diff options
Diffstat (limited to 'src')
131 files changed, 0 insertions, 16258 deletions
diff --git a/src/.gitattributes b/src/.gitattributes deleted file mode 100644 index eebe826..0000000 --- a/src/.gitattributes +++ /dev/null @@ -1,5 +0,0 @@ -text_recognizer/weights/CharacterModel_EmnistDataset_DenseNet_weights.pt filter=lfs diff=lfs merge=lfs -text -text_recognizer/weights/CharacterModel_EmnistDataset_WideResidualNetwork_weights.pt filter=lfs diff=lfs merge=lfs -text -text_recognizer/weights/CRNNModel_IamLinesDataset_ConvolutionalRecurrentNetwork_weights.pt filter=lfs diff=lfs merge=lfs -text -text_recognizer/weights/LineCTCModel_EmnistLinesDataset_LineRecurrentNetwork_weights.pt filter=lfs diff=lfs merge=lfs -text -text_recognizer/weights/LineCTCModel_IamLinesDataset_LineRecurrentNetwork_weights.pt filter=lfs diff=lfs merge=lfs -text diff --git a/src/notebooks/00-testing-stuff-out.ipynb b/src/notebooks/00-testing-stuff-out.ipynb deleted file mode 100644 index 2d6b43c..0000000 --- a/src/notebooks/00-testing-stuff-out.ipynb +++ /dev/null @@ -1,1059 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from PIL import Image\n", - "import torch.nn.functional as F\n", - "import torch\n", - "from torch import nn\n", - "from torchsummary import summary\n", - "from importlib.util import find_spec\n", - "if find_spec(\"text_recognizer\") is None:\n", - " import sys\n", - " sys.path.append('..')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.networks import CNN, TDS2d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tds2d = TDS2d(**{\n", - " \"depth\" : 4,\n", - " \"tds_groups\" : [\n", - " { \"channels\" : 4, \"num_blocks\" : 3, \"stride\" : [2, 2] },\n", - " { \"channels\" : 32, \"num_blocks\" : 3, \"stride\" : [2, 2] },\n", - " { \"channels\" : 64, \"num_blocks\" : 3, \"stride\" : [2, 2] },\n", - " { \"channels\" : 128, \"num_blocks\" : 3, \"stride\" : [2, 1] },\n", - " ],\n", - " \"kernel_size\" : [5, 7],\n", - " \"dropout_rate\" : 0.1\n", - " }, input_dim=32, output_dim=128)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tds2d" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "summary(tds2d, (1, 28, 952), device=\"cpu\", depth=3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t = torch.randn(2,1, 28, 952)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tds2d(t).shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cnn = CNN()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "i = nn.Sequential(nn.Conv2d(1,1,1,1))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "nn.Sequential(i,i)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cnn(t).shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.networks.vqvae import Encoder, Decoder, VQVAE" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vqvae = VQVAE(1, [32, 128, 128, 256], [4, 4, 4, 4], [2, 2, [1, 2], [1, 2]], 2, 32, 256, [[6, 119], [7, 238]])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t = torch.randn(2, 1, 28, 952)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x, l = vqvae(t)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "5 * 59 / 10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "summary(vqvae, (1, 28, 952), device=\"cpu\", depth=3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "up = nn.Upsample([4, 59])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "up(tt).shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "tt.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class GEGLU(nn.Module):\n", - " def __init__(self, dim_in, dim_out):\n", - " super().__init__()\n", - " self.proj = nn.Linear(dim_in, dim_out * 2)\n", - "\n", - " def forward(self, x):\n", - " x, gate = self.proj(x).chunk(2, dim = -1)\n", - " return x * F.gelu(gate)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "e = GEGLU(256, 2048)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "e(t).shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "emb = nn.Embedding(56, 256)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with torch.no_grad():\n", - " e = emb(torch.Tensor([55]).long())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from einops import repeat" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ee = repeat(e, \"() n -> b n\", b=16)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "emb.device" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ee" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ee.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t = torch.randn(16, 10, 256)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t = torch.cat((ee.unsqueeze(1), t, ee.unsqueeze(1)), dim=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "e.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.networks.residual_network import IdentityBlock, ResidualBlock, BasicBlock, BottleNeckBlock, ResidualLayer, ResidualNetwork, ResidualNetworkEncoder" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.networks import WideResidualNetwork" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "wr = WideResidualNetwork(\n", - " in_channels= 1,\n", - " num_classes= 80,\n", - " in_planes=64,\n", - " depth=10,\n", - " num_layers=4,\n", - " width_factor=2,\n", - " num_stages=[64, 128, 256, 256],\n", - " dropout_rate= 0.1,\n", - " activation= \"SELU\",\n", - " use_decoder= False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from torchsummary import summary" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "backbone = ResidualNetworkEncoder(1, [64, 65, 66, 67, 68], [2, 2, 2, 2, 2])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "summary(backbone, (1, 28, 952), device=\"cpu\", depth=3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " backbone = nn.Sequential(\n", - " *list(wr.children())[:][:]\n", - " )\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "backbone" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "summary(wr, (1, 28, 952), device=\"cpu\", depth=3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "a = torch.rand(1, 1, 28, 952)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "b = wr(a)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from einops import rearrange" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "b = rearrange(b, \"b c h w -> b w c h\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "c = nn.AdaptiveAvgPool2d((None, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "d = c(b)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "d.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "d.squeeze(3).shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "b.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from torch import nn" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "32 + 64" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "3 * 112" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col_embed = nn.Parameter(torch.rand(1000, 256 // 2))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "W, H = 196, 4" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "col_embed[:W].unsqueeze(0).repeat(H, 1, 1).shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "col_embed[:H].unsqueeze(1).repeat(1, W, 1).shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " torch.cat(\n", - " [\n", - " col_embed[:W].unsqueeze(0).repeat(H, 1, 1),\n", - " col_embed[:H].unsqueeze(1).repeat(1, W, 1),\n", - " ],\n", - " dim=-1,\n", - " ).unsqueeze(0).shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "4 * 196" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "target = torch.tensor([1,1,12,1,1,1,1,1,9,9,9,9,9,9])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "torch.nonzero(target == 9, as_tuple=False)[0].item()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "target[:9]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.inf" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.networks.transformer.positional_encoding import PositionalEncoding" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.figure(figsize=(15, 5))\n", - "pe = PositionalEncoding(20, 0)\n", - "y = pe.forward(torch.zeros(1, 100, 20))\n", - "plt.plot(np.arange(100), y[0, :, 4:8].data.numpy())\n", - "plt.legend([\"dim %d\"%p for p in [4,5,6,7]])\n", - "None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.networks.densenet import DenseNet,_DenseLayer,_DenseBlock" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dnet = DenseNet(12, (6, 12, 10), 1, 24, 80, 4, 0, True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "216 / 8" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "summary(dnet, (1, 28, 952), device=\"cpu\", depth=3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - " backbone = nn.Sequential(\n", - " *list(dnet.children())[:][:-4]\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "backbone" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.networks import WideResidualNetwork" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "w = WideResidualNetwork(\n", - " in_channels = 1,\n", - " in_planes = 32,\n", - " num_classes = 80,\n", - " depth = 10,\n", - " width_factor = 1,\n", - " dropout_rate = 0.0,\n", - " num_layers = 5,\n", - " activation = \"relu\",\n", - " use_decoder = False,)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "summary(w, (1, 28, 952), device=\"cpu\", depth=2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sz= 5" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mask = torch.triu(torch.ones(sz, sz), 1)\n", - "mask = mask.masked_fill(mask==1, float('-inf'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "h = torch.rand(1, 256, 10, 10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "h.flatten(2).permute(2, 0, 1).shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "h.flatten(2).permute(2, 0, 1).shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mask\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pred = torch.Tensor([1,21,2,45,31, 81, 1, 79, 79, 79, 2,1,1,1,1, 81, 1, 79, 79, 79, 1,1,1,1,1, 81, 79, 79, 79, 79]).long()\n", - "target = torch.Tensor([1,1,1,1,1, 81, 79, 79, 79, 79, 1,1,1,1,1, 81, 79, 79, 79, 79, 1,1,1,1,1, 81, 79, 79, 79, 79]).long()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mask = (target != 79)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mask" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pred * mask" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "target * mask" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.models.metrics import accuracy" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pad_indcies = torch.nonzero(target == 79, as_tuple=False)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t1 = torch.nonzero(target == 81, as_tuple=False).squeeze(1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "target.shape[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t2 = torch.arange(10, target.shape[0] + 1, 10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t2" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for start, stop in zip(t1, t2):\n", - " pred[start+1:stop] = 79" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pred" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "[pred[start+1:stop] = 79 for start, stop in zip(t1, t2)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "pad_indcies" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pred[pad_indcies:pad_indcies] = 79" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pred.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "target.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "accuracy(pred, target)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "acc = (pred == target).sum().float() / target.shape[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "acc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/notebooks/01-look-at-emnist.ipynb b/src/notebooks/01-look-at-emnist.ipynb deleted file mode 100644 index b70ce12..0000000 --- a/src/notebooks/01-look-at-emnist.ipynb +++ /dev/null @@ -1,151 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from PIL import Image\n", - "import torch\n", - "from importlib.util import find_spec\n", - "if find_spec(\"text_recognizer\") is None:\n", - " import sys\n", - " sys.path.append('..')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.datasets import EmnistDataset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = EmnistDataset(train=False, sample_to_balance=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "dataset.load_or_generate_data()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "EMNIST Dataset\n", - "Num classes: 80\n", - "Input shape: [28, 28]\n", - "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'A', 11: 'B', 12: 'C', 13: 'D', 14: 'E', 15: 'F', 16: 'G', 17: 'H', 18: 'I', 19: 'J', 20: 'K', 21: 'L', 22: 'M', 23: 'N', 24: 'O', 25: 'P', 26: 'Q', 27: 'R', 28: 'S', 29: 'T', 30: 'U', 31: 'V', 32: 'W', 33: 'X', 34: 'Y', 35: 'Z', 36: 'a', 37: 'b', 38: 'c', 39: 'd', 40: 'e', 41: 'f', 42: 'g', 43: 'h', 44: 'i', 45: 'j', 46: 'k', 47: 'l', 48: 'm', 49: 'n', 50: 'o', 51: 'p', 52: 'q', 53: 'r', 54: 's', 55: 't', 56: 'u', 57: 'v', 58: 'w', 59: 'x', 60: 'y', 61: 'z', 62: ' ', 63: '!', 64: '\"', 65: '#', 66: '&', 67: \"'\", 68: '(', 69: ')', 70: '*', 71: '+', 72: ',', 73: '-', 74: '.', 75: '/', 76: ':', 77: ';', 78: '?', 79: None}\n", - "\n" - ] - } - ], - "source": [ - "print(dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "def display_images(dataset, shift=0):\n", - " fig = plt.figure(figsize=(9, 9))\n", - " for i in range(9):\n", - " x, y = dataset[i + shift]\n", - " ax = fig.add_subplot(3, 3, i + 1)\n", - " x = x.squeeze(0).numpy()\n", - " ax.imshow(x, cmap='gray')\n", - " ax.set_xticks([])\n", - " ax.set_yticks([])\n", - " ax.set_title(dataset.mapper(int(y)))" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 648x648 with 9 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display_images(dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 648x648 with 9 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display_images(dataset, 9)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/notebooks/02a-sentence-generator.ipynb b/src/notebooks/02a-sentence-generator.ipynb deleted file mode 100644 index 99aa56a..0000000 --- a/src/notebooks/02a-sentence-generator.ipynb +++ /dev/null @@ -1,98 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from PIL import Image\n", - "import torch\n", - "from importlib.util import find_spec\n", - "if find_spec(\"text_recognizer\") is None:\n", - " import sys\n", - " sys.path.append('..')" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.datasets import SentenceGenerator" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "<class 'str'>\n" - ] - } - ], - "source": [ - "sentence_generator = SentenceGenerator(32)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'broad___________________________'" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sentence_generator.generate()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/notebooks/02b-emnist-lines-dataset.ipynb b/src/notebooks/02b-emnist-lines-dataset.ipynb deleted file mode 100644 index f82342b..0000000 --- a/src/notebooks/02b-emnist-lines-dataset.ipynb +++ /dev/null @@ -1,330 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from PIL import Image\n", - "import torch\n", - "from importlib.util import find_spec\n", - "if find_spec(\"text_recognizer\") is None:\n", - " import sys\n", - " sys.path.append('..')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.datasets import EmnistDataset, EmnistLinesDataset, Transpose, construct_image_from_string, get_samples_by_character" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "transform = [{\"type\": \"ToTensor\", \"args\": None}, \n", - " {\"type\": \"ApplyContrast\", \"args\": {\"low\": 0.0, \"high\": 0.15}},\n", - " {\"type\": \"RandomAffine\", \"args\": {\"degrees\": [-0.25, 0.25], \"scale\": [0.9, 1.0]}}\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "emnist_lines = EmnistLinesDataset(train=True,\n", - " max_length = 60,\n", - " min_overlap = 0.0,\n", - " max_overlap = 0.3,\n", - " num_samples = 50_000,\n", - " transform=transform,\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-01-02 22:02:47.979 | DEBUG | text_recognizer.datasets.emnist_lines_dataset:_load_data:152 - EmnistLinesDataset loading data from HDF5...\n" - ] - } - ], - "source": [ - "emnist_lines.load_or_generate_data()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "def convert_y_label_to_string(y, emnist_lines=emnist_lines):\n", - " return ''.join([emnist_lines.mapper(i) for i in y])" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/akternurra/.pyenv/versions/3.8.2/envs/text-recognizer/lib/python3.8/site-packages/torchvision/transforms/functional_tensor.py:876: UserWarning: Argument fill/fillcolor is not supported for Tensor input. Fill value is zero\n", - " warnings.warn(\"Argument fill/fillcolor is not supported for Tensor input. Fill value is zero\")\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "412 We______________________________________________________\n", - "new structure that for supporting the basic_________________\n", - "expect______________________________________________________\n", - "you come out when you saw them gang up on___________________\n", - "fashion Passing_____________________________________________\n", - "life________________________________________________________\n", - "in__________________________________________________________\n", - "that________________________________________________________\n", - "a dilution of the intermediate sera to______________________\n", - "and Wilson remaining ashore determined to catch_____________\n", - "are of two types participation______________________________\n", - "nonetheless_________________________________________________\n", - "will begin as soon as the shelter is occupied_______________\n", - "their orbits but allows the wind to bend a blade____________\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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ydu1a5YUXXlAOHTqklJeXKy+++KKSlpY2YdvR6/XKihUrFI/Ho/T29ir/9E//pISFhX1qffxyPl/72teUhoYGpaWlZcT1ER8fryxcuFBJT08fNi00NFTJyclRbrnlFuWRRx7Rjuntt9+urFixQomIiPjE91sIoYSHhyuRkZGKXq8fdZ6QkBDlxhtvVPbv36/09vYq1113nWI2mz9yvf7+/orNZlOSk5OV+Ph4JTw8XDGZTMqgoPypfoQQisViUVavXq18+OGHSmVlpdLc3Ky0trYqhw8fVjIzM8fsdzabTVm1apXyne98R3n88ceVBx98UJkzZ45iMpk+8z439KPT6ZSf//znSklJifLWW28pq1atuqzldu/erVy4cEG57bbbLvsedC1/QkJClJ/+9KdKaWmp8uyzz455bi89diaTSUlKSlKCgoI+kXvlpR8hhJKcnDzmNfdZ9SG9Xj/s81nvk/zIj/zIj/zIj/zIzzX6OT6aDnNNGxELIQBISEggIyODkydPAgNRNjfffDPR0dE0NDRw7NgxNm/ePCyiQAiBTqcjJCSEGTNmsHLlSjIyMrBarfT39+PxeIiNjeWJJ56go6ODp59+ms7OTm15vV5PWFgYHR0deDyeCW1XVlYWQUFBAHR3d9Pd3Y3P56OpqYm4uDhCQ0OJjY0lMzOT1NRUDh8+PGx5dd+mTJnC0qVLiYmJYcaMGSQkJODv74/D4SA8PJylS5fy+uuvaxE7H9WOoKAgMjIymDt3Lunp6WRnZ1NfX69FNC1atIi5c+cSHByMEIJp06aRlpZGfn4+HR0dE3JcgoODsdlspKSksHjxYhYuXEhGRgbBwcH86U9/4uDBg0yZMoXy8vIJ2V5GRgaLFy/G6XSyZcsWNmzYQFfX1RfbCQsLw9/fn87OTpzOv/r/qdEwlzLW90Px9/fHYDCMiLJJS0tjxYoVLF26lNraWh577DHcbjdRUVEsXbqU6667jtTUVGJiYvjDH/5AQEAA1113HZ2dnXR0dNDa2nrV7fw4hBCkpaVx8803YzQa2bBhAzU1NSPmS0pK4r777mPKlCn4+flp1/tHERERwZQpU4iNjcXlchEbG8tbb71FXV0d/f39n0RzRqDT6Zg1axbz5s3D6XRy7733MmvWLMxmM3q9HiEE0dHRzJgxg5KSkhHLh4WF8dBDD3HdddcRFxeHv78/HR0dzJ49my1btrBly5ZPpR2XQ1RUFNdffz3R0dGcPXv2sqK2dDodU6dOJSwsDIvF8ins5SeL2Wxm8uTJ3HfffSQkJLBmzRpeeeWVUc+tinr/TkpKYsmSJRQVFVFQUEBRUREtLS0TGi04dD/vuusu7rjjDv7u7/6O+vr6zzQiKCoqCpfLxdSpU4mOjsZoNGrT8vPzqaqq+tSuWYlEIpFIJJLPM9e0aKOm3sTFxZGenq6JNgEBAWRkZGCxWCguLiYvL49jx44NS9UxmUxYLBbi4+OZN28eWVlZBAcHoygK1dXVeDwe4uLiuOmmm2hsbOT73//+sAGJ1WrlS1/6Ert376aysnJC0oD0ej0zZ84kJSUFo9HIhQsXKCwspLy8HJ/PR0tLC5MmTeKmm24iJiaGwMBArFbrsHUIIQgMDCQlJYUlS5awZs0aIiMjCQ8P19Jh2tvbOXHiBA888ADr16+nvb2dI0eOUFdXN+ZDvNlsJjY2lpSUFMxmM7NmzaK8vByHw0FOTg7z588nMjISh8OBn58fNpuN+Ph4wsPDJ0S00ev1xMTEkJOTw/Tp01m1ahVZWVl4vV7MZjPh4eGkpaWh1+tHDGr9/f0xmUz4fD4cDsdlb3PSpEnMnDmTwsJCNm/eTEVFxVWfZz8/P5YuXcrkyZM5c+YM7777LjAwgJ0zZw4ej4eamhpaWlq0ZZYsWUJJSQlNTU2jnhedTofRaMTr9XL+/Plh01JTU5k/fz6LFy+mrq6OiIgIOjs7mTx5MkuWLGHx4sXYbDZ8Pp+WDjhp0iSMRiO1tbWcPHkSRVHQ6/UEBgYihMDr9dLX1zdmiuHlIoQgIyODJUuWEBISQn19PX/5y1+GzePv709iYiKLFi3SrsuPG2D6+fmRnJzMkiVLSElJwel0MnnyZI4ePUpra+unNgAMDQ0lNzeXe++9l97eXubOnYvX69VEJ0VR0Ol0mEymEcsGBQWRk5PDrbfeSkpKCoGBgSiKQlxcnHYNv/fee5/IoP5K0el03H333UyaNIlz586Rl5dHVVXVxy4XEBCAxWJBCIHP5xt2Xg0GAzExMTQ3N48QgNS+CGj98JNMv7xcEhMTWb9+PS6Xi5deeol77rkHf3//UecVQmA2m5kzZw5f/vKXSUhIICcnhylTpjB16lR2795NXl4eFy5cGNZfxovJZCIxMZH777+fhQsXctNNN/H++++PSCv+NDCbzdx66634+/vjcrlYsGAB8fHxWuqgEILs7GxeeOGFT1VslUgkEolEIvm8cs2KNoqi0NjYSFhYGPHx8aSlpaHTDfgmR0dHY7FYUBSFqqoqTpw4QV1dnbasTqcjISGBpKQksrOzWbhwIcHBwfT19dHS0sKRI0fwer3MmzeP2NhYent7R2w/KCiI++67j46ODmpra8c9kIWBCKEbb7yRqKgovF4vR48eZdOmTRQUFKAoCr29vWRlZZGZmUlYWJgWLTS0XfHx8UyaNInc3FyWLFlCRkYGAQEBuN1u2tvbMZvN1NXV8frrr/PQQw/xwAMP0NraitPpxG63DxM1VJ8a1dfH6/VqPjVz586lpaUFl8tFVlYWKSkpNDY20tLSQkxMjHYODIaJ6UIWi4XMzEzmzZvHjBkzyMjIwN/fn9LSUhISErjxxhuZMWMGp0+fHhGhkpiYSFJSEr29vRw8ePCytqf6dMTExLB582YOHTo0rgFiYGAgN910E+vXr2f37t2aaBMYGMiXvvQlvF4vu3fvZu/evdoyX/3qV9m4cSOHDx8eVWwKCAggICCA/v5+amtrR+x7cnIyISEheDweMjIyqK+vZ+7cuUydOpXY2Fj0ej1tbW1ER0fj8/kICwsjKSmJkpIS9Ho9Ho+HqKgopk2bhslkoru7m/r6+o+MILgchBDExsYSFxdHamoqa9eu5a233hp2DdlsNtLT04mLi0On09HR0UFvb++oYoWfnx8Wi4XU1FSWLl3KDTfcQGxsLE6nk8bGRq0vXolgNx6mTJnCwoULmTNnDgaDAZfLxeHDhzGbzdo8LS0t1NfXD1vOaDSSkpLC6tWrmTJlCjqdThu4m0wmEhISWLx4MRERETgcjk/NY2ssTCYT3/jGN4iIiODZZ5/lvffeo7q6+iOX0el0hIaGIoTAbrfjcrlQFAUhBP7+/oSFhbFy5Up27dpFfX09brcbvV5PQEAAqampmM1mPB4Pzc3NNDc343K5tH3xeDyfiYiTlpbG2rVr+ctf/sJLL73E2rVrxzwvYWFhpKSksGLFCm699VYsFgt6vZ6IiAgyMzOJiorC4/FQV1en/Q7U19ePS6TT6XRERkaydOlSsrKyOH/+POvXr+fs2bNXLdqoLwCuRlCxWq1897vfxel04vV6SUxM1PzazGYzFouFKVOmUFRUxIcffjhMyJ5oDAaD5lfW399/TYiAEolEIpFIJFfKNSva+Hw+du3aRWRkJImJiUydOlV7C7ts2TIsFgvt7e0UFxdTVFREd3e3tqy/vz+33HILq1atIi0tjYSEBPr6+igtLWXHjh1s3rxZM+212Wy43e5h4ggMDLASExNZvHgx27ZtmxDRRqfTERUVhcFgoLOzk4KCAk6ePEljY6MWaaC+gb50UGAwGPD39+frX/86t956K5MmTdIMTH0+H1VVVVRXV5OYmIjH49GMhHNzc+nv7+f06dPU1NRQWFiorTMwMFB7oO7v7+fQoUPY7XZuuOEG5s+fT2ZmJj6fD5fLRUVFBVu2bKGhoYEVK1awdu1aqqqqKC4uHvdx0ev1pKamsnDhQhYtWkRiYiIWi4Wuri7279/P+vXr6e/vx9/fn6SkpBGizfLly/nKV75Cc3MzeXl5lzUACgwMJDIyEpfLxYsvvkhDQ8O42hAQEIC/vz82m41p06Zp36enp7N48WKCgoJwuVzDRJtFixbR0NBAbW0t586dG7HOqKgoIiIi8Hg8w6KZ1Lf5ah8OCQlh5cqVlJSUsGTJEuLi4ujr68Pj8WjiyIULF/B4PJhMJqxWK0ajEZ/Px8qVK3n44YexWq3U19ezc+dOfvWrX407JVB9ox4REUFubi4RERHDRIycnBwWLVqkiYWHDx+msbFx1OssISGB7OxsvvnNb7JgwQICAgK0Prt8+XLWrVuHzWYbMUBVr+mJHKgJIVi/fj3Lly/X9v3ChQv8+Mc/pra2VkuvUw20hxIZGckNN9zAAw88gE6no7e3l76+Pvz8/DCZTOj1eoKCgli4cCFtbW0cPXp0RMpnYGDgmKmOgYGBOJ3OCRF6VPElLS0Np9PJ4cOHL+taV4VlgJMnT9Lc3Az8NRJk/vz5/PCHP6Szs5O9e/dit9uxWq1MmjSJH//4x5w7dw6Hw0FhYSEnT57UInsSExNpb2+nq6trwtNVPwqj0UhQUBAmk4nHH38cIcSY29fr9SxZsoTVq1dr17yKTqcjKCiIZcuWUVlZyZkzZ7j77rtxOBz8/ve/H9d5CwkJYfHixXzrW99i06ZNvP322/ziF78YEaV5uQghCAoKIjg4+LIiqy5Fp9MRFhbGtGnTcLvdlJaWcuLECWpra0lPT2fSpEnYbDZuv/12KisrPxHRRn0RERERQVhYGP39/bS0tNDX16f9pkkkEolEIpF8XrhmRRtFUVi6dKnmE5KRkcGaNWtwuVx897vfxWq18tJLL7F161ba2tqGLZuYmMjMmTOZNm0aFotFi5bZtm0b27dvp7KyUhMkent7mTVrFuHh4TidTu2BXFEUvF4vN954I//8z/88zKPkajEYDFx33XUoikJeXp4WITT0Yd3r9Q4zHVIHnGFhYURGRjJ79mxiYmJwuVw0NTVx5swZzp07x5///GcCAwP55je/SVJSEp2dndp6VV+a6OhoTbQxm818+9vfZt26dQQFBbF9+3aeeeYZTpw4QUNDg/awqygKpaWlHD58mLy8PGbOnEl4eDh6vX7cxwMGBjrJycn827/9G/PmzSM4OBiv10tjYyN//OMfee6558jNzcVqteLxeIb5IqhERkZq3jfBwcG0t7d/7Ha///3vc/vtt1NdXT2q38qVokYBXJoW8/Wvf520tDSsVitZWVna92azGbPZTEpKClFRUaOKNvPmzWP69Ok0NTWxadMm7Xufz8f+/ftJSEggMTGRoKAgvv3tb+NwOGhra2Pv3r2cOHECg8HAr3/9a1JSUiguLqapqYlJkyYRGRlJZGQkvb29/OhHPyIlJQWDwUBSUhI6nY53332Xs2fPTojYIYTAarVyww038MorrwADA/jc3FxuuOEGvF4vVVVVPP3006NWJgoICGDBggUsWLCAjIwMhBB0dHQQEhKCTqfj8OHDVFZWUlFRMUyQBIiPj0en09HW1jZM1B0Per2euLg4bDYbHo+H9vZ2fvrTn3Lq1KlhYmFPT8+IZe+77z6+8Y1vEBISgtPp5IknnmD37t3ccsstrFmzhsmTJ+Pn58f8+fP59re/TXZ2NtXV1fh8PoQQ2Gw2nnrqKV544QUOHjw4LELQYrHw5JNP8o//+I8jKr9dDQaDgYSEBHQ6Hbt27brsiA2j0cjKlSsBKC0txW63AwPRUllZWSxevJjo6GhycnIoKyvDYDCQmprKqlWrmDNnDjfddJOW4vn222/zne98B4/Hw+OPP05JSQl/+tOfuHjx4rjadiVERkZis9k00TQnJwen0zlqBEpSUhI///nPmTRpEn5+fto58Hq9WlSVyWQiIyODdevWcfvtt9Pd3c3TTz89rvP1yCOP8P3vf5+ysjIeeughZs2aRVdX11WnHQUGBjJv3jxyc3P513/91yteXo380el0NDU18atf/YqdO3fidDoJCgrShMl/+Id/0KoRTiTqcbbZbMybN48pU6bgdDppbm7GYDDgdDpHeOBJJBKJRCKRXMtcs6INwO233863v/1t1q9fT1ZWFo899hiNjY0kJyej0+k4e/YsFRUVwwZL/v7+rFixgilTphAcHExnZyeHDh3i2LFjfPjhh5SXlyOEICwsDJvNRn9/v/aGW00ZAbQonKFpKePB39+f1NRU7W14fn6+FmFzKV6vF7fbTU9PDz09Pej1ei3tZdKkSbhcLgoLC8nPz+f48eOUl5fT2NjInDlzUBSFnp4esrKyhhm76vV69Hq95gty66238uijj2KxWNDpdNjtdgoLC3njjTf47//+b/7rv/5LS/cICgpi6tSppKens2jRIoKCgujo6BiXaS/89Y3s/PnztcihlpYW7HY7p0+fZvPmzTgcDnw+Hw0NDfh8Pmw2G6GhocOEOo/Ho4kDoaGhHyvaGI1Gpk+fjt1u56233hpXG1RcLhf9/f309/drx8VsNrNu3TqioqK0PqfX6/H5fPz6178mJCSE/Pz8USMYTCYT06dPJz4+nrKyshHCZEVFBUVFRTQ3N+Pv709AQAAGg4EDBw5w4MABAL7yla/g9Xqprq6mu7ubQ4cOERcXR2BgIOnp6SxYsIDk5GSampoICwsjICCAGTNm8Mwzz3DrrbeO6w34UI+agIAAFi1apIk2ZrOZ4OBgAgMDcblcFBUVcfbs2RGDTIPBQFpaGrfccgvZ2dmEhIRw8eJFLl68yJw5c4iOjsbhcJCenq6lEw69nn77298SGhrKM888wzvvvDMhESiRkZFYLBaKiorYtGkTGzdupKSk5COju8LCwliyZAk33HADYWFhnD17ls2bN/Of//mf9PX1sXr1aoxGI0II+vr6OHXqFCaTicjISBobG3G5XFpKyfr16wE4ffr0MNHGZDJx++23s3XrVnbt2jXuVLGQkBAeffRRenp6ePLJJ6moqLis5UwmE+vWrcPr9dLd3a1FThmNRqKjo0lISMDPz49FixbR3NxMU1MTubm5rFixAovFQlNTE6GhoVr0zYIFCzh48CDZ2dksW7aM06dP09LSMmEi3Mdx/fXXk5uby8GDBwkNDeWNN95g69atw9JxYeD++vLLL5OZmYmfnx+tra1a+s+ePXv4+7//e9auXauVt585cyaTJk3C5/Oh1+sxGo2EhIRoxs1dXV2XJT4LIbQ0yH379hEaGsqePXuYNWvWVYtbcXFxzJw5k+nTp1/V8uo+bdu2jSeffJLjx49ropfL5dLuK1//+tevav0ft22z2UxcXBy5ubmsX7+e7OxsTbRRPb3y8vImRKyXSCQSiUQi+TS4pkWbpqYmtm3bRnp6OqmpqWRkZJCcnIzRaMRut9PU1DTi4X3ZsmXcf//9REREcP78eY4ePcpzzz1HY2MjnZ2duN1uZs6cyYIFC0hLS6O8vJz09HTMZvOwFCn1bV1LS8uEplfodDp6enqoqKgYVfTweDy0tbVRU1NDZWUlra2tJCQk8Oijj2pmjmoIfGVlJZ2dnZpvxPz58wkPD6e7u5sZM2YghOC9994jPT0dq9VKZGQkKSkpfPe732XFihVYrVZaW1sJDAwkPj6eBQsWsH37du3tvupFERISwrRp01AURYuy+clPfsK2bduu+jioD9fTp09n+fLlBAUFUVVVxcWLF6mtreXEiRNUVlbyk5/8hJiYGHbt2oXP5yMuLg6r1TpMxLBYLFrayFgViHQ6HeHh4bS0tHDzzTcTHx9PQUEBO3fu1ObR6/VX7S1hNpsxmUy4XC6am5sRQjBv3jzN5PfStufm5mK326moqBghyADMmTOHadOmERAQMKq4193dTUFBAW+++aZmPGwwGAgPD+f666/X0hPq6uo4deoUbW1tdHV14Xa7CQ4OZtq0aeTm5mrRKrNnzyYxMZGAgACSk5Ox2Wy0trZeldChKArFxcVUVVWRk5NDQEAAixcvxt/fn76+PnJyckhKSsLr9dLc3MyRI0doa2vTrjPV/yQ1NZXHH3+cBQsW4PV6OXjwINu2bePixYv8zd/8DXfccQc9PT1ERESMGvmVnp5OZGQksbGxBAQEjBr9cqUYjUZNeHO73Xg8HoKCglAUBafTqbVhaJTc+vXrufvuu5k+fTrnz5/nmWeeYceOHZroEhcXp4lOHo+HCxcuaNtS70mxsbHcdddd+Pn5MXfuXPz8/LR9Uo9XYGAgWVlZHDhwYNyijb+/P9dffz1PPPEE58+fH9X3azT0ej0ZGRns2bOHrVu3ailxTqeTI0eO4PF4mDZtGjNmzCA6OhqXy4XVasXpdPL8889TX1/PPffcQ2ZmJsHBwcTFxaEoCpWVlSQnJ/Ptb38bnU7Hxo0bPxW/H/UFwQsvvIDJZCIoKIhdu3YNq74mhCA0NJTs7GxMJhP9/f18+OGHPPvss5SUlNDZ2YnBYNCibWprazl+/Di33nqr1m/nzZvHypUrycnJAaCgoIBf/OIXH7t/FosFn8/H2bNnee655zAajZhMpnGZOPf29mIwGIiNjcVgMFxxOprazi1btlBQUDDid05RFFauXKmZn08UMTExJCQkMHv2bBYuXMj06dOJjY0lMDAQt9utvRi4ePEiixYtYsOGDXi9XiIjIzGZTOh0Ovr6+j4T82aJRCKRSCSSj+KaFm36+/spKiqitLQUh8NBSEiINljZsGEDFy9eHPZ2XqfTMX/+fJKSkqivr6egoICjR49SWlpKb2/vQI1zg0Erae31ennttde46aabiI+Pp6ioSBucqCbAwcHBE5YKNLRdqoB0KR0dHbz99tvs27eP1tZWLly4QFpaGpMnTyYoKIjTp09TUFDA2bNn6ezs1B561RQho9FId3c32dnZAGzevJlFixaRk5PDV77yFZYtW8by5cuJiYmhra2Nl19+mWXLlpGUlERERITmrwF/jZZQq0Wp9PT0cOHChXGVjbZarcyePZv777+fBQsWUFlZyZYtWzh58iTt7e00NTXhcDhYunQpJpOJpqYm7ZwMNXyFgYiM0Sr1DMVisfD//t//4w9/+ANz5swhLCyMrq4umpubtao1ixYtIj8/n/b29qsaqMDA+SsrK0On07FixQpMJhOtra1av/X5fFoUxfHjx6mvrx81jSE7O5vo6GgteigsLGzYm3efz0d7ezulpaWkp6dr3ycmJhIeHk5gYCB+fn4cP36clpYW3G43/f39WqWiW2+9leTkZFpbW9m4cSO1tbWakBkUFMTNN99MeXn5VaUQ+Hw+iouLOXXqFFOmTGHy5MnExcUxe/ZsysrKWLlyJZMnT6ahoYFNmzbx4YcfDjsGJpNJK+09Z84cTCYTJSUlWsRcX18f3d3dKIpCbW0tycnJWCwW/Pz8hnlV6PV6rFYrQUFBEybaqAJcTEwMy5cvJzo6moqKChRFoa6uTrum1Qg/j8fDsmXLmDp1Kv7+/ly8eJE9e/YMGxjabDaCgoLwer04nU5NLHO5XHi9Xq2y2ooVK9DpdMTExGj93Wg0EhcXx7p169DpdJSUlIw77UM9bsHBwezZs4fu7u7LEgCEEBiNRiwWC1u2bKGsrExLK+3v76empkYT6jIyMkhNTaW/vx+Hw8GpU6c4cOAAoaGh2j3N4/FoZswVFRUsWbKEnJwcEhMTh0VV5eTkUFtbq0UT9vf3T0hFOxhI6/J6vbS3t3PnnXcSHBysRT+pmEwmZs+eraXiFhYWsnv3bk6ePKn107i4OC21U43CaW1txeVyYTabWb9+PStWrCAhIQFFUYiPj+fFF1/UDPnHOqdqe9va2mhpaeHOO+8ct4G10+nUxLT4+HgqKyuHTVcrM3Z3d496n1SvkdOnT9PV1TVMmBFCYLFYWLVqFVVVVZcVTXQ5GI1GsrKymDFjBgsXLmTWrFlERkYOM7pua2vDz8+PkJAQEhISNCHtvvvu04zba2treeGFF0YV0iUSiUQikUg+K65p0QYGqrAUFhZy7tw5Fi5cCAwMCtUQdfXhVDXpzM3NBQZMMPfs2UNhYaEWgWGxWIiOjmbGjBkkJydjt9s5cOAAiqKQlpaGv78/nZ2dGI1GAgMDtdz8iRZtPgqn08nBgwcxGAy43W78/PxYt24dgYGB9PX1ceTIEc6fP689DPv5+WE0GsnNzcXtdmvTZs6cidfrJS8vj6ioKObPn092djYulwu9Xs+5c+fYs2cPr7/+OikpKVoEk8lkIjMzE71erw2y1CpGiqJw4cIFmpqaaG9vH9fAIDg4mEWLFnHDDTcQERHB3r172bt3LwUFBfT19Wk+EKqpbltbGwaDAb1ej81mQ6fTaV4fer0enU6niWyjERAQwN13301bWxsLFy4kJCSE0NBQpk+fTlRUFDExMdxwww1YLBby8vJoaGi4IuFGHUQ6HA7q6+u1aBqDwUBZWRkRERGa501CQgJBQUEcPnyY5ubmUY/jnDlzsNlsWCwWMjIyWL16Na+++uqweXw+34iKOjabjfDwcIQQWjSOWhGsu7ubvr4+QkNDmT17NiaTiXfffZe8vDwaGxupr69nyZIlrFq1irVr1/Laa6/R1NR0VW/D29raOHDgANHR0YSEhBAdHc2XvvQljhw5wuLFi0lISKCoqIgPPviAoqIibTn1HKalpTFr1iyCg4OprKwkPz+fkydP0tDQgM1mw2w2I4SguLiYhQsXYjabtRLDKiaTCZPJpF0jE4Hb7cbr9RIWFsacOXPIzs6mrq4On89HU1OTJj7V1NRQUlJCY2OjltrV0dFBVVXVMENm9b6ligNDPa3UQXFQUBBJSUkkJycDA31ZNXr28/MjMTGRm2++me7uboqKisYt2gQFBZGRkYHX66WsrOyyrwM/Pz/i4+MRQnDy5EnsdrvWd7xer2YifPLkSeLj4zVfIofDgdPpJDQ0lDlz5hAREYHX66WtrU2rVqX28cDAwGHtDwwM5LbbbqO9vV2rFFRfX88777wzIT5kPp+PkJAQFi1axF133UVAQAAOh2PYMbFYLKxbtw69Xk9eXh47d+4kLy9vWIRJVFSU1gcdDgc1NTUUFxcTGhrKlClTWLRoEampqZoYl5WVxZe//GXa29vZtm2bZuh8KWp5+YiICBYuXKiZG48nOlQVeP39/UeINmpluNmzZ3P06NER6cPqeVEF1UsFaZPJRHp6OjNmzOA3v/nNhJkQq/e02bNnM3nyZCIjIwEoLy8nIyMDi8VCWVkZYWFhhIWFkZycjNlsJioqivXr15OcnIxer+fChQscP36cM2fOSOFGIpFIJBLJNcM1L9ooisKpU6fYv38/CxYsAAbe2p4/f35YapT64Dpz5kxaWlrYuXMnO3fu1Ax5zWYzycnJ5ObmMn/+fMLCwiguLtbeIk+aNImAgABgYNCSmpqKEEKLtLnUL2M8GI1GgoODh6U4qHi9Xjo7O7XtBQcHs2TJEu2N67Zt2zh37hwejweDwUBkZCShoaHccccd5Ofnc+LECeLj47US2HV1dXR3d2vii9frpaCggA0bNvDcc8/h9XpxOBz09/drg5yFCxei0+k4cuQI3d3d2Gw24uLi6O/vZ+PGjRQXF9PY2HjV7Vcr06gRL52dneTn51NdXa29JTYajWRnZ+Pn58fFixepr68nPDwco9FIeno6R44coa+vT0tXUd/yJyYmcuLEiRHbNBgMZGdn86tf/Uorwzt16lS++c1vMnv2bOLi4rQUJ7vdTkdHxxWlmJjNZoxGI16vVxMJU1NT8Xg8nDhxgkmTJmkRDMuXL8disXD8+PEx3zQvXLiQmJgYdDodWVlZPPzwwyNEG7PZTHR0NDabDZ/Ph9Pp1ISt7u5uiouLOXz4sDbArK+vp6OjQ3vz7PV62bBhAx0dHTQ3N3Px4kXsdjtr164lNzeXKVOmaKW4r4a8vDw8Hg8xMTHccccd3H333WRlZZGVlYW/vz8dHR1atSAV1ctk0aJFzJ8/H6fTyd69e9m5cyfFxcW43W6tP6ppIW63G39//xHXU3Bw8ISVpFdRTWgNBoNmqhoVFQUw7P5QUVHB3r17ee+994iIiNC8NKqqqoYNqNWqUWoUmclk0s6Nw+HA6/WSkpLCjBkz0Ol0mpipEhAQQExMDKmpqZSXl1NXV3fVBrQqahRRR0fHFaWHWq1W5syZAwxc46rwrB4Xj8dDV1cXH3zwAUuWLCE0NBSDwUBAQABJSUlER0cze/ZsgoODaWlpoaKigosXLyKEIC0tTUu7USNqDAYDU6dO5Wtf+xoxMTEYDAaEEJw5c4YLFy5w9OjRcR0HGIiYstlsPPzww8yfPx8hxLDS9Hq9ntDQUO68804tcnPfvn0jRBar1aqdv/7+fux2OwUFBaxZs4a1a9eSnJyM1+ultbUVvV5PVFQUjzzyCKdOnSI/P5+WlpZRf3/6+/vx8/Nj+vTp/OM//iPz588fYYp9pajioVp9aSgGg4EpU6bwjW98g76+vhGijU6nw8/PD5/PR0dHh9Z3dDqd1ldV/6L8/PwxxajLRRUuZ86cyZIlS8jMzNQEm7q6Onbv3o2/vz/p6ekUFBSQmJjIsmXLyM7OxmazsWbNGjIyMggPD9f61/r16zEajVpKrkQikUgkEslnzTUv2sDAW+vS0lLtb9XH5dIHKoPBQHBwMNu3b6e8vFwLTTeZTCQkJLBy5Upyc3MJDw+ntraWM2fO4PF46OvrIyYmBj8/P4QQZGRk8JWvfAUYEFj8/f21B+6JwGw2M2nSJM6dO0dnZ+eIEt9DvT1MJhNxcXE4nU4uXLhAQ0MDPT09CCEICAhgzpw5pKenM2vWLN5//30qKio0s96mpiZcLpf2xl5RFGpqanjqqad4//33cbvd2kCnrq6Ow4cP09bWppXszc/Pp7KykpCQEGJiYvB4POzcuZPz58+P61j4+fkRFxfHwoULEUKwc+dOtm/fTn19vZbClpCQwKFDh2hsbOSNN97gxIkTLFiwAJPJpHm9uN1uoqOjCQ4O1kq8qtEIl6Kmm4SEhGjfzZw5UxsMw8BDvtlsxmKxXHFkxsyZM4mJiaGvr08Tv6KiomhrayM/Px+j0UhaWhpTpkzhRz/6ET09PVy8eHHUaIChfU49HomJicPmEUKQkJDAjTfeqJkqHzx4UKv4c/r0aV577TX27t2r9a3a2tphIpEqiKreLG1tbTQ1NeHz+QgICOC+++7jzJkzVy3aqF4kW7du5bbbbiM+Pl7rW2pq2qUpdrNmzeKee+5h9erVhISE8Pbbb/PDH/5QE/MmT57M4sWLmT59Ol1dXdrbfD8/vxE+L+o5HMvn6GpwOp2acKOuXxVJhvqWxMfH80//9E8cPHhQE36rq6tHGPrGx8drpcFVQeJSoWnBggXcfvvtmrA6tA9HRUVpvkEvv/zyVUfZDI2QSEpKYt26dezbt2/YfUkVlvR6/aipnRaLRTOvvf3223nzzTcpKSkZ4TumlnZXPbMsFovmmRUSEqKlUqlpsUIIsrKycLvdmqilit4/+tGPSExMpKenB6fTiclkIi0tjX/7t39jxYoVV3UshrJnzx5iYmL41re+Bfy1qqCKn58fUVFRhIeH09TUxOHDh2loaBhx3IKCgtDpdDidTi2SUG3PrFmz8Pf3p7a2lurqakwmE1FRUcTGxtLV1UVQUBAGg2FUMa63txeHw0FAQACzZ88GYO/eveMqad3b24vT6dTKlA/FYDAQEhJCVlYWS5YsYevWrcOmm0wmQkNDteOk9qvw8HBycnK4+eabufXWWzl27BiVlZVXfW+Bvwo2qampPPHEE5ow3tvbq5nqv/rqq/j5+REbG8u5c+e031ar1UpMTAyPPPIIer2ejo4O/P39CQ8P5+677yYyMpK9e/eO2s8lEolEIpFIPm0+F6KN1WodNlAZLex6KFlZWdx///2aX0loaCi33347dXV1REVFUV1dzWuvvcYf/vAHAgICuHDhAtdddx1ZWVk4nU6ys7NZsGAB3d3dBAYGsmrVKrZu3UpjY+MVe52oKIqCx+Ohu7ub1NRUHn74YaZPn86OHTu0UPra2lpsNht2u10TcnQ6HRaLBYfDoT3kqoJNXFwc69evZ8qUKcTExNDd3Y3dbteicNRUjvz8fFpbW2ltbSU/P5/33ntPe6i3Wq34+fnR09NDa2vrsOPa1NTE6dOn6evr0wbEtbW14xavVN8L1Rdh3759WiqFOsCZM2eOZl567NgxGhsbtenx8fFERETQ1dXFvffeq73dNxqNxMTEjBoV5XA4+PWvf83Pf/5zTaRRB/OqB8bChQu1FJcrjaoKCAigt7eXmpoa9u/fj5+fH/7+/tjtdnp7e/mXf/kXEhMTefTRR0lISOCJJ54YswLO7NmztYH8qVOnKCoq4uabbx42j1otKyEhAbPZTEtLC5s2bSIqKgqdTkdZWRknTpwYNuhwOBz09vZqaVo+nw+73a4NZNxuN3a7nfb2dmw2GzfddBOBgYHjMuZsaWkhLy+PyspK0tLStGO+ZcsW/vCHPwzbP51Ox4wZM5g+fTrh4eF0dXWxc+dOenp68Pl8GI1GUlNTmTt3rpbuoAoAcXFxJCQkUF9fj8fj0dLlhBDaAHg80WEqXq+XHTt2aNWc+vr6eOeddwCYOnUqM2bM0ATg6dOn8/DDD2uphfv372f37t3D1vfYY49p5dfVqJNVq1YNuw6DgoKwWq3U1dXh8XiG3Qtzc3O5//77sVgsbNq06aqvzdDQUGJjY3G5XERGRmI2mzl27BiKoqDX6wkLCyM0NJTk5GQmT57Mk08+OWIdsbGx3H///QghePTRR1m3bh3f+973RhiWh4WFYTQatWvMYDAQGhoKDPTBmpoajh49yokTJ/B4PPj5+REdHc3777/PwoULSU1NZcWKFURERLB69Wp8Ph//+7//i9frJTc3l5kzZxIYGHhVx+FSCgoKKC8vZ/v27XzwwQfAXyOq1HvV5MmTgYHIstH8ZFShSxUZ1Epa3/nOdxBCEBcXR11dHVu3buWDDz5Ap9NpYlxGRgY33XQTHR0dlJSUjLqPf/jDH/jLX/5CbGwsu3fv5ujRo+MSG3w+nxbl9cgjj/DSSy9p09RKfZGRkaxcuZIf/vCHw9qrRlj19vaSmpqK2+0mOTmZr371q9x9993AwG/It771rXEb/qr9Qu0T6v2tqqqK/fv3a2bfQ0uvq78hFouFuXPnEhERwalTp7Db7cTExJCZmUlYWBiTJ08eFtEmkUgkEolE8lnyuRBtbDYbCQkJwMCDV1VV1UcOTtQSwC6XS4tWMZvNNDU18fLLL3PgwAHKyspQFIX+/n6ee+45/vM//5OHHnqI5uZmEhMT0ev1vP7669xxxx089NBDBAQE8N5771FZWYnFYsHpdF7Rg7H6IFxTU8O0adMIDg5m7ty5mEwmoqOjaWpqYteuXcyaNYtjx47R0NCgrV+n02G1Wpk8eTL3338/drsdq9VKZmYm119/PR6Ph5UrV3L+/Hn0er1WUaS4uFgrl11RUUFZWRl/+tOfhr2FDQwMxGQy0dzczKlTp+jr66O6upqpU6eSlpZGXV0dFy9epLq6WnvoHS/qg7365rm0tBSPx6P5jyQkJLBixQpOnDjB+++/z8WLF3E4HJqgcP311/P973+fxx9/nIULF2qln/V6PatWreLnP//5iDe4LpeLnTt38rOf/YyGhgb27t1LXV0dra2t7N27l87OTurr68fVvuDgYGJjY/na175GSkoKMPDWu7GxkcjISBYsWMDy5csBeOutt8b03HjssccICwujra1NS49Ys2bNiPmMRiMBAQFaWlZ+fr4mwKgC3kfh8/no7OwcVvGooaGBDRs28M1vflNLLxpPaqCiKLS2tvLkk0/yu9/9Dhi4hsvLyyksLBw2b3R0NMuWLWPy5Mm0tbWxY8cOtm7dis/nIzg4mJkzZ7J27Vpmz55NVVUVv//97zWRLSkpibS0NE6fPo3T6dQijgAiIiK0FKbxoigK77zzDocOHSIsLAyfz0d5ebm2neuuu4558+YxY8YMFixYwHe/+138/Pxwu9309fUNE2P0ej1z5sxh//79REdHM336dAwGg1bVTU1RUX1O7Ha7Jkb4+fnxjW98g9tvv53u7m5efvllzp8/f9XnKSgoiN/+9rdatILRaCQ5OZkf/OAHLF68mOTkZEJCQjQh8qWXXhoWtaVGNqkppmok2KWRGkIIbr75ZoKDgzl58qRWHU8VtrZu3cqePXsoKirSvKFUA/rXX3+dxMREZs+eTXx8PKGhoej1erZv386zzz7L3LlzmTJlyoRXlXI4HFRVVWnXh3q96PV6IiIimD59OoqikJ+fPyLSSfWwUvuiKiaqJdHDwsIAePXVV3nrrbe0e/gPf/hDtm7dqpVyLykpGVO0sdvtdHZ2atGUu3fvHlcECwyIZw6HY0TEYVJSEhkZGVoEzaWopu7BwcH8+c9/BgZ+Y8LCwjQBd/PmzVRVVV31CxBASw2eP38+K1euxGAwcOLECWpraykpKeH48ePU1NRolRENBoPmFWUymUhKSuKRRx7BZDKxceNG8vPziY2NZc2aNdx1112ax92lUbASiUQikUgknwWfC9Fm2rRpXHfddVpkwO7du0cN//Z4PLjdbi16RC1b7fF4qK2t5cCBAxw6dIjy8nLNR8Pj8fDOO+/wy1/+ktmzZ+PxeDAajbS2tvLiiy+yYsUKUlJSuOuuu3C5XJSVlZGbm8v777/PmTNnLvuBzuPx0NnZydmzZ1m+fDlmsxmbzcaCBQvIysqip6eH5cuXa2+h9+3bpxmWKoqCxWIhLS0Ns9msRb6EhITQ29urlcnu6+sjMTGRlJQUvF4vFy5cQFEUurq6+NOf/oTH4+H8+fPD9qu3t5f+/n76+vro7Oykv7+fHTt2sHz5cr70pS8xZ84cioqKOHjwIAUFBVr6RExMDEVFRRQXF1/VOVVTLQICAliyZAnp6emaCW1UVBQBAQE8/fTTmiGkx+OhsrKSHTt28OCDD7Jq1SqcTidTpkyhsrKSCxcuEBAQwLJly1i8eDF79+7F4/Fo/jUAf/u3f4tOp2Pv3r28/PLLXLhwgb6+PlpbW/F4POMSbFSBLTU1lS9/+ctERETQ2NjIK6+8wsWLF7nvvvt46KGHMJvNNDQ0UFlZOWLQYjAYyMnJYe7cuXg8Hs1s+/rrrx+1n6n9XY2c6e7upr29XTMovrQ9JpNJE2FULk0x7Ozs5Pjx48DwCIHxDFx6e3vJy8vT0mF6enro7u4ecQ2rg35V1HnnnXdwuVzodDoyMzNZtGgRkyZNwul0cvr0aW2QrJr1RkZGYrVa6evr47777sNkMmkRSZeKB+PB4XDgcrloaGgA0NrR09ODw+HgzJkzLFq0iFmzZvHv//7v/OxnPxvRXiEE8+fPx2azkZ+fj81mw2azkZSUhE6n08Sd4OBgAgMD6erqIj8/n7lz56IoChkZGVx//fWkpaVRWlrK+++/P67+qwpjcXFx2nfr16/XPIQCAgK06L3a2lpNnIG/+oklJSXh8/moqqrSKtFdGq2g0+lITEzEaDRSUlLC6dOniY6O1oTmHTt2UFhYqFVWUgfnap9wOp1ERUVpZd4bGhp4+umnqampYcaMGVqp9fGKFkNRB/tqdS71uo2Pj9fM1H0+H5s2bRpRoczf31/zfxlqaK9GXaqiTVFRETU1NfT29qLX6ykpKdGul6SkJGJjY7WXBWPtn+p35HQ6xy009PT00NbWRnJy8rCy30FBQVo6qtFoHHFvCA4OZsqUKZoXl5p2eeLECY4ePcqOHTuoqKgYt+9SWloaN954I1/+8pfJzs7mwIEDPPXUUzQ0NGgils/nY+7cuUyfPh232013dzcOh4O+vj4sFgs2m43u7m7y8vI4f/48paWl9PT0kJOTg9VqJS4ujp6eHpkiJZFIJBKJ5DPnmhdt1PBx1dS1vLx81PBvn89HV1cXhw4dYtq0aej1enp6eujo6KC6upojR46wZ88e7cFffdBU354+++yzrFu3Dhjw26iurqaoqIiysjLi4uLIzs5m/fr1NDc3k5yczIEDB66oHaqnimr2qhqnmkwmQkJC8Hq9xMbGAgPpXYWFhTQ2NuLz+TTD3fDwcMxms1Zhxu12U1JSQn5+Pi6XS/OEiIqKwuPxUFZWBgyk/5w+fRpFUUYMKnp6eujr69O8WDweDwUFBXR0dJCSkkJsbKw2qPL398fr9WolnPv7+6mqqtK2fTWYzWaWLFmiRcoEBgbi9XrZvXs3J06c0EQIgIaGBvLy8viHf/gH4uLiWLNmDeHh4ezevZsPPviA+Ph41q5dy1133UVlZSVWq5WMjAz6+vrIy8tj2bJlWprK2bNnh4li46W2tlbzn1AHuH/6058oKyvjxhtv5OabbyY2Npbi4mI2bNhAV1fXiO0aDAbmzZtHaGgoR44cYdeuXURGRjJz5kw6OzuHzav2p7a2NqKjozGZTAQFBdHU1DTs2hjqVaJ+Pgq1nLuaBpGTk4PdbqelpeWqB1pqX1X/39nZidPpHCEyqPuqDgijoqJYsWIFVquVxYsXExERofXro0ePaulC7e3tJCUlkZubS0VFBfv27ePGG2/E4/HQ399PbGwsKSkp6PX6CYkUUyP0Lj0e/f39NDc309XVRWhoKB6Ph2PHjmn7MVQgMxqNfOUrX0Gv11NRUUF7eztz5swhOTlZi6zLzMwkNjaWzMxMWltb2bFjB1arlZycHO6++25mz55NR0cH+/fvH+b5dTV0dnbidru1SBBAi25U+0xVVRX5+fns2rVrmHm0GgV1/fXX09/fz/bt27nllluIiIgYIdoM9Xdpamri1KlTmEwm/P39URRlWClsNSoiMzOTrq4u7Ha75gOj1+txOp289957HDt2TBNpVKPgS72DxoPqNeTz+Th06JB23iMiIsjMzCQhIYH+/v5RhVh/f3+WL1/O0aNHyc7O1szUDQYDjY2NJCUlAQNlwNXfJa/Xi91ux+l0YrVasVgsxMbGEhUVxcWLF8fcR9ULaSL6uCraTJ48GT8/P61dcXFxWh9VKyxeaqwdERGB0+kkPz+fnp4eCgsLOXPmjPZ7Oh6/HRhoa0pKCnPnzmXatGlYLBYOHz7MiRMntPRgGEj5y83NJSoqCrvdTnd3N93d3fT09GjG0F1dXbS0tGiVvtQXILNmzeKGG26goaFhwsqSSyQSiUQikVwt17xoo9PpMJvNBAUF4Xa7OXfuHBcvXhzxcKxGlGzevBmfz4fBYKC9vZ3q6moKCgrYv3//CM+Wofz+97/X0n9aWlqw2+04HA62bdvG/PnzsVqtLFiwAI/Ho4kbV4rH46Gqqora2lqtcsrQktWhoaH09/eTmJioTe/r66O4uJjExERtQOVwOGhvb6ehoYH9+/dz4sQJLSxffcvscDgoKirSBlxjVUJSxZr+/n5NDKqpqaG6uprQ0FCCg4NJSUnRKtv4fD7i4+MxGo2EhYVddQi5GgUVEBBATk6Odl70ej1VVVUcPnx4WOoODAwsS0tLqa+vJz4+nvT0dOrq6jh58iT79+9nypQpeL1eVq1axZkzZ4iNjSU7O5uqqirOnj1LYmIi7e3tnD59mo6OjgkNe6+qqqK9vV07Tg6Hg40bN9Lb26t5LtTV1bFjxw6ef/75Ufuh0Whk0aJF6PV6du3axalTp7jxxhu1aIyhKIpCR0cHpaWlhIWFERsbS2pqKq2trZpXjjqwCgwM1Exs1cicscrY9/X1UVdXR1VVFXFxceTk5FBaWordbr9q0cZgMGheQ8BHVuZSBRu1yowqumRlZXH8+HEKCgqor68f1j/Onz9Pamoq8+bNw+12axFYamUbq9VKdnY2UVFRWsqNmrI00dVh1EG3KlIlJCRo6Ubqda6a165bt466ujoaGxtxOByax4fP5yM0NJS1a9cyadIksrOzKS0t5ciRI0ybNg2v18stt9yCyWRi06ZN7Ny5k46OjnHtd1dXl1YVyeVy0dzcrN3jWlpacLvdWrri3r17hwkDmZmZ3HTTTSxZsgSn08mmTZtYs2bNmAbQqtdQT0+PJnaq87a1tQ2rOGUwGEhLS6O7uxu3243L5dI8p1pbW9mwYQPt7e0oiqIJ4b29vVrK2kQQERFBbm4uPT097N27V7vXqmk/fn5+WgW+oZhMJiIjI0lLS+OXv/wlfn5+msm5wWDQTLjVym/q8oqi0NvbS0lJCTk5OZp3S3x8/JiijdFoxGq1DvNwGQ+9vb20t7fj7++P2Wymp6cHg8FAeno6mZmZmkmxXq8f9lsYEBBAREQExcXF/PnPf8blcnH27Fnq6upwOp0Tcr0ZjUZSUlLIzs7G399/2P1AFR4tFgvJycnMnz8fgOLiYlpbW+ns7KSrq0tLl1T79tDfycrKSlatWsXq1avZsmWLFG0kEolEIpF85lzzoo0ali6E0FJkHA7HmOVP9+/frw2QOjo6qK2tpaysjObm5o98A9nY2Mh///d/D4s4URSFp556invvvZeYmBhtverbuitFDfE/fvw4NpuN2NhYrUoQDAxmjEYjCQkJ2Gw2TCYT7e3tvPTSS0RHRxMYGKg9BJ86dYqysjJOnjw57KGyqqqK48ePs2DBAk6dOvWxD/BqOoNqgtrZ2UlbWxvHjh0jOjqaqKgo/Pz8SElJwWKx4Ha76e3tpbS0dMQA60qOg8vlorOzk5CQEEJCQrQBmdPppL6+nrNnz44YBLndbhoaGtixYwe33XYber2e3bt3c/bsWc3vprGxkfj4eL7//e8TFBSkVUKJi4vDaDRy4sSJjxTvrpb29natqpnb7aaoqIgDBw7gdrs5cOCA9mZ3w4YNYxpwmkwmVq5cSV9fH9u3b6ejo0MbiO3atWvE/LW1tezcuROA+++/n7lz5w5LkfL39yczM5OMjAzsdjvvvPOOlg43lmijlh1+8803ue2227TzO56BoMlkIjs7W/u7oaFhROSQiiq42mw2Fi5cqLVDLQu/ZcsWreS9ynvvvUdmZiaLFy9m1apVREZGEhwczPPPP09vby+rVq3Sqt289dZbBAQEkJiYSEVFxZi+QmPh7++vCZyjoaZdpKSkYDKZuOeee7Ty4MHBwYSHhxMVFcV1111HVFQUf/7zn2lpaaGnp0ernuZyuTAajTzwwAOYzWZNfFZTrPr7+zGbzVqkW0FBwbj8QeCv0UNer5fq6mpef/11HA4HPp+PAwcOYLfbaW1tHdUnac2aNaxfv57Y2FjKysrIy8vT0n4uHaQriqIN3k0mE4qi0NLSMub+q+lRqqDU1tZGR0cHQghOnTrFwYMHtb4ZExNDREQE/f39E2I6rTJ79mz++Z//meLiYo4dOzbi3qGWt76U0NBQli1bhtlsZteuXaSnp2vpb0MjmlSRZuh6+/v72bRpE9HR0URHRxMeHq6Vsh6N4OBgsrKytOjB8aIa0+v1euLi4ujq6tKqRqWlpQED5yYwMHDYb0BISAiTJ09m48aNvPLKK1okzkQK5BaLhczMTNLT0+nt7eXDDz9k//79mpimphLfeOONzJs3j9raWt566y2qq6tJSEigqamJjIwMAMrLy4dFJqq/bUajkdzcXCwWy4Ttt0QikUgkEsnV8rkQbdTIEdUXZqwHQEVRqKio4L333gMGHnx7enro6uq6rJDxsQbTCxYswGq1aqkJy5Yt4+zZs1cVXVJTU8NvfvMbLly4wJo1a5g+fTqhoaHD3konJCRovi6tra0cP36ct99+G4vFgsvlorCwkOLiYrq6ukYMFjo7Ozl48CBtbW2X5eugKAovv/wyBoNBE6L6+vr4j//4Dy5evMj06dNJS0sjLi4Oh8NBdXU1b775Jvn5+dTX11+Vd0RPTw9FRUX8z//8D6tXr8ZqtVJaWsrZs2c5cOCA9tZ0NFpaWvjNb36jiXK7du2irKyM/v5+amtreeWVV/jBD36geXNUV1fT3t7OrFmzUBSFPXv20NzcPOGiTVdXF8ePHycyMpKWlhZ++ctfamkA77zzDlu2bAE+OnVBCIG/vz9vv/02NTU1mj9RT0/PqN5Bdruds2fP4u/vz5133snatWsJDg7WRDyTyUR6ejqRkZGaH1B3dzednZ3DUh4uxel08sQTT1BdXc2mTZtoaWkZt0HzAw88AAxcAwcPHhw1nWeoR4/ZbNbKHPf09FBQUMCRI0dGjbLbvn07iYmJWK1W5s2bxw033EBPTw//8z//g06nIy0tjfXr1/PQQw9RUVHB4sWLWb16Nd/73vcoKCi4orbccccd5OfnaylkQ/Hz8+OrX/0qd955JzNmzADg+PHjLF68GJPJxOLFizXD4Tlz5mgD88bGRqxWq1aZTRUc1IpKR44cYcOGDfT19VFYWEh9fT0pKSmUlJRQVlY2IZ4bqgGyn58fZWVl/PrXv9YGwR937idNmkRSUhL19fVs3LiRqKgoDAYDTU1NI8Rt9R44bdo0Le21vb19WFoL/NXzKjQ0lIcffpjNmzfT39/P2bNnCQkJob29neeff35Yqk1MTAw2m426uroRkWnjISAgAL1ez9133z3sfud2u+np6Rn1OtLpdKSkpPC9730Pu91OVVUVJSUlLFmyBL1ej9VqJTo6Wpv/0t8SNRXrnnvuwWazDRN5LsVsNrNgwQJ+8YtfaL5j46WpqYnjx4/j5+fH9773Pf7jP/6DyZMnk5aWhk6no6+vD4PBwM0338zGjRs103+LxUJISAg7duwYUR59ovD398disWgpZgcPHtT6qmr8vGjRIq6//nqcTifHjh3j+PHj2rPD0ONTUlIy7LcgKSmJX/7ylwATYsQukUgkEolEMhF8LkSb8+fP8+677+J2u7WH97Ho6+ujsrJyQvdB9Riw2+3U1tZqD6RXg6IoNDY2smPHDrxeL11dXUybNm2YaFNfX6+VXFXTtTZv3ozBYMDr9dLR0UFXV9eYbzDb2to4fPjwZe+TmkYylI6ODj788EMuXLhAcnIysbGxWhWVvLw8Wltbr3qwqA5MN2/eTGdnpybalJSUUFVVpfkLjEZ/fz9lZWU89thj9PT04HK5tEGTariclZVFdnY2er2ejRs3Ul1dzVNPPYWiKLz22mtjpuaMl02bNrF9+3YtWkXlcgcvXV1d3H///Rw5coTJkyfzgx/8gOnTp5OXl8fRo0dHzN/b20t1dTUw8MY4LS2N4OBgLV1NTXcrLCykpKSEnp4e7HY7NTU19PX1cfTo0VH3S029+uMf/6hFnF0tann69PR0YEBoKigoGLXPNTc388Ybb9DZ2UlycjK1tbXk5+dTVVWllX0fyw9jy5YtdHZ2snLlStrb29m0aRNVVVVYrVYtfSY3N5dXXnmFqKgoLfXjStvy93//90ydOpUdO3aMuM+sXr2ae++9l5kzZ2I0GnE4HLz88ss8+OCD9Pb2snz5clatWoWfnx8AGzdupLy8nL6+Ps2rymQyaYKPz+ejubmZI0eOcPjwYdxuN3v27OGll17i1ltv5Y033mDv3r1X1Iax2LJlC1OnTtVSuy43kjA0NBSz2ayZAr/22ms89thjWiWoS71lFEXhzTffZM6cOaxevZqcnBwKCgo4efIk27dv1yJzoqOjSUlJYerUqXg8HoqKivB4PLz00kts2rRJOzZDqauro6mpib6+vo+tnHa5mM1mrFYrLpdrxPk+c+YMGzZsICkpiUWLFqHT6bRrJSMjgxUrVmCz2XjmmWfo6+ujtLSUO++8k+bmZq0y38yZM0fdrpr2d/DgQWw2G8nJyUydOpXt27ePELSTk5PJzc0lKCiIn/70pxOSgtTV1UVFRQUdHR186UtfIiQkhEmTJhEVFcX58+c5fPgwt912Gz/+8Y/Zvn07ISEhKIpCUFAQFouFurq6ce/DWOj1egwGg1aYoKSkRPvOYrEwe/ZsVq5cSXZ2NkePHtWqVblcLoKCgrDZbNq6Lr23qQbR6osUtcrhJyE+SSQSiUQikVwu17xoA7B//36Kiorw+XyjDvY+TSbi7aHb7aa8vJzW1lb27NkzIuzd5XJRW1urPZx7PB6qq6s1Ycfr9X7sg/l4H9y9Xq9mynj8+HGMRiM+n0/zDBnvMXA6nRQVFeFyubQ0MNV883JSulpbW0eIVqpn0M9+9jPCw8PR6XRUV1czefJkjEYj58+fp7m5ecJ9TFQcDgc9PT1XLXL09/eza9cuenp6WLNmDTabDbvdTmFh4ai+Cl6vF6fTSXV1tVaKXk0n7O7upqysjN27d5OXl0dHRwetra1UVlZy+PBhoqOjP7bi0ERGI6kmqd3d3XR0dIwqvvT393Ps2DG8Xi/x8fHU1tZy+vRpurq6aG5uHlFOeSitra3s3r2bgoICzURVFUV/97vfYbfb+fGPf0xSUhJ6vZ7Dhw9fsXin0+kICwvjzjvvZNmyZZp3kEpycjI2mw2j0ahFCFZVVfH8889z9913ExUVRV9fH2VlZezYsYMnn3xSW0dXVxf19fXY7XZWrVrFzp072blzJ6dPn+bcuXNa251OJ88++yx/+ctfNHPViUD13tm7dy+bN2++7OWsVqtWct7hcNDV1aUdm7KyslEj5kpKSqirqyMmJoaUlBQCAwNJSEjAz89P64/R0dE0Nzfz1ltvcfvtt1NYWIjH49Eqj8HICCBVtDEajWNG6l0piYmJxMXFjZpu1dvbS3NzMxcuXGDlypXs378ff39/3njjDWw2G+vWrcPpdLJhwwYttVdNQ21ubub06dOsXbt2zG23t7fzxz/+EZPJxLRp0wgJCRl1vsWLF3Pbbbdp6XITgfpy4NChQ6xfv56lS5fi5+fH+fPn2bZtG0eOHOH666/n+eefZ926dcyZM4e6ujoCAgIQQnyk8D5ehpqVq8d57ty5WCwWoqKimDlzJpGRkRw6dIinnnqK4uJiuru78fl8mh/cWDgcDk6cOEF8fDxCCG666SbsdvuYXkISiUQikUgknwafC9Gmvb1dq7YzXu+GawHVP6K9vR2HwzHiraRaMWpoWz+LdqsVcpxOpyYYTVSYuDqoraio0Eocf5RXyKWMNV9/fz8XLlygqqoKIQRut5vMzExcLhcvvPDChKdFDWW8gp6iKNqAVPVPKiws5K233hpTaFKNp/ft26d5UMBAmpz6pr6yslI7tkVFRXR3d2O1WikrK/vEw/7VY6JWbFENW0fbrlrJ7dSpU1pFpfr6etxu98eaXauDzO7ubi1CDQaOT3V1NRs2bKCxsVGLrlEH+Ffalr6+Pk2cufSaVNNo1PN44sQJXC4Xr732GjExMURGRtLU1MTZs2f58MMPqamp0c6rGn1WUFDA0qVL+dnPfkZ+fj7Nzc3DfHcURaGtrU2LHpoIbDYb4eHhlJSUsGPHjlGjuj7qmHi9Xurq6jh//jwwILhs2bKFkydPjhrx0tzcTElJCampqcTFxREdHY3ZbNZEWNXHqL29HYvFolWSu7QC16XU1dVx8eJFbDab5oN0taheLPPmzSMzM5Nt27aNmMfr9dLU1MSePXtYv349OTk5GI1G7r33Xqqrq/nggw/Yv38/5eXl2v1u+vTptLa2UlxcTEFBAT6fT/uMtv7S0lJef/11CgoKNO+jS4mMjCQqKory8vIJvb91dHTw+uuvs2bNGi019/Dhw2zbto2uri7Onz/PTTfdhNVqJTAwkLy8PHp6ehBCfOKRKYqioNfrCQ8PZ8mSJfT29uLn50dQUBBWq5X6+nr27dtHSUnJsEp9ra2tVFVVsXDhwlHX29bWxhtvvEFCQgLp6emEhIQMK28vkUgkEolE8lnwuRBthg7Cvkj4fD7NuHYo4zV+/ST4pPbnSo1gLwe1hLmKx+OhpaWFbdu2XXPHdSxaW1upr6+npKSEoqKij5zX6/WyZ88eiouL0ev1WmWepqYmamtrhwkeTU1NtLW1YTAYrsqP6Gpwu92UlpYSGhrKsWPHPtKXyuFwUFNTQ1NTEy6XS6uGcznnTY0EG237JSUlVFdXD0uJutIUGkVRsNvt+Hy+UQ1K1Si0jo4OysvLeffdd7Uy1ps2bSIwMJCWlhYqKyspLy8fMVC32+2cPn2a3NxcqqurtTS2sfZlIhBCaFXg9u7dy759+7SUu8uhu7ubM2fOUF1dzcGDB4mPj8fPz4/t27dTWlo66vXtcDjYu3cvgYGBzJ49m8jISAwGA0lJSbjdbpqbm2lpaaGxsZHu7m62bt2q+f18FGq596ioqDErV10uVquV/v5+Jk2aRHBwMDt27Bh1vs7OTk6dOsXbb7+tVTtTy9K/+eab5OXlafudlpbG4sWLKS0t5fTp09TU1NDT00NDQ8OYYovD4SA/P5+LFy/i9XqHzSeEwGQyERAQgNPpvCKx7XJwOp0cPnyYkpISIiMjqaio4MiRIxQWFuLn58cHH3zAE088gZ+fn5bm90lFMQ5lqMhlsVjIysrSBFQhBG1tbRQXF3PixIkRRQuampq0a280r5ru7m4OHTpEUlISS5YsobCwcMKitiQSiUQikUiuls+FaPNF59N40P2/TEtLC/v27ftchbifP3+ePXv2UF1d/bFRVoqiUFBQQEFBgVZpTRU6RjM4dbvdE2Jee7k4HA62bduG1Wpl+/btNDY2jik6eL3eCUv5GYrqVTGedSuKQllZmRYdYjKZALToprq6OsrLyykpKeHkyZN88MEH2rZ37tyJz+fTBvWjtb+zs1NLh0pMTOTkyZNXva+XixCC6OhohBB8+OGHnDlzZkzfoNGw2+28++67eDwempubWb58OTqdjiNHjtDW1jZq31WPR09PDy0tLcyaNYvAwEAALULp7NmzlJSUUFtbS29v72X115qaGrq7u7FYLOMSg/39/bHZbERGRhIaGqpVsxuN/v5+ampq+O///m8cDgednZ04nU7OnDlDfn7+MKEpJyeH+fPno9frsVgstLe309DQwIEDB+ju7h7zmujs7KSzs3OEEOXv709GRgZWq5Vz587x9ttvX3WbR8Pr9dLc3MzmzZtJS0sjPz+fEydO0NHRgdFo5PDhw5q3TGFhIRcuXMBms2mpS59UdKjb7aatrY3W1lZCQkIwm804nU7sdjsOh4Pz58+Tl5dHWVnZiH1obW3l4sWLWmlwNW1KxePxaN5MlZWV7N27V5b8lkgkEolE8pkjRRvJF57Tp0/z4IMPfq6itQoLC7UImyuJqrgWBUC73c7vfvc7hBAjqgR93njttddoa2tj1qxZxMTEIITQvEY2bdrE1q1bKSoqorOzc5jQcDn+OWpZ85CQEM1755NGCMHUqVPx+Xx0dXVdkWCjopqeJyQkkJycTF9fn2aUPhYOh4MzZ87gdrupq6vDbDYDA9EdBQUFFBcX09LSckV9Xy0J3tbWdsVtUBFCkJSURFJSEj/96U8pLCzklVde+ci2qGlM//Vf/0Vvby8ej2dU37GioiLNMHjq1KkcO3aMvLw8tm3bdllRX0OPhV6vJyoqikcffZSQkBC2bt3Kvn37rrrdY+F2u3nyyScxm820trbicrlQFAWz2cwNN9xAcXExQgheffVVioqKNNEuJCTkE4vks9vtfPjhhwQEBDBv3jwsFguFhYUcO3aM2tpaiouLuXjx4qgCrWpSrRrFqymMQ/H5fNTV1X1kWqpEIpFIJBLJp4kUbSRfeD6p0rOfNJ+XVK6Pw+fzjWsgfS2xf/9+Tp06RVBQEP7+/hgMBrKzs4GBAaBavehqcLvd1NfXs2vXLi1V6JPG5/Oxd+9eDh8+PO40kK6uLvbv309PTw/Nzc0fec2pA2PVe2Qon6WoJ4Rg8uTJPPfccwQGBrJx48bLrsT3cREZBQUFvP7660yaNInk5GT279/P1772tavaz5SUFKKjo0lNTWXz5s0888wzV7Wey2E0Icxut/PMM8+wdetWYKDiYUBAgJa61NHR8Yntj2rYXltbS1VVFWazmbNnz3L27FkcDgfd3d1jVj/r6+ujoaGBQ4cOAXDu3LkxrzMp2EgkEolEIrlWkKKNRCKRXCaKotDV1UV3d7eWrqKWtf44o9zLwePxUFNTQ319/Sdqmq2iKArnzp3j3nvvHbe/VFdXF3v27GH//v2XLbxci+byHo+HxsZGXn311TG9bK4Gu93OqVOnaGhowGAwXLUoqwpLv/3tb9m/f/+Ee9lcLh6PR0s59fl8BAQEaBW+PmnB0ePxcO7cOcrKyoYZH6ui0VjH1uVyUVBQwKOPPgoMmGJ/UcRxiUQikUgkX1ykaCORSCRXyFC/oKuNrBmLsaoJfVKoqVHjRRVgrjUR5krw+XwcPHiQBx98kMrKSlpaWiZs3Yqi0NraSldXl1adajzr6uvr49133+XUqVMTto9XytA2uFwuSkpKePXVVz81Q+KrufbUaBv44kQzSiQSiUQi+WIjruShRQghn3AkEolE8oXG398ft9t9zabIpKamsmLFCrZt20ZNTc1nvTsAGAwGQkNDiYmJ4cyZM5/17kgkEolEIpF8HjmhKMqcS7+Uoo1EIpFIJJ8zAgMD6e3tveb8usYbRSSRSCQSiUTyf5hRRRuZHiWRSCQSyeeMy6lI9lkgBRuJRCKRSCSSiUX3We+ARCKRSCQSiUQikUgkEolkJFcaadMKVH0SOyKRSCQSiUQikUgkEolE8n+UpNG+vCJPG4lEIpFIJBKJRCKRSCQSyaeDTI+SSCQSiUQikUgkEolEIrkGkaKNRCKRSCQSiUQikUgkEsk1iBRtJBKJRCKRSCQSiUQikUiuQaRoI5FIJBKJRCKRSCQSiURyDSJFG4lEIpFIJBKJRCKRSCSSaxAp2kgkEolEIpFIJBKJRCKRXINI0UYikUgkEolEIpFIJBKJ5BpEijYSiUQikUgkEolEIpFIJNcgUrSRSCQSiUQikUgkEolEIrkG+f+d5TMpkGhJBgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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0zkVn6HQ6fHx8aGlpcbpcQEAANpsNq9XKwMDAZW/PZDLxn//5n3h5eREYGEhSUhLBwcG88MIL5OVN5JF57WAymVi4cCFr1qxBo9FgtVrZu3cv2dnZU35Xo9Hw4x//mO9973uXLYb7+Pig1+vp7e2lp6fnstbxr0BgYCBf+tKXeP/99zl37txV2eaiRYvo7u6moKDgqmxPMDPo9Xp0Oh1Wq/WTbopAIBAIBIJrhGtOtDEYDMTHx1NdXe3SDK/BYCAiIoItW7Ywe/ZsUlNTefrpp2c00iE0NBQ3N7cZW59CWloaGo2GM2fOqANaT09Penp6RgkmkiQRERFBUFAQUVFR+Pj4UFU1cVEAPz8/EhMTCQsLIysra1oDZZ1Oh7+/P4mJiZw7d25K0cbf35+MjAzuvvtu0tPTsdlslJWVUVxcjLu7O9dddx3Nzc1YrdZJB/YeHh4sXryYkJAQvL29mT17Nh4eHlgsFmbNmkV1dbV6Dnh6epKQkMCyZct4//33MRgMXLp0aVIBa6aJi4sjPDyc5uZmcnNzmTdvHrfffjtarZaYmBjMZjOenp7U1tZy4cIFDh486HR9np6ezJs3j3Xr1rFs2TIMBgOyLPPyyy+Tn59/TYs2oaGhhISEUFVVRX19/YSiiL+/PwkJCSQkJHDp0iUKCgro6en5WFEbOp0OX19fEhISOHv2rNNjtHLlSpqbm6msrKS5ufmyjqfJZCI5OZm0tDR0Oh2yLOPj40NfXx/19fXXtGgTGBhIeno627dvZ/HixUiSRFtbGy0tLdTX19PU1OT0+xqNhm3btvGrX/2Krq6uaQvhq1atIj09HW9vb7q7u+no6ODEiRPU1NS4tD6dTofJZMLd3Z3o6GhsNhvFxcXTut69vLyIi4vDYDDQ19eHxWLBYrG4JKjOJDfeeCNr164lMzPTpeUlSSItLQ29Xk97ezv19fXTEs6MRiPr16+nubn5sr4bHBxMaGgoAM3NzZSXlzv9vRRBW6vV4u7uTmRkJDabjZKSEnp6erDb7S5v/3Lx9PRkcHAQm8026fYMBgMGg4GBgQH6+/uveJumi7u7O4mJiQQHB7Nr166rsk2dToenpye+vr6UlZVNuby7uzsGgwFAFcU/TUiShJeXF/7+/lRXV3+sCQFXiY2NpbKyUqSOCwQCwaeYa0a08fb2xtPTk4CAAG677TZOnDjB0aNHpxzk9fX1IcsyCxYsYO7cufT395Obm0tmZiZtbW0z8pDz8fGht7d3xjsny5Ytw8vLi/r6egoKCtDpdFx33XWcOXNm3KBCp9Oh1+sxmUxqh2ksXl5eREVFkZycTEhICEajcVrt0Wg0mM1mdDodkiQ5XVav15OYmMiGDRtYsWIFdXV1XLx4kQMHDpCfn891113HqlWraG1tpb6+ngsXLoz6viRJuLm5MX/+fLZt24afnx9arRZfX198fHzo6elh1apV7NixY9Rv6ObmRmxsLCtWrMDLy4uOjg5KS0uvisCRmJhIeno6+fn5lJeXs3TpUtavX48kSWi1WrV9DocDX19fp6KNXq9n1qxZrF69mq1bt5KRkYFGM+QL/tFHH6n+QNcqsbGxzJs3D1mWsVgsE3Zcw8PDWbp0KRkZGZhMJqxWKwUFBR9rAOfm5kZoaCgZGRkUFhY6/d1vvPFGKisrMZlMFBQUUF9fP63UFHd3d+bMmcMtt9yCXq9nYGCAkpISTCYT4eHhrFu3jqeeeuqqdNqni8lkIjU1lXvuuYdNmzah1+tpbW3FYrEQHx9PVVUVx48fZ3Bw0Ok9MiQkBF9fXxoaGqY1yNXr9eq2AwIC6Ovro6+vj7/85S8cPXqUkydPTii+jPTtMpvNxMXFERgYyMaNG+no6OCll16iuLjYJQFJr9cTFhbGzTffjJ+fH83NzRQUFJCXl3fVU9tuv/12AgICXL6u3d3d2b59O2azmaKiIjVq0tVrx9PTk6VLl9La2sqpU6dcFm10Oh0BAQGkpaWxdu1aAM6dO0d1dfW4a0en06nnTlhYGH5+fhgMBgICAli3bh1tbW3s2LGDysrKKz6w1+l0REVF0d3dTWtr66TCXlBQECEhIbS0tFBbW/uxr12tVovBYMDLy4vw8HAAqqqq6O3tpb+/f9r3upiYGLZu3UpUVNRVEW00Gg0+Pj7Mnz+fuXPn8vTTT095bcXGxhIZGQlAW1sbDQ0NdHd3097eflXEOZ1Opz5vPwkPPK1Wy4IFC0hKSmLHjh3T8jlU+mSyLDM4OOjS8ZIkiU2bNvH666/T0tJyVY6xQCAQCK49romRocFgYOXKlSxdupRZs2axdetWtm7dykMPPUR+fr7TDp8yY6bVavH09MTT05M//vGP/OhHP+K9996jubn5Y83sKylR2dnZVFdXX/Z6JsLHx4f4+HjS09MpLCzEz8+P73//+zzyyCOjRBtZluns7KSrq4uqqqpJo4gWL17Mhg0byMjIwG63T3vgbzQamT17Njt27JiyYxASEsLq1atZvnw5nZ2dPPvss3z44YcUFhYyODjIwoULWb16NXPmzKG4uJh/+7d/G9UZVESL//iP/+CGG27AbDar6RNK6s1nPvMZ3n33Xbq6utRjUFVVRVhYGA8++CC+vr4MDAzw1ltvUVhYeMV9Nzw8PPDz88PPzw9/f3+WLl2KTqfD4XDgcDgYGBhAo9EQHh5OQkICkiRN2qbAwEAWLlzI8uXLiY6OVteh0+kwm81TimaXiyRJqjh0uZ0/g8HA3LlzWbx4sSo4ToS7uzshISEkJSUxMDCA3W6nurpa/T2ni06nw8vLi+joaBYtWsTOnTtpaWmZdF1BQUH4+fnh4eGBp6cnFy5coKyszOWIkZiYGG677TYee+wxYCji4Pvf/z4JCQls3rxZjTaa6fuCgkajQZKkaZt6S5JEcnIy27dv55ZbbqGvr4+cnBx27dpFVlYWGRkZrFmzhtbWVpqbm6mtrZ10XXq9nvDwcMrLy10WbQwGA6GhoSxdupTQ0FA0Go36233nO9/h+PHjPPzwwxQVFY3aL41GQ2RkJA0NDdhsNsLCwrjxxhsJCwtTo0ZOnjxJY2MjnZ2dTn9HjUZDaGgo8+fP54YbbiAoKIimpibCw8Px9PQkLy9vRlNonaHVapkzZw56vd6l31ERmzZu3IjZbCY6OloVGVwdIJpMJjVKac6cOS6LVIGBgcyZM4clS5awceNGYChi7o033hgl2mi1Wvz9/WlubgaGUrHi4+Mxm834+fmxatUqGhsbyc7Opq2tTZ1cuVL4+vqybNky6uvruXjx4qSizYIFC7j++us5f/48hw4doq6u7mMNgr28vJg9ezYZGRl87WtfQ6PR8MQTT5CXl0dpaSlNTU3T2u9NmzZx++23X7XoUaPRSEJCAl/84heZO3cuf/7zn51eF5Ik8ZnPfIabb74ZgLKyMsrLy8nMzGT37t1XpVCDv78/vr6+yLJMe3s7zc3NV+1ahqFj9vDDD5OSksKuXbtc3metVktsbCwAg4ODNDc3097ePmXbJUniC1/4AqWlpZw+fZq2traPvQ8CgUAg+OfjmhBt7rnnHh588EEWL16Mw+GgsbERb29vMjIyqK2tdSrajByEKgQFBZGUlERpaSmFhYXU19dfVrs0Gg0bN26kqamJgoKCKT00LgeDwYDJZFJToFJSUvDw8Bi1jCzLXLx4kba2NsrKymhtbZ1wXSkpKSxatIg5c+Zc1oPdaDQSHx/v0qD6hhtuYOPGjfj5+fHee+/xf//3f+ParHRog4KCxokQISEhPPDAA9x66624ubkxMDDAT37yEzw9Pfnv//5vdDod119/Pbfddhtvv/22Gs3R19eHTqcjKSkJSZJ49NFHGRgYwGKx0NjYOO19ng5KGPmqVatISUlh8+bN9Pf309jYqEZ49fX1kZqaSlRU1KTrkSSJjIwMbrzxRvX3bm1tZWBggJCQECRJumKiTXBwMMHBwdjt9suKNtDr9dx2221s3ryZoKAgjhw5os56jqW2tpbm5mZiYmIIDg5m1apVdHZ2smfPnsu6ljIyMli2bBmpqanMnj17SlEyNzeXO+64g5UrV9LU1MTp06f56U9/SkVFhUvbmzdvHmvWrFG38+abb3Lo0CEyMzPx9vbm7rvv5otf/CLf+973pr0vrhAfH4/RaKSpqYm6ujqXvxcaGsqWLVtYtWoV7e3tPP3007z77rvk5OTgcDhYtmyZmjJVVFTEV77yFafrW7x4Mbm5uS5Fa7i5uXHXXXexatUqIiMj0Wq1NDc3U11draZ+LFmyhISEBOrr61VxWpIkYmJi+PGPf8xzzz3H4OAgmzZt4stf/jI6nQ6NRkN0dDS33347Hh4eZGVlOf0djUYjX/jCF8jIyCAlJUX1jkpJSWHdunV89NFHVFZWXnGhV6vVkpSUREREhCpwTIWvr686KNRqtURHR9Pf309nZ6fL0RfKM7Gzs9Pl7QJs375dvcbi4uIAiIyM5Oc//zmFhYWqcGc2m7n55pvZu3cvWq2We+65h5UrV+Lu7o5Wq1V/r29961v85je/4cCBA1fs/ixJEoGBgaxcuZKioiLq6uqorJzQww8vLy8SEhIwm81YrVbOnz//sdJOli9fzpYtW0hJSVGP1x/+8Ae6urp48sknefrpp6dMQxxJZGQkISEhV1W0CQsLIz09ndDQ0CmfO2FhYcTGxhIXF4ckSaooP2vWLE6fPn1VRJu0tDRSUlIAKC8v59y5c5SUlFzx7SrodDoWL15MRESEy2nzer2eqKgotm/fjkajobe3l/z8fC5evDhpqvvY799333309vZy5swZent7P+5uCAQCgeCfjGtCtNmyZQthYWHk5eWRnZ2tzvZoNBq8vb2xWq2TehD4+/sTGRk5SrixWq3ceOONhIeHs3v3bv72t79dVrskSWLBggUcOXKE7u7uK9LB1+v1eHh4oNVqWbVq1YQDYI1Gw4oVK6acqY2KilI9b6abLpSWlsZ9993nsliwYsUKoqOjuXjxIr/85S/HfW4ymfD19Z10fWazmZSUFLXTowhjQUFB9Pf34+bmhpubG6tXr2b//v1YLJZR61L+HxAQQGJiInFxcVdctJEkCaPRyJw5c9QKXdXV1ezatYvW1lZyc3ORJAmbzTZpapq7uzs333wzTzzxBIGBgXR0dPD+++9z4sQJEhMTuf/++6eMrIiMjGRwcJDOzk6XzV0PHTpEcXEx3t7ezJkzB4fDweuvv87Pf/7zaZdmX7duHcnJybi5ueHu7j7psv39/arI5ufnhyzLfO9736O7u5tDhw5N29w2OTmZVatWER8fP2mK4Eh6enowmUwEBQURGBhIWFgYsizzyCOPuLS9uLg4MjIy1BL2hw8fxmq14u/vr+67kiYw0yxYsIBf/vKXhISEkJeXx+7du9m7d6/TqBiFLVu2sHHjRtzc3Ni5cyc///nP1ZQjGBJUAwICiI6OJjBwXEXBj4W7uzs//vGP8fX1xcPDg5aWFg4ePMjrr79Oe3s7e/bsQavV8tBDD9HZ2cmhQ4dGfd/Pz4/bbruNiIgIYmNjqa+vx26309XVRWZmJjExMWzbtg2NRjOpaOPh4UFKSgqbNm0iIiICvV6Pw+FAkiQ8PDzw8fG5aumHbm5u3H333RgMBo4dOzblBIKvry9paWls2LABQI2YNJlM48R8Z2zevJmwsDBKSkomFTDGkpSUxA033EBKSgp+fn7Y7XY0Gg1ubm7o9fpR91/l2Ww0GklOTiY8PByTyTTu+RUZGUlMTAznz5+/YvdnrVbLl770JRITE2lvb3eaFtzc3ExHRwdbt25lwYIFVFZW8vOf/5z8/PxpD4RjY2NZuXIlq1evJjAwUD1eSrqU8lyfDn5+fnh7e1+1yBFl0kuSJJeeJTfddBOJiYnqMTYYDPj7+xMREcHu3bvp6+ublsA83bYGBARw0003qR5dRUVFeHp60tbWdlUiUNzd3YmNjVWFF1eitJSU3pUrV3LnnXeq371w4QI6nc4l0QaGotkOHjxIcXGxEG0EAoHgU8g1Idr4+/uzZ88e3n33Xaqrq1UzvOeff56+vj5efvnlSWcY3d3d8fLyUl/LskxhYSG+vr7ExcWRlJSEt7f3ZVU/0Wg0rF27lv/93/9l06ZN+Pr6Xt4OOiE0NJSFCxei0+nYsmULFotl3KyfRqNh8+bNqhHqZINsd3d3jEbjuMgjV9BoNGi1WgYHB13qMColhOvr6yccFGi1WrRaLbIsj4uUioiIYNWqVSxduhQY+s1effVVioqKaGlpYceOHcyZM4cVK1aQnp6OyWQChnwaIiMjR0Wi2Gw20tPTqaurIycn54qUfFfo6+ujv79fNeq0Wq384Q9/YOfOnfT19WG1WjEYDOTm5qqmwmNxc3Nj+fLlavRReXk5ly5doqmpiY0bN2IwGOju7h73G2g0GmJiYvjFL35BVFQUPT09vPnmm/z2t7912mZJkti4cSOxsbEMDAxQXFzM6dOnKSgowN/fn7CwMBoaGlxOEdBoNCQkJODn54ckSU4rqo2NGJIkibCwMBITE6mtraWsrGxaHe3Q0FA1ascVbxpl20o7zGYzaWlphIeHU19f7/Q8N5vNqr+T3W6npKSEgwcP4u3tzfbt21m5ciVubm7ExMS43H5X0Wq1/PKXv2TBggVYrVYSExORJGnKVCaF5cuXExoaSmZmJs8///w43w6TyYSnp6d6jU6Ep6cn9957L5IkkZ2d7dJ15e7uTnx8PIGBgRgMBiRJIicnh2PHjnHhwgU6OzuxWq2YTCbmzp3LrFmz8PDwGDUA8fDw4LrrrsPPz4/u7m4OHz5Mb28vnZ2dnD59mhtvvNGpiezy5ctZt24dg4ODzJ49G3d3dywWiyqOG43GaQ+kPw5Go5F7772XhoYG2tvbnZ63Hh4ezJkzh1WrVhEVFUVnZye9vb0EBgaqA2tXiI+PZ926dQQGBuLh4UFsbCwJCQkEBwfz4Ycf0tHRMc7o3sfHh7Vr15KQkKAKEM3Nzeq5MhbFk+wzn/kMa9asITo6GoDu7m7a2tro6enh3LlzpKamEhQUREBAwPQOnItoNBp8fX3JyMggLCwMNzc3p8fJZrMxMDBAYGAg3t7exMXFYbFY+OMf/8jFixdd3q6Pj48akRQWFoZWq6WhoUFN0dZoNNN+Dl933XUYDAZOnjx51UyIY2NjWb58uVrhz5mA7+3tzdatW5k9ezYwJIpXV1cTGxuLwWBg8+bNtLS0cPjw4RkXUAwGA5GRkXz1q19lyZIlREVFodFo8PDwoKuri6NHj066TUmSiI2NJSkpiYKCAmpray+7kqGvry8bNmxQfZGmmvDw8fEhPDyc5ORk1q1bp14ng4OD6HQ6ysrKePvtt13adkBAAMnJyWRnZ18xYUwgEAgE1y7XhGhTVVXFmTNnOHXqFFarVe2Qp6SkYLfbOXDgwKTf7erqUh+ekiTR3d3Nu+++y6ZNm0hISCAlJYU5c+Zw9uzZabdLo9EQHx9PWVkZ3t7eeHt7X/Y+TobBYMDT01MNNbbZbOMGk0r4d01NjdMS10pY+kQpY64gyzI2m82lWRyTyURtbS05OTnjBoVeXl6YTCZ1Run8+fOjOjcBAQHMmTMHHx8fHA4HxcXFHDp0SK1o87e//Y0FCxawYsUKwsLC1FlxNze3UdE7DoeD2tpaDAaDao44ODh4xUoLWywWmpub6enpQaPR0NbWxvHjx6murlZFD41GQ1dX14TH32AwEBISwtKlS5EkidzcXA4fPkxXVxfp6ekkJSWRlZXFyZMnR0VK+fv7c9NNN7FhwwZWrVqF2WzGbrdPWLY5Pj4em82mltjWarXcddddVFRU8P7773PhwgVqa2vp6elh27ZtPPjggzz//PPU1dW5ZMqpDPCUqC9nAyQvL69x0VayLLNy5Ur0ej1Hjx4dF2nhDJPJhJeXF0aj0aV0hpCQEDUix263MzAwQGRkJOvWreO1115z6tGyevVq5s+fj91up76+nm9/+9u0tbXxzW9+kxtvvJHIyEjc3NxmPFLFYDCwaNEiFi5cyNGjRzl//jwOhwO73e7yea1U/amurqawsHDc54qp+eDg4KTHwN3dnQ0bNqhi0VQimV6vJyYmhgcffFAVbLq7u9m3bx+7d++mvr4eWZbZu3cvmzZtIjg4mLCwMLy8vNT7jRJhFhoaiqenJ0VFRTz77LNqO5VrT6vVTjg7rfi3KOJnXl4eJSUlNDc3s3TpUubPn4+bm9sVT4kaiUajISQkhHfffdfpfTU5OZnExETVb6i3t5e3336b9vZ2HnjggXFtjouLw2w2A/8w8Yeh6zM1NZX58+djNptJTU3loYcewmw24+vrS19fH3v27Bk1aNXr9dx111185jOfwcPDg5qaGoqKiqisrGT9+vVq2s9YtFot69atY+7cuRiNRnUQrwh0x44dQ6/XT1kF0t3dXTUqn+69W6PRYDKZiIyMxGw2TynIKc9WpcqV0Whk0aJFzJ49m9LSUpe3f+utt7J9+3aio6NpbW2lpqaGY8eOcdNNNzF37lynkyuTER8fT2trK+3t7ZOmQM80s2fPZuHChWrEZFBQEDExMVRWVtLY2Djquv/BD36gLqsUANi3bx/r168nNTWV9PR0WltbsVqt7N69e0bbGRwczOc+9zk2b96Mn5+f2r8ICwsjJSWF9PR0ioqKRn1HqTSYnp7Opk2biI+Pp6SkhA8++IDjx49fVgqaj48PK1euVKPe/P398fHxob+/n9LS0lETVD4+Plx//fWsXLmS+Ph49TpRnoezZs1i7ty5hISE0NDQ4HS7Wq0WnU6Hh4eHS1GmAoFAIPjX45oQbc6dO0dRUdE4g12tVovZbHYa7qxUi1A6SJ2dnRw+fJjY2FhiY2MJCQlRywNfDgEBAQwODqrhzlcKJRJgsoGzyWQiOzt7XEdqppBlGbvdrnonTNXh1Gq1lJWVTeiL4u7urg6Ourq6OHTo0Kj1mc1mdea1vr6e/fv3k5OTQ1dXF4ODg2RmZmKxWJBlWfVIgKHZKaVTJMsy/f39nDp1itmzZxMREcGiRYsYHByc1Bj341JfX09FRQXz5s2jr6+PgoKCcca2DofDaXnzuLg4YmJi6OzsJDs7m/LycoKDg1m2bBkBAQG88cYb5ObmjhpM+/v7c/fdd5ORkcHBgwcZHBwkKSkJHx8fIiIiqKurY8WKFfj5+ZGWlsbAwIBaclySJFasWMHzzz/Pnj17KCsrw2az4enpiY+PDxs3bmTXrl00Nze7LNoog/KpzhEvLy98fHxGiTaNjY1qeLnFYuHEiRMun886nU71N3El6iAoKEiNBLJarbS2tmI2m1m+fDkffvghNptt0n247rrrSEhIoKenh+zsbN566y0iIiLYunWr6omhHI+ZxMPDg23btnHs2DFeeuklLl68SFBQEImJiSQkJJCZmTnlwNLT05Pm5mYqKirGLatcmxqNBpvNNmmKkV6vJy4uTjX7nOo3MhgMzJo1i02bNqnnRk5ODpmZmarfhE6n44UXXmDRokUEBwcze/ZsZs2apQ5YbDYbHR0dGAwG9Ho9bW1tnD59etR2FMF0bHtMJhOhoaGq34Ysyzz99NOUlZXh5+eHw+Ggp6eH+vp6ysvLr5pnCAzdK4uKipwew7S0NFauXEl6ejoxMTFcunSJDz/8kPT0dFVwVK5PjUbDwoULSU1NVVMPletMkiSioqIICwtThbTQ0FB0Oh0Gg4E1a9Zw9OhRVbRRnjtbt24lLS2NkpIS8vPzOXfuHM3NzaxevRqr1Yrdbh91rSjPi8TERLy8vGhsbKSyspILFy5w4MABrFYrxcXFGI3GSX11vLy8mD9/PnPmzFHX8eqrr077+Lq5ueHl5TUuhWsi3N3dRxm922w2NRKiuLiYkpKSKc91o9HI5s2bycjIoLOzk8LCQi5evMipU6fYtGnTqN9rOsKNn5+f6vU1k8JiSEgIJpMJi8VCRETEqM8ULx7FAP/mm28mNTWVQ4cOceLEiVFRtHfddRdBQUFYrVZqamo4ffo0Bw4cIDg4WK1YmZGRQUlJCQcOHJjRfoqfn58qlg8MDDA4OKj6AYaHh7NgwQLefPPNUc9NX19fFi1axL333suyZcswmUzExMTQ3NxMSUmJ03uATqdj7ty5VFVVERERoU4cJSUlkZSUpPZNN2zYgI+PD42NjezatWtUf8jHx4f09HQ2bNhAREQERqOR3t5e9Ho9bm5ueHt7ExYWRnh4OBaLxekkgoeHBzabjdbW1suOEhIIBALBPzefuGgTFxdHVlbWhCk2NTU1JCQk4OnpidFonHAwPDg4OKpzoHQocnJySEhIUGfhLhe9Xk9gYOCoGZKZQvE/6enpUSNuJgrx1el0+Pj4cOjQIWpra6+IaAOoUSqudAr6+vqoqamZcMZ7cHCQgYEBdeb18OHDozqhbm5umM1mBgcHOX/+PDt37qSxsVGNnrDZbGr0ysjBQldXF9XV1ao/hWLMuWrVKm644QbWr19Pb2/vFRNt6urqKCoqIiUlRY2KmY7JpLe3N6mpqXh4eJCfn09jYyPBwcGkpqYyZ84cdXZ6rGimzJhfvHiRxx9/HKvVyqOPPkpMTAxbtmzh+PHjfOMb31CvFXd3dzIzM+ns7KS+vp6AgADeeecdysvLR507ShTAdD0+XDVKVlLuFAYGBsjKymLhwoVER0cTHx9PQECAy6He0zVoVkrXOxwOWltbycnJISIigvnz5xMSEkJ3d/ekHWWlhLHFYuHUqVPAkPl2bGwsXl5eaLVa1WtlppAkCS8vL7Zu3crdd99NQUEBg4OD+Pn5kZiYyIoVK9i9e/eUoo3RaCQ3N5fc3Nxxn/n4+Kjic2dnJ0eOHJlwHYpnycDAgGqS7QyDwYCPjw+hoaHY7XZ6e3t56623KC0tVZcZHBzk7bff5stf/jJ+fn6qsJ6ZmYksy/T09KjG825ubhPe5ya7N0VERJCamqr6bRQUFHDq1ClCQkJYt24dc+bMoampib1796oVqK4WDoeDqqoqNaV0LHq9npSUFFJTUwkPD1ejE7u6urj//vvx8PBQK80oxMXFcd999+Hh4aH6fynXxkhR02Aw4HA46O7uxmq1Ehoaql6TGo0Gd3d3wsLCVGP3vLw8zp07R1NTEyEhIQQGBlJRUUFvb+8ocVqWZbq7u/H39weGqisePnyY48ePq9cLQEtLC5IkjXt2BwcHs2DBAh599FFWrlyJ0WikrKxsnGij1+vR6/WTRilJkoRWq3WapjkSLy+vUdFxbW1tmEwmFi1aREVFBS0tLZPe0xXBOiQkRI2ayMnJ4ezZs5SVlREUFERwcDCdnZ20trbS2to6rWe1yWSio6NjyrTT6ZKenk5ycjJFRUWsWbNmVBRoWloa/v7+aDQa/P39+e///m9VBGxvb6empga73Y7ZbFbTvpqamjh79iwHDhygqKiIixcvsmXLFvz8/AgNDSU6Ohpvb+9pGWA7QzlPAwMD6e/vV/3t/Pz8cHd3x8PDg6ioKHWbyrNz1qxZ3HbbbWzbto329nZKSkqw2+2YTCZCQkKcmhd7eHjw4IMPsmvXLtatW6cWiwgJCSEkJAQYEoUeeeQRPDw8KCwspLu7W71nw9DzPjAwEH9/f9zd3bFarVRVVeHj40NgYCB6vR6TyURYWBjV1dWTnnd6vR4fHx9aWlooLCwUqVECgUDwKeUTF20OHjzILbfcQk1NzbjPjhw5QnR0NPPmzWPBggXjZl0nQ5ZlampqKC8vZ9asWaonynRRZvUXLlyoDkRmEq1WS11dHWfOnFENhNva2kYN2LVaLb6+vuh0Oo4ePXrFPFuUTr6rlbbq6+vVtK2xYclKuHhBQQFHjhwZ99t6eXkREhLCwMAA586do6CgYNLOrdVqVQcLg4ODaulYZdBQXFyMyWRSIxESEhKmu+su09vbS2NjIxaL5bLOh4iICG6//XbVwHjBggXExcWp5sunTp3i/fffH/UdZaCg0+lYuXKl+v6+ffu49957+dnPfoZOp6Ouro62tjbee+89kpKSWLhwIX/+85/5yU9+QmNjI9XV1aMEioGBAfLy8vD09CQ1NZWioqJJzb4vl4aGBmpqatQ0KqWS0Q9/+EMWL17MokWLSElJuWKd0IKCApYvX45WqyUvL4+XX36ZhIQEHn/8cRYvXqxW/ZoI5RocGBigvb0dnU7Hl7/8Zfz9/VUx6HIrzkyGwWAgMDCQqKioUSWpFdE2PDzcadShgs1mIy8vb9x1qeyXLMuq6PrBBx9MuA5lEDfW/2Qy3NzcMJlMamROdnY2r7766oSibmZmJvPnz6enp2eUAGWxWMjOzmb16tU4HI5p+WLMmzePJUuWEB0dTU9PD0VFRcTGxrJ48WLmz5+PVqulpKSEo0ePcunSJZfXOxNYrVYOHjyoltAey6xZs8jIyGDu3LkMDg6SnZ3Nrl27uPPOOwkNDeXIkSN88MEHnDt3Tv1Oe3s7er0ed3d3JElSDYvhH5WjlJn5vLw8Dh48CKCmLsHQfTgmJoaMjAzCw8OpqKhg7969tLe3q9XxfHx8ePzxx6moqBgl3A0MDFBbW4vD4VDF99OnT1NeXj5q3ya7p/zP//wPt956K2azWRXhw8LC0Gg0OBwO9RqLiooiNjaWPXv2XObRH43RaMRsNqvXd0FBAaGhoYSEhJCSkkJxcfGkg2eDwcDs2bNZunQpUVFRWCwWjh8/TmZmJuHh4Wokys6dOzlx4gQfffTRtM7hJUuW8Nprr6HX64mPj5+R/QWYP38+X/nKV9QUOq1Wq6ZQKynVMHSfCQsLw263ExcXx9y5czlz5gwdHR1cf/316PV67HY7e/fu5e9//zunT59GkiTeeustFi9ezKZNm9T7VHBw8IyJNp6enoSGhmIymaivrycvLw93d3eSk5MxGo0YDAbCwsLGRULPmjWLdevWUV1dzbPPPsvx48ex2Wy4ublNKbZ7eHjwhS98gW3bthEeHq6ejyNTz3U6HSEhIdjtdnXyJSAgQI0cjIqKIiQkBKPRyMDAAHV1dRw4cIC5c+eyePFivL29cXd3JyQkZFJDf61Wq0bpdHZ2qmXkBQKBQPDp4xMXbZyFECv+LdHR0SQmJrok2ihVns6ePatWulm9evW02+Xr68sXv/hFBgcHWbRoEXl5eeM6pB8XxZ/Dw8ODz3/+82g0GvLz80flRev1embNmnXFfRiio6PZsmWLmvYxFcePHycjI4Ply5dz/PjxcZ+/9957nDlzhurq6lHvR0VFkZGRQWJiIkVFRfz973+nsbFx0v3r6OiYcnBcX19PdXU1QUFBauj7lThe7e3tlJeXq4MVV817FZSQakmSWLhwoRpdZbVaKSwsnHBgMmfOHK6//vpxZs7Z2dksWrSIe+65h5qaGhITE9V9DgwMVGfTf/GLX/Dqq6+Om+nu6+vjgw8+wMvLi8cee4ysrCyXq1i4Sn9/P1arVRUKOjo6uHjxIkePHlVnbefOnTvj/gcKPT092O12rFYrdXV1ZGVlcf78ef7t3/6NhIQEDh48OGnp8crKShoaGtRIDQ8PDxYsWEBPTw/79u3D39+fpUuXzqiprZ+fH6mpqeP8DZqamsjPzyc9PZ0lS5ZQVFTkVEipqanBaDTi7+8/LoKxoaGBqqoqLl26xIEDByZNj1Jm+9vb2126lvz8/NQy97W1tZw5c2bcOavQ3d3NwMAADQ0N4wS76upqLBYLTz75JE899dSU21V44IEHuO666zCbzbS0tKDX6/nud7+Lt7c3PT097NmzhxdffPGqGbzCkJAVHBzMnj17KC0tJTc3d1wKMAxFQgQHB2MwGGhoaKC0tJTY2Fi2bNmC1Wrl5MmT5OfnqwNNh8PBU089xZ49e9Q0KB8fHxYuXIivry+PPvookiTx1FNP8cILL5CVlTVh+4KCgsjIyFBFsuzsbPz9/UlMTFTTtFpbWzl58uS4e7DNZuPixYv09fXR1tbGnj17OHHihEvHRZIktmzZAsA3vvENDhw4QEJCAk888QT33nsvL7/8Mj/72c9YtWoV0dHRNDU1UVhY6LTEu6s0NDRQUlJCamoqDoeDP//5z0RHR3PnnXeybds2bDYbmZmZE35XMclet24dkiSpflELFiwgNTWV5ORkLBYLBw8e5MyZM9MWo2NiYujt7cXLy0u9lmaC3//+97zwwgt4enoSHBxMWloanp6e3HzzzcydOxdJkmhsbMTDw4Pjx49TUFBAfn4+WVlZtLS0YDQaSUlJQaPRkJWVxd69e9UoPlmW1Qpcqamp+Pj4EBcXx7Zt2yZMnb4c/P39iYuLw83NjRMnTnD48GEiIyPViCC73T7ufhgeHk5CQgL9/f3cf//9o66Bb37zm8THx/PDH/5wUmN3JbU7KipKjeYaS0tLC2fPnqWoqIjCwkIuXbqk3rv9/f1ZvHgx0dHRanTS8ePHefvtt1m9ejXR0dEYjcZxaYdjUYy2AU6dOnXVvI4EAoFAcO3xiYs2Hh4ek5rm5ufnU1lZSWhoKPPnz3dpfdXV1Wp6jsViobu7e9qVK9LT03nssce49dZb0Wg0rFmzhp///OczKtr4+PioKUJ2u50VK1YAQ/4+IyM4IiIieOmll/jGN75xxQx2YSgFJyQkxOXogQMHDhATE0NkZCS+vr5YLJZRn1dWVo4y6FWYN28eCQkJNDU18eSTT1JVVXVZAsvg4KBaZamgoIDc3Fy2bt06KhrlSnA5BpMKykydJEmEhoaqaTt79+7lpZdeGpeqEhgYyObNm9m4cSP/8z//M64dDoeDiooK7rrrrlFtUlISlApVFRUVTgWm0NBQlyI4Pi6KoPrqq69SV1fHsmXLWLdu3ZQVsC6XsR4cNpuNxsZGmpqapizHm5eXp4phGzduZNWqVeh0Ojo7Ozl58iQREREsXLhQLVk/Eygzym+++eaotpeWlrJr1y7i4+P56le/yosvvuhUtDly5AjJycksXLhwVHSGwhtvvMGhQ4emLAWtDEyVlLt58+YRGRlJcXGxGrmhsGDBAm655Rb6+/v54x//yMmTJ51GBcqyTH19/bjIvuLiYlpaWqadAmoymTAajbi5uREQEKBWoFIMvyeKArnSmM1m4uPjyc/Px9fXl+Li4glFm89//vPqjH1fXx++vr58/etfJzg4mGeeeYYdO3ZM2PaKigpVFNdoNBw8eJCFCxfypS99iYGBAQ4dOjShWblCUlISn//854mNjaW/v5+BgQEeeOAB1QuqubmZV199lfLy8nHnm5Jy1dvby8DAwLRLVPv7+7NmzRouXbqkmt+/8cYb/OhHP+KHP/yhKra2t7cTHBzM7t27mTt37rS2MRGKkKzsQ35+PtnZ2aSmppKRkeG0Gpy/vz9f+MIXVIG8v7+fdevWMWvWLMxmM/39/fz973/n9ddfp7Ozc9qivsFgwNvbW53MmSl6enqwWq2qIJiZmYlGo2HOnDnMmjWLtrY2XnzxRZ566inVL0bx5YGh+4C3tzcWi4XnnnuOrKyscX2RvLw89f7e398/4Xl+ufj4+ODr60tOTg579uwhOzub5uZmYmNjcXNzY3BwkPz8fLVNPj4+bNiwgXnz5nHkyJFx14C/vz8hISFOU9BaW1tZu3atmsoUGxvLunXrWLt2rWrWfdNNN1FbW0t/fz92u31U38lsNhMXF4e/v786KbNv3z6Ki4vx9vZm4cKF2Gw2tZ80Wd/OYDCwbNkyJEni4sWL4/pZAoFAIPj08ImLNlqtlgULFlBTUzNuhvnSpUucO3eODRs2MH/+/EmjOkZSVlbGwMAAXV1dFBQUEB0dza233upy2e+7776bz372s0RHR/OnP/2Jxx9/HKPRSGVl5aSz8peDUjZZKeOolNE8d+6c+gBX/Ab8/f3Zt2+fS0axl4ter8dgMLjcKaivr+fYsWPExcWRmpo6bhCn+NGMxNPTk4SEBMLCwujq6uL06dPjOvsGgwGz2UxERITTClhWq1U1Ze7t7aW7uxu9Xk9QUJCLe3z5KILJx6Wnp4fi4mKysrLIzc0dFw2zfft27rjjDjw8PMbN/gYFBeHv709nZyd5eXnj2jdy0D9r1qxJBYrm5mbVhyA4OPiKeX0og3SHw0F7ezsNDQ309vZOWplmJrHZbPT396ti7pkzZ0hKSpqwlLHC0aNHWbRoEStWrFBNI/v7+3nyySc5f/48fn5+o1JSZgLFi2TsAF0ZwHZ2djJ79uwpfX2OHDnCnDlzCA8Px9PTc1wqQHFxMeXl5ZOmhi1ZsoQnnngCSZLo6uri4YcfJiQkhJiYGHx9fSktLaWgoGCU4OLj40NwcDC1tbW89957dHZ2Tnm/GhwcHCcSKwLAdK8vJc1Dq9Xi4eGhpmeUlZXxzDPPcPr06avqBWEymZg/fz4PPPAATz31FHfccQc7d+6cMKVSSblT7l9K2kVraytZWVm0tbVNKACMPU46nU6d3Kivr58yStFoNKqVeGCoglVkZCTu7u40NTWRmZnJX//61wm3bbfbaW9vV4VzV9Hr9axevRq73U5bW5sqoFgsFo4cOcLjjz9OZ2cn//7v/05ZWRlms5n09HS++93vEh4eTl1d3ceOolS+rwi51dXVNDQ00N/f77Qyj1arxd/fX41GjY+PVyvk2Ww2VRzs7u6+rNRJjUZDdHS0WnltJlF+o5FijNFoxGazUVhYOGnUoZubG/Hx8Wzbto1nnnmGffv20dDQMO43GBwcVJ87XV1dM3at+fr6EhcXR3h4OJcuXSIzM5P6+noaGxtpaWkhODgYWZapq6tTowLj4uJYsmQJJpOJDz74YNxv0dvbi1arJSEhYdLorcHBQcrKylQD76ysLIxGIxkZGciyzO7duykpKZk0RTo5OZmoqCjc3d1paGhQ297U1MTRo0dpb2/H19eXtrY2Ll26NGm6lkajwcfHBxjq217JiTuBQCAQXNt84qJNZ2cnS5cupaSkZJyDvpeXlzrz5OXlxU033eRUtJFlmdbWVux2OzabjaamJrVKgJeX15RVkby8vNSw8OzsbF577TW+/vWvk5eX51LZ2+kQFRWlmiKmpKSoA8j6+nq1UxUWFsby5cvp6+ubMGplJlFCgCdLaRiLzWYjOzsbg8HAqlWrxok2E+Hh4UFoaKjaWZmoo2I2m4mJiSEpKcmpaNPd3U11dTU2m43u7m5aWlro6urCbDZjMpno6em5IilSiofDx1m3LMsUFxdz9uxZzpw5w4kTJyYMe05KSiIxMZGysrJxURFBQUG0t7fzxhtvjPvN3N3dmT9/Ptu2baO7u5vFixdPKC7Iskx+fj4BAQGsW7eOsrKyGfOOmIiqqip1sFdZWUldXR1Lly7Fw8Njxv2iRtLT06NWQFLC+a+77jqnUTJK6PuxY8fUUrwWi4V9+/bR2NiozpRO5kVwufT393Py5Mlx51dfXx8NDQ2YTCZmzZpFZWXlpAPD0tJSLl68qF5LY2eapzrWwcHBrFixQp2NT0lJUe/Dbm5u+Pv7c+ONN/Lcc88hyzJubm54enqq3krOhD/lHuBslvvjXreKqFVQUMCZM2c4deoUVVVVV1T0Hst1113Hww8/TFpaGm5ubqxfv5633357QoFD8U7TarV4e3tjMBjo7Ozkz3/+M2fOnHF5oObm5sby5csZHBzk4sWLU05SaLXaUWJXZGQkg4OD1NbWkpWVxXvvvec0Jclms01LnNDpdISGhvLAAw/w4YcfjhImBgYG1LS2v/71rxw4cIDGxkYMBgP9/f1qie6ZTH1VUlwVg9i2tjb1mTyRH4skSepvpdFoCA0NZWBggObmZqqqqnj11Vc5f/78ZQk2S5Yswc3NjVmzZtHY2EhZWdlM7OKkBAQE4OXlRUtLi+orNxEmk4m5c+dy7tw5du7cqT5zJ0PxmpvIo3C6GI1GgoODiYmJwc/Pj3379lFbW0tfXx+9vb1YrVZVZFMixWAo1VuJIJoopV5Zx/XXX+/0eadUALNarXR0dNDd3Y0kSdTX13PgwAGnVSJTUlIIDAzE4XDQ0NBAQUEBdXV1qs+Ukr4/MDDgVKDTarVq9FdHRwdGo1GtBlZQUHBFn5sCgUAguLaYfFR8lbh48SIpKSmsXLmS+fPnExERgYeHB0FBQSxfvpzk5GTc3NwwGo2sW7eO0NDQSdel+GYoHePe3l4aGhpobGx0yYz4uuuuw8vLi3PnzrFjxw5KS0txOBzs27dvxnOJ/fz8cHNzw8fHh5iYGLRaLbIsY7FY1E5fZGQka9ascRo+OxMYDAZ1ADudDnFDQwNtbW3ExcWNqsjhbDve3t6YTCY0Gs2oEHA3Nze0Wi1BQUEsWbKENWvWTLoepdJMfX09NpttVOi33W7H29t7xit9KSgiUUdHx7QrfCiznLIsc+bMGV5//XXeeecdcnJyJuwAKil0NpttXASUXq+nuLiYl19+edz3goODuf766/nc5z5HVVUVqamphIaGTjiLrPhGrVixwuUUxMtBmQ1V0irKy8spLy9HlmW8vb2v2HZhyIuoubmZ3t5eZFmmqqoKs9mM0Wh0KgxeunSJl156iTfffJOXXnqJF154gbKyMtWMenBwcMbTyvr7+7l48eK495WoLI1Gw/r1652KRV1dXRw9epSGhgbS0tKm3QZFwJUkiaSkJCIjI1XhuKmpCU9PT7Zu3aoeO3d3d7U9zgYgSsqe4k8xEYooermRbLIs09vbS1FREfv37+fAgQM0NDRcVcEmISGBDRs2sHbtWsxmMzfccAMJCQku3S+U47x7926eeeYZioqKnJYCHonBYCAtLU2dzXclMnRkyqbZbKa+vp7z58/z0UcfceDAgSm/P53nhY+PD2vXrmXLli28/vrro0R7xe/Lbrfz9NNPq89bm8024wbpSrttNpt6XtTW1tLS0oK3t7dTP5mRFbo8PDzo6uoiPz+fw4cP8+6771JRUTGtc1en05GcnMz999+PyWRSI+kmugfMJHPnzsXHx4fKykpOnz496bliNpuZO3cuzz//PBcvXpz0XDSbzWo0p9VqnZH+ktlsJjQ0VE0/KywsHPWc7O3tpb29nfb29lETF76+vphMJrq6uiYUHZWoKiUl3VWU52deXp5a8W4skiTh6enJvHnz8Pb2pre3l+rqaoqLi0e1vbu7W03fd4Zer2f+/PnIssysWbNYtmwZ27dv5/bbb2f+/PkzPmkgEAgEgmuXTzzS5sUXX+R///d/ufPOO0lISFCNShMTE7n11lvV0oxWq5X4+HhuuukmXnnlFXp7e8fNaClVo0a+r4Q/K/4Gk3UyJUniscceo6SkhGeeeYZz584RHByM0Whk9+7dMy7aKG0xGAwYDAa1zU1NTTgcDoxGIxEREcTGxvLWW2/N6LbH4ufnh6+vr9MB7GR0dXVRX1/P2rVreeONN5x2WJXZUiUVKzQ0lMbGRiRJIjIyEovFQlJSErfffjtJSUkAk85mdXV1jfINaGlp4cSJE7i7u+Pp6Tku1W6m6OzspLKykoqKCpdNmxUGBwexWCzIskxRURFFRUU0NjZOuo+SJE3aAa6traW2tnZCX5KEhARmz56tlmMNCAjgxhtv5J133qG+vl49ZiaTidTUVGAoReNKiidK2Lxy/VksFioqKmhoaMDT09PlqmWXQ3NzM9XV1epMqVKSWPGOmGy2sqSkhJKSEl588cVxnykC3HTPAWcogoWbm9s4n5De3l5KSkqQZZmvfOUr7N+/n56enkmvt4KCAuLj47nhhht4+eWXXR74j8VgMHDx4kX27NlDR0cH8+bN46abbmLevHmjykor6T1jzyFlGVmW0Wq1zJ8/36loY7PZaG9vn/SamAzFjNRut1NXV8fzzz/P888/f9WNOzUaDY899hjx8fGcOHGCRYsW8aUvfWnSSAYY+t1lWWZwcJCCggLVF2U6bVdE8PDwcD788EP2798/7eo97e3tnD9/njNnznD27FmXRJ/ppAhGR0fzjW98A29vby5dujTqnDQajcTGxlJXV0ddXZ36PBxZrWcmq7Up4p5Syry9vZ2uri7Cw8OZPXs2Z8+enXIdSgr28ePHOXv27LSfOXq9nvDwcL7zne9w/fXX4+npSXx8PFlZWVdctFm5ciUBAQGcPHlyUqNqGIoaiY+P59lnn3X6bE9ISFAF7I/j+zYSJdImODgYnU5HaWmpS99TKpBN1l6l76iU3XZV0NVqtXR3dzs1cIchEVsxGlYqaU7lHzYRkiSpVbIkSeLhhx8mICCAsLAwBgYGCAkJ4be//S2XLl264oUqBAKBQPDJ84mLNgcPHuTixYusWbOGO++8k5tvvpnGxka8vb3p7Oxk9+7dvPPOO/T39/PII4/wn//5n8THx/Pcc89RUlKirkfpKBQXF496CHd3d5Obm8uGDRvIzMyc9EHu5+dHSEgIH374odphs1gsaunZmS7vC6jRNUo4sVKuOjw8nDVr1rB9+3Z6e3v55je/OePbnozpPvxLS0t57bXX+NrXvsahQ4doaWlx2rlT1h8REcHXv/51NY1j8eLFVFVVERQUxNKlS9HpdKr/yNgoI1mWqa6uHlXGuqurSy3zq1RoulIMDAzQ399PRETEtL5XXV3Nq6++yqJFi8jIyCA3N5eWlpZJB6h1dXXs3LmTHTt2jPtsrI/NSGRZJi8vj3fffZe9e/fS0tLCt7/9beLi4jh06BClpaVoNBpuueUWAgICOHXqFIGBgVesnDwMDUwrKytHpfj19vZSW1s7o8LHWJQIH8WwVZZlCgoKqKioQKfTORVtnFFfX09ubu60Z2ud0dnZSXV1Ndu2bePYsWOqgAtDUQqLFy8GhmbJt23bxs6dO51W/Oro6KC9vZ0bbrhh0tLek6Fcp5WVlTz++OOqp8P69etZtWrVKIFXq9Wi0+nQ6XR4eXmNWk9AQACyLNPS0oLBYOChhx5i9uzZkw7s6urq2L17t8sDNIWenh76+vrQarVqpMknUWklMjKS6OhoMjMzee+993jppZewWq289dZbk/pWKB4oDoeDqqqqSdMlnREQEMDatWtxc3Pj1KlTlJaWupTqqvhzKREuL7zwAhUVFS6LZt3d3S6LvR4eHsyZMweA8vLyUc9Uxb/t1ltvHSXmKNEWAwMDM1I9SkGJym1tbcXhcJCXl8f58+eZPXs2mzdv5vXXX5/we4rAZrPZeO2119ixYwdZWVmXFQ2UlpbGs88+y9y5c7n//vv59a9/jUajobW1ddrn/3TZunWrmmLrrIS0m5ubWqHM2aTXunXr8PHxmdHnrnJ/ViIiXU3dhqF2TyYMNzY2Ul1dzbp167juuus4ceKES/0eSZJoaWnhvffec7qMTqfDbDaj0+nUc2W6IjT8w8hcidBbs2aNKuZLksQtt9wCoFY5FQgEAsG/Np+4aNPW1sb3v/99tm/fzvr16wkPD0eSJGpqavjVr37Fvn37aG5uxs3NjebmZn70ox9xxx13kJSUxHe+851xpp1jBQOr1UpFRQV3332300iSDRs28NZbb6nVVvR6PdHR0TNiOOuMyspKTp48SWtrqyra/OhHP2LFihXo9foJq79MxeXMuiidi+kaICppSrNmzcLT09PpYGNwcJDe3l76+/sJDw9n69atbNq0Se3oLF26FBjqrDkcDgoLC/npT386bgZT6WTn5eWpHbn29nbOnTunVlzKz8+/Yh2Zrq4umpubycjImPb38vLysNlspKenU1BQoBoRtrW1jVv+29/+NpIkTdvL6Pjx45w6dYquri40Gg1PPfUU//Ef/8HnPvc5br/9dnXwGBISwu9+9zt27tyJVqslKSmJr3zlK7z99tszWv5blmXsdjv5+fmj9sVisVBQUMDmzZs5f/78FfMgqq+vH2eMuXPnTpKTk9FqtZw9e3baM6EhISHMmzdvJptKQ0MDFy5c4OTJk5w4cYLf//73NDU1kZiYqKav7dmzh4SEBL70pS/h5+fHzp07uXDhwoTru3DhApIk8cADD3D69GlaW1unPMa+vr4EBgZit9tpbm7m8ccfJzMzU/WPcHYuGgwGoqOj+f73v6/eNxcsWIBGo6GkpAS9Xs+GDRvQarUUFxdTV1c3Ydrnvffey5EjRygtLZ1U6BjLSy+9RENDA8uWLSM4OJiVK1e6FC0xk0RHR/Pcc89x5MgR3n77bfR6PZ6enjQ1NfHss89OWlHnL3/5C1FRUYSHhxMVFUVGRsa0Iy2SkpL46U9/is1m48knn3TJf02539tsNux2O/v376e6utrlAabdbufFF1/ki1/8okvLazQa9Hr9hCJpT08P58+f59y5c6PO0ZiYGG644QZVdJ0plDRNZVtVVVUcPXqUWbNmqeLoRN/p6upSj5dSiv1yBBslLTo4OJif/vSnvPHGG3zrW98iNzeX8+fPf6x9m4rAwEBmzZpFbm6uWrZ8MrRaLV5eXgQEBIy6R5rNZjw9PWlsbCQ5OZkvfvGLM14EoKOjg5KSEmpqaoiIiGDt2rXs2bNnyggyRdSYNWsWa9euHee3V1FRQX5+Pn5+fvz2t7/l17/+NW+99ZbT8/73v/89d9xxBwUFBU6fFUrEXFdXF3a7nfDwcK677jpyc3MnNXuejKCgIG688Ub1dXFxMfv27aOlpYWEhATuvPNOMjIyLitCWiAQCAT/fHziog0MueK/8MIL7N27VzXkHRgYICcnR02p6O/vp7S0lB/84AcsX75cLUHpcDgYGBigr69PFR5Gdvo6Ojo4ceIEv/nNb7jlllsoLCykvLx8XEfrW9/6Fo8++iiXLl0Chh6Yv/jFL674vufn5/Pcc8+h1+t57LHHePTRR1m+fDkBAQEcOnSIX/3qVy6v63LDko1GIw6Hg9ra2ssa6Chmjr/97W+5//77J61ApVR8am9vJzw8XJ2dVxjZ+XA4HJSVlY0yZh67Tbvdru7v4OAg7e3tlJeXc9ttt81oVZ+x9PX10d7ePu3OUnd3tyo2zZ8/n9tvv5309HQ++OADdu7cOW4m+XKNp5VBkSKW/PGPfwRg8+bNBAYGIkkSfX19vPLKKzz55JN0dHSQlpZGWloaS5cuZc6cOfzsZz9zWgHkcs6zsQJob28vdXV1qnB3pUK8J6r2VVFRwYYNG5AkicbGxgk74v7+/ri7u09oqunn58esWbNmvMoLDKVtrV+/nqioKPr6+vDx8cHb25u6ujq+9rWvERgYyE9+8hNuvvlmTCYTBoNhXHUxGBIJm5qaCA8Px2w2uxS98cgjj/Doo49SVVXF008/ze7du9XBzMhUlZEoqUmSJOHl5cWjjz6qfqZ4LqxcuRJJktQSvX/5y184evTohKKN2WzGx8dH9QxxhaKiIkJDQ9VqditWrGDnzp10dXWN8jm7Umg0Gv785z+TnJzM73//e3JyckhNTaWtrY0nn3yShoaGSa/nvLw8dT9DQ0NZunQpu3fvdlmoCAgIIC4uDh8fH2pra12+bxQWFvLGG2+watUqli1bRkZGBpcuXZqWf1pTUxMGgwEfHx+MRqPTga/VaiUnJ4e///3v4wT1zs5Ojhw5Mu4eoEQ3/OlPf3K5Ta5gt9vHXdcWi4WamhqWLVs24Xfa2tp46aWX2LhxI8uWLSMlJYULFy5cVirutm3buOmmm/jBD37Aa6+9htlsxsPDgxMnTjhNV/q4SJLEqlWrcHd3p7Ky0qUUOqPRyKOPPkp2djZ2ux03NzfCw8MJDg4mJyeH1atXExERod5La2pqZuS+2NHRQXV1NR0dHcTExPCNb3yDZcuWUVVVRW9vL1lZWVRUVKj9AyViqLy8nIqKCuLj49m+fTvZ2dmjJkUiIiKIi4vDYDCQnJzMV7/6Vfbu3ev03A0ODlaFxano6emhrKyMhIQEvLy8mD17NmvWrFGf/woDAwPYbLYJfZsCAgJYuHAhq1evBoaenT/5yU84duwYGo2GDRs2cOedd05aFVIgEAgE/3pcE6LNwMAADQ0NtLa2qoNth8MxqvMoyzJ9fX2UlJTQ3t6O0WhUSz4XFBTw+uuvI8syTU1NozqtyoyaxWLhwQcfpLa2llOnTnHixAlyc3OBoQekXq+ns7OT/v5+AgMDiY+P56233mLz5s1XdN/b29spLi7GZDJht9u55557CAgIoLCwkCNHjqgikitUV1dTXl5OX18fLS0tLkeahIeHExISgtVqdbnk90iUspQREREEBwfT3d09odDS1dVFdnY2KSkpxMXFYTQaVaGps7MTd3d3NQy6s7OTXbt2jQqVV2ax+vv7sVqto6o4KVU8zpw5w49+9CNWrlzJsWPHJp3d/jhYLBaKiopGzYK5gnIuZmZmEhsbS0BAACaTidraWvLy8ujs7Jww4ma6jB34tLW18corr1BQUKCWD1Wqfymd9tLSUg4ePKhW5XB27rgqriiCqlLZY2zVLYvFQk5ODt/61rdYuXIlJ0+enLIyyXSEnb6+Pvr7+7HZbOMG7VlZWdx7772kpqai1+spKysbJVK5u7uzadMm1qxZw+HDh3nhhRcAuOeee0hLS2PFihXodLoZr+jW29vL22+/TXx8PL6+vkiSRHNzM9nZ2Vy8eJGcnBwMBgOvvPIKaWlpJCYm8pnPfIaSkpJx547i6+Dl5UViYqJLFegUg/QTJ06wd+/eUYMvT09PgoODx4kpysBDlmU0Gs0oU3IlXUJJgVMEw/LyctXfaSzKe9MRRcvLy3F3dychIYHU1FTS0tL4+te/Tnl5Obt27VKNqC/X28cV5s+fz5EjR9Ry6orIdfbsWae+GSUlJZSVleHr64u7uztLly7lK1/5Cr/+9a/p7u526l2kpM2tWLGCgYEB3nzzTZevkfLyct566y1qa2tJTU1l8eLF7N27l8HBQaxW65THSpZlamtrMRqNeHp64ubm5nTgW1JSwn/9139RUFAwbn8cDseEETi1tbXs3bt32v48U7VbSbcaez+qqalRK7RVV1eP+ryvr4+LFy8SHR2tRr7NmTNHjU5yVexyd3cnIiICs9nM+++/T2trKwaDQU0jvJxnsCsoFcJuu+023N3dqa6unjLyw2AwEB4ezg033EBaWprqTWUymfDw8Bhlct/Y2Mi+ffvYt2/fjJhHK+dEf38/7u7uxMbGotfraW1tpb+/n8WLF9Pc3ExXVxeSJJGZmcmRI0fIycnhww8/RK/Xs2bNGh566CFef/11tW+3cuVKEhISyM3NJTg4mDlz5rBs2TK1FPdE6HQ6LBbLuMjusSh9mcLCQlasWIG3tzdBQUGkp6djtVrVaCTF4629vZ2enh7q6upGiYjz5s1jw4YNxMTEIMsyWVlZZGZmqunEyn35SqaBCwQCgeDa4poQbWAoUsIVkaGvr2/cDFlNTQ3vvvvuOLNTBbvdzqVLl7j++uuZO3cunp6eWCwWcnNzkSQJb29vcnNz6e3tJTU1lSVLluDr68vzzz9/RctsK9UPWlpasFqttLW1qQayBw4c4PTp09Pq/BQVFXH27FkiIiLUEseu4Ofnpw7WlEgnV1Byt2NiYggJCVEjAhQ/mrHYbDYuXLhAaGgo/v7+ZGRkkJOTw5kzZ/D09CQ9PR2DwUB5eTlVVVUcOnRo1PEfGBigqamJ06dPU1RURHt7u/q5w+HAarVSW1uLTqfj1ltvVdPL6urqZrR6jFKV7HJm75WSzkuXLiU6OhqTycScOXNIS0ujurp6RkSbiSgpKVFL6sLQ8RoZedHc3MypU6eora3Fzc1t0plSh8NBXV0dAQEB6v5MNki0Wq3U1NRw+vRpBgYG6OjoGDcIUsr63nXXXfT395ObmztpdIXFYlHLSVut1ikHpyUlJZw9e5aKiopxA0LFkDk8PJzly5dTV1fHsWPHyMnJAYY61bGxsdx0000EBwdz9OhRKioquPnmm1m1ahUBAQFYLJZpe8VMRXd3N88//zyJiYlERUWh1WqprKwkPz+fiooK9XdRKtUsWbJENfeeCCXa6vrrr+fAgQNT3s+UCITdu3eP8gwD8Pb2Jjg4GDc3t1ED3r6+Pjo7O+np6RnnJ6VcdzqdDkmS6O/vp6Kigrq6ukm9hKxW67Sv187OTsrLy9XzPDIykq1bt1JaWkpbWxuFhYVUV1dfEcNrvV7PggULOHPmDK+88grV1dXqYK2rq2tKz6SOjg7y8vLw8vIiKiqKgIAAYmNjSUtLo7Ky0qk/zYIFC9iwYQOLFy+mr6+P/fv3uyzaKINLxW9q3rx53HXXXVRXV1NSUsKFCxemPF5tbW2jSmE7o7W11WmJ5YmwWCx0dXVdlieIMxwOxzhxpLe3l9bWViRJIjY2lpqamlHHsr+/n+LiYrKysvjsZz9LXFwc119/PR4eHtTU1HD27Fn6+/unbGt4eDgw9LxWoqkUE9/pllGfDkajkbVr17JkyRIcDgfFxcVT/r5KepSnpydhYWHq+0rFscDAQPV3b2ho4Pz58+Tl5c3Y83ZkFJ+7uzuRkZEEBQUxODhIUlISvb29dHV14XA4MBgMnD9/nsbGRo4cOYJWq2Xjxo1qClF/fz+hoaFqGvrrr79OREQEy5YtY/PmzXR1dXHhwoVxwo2Pjw96vR6LxeJS9Ft/fz8NDQ3qNWs2m5k9ezZ2u53g4GB1uba2NlpbW9WJu+bmZlUojY+PZ+nSpXh5edHb28srr7wyKupYGA8LBALBp49rRrT5OHR2dqrpARM9zAYGBjhw4AApKSlqKLcScSBJEr6+vhQVFREWFsa6detYvHgxeXl5tLS0UFdXpw44ZvJBabVaKS0tpaysTJ1JVTxZ3nrrLQ4ePOi04shE5OfnI8sygYGB9PT0uByirJhgenh4qB3KqfD09MTPz4/o6GhWrlyJt7f3uNS0iaioqODDDz9Ep9Mxe/ZsXn75ZZ588knS09O59dZb8fDw4Ny5cxQWFo7Lt+/v76eyspKXXnqJ06dP09TUNG57sixTWFjIhg0b8PPzQ6fTsWfPnhkVbQYGBujp6bmsKB6bzcapU6c4cOAAGRkZhISE4OXlxfz58zl9+rQqGlwJpmpvY2OjKopMxuDgIGfPnlVFgtbW1kmFgN7eXi5dusQLL7wwTiQaub6ioiK2b99OR0cHLS0tk4o2paWlnD9/noiICBwOx5SiZFZWFnq9noKCggmvhaqqKuLi4oiIiOCBBx5QI38ANVqtv7+fJUuWcPvtt7N7924WLlyoClbl5eX88pe/dNqG6dLX18e+ffs4c+aMKoB0dnaOM4nOzc2loqKCsrIywsLCJhUG7HY7NpuNTZs28ZOf/GTKa9RisZCdnc3u3bvHHTM3NzdMJhOSJI0ySu3r66Ouro6ysjJSUlKAf1Tnqa+vR6PREBQUhFarVb1DKisrJxUiuru7Lysipru7m9LSUvLy8tSqMwaDgbVr1+Lr66v6G800Hh4ePPjgg/zpT3/i4MGD9PX1kZKSwty5cyktLSU4OJj8/Hyn68jOzkaSJAYGBkhISKCtrY0NGzaQlZU1agA4lnXr1rFhwwaioqKoqqoiNzd3Ws+pwcFBWlpaOHPmDLfeeitf+tKX6O3t5dixYzz77LNOj5di5qukJl8JXJ3MkWXZ5aiDkZXGRrZ7YGCA9vZ2Ghsbue666zh79uwo35uBgQGqq6vViIeYmBi2bNnC/Pnzyc3NVf3dqqurnQr6ixcvxmq1smvXrlHvKSWlrxQmk4nbb7+dsLAw2tra1OqFzlBSS5U0HOVYKMdPiYweGBigpKSEysrKGTW0HxwcpLOzk+bmZjw9PdXKk0q73N3dMZlMtLe3q4UdYKif8cYbb5Cbm8tnP/tZVq9erQo/fX19nD59mueee06tXLhgwQKuv/56bDYbmZmZo8655ORkvLy8qKqqctkgWvE9cjgcuLm54evry9y5c9X+lcPhoKOjg7a2Npqbm/Hy8uLMmTPq/Tk0NJQ5c+YgSRK1tbW89NJLaiSXXq8fValLIBAIBJ8O/iVEG5vN5rRzabPZ+Mtf/oLBYMDLywur1UpxcTEwFIKflJREUlISN9xwA9nZ2fy///f/eO+999DpdLz22muEh4fT1dU1rVz/qSgpKVGrGCht/MMf/oBer+fw4cOX1fEpKyujpaUFo9GodnZcoby8nLy8POLj411OSVi0aJEaHp6cnEx5ebnaCZmqk22xWCgsLCQnJ4d9+/bhcDjIzMyc0JdjLD09PRw6dGjS31uWZQ4fPkxycjKJiYlER0dPWkXicrHZbGpkxnSx2+2UlJTwxBNPsGjRIpKTk0lJSbmini4zyeDgIAcPHqS1tRWNRkNxcbHTmeWmpiYOHjw46b719fVx5MgRFi1aRGpqqtNKNLm5uTgcDoKDg5EkadSAaiIaGho4derUpOJldna2WlLVz8+PV199ddTnhw8fJjg4mK9+9av87Gc/49///d8JDQ1Fo9Gopd+VFMuZpr29fUqRraenh9OnT0/6uVIGOjg4mKioKMLCwujp6XEqdh05coSLFy9O6NPR2dlJQ0MDHR0d44yjs7Ozeeedd0hMTFQ9yM6dO8fu3bvx8PBg/fr1GAwG3n77bXbv3u3UX6evr++yRNaenh7y8vI4cOAAs2bNUj0llDSFpqamK2JObDKZuO+++/jKV77C4OAgBoOBhIQEEhISOHnyJCtWrODIkSNOo5zOnDlDY2Mjvb29+Pn5sXz5clVIP3bs2KRpLIsWLWLOnDm0tLRw+PBhamtrp9V2u91OS0sLH374IfPmzWPu3LkYjUZmz55Nenr6KGFhImpra2loaLiiaWeuYLfbRw3aJ6Ovr4+Ojg46OzvHXV/9/f1UV1dz8OBBHn74YY4ePcr58+dHCWayLNPc3ExWVhb+/v74+PiowkFjYyNZWVnU19dPeo1JksQdd9yhXhswNAj/1re+hZ+f38c7CFPg4eHB2rVrMRgMVFdXu1Q1T/GK8/f3HyVeKRFkgYGB6vo++OADMjMzZ7RqW2dnJ6dPn+bNN98kPT0dX19fNdVSSZ3q6uoiKyuLv//976MiYJX3y8rK1OeK3W6nra1tVN8oNzcXPz8/fve737F27Vo16gWG7qGf+cxnCAgI4KOPPnLZINzhcNDZ2Ul3dzcajUatrKdEMjscDgICAvD398doNNLZ2TmqQpdWq0Wr1dLX16c+b0dWElTEnysZCS4QCASCa4t/CdHGVT744AMMBoM6ywFDnZLnnnuO73znO+zatYvnnntOrdjkcDjYvXs3v/zlL/nhD3/I6dOnZyxMe+yA3263T9lBnoquri6XjTtHUlBQwJ/+9Cdef/11l1OqlCoKSpg0DB1LV3wzOjs7OXz4MPn5+dOuCqLMdk6Gw+Hgl7/8JZ2dnfj7+3PhwoVplQp1BUW02b9//2Wvo6WlhczMTCoqKigsLMTLy4uioqIZbOWVwW63c+bMGVX0bGpqctr57+3tdVqJqquri1/84heqp5Qzj4XS0lIaGxtxc3NDo9G45P3gzCR0x44d7N27l+TkZFJTU8d9npOTgyzLzJs3j+3btxMZGQkM/f779+/nySefnHL7nxRms5no6Gg2btxITEwMkiSpgwJnnDp1atLP2tvbqa+vp729naNHj44aIFdWVnL06FHuvPNOCgoKyMnJ4Z133qGwsBCz2cyePXtoaWmhtbV1ykHd/v37aWlpuSzhpqSkhL/97W+cOnWKO+64g8DAQDw9Pa+IYfRIRhqfP/HEE1gsFn784x/j4eHBQw89NOVxLysro6ysjOzsbI4ePcrdd99NQEDAlCmYOp0OrVZLXV0d77zzzmW1fXBwkHfeeYfS0lK2bt1KVFTUpOl2E/Hqq6+6dN+/EtjtdiwWC5cuXcLDw4Ompian9/vi4mJeeeUViouL2bVr17jja7FY2LVrFw8//DC33347LS0tVFRUjHoutra28s477xAaGkpaWhre3t5ER0ezfft2AgIC2L9//6TP0fDwcHp6etTntLu7O3fccQePPPII+/fvv2IeJZ6ensTHx6tFB77+9a+75JfX1tbGhx9+yL333ovD4VCrNJ44cYLGxkYefPBBPDw8+O53v8uBAwdmNMpGoaioiNdff52ysjJCQkKIiopSI1Da2tro6ekhNzd3QgFdESWn8u5pa2vjvvvuG/d+YmIit9xyi1p8wFX27t1La2srKSkpLFiwgOTkZHx8fAgICMBqtaq+f/n5+ezYsYOcnJxRE1E9PT20tbXR3d3N//3f/406T728vAgMDFS//88w2SMQCASCj8+nSrQZ6YUz9kG3fv162traRg1AHQ4HR48e5bHHHqOysnLG8+qvJTo6Oib0A5qMhoaGCTuYrvq89Pf3T1iVZ6bYu3cvRqOR1tbWKzJg6+3t5eTJkx9rHRaLhfb2djXk+kp5GcwksizT2tqqeu/MRFUeWZZ58803sdlsTg1Hu7q66O7untGBjcVi4cKFC5NWysrPz+drX/saMTExeHp6snv3bnJycjh79izZ2dkz1o6ZZP369cybN4/ExESSkpLUCixtbW0fa2Dd19eHxWJRZ6pH3iusVivV1dWcOHGCkydPcunSJfLy8ujt7aW7u1tNZXTl/nLmzBm1IuDl0NPTw8WLF9FoNPj7++Pl5YXdbleFxiuBRqPhr3/9K1FRUeTl5bFnzx6qqqrw9/fngQce4JlnnqGsrGxKIUoZFOt0OgIDA6ccLDY2NtLc3Ex9fb062XC5KJVtAgMDR5mdTsWhQ4doa2tzWfCfSRwOB83NzezcuROz2cz58+edDtA7OzvJz8+nra1tQvFfidrNyspi7dq1vPnmm9TW1o7bt127dlFWVsbq1atJTk7G29tbrZTmDDc3N06dOkVRURFpaWn85je/QafTsWPHDrq7u6+Y8OXm5oa/vz8wJFCUl5e7FGljsVjYv38/t956K0ePHuWDDz7go48+or6+Hl9fX8rKymhsbCQ/P/+KRVs1NzerKWl+fn6qaFNTU0N7ezsDAwNXzLw5KCiIzs5Ovvvd7/Lhhx+6/L22tjbOnTtHbW0txcXFFBYWEhwczLJly6itrVXT4svLyzl16tS4iY09e/bQ1taG0WgcV1Wyq6tLjcrLzMy84pXxBAKBQHBt8KkSbZw93GprayccNA8ODlJSUvKJdEivNtN5+F9uefGx67hS1NXVIUnSFZ39/bjrVo7hP1unayZ++7GUlZWpVV2u5rZlWXbqoaL4oNx///1qBZGenh56enquWZFNqcx04cIFPDw80Gq19Pf309jY+LHarFR/e+mllyZMvaytreX3v/89tbW1ahUy+IcZsqsoVXs+TlttNhs5OTno9Xo1beZKie69vb0899xzZGRkcPz4cV599VXOnz+v+n709PTwwx/+kK9//etTeuoo52NmZiY6nU6tRjMZb775JsXFxWr1xY+Dw+FQfUk0Go3L96XS0lLsdvsndj3Y7XZVpG9ubnZq5q54LdXU1Ey4f4ODgzQ2NvKb3/yGrVu3Tpruq3i4yLJMRUUFPj4+mM1ment7nd7D5s2bR3x8PMuXL6e/v5/jx4/z8ssvMzAwoJad9vb2nvGIFSXisaenhxMnTrh8LVitVgoLCykvL+fAgQMcO3aMmpoaenp6VONmpULglUL5zSorK9V0PI1GQ3t7O729vdO+v0yHsrIyTp06RV1d3bT2UZZlLBYL3d3dtLW1UVtbS2BgIO3t7dTW1lJaWqpWj6qrqxt3LhYXF1NbW4tWqx33WVdXF7W1tTQ1NXHmzJl/uv6DQCAQCC6PT5Vo4wxnHc5POl9fMH0+DSLbvxKfZBSbw+Fweo0PDg5eUYPomaahoUGNWFKikqbaR1ew2+00NjZy6NChCQeVvb29FBQUfOzfcqbut65EEswEPT09/PWvf+XkyZMUFRWRm5urCi29vb383//9HxqNxuXjovhhuIJSSa+/v39GRJO+vr5p/36fdASqLMuUlZWh0Wiw2WwulSp39nzo6+vj3LlzGI1Gp0JnX18flZWVtLa24ubmhl6vZ3Bw0KloU1RUxO23305AQABnz57ljTfeUO8tp06dYv78+Wi1Wvbv3z+jx7W/v5+qqio++OAD3nnnHZdThvv7+ykrK+Ppp5/m8OHDVFVVqZX7BgYGZtTg3xmKT1Z/f78qUihmv1eSpqYm3n333Wl7RcHQ/VJJz+rt7R1Vva6hoUH17proXLRarZP+Rkq1PiWCUSAQCASfDqTpzFpLkiSSZwUCgUAgcAFJkjCbzZhMJpqbm4Vx6D8RISEhtLS0zHgE0Te/+U3c3Nw4dOjQKG+7+Ph4tmzZQn19Pe++++6Mi47u7u6sWbOGc+fO0dzc7HKEhiRJBAQEOE1d/VfG39//sivaTYSnpye9vb2XfS/w8PAgLS2NRx99lMcff9ypZ5xAIBAI/inJkmV50dg3hWgjEAgEAoFAcBUwm80MDg5OGEkTGBjI4ODgFfNoEfxrEBwczOrVq9mzZ88VMX8WCAQCwSeKEG0EAoFAIBAIBAKBQCAQCK5BJhRtputp0wJUzkx7BAKBQCAQCAQCgUAgEAgEQNREb04r0kYgEAgEAoFAIBAIBAKBQHB10HzSDRAIBAKBQCAQCAQCgUAgEIxHiDYCgUAgEAgEAoFAIBAIBNcgQrQRCAQCgUAgEAgEAoFAILgGEaKNQCAQCAQCgUAgEAgEAsE1iBBtBAKBQCAQCAQCgUAgEAiuQYRoIxAIBAKBQCAQCAQCgUBwDSJEG4FAIBAIBAKBQCAQCASCaxAh2ggEAoFAIBAIBAKBQCAQXIMI0UYgEAgEAoFAIBAIBAKB4Brk/wNl8bQ7etXlQAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "num_samples_to_plot = 14\n", - "\n", - "for i in range(num_samples_to_plot):\n", - " plt.figure(figsize=(20, 20))\n", - " data, target = emnist_lines[i]\n", - " sentence = convert_y_label_to_string(target.numpy()) \n", - " print(sentence)\n", - " plt.title(sentence)\n", - " plt.imshow(data.squeeze(0), cmap='gray')\n", - " plt.xticks([])\n", - " plt.yticks([])" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "EMNIST Lines Dataset\n", - "Max length: 50\n", - "Min overlap: 0.0\n", - "Max overlap: 0.2\n", - "Num classes: 80\n", - "Input shape: (28, 1400)\n", - "Data: (35000, 28, 952)\n", - "Tagets: (35000, 50)\n", - "\n" - ] - } - ], - "source": [ - "print(emnist_lines)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/notebooks/02c-image-patches.ipynb b/src/notebooks/02c-image-patches.ipynb deleted file mode 100644 index fedea91..0000000 --- a/src/notebooks/02c-image-patches.ipynb +++ /dev/null @@ -1,525 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from PIL import Image\n", - "import torch\n", - "from torch import nn\n", - "from importlib.util import find_spec\n", - "if find_spec(\"text_recognizer\") is None:\n", - " import sys\n", - " sys.path.append('..')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.datasets import EmnistDataset, EmnistLinesDataset, Transpose, construct_image_from_string, get_samples_by_character" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "emnist_lines = EmnistLinesDataset(train=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-01-10 17:44:25.666 | DEBUG | text_recognizer.datasets.emnist_lines_dataset:_load_data:153 - EmnistLinesDataset loading data from HDF5...\n" - ] - } - ], - "source": [ - "emnist_lines.load_or_generate_data()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "def convert_y_label_to_string(y, emnist_lines=emnist_lines):\n", - " return ''.join([emnist_lines.mapper(int(i)) for i in y])" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "office in Arkansas after the______\n", - "in________________________________\n", - "by a oneshot technique____________\n", - "office Incumbent__________________\n", - "of the revolutionary______________\n", - "they______________________________\n", - "the scene but_____________________\n", - "Knox Ky___________________________\n", - "workers wife refused to have______\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "num_samples_to_plot = 9\n", - "\n", - "for i in range(num_samples_to_plot):\n", - " plt.figure(figsize=(20, 20))\n", - " data, target = emnist_lines[i]\n", - " sentence = convert_y_label_to_string(target.numpy()) \n", - " print(sentence)\n", - " plt.title(sentence)\n", - " plt.imshow(data.squeeze(0), cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "data, target = emnist_lines[8]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "from einops.layers.torch import Rearrange\n", - "slide = nn.Sequential(nn.Unfold(kernel_size=(28, 46), stride=(1, 46)), Rearrange(\"b (c h w) t -> b t c h w\", h=28, w=46, c=1))" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "from einops import rearrange" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "data = data.unsqueeze(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1, 1, 28, 952])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "patches = slide(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1, 34, 784])" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "x.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "# remove batch size\n", - "patches = rearrange(x, 'b t (h w) -> b t h w', h = p, w = p)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "patches = patches.squeeze(0)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([20, 1, 28, 46])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "patches.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "<Figure size 1440x1440 with 5 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(20, 20))\n", - "for i in range(5):\n", - " ax = fig.add_subplot(1, 5, i + 1)\n", - " ax.imshow(patches[i].squeeze(0), cmap='gray')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Testing the data loader for EmnistLines" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'fetch_data_loaders' from 'text_recognizer.datasets.util' (/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/datasets/util.py)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m<ipython-input-18-5d40384147e9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtext_recognizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutil\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfetch_data_loaders\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mImportError\u001b[0m: cannot import name 'fetch_data_loaders' from 'text_recognizer.datasets.util' (/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/datasets/util.py)" - ] - } - ], - "source": [ - "from text_recognizer.datasets.util import fetch_data_loaders" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2020-08-30 21:31:41.007 | DEBUG | text_recognizer.datasets.emnist_lines_dataset:_load_data:164 - EmnistLinesDataset loading data from HDF5...\n" - ] - } - ], - "source": [ - "dls = fetch_data_loaders([\"train\"], \"EmnistLinesDataset\", {}, batch_size=2, shuffle=True, cuda=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "dl = dls[\"train\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "d, t = next(iter(dl))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "patches = sliding_window(images=d, patch_size=(28, 28), stride=(1, 14))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "might as well stand their_________\n" - ] - }, - { - "data": { - "text/plain": [ - "<matplotlib.image.AxesImage at 0x7f3e806538e0>" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(20, 20))\n", - "sentence = convert_y_label_to_string(t[0].numpy()) \n", - "print(sentence)\n", - "plt.title(sentence)\n", - "plt.imshow(d[0, 0], cmap='gray')\n", - "# plt.imshow(d[0, 0], cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 5 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(20, 20))\n", - "for i in range(5):\n", - " ax = fig.add_subplot(1, 5, i + 1)\n", - " ax.imshow(patches[0, i].squeeze(0), cmap='gray')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/notebooks/03a-line-prediction.ipynb b/src/notebooks/03a-line-prediction.ipynb deleted file mode 100644 index 13f4ff1..0000000 --- a/src/notebooks/03a-line-prediction.ipynb +++ /dev/null @@ -1,419 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from PIL import Image\n", - "import torch\n", - "from torch import nn\n", - "from importlib.util import find_spec\n", - "if find_spec(\"text_recognizer\") is None:\n", - " import sys\n", - " sys.path.append('..')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.datasets import EmnistDataset, EmnistLinesDataset, Transpose, construct_image_from_string, get_samples_by_character" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.models import CRNNModel\n", - "from text_recognizer.networks import ConvolutionalRecurrentNetwork" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-01-04 21:35:35.605 | DEBUG | text_recognizer.datasets.emnist_lines_dataset:_load_data:152 - EmnistLinesDataset loading data from HDF5...\n" - ] - } - ], - "source": [ - "emnist_lines = EmnistLinesDataset(train=False)\n", - "emnist_lines.load_or_generate_data()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "def convert_y_label_to_string(y, emnist_lines=emnist_lines):\n", - " return ''.join([emnist_lines.mapper(int(i)) for i in y])" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "data, target = emnist_lines[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([34])" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "target.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-01-04 21:37:05.918 | DEBUG | text_recognizer.models.base:load_weights:432 - Loading network with pretrained weights.\n" - ] - }, - { - "ename": "TypeError", - "evalue": "'NoneType' object is not subscriptable", - "output_type": "error", - "traceback": [ - "\u001b[0;31m----------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m<ipython-input-16-df17e62a822a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\"patch_size\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \"stride\": [1, 14],}\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mline_ctc_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCRNNModel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ConvolutionalRecurrentNetwork\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"IamLinesDataset\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#, network_args)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/models/crnn_model.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, network_fn, dataset, network_args, dataset_args, metrics, criterion, criterion_args, optimizer, optimizer_args, lr_scheduler, lr_scheduler_args, swa_args, device)\u001b[0m\n\u001b[1;32m 49\u001b[0m )\n\u001b[1;32m 50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad_token\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset_args\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"args\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"pad_token\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 52\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mapper\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mapper\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEmnistMapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpad_token\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad_token\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable" - ] - } - ], - "source": [ - "network_args = {\n", - " \"encoder\": \"ResidualNetworkEncoder\",\n", - " \"encoder_args\": {\n", - " \"in_channels\": 1,\n", - " \"num_classes\": 80,\n", - " \"depths\": 2,\n", - " \"block_sizes\": 128,\n", - " \"activation\": \"leaky_relu\"},\n", - " \"flatten\": True,\n", - " \"input_size\": 128,\n", - " \"hidden_size\": 128,\n", - " \"num_classes\": 80,\n", - " \"patch_size\": [28, 28],\n", - " \"stride\": [1, 14],}\n", - "line_ctc_model = CRNNModel(\"ConvolutionalRecurrentNetwork\", \"IamLinesDataset\") #, network_args)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "office in Arkansas after the______\n", - "in________________________________\n", - "by a oneshot technique____________\n", - "office Incumbent__________________\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "num_samples_to_plot = 4\n", - "\n", - "for i in range(num_samples_to_plot):\n", - " plt.figure(figsize=(20, 20))\n", - " data, target = emnist_lines[i]\n", - " sentence = convert_y_label_to_string(target.numpy()) \n", - " print(sentence)\n", - " plt.title(sentence)\n", - " plt.imshow(data.squeeze(0), cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "data, target = emnist_lines[3]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "office Incumbent__________________\n" - ] - }, - { - "data": { - "text/plain": [ - "<matplotlib.image.AxesImage at 0x7f05bd77af70>" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(20, 20))\n", - "sentence = convert_y_label_to_string(target.numpy()) \n", - "print(sentence)\n", - "plt.title(sentence)\n", - "plt.imshow(data.squeeze(0), cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "data = data.to(\"cuda:0\")" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('offiee ineumbent', 0.19405342638492584)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "line_ctc_model.predict_on_image(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p, _ = line_ctc_model.predict_on_image(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p = line_ctc_model.swa_network(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "p, _ = p.max(2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "torch.exp(p.sum()).item()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.models.metrics import cer, wer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "target.unsqueeze(0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cer(p, target.unsqueeze(0))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "wer(p, target.unsqueeze(0))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/notebooks/04a-look-at-iam-lines.ipynb b/src/notebooks/04a-look-at-iam-lines.ipynb deleted file mode 100644 index de59a85..0000000 --- a/src/notebooks/04a-look-at-iam-lines.ipynb +++ /dev/null @@ -1,383 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from PIL import Image\n", - "import torch\n", - "from torch import nn\n", - "\n", - "from importlib.util import find_spec\n", - "if find_spec(\"text_recognizer\") is None:\n", - " import sys\n", - " sys.path.append('..')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.datasets import IamLinesDataset, AddTokens" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "transform = [{\"type\": \"ToPILImage\", \"args\": None}, \n", - " #{\"type\": \"RandomResizeCrop\", \"args\": None}, \n", - " {\"type\": \"RandomRotation\", \"args\": {\"degrees\": 0.8, \"fill\": 0}}, \n", - " {\"type\": \"ColorJitter\", \"args\": {\"brightness\": 0.5, \"contrast\": 0.5, \"saturation\": 0.5, \"hue\": 0.5}}, \n", - " {\"type\": \"ToTensor\", \"args\": None}, \n", - " {\"type\": \"Normalize\", \"args\": {\"mean\": [0.912], \"std\": 0.168}},\n", - " #{\"type\": \"RandomAffine\", \"args\": {\"degrees\": [-0.25, 0.25], \"scale\": [0.98, 1.0]}}\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'type': 'ToPILImage', 'args': None},\n", - " {'type': 'RandomRotation', 'args': {'degrees': 0.8, 'fill': 0}},\n", - " {'type': 'ColorJitter',\n", - " 'args': {'brightness': 0.5, 'contrast': 0.5, 'saturation': 0.5, 'hue': 0.5}},\n", - " {'type': 'ToTensor', 'args': None},\n", - " {'type': 'Normalize', 'args': {'mean': [0.912], 'std': 0.168}}]" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transform" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IAM Lines Dataset\n", - "Number classes: 54\n", - "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'a', 11: 'b', 12: 'c', 13: 'd', 14: 'e', 15: 'f', 16: 'g', 17: 'h', 18: 'i', 19: 'j', 20: 'k', 21: 'l', 22: 'm', 23: 'n', 24: 'o', 25: 'p', 26: 'q', 27: 'r', 28: 's', 29: 't', 30: 'u', 31: 'v', 32: 'w', 33: 'x', 34: 'y', 35: 'z', 36: ' ', 37: '!', 38: '\"', 39: '#', 40: '&', 41: \"'\", 42: '(', 43: ')', 44: '*', 45: '+', 46: ',', 47: '-', 48: '.', 49: '/', 50: ':', 51: ';', 52: '?', 53: '_'}\n", - "Data: (1861, 28, 952)\n", - "Targets: (1861, 97)\n", - "\n" - ] - } - ], - "source": [ - "dataset = IamLinesDataset(train=False, pad_token=\"_\", transform=transform, lower=True)\n", - "dataset.load_or_generate_data()\n", - "print(dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(28, 952)" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.input_shape" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(97, 54)" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.output_shape" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision.transforms import ToPILImage" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "source": [ - "def convert_y_label_to_string(y, dataset=dataset):\n", - " return ''.join([dataset.mapper(int(i)) for i in y])\n", - "\n", - "# convert_y_label_to_string(dataset.targets[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "but since starting salaries would depend on grade a______________________________________________\n", - "or b in the finals next may, and since mating____________________________________________________\n", - "prospects would depend upon salaries, scholarship for____________________________________________\n", - "these fine young people was closely geared to____________________________________________________\n", - "economic and biological ends which, essentially,_________________________________________________\n", - "were really means. so, seeing them revolve in____________________________________________________\n", - "circles, harry had the feeling that moke (or what________________________________________________\n", - "moke consciously or unconsciously symbolised, any-_______________________________________________\n", - "way in harry's mind) had these splendid young____________________________________________________\n", - "people by the short hairs, and was diverting them ...____________________________________________\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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1IDVv3jx+//d/n4GBAV588UXq6uomjMGJtj3A+Pg4lmXh9XpRSrFmzRq8Xi9nzpyhv79/0n1uz8vEnKseI8Dtt9/Ozp07aWho4Ic//CHd3d2u91mWZT/PSCSCx+OZ5JyHw2H7OTufiRuWZU2wh77HPO7z+QgGg4TDYcLh8IRz2jbOtpz2MNeiec45xnA4nHC8s0Wid3qmmGvuo8J8r6dzXKOPT7UOptueRtsP3D+nILoGrvR5hkKTqnsLgiAIgiAIgjD7XHA7OCPRRjuCwWCQiooKli5dSlNTE0ePHuWWW26hubnZduYzMjLYuHEjt9xyC++99x49PT2UlpaSkpLCyZMnyc/P5+6776auro5Tp04xNDREOBwmLS2N66+/nurqatth1U6mdmDiOSb6mHmtdkZNtIhiOtmRSITLly8zNjY24T5TZDHb1Q6ViY7u0A6o6Ww5nS9npMeVCjZ6vs75mcLZgw8+SEZGBqdPn6aystKOcHH2Nz4+bs/RbLuoqIh169bx/vvvU11dbTt/WnTS/c+ZM4f6+nr6+/ttsU3b0O/3c/fddxMOh6mtraWjo2OCPZy21jjtr6+tqKigvr7eFirMMU/l/Ot+9RrQ7RYWFrJlyxZGR0epr69ncHDQbs/r9U4QbJzP02xTX+f1em2bxnPUzcgqp3NuCnvaKV+1ahVbt27l8OHDHD9+nNHR0QnX6/E524o3dtNW+v025+Acjyl26bViruXs7Gz6+/sZGxuLOy/nePSzj/fM/H4/4+Pj9o+55pz21X877TtbkTPx5pPo+HQwxw8fCnDOaDHzPTGPwYfPxHz/p9OXIAiCIAiCIAjXLjPOaaOUYtWqVSxduhS/309PTw8ej4f58+eTlJSEx+MhEAhQWFjIzTffTH19PR988AENDQ2Mjo6SnJxMfn4+mzdvZmhoiJaWFqqrq6mvr6e9vZ3Lly+jlGLTpk0kJSXZDpfTGV68eDHr1q1j6dKl5OTkTHDc9LXxnDXnMe18joyM2IKE2YZzu5X+3WzHHKd5velAxfvWPN6WBn29/nGL5jDnY45B/6xevZrS0lIaGxs5efIkbW1tE8Qtsy/dn3k8GAySn59Pbm4uBw8epLe3d8K2LP3j8/mwLIuenp4Joo4+v2zZMvLz8zl79iynTp1icHAwbqSURtvO+ePxeBgZGaG7u9s1emA64pe2qe7X5/Nx5513UlBQQHV1NSdPnrQFEedzN8fl7NNtTbgJIGb/zrmZbZrPKiMjg23btpGXlzchwsvtHrd+TVEn3pp2zsvsw3zezjlqG956663MnTt3wrvrJBAIUFpayuLFi+1+3a7zeDxkZ2dzyy23sHDhQlJSUibZ2A0tdjjtO1vEa2+qfhKJR877zL+1/b1eL3l5ecybNw+/3z+pLa/Xi9/vtz8L9Y/P55sgbpnr0vxxHhcEQRAEQRAE4ZNnxiW/MzMz2bZtG2lpadTU1JCTk0NJSQltbW2Mjo6ilCI1NZWioiKKi4v5zne+Yx9PTk4mJSWFoqIiNm7cyHe/+10uX74MwPz580lJSaGvr4+enh7WrFnDsWPHJkQSQNSRKykp4aabbiIQCNDf309TUxPV1dUMDg6SnZ1NJBKxt2nFE0mcbTqjU7Tjor/VdrYT71tqpdSEb8JNh9TsNxAIkJaWht/vt7eF6WvjOXDx8p+YzpZ2HP1+P9nZ2XzqU5+iubmZo0ePcuHChUnRLU4H3ozqUEqRn59PTk4OPT091NTUTNjqpe/xeDz29jaIRtx4PB7GxsYIh8MEg0G2bt1KU1MTJ06c4OLFi7aT77SvOUdnZJIeW1JSEtXV1Vy8eJGRkRGUUvj9frxeL6Ojo5MidvR4k5KS7DE6I2OKi4u54447qKmp4ciRI9TV1U2wL2BHoZjP1bnlyPmMnI66vt6MqNB/O4U/fb/X68Xn87F06VKuu+46du/eTXNz84RtTaZAYfapj5tiXLw5mLitL7c5mHNOTU1ly5YtnDt3jq6urrjRMykpKbbIW1NTYz9Xpz2TkpJYu3YtGzduxO/309vba0c/aeJFjWh76og4pzDk9k5NF9N+V3KfG87IOzMCy1y/JSUltliuo/nGx8fx+/3MnTuX4eFhO9LNvFdHi5lj8Hq9ZGRkMDw8bAvWTuFZEARBEARBEIRPlhmLNuvWrWPlypV4vV4CgQCZmZkcPHiQxx57zM7/kZycTFpaGl1dXbZz7vP5aGtrIzc3lwULFhAOhxkbGyMnJ4ecnBzKy8vt6Im5c+eyZ88e0tPTiUQi9palSCRCUlISDzzwAMFgkMOHD5OTk8OGDRuIRCKcPXuWzZs309bWRm1trd3/dL/91nMKBAL4/X4ikciknCbxiCfimI6y1+u1nciSkhKuu+46MjMzaW5uZu/evQwNDU3I7aGdLO2AmblrzDklJSWRnJxsb7MaHx8nIyODHTt2sGHDBh599FGam5unNQ/dD0TzYKxevZrc3Fzeffdd26kztwtpZzIrK4uWlhYyMzNZuHAhycnJdHd309TURElJCWvXruU73/kOnZ2dpKWlEQwGsSyLrq4uxsbGJggQpuPqfFY+n4/s7GzOnTvH6OgoXq+XtLQ0MjIySEtL4+LFi3R3d9uCgt/vJxAIkJqaSl5eHg0NDfT19dnPRgtO9913HwMDAzz11FPU1dUxNjZm9+sUVkyhQm+H8/l8doSHU4jSttLbj/RczHmZOUn0GtD9eL1esrOzufPOO2lububQoUNcunRpQj4e3Z4z6sacgxaddF/hcHhSLhl93oxgMgUuveXLKVwEAgHmz59POBy2xbR4ETSpqam0tbUxODg4QRAz8Xq95OTk8NBDD/Hyyy8zMDAw4+08pvjoFsWl7TVT4cYpds4mplBsrhUtTBYXF3Pp0iX7en0uKSmJ7du3895779mJwM32tNipP9f0Z8Stt95qRzqakVaJck0JgiAIgiAIgvDxMWPRpqioiOeee46zZ8/S0NCAZVmEQiGCwaDtKPf09NDa2kpBQQHf+ta3qKqqsiNgALKyssjPz6eiooJly5aRnZ3Nu+++S1NTExUVFRQWFrJq1SrOnz/P9ddfT0ZGBl1dXRw7doySkhJWrlzJN77xDZYsWUJvby8tLS0UFxezceNG3njjDc6fP8/Y2BhJSUl4vV4GBgZc56Kd0EAgQDgcJiMjg/vuu4+VK1fS1dVFIBDgxz/+MQ0NDbPmxITDYRYuXMgDDzzAmTNnqKuro7S0lHvuuYenn37adpbNJLQatwS7SinKy8vZuXMnXV1dHD9+nIGBAbZu3UokEmH37t1cvnzZTiaayCFzOuyZmZnk5OQQiUS4ePEihYWFdrSDFhUKCgpYu3Ytvb29DA8P8wd/8Ae0tLQQDAYBeP/999m5cyePP/44AwMD3HfffZSVldkC1htvvMHbb7/N8PCwLU7k5ubS0dExafuT3+8nJSWFoaEhQqEQPp+Pe++9l/LycgKBAMPDwwwPD/PEE0/Q1dUFwPr167n55pvJyMigr6+PwcFBHnvsMYaGhrAsi0AgwIoVK9i+fTtf/epXqa+vnxTdZY7BubVMCzbXXXcd6enptLa2curUKfscfLgVSotOWixxe57a/joJs8fjobCwkC9+8YuUlJTw9a9/3Z6bGankTLzr9pzdkvM6t5dZlhU3Ya1u062d3NxcPve5z3HhwgU7+ineWlu0aBH9/f1cunRpgoBkkpOTw44dOwA4ceIEly9fnrLamBb6ppOEON41pu3j8VEJGlO9m8FgkDVr1vDmm28yOjpqr0GdFN7n89HY2DjhnMbn83HPPfcwd+5cKisr6erqYt26dSxatIjnn3/etr+5rUwQBEEQBEEQhE+eGYs2bW1tLF26lMHBQZqbm1FKkZmZSUVFhe1st7W10djYyFe/+lWWL19OcnIyBw4coKuri1AoRG5uLvfffz933HEH+/fv5+mnn6azs9OOcCktLeXQoUPcfvvtHD16lMLCQhYsWEBKSgplZWW8++677Nixg+TkZE6ePMmxY8eYO3cun/70p3nssccoKCjg5ptvpqCggL6+PhobG8nLy2PXrl2Mjo7aER35+fns3LmToaEhfvGLX/Dwww8TiUR49tlnqaqqYsWKFWzZssUucT0bBINBHn74YQ4cOEBVVRV9fX00Nzdz//33c9ttt7Fnzx7C4TArV66kvLyccDjMmTNn2L59O/39/bz55pt0dHQAH+b8uPfeezl06BAlJSVUVFRw6dIlXnnlFbZt28brr7/O0NDQpCpcGh05ob9h186bz+dj8+bNtjjyx3/8x2RkZNDU1MQTTzzB0NAQpaWlrFu3jvLycl588UX+8i//kqeffpr6+npWr17N2rVr2bx5M16vl9OnT/Poo4/S1tbGq6++SnV1NcnJyezYsYNIJEJBQQGbNm2irKyMlJQUgsEgb7/9Nq+++irhcBiPx0NqaipZWVk0NjYC8OijjxIOh9m3bx9VVVUEg0FuuOEGAoEAPp+PRx55hNLSUs6dO8euXbtISUnhs5/9LHPmzGF4eBiPx8OyZcv40z/9U7797W9z7tw5+znHEz3cojK2bdtGeXk5paWltLS0cOHChUkVrXSb2r6BQGBC+0lJSeTk5DBv3jw6Ozu5dOkS4+PjrFy5krvuuovVq1fz53/+5xQUFPC5z32OzMxMampqePPNN+nt7bXb1WJaPMwIEy0gafHH3PZmiihmFSM9B1Nwys7OZvXq1dx000184QtfmBDl4dwu6PP57MpwWjhz4vP5KC4uZseOHfzrv/4rAwMDjIyMTPkOmluBphJe4qHvm6rS00wEoplgCqfmHNLS0li2bBm1tbV2FJMeo8/nY86cObzwwguTBEczCmfp0qU888wzhMNhrrvuOlavXs33vve9Cdc7n5nP57M/MwVBEARBEARB+PiZsWdTWVnJ3Llzuemmm9i4cSMDAwO0trZy9uxZampqGBgYIBQK2SWJtWMWDoftsPxQKMTjjz9u56QZGhpifHycsbExzpw5Q1NTE11dXTz//PMMDw+TlZWFz+fD6/Vy+PBh7r//ft5//33279/PhQsXbCctKSmJO++8k7y8PNrb21FKsX79elJTU5k3bx5tbW1UV1fblYvS0tIoLCzke9/7HuFwmMLCQk6fPs2FCxcYGBjg1KlTk5zORJhOYzxGR0dpb2+nvLyctLQ0PB4PCxcuZPHixaSmpnLw4EGCwSCbN2+mvLyc2tpaCgsLOXz4MH/4h3/I2bNnbSElNTWV8vJyW9SaM2cOtbW1HD161M6T09PTYzuX+lt0s9y3s9KMjqTwer2sWLGCYDBIQ0MDp0+fZt26dbbwk5+fz2233UZhYSEvv/wyPT09zJs3z95qlJcXLS/v8Xh44403WLx4MaFQiMOHD/PBBx+Ql5fHDTfcQG1tLR6Ph/vvv5/Ozk727dtHJBLh9ttvp7Oz0x5bZmYmc+bMsddTcnIyCxcu5Ac/+AEnT55kcHCQ/v5+3n77bYaGhti0aRMrV660BYBHHnmEgoICXnzxRQKBACkpKSxatIiNGzdy7tw5jh8/PqlSj7aTs6y7xuPxsGLFCpYuXUpLSwt5eXl26XbTxiUlJQQCAQYHBxkZGeGWW25h27ZttLS08Pzzz9PT08PatWt54IEH6OrqYs2aNbz00kuMjY2xbNky8vLy+P73v4/P5+PLX/4y+/fvp62tDb/fzw033MAbb7xhj8kUEZxbrjRu1YXmzp1LOBymr69vkhjhtsXQFIcWLVrELbfcwquvvmo/M7c+9LrSOVecEUb6nrlz51JaWkpXVxdVVVWEQqFpiQbOnExOpvN+apxJzZ33mNvMEhEvMmk6aBsnJSXZecB27do1IdprfHyc4eFhLl68yODg4CS7+3w+8vPz+fSnP82Pf/xjBgYGuPnmm8nKyuLZZ5+1twqC+7oQBEEQBEEQBOGTZcaiTV9fH4cOHeL8+fP4fD7Gxsbo7++ntbWV/v7+CdWXxsfH6e/vn5S8NhQK0dbWNiGBqnaMQqEQw8PDjI+P20mKT58+bW+P6O3tZXx8nNbWVpqbmxkcHMTj8dDX18epU6cIhUIcO3aMtrY2zpw5w/vvv093dzeZmZl2GWI9tvb2dl544QU6OztRSnHu3Dny8/O56aabOHLkCO3t7TQ3N5Obm8vIyAjDw8OMjo7GTW6cKH+OdmTHx8d56623WLVqFcFgkLGxMerq6mhoaGDFihXMnz/fFpSUUrS3t3P+/Hnq6+s5c+YMixcvxu/309raysDAAI2NjdTU1NDa2kpnZyeNjY10dHSglOLEiRMTIhTcKgY5I3B0FSid2DQtLY2hoSF6e3vp6OjgwIEDjI2NkZWVRWdnJ21tbXY+jOrqaoqKirh48SJnz54lOTnZLs29aNEiILqNJjMzk6KiIrq6uqirq8OyLIaGhpg/fz6ZmZlcunSJ+vp6zp8/z/j4OD6fj+XLl5OSksKxY8eAD/PLLFiwAL/fTzgctnOojI+PEwwGaWtro7+/n7q6OpRSPPDAA2RnZ5OVlYVSipKSEtLS0njnnXfiRn2Y9jLzxmgBYsOGDSxatIiCggIuXbpEVVWVbVM9/61btxIKhbh8+TLp6emEQiEqKyv5zGc+Q1VVFZZlsXbtWk6fPk1ZWRnl5eVs374dn89HV1cXhw4doqamhoqKCrxeL7W1tQwMDFBQUEBycnLcd9Utz41+D818O0uXLmXTpk00Njby9ttvMzo6it/vZ/v27YyMjHDq1CkuX75siw9z5swhOzub4eFhkpKSWLFiBcuXL+fnP/85KSkptshiJtJNS0tj4cKFNDc3MzAwQHFxMYWFhUQiEaqqquzoEZ/Px8KFC1mwYAEHDx60Ew/rZzyV4OJMkuyct8fjIS8vz65w1d7eTmtrq+vWK6UU6enplJWV0dfXR1NT04TkvzpiL57tryahr9/vJxgMMjo6aotYw8PDE6q/6f69Xi/Dw8OukWG5ubns2LGDDz74gPb2djZs2MCcOXM4f/4858+fnxBR5UyCLQiCIAiCIAjCJ8+MRZvx8XHOnz8/obqOxqw+YlZ1cVZn0sfcks2alV60E9LS0mIn31RKsXfv3klj6unp4cCBA/T09HDhwgU7h4tGixADAwO249Xb20tlZaUtGB05coR169ZRVFTEunXraG1tpaenh4yMjAl5Yaayz1Tnzp49i8fjISUlhZGREbq6uhgaGrJFr9HRUTs5aHV1NR988AEABw4cIC8vj3A4bEfS9Pf3c/DgQbq7u2lra5uwFaquro5wODyhspC2oWlvjfN4VVUV6enptLW12c7tBx98QDgcZnh4mOrqavr7++nv78fv9/Pmm2+SkpJCbW0t7e3t5OTk2OPt6upieHiY4uJiIpEIo6Oj1NbW0tnZCUTzllx33XWUlZVRWFjIrl276O7utnN5ZGRk4PV6bUFgdHSUQ4cOEQgEKCoqsrfbjI6OMjY2RktLC2+//Tb9/f20tLQwOjpKWVnZpJw8IyMjtnDiZhPnM9Xj0Y70+vXrWbBgAR988AGVlZU0NTVRUFBAZmYmhYWFzJ8/nwULFtjiw9jYGPX19VRVVXHdddexaNEiMjIySE9PZ/fu3ZSVleH3+/H5fGRmZlJbW8u+ffsYHBy0k8n29PTYzzo1NXXC+LxeL8nJycyZM4eUlBRSUlKYM2eOne+ko6PD3hqm38OVK1cyd+5c2tvb7epuCxYs4J577mH//v3U1dWRnZ1NTk4O2dnZdsWztrY21qxZw4YNG5g7dy7z5s3jM5/5DHv27KG1tdX+PEhJSWHz5s0kJSXR0dFBQUEB5eXl5ObmcvnyZerq6hgeHgaiglBRURFpaWns3r2bjIwM5s2bR3Z2NrW1tbS2tk5IEu3ETGLtFG1SUlJYtmwZmZmZeL1e8vPzycrKIhKJ0NLSMuk5p6enU15ezpYtW+jp6eH999+npqbGztmj7Z0oD40z4bTb+jLP6d/nzZtnP6f58+dTVFTE6dOnJ2x/0tE+5rM0ycrKYunSpRQXF/PKK6+wYMEC5s2bR0NDAzU1NXZkjtuYpOS3IAiCIAiCIFwbzEi0cZYrdmI6DqYj4/zWWYs6zgo1zjLFZr+Jqr2Mj48zMjLCwYMH7fbdtnNcuHDBdUy6vePHj9PR0cHy5cspLy+nqKiIDz74gNbWVoaHhwmFQpMcO2e57OkwNjZmJ6vV93k8Hl577TXbbnqsZn8nT56cZI+xsTGOHTs2yXHUCaKduFX0cdseNTIywpNPPkkgEMCyrAnf5LvZMhwO884770xoVylFZWUlXq+Xs2fPUlZWRmZmJg0NDZw4ccKu8mRZFsePHycUCpGWlsaCBQs4f/683UYgEODixYt2FJYW3Z588kkWLFhAcnIyw8PDdHV10dfXZ5f9PnXqlD03n8/HSy+9RE9PD+3t7Xg8HlpbW+1oC9N+ZvltE308GAyyYMECtm7dSkpKCunp6Xb0S3FxMVlZWZSUlLBo0SKOHz/OoUOH7Oiww4cP28Lhc889x5YtW5g/fz7d3d12zh4tQJaWllJXV0dbWxsZGRkMDg6Sn5+P1+u1t4OZz08pRXZ2NsXFxRQVFZGfn09+fj5LlizB5/Oxa9cuKisrbdFBv686Oqqvr4/y8nKWLFnCpk2bWLt2LS+88AKZmZmUlZWxaNEiiouL6ejoYN++fSil2Lp1KzfccAMdHR2sWLGClStX0tvby759++jt7bWrQN1///0888wzWJbFrbfeSlpaGo2NjTQ1NTFnzhy7klRRURHp6ekMDw8zODjI6tWrWbNmDTfccAM//elP6e3tjSva6GdmlmbXx4LBIPPmzeOee+6htbWVc+fOkZGRYUez6RxC+rPJ5/OxYMECNmzYwPLly+nv7yclJYULFy7YwonP5yMnJ8dOgK3XpX6nzc9D871ziqf6nPlM1q5dy+HDh8nLy6O4uBifz8fx48ftzxyPxzOh0p2OttHiYFJSEgsXLmT16tWcPXuW5uZmPv/5z1NXV0d9fT0DAwP4/X57vM7P3NnO1SMIgiAIgiAIwpUxI9HGLM9rRtNo4v1DX1czcStXrdFbF0zMSkraGXMmGXWrduKM7HHDLb/L2NgYFy5coKmpiVdffXWSEGJWAdLbYyKRyIStOdN1dpxtu/U11T1THXdiPj89Tud4zTwoWqhKlNfHnLPzmr6+Ps6cOWP//dxzz02wnU64Oj4+TnJyMuvWrcPv9/Pqq6/ajnEkEqGzs9OOyDHHOTw8PEGYiYcWsGpqauw2PR4PZ8+etXMigXuVKCcej4euri727t3LgQMH8Pl8rF+/nrvuuouKigq6u7s5e/Yshw8f5vHHH7cjg9zW6cGDB2ltbeWzn/0s27Zts0WRb37zm9TV1bF27VqCwSBbt25l9erVrFixgr1799Lb22vb0UyYnJSUxB133MGSJUvsqmlpaWlUVFTYZczd1l1vby87duwgOzubtrY2Dh8+zEsvvcTChQu56667mDdvHv39/Rw4cIAf/ehHXLp0iXA4zK233sqyZctoa2vju9/9LidPnuSb3/wmZWVlVFZW0tvba2/zuXDhAq+//rod7XLmzBn27t1LJBIhJyeH9evX4/F4WLBgAUlJSQwMDPDbv/3bFBcX88QTT7Bp0yZSUlIS5qsx/6sJBoMEg0HKysrYtGkTPT097Nu3j4ceeojx8XGqq6upqamxP588Hg9paWkEAgE2btzImjVreOKJJzhx4gRz5861I4J0XqfPf/7ztLa2cujQIXp6eujr67O3eum568gyM+rN7b3R1cUCgQCLFy/m6NGjbNy4EY/HQ2VlJaFQyC7ZnZycTE5ODsuXL2f16tWkpqZSWVnJG2+8wfj4OPPnz2fx4sUopXjzzTfZvHkz4+PjHD16lOLiYubPn8/p06dpamqyq5nBh59x03kXBEEQBEEQBEH46FEz+Qe5x+OxdBntqZJ+OquraEfZrfrOleIch7NssFv1GmCC4+QUgfx+vx0FNNOKUW4VZaaqQjMVzjLcs0Wi0sb6nFk16Gox+1q+fDnz58/nzTffJBwOM2fOHB5++GFycnI4evQou3fvdhVSZgszCa7pPOt5OwUWPfZEa9jn89mREjNN6KojO/Q4dCSJ1+vF7/eTlZVFRUUF586dmyC+mAKiHsONN97IsmXLWL58OevXr6eiooJ9+/bx9a9/3S7H7XyeOueMzlEVCoVIT0/nS1/6kh0dVF9fz9DQ0ITnsXTpUlatWkVPTw/79+9nzpw5PPvss3zlK1+ZVOVI50ry+Xx87WtfY+XKlbYgV1VVRVVVFefPn+fOO+9k/fr1JCUlUVlZyS9/+UvWr1/Pvffey3/8x39w+PBh10ibrKwsCgoKCAaDvP/++7Y9vvzlL7N69WoKCwvJzMykra2NCxcu8OKLL9rbjXJycuyqZY2NjXbenczMTG688UZ27tzJD37wA44cOWILtIFAgDVr1vDoo4/y0ksv0dnZyfXXX89//ud/cvToUdLT07ntttuYP38+GRkZPPHEEzQ0NNh5tQKBgL3mtBithZ7U1FT++Z//mQMHDlBWVkZdXR3vvfeeXUGtqKiIvr4+brzxRkZGRuycRw8++CA//OEPSUpK4oEHHiAYDLJnzx56enr4xje+wd///d8TDAa5/fbbCQQC7N69m6qqqgmVx5xifDgcnta2UEEQBEEQBEEQrpqjlmWtdx6ccU4bpyPvJmzof/i7CTqzWSrX7NvNuTedU/Na7Xhrx9qsmGRGzky3fzdH3lk1xrSZ7sd5n1ulGbNM9GzgfHbOKkGmw2YmMJ7JM3MTe8w5dHd3U1hYyO/8zu8wOjrKhg0bOHv2LL/4xS84f/68/QziYY57pmKgvt7cxmKuDWdVI30MPiwDbuYz0eiyyG4RaBrTjuYcdOU0LUaY63J0dJTh4WEuX75sJ+qGDyPTzGt9Ph+5ubksWrSIpUuXkp2dzb59+zh48CA9PT0TklKba9GyLPr7+yckLu7q6uJ73/seY2Nj9nYz5za6c+fO0dDQYNvFsiyefvppzp8/P8HR12tYi4/f//73CQaDeDweQqGQXW0uFArx8ssvs3fvXizLsivR7dixgyeffJLTp0+7rouMjAy2b9+O1+vlrbfeskuZAzz77LPU1tZSUVHBypUrKSoq4vXXX6egoIDFixczOjpqJ9PWuY9uu+02Ojs76evrY3h42K74dfToUbtPveZCoRBvv/02Pp+PpqYmmpub7apuZWVl/OxnP+MrX/kKCxcuJBgMkpOTw7p161i/fj1vvfUWTz/9NCMjIyQlJQHY/f34xz/ma1/7mj2OJUuW0Nvby7x586ioqOCZZ57B5/Ph8/lISUnh3nvvpa+vj5SUFLZs2cLixYu5fPkyaWlp3Hzzzbz55psMDQ3xyCOP0NjYyFtvvWVvQdQClK7aZ+bNkZLfgiAIgiAIgvDJckWJiJ04S+I6hQiYGGLvdJR1G1cSheMUB9xED7fIB2eeHPhwa4CZ28IUZvR9+riO8HGLRnHbomXm+zHvMSvbaJxjM518Mw+OmzjkVtXGHIv5X7dSxs57EwkRbiWRE+U8UkrR0dHByZMnaWpqIikpiVOnTtHd3U1HR0fCKk6aeHZ0u85MTKvn7JYzye13t7nFE+jM+3ReEjdB0xQ4nGvLxHxeZrSYuQ6daysSidiRKKmpqSxcuJDOzk6effZZurq6JoiRTsHSbE//3t3dHXeroRZizKigwcFBfvnLX9riULx3vr+/38694hxTb28vAwMDdtSS1+vl1KlT1NXVMTg4OGkcXq+X22+/3RaRdJJpTUdHB5WVlZw5c4b8/HxWrFhhVz0zRZnBwUG7/ZaWFrZt20ZBQQGjo6McO3aMt956i7GxMbt/XQHv5MmT3H333fzkJz8hEomQlZVFcnIySUlJVFRUkJ2dzbJly4hEInaVKp1Lpru7my984Qukp6cTCATo6uriyJEjnDx5kqVLl/LOO+9w9OhRmpqaGBsbIzc3l507d/LUU09x4cIFTp06xdy5c1m6dCmLFi2ioaGB7du3c8cdd3Dq1CkOHz7M8PAweXl5vPrqq5SWluL1erl48SIXL14EoomfN2/eTH5+PpcvX8bj8ZCVlUVubq5tI0EQBEEQBEEQPjlmLNq4OW+JBBGnMxuvHTMRscaMJDDPmYKG2b9Zyjhekk/zfreEoM7xmrkyzKSd5r3OPvXvTgfZdICdYzerbDnt4hyjOQ+nYxzvfjcn33m/m2hm3mfONdF2r0Sii45O6OjooK+vD5/Px9DQ0ITIF6c93ISKeBWw4o093nOdCre17fzd7ViiLVLO+8zxOKOATJy2dz47y7Job2+ntraWlJQULl68SFtbm70tJ9G76hZhFW/txJtTOBympaUl7j163Zu2ca5XLfaY1+lIITcRLD09nfHxcZqbm2lsbLRzCOm2dSSPrrh1+fJlfD4ffX199Pf3Mzo6OulzobGxkZMnT3Lx4kWGh4dpaGigvr6ecDhMSUkJubm5dp6uvr4+1qxZQygUYmRkBJ/PR3Z2NosXL7a3Qu3bt4+Wlha6u7tJSUlhyZIlAOTk5DAyMkJ3d7dduj01NZWcnBzmzZvH66+/zpkzZ+z3JBQKsWfPHo4dO8bY2BhHjhwhOzubuXPnkp6eTldXFz6fj7a2NqqqqqivryczM5PLly9z6dIlsrKyGBoaoqCggLVr1zI8PExycjIbN25k//79DA4OkpqaypIlS1izZg3PP/+8iDaCIAiCIAiC8AkzY9HGDbfIBTeHMpHj53ZeiwRuVXycuFWO0e06BYbpOOK6LbfkyW7ikzMaRYtF8UQVt8pTpihibp3RET1mf057mWKSm8DjZq94x8056Tacz9DZjznfeIKKcztPKBSyqwa52ditCpizr0TEm7ub8ON2jXMu5vFETOeaRP0lilTS49brzdlXJBKhvb2dd999l/379xMMBhkbG0soIjnXpNn/VPmM3ASoRCSanz6v29E/tbW1k/rU1yUlJXHmzBna29vp6emJO3bLiiajbmxsnNSWczzd3d3s3bt3gmCrr0tNTWXx4sX4fD4GBgYIh8M0NjbaOYY6OzvJycnBsizeffddGhsbbeFlZGSEoqIiUlNT8Xg8tvDS3NxMcnIy2dnZBINBSktL6ejo4MyZM3R3dxOJRBgbG6O1tZVdu3YxOjqK1+vl/Pnzdjl2LUylpqbS1tZGdXW1HSl14sQJQqEQTU1NnDlzhrS0NAoLCxkfH2fZsmWMjo5SVVXFwMAACxcuZMWKFSxevHhWclkJgiAIgiAIgnB1XFEi4pni8/lcc4VcDW6OvnauvF7vjJPBTqc/Uxhy4tzedLV5aLxerz0f3Z8+5pYo15lcN972nJmOwenUmkl5TeFA51Qxc8yYY9ftmVvPzPvAfQuXM1mys82PAnMcH0fp43gJszVuyajNnDaJ8kp91LaabaZKfq1FKv2ufRSfLSZOOyYlJbF+/XqWLFlCMBiktbWV/fv309fXN2HLpN/vn9SWXvs62bmuKAUfPs/U1FR27tzJnj17uHjxoi0GudlDfx6Y6Fw2ejzaTuY69vv9ZGRksGTJEn73d3+Xv/u7v6OxsRGv18uWLVtYtWoVLS0tPPXUU4TD4WltWRQEQRAEQRAE4apxTUT8sYg2HwWmo29W0fkoqg3B1M4kzG6lp3hltGezotO1hptQNRUfRVWyeP1ciYDzcY1vulxpNTO95q5WxDK3PznHMJu2SlQd7WpxE0vcBLUrISkpiZUrV1JbW+uaw+dq8Xq9pKSkcOONN3LXXXdx+PBhXnjhBcLhMDfddBObN29maGiIH/zgB3a1MKkeJQiCIAiCIAgfC7NTPepqMZ2/q3GsnMKIzqnhxmyIKdMRERKNYTqY9piNCIl41aim63xPVyBKZN9459yOz2TOZlnymZJIHHA7F2+dTkfI+TgidWaCm5gwHbHELC0+k/ucmNv+9LN3tu0UYq+kr49KsNFjSYRzS6MZgeNcVyY6+ub48eMTthHGSwTuFoHjFpmm33f9vtx8881UVFRQU1PDSy+9RDgcJisrizvuuIPa2lpee+01W7CR6lGCIAiCIAiC8MkyI89mOv9wv9Jv8j8KzG0n19I2kXjO/nQcTdOZnWpObn24Od+J+prOM08UGRPvnNvxRGvHeU5vi9HHpspP48xvYm7T0iQq9+02B6cdZ3Ptz0SkMLfPuZ2brrDk3HpkYtowXnvTjUbS7Zjr3c325hqZrQgc01ZmvqxEFeiuBHPs08n3Y0bnaDHH6/Xa4zSTRWs7mwmp9bVu1eDM+WzatImVK1fS1dXF7t277epfd999N2fOnOHEiRN0dXXZ13+UW88EQRAEQRAEQZiaKyr5ncgpdXOs9D1O59n81l3ni5kttNPkrLRzpX057zMd9ETnZko8+5o2NOdzJW1NF7c+4s1tqiTTMzk+Fea83JJcO6uIueV3cXOknWvFvE+LGW5Jsc2xmMfcbDXdtTETkSJRW86+zUpqbqXdnXNyqyDlTParx+qsfub2npkJpp22d9rGjFgxEzXHs4spOpltOZ+neb2+Rj/n2YyMchOhEl3rVjXMKZw6K285k5Gb+bzMZ+31elm8eDEbNmygra2N9957j46ODiKRCH6/n1AoxMmTJ2lsbJxQxv1aErsFQRAEQRAE4TeRK95D4HRA3CoHud1jXmMmE57tnB+m8zOdsZn3mWMzjznPOYWUqSoamWNwVsUyxQc3h9pZ+vpqcZun2zidx69ECJrOunA64+Y95rNMJAA5n4MZNTITmyWKOHGOyfkczXE6BQxzXk6bmMdnwlS2jbfGnPZyG795zCkcxBuDmyjr9n44RRi3xOLOPvT10xG0nJFVzn6cTPezYarrnDadyeeOm/Do/Mw0BUG3c04RU0fa5ObmsmXLFsLhMDU1NdTV1REOh+0onlOnTtHY2MjAwIBE1giCIAiCIAjCNcSMwzCc39yPj4/bkQhu1X+c97g5TvEEgkRMd9uOHpP5zbuzP6co4GzbdMzNb+fj9RcvEanpaJkljZ3fnDuPXymJxBVdlSbeHNyOJ8qvkYhE20LM/tyilfTvTpHEGTkVb5uTXp9uYzCjcHSkhb5Xz9eMNDCfmT6nt9+Zz0xHNzjHb/Zr9mPOT7fr9vzdxJMrSUit23bOVx/XooAZReO0mxPdnttx8zNCHzPbcX6mOJ+X8954c3Zbm8732ilYmef0c4mHWySM23wTCVzTQa9Lp7Blno+3NsbGxiZ8hgAEAgGuv/56li9fzuHDhzl79iwjIyP2vWNjY5w4cYK+vr5Ja1EQBEEQBEEQhE+WK947o7/BNbc4OR1l8x/92sE1czGYx922UcTDdDaduCUo1tsDnMfNfCWJRBK95QDchRAzcsgtz4mZC8S0icfjmWAPM1Gp04ZXGonk3BJktue2FSSercxcOjPFLcmsW39OW+nxOx1s5/NyCiLTxW2dJkKLMabIpJ+ZWeZcz8kci+5Lz0n/13wepjholnfX15h9z1SwMcWg6Zwz322nDRLNzU1g0Nfr7UfOn5km73a+u4lwlp/X8zSFZn1sOrl/TOEuXn/mZ0S8dRwPbS/zejdR0nwGToHR3M4XCASYO3cuDz30EL/85S+prq6mt7fXdd3rY9daDjBBEARBEARB+E1mRtujnFEQ5jfT2tFwigTaIfP5fBOcOxOnE6Lb0+ecTospfmgHajqCgrOKi+kw6nvNMThzXMQTbqbKKWLawcl0v9FOJDrppLzOLRHOqkemjbQNne26OdFaXAqHw9Oq+OV8Zm7txZuX83k6HVhzHqbj7Zz3TEj0fExMp9153K2UtXPs+pyzL+f61XY256vb02gxwi0ixXyXPipbuGHOwbmmzfXjxpX2adrETaCNl0Da7bmYYzHz6JjtzUS01O+Xzq2l20v0eeFm/5kIdOa7GQwGKS8v54/+6I94+umnOXjwIOFweEpx2rkepXqUIAiCIAiCIHxyzLh6lHZ2zLwc8RwZ07mezjfNpgPudKzi5VJx24oULzojUf9uVWpMYcN0aJyOnB6fbt8piJh2cN4TDzP6ZCpM59DtW3mNeU7bI55gZApBem7OcuSmrZwRRGbUiVt7+jpz7Nqh1WKgs/rOVMxkS8d0y5mbaOfbHK9zjM6ILVNESSS0JRq7PucURJwiibNfN3Ep3pyna49E75KbPUwhZSpxMZ445yboTPWs3drTOMUtp331OjfvNcXpeJjPRa8T854rjZZzYyqRy+/3s3btWrZs2cJrr73Gnj17CIfDU34OOt+/2UzMLAiCIAiCIAjCzJlxImI3ZyGRcz3dbQHaWdTXRiLRqiYapzjgdDBNQcJM1KkxBRU359Qtx4X+dtzpHLptz3CKJc45uwk3iezijKBIhB6Pm1ji7MPNqZ4uplij73Xrxxy7+TzjCWlOm0w1tqmEg+lwJblGplo3MLHiVLy+3KKH3NpytgmTn5/buxfv2U9nzs51HC+3Tjy0GOKMDon3GZFobU8lzLiNw/yccApAZiUwmLxOnZExid5nN8xtlFeLWQVqpvj9frZs2UJpaSmnT5/m4MGDhEIh1zXnJswkEgMFQRAEQRAEQfh4ueLqUSZTOXFX0kY8hy2RU2SW+jWdf7e+Ep3T/3WWME6EM5fH+Pi4q8A1W06dG2aS3qt1tPQ2ikTCyGz080lxpaXQp5qvcx2Y22L0efNvZ2ntRP067T3VGp7J2J1jvJp1arYD8ecWT+iLd810iSd2TCU+OQVD5/l4nwfOROvOsVzJWruad2vZsmUkJydz4cIFampq6OrqstvUnxFTVevSa+BX9f0WBEEQBEEQhF8XZkW0+aiYaTLM6SQnnU7UypX0Hy/K6ONkNh2sq7HhrwJm0t/ZbtdNXIkXPTSdiKePw3E2I8bi5Z6aaVszKbc+W2vqSgSrmYxjJkKGmVzaWQJc/zdRP1eCUork5GSampq4dOkS7e3t9pid/bpFxJmIYCMIgiAIgiAInzxqJv8wV0rJv+IFQRASYFbJM3PhuOVymm0CgYCdB8gUZXRyZVOsdMs35ZxDOBxmdHR01scpCIIgCIIgCMIkjlqWtd558JqOtBEEQfhVw7ImV4CKVzVvtrkSgUVEGUEQBEEQBEG4dpmpaNMBXPgoBiIIgiAIgiAIgiAIgvAbSqnbwRltjxIEQRAEQRAEQRAEQRA+Hq6shI4gCIIgCIIgCIIgCILwkSKijSAIgiAIgiAIgiAIwjWIiDaCIAiCIAiCIAiCIAjXICLaCIIgCIIgCIIgCIIgXIOIaCMIgiAIgiAIgiAIgnANIqKNIAiCIAiCIAiCIAjCNYiINoIgCIIgCIIgCIIgCNcgItoIgiAIgiAIgiAIgiBcg4hoIwiCIAiCIAiCIAiCcA3y/wOWvOk5vgLhPQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for i in range(190, 200):\n", - " plt.figure(figsize=(20, 20))\n", - " plt.xticks([])\n", - " plt.yticks([])\n", - " data, target = dataset[i]\n", - " target = [x - 26 if x > 35 else x for x in target]\n", - " sentence = convert_y_label_to_string(target, dataset) \n", - " print(sentence)\n", - " plt.title(sentence)\n", - " plt.imshow(data.squeeze(0).numpy(), cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.networks.util import sliding_window" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "patches = sliding_window(data.unsqueeze(0), (28, 46), (1, 46))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "patches.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "patches = patches.squeeze(0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig = plt.figure(figsize=(20, 20))\n", - "for i in range(6):\n", - " ax = fig.add_subplot(1, 6, i + 1)\n", - " ax.imshow(patches[i].squeeze(0), cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/notebooks/04b-look-at-iam-paragraphs-predictions.ipynb b/src/notebooks/04b-look-at-iam-paragraphs-predictions.ipynb deleted file mode 100644 index 5662eb1..0000000 --- a/src/notebooks/04b-look-at-iam-paragraphs-predictions.ipynb +++ /dev/null @@ -1,269 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import cv2\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "from omegaconf import OmegaConf\n", - "\n", - "from importlib.util import find_spec\n", - "if find_spec(\"text_recognizer\") is None:\n", - " import sys\n", - " sys.path.append('..')\n", - "\n", - "from text_recognizer.datasets import IamDataset\n", - "from text_recognizer.datasets import IamParagraphsDataset\n", - "from text_recognizer.models import SegmentationModel" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "path = \"../training/experiments/SegmentationModel_IamParagraphsDataset_UNet/1207_082955/config.yml\"" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "config = OmegaConf.load(path)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model = SegmentationModel(\"UNet\", \n", - " \"IamParagraphsDataset\", \n", - " network_args=config.network.args, \n", - " dataset_args=config.dataset.args)\n", - "model.load_weights()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2020-12-07 20:38:30.094 | INFO | text_recognizer.datasets.iam_paragraphs_dataset:_load_iam_paragraphs:250 - Loading IAM paragraph crops and ground truth from image files...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IAM Paragraph Dataset\n", - "Num classes: 3\n", - "Data: (308, 256, 256)\n", - "Targets: (308, 256, 256)\n", - "\n" - ] - } - ], - "source": [ - "paragraphs_dataset = IamParagraphsDataset(False, **config.dataset.args)\n", - "paragraphs_dataset.load_or_generate_data()\n", - "print(paragraphs_dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x360 with 3 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x360 with 3 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x360 with 3 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x360 with 3 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "<Figure size 720x360 with 3 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x360 with 3 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x360 with 3 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x360 with 3 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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bmDT8WjNbocTJkO+kOy4fBxUVFaqtrVVzc7MqKyt19NFHa//+/SkniNfU1KisrExlZWUaP358u9OaO3eupk+fnoe5RswUVSZyebmYXE2r2DZkJVBwFVUmMpWL9b3Y1nUE3L3DJmm5EsfW9ytxrH2ipBMlPSPpVUnrJfUOxjVJ90vaKelFSeemm37wPI+yjRgxIuX+Aw880Gqc6dOn+8KFC12SDx8+3MvLy1uNM23aNJfks2fP9vnz5/ukSZMifV20zrdSz0Shinq5FXPzEs+EYp6LqJdNKTbPYL1Nblz49AiLFi1SU1NTq/8+hw8frtNPP73L36SbNWuWDh06pJ07d+q9997L6mrwmbj33nt1/fXX53SapchL/CKPcfhciBJ7GFojE6WVCTKQXraZoOCKkWXLlumxxx5Tr1699I1vfEPr16/XxIkTNXz48FbjHvk7jpJUX1+vrVu3dvk3FmfMmKGf/OQnXZpGoWPjQiQ7UoobIzJBJtpSilk4LOtMZLtLLIymGOwajKJVVFTkZDpjxoxpuX377bf7lClTXJJffvnlPmHCBJ80aZJXVVW5JL/uuutaPXfOnDk+atQol+SNjY1eWVnZbl/Tp09vNWzy5MmRL8tcNzLRqWWGLEX9npEJ1v1CEPX7m6tMRF5secyDVCht3Lhx/tBDD4XaR3JhJ6mliDuyjR49OuNpJp8vN3HixMiX4+FGJiJd9sgCmSi+hs6J4H3Kah3uJhSFQYMG6aqrrgq1jyN/1mf58uVtjvfrX/9akrRhw4ZWv7OY/A3O8ePH65prrtG4ceM0YsQInXbaaZKksWPHtnkYFaXBzNI2oJiRgeLEOVzIuwULFqi2tjajcWtra3X00Ufr7rvvDnmuUnmJn69SKuLw+ddV+dr4koniV+h5yHchmm0mKLiANrBxwWFx+IzsCAUX8i2umYh7wdXln/YBUHw6e1HOYryYZ64+xOO6kUJm2lu3i3GdTyeswqbYM0LB1Y5bb71Vd955Z9SzARQUftKmfek2UsW+sSlWma7PpbreZ6PYz02j4ArMnz9fGzdu1JlnnqkTTzyx5QT03/72txoyZIikxHWu6urq2nx+ZWWlVqxY0e70x44d2+b1scaPH68lS5Z0/QUABYIfiG5bsW9sSl2u19diW/9LAedwddKsWbM0aNAg9erVSy+99JIuuOACbdq0STt37tRdd90lSXriiSf097//Xaeddpp69+6t7du368Ybb2yZRmNjY5vfxps8ebLuv//+vL0WtFbq56sUy4d5sbyOOCATM6PsvksKed7jjJPmY2zatGktxdjhn+CZP3++PvzwQ9188826+uqrdckll2jz5s3avXu3Fi9e3GoaixcvVq9evXTFFVeopqZGDQ0NLY9VV1dr6dKlrZ7z3HPP6YILLmi5P2zYMD311FMt97myfGtsXGZG2X3ssDzIBOtAa6W+TCi4ilhDQ4NqampaDZ8wYUKbxZkkjRo1Sv369dPPf/7zlmHTpk3T6aefrrfeeku33HJLaPNbyEp945KJUv+w7YxCXmZkIqGQ38M4K8TlSsEVY/PmzUs5pJitxx57TN/5zndyOEcJixcv1oQJEyRJs2fPVs+ePbVjxw5973vf044dO1RVVdXqORUVFbr00ku1e/fuovxyARuX3CjED9GoxXWZkYnsxPV9LGb5XuYUXOi05HPKDh/yTFZXV6f6+vo2n1tXV6fm5uaWK8tPnTpVb7/9tpqbm3XmmWfqzjvv1Lx58/TJJ59o165dLYdCR40aJUl6/PHH9atf/Urf/e53W6Y5adIkPfjggzl/nZlg4xIfbLjaFveNS66RidbIRqq4Z4KCC3nxxBNPqKysTP369dMPfvADnXTSSRo4cKC++tWv6sMPP9Tq1atbFVfPPfecnnzySe3cuVOrV6/O6/yycSkspbjhifvGJdfIRNeUQkbingkKLqANpbxxcfeSvURBIW2U4r5xyTW2E/ESx6zEPRNch0sdn3QOlKJM/hErxqKMC7cCmck2B+SGPVyaN2+ezj//fM2ePVtr166NajYQM6X833y+PxOKsXDLRFc3QHH/bz7XospEWHko1fW+mHBIMQujR4/WxRdfrO7du2v//v2qra2VJD3wwAO65pprWsbLxR6w+fPn67rrruvSNJA/pbpxkeL5EzOlunHqqKii4MqfqDJRqut9oaDg6qKPPvpIxxxzTMv9iooKnXfeebrlllv00EMPtfzkz8iRI1VVVaUrrrhCd999t3bu3KkvfOELam5u1r/+9S/94he/aJnGrFmzNHToUK1Zs0bvvfee5s6dq5tuukmjR4/W1772tbTzNHToUD399NO5f7FoFxuX4sQGrPNKNRPFlAfW/9zKOhPuHnmT5HFpw4cPz/o5VVVVKffLy8tT7v/hD39wd/fbbrvNJfmcOXNcki9cuNAl+bBhw9L2MX/+/A4fnzx5cuTLrphaKWeilEW93sW5OZkoCVGvZ4XUPMt1mD1cObRmzRpddtlloUy7qqpKBw4c0IUXXqgXX3xRZ599tmpra1sOf9bU1MjdW11a4aWXXtLgwYNbTa++vl4vv/xymz8FJEnXXnut7rvvvlBeSyHwEv1vXlJR/UcflWLck1CqmSAPuUEmEk+IvCkGlWqU7dZbb835NMeMGdPuY1OnTvVt27b5unXrXJLX19f7rFmzXPp0T9qsWbO8trbWKyoqXJKPHTvWKysr25zeyJEjfeXKlX755ZenDB81alRG81pXVxf5e3BkIxOdWmbIsajfUzJBBuIm6ve4K5nIZCV/WNKbkrYkDZspabekzUEbkvRYnaQdkrZLujSjmYjBgouyLVu2zJcsWZKz6Q0fPtzvvvvudh9/7rnnfM2aNWmnk1wIHT7UumrVqpZhc+fO9T//+c8+c+ZMr6ur8w0bNqQ8/9577/WhQ4e6JJ8wYYJv3rzZZ8yYEfnyzqSRiVCWKbIU9XtGJshE3ET9nmWaibZa2kOKZnahpPclPeLuXwqGzZT0vrvPO2LcwZKWSzpPUj9J6yV9wd0Ppumj45lAzoT1e4ydtWrVKu3bty9W8yRJ3sGuYjIRvXSfW8UiTodhyET8lUIuCiUTbUl74VN3/6OZDchwesMlrXD3fZJeM7MdSoTqz9nMFMITt8KmoqIi6lnIGpmIXiYfuqWw8YkLMhEP2RYjZCS/unXhudea2Qtm9rCZnRAM6y+pKWmc14NhQCkgEzFiZjlp6BIyEWO5ygh5yUxnf9pngaQ7lDiOeYekuyR9L5sJmFmNpJpO9g/ETVFkIk4X2oyLdBsR9hK0qygyIbVe90s1C5noTNGVaYYKvaDrVMHl7nsP3zazByU9FdzdLaksadSTg2FtTaNBUkMwjUg+scaNG6eqqiqVl5ersrJS/fv31/jx43XmmWe2jLNs2TI1Nze3XCW+pqZGDQ0NOen7kUce6fJ0EA/FkomO5GojU2wbq1xtBIqtcCuWTLS1vuZrHS62rLSn0AupTHWq4DKzvu6+J7g7QtKW4PZqSb8ys58pcTLkIEnPd3kuQ/LBBx+ovLxckrRixQrV1NTojjvuSBnnyiuvlJQ412jVqlW6+OKLWwqu5cuX69lnn9XevXvV2NjY8pzKykqtWLGiw74ptopLsWQiH/iB6LYV20aHTHQdWSkumXxLcbmkiySdJGmvpP8T3P+KEruKd0n6/uFgmdkPldhtfEDSDe6e9heh4/jffC7ccccd2rVrlxYtWqTbb79dP/rRj/T888/rb3/7m77//e9LksaPH68lS5Zo+vTpmjt3bsuwkSNHhnYRVaSX5htZRZuJYvvwLrbXEyUyUXyK+bXlQ7bfUuRK8wVo4sSJOuuss/SPf/xD77zzjgYOHKimpibt27dPjzzyiOrq6lRfXy9JuvXWWzV27FitXbtWU6dOjXjOC0e2Qcq1uGaiVD6gS+V1ZoNMfKqU1o9Seq3ZouAqQfX19TruuON03HHHacKECS3D58yZoy9+8YtavHixJk6cqGeeeUYffPBBS2GG9rFx6bxi+IAuhteQa2QiO8W0DhXTa8klCq4SNW/ePN14441Rz0bRYOMSrag/4KPuP47IRDSiXhej7j/Ocn7hUxSG119/PepZAHKGk4WBhGzWczIRb+zhAtrAf/OlhesspUcmSkfy+k8W2schRSAH2LgAqcgEkCrbTHTlp30AAACQAQouAACAkFFwAQAAhIyCCwAAIGQUXAAAACGj4AIAAAgZBRcAAEDIKLgAAABCRsEFAAAQMgouAACAkFFwAQAAhIyCCwAAIGQUXAAAACGj4AIAAAgZBRcAAEDIKLgAAABCRsEFAAAQMgouAACAkKUtuMyszMyeNbOXzGyrmU0Jhvc2s9+b2avB3xOC4WZm95rZDjN7wczOCftFAPlEJoBUZALIgLt32CT1lXROcLuXpFckDZY0V9KMYPgMSXOC20MkrZVkkr4uaWMGfTiNFqdGJmi01EYmaLTUlm6dbbUOZ/0EqVHStyVtl9Q3KWzbg9sLJVUljd8yHkGiFUojEzRaaiMTNFpqy7Z+yuocLjMbIOlsSRsl9XH3PcFDb0jqE9zuL6kp6WmvB8OAokMmgFRkAmhbj0xHNLPjJK2UdIO7v2tmLY+5u5uZZ9OxmdVIqsnmOUCckAkgFZkA2pfRHi4z+4wSIXrU3Z8MBu81s77B430lvRkM3y2pLOnpJwfDUrh7g7uf6+7ndnbmgaiQCSAVmQA6lsm3FE3SIknb3P1nSQ+tllQd3K5W4pj94eHjgm+hfF3SO0m7lIGCRyaAVGQCyEAGJz9+U4kTxF6QtDloQySdKOkZSa9KWi+pdzC+Sbpf0k5JL0o6N4M+Ij/5jUZLbmSCRkttZIJGS23ZnjRvwYocqWyP6wNhc3dLP1Z4yATihkwAqbLNBFeaBwAACBkFFwAAQMgouAAAAEJGwQUAABAyCi4AAICQUXABAACEjIILAAAgZBRcAAAAIaPgAgAACBkFFwAAQMgouAAAAEJGwQUAABA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- "text/plain": [ - "<Figure size 720x360 with 3 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x360 with 3 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for ind in range(10):\n", - " x, y = paragraphs_dataset[ind]\n", - " y_hat = model.predict_on_image(x).cpu().numpy()\n", - " fig = plt.figure(figsize=(10,5))\n", - " ax1 = fig.add_subplot(131)\n", - " ax1.matshow(x.squeeze(0), cmap='gray')\n", - " ax2 = fig.add_subplot(132)\n", - " ax2.matshow(y.squeeze(0), cmap='gray')\n", - " ax3 = fig.add_subplot(133)\n", - " ax3.matshow(y_hat.squeeze(0), cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/notebooks/04b-look-at-iam-paragraphs.ipynb b/src/notebooks/04b-look-at-iam-paragraphs.ipynb deleted file mode 100644 index dc0aef6..0000000 --- a/src/notebooks/04b-look-at-iam-paragraphs.ipynb +++ /dev/null @@ -1,264 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import cv2\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "\n", - "from importlib.util import find_spec\n", - "if find_spec(\"text_recognizer\") is None:\n", - " import sys\n", - " sys.path.append('..')\n", - "\n", - "from text_recognizer.datasets import IamDataset\n", - "from text_recognizer.datasets import IamParagraphsDataset\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IAM Dataset\n", - "Number of forms: 1539\n", - "\n" - ] - } - ], - "source": [ - "dataset = IamDataset()\n", - "print(dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "transform = [{\"type\": \"ToTensor\", \"args\": None}, {\"type\": \"RandomAffine\", \"args\": {\"degrees\": [-10, 10], \"scale\": [0.8, 1.1]}}, {\"type\": \"RandomHorizontalFlip\", \"args\": {\"p\": 0.1}}]\n", - "ttransform =[{\"type\": \"Unsqueeze\", \"args\": None}, {\"type\": \"RandomAffine\", \"args\": {\"degrees\": [-10, 10], \"scale\": [0.8, 1.1]}}, {\"type\": \"RandomHorizontalFlip\", \"args\": {\"p\": 0.1}}, {\"type\": \"Squeeze\", \"args\": None}]" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2020-12-05 22:39:25.402 | INFO | text_recognizer.datasets.iam_paragraphs_dataset:_load_iam_paragraphs:250 - Loading IAM paragraph crops and ground truth from image files...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IAM Paragraph Dataset\n", - "Num classes: 3\n", - "Data: (1229, 256, 256)\n", - "Targets: (1229, 256, 256)\n", - "\n" - ] - } - ], - "source": [ - "paragraphs_dataset = IamParagraphsDataset(True, transform=transform, target_transform=ttransform)\n", - "paragraphs_dataset.load_or_generate_data()\n", - "print(paragraphs_dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x360 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x360 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x360 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x360 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x360 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x360 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x360 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "<Figure size 720x360 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x360 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x360 with 2 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "for ind in range(10):\n", - " x, y = paragraphs_dataset[ind]\n", - " fig = plt.figure(figsize=(10,5))\n", - " ax1 = fig.add_subplot(121)\n", - " ax1.matshow(x.squeeze(0), cmap='gray')\n", - " ax2 = fig.add_subplot(122)\n", - " ax2.matshow(y.squeeze(0), cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/notebooks/05-sanity-check-multihead-attention.ipynb b/src/notebooks/05-sanity-check-multihead-attention.ipynb deleted file mode 100644 index 54f0432..0000000 --- a/src/notebooks/05-sanity-check-multihead-attention.ipynb +++ /dev/null @@ -1,169 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import cv2\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import torch\n", - "from torch import nn\n", - "from importlib.util import find_spec\n", - "if find_spec(\"text_recognizer\") is None:\n", - " import sys\n", - " sys.path.append('..')\n", - "\n", - "from text_recognizer.networks.transformer.attention import MultiHeadAttention" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "temp_mha = MultiHeadAttention(hidden_dim=512, num_heads=8)\n", - "def print_out(Q, K, V):\n", - " temp_out, temp_attn = temp_mha.scaled_dot_product_attention(Q, K, V)\n", - " print('Attention weights are:', temp_attn.squeeze())\n", - " print('Output is:', temp_out.squeeze())" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "test_K = torch.tensor(\n", - " [[10, 0, 0],\n", - " [ 0,10, 0],\n", - " [ 0, 0,10],\n", - " [ 0, 0,10]]\n", - ").float()[None,None]\n", - "\n", - "test_V = torch.tensor(\n", - " [[ 1,0,0],\n", - " [ 10,0,0],\n", - " [ 100,5,0],\n", - " [1000,6,0]]\n", - ").float()[None,None]\n", - "\n", - "test_Q = torch.tensor(\n", - " [[0, 10, 0]]\n", - ").float()[None,None]\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Attention weights are: tensor([8.4333e-26, 1.0000e+00, 8.4333e-26, 8.4333e-26])\n", - "Output is: tensor([1.0000e+01, 9.2766e-25, 0.0000e+00])\n" - ] - } - ], - "source": [ - "print_out(test_Q, test_K, test_V)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Attends to the second element, as it should!" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Attention weights are: tensor([4.2166e-26, 4.2166e-26, 5.0000e-01, 5.0000e-01])\n", - "Output is: tensor([550.0000, 5.5000, 0.0000])\n" - ] - } - ], - "source": [ - "test_Q = torch.tensor([[0, 0, 10]]).float()[None,None]\n", - "print_out(test_Q, test_K, test_V)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Focuses equally on the third and fourth key." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Attention weights are: tensor([[4.2166e-26, 4.2166e-26, 5.0000e-01, 5.0000e-01],\n", - " [8.4333e-26, 1.0000e+00, 8.4333e-26, 8.4333e-26],\n", - " [5.0000e-01, 5.0000e-01, 4.2166e-26, 4.2166e-26]])\n", - "Output is: tensor([[5.5000e+02, 5.5000e+00, 0.0000e+00],\n", - " [1.0000e+01, 9.2766e-25, 0.0000e+00],\n", - " [5.5000e+00, 4.6383e-25, 0.0000e+00]])\n" - ] - } - ], - "source": [ - "test_Q = torch.tensor(\n", - " [[0, 0, 10], [0, 10, 0], [10, 10, 0]]\n", - ").float()[None,None]\n", - "print_out(test_Q, test_K, test_V)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/notebooks/05a-UNet.ipynb b/src/notebooks/05a-UNet.ipynb deleted file mode 100644 index 77d895d..0000000 --- a/src/notebooks/05a-UNet.ipynb +++ /dev/null @@ -1,482 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from PIL import Image\n", - "import torch\n", - "from torch import nn\n", - "from importlib.util import find_spec\n", - "if find_spec(\"text_recognizer\") is None:\n", - " import sys\n", - " sys.path.append('..')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.networks.unet import UNet" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "net = UNet()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "x = torch.rand(1, 1, 256, 256)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ModuleList(\n", - " (0): _DilationBlock(\n", - " (activation): ELU(alpha=1.0, inplace=True)\n", - " (conv): Sequential(\n", - " (0): Conv2d(1, 32, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3))\n", - " (1): ELU(alpha=1.0, inplace=True)\n", - " )\n", - " (conv1): Sequential(\n", - " (0): Conv2d(1, 32, kernel_size=(1, 1), stride=(1, 1))\n", - " (1): ELU(alpha=1.0, inplace=True)\n", - " )\n", - " (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (down_sampling): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " )\n", - " (1): _DilationBlock(\n", - " (activation): ELU(alpha=1.0, inplace=True)\n", - " (conv): Sequential(\n", - " (0): Conv2d(64, 64, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3))\n", - " (1): ELU(alpha=1.0, inplace=True)\n", - " )\n", - " (conv1): Sequential(\n", - " (0): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n", - " (1): ELU(alpha=1.0, inplace=True)\n", - " )\n", - " (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (down_sampling): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " )\n", - " (2): _DilationBlock(\n", - " (activation): ELU(alpha=1.0, inplace=True)\n", - " (conv): Sequential(\n", - " (0): Conv2d(128, 128, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3))\n", - " (1): ELU(alpha=1.0, inplace=True)\n", - " )\n", - " (conv1): Sequential(\n", - " (0): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))\n", - " (1): ELU(alpha=1.0, inplace=True)\n", - " )\n", - " (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (down_sampling): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " )\n", - " (3): _DilationBlock(\n", - " (activation): ELU(alpha=1.0, inplace=True)\n", - " (conv): Sequential(\n", - " (0): Conv2d(256, 256, kernel_size=(5, 5), stride=(1, 1), padding=(6, 6), dilation=(3, 3))\n", - " (1): ELU(alpha=1.0, inplace=True)\n", - " )\n", - " (conv1): Sequential(\n", - " (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n", - " (1): ELU(alpha=1.0, inplace=True)\n", - " )\n", - " (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " )\n", - ")" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.encoder_blocks" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "ModuleList(\n", - " (0): _UpSamplingBlock(\n", - " (conv_block): _ConvBlock(\n", - " (activation): ReLU(inplace=True)\n", - " (block): Sequential(\n", - " (0): Conv2d(768, 256, kernel_size=(3, 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ReLU(inplace=True)\n", - " )\n", - " )\n", - " (up_sampling): Upsample(scale_factor=2.0, mode=bilinear)\n", - " )\n", - " (2): _UpSamplingBlock(\n", - " (conv_block): _ConvBlock(\n", - " (activation): ReLU(inplace=True)\n", - " (block): Sequential(\n", - " (0): Conv2d(192, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (2): ReLU(inplace=True)\n", - " (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", - " (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", - " (5): ReLU(inplace=True)\n", - " )\n", - " )\n", - " (up_sampling): Upsample(scale_factor=2.0, mode=bilinear)\n", - " )\n", - ")" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.decoder_blocks" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Conv2d(64, 3, kernel_size=(1, 1), stride=(1, 1))" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "net.head" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "yy = net(x)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "y = (torch.randn(1, 256, 256) > 0).long()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([1, 3, 256, 256])" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "yy.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[[1, 0, 1, ..., 0, 1, 0],\n", - " [1, 0, 1, ..., 0, 1, 0],\n", - " [1, 1, 0, ..., 1, 1, 0],\n", - " ...,\n", - " [1, 0, 0, ..., 0, 1, 1],\n", - " [0, 0, 1, ..., 1, 1, 0],\n", - " [0, 0, 1, ..., 0, 0, 0]]])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "loss = nn.CrossEntropyLoss()" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor(1.2502, grad_fn=<NllLoss2DBackward>)" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "loss(yy, y)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[[[-0.1692, 0.1223, 0.1750, ..., -0.1869, -0.0585, 0.0462],\n", - " [-0.1302, -0.0230, 0.3185, ..., -0.3760, 0.0204, -0.0686],\n", - " [-0.1062, -0.0216, 0.4592, ..., 0.0990, 0.0808, -0.1419],\n", - " ...,\n", - " [ 0.1386, -0.2856, 0.3074, ..., -0.3874, -0.0322, 0.0503],\n", - " [ 0.3562, -0.0960, 0.0815, ..., 0.1893, 0.1438, 0.2804],\n", - " [-0.2106, -0.1988, 0.0016, ..., -0.0031, -0.2820, 0.0113]],\n", - "\n", - " [[-0.1542, -0.1322, -0.3917, ..., -0.2297, -0.2328, 0.0103],\n", - " [ 0.1040, 0.2189, -0.3661, ..., 0.4818, -0.3737, 0.1117],\n", - " [ 0.0735, -0.6487, -0.1899, ..., 0.2213, -0.1529, -0.1020],\n", - " ...,\n", - " [-0.2046, -0.1477, 0.2941, ..., 0.0652, -0.7276, 0.1676],\n", - " [ 0.0413, -0.2013, -0.3192, ..., -0.4947, -0.1179, -0.1000],\n", - " [-0.4108, 0.0199, 0.2238, ..., -0.4482, -0.2370, 0.0119]],\n", - "\n", - " [[ 0.0834, 0.1303, 0.0629, ..., 0.4766, -0.0481, 0.2538],\n", - " [ 0.1218, 0.1324, 0.2464, ..., 0.0081, 0.4444, 0.4583],\n", - " [ 0.1155, 0.1417, 0.2248, ..., 0.6365, -0.0040, 0.3144],\n", - " ...,\n", - " [ 0.0744, -0.0751, -0.5654, ..., -0.2890, -0.0437, 0.2719],\n", - " [ 0.1057, -0.1093, -0.3803, ..., 0.0229, 0.1403, 0.0944],\n", - " [-0.0958, -0.3931, -0.0186, ..., 0.2102, -0.0842, 0.1909]]]],\n", - " grad_fn=<MkldnnConvolutionBackward>)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "yy" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "from torchsummary import summary" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "==========================================================================================\n", - "Layer (type:depth-idx) Output Shape Param #\n", - "==========================================================================================\n", - "├─ModuleList: 1 [] --\n", - "| └─DownSamplingBlock: 2-1 [-1, 64, 128, 128] --\n", - "| | └─ConvBlock: 3-1 [-1, 64, 256, 256] 37,824\n", - "| | └─MaxPool2d: 3-2 [-1, 64, 128, 128] --\n", - "| └─DownSamplingBlock: 2-2 [-1, 128, 64, 64] --\n", - "| | └─ConvBlock: 3-3 [-1, 128, 128, 128] 221,952\n", - "| | └─MaxPool2d: 3-4 [-1, 128, 64, 64] --\n", - "| └─DownSamplingBlock: 2-3 [-1, 256, 32, 32] --\n", - "| | └─ConvBlock: 3-5 [-1, 256, 64, 64] 886,272\n", - "| | └─MaxPool2d: 3-6 [-1, 256, 32, 32] --\n", - "| └─DownSamplingBlock: 2-4 [-1, 512, 32, 32] --\n", - "| | └─ConvBlock: 3-7 [-1, 512, 32, 32] 3,542,016\n", - "├─ModuleList: 1 [] --\n", - "| └─UpSamplingBlock: 2-5 [-1, 256, 64, 64] --\n", - "| | └─Upsample: 3-8 [-1, 512, 64, 64] --\n", - "| | └─ConvBlock: 3-9 [-1, 256, 64, 64] 2,360,832\n", - "| └─UpSamplingBlock: 2-6 [-1, 128, 128, 128] --\n", - "| | └─Upsample: 3-10 [-1, 256, 128, 128] --\n", - "| | └─ConvBlock: 3-11 [-1, 128, 128, 128] 590,592\n", - "| └─UpSamplingBlock: 2-7 [-1, 64, 256, 256] --\n", - "| | └─Upsample: 3-12 [-1, 128, 256, 256] --\n", - "| | └─ConvBlock: 3-13 [-1, 64, 256, 256] 147,840\n", - "├─Conv2d: 1-1 [-1, 3, 256, 256] 195\n", - "==========================================================================================\n", - "Total params: 7,787,523\n", - "Trainable params: 7,787,523\n", - "Non-trainable params: 0\n", - "Total mult-adds (M): 35.93\n", - "==========================================================================================\n", - "Input size (MB): 0.25\n", - "Forward/backward pass size (MB): 1.50\n", - "Params size (MB): 29.71\n", - "Estimated Total Size (MB): 31.46\n", - "==========================================================================================\n" - ] - }, - { - "data": { - "text/plain": [ - "==========================================================================================\n", - "Layer (type:depth-idx) Output Shape Param #\n", - "==========================================================================================\n", - "├─ModuleList: 1 [] --\n", - "| └─DownSamplingBlock: 2-1 [-1, 64, 128, 128] --\n", - "| | └─ConvBlock: 3-1 [-1, 64, 256, 256] 37,824\n", - "| | └─MaxPool2d: 3-2 [-1, 64, 128, 128] --\n", - "| └─DownSamplingBlock: 2-2 [-1, 128, 64, 64] --\n", - "| | └─ConvBlock: 3-3 [-1, 128, 128, 128] 221,952\n", - "| | └─MaxPool2d: 3-4 [-1, 128, 64, 64] --\n", - "| └─DownSamplingBlock: 2-3 [-1, 256, 32, 32] --\n", - "| | └─ConvBlock: 3-5 [-1, 256, 64, 64] 886,272\n", - "| | └─MaxPool2d: 3-6 [-1, 256, 32, 32] --\n", - "| └─DownSamplingBlock: 2-4 [-1, 512, 32, 32] --\n", - "| | └─ConvBlock: 3-7 [-1, 512, 32, 32] 3,542,016\n", - "├─ModuleList: 1 [] --\n", - "| └─UpSamplingBlock: 2-5 [-1, 256, 64, 64] --\n", - "| | └─Upsample: 3-8 [-1, 512, 64, 64] --\n", - "| | └─ConvBlock: 3-9 [-1, 256, 64, 64] 2,360,832\n", - "| └─UpSamplingBlock: 2-6 [-1, 128, 128, 128] --\n", - "| | └─Upsample: 3-10 [-1, 256, 128, 128] --\n", - "| | └─ConvBlock: 3-11 [-1, 128, 128, 128] 590,592\n", - "| └─UpSamplingBlock: 2-7 [-1, 64, 256, 256] --\n", - "| | └─Upsample: 3-12 [-1, 128, 256, 256] --\n", - "| | └─ConvBlock: 3-13 [-1, 64, 256, 256] 147,840\n", - "├─Conv2d: 1-1 [-1, 3, 256, 256] 195\n", - "==========================================================================================\n", - "Total params: 7,787,523\n", - "Trainable params: 7,787,523\n", - "Non-trainable params: 0\n", - "Total mult-adds (M): 35.93\n", - "==========================================================================================\n", - "Input size (MB): 0.25\n", - "Forward/backward pass size (MB): 1.50\n", - "Params size (MB): 29.71\n", - "Estimated Total Size (MB): 31.46\n", - "==========================================================================================" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "\n", - "summary(net, (1, 256, 256), device=\"cpu\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/notebooks/05a-test-end-to-end-model.ipynb b/src/notebooks/05a-test-end-to-end-model.ipynb deleted file mode 100644 index 7723b12..0000000 --- a/src/notebooks/05a-test-end-to-end-model.ipynb +++ /dev/null @@ -1,80 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - }, - { - "ename": "ImportError", - "evalue": "cannot import name 'ParagraphTextRecognizor' from 'text_recognizer' (/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/__init__.py)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m<ipython-input-4-f0e40de01802>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtext_recognizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mIamDataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtext_recognizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mIamParagraphsDataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtext_recognizer\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mParagraphTextRecognizor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mImportError\u001b[0m: cannot import name 'ParagraphTextRecognizor' from 'text_recognizer' (/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/src/text_recognizer/__init__.py)" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import cv2\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "from omegaconf import OmegaConf\n", - "\n", - "from importlib.util import find_spec\n", - "if find_spec(\"text_recognizer\") is None:\n", - " import sys\n", - " sys.path.append('..')\n", - "\n", - "from text_recognizer.datasets import IamDataset\n", - "from text_recognizer.datasets import IamParagraphsDataset\n", - "from text_recognizer import ParagraphTextRecognizor" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ParagraphTextRecognizor" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/notebooks/06-try-transformer-model-predictions.ipynb b/src/notebooks/06-try-transformer-model-predictions.ipynb deleted file mode 100644 index d39e111..0000000 --- a/src/notebooks/06-try-transformer-model-predictions.ipynb +++ /dev/null @@ -1,358 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import importlib\n", - "import cv2\n", - "import yaml\n", - "\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import torch\n", - "from torch import nn\n", - "from importlib.util import find_spec\n", - "if find_spec(\"text_recognizer\") is None:\n", - " import sys\n", - " sys.path.append('..')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def convert_y_label_to_string(y, dataset):\n", - " return ''.join([dataset.mapper(int(i)) for i in y])" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.models import TransformerModel\n", - "from text_recognizer.datasets import IamLinesDataset" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "dataset = IamLinesDataset(train=False,\n", - " init_token=\"<sos>\",\n", - " pad_token=\"_\",\n", - " eos_token=\"<eos>\",\n", - " transform=[{\"type\": \"ToTensor\", \"args\": {}}],\n", - " target_transform=[\n", - " {\n", - " \"type\": \"AddTokens\",\n", - " \"args\": {\"init_token\": \"<sos>\", \"pad_token\": \"_\", \"eos_token\": \"<eos>\"},\n", - " }\n", - " ],\n", - " )\n", - "dataset.load_or_generate_data()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "config_path = \"../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1213_175148/config.yml\"\n", - "with open(config_path, \"r\") as f:\n", - " experiment_config = yaml.safe_load(f)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'CNNTransformer'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "experiment_config[\"network\"][\"type\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2020-12-30 01:24:06.949 | DEBUG | text_recognizer.models.base:load_weights:432 - Loading network with pretrained weights.\n" - ] - } - ], - "source": [ - "model = TransformerModel(network_fn=experiment_config[\"network\"][\"type\"], dataset=experiment_config[\"dataset\"][\"type\"], dataset_args=experiment_config[\"dataset\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2020-12-30 01:25:47.777 | DEBUG | text_recognizer.models.base:load_from_checkpoint:379 - Loading checkpoint...\n" - ] - } - ], - "source": [ - "ckpt_path = \"../training/experiments/TransformerModel_IamLinesDataset_CNNTransformer/1213_175148/model/best.pt\"\n", - "model.load_from_checkpoint(ckpt_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "model.eval()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "data, target = dataset[11]\n", - "sentence = convert_y_label_to_string(target, dataset) " - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([98])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "target.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "data = data * (data > 0.1)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "from torchvision import transforms" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [], - "source": [ - "ra = transforms.RandomAffine((-1.1, 1.1), scale=(0.8, 1))" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/akternurra/.cache/pypoetry/virtualenvs/text-recognizer-N1c_zsdp-py3.8/lib/python3.8/site-packages/torchvision/transforms/functional_tensor.py:876: UserWarning: Argument fill/fillcolor is not supported for Tensor input. Fill value is zero\n", - " warnings.warn(\"Argument fill/fillcolor is not supported for Tensor input. Fill value is zero\")\n" - ] - } - ], - "source": [ - "data = ra(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "<Figure size 4320x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(60, 20))\n", - "plt.title(sentence)\n", - "plt.imshow(data.squeeze(0).numpy(), cmap='gray')\n", - "plt.xticks([])\n", - "plt.yticks([])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "('becane gool big alls at boasty<eos>', 0.31098294258117676)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.predict_on_image(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "data, target = dataset[0]\n", - "sentence = convert_y_label_to_string(target, dataset) " - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "([], [])" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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dIAiCIAiCIMyRfsuyZnjAzVe04dprryUQCNDb20tnZyeQ+mPZ4/Gwdu1a6urqeOaZZ4jH4/Mq31ne5WS+wszVYFTm5eVxww03MD09zYsvvsjk5OSMY5wGjRZFzHbp/Xrf9PS0/d0sw1mO27XweDwXLbgAXHvttWzcuJH29naee+652U+4jMx2jcw+e71eiouL+eAHP0hRUREPPfQQHR0d9tjNtR7nmM61LfNp9+tJunsFuKxtvBIC7MXgvGeuFOnuTXO/3mfeg3rO6m1u5znvVbf71+2a6jLdjtP7zXHJNC+cYqVlWUxPT6f023we6c8ejwe/308sFrPPcatD7xcEQRAEQRCENygX3DbOOxFxZWUl69evp6amBpgpsPj9fsrLy6mrq8Pj8czbeHIKDvNhth/ssxnr8zl+vuVfDDk5OdTW1rJ8+XLbe8mtDjfDyNxnjovbd/O/W7lu2y/WOPJ4PBQVFVFaWsro6OhFlXEpzMXbRhMIBKiurqa9vZ1wOExtbS1FRUWz1uEc33R1znW+KKXweC5/zvDZrqHH4yEnJwefL1XbTXevXO75f7HlXS3i1sXgdm9qnIKf83O6+1Jvd97r+pq5za9Mc8Msz+354dzn9kyanp627w3zL11f1q9fT3l5+Yy5mKkeQRAEQRAEQXgzMG9LUClFIBDA5/PNMNSUUni9XnJzc8nPz7e3mcfNZtxdbAiKLvtKcyWM03QsWLCA1atXMzw8TFtbm+ub/kyeQ5mMuHTnuOHmuXMp+P1+vF4vQ0NDl1TOlSYrK4tFixZx+vRpXnvtNRYuXMiCBQtmvf7O+e7cdrWQSUzyer1ce+21VFVVEQwGX+eWXV1cikh5Oeo2P5veKk7xJZ0A6zy2pKSEqqoqCgsLZ52fbn13evikO36ugo7zXnHi9XrZuHEjJSUleL1e1/G52u4tQRAEQRAEQbhczFu0GRsbY2xsjFgs5uqGb/6lc/NPRyahIRPpXPJnK/P1MsTSecDMdk5JSQlLly6lqamJc+fOzQg1c453pjoyiQZzHfPLNV5erxev18v4+PhlKc/kcrVRKWWLNp2dnRw/fpzS0lIKCgquqIGYyVNiPvh8PvLz82cYuXMlJyeHm2++mcLCwjl7zF2Osc/KysLv919yOZebK/GscBNMZruf0+13iqrpvGpKS0tZt24dW7ZsYdWqVQQCAYAUQXg2Lxu3Op0Cjlv7p6enXYVnt/rM8fD5fFxzzTVkZ2fbc3E2oUcQBEEQBEEQ3izMe8nvSCRCVlYWgUAg7VvYqakpOwlxuvwDVxI3Y+Zi63WGt7iFJThzNcylzHRlm98DgQB5eXmMjY0RiUTSluMW2uDxeJiampq1LXPB7Odc+5gOj8dDXl4eBQUFrn26WEyD9nIRDAZZtGgRExMTRKNRsrOzCQaDl60Ot7G8XGUvWLCA5cuXc+zYMdcwtEzX0ev1smjRIoqKimhtbSUUCs3p/rkcba+srCQSidDT0zPnczL1Jd39+5sy9s08LZkEEBNnm3U402w5dtzCoLZv3866desoKCigt7eXvr4+zp8/7/qsdmvLbH0zxRvzuZhOrEkn2Ohypqen8fl8rFmzhmg0aj/TLqfnnyAIgiAIgiBczczb08bn8xEMBgkEAq5vUn0+n/223CkmzMWodsuvMBecBsBcPGsuxnAzPXdM42IuOSDMet3GwmmIaNFmYGAgRaBxGm+mUaT3OQWb+XprZAqVulh0mbFYjPHx8csq2ujxvFy5XwKBALm5uQBEo1Gmp6cJBoMEg8HL5glyJYWD0tJStm3bxsKFC9OGzaTD7/dz3XXX8dprrxGJRC7JKJ7PuR6PhxtvvJG1a9fOq45M45jOG/A3gQ47y8vLY/Xq1SmCks7xoj/rv3TjNzU1ZYuzWsDRn01hSHtaWZaF1+tl+fLlHDhwgK997Wv86le/YufOnTPOMzGTA+s+mGQ6z9l3t3vT3K7bodsLCQGxsLCQcDjM4ODgjGTE5n8RbwRBEARBEIQ3I/O2cCcnJ/H5fPj9fvsHt/mj2xRs9H+3H+PpSPdWdjYyhQuY+zPlYHBrS6YQsHR1p2uXW9iC87Nbe2OxmGuYix4rbbA5c0Xoc7TxZhpNs4kbmbww0olOmdBzQOezmZqaIhaLzfn8uaL7PZeQnkx9yM/Pp6Kigmg0itfrZXp6mry8PHw+3xVfRehicF7PSCTC4OAg5eXl9r5M94fepq9RfX09p0+fdl2tLB2Xw2jWHnrOhLNXkktt91yFQsuyyMnJYenSpWzevNnersddXxPnve6cp6aQop9RWrgx7039fNDfly5dSktLC62trbS1tXHmzBmqqqrw+XwZ72stiDiflaawlEmINkOanAKRLjOdEJqTk8NNN93E3r17GRsbS+mPKWRfTNJ7QRAEQRAEQXgjMG/LSAs2pnu/+T8YDGJZFiMjIzO8PS41VOlyMJ9wFGd75yICzHX/bAa0x+OxjalMS9mab+ednjrT09O24JDuvPliGmnzQRtnWVlZtqeWZrYlxGerzymMmfXNBaeYp5SisLCQ6upqQqEQU1NTtlhztb7Nd7YrFosxOjrKxMREyna/309ZWRlr1qyhurqa06dPc/ToUaLRqF2ODmEbHR1NK1Dpa6YNZh0WeanebBc7vy6FS61rPvMsPz+fJUuWpAitzjL0uKarQ4uSzrmuxR7T+0+pXy+lvWbNGgYGBgiFQrbI4fV6ycnJIRQKpdRnCkfONpqefpDq5eL0qDE9AHW/3ISXqampGV6LOqn9tddeyyuvvGKLvOnqEgRBEARBEIQ3Ixcl2pjeMNow0D+28/PzycrKIhKJ2D/OTUFB/1i/3N4Kutzi4mKGh4ddw4Mu1jhzGin681z6MN+QJLMOj8djixuzhbg4jT6nEOHmYXQxzDfUxnme3+8nOzubrKysOQsszvG/XP3LNCeCwSD5+fm2YObz+WyD80rMXbc2mx4Mei5YlsXk5OSMMXOeOzk5STgcprCwEEgYwJWVldTW1lJRUUEwGGRsbMzOTxWLxZiamrLFwpKSEqLRqKsIEwwGWb58OdXV1eTn5+P3+5mammJ4eJhDhw4xNDR0UfNLKUVOTo69zLj2urmaSefBl+6a5uXlUVZWRktLywyh0OPxkJ2dTW1tLYFAgP7+fgYGBlLCCE2R1rlNf9btMdvm9XqpqqqycxRNTU3Z1zs7O5twOGy3I9Pc0l57boK927nmWJgeX6bwkuk54PP5KC0tpbm5+Yp45gmCIAiCIAjC1c68RRu/3088Hk9Zycj0dMjPzycQCNgGn/nG1+m6f7kNMr/fz44dO9i1a1fa5KlmqJZpgPv9fnw+H7FYbMYqTZrs7GyKi4spLy8nGAzS2tpKb2/vFTMunWETMPeVXZxluO1/vQ1iM/zGDIdwGq/p2mZ6H5keJJfSj0wimPYMUCqxkpQZcnKp6HtCz6n8/Hwsy6Kjo4Px8XG7XcFg0J5z+fn5eDweJiYmGBwcZHBwkJGRkZT2mN4PU1NTLFiwAJ/Px/Lly6mrqyM7O5toNMqFCxcYHBy0jzPRwpo5r/UYlJeXU1tbS3l5OdnZ2bZHhtfrpbS0lK1bt3Lw4EFGR0ddPW8y4fP5yM7OtvNluYkh80ELTEop4vF4Ro+1+ZTpts28x5weKebzLxgMUlRURHd3tz2/gsEgBQUFFBQU2KJOOBwmHA6neJLM9tx0u4/0Z6UU5eXldi4pfd3TJSt38y7Mzs6mrKyMuro6pqen6evr48KFCyl5j2bztEr3jDL7Z27XK8319fVlbKuERgmCIAiCIAhvVuYt2gSDQXt1KI3pzp+Xl4ff72doaCjFbV67umtRZ3R01E6wa/7ovhSjyufz8b73vY+DBw8yNjaWstKI1+u1jSId6jI5Ocng4CDRaJScnBxqamoYGBigv79/hoEQCASoqKigvr6edevWkZeXx9GjR9m7dy/9/f22QTgX48E04sw+m2/ITTKJBG7jpo3InJwcCgoK7DL0cu2QWFrZ5/OljNPrgdnXuRpaWkDQnhilpaWcP3/+inpiRKNRQqGQLZTk5eWlrIp2Kej7JSsri+rqapYvX87ChQuZmpri8OHDvPbaa0xPT5Odnc3ChQtZuXIltbW19r0Tj8dpb2/n7NmzNDc309fXN6MOpRR+v5/c3FwWLVrEjTfeiN/v5+jRoxw9ejRFGHJ6Pvh8vhSDXo97dXU1GzduZPny5Zw+fZoDBw7Q399PJBLB6/VSU1PDe97zHiKRCI2NjbZw46wjHT6fz/bkSyeczgUtjhQXF1NVVYXX6yUcDtPT08Pg4KCrp5Y+L13Yp95vhvn4fD4CgYAdMmpZFtFolGg0aucCco6t9uAKhUL4fD6KioooLS2lpqaG8vJyAC5cuEBTUxOTk5MzcgppDz83kcgt5Mp81ujyzWsSj8cJh8MpnpNu+P1+qqqq2Lx5M9dddx0TExN0dHTw05/+lL6+PtsLRj9nQ6HQDG+gTKF2fr/f9vjSnmR+v59gMEgkEkmZr26IaCMIgiAIgiC8WZm3aOPxeFK8UbQgoikuLsbr9TI0NGQnnAXIzc3l1ltv5aabbqKmpobGxka+/OUvzyvR6WzE43E7cazzTXNBQQHvec97qKurs708hoeHefLJJ2lsbMTn8/HBD36QxsZGdu/eTW9vb8r5S5YsYe3atQQCAX7yk58wPDzMfffdRzAYZPfu3XR1dV2W3BhmGfpteLpy3YxMU6C6+eabuemmm4hEIoTDYRoaGti7dy9TU1OsXbuW2tpannvuOUZHR6+I+OHsj2UlkipHIhFisZhrv9zasW7dOnp7e+np6SErK4sbbriBeDxOa2urPb8u99v28fFxhoaGWLt2LdPT0xQUFDAxMTEjREN74sw13415jerr61mzZg3j4+M0NjaSlZXFRz/6UT772c8SiUTYuHEjW7duxe/388gjj9Df34/H46GgoICtW7dy/fXXs3nzZh566CHb88gUCLRw9+lPf5qXX36ZF154gf7+/hltdXqEeDweQqGQ7UHh9/vZsGEDt912GwBf//rXU3JWaVGgubmZRx55hAceeIBIJMLx48cJh8MopWyxKdM4abFIh2pdbEJyn8/Hxo0b+fCHP0x5eTlFRUUMDQ3x/PPP861vfYtIJDLj+aBFtOzsbFuAjcfjTE5Ozsiz4/V6ycrKorS0lLVr11JeXk5eXh6WZdHV1UVTU5MdzmMKRPq87OxsAKqqqrjrrrvIzc2lpaWFhoYG2tvbmZiYcJ1Tzs9OUUYLI2biX1Ngmp6etq+B1+slOzub/Px82/PGvP7OeiorK7n55pupqanh4YcfpqOjg49//OPU19cTi8UYGBgAEgLre97zHh599FEmJibsUCotdjo9eLQgunTpUoqKihgdHeX06dNMTEyQl5dHRUUF4XDYfl6kC7+6mvNNCYIgCIIgCMKlMC/RRhubJSUlKX/XXXcd5eXlnDp1inXr1hEOh+nr67PDj/Ly8vjABz6Az+dj9+7dDA0N8dGPftQ1V8mlMDU1xcTEhJ1/BLDzc3z4wx/m+eef59FHH2V8fJy8vDzWrVvHJz/5ST7zmc8wPDzMrl27uPXWWyktLeWb3/ymLUxVVlZy9913c+rUKZ599lnb6HvyySf50Ic+xJEjR+jt7Z2zx0q6/rqFQZnhXG44ywgGg6xYsYKdO3dy9OhRPve5zxGPx9myZQtLlizhjjvu4KWXXuLd7343u3fvnncIy3xwiiher5fJyUk7se9s6Bwyg4ODQMLbaWBggJdeeon777+fL33pS7ZX1GyCjTZinSttmdt1nUopIpEIY2NjlJSUYFkWS5YsYWJiIiUUxOv1cvvtt5Obm8sLL7xgG65zGZdt27aRl5fH888/T09PD5ZlUVhYyDvf+U6ys7NZtGgRt912GwMDA3z3u9+1w6CmpqYYGBjgueeeY2hoiDvuuIN3vetdPProoymGt/aMeOc738mDDz7IU089xcTEhOu1NsURv99PXl6eLYh5PB7e9ra3sWXLFkZHR/nqV786Q2g1yzx//jzf+973WLt2LZOTkzQ2NpKdnc3tt9+OUopnnnmGaDQ6QzSZnp6msLAwJV/KXDGvZ0FBAW9961t5//vfz9e+9jUaGxuJRqPcdNNNfPjDH6avr48f/vCH9rm5ubksW7aM7du3s2HDBoaHh+nt7bWfIX19fezZs4fm5mZ7PLZt28Ytt9zCkiVLOHr0KHv27GFoaIj8/Hxuu+023vKWt9Da2sojjzxiJ/jVolBubi7FxcXccsstLF++nD179nD69GlGRkaIx+MpIqTpTaPzgpkirR47t/lrJgCGxL0TDAZTPCIrKytn3D/aG6ikpASfz0dnZyc+n4/bb7+d8fFxHn/8cTo7O5menubIkSPU1NRw4cIFhoeHycrKYsmSJZSVldltNUNQzaXJ9Tytr6/nrrvuorq6mi9+8Yt2+JbH4yE3N5fKykq6urrwer3289hcxtwpVIlwIwiCIAiCILzZuChPmy1btnDPPfcwNjbG+Pg4zc3NHDhwgJaWFq655ho8Hg/xeNzOR3DjjTfaLv/hcJi6ujr7jT/MTHRpkimHg0YbLvqHvWmwVFRU8Hu/93scPHiQEydO2Ll2RkdHuXDhAp2dnWzfvp2f//znHDp0iNraWqqrq9m8eTOHDh0C4O6776azs5OzZ8+meDSYxsNsIUxmrhGzn+n6pxPPZmdnzyjbNHi1IaQ/b9y4kVWrVvHcc89x5swZO9SgoaGBvLw8Nm3aRGtrK1lZWfT09NhhR87rcCmkK296etr20vJ6veTn59urFrmhzz9//nzK946ODl566SXuvPNO/uu//ove3t4Z4+gmzJhhJOlypuh9WrQpLCwkOzub+vp6zp07x8DAQIpXw69+9Sve9773kZubayfANg3pYDAIYBvkgUCA+vp6br/9dh566CFb3NReGIWFheTk5PD2t7+d1tZWDh8+7JqfaXp6mnPnzrFv3z7e/va38+ijj9r9VkpRWlpKfX09+/fv52c/+1lawcZ53bKysliwYAHnzp3DsizKy8t5y1vewtjYGD/4wQ9cw5bM621ZFk1NTdx+++20trbi9XqJRqMcOHCAL33pS5w+fZqWlhZb/NKeMcFgkNLSUqampuw5Yc7t2doNUFJSwtatW/nIRz7C5z//eZqammyvlYaGBuLxOJ/4xCf46U9/SjQaJRAIsGPHDq699lpOnjzJ008/zcjIiC1K5efns3jxYnbu3Mndd9/Ngw8+SH19PUuWLKGxsZHvf//7jIyMEIlEiMfjDA0N8fjjj1NbW8uGDRv47Gc/yxe+8AVbpCorK2PZsmUsW7aMI0eO8Nhjj9HV1WULmabAYQoveizM+Wlu0wKPMxxVf9bjG41G7fOrq6vZsGEDDQ0NMwSekpISbrnlFhYuXMg3vvENqqurKS8v58yZM3R3dxOPx/F4PCxatIiJiQmmpqbs+3nZsmX2inemN6b5jNbzffPmzWzatAmPx8O//Mu/EAqFiMVi9v5AIEBhYSHd3d14vV474bUpaJuCjSAIgiAIgiC8GZn3eqlZWVmMjIzwy1/+ki9/+ct86Utf4uGHH+all16iu7vbFhvMsKjVq1fT1NREZ2cneXl59htqp8s8pIoYcxFs9HHwa68awDaYS0pKKCgo4NixYzOSI4+OjnLo0CGqq6tRSjE+Ps7+/ftpaWnhhhtusJc2X7x4MV1dXfT19aUIAVu2bKG7u9sWcjLlacnUTyf6LXW6cAB9TEFBAe9617vIzc21jaiKigoikYhtGGuDLBqNcv78eVpbW7nhhhuoqqpKCaNwa7dSihUrVrBx40aqqqpmvQ76nHRYVmJZ6HA4zOjoqL2yUbpjdb91fhW9LRaL8corr1BcXMzSpUspLi7OeL55zdPVpduux3xycpLx8XHi8TgFBQWsWLGCzs5OhoeHU84dGxvD4/FQXFxsh72Yda1atYr6+noWLFgAJDyhtm/fzquvvsrQ0JBt2GZnZ7N06VI714nOr9Te3u4a+mFZFqFQiLa2Nvr6+lKuY0lJCatXr2bVqlW8+uqrcxJsNMFgkEWLFjE+Po7H42H9+vWMjY3R1NRkewRlQilFKBTi0KFDdHZ22td8cHCQvXv38ra3vY3y8nJ7jufl5bFz504+85nP8Ed/9EesW7fOziOU6f5weqUFAgFWr17N1q1beeSRR2hubmZiYsIWQkZHR2lpaSEcDlNaWmonAA4GgwwPD7N//346OjoYHBwkFAoRCoXo7e3l5MmTvPDCC5SXl3P77bezbt06urq6OHHiBP39/XbojvaCGhsb4/z58xw5coSJiQl27txp58Pavn0711xzDS0tLezZs4fOzk6i0egModmZfNjtOaBFQ7ck1Ga4kA5NMpPHZ2VlUVJSQmlpKSdPnkypUye6PnfuHNPT01RXV7Ny5UogETI4PT2Nz+cjNzeXzZs309XVlfL8Ky0ttQUjZ9tNcbugoICNGzdSVFTE008/zcDAgB0Wp/uhn999fX226OlWrgg2giAIgiAIwpuZeYs2Y2NjDA0NcebMGfbu3cuhQ4c4ffo0vb299o96vdywz+dj9erVhMNhhoaGgEQeh+rqao4ePZrWGJ3NyE7bmWQokfYeKSgooKSkhP7+foaHh1Pq08b/wMAA5eXl9lvb9vZ2uru7KSoqspOYFhYW2slFtdDh8/nYtGkTTU1NjI6OzmjLbPla3PI76M/6jbTz7bnTWPX7/axcuZLKykr8fj/XXnstwWCQ5uZmwuHwjHHs7++ns7OTpUuXMjo6SmVlpR2m5BaqtWDBArZv385b3/pWWwybK+mu3fT0NMPDwwwNDVFTU5P2/HTjpw2//v5+GhsbWblyJUuWLLGXRk9Xf7p55laHKdyEQiGWLVtmJ1d1rpQzNTXF0NBQSliIWZ/Oy5GXl2d7sqxcuZLjx4/bZfl8PsrLy9m0aROHDh0iEAiQlZXF+Pg4Y2NjMwxTM7dKIBCwl2vW9erkxjk5ObZwMhe091hRUZEtTi1btoyRkRHa2trSrr7kHMt4PM6xY8fsMBp9r+3du5fy8nIqKyvJycmxx6+3t5fjx4/boUk6x5LbnNT3uNO7YunSpdTW1jI4OMjLL79sJ9g2hYuxsTE6OzspKCiwBSO90t34+HhK/hp9zujoKOfOnePQoUPccssthMNh2tvbU5KP6+ujRZTx8XE6Ozs5dOgQN910E0VFRWzZssVedamtrY3Ozk7Gx8dTcvdkEn3NOeW8r908ytzGTwsz2nNGr0Bm1u3xeIhEInR1ddmhrVqIDwQCtti1adMmAFu01t4+OqGwz+dLEZHM0C4tsi1YsACPx0Nra2tKviPzPjfzG5l9NO8BQRAEQRAEQXgzMy/RxrIs+vr67LwD2hNBGyv6bbM2Nr1eL2vWrKGrq4t4PM7ChQtZuHAh8XjcDnkxy3aKGvPF5/OleJcUFRVRUlJCW1uba4iRXtFKh7AARCIR+vr66Ovro76+PuWNvN/vtwWbsrIyioqKaG5utsNXnH2YzxtgnSy0pKSE8vJySkpKUjw33DxilFLk5eXZx65YsQKApqamGSu3QOJN+ejoKHl5eZw5c4a6ujqysrJmtNvj8VBSUsLmzZvZsWMHGzZssFehMstzYzaxTc+h3t5elixZknGMMu2bnp7m0KFD5ObmsmTJEntlnEvB2e7JyUn6+/u54YYbGB0dJRwOp4ST6XOGhoZYuHAhJSUlroZ3MBi0RSWfz0dFRYW93LYe62XLllFaWsq+ffts4VEbrG4opSgqKqKurm6Gx1p5eTk1NTW2QDaXvuoyA4EAeXl5tnBSWFjI+Ph4SojWbMKCZSUS8prLkVtWImyqu7ubyspK2/MoEolw5MgRnnjiCY4ePWoLwM5+FxQUsHTpUtatW8fatWtZtWoVtbW19spNq1evprCwkEOHDtHd3T1DAICEmNTZ2Ulubi5er5fKykri8Ti9vb12cnJn3yzLYmJigpdeeom6ujq6uroYHBx0TVCsn4GWZTE2NsapU6eoq6tjxYoVrFq1CstKLOmuV1tKJ9hk8iBxCjTprqUTM+Ro2bJlFBcXc+LECdv7xhSA4vE44+PjtqDb09NDbm4upaWllJaWUldXx/XXX8/Ro0cZGBiwn7nxeJxQKERpaSnZ2dm2SGR6Dpp9MMUZ879uy+TkJKOjo/bz2SnWzXcMBEEQBEEQBOGNyLw9bXSyVf3WVb/ZNX9M67wU09PTLFiwAMuyKCoq4oYbbqCiooJ9+/bNSEY6FzL9MNdeDJFIxP6ek5NDfn6+65LIkAgTWLRokZ1fR/elq6uLw4cPs2nTJvsNellZGQUFBbbQs23bNhobGxkcHHTN8zGbV41pbOi219TU2ElON2zYYIckmXkszHJ0W+LxODk5Ofj9fiYmJlw9f8z2RKNRjh49yqpVq2wDVhtAehni66+/nne+851UVlYSiUTIyclJGetLoa+vj7a2NhYvXpzyRt7JbPWEQiGOHTtmJ5XOlLD5YohGo7S1tfGOd7yDw4cPp4gQZht1klndF7PdgUBghtCmDWCfz0dOTg7r1q1jzZo17Nu3j9bWVkZHR+28Km590klaly9fzubNm2d4wOhcSIFAwE6E68Q5tkr9OgFtaWmp3e6RkREAO1QwUzidiXPOaw+UV155hbKyMqqrq23PC53/JDc3l7GxsRTPIU1dXR333nsvf/AHf8B9993H+9//fnbu3ElOTg5FRUWUlZUxMTHB0aNH7fqd7YnFYvT39+Pz+cjKymL58uV0dnayZ88exsfH7ba6iQs6d41ektwUlUwPEi0+eDweioqKmJiYYMeOHYyMjHD+/Hk6Ojro7++f8fwzRQtn+93EYLOds10Ty7JsL8iKigpqa2uxLIvDhw+n5MoxRSct8GhPI4/Hw4oVK9ixYwd33HEHxcXF/OQnP7ETKOvre+7cOaqrq6msrLSXqNfjo0OfPB4P4+Pjdr6h7Oxs177o5cjLysrIzc0lKysLIEXsmmvyd0EQBEEQBEF4ozLvRMT6Dazf77cNDUj8eM7JySErKysl2eTY2Bj19fXs2LGDWCzGoUOHUpJfOsn05jSTce/1eqmoqGBsbCwlmWw4HCY/P3/GOXrp5GuuuYZdu3alCC/hcJju7m6WL18OwL59+9iwYQPj4+NEo1GWLFnCe9/7Xv7qr/4qxdhzC+dw9kUfZ+7z+/0sXbqURx55hJMnT3L27FkCgYAd9uM0IvVn7aXR39+P1+ult7fX9qxwvqm3LIvi4mKqqqpoa2tj//79/Mmf/AmLFi1ieHiYsbExO5fKAw88wIYNG/jCF75AeXk5tbW1LFq0KOO1mM+b7lAoxMjICDU1NSxcuHCG15Um3RwxxYCDBw+yaNEiVqxYwb59+1I8S9J5K8yGPk/nBlqzZg0nT560Vw1z9rW4uJjh4WHC4fCMkL+amhpGR0ftfEqRSITXXnuNP/7jP+Yb3/iGnS+opaWF5557DkgIo4FAgMrKSkpKSujt7U3pR3l5OXfccQcrV64kHA4zPj6eEhbT1tZGd3c3119//Zz77/f7uf7663n/+9/PHXfcQVZWFseOHaOpqYlbb70Vn89Ha2sroVAoJe+I6fWRLmG2uf306dPccsstVFRUkJ2dzfj4uD1eFRUVtLS02Pl0TIO8p6eHH/3oR0QiEdsLSHv2veUtb2FoaIgLFy7MWNnKbKNepru7u5vFixfj9/uZnJy0k+m6tV8/W3QoYV5enu2Vo9vtTC4eCASoqamxE0Tfe++9nDp1Ckh4+5w+fTplfMyyzPKcXjhO4df5PHDifP6Mj49z7733cvDgQfbt25cSQmbWo73XCgsLGRwcxOv1curUKd773vfy7ne/m//4j//gn//5nwmFQrb3i2VZRKNRzpw5w49//GM+8YlPcPjwYRoaGmhtbbXz3ujn7PT0NKFQiKqqKm699VaefPLJlFBNpRRDQ0M0NTXx6U9/mpycHA4ePMj58+cJhUIpeYAEQRAEQRAE4c3MvEWbqakpOxxDizN6KVyv18vw8LC9ig7AoUOH+PnPf86uXbt4+OGH2b9/P5AwbPRqT3r1kWAwSE5ODh6Px056OlcxwOv1snTpUjo7O20Dob+/n6amJjvprmlclZaWsnr1agoKCti7dy+QCKeanJy03/yPjo5SUVHBs88+y4oVK/jIRz7CH/7hH2JZFo899pgdhmEmTTUNOWeSUNOw1f+9Xi/V1dXcd999fPWrX+Wpp55ibGyM0tJStm/fzk033eRqGOvxCgQCDA4OkpOTY393Eyv8fj/XXHMNW7Zs4cc//jFjY2M8+OCDPPDAA3R1dREKhVi4cCFLly7liSee4O/+7u/svDNve9vbuPPOO/nP//xP29ugoKCA4uJicnJyiMVinD9/3tXDx4nuQ0dHB0899RQPPPAAf/mXf5mSK0aj+1FSUsLExERKwlO9X+ff8Pv91NTUMDw8PCN8ZL6YYoNe6UrPUT3ntTDo8/m48cYb+dGPfkR3d/eMsgKBAG1tbfT399seG1/5yld4+OGH+da3vkVraytPPfUUu3fvtoWKWCzGd7/7XX7nd36H6upqdu3axcjICFVVVezcuROfz8f+/fs5ceKE7S1lcurUKX7xi1+watUqvvjFL/LFL36RM2fOpKzU5QyHW758OX6/n/3799PX18c//dM/MTw8zL59+8jLy+Otb30rd955J7t27eKFF16gq6srxbNE/+n7zFy62qzLXDZde62Y+ZtGRkYIh8MzPCh6enpcRVCdgPvChQu0tLTMWHHJPFaHnPX29jIxMWE/b7QIo4VmXYf21qurq+OTn/wkP/rRj1iwYAErV660lwPX80I/AwsLC9m0aRPbtm2jr6+Pffv2sWPHDtavX08oFKK9vX2GWOM2T00Pq7k+B51LamuUUnYC8MLCQvr6+mYkuNZtCAaDbNiwgQ984AMcOHCALVu2UFNTg2VZfPvb36awsJDrr7+ez3/+8/ziF7/g6NGjDA4OEo1GmZqaIhQK8fjjj/Piiy9SX1/P9ddfz6233sr4+Ditra2cPXuWzs5OYrEYu3btwu/386lPfYrt27fz4osv0t7eTiwWs0M/Fy1aRHt7OxMTE/zZn/0ZWVlZNDc3c/DgQQ4ePEhvb6/dDzePR0EQBEEQBEF4ozNv0QYgJyeHnJwcfD6fbQjq3A8vvPACk5OT+Hw+qquruf/++2lsbKS2tpZ7772X+vp6+vr6mJiYoLCwkIqKCgoLC4nFYnR3d3Py5EkaGhpcDRXtWu806LThU1xcTE9PD1NTU9TU1BAMBhkdHbVX+NFhLMuXL2f79u2sXbuWH/zgB7bBoT0bQqEQw8PD9PT0kJ2dTV9fHz/84Q/52Mc+xl133UUsFuNjH/sYO3bsoK2tzV4FSOdg0G+FzeSt6d6Ea8Oxp6eHp59+2vZk0Ibk0NBQSkJV02ugqKjIzq+hl5PWRqj5Jtrr9bJ27VoqKyvp7u62jdsXX3yRiYkJ6uvrycrK4siRIxw/fpz29nZGR0eZnp6ms7OThoYGKioq+NznPkdJSQkej4doNGqvfBOLxWhububBBx90vWZOLwzLSqzcdfDgQW6//XbuueceXnjhBQYGBmaspBMMBvn93/99Ghoa7HmhjVJ9XDgcZmJiwnUVqfmi2+vxeCgvL+euu+7iscce47bbbrO9QPQ1CAQC3HjjjQQCAfr6+lJCkbSgU1JSYs8N3fdIJEJ7ezsVFRUsXLiQ973vfWzbto0jR45w7Ngx+vv7aWlp4dlnn2Xp0qXceuutdg6V3bt309HRwcjICBUVFUxMTNhJvjXxeJyGhgb+9m//lptvvpm/+Iu/oKenh4GBAcbHx+nr6+PYsWOcOHHCnls9PT22YFJfX297kEWjUfbs2UNTUxMrVqxg6dKl3H///YyMjNjedvF4nGg0SiQSsYWDn//855w7dy7FC0gnqvX5fExNTdnL0WsBTAs6Om+QOW+c4ULm58nJSYaGhhgdHU3xSHEeNz09zcDAAD09PUxMTNDd3c2WLVtYvXo1zz//vJ04WV+39evXs3nzZqqrq9m/fz979uwhLy+Pu+++my1bttDQ0EBHRwfxeJzS0lLWr19PRUUFoVCIl19+mVOnTjE5OcnPfvYzPv3pTzM2NmYL2n6/37Vveu5lEmn0/HPeB2a/zfP9fj9r166lrKyMiooK6urqaGpq4sKFC/bxOtfSjh07WLp0Kc8++yy7d+9OEYG0V+CePXvsufu7v/u7HDt2jJMnT9Lb2wskhLn29nYGBgbw+/3k5eWxYMECqqurueaaa+wkx729vTz55JO89tprbNq0iY0bN7J582b72dvX10dzczMvv/wyo6OjvPTSS5SVlbFo0SKWL1/O1q1b+fGPf5yyEqEgCIIgCIIgvNmYt2hjWZYdkuH8oRyLxTh37pz9lnT16tU0NDTw2GOPUVxcTGFhIfn5+axatco2zoeHh2lubmZoaIjBwUH7LTi4hxeZuWc02oApKCigsbHRzoVQVVXFtm3bbDFH57DxeDwMDw/zzDPP2MdDYiUUHSK0devWlGWaq6qq6Orq4gc/+AHnzp0DsMUmn89ne2WEw2HbgDfFBxPzezweZ2JigoGBAUZHR+0x1WEY+k2yOQ46hKG8vJz9+/fbXg6nTp3Csiw7BEqPy7Jly9i4cSOxWIyDBw/auSRCoRCvvvoqbW1t+Hw+RkZG6OvrSwkxmZyc5MyZM0QiEcrKysjPz7e9kczlis08JG7hG+b80XNlaGiIJ554go0bN/Lud7+bSCRCLBazr/PZs2d57bXXqKmp4cyZM8Cvw9q2bt3KsWPHGBkZITs7m9zc3LTJZOeDKdisXr0av9/P97//fXbu3MnmzZsJBAL09/eTk5PDxo0bueeee9i1a5edbNvsb3l5OWNjY4yNjRGLxfB4POTk5LB+/XoOHDhAR0eHLUxpD6l169YRi8W4cOEC/f39DA4OEgwGbe+Vnp4eO6wlFArR3d09437Q9+ipU6cYGRnh+PHj9rLpOlmsNpz18XplLD03zPxUOrxrYGCApqYmysrK7HmvhRW97LVeMW7lypWMjY3R0tKSck1WrFhhe7GZQpb2tnGKdm5zx0lhYaEtIJnhh6YXi5mvSa8E19jYCMCaNWv42Mc+Zof7+P1+YrEYY2NjtLa2cvDgQc6ePWuHwL3yyitUV1ezcOFC1qxZY68yNjIyQnt7O319fXR1dTE0NIRSiqamJvbs2cPmzZvZunWrfc/rhMtmn93udfN/ppAoZ+ik1+uluLiYTZs2sWHDBnbv3k1xcTELFy7kAx/4AOfOnaOrq4uxsTGWLFlCbW0tsViMY8eOcejQIYaHh1M8yyBx/4VCIQYGBujq6iIvL4/BwUFbGNdt1c9CSKw4GAqF6Onpwefz2QKkXr1vfHycgYEBCgsL8Xq99jzU944OOxwaGqK/v5+Ojg7OnTtHTU2N/Yy+FM86QRAEQRAEQbiambdoE41GOXDgAAMDA3ZuC9NYCIfD9kpIAwMDnD9/nrNnz5KdnU1VVRVVVVUUFRXZOUO6u7tpb29nZGTE9ngxDRa3N+zOxK4670RRUREXLlwgFosxOjrK4OAg4XCYvLw8CgsL7ZCsnp4eTpw4QWNjox1OAzA8PGwbbQsWLODcuXMMDw9TUlKCz+fj2LFjdHd309rait/vp6KigkAggN/vZ2pqirGxMdtbQYd4uIU/mP2Kx+MMDQ3ZOS/09tzcXAKBAENDQ6791slUjxw5YocHnDhxgqKiIrKysmxPkMWLF3PdddcBcO7cOVpaWlLGVHsowMwcMrqdw8PDhEIh20tCew+ZHkCZvAMyCTcNDQ1kZ2ezcOHClOWCIWGMQyLMbWxszD53enqaoqIi7rzzTsLhMIsXLwagoaHBtf754PP57HFbtWoVjY2NHD58mJKSEioqKti6dSvj4+O2kNfa2srevXvtJeVNAoEAx44ds/fp8ZyYmGDfvn2cO3eOqakpgsGgHW6ml0GORCKMjIykCHnOcQyHw5w/f95eAcxEJ4Y9e/YsLS0t9rUDbM8f8xxzOXtnQlztHRSNRunr6+PMmTMp3h66Pu3NFolE7ATkzntYh7t0d3fbHnO6HHNFJl23E7c8MIODg1hWYhlpZ04bfYx5X+t6u7q6gMQ8XL9+vb06XDQaZXR01A7n0R5z+u/48eP09fVRW1tLZWUlU1NT9opzfX199ipj+poPDw/zy1/+kvHxcerq6qiuruaGG25gYGCAsbEx21ttdHSUnp4e29suE+Yzwfk8tCyLYDBIWVkZdXV11NXV0dvby4EDB/D7/WzZsoVVq1axbNkyampqGBgYID8/n0gkwpkzZzhx4gTd3d0p3n3mNYnFYoyMjDAyMuKaa8fMrwTY4u7IyIidO8gUBcPhMM3NzSlhic6cSeaxY2NjDAwM0NHRQVlZmeS2EQRBEARBEN7UXFQiYp0w1W1ZadPQ379/f4qhOjw8zMmTJ+3jTfd+pzjjlvDVWZdGL8utw4z02+umpiba2tooLCy038hOTEzQ399vv70122xZFoODg4RCIc6fP09hYSEDAwNUVFTQ1NTE0NCQbVDppYLN880lgN3Gxg2dx+PIkSMpx+nws56eHnub2U69HPWFCxfsetva2giFQnY4QlFREZs2baKmpobnnnuOkydPMjk5OcMLwem15CayxONx2yPJNBT1sXPtr7NcHX7j9/vJyckhOzubYDBoCwyRSIRDhw6l5FAJhUIcP36cj3/842RnZxOPxzl27BgdHR1zqjcTeXl5FBcXU1tbS05ODk888QTj4+Ps2rWL2267jeXLl1NUVEQ8Hqe5uZnvfOc7jIyMuPY5FArR0NCQ4lEyMTGREv6nBRHtpQGpRqs5Vk7MFXgyoQ1lM6eNxrzeWoDRiXnTeUw5l6o2y1JKcfLkSXJzc+0QK12Gx+MhFovZooC5z7ISS6drMclNpDLrMcfk8OHDxONxO0eO23gppfD7/WRnZ6csLd3R0UFnZycvv/yyvax9NBq181qZzybd50gkQltbG+3t7bZQZebwcQpPsViMpqYmurq6WLRoERs2bGDlypXE43EGBweBhGjW2dlJJBKxV3rS5zv7m+67+TkYDFJaWkpVVRU9PT0899xz9sp6fX19vPbaa2zYsIHq6mri8ThHjhzhxIkTDA0N2TllnNfBrMdcCcr5/DDbbop6Pp/PFiCdq6KZ803Pf12+Hl9zrmovHZ3rSLxsBEEQBEEQhDcraq4GNoDH47F0GMKcCk/+kNY/7E2RxvyBnqauOb9BDQaD1NbW8o53vIOvfOUrKUsg6x/0pqFg7jMNhHSGaFZWlm2omh4yuj9Ow1f3ca5j5BR5PB4Pd955JzU1NTQ0NHD48GHX89zq0kuHX3fdddTV1dHY2MjLL79sh2s5PRXmc/3d2qDLuRyYxtds4+fxeMjOzmbBggXEYjFCoZDrUtFzRV/HtWvXsnHjRqampjh69ChnzpxJGTefz2fPI3MlHKfwOFcyiZMX0/75Hm/mK9HLiN9222189atfTevtYZ6Tbr+uw008cbYDEnmXtm3bxpkzZ+ykzekw7xlt1Ovv6dqclZVFZWUl99xzD1//+tdneHPoNujQHmcf3Tx89DlmCJEz+bgeZ6fAoZ8pOiwMUj2gnJ5O6cbXbUz1Pqe4qoVQs51ObylzXujxdatLt90p7Hm93pRnvXm+UorJyUk755auR6lEomQtujlXzTLH0qzLPFaHVckS4IIgCIIgCMIbmAbLsrY4N15UThtTCHEzrvR+/aNch9GkW1HGjdmWe9bGgfbWaG5uThFsnIaMM4GxaQyYxolbfXq5Zmd5ptGoj9XGoGn0uQkzZvluY6GXKdfJQtO1zVlmNBrl/Pnzdk4b0ytCt1OPg9Mgmw9z7d9cSechkY7p6Wk758XlQM+H06dP09zcbOfc0G0yjVV9vO6zXnFovmiDdT4i32ztn01QcR5v1qsFCL2kdqbyMgl2mfqi7xvzGaLFln379s2hp6n3jFMoyUQ8HufEiRMAdjJkc/6azye3Z4VeIcqc9+aqWfpcfY7zPtMinw4FsyxrRkiYOU66HlPY0H0222Xe325jYbbfTQxy1m3OR9MDxixP990pzJh91G3T11kLPZD6/Nb3kHk99PGmqGTOHZ3/SAtFFyuaCoIgCIIgCMLVzkWtHjXbG3bzODM5qymUpDPyZxMA3IxN02U+XdtmMyRNA8lNzHC+aTfLMw15wH6TbBr78zHKtVEXDodTViQy68hkmJsGjHm807jTxpL55toNNw8Y5/inO3euHiCXGt5wOYQjLew539ZrozCdyHWxxqJbUm0n8/VmytT/2a6FNpz1Kkpmeena4QxNyVS/NtjdQm/MNl4O49spYExOTtLd3W0v0+30Qkl3r+vv+rngtkqRvrdMAUP31xRGzHvIeT+a5ehjNG7hcqbgZwpYznaZbTFX5TL367nt9DA0hRnTK8Yp1prPTS28mHXpY3Xiai0kp3vOmuNi3h9Ogc4Usy71+SEIgiAIgiAIVysXJdqkw/njezYjOlMYRbry0/04zyQaZNpv1p/O60EbgE4hxGlU6P9uhp2b0evcpusJBAJAQoSai7t/ulAOZ7vcxuByjH0mLrcxlU48cOvDfAUPpxGcbhzdPl+s0PB6egfM5VrolY0y4ZwL8xWUMl3DyzUemYSV2epx9s8tQbfzu9v9oesxw6ecoonz+WE+N2fzaMz0bHW2xSkiK6VsEc05v511uj2n3YQf/d8UgvWx+llpel86PS/1fh12ZXrbaNHJ2V4tGEkyYkEQBEEQBOHNyrxFm3SeAW4GC6S6tOvtbgbfXAzs+QgA6QxsZ3mmwZDu+Ezf3Qx6MxFnuna7rbakv3u9Xjs/y1zEidk8XTJtm6uoNdsxrxfON/zOfZdadqbt6eq7UuMyX9HpUpmammJyctJVtMkkWM2VuRw7m+fRpdQ127zXAoDb/ZTuHjPno9vxmUTadNvSCb5u7Z6r4Krb6fRumW2cMl2PdN5K5vPUKdxAqieg2T+niGXW4SYciVAjCIIgCIIg/DZwUZ42s4kn5o9up9u9c7+bsHEpRttsRoabh4Rbf5xvnWdrvz7WbXsm3Mqampqit7d3zp4HlyJezPfYy+05czFlzvWc+c6jTOElvw3E43Gi0SiRSGRW8eRSxuY3Oa5Og3+2e9Y51zJ5vjjPM+tyChmZws2com8m5ioEuWGG52V6BprClFPodhO5zDxPZlt0SJTbmJr1OMPXnG1xa6eERwmCIAiCIAhvVi7K00Yz1zfubvvc8jRcCs433m51Otuuj8m0Uko648JZt8bNiHHzDsn0ljgajdLW1jaHXmfmchszcxHr5lvfxbbv9TT8L5f3x3y4HKLIfMc2Ho8zPj5Odnb2nPp8uefXb0LMcRMknOGQGuc9bD4f3EKfNHpfJqHIzVvHWY557sXMSS2cuInXZohROq9IZzvcPArN55t5rs514/f7U8bbeb5Zjr4GZpJis35nO0W4EQRBEARBEN6MXNTqUfPxIkmXkyWT90267ZneCps/9NPhXEo23Vtb0wgwz9FvjzOtbJVOjJrPuE1NTTE0NJTxmLkYzJdqxKTzIsrUjosVDObSnyv1Rn22ctN5W10JwcI0XDMdOxcDf77EYjGGh4cZHx9/04eeuIX16Ps93UpykPoMcRNnM3n56WPSMdszIl04kls5mTAFKdO7T9fhHBPzv3NeOFfIMsM+M3nFmM9ZXa4WjpwJk93EIeezWVaPEgRBEARBEN6sqPn80FVK9QEXrlxzBEEQBEEQBEEQBEEQfutYbFlWmXPjvEQbQRAEQRAEQRAEQRAE4fUhfRyAIAiCIAiCIAiCIAiC8BtDRBtBEARBEARBEARBEISrEBFtBEEQBEEQBEEQBEEQrkJEtBEEQRAEQRAEQRAEQbgKEdFGEARBEARBEARBEAThKkREG0EQBEEQBEEQBEEQhKsQEW0EQRAEQRAEQRAEQRCuQkS0EQRBEARBEARBEARBuAoR0UYQBEEQBEEQBEEQBOEq5P8HWVjnahOypUIAAAAASUVORK5CYII=\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(20, 20))\n", - "plt.title(sentence)\n", - "plt.imshow(data.squeeze(0).numpy(), cmap='gray')\n", - "plt.xticks([])\n", - "plt.yticks([])" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('he rove fron his breakfait-nook bench<eos>', 0.6715805530548096)" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.predict_on_image(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/notebooks/07-look-at-lexicon.ipynb b/src/notebooks/07-look-at-lexicon.ipynb deleted file mode 100644 index b7a5a0e..0000000 --- a/src/notebooks/07-look-at-lexicon.ipynb +++ /dev/null @@ -1,1119 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "from pathlib import Path\n", - "import numpy as np\n", - "from PIL import Image\n", - "import torch.nn.functional as F\n", - "import torch\n", - "from torch import nn\n", - "from torchsummary import summary\n", - "from importlib.util import find_spec\n", - "if find_spec(\"text_recognizer\") is None:\n", - " import sys\n", - " sys.path.append('..')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "path = Path(\"../\").resolve().parent / \"data\" / \"processed\" / \"iam_lines\" / \"iamdb_1kwp_lex_1000.txt\"" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PosixPath('/home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/data/processed/iam_lines/iamdb_1kwp_lex_1000.txt')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "path" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "with open(path, \"r\") as f:\n", - " lex = (line.strip().split() for line in f)\n", - " lex = {line[0]: line[1:] for line in lex}\n", - " #print(len(lex))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'!': ['▁', '!'],\n", - " '\"': ['▁', '\"'],\n", - " '&': ['▁', '&'],\n", - " \"'\": ['▁', \"'\"],\n", - " \"'30s\": ['▁', \"'\", '3', '0', 's'],\n", - " \"'61\": ['▁', \"'\", '6', '1'],\n", - " \"'d\": ['▁', \"'\", 'd'],\n", - " \"'ll\": ['▁', \"'\", 'll'],\n", - " \"'m\": ['▁', \"'\", 'm'],\n", - " \"'re\": ['▁', \"'\", 're'],\n", - " \"'s\": ['▁', \"'\", 's'],\n", - " \"'ve\": ['▁', \"'\", 've'],\n", - " '(': ['▁', '('],\n", - " ')': ['▁', ')'],\n", - " '*': ['▁', '*'],\n", - " '+2.8': ['▁', '+', '2', '.', '8'],\n", - " '+3.6': ['▁', '+', '3', '.', '6'],\n", - " ',': ['▁', ','],\n", - " '-': ['▁', '-'],\n", - " '-2.6': ['▁', '-', '2', '.', '6'],\n", - " '-5.4': ['▁', '-', '5', '.', '4'],\n", - " '.': ['▁', '.'],\n", - " '...': ['▁', '.', '.', '.'],\n", - " '0m': ['▁', '0', 'm'],\n", - " '1': ['▁', '1'],\n", - " '1,157': ['▁', '1', ',', '1', '5', '7'],\n", - " '1,400': ['▁', '1', ',', '4', '0', '0'],\n", - " '1,500': ['▁', '1', ',', '5', '0', '0'],\n", - " '1-2': ['▁', '1', '-', '2'],\n", - " '1.8': ['▁', '1', '.', '8'],\n", - " '1/2': ['▁', '1', '/', '2'],\n", - " '1/2-in.-long': ['▁', '1', '/', '2', '-', 'in', '.', '-', 'long'],\n", - " '1/4': ['▁', '1', '/', '4'],\n", - " '10': ['▁', '10'],\n", - " '10,000': ['▁', '10', ',', '0', '0', '0'],\n", - " '100': ['▁', '10', '0'],\n", - " '100,000,000': ['▁', '10', '0', ',', '0', '00,000'],\n", - " '104': ['▁', '10', '4'],\n", - " '11': ['▁', '1', '1'],\n", - " '12': ['▁', '1', '2'],\n", - " '12,000-word': ['▁', '1', '2', ',', '0', '0', '0', '-', 'word'],\n", - " '125': ['▁', '1', '2', '5'],\n", - " '13': ['▁', '1', '3'],\n", - " '13,000': ['▁', '1', '3', ',', '0', '0', '0'],\n", - " '14': ['▁', '1', '4'],\n", - " '15': ['▁', '1', '5'],\n", - " '15,000,000': ['▁', '1', '5', ',', '0', '00,000'],\n", - " '15-17': ['▁', '1', '5', '-', '1', '7'],\n", - " '15-nation': ['▁', '1', '5', '-', 'n', 'ation'],\n", - " '15-year-olds': ['▁', '1', '5', '-', 'year', '-', 'old', 's'],\n", - " '150,000,000': ['▁', '1', '5', '0', ',', '0', '00,000'],\n", - " '16': ['▁', '1', '6'],\n", - " '16,000': ['▁', '1', '6', ',', '0', '0', '0'],\n", - " '160': ['▁', '1', '6', '0'],\n", - " '163,000,000': ['▁', '1', '6', '3', ',', '0', '00,000'],\n", - " '167': ['▁', '1', '6', '7'],\n", - " '17': ['▁', '1', '7'],\n", - " '18': ['▁', '1', '8'],\n", - " '18.1': ['▁', '1', '8', '.', '1'],\n", - " '1830': ['▁', '1', '8', '3', '0'],\n", - " \"1830's\": ['▁', '1', '8', '3', '0', \"'\", 's'],\n", - " '1834': ['▁', '1', '8', '3', '4'],\n", - " '1897': ['▁', '1', '8', '9', '7'],\n", - " '19': ['▁', '1', '9'],\n", - " '19.5': ['▁', '1', '9', '.', '5'],\n", - " '1910': ['▁', '1', '9', '10'],\n", - " '1913': ['▁', '1', '9', '1', '3'],\n", - " '1914': ['▁', '1', '9', '1', '4'],\n", - " '1914-18': ['▁', '1', '9', '1', '4', '-', '1', '8'],\n", - " '1918': ['▁', '1', '9', '1', '8'],\n", - " '1920': ['▁', '1', '9', '2', '0'],\n", - " '1930': ['▁', '1', '9', '3', '0'],\n", - " '1931': ['▁', '1', '9', '3', '1'],\n", - " '1932': ['▁', '1', '9', '3', '2'],\n", - " '1934': ['▁', '1', '9', '3', '4'],\n", - " '1936': ['▁', '1', '9', '3', '6'],\n", - " '1939': ['▁', '1', '9', '3', '9'],\n", - " '1943': ['▁', '1', '9', '4', '3'],\n", - " '1944': ['▁', '1', '9', '4', '4'],\n", - " '1950': ['▁', '1', '9', '5', '0'],\n", - " '1951': ['▁', '1', '9', '5', '1'],\n", - " '1952': ['▁', '1', '9', '5', '2'],\n", - " '1953': ['▁', '1', '9', '5', '3'],\n", - " '1954': ['▁', '1', '9', '5', '4'],\n", - " '1956': ['▁', '1', '9', '5', '6'],\n", - " '1957': ['▁', '1', '9', '5', '7'],\n", - " '1958': ['▁', '1', '9', '5', '8'],\n", - " '1959': ['▁', '1', '9', '5', '9'],\n", - " '1960': ['▁', '1960'],\n", - " '1960s': ['▁', '1960', 's'],\n", - " '1961': ['▁', '1', '9', '6', '1'],\n", - " '1963': ['▁', '1', '9', '6', '3'],\n", - " '19th': ['▁', '1', '9', 'th'],\n", - " '1superceded': ['▁', '1', 'superceded'],\n", - " \"1tho'\": ['▁', '1', 'tho', \"'\"],\n", - " '2': ['▁', '2'],\n", - " '2,000': ['▁', '2', ',', '0', '0', '0'],\n", - " '2,415,000,000': ['▁', '2', ',', '4', '1', '5', ',', '0', '00,000'],\n", - " '20': ['▁', '2', '0'],\n", - " '20-month-old': ['▁', '2', '0', '-', 'month', '-', 'old'],\n", - " '200': ['▁', '2', '0', '0'],\n", - " '20th-century': ['▁', '2', '0', 'th', '-', 'cent', 'ur', 'y'],\n", - " '21': ['▁', '2', '1'],\n", - " '210million': ['▁', '2', '10', 'million'],\n", - " '22': ['▁', '2', '2'],\n", - " '23.1': ['▁', '2', '3', '.', '1'],\n", - " '24': ['▁', '2', '4'],\n", - " '24-strong': ['▁', '2', '4', '-', 'strong'],\n", - " '25': ['▁', '2', '5'],\n", - " '27': ['▁', '2', '7'],\n", - " '28.5': ['▁', '2', '8', '.', '5'],\n", - " '280,000': ['▁', '2', '8', '0', ',', '0', '0', '0'],\n", - " '287': ['▁', '2', '8', '7'],\n", - " '288': ['▁', '2', '8', '8'],\n", - " '2bhoys': ['▁', '2', 'b', 'ho', 'y', 's'],\n", - " '2ole': ['▁', '2', 'o', 'le'],\n", - " '2pianna': ['▁', '2', 'p', 'i', 'an', 'n', 'a'],\n", - " '2skint': ['▁', '2', 's', 'k', 'in', 't'],\n", - " '3': ['▁', '3'],\n", - " '3,000': ['▁', '3', ',', '0', '0', '0'],\n", - " '3.6': ['▁', '3', '.', '6'],\n", - " '3/0': ['▁', '3', '/', '0'],\n", - " '3/4': ['▁', '3', '/', '4'],\n", - " '30': ['▁', '3', '0'],\n", - " '30-day': ['▁', '3', '0', '-', 'day'],\n", - " '30-minute': ['▁', '3', '0', '-', 'minute'],\n", - " '300,000': ['▁', '3', '00,000'],\n", - " '32': ['▁', '3', '2'],\n", - " '33': ['▁', '3', '3'],\n", - " '34': ['▁', '3', '4'],\n", - " '35': ['▁', '3', '5'],\n", - " '357million': ['▁', '3', '5', '7', 'million'],\n", - " '36': ['▁', '3', '6'],\n", - " '37,000,000': ['▁', '3', '7', ',', '0', '00,000'],\n", - " '37.2': ['▁', '3', '7', '.', '2'],\n", - " '38': ['▁', '3', '8'],\n", - " '4': ['▁', '4'],\n", - " '4.8': ['▁', '4', '.', '8'],\n", - " '40': ['▁', '4', '0'],\n", - " '400': ['▁', '4', '0', '0'],\n", - " '400,000': ['▁', '4', '00,000'],\n", - " '420000': ['▁', '4', '2', '0', '0', '0', '0'],\n", - " '43': ['▁', '4', '3'],\n", - " '450': ['▁', '4', '5', '0'],\n", - " '5': ['▁', '5'],\n", - " '5,000': ['▁', '5', ',', '0', '0', '0'],\n", - " '5.30': ['▁', '5', '.', '3', '0'],\n", - " '5/8': ['▁', '5', '/', '8'],\n", - " '50': ['▁', '5', '0'],\n", - " '50,000': ['▁', '5', '0', ',', '0', '0', '0'],\n", - " '500': ['▁', '5', '0', '0'],\n", - " '53-year-old': ['▁', '5', '3', '-', 'year', '-', 'old'],\n", - " '55': ['▁', '5', 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'86': ['▁', '8', '6'],\n", - " '88': ['▁', '8', '8'],\n", - " '88-year-old': ['▁', '8', '8', '-', 'year', '-', 'old'],\n", - " '89': ['▁', '8', '9'],\n", - " '89-year-old': ['▁', '8', '9', '-', 'year', '-', 'old'],\n", - " '9.30': ['▁', '9', '.', '3', '0'],\n", - " '9.40': ['▁', '9', '.', '4', '0'],\n", - " '90-day': ['▁', '9', '0', '-', 'day'],\n", - " '90-minute': ['▁', '9', '0', '-', 'minute'],\n", - " '91': ['▁', '9', '1'],\n", - " '950': ['▁', '9', '5', '0'],\n", - " '97.5': ['▁', '9', '7', '.', '5'],\n", - " ':': ['▁', ':'],\n", - " ';': ['▁', ';'],\n", - " '?': ['▁', '?'],\n", - " 'a': ['▁', 'a'],\n", - " 'abandon': ['▁', 'a', 'b', 'and', 'on'],\n", - " 'abandoned': ['▁', 'a', 'b', 'and', 'on', 'ed'],\n", - " 'abandoning': ['▁', 'a', 'b', 'and', 'on', 'ing'],\n", - " 'abashed': ['▁', 'a', 'bas', 'he', 'd'],\n", - " 'ability': ['▁', 'a', 'b', 'il', 'ity'],\n", - " 'able': ['▁', 'able'],\n", - " 'able-bodied': ['▁', 'able', '-', 'bo', 'die', 'd'],\n", - " 'abolish': ['▁', 'a', 'bo', 'l', 'ish'],\n", - " 'abolished': ['▁', 'a', 'bo', 'l', 'ish', 'ed'],\n", - " 'abolition': ['▁', 'a', 'bo', 'li', 'tion'],\n", - " 'abortion': ['▁', 'a', 'b', 'or', 'tion'],\n", - " 'abou': ['▁', 'a', 'bo', 'u'],\n", - " 'about': ['▁', 'about'],\n", - " 'about-': ['▁', 'about', '-'],\n", - " 'above': ['▁', 'a', 'bo', 've'],\n", - " 'abreast': ['▁', 'a', 'br', 'east'],\n", - " 'abroad': ['▁', 'a', 'b', 'ro', 'ad'],\n", - " 'absence': ['▁', 'a', 'b', 's', 'ence'],\n", - " 'absent': ['▁', 'a', 'b', 's', 'ent'],\n", - " 'absolutely': ['▁', 'a', 'b', 'solut', 'e', 'ly'],\n", - " 'abstraction': ['▁', 'a', 'b', 's', 'tr', 'action'],\n", - " 'abundance': ['▁', 'a', 'b', 'un', 'd', 'ance'],\n", - " 'ac-': ['▁', 'ac', '-'],\n", - " 'academic': ['▁', 'ac', 'a', 'de', 'm', 'ic'],\n", - " 'accent': ['▁', 'ac', 'cent'],\n", - " 'accents': ['▁', 'ac', 'cent', 's'],\n", - " 'accept': ['▁', 'accept'],\n", - " 'acceptable': ['▁', 'accept', 'able'],\n", - " 'accepted': ['▁', 'accept', 'ed'],\n", - " 'accepting': ['▁', 'accept', 'ing'],\n", - " 'accessories': ['▁', 'ac', 'ce', 's', 'so', 'ries'],\n", - " 'accident': ['▁', 'ac', 'c', 'id', 'ent'],\n", - " 'accidental': ['▁', 'ac', 'c', 'id', 'ent', 'al'],\n", - " 'accommodate': ['▁', 'ac', 'com', 'mo', 'date'],\n", - " 'accommodation': ['▁', 'ac', 'com', 'mo', 'd', 'ation'],\n", - " 'accompanied': ['▁', 'ac', 'com', 'pan', 'i', 'ed'],\n", - " 'accompanist': ['▁', 'ac', 'com', 'pan', 'is', 't'],\n", - " 'accompany': ['▁', 'ac', 'com', 'p', 'any'],\n", - " 'accomplished': ['▁', 'ac', 'com', 'p', 'l', 'ish', 'ed'],\n", - " 'accomplishments': ['▁', 'ac', 'com', 'p', 'l', 'ish', 'ment', 's'],\n", - " 'according': ['▁', 'ac', 'c', 'or', 'd', 'ing'],\n", - " 'account': ['▁', 'ac', 'count'],\n", - " 'accountancy': ['▁', 'ac', 'count', 'an', 'c', 'y'],\n", - " 'accra': ['▁', 'ac', 'c', 'ra'],\n", - " \"accra's\": ['▁', 'ac', 'c', 'ra', \"'\", 's'],\n", - " 'accuracy': ['▁', 'ac', 'cur', 'ac', 'y'],\n", - " 'accurate': ['▁', 'ac', 'cur', 'ate'],\n", - " 'accurately': ['▁', 'ac', 'cur', 'ate', 'ly'],\n", - " 'accused': ['▁', 'ac', 'c', 'used'],\n", - " 'achieved': ['▁', 'a', 'ch', 'i', 'e', 'v', 'ed'],\n", - " 'achievement': ['▁', 'a', 'ch', 'i', 'e', 've', 'ment'],\n", - " 'acquaintance': ['▁', 'ac', 'q', 'u', 'a', 'in', 't', 'ance'],\n", - " 'acquaintances': ['▁', 'ac', 'q', 'u', 'a', 'in', 't', 'ance', 's'],\n", - " 'acres': ['▁', 'ac', 're', 's'],\n", - " 'across': ['▁', 'a', 'cross'],\n", - " 'act': ['▁', 'act'],\n", - " 'acting': ['▁', 'act', 'ing'],\n", - " 'action': ['▁', 'action'],\n", - " 'actions': ['▁', 'action', 's'],\n", - " 'active': ['▁', 'act', 'ive'],\n", - " 'activists': ['▁', 'act', 'i', 'vi', 'st', 's'],\n", - " 'activities': ['▁', 'act', 'i', 'v', 'it', 'ies'],\n", - " 'activity': ['▁', 'act', 'i', 'v', 'ity'],\n", - " 'acton': ['▁', 'act', 'on'],\n", - " 'actor': ['▁', 'act', 'or'],\n", - " 'actress': ['▁', 'act', 're', 's', 's'],\n", - " 'acts': ['▁', 'act', 's'],\n", - " 'actual': ['▁', 'act', 'ual'],\n", - " 'actually': ['▁', 'act', 'ual', 'ly'],\n", - " 'adamafio': ['▁', 'ad', 'a', 'ma', 'f', 'i', 'o'],\n", - " 'adaptation': ['▁', 'ad', 'ap', 't', 'ation'],\n", - " 'adapted': ['▁', 'ad', 'ap', 'ted'],\n", - " 'adapting': ['▁', 'ad', 'ap', 't', 'ing'],\n", - " 'add': ['▁', 'ad', 'd'],\n", - " 'added': ['▁', 'ad', 'd', 'ed'],\n", - " 'adding': ['▁', 'adding'],\n", - " 'addition': ['▁', 'ad', 'd', 'it', 'ion'],\n", - " 'additions': ['▁', 'ad', 'd', 'it', 'ion', 's'],\n", - " 'address': ['▁', 'ad', 'dr', 'es', 's'],\n", - " 'addressed': ['▁', 'ad', 'dr', 'es', 's', 'ed'],\n", - " 'addresses': ['▁', 'ad', 'dr', 'es', 'se', 's'],\n", - " 'addressing': ['▁', 'ad', 'dr', 'es', 's', 'ing'],\n", - " 'adenauer': ['▁', 'adenauer'],\n", - " \"adenauer's\": ['▁', 'adenauer', \"'\", 's'],\n", - " 'adequate': ['▁', 'ad', 'equa', 'te'],\n", - " 'adhem': ['▁', 'ad', 'he', 'm'],\n", - " 'adjust': ['▁', 'ad', 'just'],\n", - " 'adjustment': ['▁', 'ad', 'just', 'ment'],\n", - " 'administration': ['▁', 'ad', 'ministr', 'ation'],\n", - " \"administration's\": ['▁', 'ad', 'ministr', 'ation', \"'\", 's'],\n", - " 'administrative': ['▁', 'ad', 'ministr', 'at', 'ive'],\n", - " 'admiralty': ['▁', 'ad', 'm', 'i', 'r', 'al', 'ty'],\n", - " 'admire': ['▁', 'ad', 'm', 'i', 're'],\n", - " 'admit': ['▁', 'ad', 'm', 'it'],\n", - " 'admitted': ['▁', 'ad', 'm', 'it', 'ted'],\n", - " 'admitting': ['▁', 'ad', 'm', 'it', 't', 'ing'],\n", - " 'adopted': ['▁', 'a', 'do', 'p', 'ted'],\n", - " 'adopting': ['▁', 'a', 'do', 'p', 't', 'ing'],\n", - " 'adoption': ['▁', 'a', 'do', 'p', 'tion'],\n", - " 'adult': ['▁', 'ad', 'ul', 't'],\n", - " 'advance': ['▁', 'ad', 'v', 'ance'],\n", - " 'advanced': ['▁', 'ad', 'v', 'ance', 'd'],\n", - " 'advancing': ['▁', 'ad', 'v', 'an', 'c', 'ing'],\n", - " 'advantage': ['▁', 'advantage'],\n", - " 'advantages': ['▁', 'advantage', 's'],\n", - " 'advertisement': ['▁', 'ad', 'ver', 't', 'is', 'e', 'ment'],\n", - " 'advertisements': ['▁', 'ad', 'ver', 't', 'is', 'ements'],\n", - " 'advice': ['▁', 'advi', 'ce'],\n", - " 'advisability': ['▁', 'advi', 's', 'a', 'b', 'il', 'ity'],\n", - " 'advise': ['▁', 'advise'],\n", - " 'advised': ['▁', 'advise', 'd'],\n", - " 'advisers': ['▁', 'advise', 'r', 's'],\n", - " 'advocate': ['▁', 'ad', 'v', 'o', 'c', 'ate'],\n", - " 'af-': ['▁', 'a', 'f', '-'],\n", - " 'affairs': ['▁', 'a', 'f', 'f', 'air', 's'],\n", - " 'affected': ['▁', 'a', 'f', 'fe', 'c', 'ted'],\n", - " 'affection': ['▁', 'a', 'f', 'fe', 'c', 'tion'],\n", - " 'affilia-': ['▁', 'a', 'f', 'f', 'il', 'i', 'a', '-'],\n", - " 'affiliations': ['▁', 'a', 'f', 'f', 'il', 'i', 'ation', 's'],\n", - " 'affluence': ['▁', 'a', 'f', 'f', 'l', 'u', 'ence'],\n", - " 'affluent': ['▁', 'a', 'f', 'f', 'l', 'u', 'ent'],\n", - " 'afford': ['▁', 'a', 'f', 'for', 'd'],\n", - " 'afraid': ['▁', 'a', 'fr', 'a', 'id'],\n", - " 'africa': ['▁', 'africa'],\n", - " \"africa's\": ['▁', 'africa', \"'\", 's'],\n", - " 'african': ['▁', 'african'],\n", - " 'africans': ['▁', 'african', 's'],\n", - " 'after': ['▁', 'after'],\n", - " 'afternoon': ['▁', 'after', 'no', 'on'],\n", - " 'afterwards': ['▁', 'after', 'ward', 's'],\n", - " 'again': ['▁', 'again'],\n", - " 'against': ['▁', 'against'],\n", - " 'age': ['▁', 'age'],\n", - " 'age-structure': ['▁', 'age', '-', 's', 'tru', 'c', 'ture'],\n", - " 'aged': ['▁', 'aged'],\n", - " 'ageing': ['▁', 'age', 'ing'],\n", - " 'agent': ['▁', 'a', 'g', 'ent'],\n", - " 'agents': ['▁', 'a', 'g', 'ent', 's'],\n", - " 'ages': ['▁', 'age', 's'],\n", - " 'agitation': ['▁', 'a', 'g', 'it', 'ation'],\n", - " 'ago': ['▁', 'a', 'go'],\n", - " 'agree': ['▁', 'agree'],\n", - " 'agreed': ['▁', 'agree', 'd'],\n", - " 'agreement': ['▁', 'agree', 'ment'],\n", - " 'agreements': ['▁', 'agree', 'ment', 's'],\n", - " 'agriculture': ['▁', 'a', 'gr', 'ic', 'ul', 'ture'],\n", - " 'ahead': ['▁', 'a', 'head'],\n", - " 'aid': ['▁', 'a', 'id'],\n", - " 'aide': ['▁', 'a', 'i', 'de'],\n", - " 'aided': ['▁', 'a', 'id', 'ed'],\n", - " 'aides': ['▁', 'a', 'id', 'es'],\n", - " 'aim': ['▁', 'a', 'im'],\n", - " 'aimed': ['▁', 'a', 'im', 'ed'],\n", - " 'aiming': ['▁', 'a', 'im', 'ing'],\n", - " 'air': ['▁', 'air'],\n", - " 'aircraft': ['▁', 'air', 'craft'],\n", - " 'aired': ['▁', 'air', 'ed'],\n", - " \"airliner's\": ['▁', 'air', 'line', 'r', \"'\", 's'],\n", - " 'airmen': ['▁', 'air', 'men'],\n", - " 'airport': ['▁', 'air', 'port'],\n", - " 'akin': ['▁', 'a', 'k', 'in'],\n", - " \"aladdin's\": ['▁', 'al', 'ad', 'd', 'in', \"'\", 's'],\n", - " 'alan': ['▁', 'al', 'an'],\n", - " 'alarm': ['▁', 'al', 'arm'],\n", - " 'alarmed': ['▁', 'al', 'arm', 'ed'],\n", - " 'alas': ['▁', 'al', 'as'],\n", - " 'alcoholic': ['▁', 'al', 'co', 'ho', 'li', 'c'],\n", - " 'algeria': ['▁', 'al', 'g', 'er', 'i', 'a'],\n", - " 'alike': ['▁', 'a', 'like'],\n", - " 'alive': ['▁', 'a', 'live'],\n", - " 'all': ['▁', 'all'],\n", - " 'all-regular': ['▁', 'all', '-', 'regular'],\n", - " 'alleged': ['▁', 'al', 'leg', 'ed'],\n", - " 'allen': ['▁', 'all', 'en'],\n", - " 'alleviation': ['▁', 'alleviation'],\n", - " 'alley': ['▁', 'al', 'le', 'y'],\n", - " 'alliance': ['▁', 'all', 'i', 'ance'],\n", - " 'alliances': ['▁', 'all', 'i', 'ance', 's'],\n", - " 'allied': ['▁', 'all', 'i', 'ed'],\n", - " 'allies': ['▁', 'all', 'ies'],\n", - " 'allow': ['▁', 'allow'],\n", - " 'allowance': ['▁', 'allow', 'ance'],\n", - " 'allowances': ['▁', 'allow', 'ance', 's'],\n", - " 'allowed': ['▁', 'allow', 'ed'],\n", - " 'allowing': ['▁', 'allow', 'ing'],\n", - " 'ally': ['▁', 'al', 'ly'],\n", - " 'almost': ['▁', 'al', 'most'],\n", - " 'alone': ['▁', 'al', 'one'],\n", - " 'along': ['▁', 'a', 'long'],\n", - " 'alongside': ['▁', 'a', 'long', 'side'],\n", - " 'aloud': ['▁', 'a', 'lo', 'ud'],\n", - " 'already': ['▁', 'al', 'read', 'y'],\n", - " 'also': ['▁', 'also'],\n", - " 'alter': ['▁', 'al', 'ter'],\n", - " 'alternative': ['▁', 'al', 'ter', 'n', 'at', 'ive'],\n", - " 'alternatively': ['▁', 'al', 'ter', 'n', 'at', 'ive', 'ly'],\n", - " 'alternatives': ['▁', 'al', 'ter', 'n', 'at', 'ive', 's'],\n", - " 'although': ['▁', 'al', 'though'],\n", - " 'altogether': ['▁', 'al', 'together'],\n", - " 'altos': ['▁', 'al', 'to', 's'],\n", - " 'always': ['▁', 'always'],\n", - " 'am': ['▁', 'am'],\n", - " 'amateur': ['▁', 'am', 'ate', 'ur'],\n", - " 'amazed': ['▁', 'a', 'ma', 'z', 'ed'],\n", - " 'amazing': ['▁', 'a', 'ma', 'z', 'ing'],\n", - " 'ambassador': ['▁', 'am', 'bas', 's', 'ad', 'or'],\n", - " 'amber': ['▁', 'a', 'mber'],\n", - " 'ambition': ['▁', 'am', 'b', 'it', 'ion'],\n", - " 'ambitious': ['▁', 'am', 'b', 'it', 'i', 'ous'],\n", - " 'ambulance': ['▁', 'am', 'b', 'ul', 'ance'],\n", - " 'ambulances': ['▁', 'am', 'b', 'ul', 'ance', 's'],\n", - " 'america': ['▁', 'america'],\n", - " \"america's\": ['▁', 'america', \"'\", 's'],\n", - " 'american': ['▁', 'american'],\n", - " 'american-born': ['▁', 'american', '-', 'b', 'or', 'n'],\n", - " 'americans': ['▁', 'american', 's'],\n", - " 'amid': ['▁', 'am', 'id'],\n", - " 'ammunition': ['▁', 'am', 'm', 'un', 'it', 'ion'],\n", - " 'among': ['▁', 'among'],\n", - " 'amount': ['▁', 'a', 'mo', 'un', 't'],\n", - " 'ample': ['▁', 'amp', 'le'],\n", - " 'amusement': ['▁', 'am', 'use', 'ment'],\n", - " 'amusing': ['▁', 'am', 'us', 'ing'],\n", - " 'an': ['▁', 'an'],\n", - " 'analogy': ['▁', 'an', 'a', 'lo', 'g', 'y'],\n", - " 'analysed': ['▁', 'an', 'a', 'ly', 's', 'ed'],\n", - " 'anchor': ['▁', 'an', 'ch', 'or'],\n", - " 'ancient': ['▁', 'an', 'c', 'i', 'ent'],\n", - " 'and': ['▁', 'and'],\n", - " 'andrei': ['▁', 'and', 're', 'i'],\n", - " 'andrew': ['▁', 'and', 're', 'w'],\n", - " 'anecdotal': ['▁', 'an', 'e', 'c', 'do', 't', 'al'],\n", - " 'angel': ['▁', 'ang', 'el'],\n", - " 'angeles': ['▁', 'ang', 'el', 'es'],\n", - " 'angelo': ['▁', 'ang', 'e', 'lo'],\n", - " 'anger': ['▁', 'ang', 'er'],\n", - " 'anglais': ['▁', 'ang', 'la', 'is'],\n", - " 'angle': ['▁', 'ang', 'le'],\n", - " 'anglesey': ['▁', 'anglesey'],\n", - " \"anglesey's\": ['▁', 'anglesey', \"'\", 's'],\n", - " 'anglesey-road': ['▁', 'anglesey', '-', 'ro', 'ad'],\n", - " 'angola': ['▁', 'an', 'go', 'la'],\n", - " 'angrily': ['▁', 'an', 'gr', 'i', 'ly'],\n", - " 'angry': ['▁', 'ang', 'ry'],\n", - " 'ann': ['▁', 'an', 'n'],\n", - " 'anna': ['▁', 'an', 'n', 'a'],\n", - " 'announced': ['▁', 'an', 'no', 'un', 'c', 'ed'],\n", - " 'announcement': ['▁', 'an', 'no', 'un', 'ce', 'ment'],\n", - " 'announcing': ['▁', 'an', 'no', 'un', 'c', 'ing'],\n", - " 'annoyed': ['▁', 'an', 'no', 'y', 'ed'],\n", - " 'annual': ['▁', 'an', 'n', 'ual'],\n", - " 'another': ['▁', 'another'],\n", - " 'answer': ['▁', 'answer'],\n", - " 'answered': ['▁', 'answer', 'ed'],\n", - " 'answering': ['▁', 'answer', 'ing'],\n", - " 'antagonism': ['▁', 'ant', 'a', 'g', 'on', 'is', 'm'],\n", - " 'anthony': ['▁', 'an', 'th', 'on', 'y'],\n", - " 'anti-apartheid': ['▁', 'ant', 'i', '-', 'a', 'part', 'he', 'id'],\n", - " 'anti-bomb': ['▁', 'ant', 'i', '-', 'bomb'],\n", - " 'anti-german': ['▁', 'ant', 'i', '-', 'german'],\n", - " 'anti-nato': ['▁', 'ant', 'i', '-', 'nato'],\n", - " 'anti-negro': ['▁', 'ant', 'i', '-', 'negro'],\n", - " 'anti-nuclear': ['▁', 'ant', 'i', '-', 'nuclear'],\n", - " 'anti-soviet': ['▁', 'ant', 'i', '-', 'soviet'],\n", - " 'anti-tory': ['▁', 'ant', 'i', '-', 'tory'],\n", - " 'anticipation': ['▁', 'an', 'tic', 'ip', 'ation'],\n", - " 'antonioni': ['▁', 'ant', 'on', 'ion', 'i'],\n", - " \"antonioni's\": ['▁', 'ant', 'on', 'ion', 'i', \"'\", 's'],\n", - " 'any': ['▁', 'any'],\n", - " 'any-': ['▁', 'any', '-'],\n", - " 'anybody': ['▁', 'any', 'body'],\n", - " \"anybody's\": ['▁', 'any', 'body', \"'\", 's'],\n", - " 'anyone': ['▁', 'any', 'one'],\n", - " 'anything': ['▁', 'any', 'thing'],\n", - " 'anyway': ['▁', 'any', 'way'],\n", - " 'apart': ['▁', 'a', 'part'],\n", - " 'apartheid': ['▁', 'a', 'part', 'he', 'id'],\n", - " 'apathetic': ['▁', 'a', 'pa', 'the', 'tic'],\n", - " 'apathy': ['▁', 'a', 'pa', 'th', 'y'],\n", - " 'apex': ['▁', 'ap', 'ex'],\n", - " 'apocalypse': ['▁', 'a', 'po', 'c', 'a', 'ly', 'p', 'se'],\n", - " 'apologising': ['▁', 'a', 'po', 'lo', 'g', 'is', 'ing'],\n", - " 'appalled': ['▁', 'app', 'all', 'ed'],\n", - " 'appalling': ['▁', 'app', 'all', 'ing'],\n", - " 'apparatus': ['▁', 'app', 'ar', 'at', 'us'],\n", - " 'apparent': ['▁', 'app', 'ar', 'ent'],\n", - " 'apparently': ['▁', 'app', 'ar', 'ent', 'ly'],\n", - " 'appeal': ['▁', 'appeal'],\n", - " 'appealing': ['▁', 'appeal', 'ing'],\n", - " 'appeals': ['▁', 'appeal', 's'],\n", - " 'appear': ['▁', 'appear'],\n", - " 'appearance': ['▁', 'appear', 'ance'],\n", - " 'appeared': ['▁', 'appear', 'ed'],\n", - " 'appears': ['▁', 'appear', 's'],\n", - " 'appeasement': ['▁', 'app', 'e', 'a', 'se', 'ment'],\n", - " 'applauding': ['▁', 'app', 'la', 'ud', 'ing'],\n", - " 'appliances': ['▁', 'app', 'li', 'ance', 's'],\n", - " 'application': ['▁', 'app', 'li', 'c', 'ation'],\n", - " 'applications': ['▁', 'app', 'li', 'c', 'ation', 's'],\n", - " 'applied': ['▁', 'app', 'li', 'ed'],\n", - " 'apply': ['▁', 'app', 'ly'],\n", - " 'appointed': ['▁', 'ap', 'point', 'ed'],\n", - " 'appointment': ['▁', 'ap', 'point', 'ment'],\n", - " 'appreciable': ['▁', 'app', 're', 'c', 'i', 'able'],\n", - " 'appreciably': ['▁', 'app', 're', 'c', 'i', 'ably'],\n", - " 'appreciated': ['▁', 'app', 're', 'c', 'i', 'at', 'ed'],\n", - " 'appreciation': ['▁', 'app', 're', 'c', 'i', 'ation'],\n", - " 'apprenticeships': ['▁', 'app', 'r', 'ent', 'i', 'ce', 'ship', 's'],\n", - " 'approach': ['▁', 'ap', 'pro', 'a', 'ch'],\n", - " 'approached': ['▁', 'ap', 'pro', 'a', 'ch', 'ed'],\n", - " 'approaches': ['▁', 'ap', 'pro', 'a', 'che', 's'],\n", - " 'appropriate': ['▁', 'ap', 'pro', 'pri', 'ate'],\n", - " 'appropriated': ['▁', 'ap', 'pro', 'pri', 'at', 'ed'],\n", - " 'approval': ['▁', 'ap', 'pro', 'val'],\n", - " 'approximately': ['▁', 'ap', 'pro', 'x', 'im', 'ate', 'ly'],\n", - " 'april': ['▁', 'a', 'pri', 'l'],\n", - " 'archbishop': ['▁', 'ar', 'ch', 'b', 'is', 'hop'],\n", - " 'arches': ['▁', 'ar', 'che', 's'],\n", - " 'archipelago': ['▁', 'ar', 'ch', 'i', 'pe', 'la', 'go'],\n", - " 'architect': ['▁', 'ar', 'ch', 'it', 'e', 'c', 't'],\n", - " 'architecture': ['▁', 'ar', 'ch', 'it', 'e', 'c', 'ture'],\n", - " 'are': ['▁', 'are'],\n", - " 'area': ['▁', 'are', 'a'],\n", - " 'areas': ['▁', 'are', 'as'],\n", - " \"aren't\": ['▁', 'are', 'n', \"'\", 't'],\n", - " 'arguably': ['▁', 'ar', 'gu', 'ably'],\n", - " 'argued': ['▁', 'ar', 'gu', 'ed'],\n", - " 'argues': ['▁', 'ar', 'gu', 'es'],\n", - " 'arguing': ['▁', 'ar', 'gu', 'ing'],\n", - " 'argument': ['▁', 'ar', 'gu', 'ment'],\n", - " 'arguments': ['▁', 'ar', 'gu', 'ment', 's'],\n", - " 'arise': ['▁', 'a', 'rise'],\n", - " 'arises': ['▁', 'a', 'rise', 's'],\n", - " 'arm': ['▁', 'arm'],\n", - " 'armament': ['▁', 'arm', 'a', 'ment'],\n", - " 'armaments': ['▁', 'arm', 'a', 'ment', 's'],\n", - " 'armed': ['▁', 'arm', 'ed'],\n", - " 'armoured': ['▁', 'arm', 'our', 'ed'],\n", - " 'arms': ['▁', 'arm', 's'],\n", - " \"arms'\": ['▁', 'arm', 's', \"'\"],\n", - " 'army': ['▁', 'arm', 'y'],\n", - " 'arnold': ['▁', 'ar', 'n', 'old'],\n", - " 'arose': ['▁', 'a', 'ro', 'se'],\n", - " 'around': ['▁', 'a', 'round'],\n", - " 'aroused': ['▁', 'ar', 'ous', 'ed'],\n", - " 'arrange': ['▁', 'ar', 'range'],\n", - " 'arranged': ['▁', 'ar', 'range', 'd'],\n", - " 'arrangement': ['▁', 'ar', 'range', 'ment'],\n", - " 'arrangements': ['▁', 'ar', 'range', 'ment', 's'],\n", - " 'arranging': ['▁', 'ar', 'r', 'ang', 'ing'],\n", - " 'arrears': ['▁', 'ar', 're', 'ar', 's'],\n", - " 'arrested': ['▁', 'ar', 'rest', 'ed'],\n", - " 'arrival': ['▁', 'ar', 'r', 'i', 'val'],\n", - " 'arrive': ['▁', 'ar', 'r', 'ive'],\n", - " 'arrived': ['▁', 'arrived'],\n", - " 'arrives': ['▁', 'ar', 'r', 'ive', 's'],\n", - " 'arrogant': ['▁', 'ar', 'ro', 'g', 'ant'],\n", - " 'art': ['▁', 'ar', 't'],\n", - " 'arthur': ['▁', 'ar', 'th', 'ur'],\n", - " 'article': ['▁', 'ar', 'tic', 'le'],\n", - " 'articles': ['▁', 'ar', 'tic', 'le', 's'],\n", - " 'articulation': ['▁', 'ar', 'tic', 'ul', 'ation'],\n", - " 'artistic': ['▁', 'ar', 'tist', 'ic'],\n", - " 'artistically': ['▁', 'ar', 'tist', 'ical', 'ly'],\n", - " 'artistry': ['▁', 'ar', 'tist', 'ry'],\n", - " 'artists': ['▁', 'ar', 'tist', 's'],\n", - " 'as': ['▁', 'as'],\n", - " 'ascents': ['▁', 'as', 'cent', 's'],\n", - " 'ash': ['▁', 'as', 'h'],\n", - " 'ashen': ['▁', 'as', 'he', 'n'],\n", - " 'ask': ['▁', 'as', 'k'],\n", - " 'asked': ['▁', 'asked'],\n", - " 'asking': ['▁', 'asking'],\n", - " 'aspect': ['▁', 'a', 'spect'],\n", - " 'aspects': ['▁', 'a', 'spect', 's'],\n", - " 'aspiring': ['▁', 'as', 'p', 'i', 'r', 'ing'],\n", - " 'assault': ['▁', 'as', 's', 'a', 'ul', 't'],\n", - " 'assembler': ['▁', 'as', 'se', 'm', 'bl', 'er'],\n", - " 'assembly': ['▁', 'as', 'se', 'm', 'b', 'ly'],\n", - " 'assess': ['▁', 'as', 'se', 's', 's'],\n", - " 'assessment': ['▁', 'as', 'se', 's', 's', 'ment'],\n", - " 'assistance': ['▁', 'as', 's', 'istance'],\n", - " 'assistant': ['▁', 'as', 's', 'is', 't', 'ant'],\n", - " 'assistants': ['▁', 'as', 's', 'is', 't', 'ant', 's'],\n", - " 'associate': ['▁', 'associat', 'e'],\n", - " 'associated': ['▁', 'associat', 'ed'],\n", - " 'associates': ['▁', 'associat', 'es'],\n", - " 'association': ['▁', 'associat', 'ion'],\n", - " 'assortment': ['▁', 'as', 's', 'or', 't', 'ment'],\n", - " 'assumption': ['▁', 'assumption'],\n", - " 'assurance': ['▁', 'as', 's', 'ur', 'ance'],\n", - " 'astronaut': ['▁', 'as', 'tr', 'on', 'a', 'u', 't'],\n", - " 'astute': ['▁', 'a', 'st', 'u', 'te'],\n", - " 'at': ['▁', 'at'],\n", - " 'ately': ['▁', 'ate', 'ly'],\n", - " 'atkinson': ['▁', 'at', 'k', 'in', 's', 'on'],\n", - " 'atlantic': ['▁', 'at', 'l', 'an', 'tic'],\n", - " 'atmosphere': ['▁', 'atmospher', 'e'],\n", - " 'atmospheric': ['▁', 'atmospher', 'ic'],\n", - " 'atomic': ['▁', 'a', 'to', 'm', 'ic'],\n", - " 'atoms': ['▁', 'a', 'to', 'm', 's'],\n", - " 'attach': ['▁', 'at', 't', 'a', 'ch'],\n", - " 'attached': ['▁', 'at', 't', 'a', 'ch', 'ed'],\n", - " 'attack': ['▁', 'at', 't', 'a', 'ck'],\n", - " 'attacked': ['▁', 'at', 't', 'a', 'ck', 'ed'],\n", - " 'attacks': ['▁', 'at', 't', 'a', 'ck', 's'],\n", - " 'attainable': ['▁', 'at', 'tain', 'able'],\n", - " 'attempt': ['▁', 'attempt'],\n", - " 'attempted': ['▁', 'attempt', 'ed'],\n", - " 'attempting': ['▁', 'attempt', 'ing'],\n", - " 'attempts': ['▁', 'attempt', 's'],\n", - " 'atten-': ['▁', 'at', 'ten', '-'],\n", - " 'attend': ['▁', 'at', 't', 'end'],\n", - " 'attendance': ['▁', 'at', 't', 'end', 'ance'],\n", - " 'attended': ['▁', 'at', 't', 'end', 'ed'],\n", - " 'attending': ['▁', 'at', 't', 'end', 'ing'],\n", - " 'attention': ['▁', 'at', 'ten', 'tion'],\n", - " 'attitude': ['▁', 'at', 't', 'it', 'u', 'de'],\n", - " 'attitudes': ['▁', 'at', 't', 'it', 'ud', 'es'],\n", - " 'attracted': ['▁', 'at', 'tr', 'act', 'ed'],\n", - " 'attractive': ['▁', 'at', 'tr', 'act', 'ive'],\n", - " 'aubrey': ['▁', 'a', 'u', 'b', 're', 'y'],\n", - " 'audacity': ['▁', 'a', 'ud', 'ac', 'ity'],\n", - " 'auden': ['▁', 'a', 'ud', 'en'],\n", - " 'audience': ['▁', 'a', 'ud', 'i', 'ence'],\n", - " 'audio-tv': ['▁', 'a', 'ud', 'i', 'o', '-', 't', 'v'],\n", - " 'audited': ['▁', 'a', 'ud', 'it', 'ed'],\n", - " 'august': ['▁', 'a', 'ug', 'u', 'st'],\n", - " 'auntie': ['▁', 'a', 'un', 't', 'i', 'e'],\n", - " 'austerity': ['▁', 'a', 'u', 'ster', 'ity'],\n", - " 'australia': ['▁', 'a', 'us', 'tr', 'al', 'i', 'a'],\n", - " 'austria': ['▁', 'a', 'us', 'tri', 'a'],\n", - " 'austrian': ['▁', 'a', 'us', 'tri', 'an'],\n", - " 'authentic': ['▁', 'a', 'u', 'then', 'tic'],\n", - " 'author': ['▁', 'author'],\n", - " 'authorised': ['▁', 'author', 'is', 'ed'],\n", - " 'authorities': ['▁', 'author', 'it', 'ies'],\n", - " 'authority': ['▁', 'author', 'ity'],\n", - " 'automatically': ['▁', 'a', 'u', 'to', 'm', 'at', 'ical', 'ly'],\n", - " 'automation': ['▁', 'a', 'u', 'to', 'm', 'ation'],\n", - " 'autumn': ['▁', 'a', 'u', 't', 'um', 'n'],\n", - " 'available': ['▁', 'a', 'v', 'a', 'il', 'able'],\n", - " 'avenue': ['▁', 'a', 've', 'n', 'ue'],\n", - " 'average': ['▁', 'a', 'ver', 'age'],\n", - " 'averages': ['▁', 'a', 'ver', 'age', 's'],\n", - " 'avert': ['▁', 'a', 'ver', 't'],\n", - " 'aviation': ['▁', 'a', 'vi', 'ation'],\n", - " 'avoid': ['▁', 'a', 'v', 'o', 'id'],\n", - " 'avoided': ['▁', 'a', 'v', 'o', 'id', 'ed'],\n", - " 'avon': ['▁', 'a', 'v', 'on'],\n", - " 'awake': ['▁', 'a', 'w', 'a', 'ke'],\n", - " 'awarded': ['▁', 'a', 'ward', 'ed'],\n", - " 'awards': ['▁', 'a', 'ward', 's'],\n", - " 'aware': ['▁', 'a', 'w', 'are'],\n", - " 'awareness': ['▁', 'a', 'w', 'are', 'ness'],\n", - " 'away': ['▁', 'a', 'way'],\n", - " 'awful': ['▁', 'a', 'w', 'ful'],\n", - " 'awfully': ['▁', 'a', 'w', 'ful', 'ly'],\n", - " 'b': ['▁', 'b'],\n", - " 'b.': ['▁', 'b', '.'],\n", - " 'b.b.c.': ['▁', 'b', '.', 'b', '.', 'c', '.'],\n", - " 'babe': ['▁', 'b', 'a', 'be'],\n", - " 'babel': ['▁', 'b', 'a', 'be', 'l'],\n", - " 'bably': ['▁', 'b', 'ably'],\n", - " 'baby': ['▁', 'b', 'a', 'by'],\n", - " \"baby's\": ['▁', 'b', 'a', 'by', \"'\", 's'],\n", - " 'back': ['▁', 'back'],\n", - " 'backbone': ['▁', 'back', 'b', 'one'],\n", - " 'backed': ['▁', 'back', 'ed'],\n", - " 'backers': ['▁', 'back', 'ers'],\n", - " 'background': ['▁', 'back', 'ground'],\n", - " 'backing': ['▁', 'back', 'ing'],\n", - " 'backstage': ['▁', 'back', 'st', 'age'],\n", - " 'backward': ['▁', 'back', 'ward'],\n", - " 'bad': ['▁', 'b', 'ad'],\n", - " 'badly': ['▁', 'b', 'ad', 'ly'],\n", - " 'baffled': ['▁', 'b', 'a', 'f', 'f', 'led'],\n", - " 'bag': ['▁', 'b', 'a', 'g'],\n", - " 'bagaya': ['▁', 'b', 'a', 'gay', 'a'],\n", - " 'baker': ['▁', 'b', 'a', 'k', 'er'],\n", - " 'balance': ['▁', 'b', 'al', 'ance'],\n", - " 'balance-sheet': ['▁', 'b', 'al', 'ance', '-', 'she', 'e', 't'],\n", - " 'balances': ['▁', 'b', 'al', 'ance', 's'],\n", - " 'bald': ['▁', 'b', 'al', 'd'],\n", - " 'ball': ['▁', 'b', 'all'],\n", - " 'balloon': ['▁', 'b', 'all', 'o', 'on'],\n", - " 'ballyhoo': ['▁', 'b', 'al', 'ly', 'ho', 'o'],\n", - " 'baltic': ['▁', 'b', 'al', 'tic'],\n", - " 'ban': ['▁', 'b', 'an'],\n", - " 'ban-': ['▁', 'b', 'an', '-'],\n", - " 'ban-the-': ['▁', 'b', 'an', '-', 'the', '-'],\n", - " 'ban-the-bomb': ['▁', 'b', 'an', '-', 'the', '-', 'bomb'],\n", - " 'bank': ['▁', 'bank'],\n", - " \"bank's\": ['▁', 'bank', \"'\", 's'],\n", - " 'banking': ['▁', 'bank', 'ing'],\n", - " 'bankrupt': ['▁', 'bank', 'r', 'up', 't'],\n", - " 'banks': ['▁', 'bank', 's'],\n", - " \"banks'\": ['▁', 'bank', 's', \"'\"],\n", - " 'banned': ['▁', 'b', 'an', 'n', 'ed'],\n", - " 'banzie': ['▁', 'b', 'an', 'z', 'i', 'e'],\n", - " 'bar': ['▁', 'b', 'ar'],\n", - " 'barb': ['▁', 'b', 'ar', 'b'],\n", - " 'barbara': ['▁', 'b', 'ar', 'b', 'ar', 'a'],\n", - " 'barbarously': ['▁', 'b', 'ar', 'b', 'ar', 'ous', 'ly'],\n", - " 'barclay': ['▁', 'b', 'ar', 'clay'],\n", - " 'bare': ['▁', 'b', 'are'],\n", - " 'bargain': ['▁', 'b', 'ar', 'g', 'a', 'in'],\n", - " 'bargaining': ['▁', 'b', 'ar', 'g', 'a', 'in', 'ing'],\n", - " 'bark': ['▁', 'b', 'ar', 'k'],\n", - " 'barrier': ['▁', 'b', 'ar', 'r', 'i', 'er'],\n", - " 'barriers': ['▁', 'b', 'ar', 'r', 'i', 'ers'],\n", - " 'barry': ['▁', 'b', 'a', 'rry'],\n", - " 'base': ['▁', 'base'],\n", - " 'based': ['▁', 'bas', 'ed'],\n", - " 'bases': ['▁', 'base', 's'],\n", - " 'basic': ['▁', 'bas', 'ic'],\n", - " 'basin': ['▁', 'bas', 'in'],\n", - " 'basing': ['▁', 'bas', 'ing'],\n", - " 'basis': ['▁', 'bas', 'is'],\n", - " 'baskerville': ['▁', 'bas', 'k', 'er', 'v', 'il', 'le'],\n", - " 'basses': ['▁', 'bas', 'se', 's'],\n", - " 'basting': ['▁', 'bas', 't', 'ing'],\n", - " 'bathing': ['▁', 'b', 'a', 'thing'],\n", - " 'bats': ['▁', 'b', 'at', 's'],\n", - " 'batsman': ['▁', 'b', 'at', 's', 'man'],\n", - " 'battalions': ['▁', 'b', 'at', 't', 'al', 'ion', 's'],\n", - " 'batting': ['▁', 'b', 'at', 't', 'ing'],\n", - " 'battle': ['▁', 'b', 'a', 'ttle'],\n", - " 'bavaria': ['▁', 'b', 'a', 'v', 'ar', 'i', 'a'],\n", - " 'bavarian': ['▁', 'b', 'a', 'v', 'ar', 'i', 'an'],\n", - " 'bavarians': ['▁', 'b', 'a', 'v', 'ar', 'i', 'an', 's'],\n", - " 'bay': ['▁', 'b', 'a', 'y'],\n", - " 'be': ['▁', 'be'],\n", - " 'beach': ['▁', 'b', 'each'],\n", - " 'beaches': ['▁', 'b', 'each', 'es'],\n", - " 'beacon': ['▁', 'be', 'a', 'con'],\n", - " 'beaks': ['▁', 'be', 'a', 'k', 's'],\n", - " 'bean': ['▁', 'be', 'an'],\n", - " 'bear': ['▁', 'be', 'ar'],\n", - " 'bearer': ['▁', 'be', 'are', 'r'],\n", - " 'bears': ['▁', 'be', 'ar', 's'],\n", - " 'beastly': ['▁', 'b', 'east', 'ly'],\n", - " 'beasts': ['▁', 'b', 'east', 's'],\n", - " 'beaten': ['▁', 'be', 'a', 'ten'],\n", - " 'beautiful': ['▁', 'be', 'a', 'u', 't', 'i', 'ful'],\n", - " 'beautifully': ['▁', 'be', 'a', 'u', 't', 'i', 'ful', 'ly'],\n", - " 'beauty': ['▁', 'be', 'a', 'u', 'ty'],\n", - " 'became': ['▁', 'be', 'came'],\n", - " 'because': ['▁', 'because'],\n", - " 'beckoning': ['▁', 'be', 'ck', 'on', 'ing'],\n", - " 'become': ['▁', 'be', 'come'],\n", - " 'becomes': ['▁', 'be', 'come', 's'],\n", - " 'becoming': ['▁', 'be', 'com', 'ing'],\n", - " 'bed': ['▁', 'b', 'ed'],\n", - " 'bedlam': ['▁', 'b', 'ed', 'la', 'm'],\n", - " 'beds': ['▁', 'b', 'ed', 's'],\n", - " 'bedspreads': ['▁', 'b', 'ed', 's', 'p', 'read', 's'],\n", - " 'beech': ['▁', 'be', 'e', 'ch'],\n", - " 'been': ['▁', 'been'],\n", - " 'before': ['▁', 'before'],\n", - " 'befriended': ['▁', 'be', 'friend', 'ed'],\n", - " 'began': ['▁', 'be', 'g', 'an'],\n", - " 'begin': ['▁', 'be', 'g', 'in'],\n", - " 'beginner': ['▁', 'be', 'g', 'in', 'n', 'er'],\n", - " 'beginning': ['▁', 'be', 'g', 'in', 'n', 'ing'],\n", - " 'begins': ['▁', 'be', 'g', 'in', 's'],\n", - " 'begun': ['▁', 'be', 'g', 'un'],\n", - " 'behan': ['▁', 'be', 'h', 'an'],\n", - " 'behave': ['▁', 'be', 'have'],\n", - " 'behaviour': ['▁', 'be', 'h', 'a', 'vi', 'our'],\n", - " 'behind': ['▁', 'behind'],\n", - " 'beier': ['▁', 'be', 'i', 'er'],\n", - " 'being': ['▁', 'being'],\n", - " 'belgian': ['▁', 'be', 'l', 'g', 'i', 'an'],\n", - " 'belgium': ['▁', 'be', 'l', 'giu', 'm'],\n", - " 'belgrade': ['▁', 'be', 'l', 'gr', 'a', 'de'],\n", - " 'belief': ['▁', 'be', 'li', 'e', 'f'],\n", - " 'believe': ['▁', 'believe'],\n", - " 'believed': ['▁', 'believed'],\n", - " 'believes': ['▁', 'believe', 's'],\n", - " 'bell': ['▁', 'be', 'll'],\n", - " \"bell's\": ['▁', 'be', 'll', \"'\", 's'],\n", - " 'belmondo': ['▁', 'be', 'l', 'mon', 'do'],\n", - " 'belonged': ['▁', 'be', 'long', 'ed'],\n", - " 'belongs': ['▁', 'be', 'long', 's'],\n", - " 'below': ['▁', 'be', 'low'],\n", - " 'belt': ['▁', 'be', 'l', 't'],\n", - " 'ben': ['▁', 'be', 'n'],\n", - " 'bench': ['▁', 'be', 'n', 'ch'],\n", - " 'benches': ['▁', 'be', 'n', 'che', 's'],\n", - " 'bend': ['▁', 'b', 'end'],\n", - " 'bending': ['▁', 'b', 'end', 'ing'],\n", - " 'benefits': ['▁', 'be', 'ne', 'f', 'its'],\n", - " 'bent': ['▁', 'b', 'ent'],\n", - " 'ber': ['▁', 'be', 'r'],\n", - " 'berlin': ['▁', 'berlin'],\n", - " \"berlin's\": ['▁', 'berlin', \"'\", 's'],\n", - " 'bernhard': ['▁', 'be', 'r', 'n', 'hard'],\n", - " 'berry': ['▁', 'be', 'rry'],\n", - " 'bertrand': ['▁', 'bert', 'r', 'and'],\n", - " 'beset': ['▁', 'be', 'set'],\n", - " 'beside': ['▁', 'be', 'side'],\n", - " 'best': ['▁', 'best'],\n", - " 'best-seller': ['▁', 'best', '-', 's', 'ell', 'er'],\n", - " 'bet': ['▁', 'be', 't'],\n", - " 'betjeman': ['▁', 'be', 't', 'je', 'man'],\n", - " 'betrayal': ['▁', 'be', 'tr', 'a', 'y', 'al'],\n", - " 'betrayed': ['▁', 'be', 'tr', 'a', 'y', 'ed'],\n", - " 'better': ['▁', 'better'],\n", - " 'better-': ['▁', 'better', '-'],\n", - " \"betti's\": ['▁', 'be', 't', 't', 'i', \"'\", 's'],\n", - " 'between': ['▁', 'between'],\n", - " 'bevel': ['▁', 'be', 've', 'l'],\n", - " 'bevelled': ['▁', 'be', 'v', 'ell', 'ed'],\n", - " 'beware': ['▁', 'be', 'w', 'are'],\n", - " 'bewildered': ['▁', 'be', 'w', 'il', 'd', 'er', 'ed'],\n", - " 'beyond': ['▁', 'beyond'],\n", - " 'bidet': ['▁', 'b', 'i', 'de', 't'],\n", - " 'big': ['▁', 'big'],\n", - " 'bigger': ['▁', 'big', 'g', 'er'],\n", - " 'biggest': ['▁', 'big', 'g', 'est'],\n", - " 'bill': ['▁', 'b', 'ill'],\n", - " 'bills': ['▁', 'b', 'ill', 's'],\n", - " 'binding': ['▁', 'b', 'in', 'd', 'ing'],\n", - " 'biological': ['▁', 'b', 'i', 'o', 'lo', 'g', 'ical'],\n", - " 'bird': ['▁', 'b', 'i', 'r', 'd'],\n", - " 'birds': ['▁', 'b', 'i', 'r', 'd', 's'],\n", - " 'bishop': ['▁', 'b', 'is', 'hop'],\n", - " 'bit': ['▁', 'b', 'it'],\n", - " 'bite': ['▁', 'b', 'it', 'e'],\n", - " 'bits': ['▁', 'b', 'its'],\n", - " 'bitter-sweet': ['▁', 'b', 'it', 'ter', '-', 's', 'we', 'e', 't'],\n", - " 'bitterest': ['▁', 'b', 'it', 'ter', 'est'],\n", - " 'bitterly': ['▁', 'b', 'it', 'ter', 'ly'],\n", - " 'bituminized': ['▁', 'b', 'it', 'um', 'in', 'i', 'z', 'ed'],\n", - " 'black': ['▁', 'bl', 'a', 'ck'],\n", - " 'black-': ['▁', 'bl', 'a', 'ck', '-'],\n", - " 'black-listed': ['▁', 'bl', 'a', 'ck', '-', 'li', 'st', 'ed'],\n", - " 'blackbird': ['▁', 'bl', 'a', 'ck', 'b', 'i', 'r', 'd'],\n", - " 'blacks': ['▁', 'bl', 'a', 'ck', 's'],\n", - " 'blame': ['▁', 'bl', 'a', 'me'],\n", - " 'blamed': ['▁', 'bl', 'am', 'ed'],\n", - " 'blander': ['▁', 'bl', 'and', 'er'],\n", - " 'blank': ['▁', 'bl', 'an', 'k'],\n", - " 'blend': ['▁', 'bl', 'end'],\n", - " 'blight': ['▁', 'b', 'light'],\n", - " 'blind': ['▁', 'bl', 'in', 'd'],\n", - " 'blinked': ['▁', 'bl', 'in', 'k', 'ed'],\n", - " 'block': ['▁', 'block'],\n", - " 'blocks': ['▁', 'block', 's'],\n", - " 'bloem-': ['▁', 'b', 'lo', 'e', 'm', '-'],\n", - " 'blond': ['▁', 'bl', 'on', 'd'],\n", - " 'blood': ['▁', 'b', 'lo', 'od'],\n", - " 'bloodstained': ['▁', 'b', 'lo', 'od', 's', 'tain', 'ed'],\n", - " 'bloody': ['▁', 'b', 'lo', 'od', 'y'],\n", - " 'blouse': ['▁', 'b', 'lo', 'use'],\n", - " 'blouses': ['▁', 'bl', 'ous', 'es'],\n", - " 'blow': ['▁', 'b', 'low'],\n", - " 'blowflies': ['▁', 'b', 'low', 'f', 'l', 'ies'],\n", - " 'blown': ['▁', 'bl', 'own'],\n", - " 'blue': ['▁', 'bl', 'ue'],\n", - " 'blunt': ['▁', 'bl', 'un', 't'],\n", - " 'bluntly': ['▁', 'bl', 'un', 't', 'ly'],\n", - " 'bluster': ['▁', 'bl', 'u', 'ster'],\n", - " 'board': ['▁', 'board'],\n", - " 'boat': ['▁', 'bo', 'at'],\n", - " 'boat-train': ['▁', 'bo', 'at', '-', 'train'],\n", - " 'bobby': ['▁', 'bo', 'b', 'by'],\n", - " 'bodies': ['▁', 'bo', 'd', 'ies'],\n", - " 'body': ['▁', 'body'],\n", - " 'boeing': ['▁', 'bo', 'e', 'ing'],\n", - " 'bogy': ['▁', 'bo', 'g', 'y'],\n", - " 'boiled': ['▁', 'bo', 'il', 'ed'],\n", - " 'boils': ['▁', 'bo', 'il', 's'],\n", - " 'bold': ['▁', 'b', 'old'],\n", - " 'boldly': ['▁', 'b', 'old', 'ly'],\n", - " 'bolt': ['▁', 'bo', 'l', 't'],\n", - " 'bolted': ['▁', 'bo', 'l', 'ted'],\n", - " 'bomb': ['▁', 'bomb'],\n", - " 'bombay': ['▁', 'bomb', 'a', 'y'],\n", - " 'bombed': ['▁', 'bomb', 'ed'],\n", - " 'bombers': ['▁', 'bomb', 'ers'],\n", - " 'bonded': ['▁', 'b', 'on', 'd', 'ed'],\n", - " 'bone': ['▁', 'b', 'one'],\n", - " 'bones': ['▁', 'b', 'one', 's'],\n", - " 'bonn': ['▁', 'b', 'on', 'n'],\n", - " \"bonn's\": ['▁', 'b', 'on', 'n', \"'\", 's'],\n", - " 'book': ['▁', 'book'],\n", - " 'booklet': ['▁', 'book', 'le', 't'],\n", - " 'books': ['▁', 'book', 's'],\n", - " 'booming': ['▁', 'bo', 'o', 'm', 'ing'],\n", - " 'border': ['▁', 'b', 'order'],\n", - " 'bore': ['▁', 'bo', 're'],\n", - " 'bored': ['▁', 'b', 'or', 'ed'],\n", - " 'boredom': ['▁', 'bo', 're', 'do', 'm'],\n", - " 'bores': ['▁', 'bo', 're', 's'],\n", - " 'born': ['▁', 'b', 'or', 'n'],\n", - " 'borough': ['▁', 'bo', 'rough'],\n", - " 'borrow': ['▁', 'b', 'or', 'ro', 'w'],\n", - " 'borstal': ['▁', 'b', 'or', 'st', 'al'],\n", - " 'bosoms': ['▁', 'bo', 'so', 'm', 's'],\n", - " 'bossed': ['▁', 'bo', 's', 's', 'ed'],\n", - " 'bosses': ['▁', 'bo', 's', 'se', 's'],\n", - " 'both': ['▁', 'both'],\n", - " 'bottle': ['▁', 'bo', 'ttle'],\n", - " 'bottom': ['▁', 'bo', 't', 'to', 'm'],\n", - " 'bought': ['▁', 'bo', 'ug', 'h', 't'],\n", - " 'boun': ['▁', 'bo', 'un'],\n", - " 'bound': ['▁', 'b', 'ound'],\n", - " 'boutiques': ['▁', 'b', 'out', 'i', 'q', 'ue', 's'],\n", - " 'bow': ['▁', 'bo', 'w'],\n", - " 'bow-street': ['▁', 'bo', 'w', '-', 'st', 're', 'e', 't'],\n", - " 'bowed': ['▁', 'bo', 'w', 'ed'],\n", - " 'bowing': ['▁', 'bo', 'w', 'ing'],\n", - " 'bows': ['▁', 'bo', 'w', 's'],\n", - " 'box': ['▁', 'bo', 'x'],\n", - " 'boxes': ['▁', 'bo', 'x', 'es'],\n", - " 'boxing': ['▁', 'bo', 'x', 'ing'],\n", - " 'boy': ['▁', 'bo', 'y'],\n", - " 'boycotted': ['▁', 'bo', 'y', 'cott', 'ed'],\n", - " 'boycotting': ['▁', 'bo', 'y', 'cott', 'ing'],\n", - " 'boyd-orr': ['▁', 'bo', 'y', 'd', '-', 'or', 'r'],\n", - " 'boyle': ['▁', 'bo', 'y', 'le'],\n", - " 'boys': ['▁', 'bo', 'y', 's'],\n", - " 'braces': ['▁', 'br', 'a', 'ce', 's'],\n", - " 'brain': ['▁', 'b', 'rain'],\n", - " 'brain-activity': ['▁', 'b', 'rain', '-', 'act', 'i', 'v', 'ity'],\n", - " 'brain-children': ['▁', 'b', 'rain', '-', 'children'],\n", - " 'brains': ['▁', 'b', 'rain', 's'],\n", - " 'brandy': ['▁', 'br', 'and', 'y'],\n", - " 'brash': ['▁', 'br', 'as', 'h'],\n", - " 'brass': ['▁', 'br', 'as', 's'],\n", - " 'brauchitsch': ['▁', 'br', 'a', 'u', 'ch', 'its', 'ch'],\n", - " 'breach': ['▁', 'br', 'each'],\n", - " 'bread-and-butter': ['▁', 'b', 'read', '-', 'and', '-', 'but', 'ter'],\n", - " 'break': ['▁', 'b', 're', 'a', 'k'],\n", - " 'breaking': ['▁', 'b', 're', 'a', 'k', 'ing'],\n", - " 'breaks': ['▁', 'b', 're', 'a', 'k', 's'],\n", - " 'breath': ['▁', 'b', 're', 'a', 'th'],\n", - " 'breathing': ['▁', 'b', 're', 'a', 'thing'],\n", - " 'breathless': ['▁', 'b', 're', 'a', 'th', 'less'],\n", - " 'breeding': ['▁', 'b', 're', 'ed', 'ing'],\n", - " 'breezily': ['▁', 'b', 're', 'e', 'z', 'i', 'ly'],\n", - " 'brehm': ['▁', 'b', 're', 'h', 'm'],\n", - " 'brella': ['▁', 'br', 'ell', 'a'],\n", - " 'brenda': ['▁', 'br', 'end', 'a'],\n", - " 'brendan': ['▁', 'br', 'end', 'an'],\n", - " \"brendan's\": ['▁', 'br', 'end', 'an', \"'\", 's'],\n", - " 'brentano': ['▁', 'br', 'ent', 'a', 'no'],\n", - " 'brezhnev': ['▁', 'b', 're', 'z', 'h', 'ne', 'v'],\n", - " 'brian': ['▁', 'br', 'i', 'an'],\n", - " 'bridal': ['▁', 'br', 'id', 'al'],\n", - " 'bride': ['▁', 'br', 'i', 'de'],\n", - " 'brief': ['▁', 'brief'],\n", - " 'brief-': ['▁', 'brief', '-'],\n", - " 'briefcase': ['▁', 'brief', 'case'],\n", - " 'briefing': ['▁', 'brief', 'ing'],\n", - " 'brigadiers': ['▁', 'br', 'i', 'g', 'ad', 'i', 'ers'],\n", - " 'bright': ['▁', 'b', 'right'],\n", - " 'brighter': ['▁', 'b', 'right', 'er'],\n", - " 'brightly': ['▁', 'b', 'right', 'ly'],\n", - " \"brighton's\": ['▁', 'b', 'right', 'on', \"'\", 's'],\n", - " 'brilliant': ['▁', 'br', 'ill', 'i', 'ant'],\n", - " 'brilliantly': ['▁', 'br', 'ill', 'i', 'ant', 'ly'],\n", - " 'bring': ['▁', 'br', 'ing'],\n", - " 'brings': ['▁', 'br', 'ing', 's'],\n", - " 'bristled': ['▁', 'br', 'is', 't', 'led'],\n", - " 'bristol': ['▁', 'br', 'is', 'to', 'l'],\n", - " 'britain': ['▁', 'britain'],\n", - " \"britain's\": ['▁', 'britain', \"'\", 's'],\n", - " 'british': ['▁', 'british'],\n", - " 'british-owned': ['▁', 'british', '-', 'own', 'ed'],\n", - " 'britishers': ['▁', 'british', 'ers'],\n", - " 'brittle': ['▁', 'br', 'i', 'ttle'],\n", - " 'broad': ['▁', 'b', 'ro', 'ad'],\n", - " 'broadcast': ['▁', 'b', 'ro', 'ad', 'c', 'a', 'st'],\n", - " 'broadcasting': ['▁', 'b', 'ro', 'ad', 'c', 'a', 'st', 'ing'],\n", - " 'broke': ['▁', 'b', 'ro', 'ke'],\n", - " 'broken': ['▁', 'b', 'ro', 'k', 'en'],\n", - " 'bronx': ['▁', 'br', 'on', 'x'],\n", - " \"brook's\": ['▁', 'b', 'ro', 'o', 'k', \"'\", 's'],\n", - " 'brother': ['▁', 'brother'],\n", - " 'brother-': ['▁', 'brother', '-'],\n", - " 'brother-in-law': ['▁', 'brother', '-', 'in', '-', 'law'],\n", - " 'brought': ['▁', 'brought'],\n", - " 'brown': ['▁', 'brown'],\n", - " \"brown's\": ['▁', 'brown', \"'\", 's'],\n", - " 'bru\"cke': ['▁', 'br', 'u', '\"', 'ck', 'e'],\n", - " 'bruce': ['▁', 'br', 'u', 'ce'],\n", - " 'bruno': ['▁', 'br', 'un', 'o'],\n", - " 'brunswick': ['▁', 'br', 'un', 's', 'w', 'i', 'ck'],\n", - " 'brussels': ['▁', 'br', 'us', 's', 'el', 's'],\n", - " 'brutal': ['▁', 'br', 'u', 't', 'al'],\n", - " 'bryan': ['▁', 'br', 'y', 'an'],\n", - " 'bu\"ckerei': ['▁', 'b', 'u', '\"', 'ck', 'e', 're', 'i'],\n", - " 'buck': ['▁', 'b', 'u', 'ck'],\n", - " 'buckingham': ['▁', 'b', 'u', 'ck', 'ing', 'h', 'am'],\n", - " 'buckley': ['▁', 'b', 'u', 'ck', 'le', 'y'],\n", - " 'budge': ['▁', 'b', 'ud', 'g', 'e'],\n", - " 'budgerigar': ['▁', 'b', 'ud', 'g', 'er', 'i', 'g', 'ar'],\n", - " 'budget': ['▁', 'budget'],\n", - " 'budgetary': ['▁', 'budget', 'ary'],\n", - " 'budgette': ['▁', 'budget', 'te'],\n", - " 'buganda': ['▁', 'b', 'ug', 'and', 'a'],\n", - " 'build': ['▁', 'b', 'u', 'il', 'd'],\n", - " 'building': ['▁', 'building'],\n", - " ...}" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lex" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/notebooks/07-try-gtn.ipynb b/src/notebooks/07-try-gtn.ipynb deleted file mode 100644 index 4ef444b..0000000 --- a/src/notebooks/07-try-gtn.ipynb +++ /dev/null @@ -1,202 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import gtn\n", - "from IPython.display import display, Image" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Make some graphs:\n", - "g1 = gtn.Graph()\n", - "g1.add_node(True) # Add a start node\n", - "g1.add_node() # Add an internal node\n", - "g1.add_node(False, True) # Add an accepting node\n", - "\n", - "\n", - "# Add arcs with (src node, dst node, label):\n", - "g1.add_arc(0, 1, 1)\n", - "g1.add_arc(0, 1, 2)\n", - "g1.add_arc(1, 2, 1)\n", - "g1.add_arc(1, 2, 0)\n", - "\n", - "\n", - "g2 = gtn.Graph()\n", - "g2.add_node(True, True)\n", - "g2.add_arc(0, 0, 1)\n", - "g2.add_arc(0, 0, 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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Pz+XLl5csWWJqahoZGZmbmwvjfnw+HwBgbm6OXd/Y2KixeX19ffGlPQAAUql0xowZGisPIw54C5uZmbW1teGveaU+c2dnJwBAs84N/Df+iz/rwNTUFABQW1urgTRDQ0P4w6CV9sYCsBK9VCo9c+ZMTk4Og8EIDQ11dXUFAGBb40cdQ0ND+C8e+/T09AAARCLRyZMnMzIyzM3Nly5dCl0Lb8/Ozk6izFtYWCiVSv/5z39qLAE+sXgL92Fw/OASrhFpvFRPbFYBtPjYZAj1z7SbgmMAIuZvP//88+guAxJCZmYm9heRyWRCdtbBvTxa5gDBThhfRiU0NDQiIgJ/zSv9G5VK7erq0qCkJMTFxeXatWsCgQDrpmFnOn78eA2kdXd3wx84HM7atWs1U2mYWL169QC/RVFUJpOZmpr6+PicO3cuKipqxBQbGIlEQqPRjh07NtqKvEJPT8+yZcsGuIBCofT09FhaWvr6+p46dWrBggXYr2g0GvacaMPOnTsDAwMjIiK0EQIDP/hKlRKJRGW0+Iq/MZnMrq4ujYccWFYB5m/aZBV0dHTAH958882wsDDNVBomPvzww96NKIrK5XJ9ff3Q0NAlS5YEBwffvHnz3LlzMFw+8kr2prOz09DQcKwZs7u7u09/g24GR+YrVqwICAi4fv36qVOnhEIhVnaAwWBoP904e/YsnU7fsmWLlnKg46gMd/HRHaASL4EzUXwazpCIjIw0NjbOy8vDWq5evUqlUpcuXaqBNEwNMzMzzfQZGchkMplMRlF09uzZx44de/bsWUZGxvz58ykUCswCbWlpGW0d/5+WlpaxX9OaTCaTSCQqlfree++dOXOGz+enp6fPmjWLRCLBCZJAIMAuZrFYWpr38uXLjY2NeGeDuaYaADWxsLDAWtrb21Wml6/0b05OTo8ePaqtrZVKpVQqdaj3MzEx2bp1a2pq6kcffcRgMDo7O3/88ceEhAR8xFJ9sGWAsRNPx4MgCIlEUiqVM2fOjIyMDA0NxQI8GI6OjiiKVlVVaXCwAx44De7vcED14fF4Y9OYAAAEQaBJ586du3z58vnz5/eeJMPIeW1tLbZWxGazVQLuQ+LKlStJSUkLFy48cOAAAECpVNbU1NDpdM3qw/N4PBRFsbi6Uqmsra1Vifa/4m+urq6nT5+WyWSPHj2CW5iGyubNm83NzWNiYiZOnFhVVRUfH9/n0EsdsBTKMfiIoCjq5eW1YsWK8PDwAXoMKpVqZ2dXUVGxePFije+Vn5+flZUFAKirq/v666+DgoLwhXeHxIMHD7AyGWMKMpns5+e3YsWKhQsXGhsb93eZsbExi8Xi8Xhz5syBLa6urjk5OZrdtKio6L333hOLxVevXsUaEQSBmZAa8ODBAwcHB6yjampq6mP1FR88OX36NGwclZ3IKsCC70wmc7S2aQ6AOplckKioKH9//+HURV3g4qrGmXrDh1wuV6lYPAD+/v4ffvgh9vHChQsIgjQ3Nw+PakPD19cXrxssSqJS3/GV+ds777wD48Uwt2UUefHiBdxB5O/vT8AhrkSjfsQ1ICDg1q1bY2EJkcvlUqlUX1/f0VZEFRKJhC9uPzDTp0+/du0a9tHPzw9FUS6XOzyqDYGOjo7i4mIOh4O15Ofnu7i44FfngUq8xNjYGP4/rly5Mrqz/OzsbDhdCQkJGUU1tAdW8jh16tRoKwKysrKCgoIIPPp9VOBwODweD0s5YjAYs2bN+uWXX0ZXKwBATk4OgiBBQUFYS15eXkBAgOp1Kn3i4cOHYXtKSspI9MH9AGtRUqlU9Q/f6Y+BN1yMQH3S0NDQgIAAoqRpxtOnT3vnzmrGwFYabnuKRCJ9ff1Dhw5hLVlZWSiKjvqQksPhLFq0CPvY0tJCJpN7n/2k6m8CgQCe6Th+/Hh8pshIgo1mFy9erKWoQTdcjEB9UjjHKCsrI0SaZmzZssXS0lL7wtWDWmkE7BkREYHfxykWi1ksFoGlbjQAbsO7ePEi1rJ3715DQ8PehZb7qM+1adMm+LgTkog0VBQKxfTp06ECJSUl2ohSZ8PFCNQnVSgUnp6e2r87NObFixdMJlP7TdDqWGkE7Hn+/HkEQZ48eYK17Nq1y8jISP1DDwlnwYIFXl5e+BYPD48+y9v14W9Pnz6FXYGJiQmxp+GoA1ZAZd68eVqKunHjhkppawsLC0NDQ+zjiNUnPX36NIIgBQUFhEgbKrDOfkdHh5ZyBrXSyNizp6dn4sSJn3zyCdbS3t7OYrFiY2O1F64BV69eRRAEf5AATPnoXSxI2V99ZSxLOjw8fLjU7AssAYJKpT569Ihw+UZGRiEhIdjHEatPqlQqQ0JC3NzcJBLJCC9v3L59m0QinThxQntRg1ppxOy5f/9+PT09vPxjx46RyWQtB0QaIJFIXF1dVeqOBQYG9jdj79vfJBIJtoSPn5sOK3K5HNurumHDBsLl37x5U19fH5tHjUx90qamJi6Xu3///mXLlpHJZCsrK/XXmrSnvb3d0dHRycmJkLHWwFYayfq5YrHYyspq/fr1WItCoQgMDGSz2dp340MiNjbW0NAQXw8Kdm79nf7b73kd8CQ7AICenl7v0hTDwYYNG6CzoShqbm6emppKYM3G3hsuCK9PqlAoamtrL1y4sHv37ujo6KlTp8LIE/yLAAB0Oh1BEEKChOogl8sXLVpkbm5ubm5uYWGhvT0HttKI1XuFpKWloSiKL3/09OlTFosVHh4+HKcU9snPP/+s8g/t6emZNGnSnDlz+vvKQOe/fffdd/BxMTIyGu7wGhzxw3vdu3cvMTGRRqO5urriYz7asH37dpX6UITUJ21ra9u1a1dkZOTkyZOxjRg0Gk0l+5REIunr65eUlKxfv55Gow18JB9RrFu3jkaj5efnd3Z2EmLPga00fPVeBQJBZWXl9evXc3Jyvv/++8TExJiYmPfff5/BYOjr6+NzoWB+/HAMjnpz5coVGo0WFxeHb0xJSdHX18fHclQY5HzTjRs3wieGwWAMUyqQQqFITEyEd6FSqdhd/vzzz+DgYABAWFjY48ePtblFbm5uUlISvkUqlZqZmWFz3G+//RYAgH+n3LlzR535vUKh8PDwQBBEdVkTB4IgKIrCv0suly9ZsoTJZA6ryykUivj4eBKJBEd3EO3tOYCViLKnUqn85ptvYBWgSZMmWVpaYsfrYk+Inp4e9jpDUVTlCN/s7GwSibRly5ZhPbvjypUrTCYzIiIC35eWlJTQaDR8VLY3g/ibXC6HpY7hwDI1NZUYff9Le3t7eHg4Zsrex77l5uY6OzujKLpq1SrNqqZeunTphx9+wLcUFhYSWJ80Pz9/AGeD/nb8+HHs+u7u7iVLltBotGEaWEokkpUrV1IolD5jJNrYcwArEWjPlJSUge2JWTU1NRUe4asyWUpPT6dQKFFRUcN01F5WVhaNRouIiMDLb2tr6324cW8G8TelUimXy9evX4/9neHh4USdH3nt2jVsNx6Dwfjjjz/6U+DkyZNOTk4UCiUyMnKAzro3XC43ICDg+/+yf//+uLi4hIQEYuuTBgUFqbyG8fRO/oYmRRBk3bp1xCYVVFdXe3p6GhoaXrhwob9rtLFnf1Yi0J7t7e0Db8+F23aOHj0Krw8LCzM1NX348CFeyPnz55lMppeX15D+ukGRSCSxsbEAgLi4OLxfSSSSgICACRMmDLp+Nri/Qb777jsajQb/YGNj42+//VabZIW6urrIyEhsGObg4DDo/FAqlaalpdnY2NBotNjYWHXezYWFhb3TBREEqaio0LLeqwr379/vc0hJIpHwBXdVyMnJMTY2ZrPZhAzUe3p69u3bx2QyPTw81BkuamBPSG8raV8/V4WNGzf29/6Cg/PffvsNu1gsFvv6+lpbW9fX1+OF1NbWent7GxgYJCYmEtLR5efnu7m5MZlMldw0uVweHh4O4w6DClHX35RKZUlJCX4zz7hx45KTk4ca3b59+3ZUVBTemkuXLlX/CNXu7u4ffvjB2tqaTCYvWLCAy+WOyhF7GEKhMDU11d3dnUQiqTwiKIouW7ZsYPVqamreffddBEEWLlx4584dzXTo6ek5ceKEi4uLvr7+zp07h9RhjjV7QhoaGvqsa0QikWg0Wu+um8/nu7u729nZ8Xg8fHtXV9eOHTv09PRcXV0zMjI0Ds+WlpbCoinz589XeTFJJJLw8HD1Y/hD8Dco/csvv8R3GmQyOSgoaPfu3Xfu3OnvLcLn8y9cuBAfH6+y947NZvc3hhwYuVx+5syZWbNmIQji5OSUlJQE8/RGkurq6i1btpiZmdFotLCwsNOnT2P9PwCAQqEEBwerubSdm5sL87M5HM6xY8fUX0F6/PhxYmKinZ0diqJDHRniGQv2hNTX1+/bt8/X19fExETl/UUmk/X09PAHYuB5/vy5t7e3hYVF74Opqqurly9fjqKonZ3djh078AeRDkx7e/vRo0fhFhsvL6/edb7b2to4HI6RkZH6C2ZD8zdIS0tLfHx877p/ZDLZwcHhnXfemTdvXlhY2KxZs95++218OQe8px09ehSbXmvM3bt3o6OjDQwMjIyM1q5de/369eF+PQuFwszMzLlz55JIJBsbm6SkJGxD4ebNm+E6G4qinp6e/RXl7o+LFy8uXLiQRqNRKBQ/P7/t27f/8ssv5eXlLS0tUBSfz6+vry8oKEhLS1uzZg2c+o4bN27jxo1EzVJG3p6Qhw8fpqSk+Pr6Ighibm6+Zs2aH3/8UWWwwGAw+syQwujs7AwODqbRaAcOHOj92+rq6ri4OHh0LpvNXrNmTVpa2rVr1+rr62EygFAobGlpKSsry87OTkhI8PPzo1AoNBpt0aJFfS6ilJSU2Nvbjx8/Xp1hJIYm/gZpb28/cuTIkPaDGhsbr1y5ksvlErsi+fLly6+//trd3R0AYGNjEx8fX1BQQOz5hm1tbdnZ2REREXQ6nUKhzJs379SpUyq34PP5hoaGsIvQeBvRy5cvMzIyVq9eDaOIfZqRwWD4+Phs27btypUrw3GM4wjYU6lUPnv27PTp0zExMbAssYWFxcqVK8+dO4e9heFeUuhsJiYmKgdf9IlMJtuxYweZTF68eHGfMx2ZTMblcrdu3erj44NlI6iAoqizs3N0dHRmZmafeTlSqTQlJYVGo82ePXuoCcaIUqlU01v6o6Oj49q1a4WFhTwej8fjPX/+XCQSiUQiExMTOp0+YcIEFxcXNzc3f3//t956a1jrjVZUVGRlZeXk5FRVVZmams6ZMycgIIDD4Tg4OGggTSaTFRcX5+fnc7nc69evAwBmzJgRFhYWFhamsmkXY8+ePXv37r116xYs5K4lUqm0pqamtbUV2tPY2JjBYFhbW2tWf0kDVOwZFBQ0bdo0Ly8vT0/PIVW8hbS3t/N4vLt37xYVFRUVFfF4PARBPDw85s2bFxIS4u3tTSK9svv57Nmz8+fPR1HUzMysoKBA/TI2eXl5q1ev5vP5O3fujImJGSB03NjYCM94EAgEdDqdwWCwWCwHB4cBvpKfnx8bG1tTU/P5559v3rxZRedBIcDfxiBVVVVnz569ePHizZs3xWKxtbW1n5/f1KlTp06dOmnSpN6FtDAaGxvLyspKSkqKi4sLCwuFQuH48eMDAgJCQkLmzJmDL9XeJ93d3XV1dWOwwJGWQHtyudySkpIXL16gKOru7u7s7Dxu3LgJEyZYWVlZWlriI7Ryufzly5cvXrx4+fLls2fPHj9+XFlZCQuo0Ol0b29vX19fHx8fHx+fAUyqVCrZbLZUKi0oKIB9oPp0dXUlJycnJSVZWVnFxcV99NFHGrwgVLhx40ZycvLZs2cDAwO///57FxcXDYS8nv6GIZVKi4uL8/Lybt26VVJS8vz5cwAAi8VydHQ0Nzen0+l0Or29vb2zs/Pp06fV1dVisRhBEDabDZ8JDoeD5W3rgNTU1JSUlJSWlj558qSlpaWxsbG1tbV3TW6YBAtTNx0cHJydnV1dXV1dXW1tbdXvE86fPxVLNpIAAAE3SURBVD9lyhTN6nMDAOrq6lJSUn766SdjY+MPPvhgxYoVHh4eQxXS0tKSnZ2dnp5eXl7O4XASEhL6qJKgNq+5v6lQV1f36NGjx48f19TU8Pl8oVCIjdOsrKwcHR0dHR0nT56s8Yklf1lUToFBEGSAsnYjTHNzc1paWkZGxuPHj+3t7TkcDofD8fT0xNeuU6GhoeHhw4cFBQV5eXmlpaUMBmPx4sXR0dHTpk3TUpm/lr/p+CtTVFR08eLFq1evFhcXS6VSFEVtbW3NzMyYTKaxsbFIJIITuZqaGpFIBABwdnYOCAgIDAycN2+e9sNRiM7fdPzl6Orq4vF4VVVVPB5PIBAIhcK2tjY6nc5kMg0NDW1tbV1cXHqXsiMEnb/p0DFyDC2aqUOHDm3Q+ZsOHSOHzt906Bg5/g+1f34NLPiPzwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "<IPython.core.display.Image object>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<IPython.core.display.Image object>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gtn.draw(g1, \"g1.png\")\n", - "gtn.draw(g2, \"g2.png\")\n", - "display(Image(\"g1.png\"), Image(\"g2.png\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<IPython.core.display.Image object>" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "intersect = gtn.intersect(g1, g2)\n", - "gtn.draw(intersect, \"intersect.png\")\n", - "Image(\"intersect.png\")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.0, 0.0, 1.0, 0.0]\n" - ] - } - ], - "source": [ - "score = gtn.viterbi_score(intersect)\n", - "gtn.backward(score)\n", - "\n", - "# print gradients of arc weights \n", - "print(g1.grad().weights_to_list()) " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1.0, 0.0, 0.5, 0.5]\n" - ] - } - ], - "source": [ - "import gtn\n", - "\n", - "# Make some graphs:\n", - "g1 = gtn.Graph()\n", - "g1.add_node(True) # Add a start node\n", - "g1.add_node() # Add an internal node\n", - "g1.add_node(False, True) # Add an accepting node\n", - "\n", - "# Add arcs with (src node, dst node, label):\n", - "g1.add_arc(0, 1, 1)\n", - "g1.add_arc(0, 1, 2)\n", - "g1.add_arc(1, 2, 1)\n", - "g1.add_arc(1, 2, 0)\n", - "\n", - "g2 = gtn.Graph()\n", - "g2.add_node(True, True)\n", - "g2.add_arc(0, 0, 1)\n", - "g2.add_arc(0, 0, 0)\n", - "\n", - "# Compute a function of the graphs:\n", - "intersection = gtn.intersect(g1, g2)\n", - "score = gtn.forward_score(intersection)\n", - "\n", - "# Visualize the intersected graph:\n", - "gtn.draw(intersection, \"intersection.pdf\")\n", - "\n", - "# Backprop:\n", - "gtn.backward(score)\n", - "\n", - "# Print gradients of arc weights \n", - "print(g1.grad().weights_to_list()) # [1.0, 0.0, 1.0, 0.0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/notebooks/Untitled.ipynb b/src/notebooks/Untitled.ipynb deleted file mode 100644 index 841a37d..0000000 --- a/src/notebooks/Untitled.ipynb +++ /dev/null @@ -1,385 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "from PIL import Image\n", - "import torch\n", - "from torch import nn\n", - "\n", - "from importlib.util import find_spec\n", - "if find_spec(\"text_recognizer\") is None:\n", - " import sys\n", - " sys.path.append('..')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.datasets import IamLinesDataset" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "transform = [{\"type\": \"ToPILImage\", \"args\": None}, \n", - " #{\"type\": \"RandomResizeCrop\", \"args\": None}, \n", - " {\"type\": \"RandomRotation\", \"args\": {\"degrees\": 0.8, \"fill\": 0}}, \n", - " {\"type\": \"ColorJitter\", \"args\": {\"brightness\": 0.5, \"contrast\": 0.5, \"saturation\": 0.5, \"hue\": 0.5}}, \n", - " {\"type\": \"ToTensor\", \"args\": None}, \n", - " {\"type\": \"Normalize\", \"args\": {\"mean\": [0.912], \"std\": 0.168}},\n", - " #{\"type\": \"RandomAffine\", \"args\": {\"degrees\": [-0.25, 0.25], \"scale\": [0.98, 1.0]}}\n", - " ]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "target_transforms = [\n", - " {\"type\": \"ToLower\", \"args\": None},\n", - " {\"type\": \"ToCharcters\", \"args\": {\"pad_token\": \"_\", \"eos_token\": \"</s>\"}},\n", - " {\"type\": \"ToWordPieces\", \"args\": {\n", - " \"num_features\": 64, \n", - " \"tokens\": \"iamdb_1kwp_tokens_1000.txt\", \n", - " \"lexicon\": \"iamdb_1kwp_lex_1000.txt\",\n", - " \"use_words\": False,\n", - " \"prepend_wordsep\": False,\n", - " }\n", - " }\n", - " \n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.datasets.transforms import ToText" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-02-24 21:43:47.687 | DEBUG | text_recognizer.datasets.transforms:__init__:201 - Using data dir: /home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/data/raw/iam/iamdb\n" - ] - } - ], - "source": [ - "to_text = ToText(\n", - " num_features= 64, \n", - " tokens=\"iamdb_1kwp_tokens_1000.txt\", \n", - " lexicon=\"iamdb_1kwp_lex_1000.txt\",\n", - " use_words=False,\n", - " prepend_wordsep= False,)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2021-02-24 21:42:02.700 | DEBUG | text_recognizer.datasets.transforms:__init__:201 - Using data dir: /home/akternurra/Documents/projects/quest-for-general-artifical-intelligence/projects/text-recognizer/data/raw/iam/iamdb\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "IAM Lines Dataset\n", - "Number classes: 54\n", - "Mapping: {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'a', 11: 'b', 12: 'c', 13: 'd', 14: 'e', 15: 'f', 16: 'g', 17: 'h', 18: 'i', 19: 'j', 20: 'k', 21: 'l', 22: 'm', 23: 'n', 24: 'o', 25: 'p', 26: 'q', 27: 'r', 28: 's', 29: 't', 30: 'u', 31: 'v', 32: 'w', 33: 'x', 34: 'y', 35: 'z', 36: ' ', 37: '!', 38: '\"', 39: '#', 40: '&', 41: \"'\", 42: '(', 43: ')', 44: '*', 45: '+', 46: ',', 47: '-', 48: '.', 49: '/', 50: ':', 51: ';', 52: '?', 53: '_'}\n", - "Data: (1861, 28, 952)\n", - "Targets: (1861, 97)\n", - "\n" - ] - } - ], - "source": [ - "dataset = IamLinesDataset(train=False, pad_token=\"_\", transform=transform, target_transform=target_transforms, lower=True)\n", - "dataset.load_or_generate_data()\n", - "print(dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "but▁since▁starting▁salaries▁would▁depend▁on▁grade▁a\n", - "or▁b▁in▁the▁finals▁next▁may,▁and▁since▁mating\n", - "prospects▁would▁depend▁upon▁salaries,▁scholarship▁for\n", - "these▁fine▁young▁people▁was▁closely▁geared▁to\n", - "economic▁and▁biological▁ends▁which,▁essentially,\n", - "were▁really▁means.▁so,▁seeing▁them▁revolve▁in\n", - "circles,▁harry▁had▁the▁feeling▁that▁moke▁(or▁what\n", - "moke▁consciously▁or▁unconsciously▁symbolised,▁any-\n", - "way▁in▁harry's▁mind)▁had▁these▁splendid▁young\n", - "people▁by▁the▁short▁hairs,▁and▁was▁diverting▁them▁...\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 1440x1440 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "for i in range(190, 200):\n", - " plt.figure(figsize=(20, 20))\n", - " plt.xticks([])\n", - " plt.yticks([])\n", - " data, target = dataset[i]\n", - "# print(target)\n", - " print(to_text(target))\n", - "# target = [x - 26 if x > 35 else x for x in target]\n", - "# sentence = convert_y_label_to_string(target, dataset) \n", - "# print(target)\n", - "# plt.title(sentence)\n", - " plt.imshow(data.squeeze(0).numpy(), cmap='gray')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "target.tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dataset.target_transform" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from text_recognizer.networks.transducer import load_transducer_loss, Transducer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t, i =load_transducer_loss(64, \n", - " 0,\n", - " \"iamdb_1kwp_tokens_1000.txt\", \n", - " \"iamdb_1kwp_lex_1000.txt\",\n", - " \"1kwp_prune_0_0_optblank.bin\",\n", - " \"optional\",\n", - " False,\n", - " False,\n", - " False,\n", - " None,\n", - " \"mean\"\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "t(target, target)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "target.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/notebooks/g1.png b/src/notebooks/g1.png Binary files differdeleted file mode 100644 index 09dd49e..0000000 --- a/src/notebooks/g1.png +++ /dev/null diff --git a/src/notebooks/g2.png b/src/notebooks/g2.png Binary files differdeleted file mode 100644 index a3cf21e..0000000 --- a/src/notebooks/g2.png +++ /dev/null diff --git a/src/notebooks/intersect.png b/src/notebooks/intersect.png Binary files differdeleted file mode 100644 index 63b7f2f..0000000 --- a/src/notebooks/intersect.png +++ /dev/null diff --git a/src/notebooks/intersection.pdf b/src/notebooks/intersection.pdf Binary files differdeleted file mode 100644 index c425a9f..0000000 --- a/src/notebooks/intersection.pdf +++ /dev/null diff --git a/src/tasks/build_transitions.py b/src/tasks/build_transitions.py deleted file mode 100644 index 91f8c1a..0000000 --- a/src/tasks/build_transitions.py +++ /dev/null @@ -1,263 +0,0 @@ -"""Builds transition graph. - -Most code stolen from here: - - https://github.com/facebookresearch/gtn_applications/blob/master/scripts/build_transitions.py - -""" - -import collections -import itertools -from pathlib import Path -from typing import Any, Dict, List, Optional - -import click -import gtn -from loguru import logger - - -START_IDX = -1 -END_IDX = -2 -WORDSEP = "▁" - - -def build_graph(ngrams: List, disable_backoff: bool = False) -> gtn.Graph: - """Returns a gtn Graph based on the ngrams.""" - graph = gtn.Graph(False) - ngram = len(ngrams) - state_to_node = {} - - def get_node(state: Optional[List]) -> Any: - node = state_to_node.get(state, None) - - if node is not None: - return node - - start = state == tuple([START_IDX]) if ngram > 1 else True - end = state == tuple([END_IDX]) if ngram > 1 else True - node = graph.add_node(start, end) - state_to_node[state] = node - - if not disable_backoff and not end: - # Add back off when adding node. - for n in range(1, len(state) + 1): - backoff_node = state_to_node.get(state[n:], None) - - # Epsilon transition to the back-off state. - if backoff_node is not None: - graph.add_arc(node, backoff_node, gtn.epsilon) - break - return node - - for grams in ngrams: - for gram in grams: - istate, ostate = gram[:-1], gram[len(gram) - ngram + 1 :] - inode = get_node(istate) - - if END_IDX not in gram[1:] and gram[1:] not in state_to_node: - raise ValueError( - "Ill formed counts: if (x, y_1, ..., y_{n-1}) is above" - "the n-gram threshold, then (y_1, ..., y_{n-1}) must be" - "above the (n-1)-gram threshold" - ) - - if END_IDX in ostate: - # Merge all state having </s> into one as final graph generated - # will be similar. - ostate = tuple([END_IDX]) - - onode = get_node(ostate) - # p(gram[-1] | gram[:-1]) - graph.add_arc( - inode, onode, gtn.epsilon if gram[-1] == END_IDX else gram[-1] - ) - return graph - - -def count_ngrams(lines: List, ngram: List, tokens_to_index: Dict) -> List: - """Counts the number of ngrams.""" - counts = [collections.Counter() for _ in range(ngram)] - for line in lines: - # Prepend implicit start token. - token_line = [START_IDX] - for t in line: - token_line.append(tokens_to_index[t]) - token_line.append(END_IDX) - for n, counter in enumerate(counts): - start_offset = n == 0 - end_offset = ngram == 1 - for e in range(n + start_offset, len(token_line) - end_offset): - counter[tuple(token_line[e - n : e + 1])] += 1 - - return counts - - -def prune_ngrams(ngrams: List, prune: List) -> List: - """Prunes ngrams.""" - pruned_ngrams = [] - for n, grams in enumerate(ngrams): - grams = grams.most_common() - pruned_grams = [gram for gram, c in grams if c > prune[n]] - pruned_ngrams.append(pruned_grams) - return pruned_ngrams - - -def add_blank_grams(pruned_ngrams: List, num_tokens: int, blank: str) -> List: - """Adds blank token to grams.""" - all_grams = [gram for grams in pruned_ngrams for gram in grams] - maxorder = len(pruned_ngrams) - blank_grams = {} - if blank == "forced": - pruned_ngrams = [pruned_ngrams[0] if i == 0 else [] for i in range(maxorder)] - pruned_ngrams[0].append(tuple([num_tokens])) - blank_grams[tuple([num_tokens])] = True - - for gram in all_grams: - # Iterate over all possibilities by using a vector of 0s, 1s to - # denote whether a blank is being used at each position. - if blank == "optional": - # Given a gram ab.. if order n, we have n + 1 positions - # available whether to use blank or not. - onehot_vectors = itertools.product([0, 1], repeat=len(gram) + 1) - elif blank == "forced": - # Must include a blank token in between. - onehot_vectors = [[1] * (len(gram) + 1)] - else: - raise ValueError( - "Invalid value specificed for blank. Must be in |optional|forced|none|" - ) - - for j in onehot_vectors: - new_array = [] - for idx, oz in enumerate(j[:-1]): - if oz == 1 and gram[idx] != START_IDX: - new_array.append(num_tokens) - new_array.append(gram[idx]) - if j[-1] == 1 and gram[-1] != END_IDX: - new_array.append(num_tokens) - for n in range(maxorder): - for e in range(n, len(new_array)): - cur_gram = tuple(new_array[e - n : e + 1]) - if num_tokens in cur_gram and cur_gram not in blank_grams: - pruned_ngrams[n].append(cur_gram) - blank_grams[cur_gram] = True - - return pruned_ngrams - - -def add_self_loops(pruned_ngrams: List) -> List: - """Adds self loops to the ngrams.""" - maxorder = len(pruned_ngrams) - - # Use dict for fast search. - all_grams = set([gram for grams in pruned_ngrams for gram in grams]) - for o in range(1, maxorder): - for gram in pruned_ngrams[o - 1]: - # Repeat one of the tokens. - for pos in range(len(gram)): - if gram[pos] == START_IDX or gram[pos] == END_IDX: - continue - new_gram = gram[:pos] + (gram[pos],) + gram[pos:] - - if new_gram not in all_grams: - pruned_ngrams[o].append(new_gram) - all_grams.add(new_gram) - return pruned_ngrams - - -def parse_lines(lines: List, lexicon: Path) -> List: - """Parses lines with a lexicon.""" - with open(lexicon, "r") as f: - lex = (line.strip().split() for line in f) - lex = {line[0]: line[1:] for line in lex} - print(len(lex)) - return [[t for w in line.split(WORDSEP) for t in lex[w]] for line in lines] - - -@click.command() -@click.option("--data_dir", type=str, default=None, help="Path to dataset root.") -@click.option( - "--tokens", type=str, help="Path to token list (in order used with training)." -) -@click.option("--lexicon", type=str, default=None, help="Path to lexicon") -@click.option( - "--prune", - nargs=2, - type=int, - help="Threshold values for prune unigrams, bigrams, etc.", -) -@click.option( - "--blank", - default=click.Choice(["none", "optional", "forced"]), - help="Specifies the usage of blank token" - "'none' - do not use blank token " - "'optional' - allow an optional blank inbetween tokens" - "'forced' - force a blank inbetween tokens (also referred to as garbage token)", -) -@click.option("--self_loops", is_flag=True, help="Add self loops for tokens") -@click.option("--disable_backoff", is_flag=True, help="Disable backoff transitions") -@click.option("--save_path", default=None, help="Path to save transition graph.") -def cli( - data_dir: str, - tokens: str, - lexicon: str, - prune: List[int], - blank: str, - self_loops: bool, - disable_backoff: bool, - save_path: str, -) -> None: - """CLI for creating the transitions.""" - logger.info(f"Building {len(prune)}-gram transition models.") - - if data_dir is None: - data_dir = ( - Path(__file__).resolve().parents[2] / "data" / "processed" / "iam_lines" - ) - logger.debug(f"Using data dir: {data_dir}") - if not data_dir.exists(): - raise RuntimeError(f"Could not locate iamdb directory at {data_dir}") - else: - data_dir = Path(data_dir) - - # Build table of counts and the back-off if below threshold. - with open(data_dir / "train.txt", "r") as f: - lines = [line.strip() for line in f] - - with open(data_dir / tokens, "r") as f: - tokens = [line.strip() for line in f] - - if lexicon is not None: - lexicon = data_dir / lexicon - lines = parse_lines(lines, lexicon) - - tokens_to_idx = {t: e for e, t in enumerate(tokens)} - - ngram = len(prune) - - logger.info("Counting data...") - ngrams = count_ngrams(lines, ngram, tokens_to_idx) - - pruned_ngrams = prune_ngrams(ngrams, prune) - - for n in range(ngram): - logger.info(f"Kept {len(pruned_ngrams[n])} of {len(ngrams[n])} {n + 1}-grams") - - if blank == "none": - pruned_ngrams = add_blank_grams(pruned_ngrams, len(tokens_to_idx), blank) - - if self_loops: - pruned_ngrams = add_self_loops(pruned_ngrams) - - logger.info("Building graph from pruned ngrams...") - graph = build_graph(pruned_ngrams, disable_backoff) - logger.info(f"Graph has {graph.num_arcs()} arcs and {graph.num_nodes()} nodes.") - - save_path = str(data_dir / save_path) - - logger.info(f"Saving graph to {save_path}") - gtn.save(save_path, graph) - - -if __name__ == "__main__": - cli() diff --git a/src/tasks/create_emnist_lines_datasets.sh b/src/tasks/create_emnist_lines_datasets.sh deleted file mode 100755 index 6416277..0000000 --- a/src/tasks/create_emnist_lines_datasets.sh +++ /dev/null @@ -1,4 +0,0 @@ -#!/usr/bin/fish -command="python text_recognizer/datasets/emnist_lines_dataset.py --max_length 34 --min_overlap 0.0 --max_overlap 0.33 --num_train 100000 --num_test 10000" -echo $command -eval $command diff --git a/src/tasks/create_iam_paragraphs.sh b/src/tasks/create_iam_paragraphs.sh deleted file mode 100755 index fa2bfb0..0000000 --- a/src/tasks/create_iam_paragraphs.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/usr/bin/fish -poetry run create-iam-paragraphs diff --git a/src/tasks/download_emnist.sh b/src/tasks/download_emnist.sh deleted file mode 100755 index 18c8e29..0000000 --- a/src/tasks/download_emnist.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/usr/bin/fish -poetry run download-emnist -poetry run create-emnist-support-files diff --git a/src/tasks/download_iam.sh b/src/tasks/download_iam.sh deleted file mode 100755 index e3cf76b..0000000 --- a/src/tasks/download_iam.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/usr/bin/fish -poetry run download-iam diff --git a/src/tasks/make_wordpieces.py b/src/tasks/make_wordpieces.py deleted file mode 100644 index 2ac0e2c..0000000 --- a/src/tasks/make_wordpieces.py +++ /dev/null @@ -1,114 +0,0 @@ -"""Creates word pieces from a text file. - -Most code stolen from: - - https://github.com/facebookresearch/gtn_applications/blob/master/scripts/make_wordpieces.py - -""" -import io -from pathlib import Path -from typing import List, Optional, Union - -import click -from loguru import logger -import sentencepiece as spm - -from text_recognizer.datasets.iam_preprocessor import load_metadata - - -def iamdb_pieces( - data_dir: Path, text_file: str, num_pieces: int, output_prefix: str -) -> None: - """Creates word pieces from the iamdb train text.""" - # Load training text. - with open(data_dir / text_file, "r") as f: - text = [line.strip() for line in f] - - sp = train_spm_model( - iter(text), - num_pieces + 1, # To account for <unk> - user_symbols=["/"], # added so token is in the output set - ) - - vocab = sorted(set(w for t in text for w in t.split("▁") if w)) - if "move" not in vocab: - raise RuntimeError("`MOVE` not in vocab") - - save_pieces(sp, num_pieces, data_dir, output_prefix, vocab) - - -def train_spm_model( - sentences: iter, vocab_size: int, user_symbols: Union[str, List[str]] = "" -) -> spm.SentencePieceProcessor: - """Trains the sentence piece model.""" - model = io.BytesIO() - spm.SentencePieceTrainer.train( - sentence_iterator=sentences, - model_writer=model, - vocab_size=vocab_size, - bos_id=-1, - eos_id=-1, - character_coverage=1.0, - user_defined_symbols=user_symbols, - ) - sp = spm.SentencePieceProcessor(model_proto=model.getvalue()) - return sp - - -def save_pieces( - sp: spm.SentencePieceProcessor, - num_pieces: int, - data_dir: Path, - output_prefix: str, - vocab: set, -) -> None: - """Saves word pieces to disk.""" - logger.info(f"Generating word piece list of size {num_pieces}.") - pieces = [sp.id_to_piece(i) for i in range(1, num_pieces + 1)] - logger.info(f"Encoding vocabulary of size {len(vocab)}.") - encoded_vocab = [sp.encode_as_pieces(v) for v in vocab] - - # Save pieces to file. - with open(data_dir / f"{output_prefix}_tokens_{num_pieces}.txt", "w") as f: - f.write("\n".join(pieces)) - - # Save lexicon to a file. - with open(data_dir / f"{output_prefix}_lex_{num_pieces}.txt", "w") as f: - for v, p in zip(vocab, encoded_vocab): - f.write(f"{v} {' '.join(p)}\n") - - -@click.command() -@click.option("--data_dir", type=str, default=None, help="Path to processed iam dir.") -@click.option( - "--text_file", type=str, default=None, help="Name of sentence piece training text." -) -@click.option( - "--output_prefix", - type=str, - default="word_pieces", - help="Prefix name to store tokens and lexicon.", -) -@click.option("--num_pieces", type=int, default=1000, help="Number of word pieces.") -def cli( - data_dir: Optional[str], - text_file: Optional[str], - output_prefix: Optional[str], - num_pieces: Optional[int], -) -> None: - """CLI for training the sentence piece model.""" - if data_dir is None: - data_dir = ( - Path(__file__).resolve().parents[2] / "data" / "processed" / "iam_lines" - ) - logger.debug(f"Using data dir: {data_dir}") - if not data_dir.exists(): - raise RuntimeError(f"Could not locate iamdb directory at {data_dir}") - else: - data_dir = Path(data_dir) - - iamdb_pieces(data_dir, text_file, num_pieces, output_prefix) - - -if __name__ == "__main__": - cli() diff --git a/src/tasks/prepare_experiments.sh b/src/tasks/prepare_experiments.sh deleted file mode 100755 index 95a538f..0000000 --- a/src/tasks/prepare_experiments.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/usr/bin/fish -experiments_filename=${1:-training/experiments/sample_experiment.yml} -poetry run prepare-experiments --experiments_filename $experiments_filename diff --git a/src/tasks/test_functionality.sh b/src/tasks/test_functionality.sh deleted file mode 100755 index 5ccf0cd..0000000 --- a/src/tasks/test_functionality.sh +++ /dev/null @@ -1,2 +0,0 @@ -#!/usr/bin/fish -pytest -s -q text_recognizer diff --git a/src/tasks/train.sh b/src/tasks/train.sh deleted file mode 100755 index 60cbd23..0000000 --- a/src/tasks/train.sh +++ /dev/null @@ -1,68 +0,0 @@ -#!/bin/bash - - -# Add checkpoint and resume experiment -usage() { - cat << EOF - usage: ./tasks/train_crnn_line_ctc_model.sh - -f | --experiment_config Name of the experiment config. - -c | --checkpoint (Optional) The experiment name to continue from. - -p | --pretrained_weights (Optional) Path to pretrained weights. - -n | --notrain (Optional) Evaluates a trained model. - -t | --test (Optional) If set, evaluates the model on test set. - -v | --verbose (Optional) Sets the verbosity. - -h | --help Shows this message. -EOF -exit 1 -} - -experiment_config="" -checkpoint="" -pretrained_weights="" -notrain="" -test="" -verbose="" -train_command="" - -while getopts 'f:c:p:nthv' flag; do - case "${flag}" in - f) experiment_config="${OPTARG}" ;; - c) checkpoint="${OPTARG}" ;; - p) pretrained_weights="${OPTARG}" ;; - n) notrain="--notrain" ;; - t) test="--test" ;; - v) verbose="${verbose}v" ;; - h) usage ;; - *) error "Unexpected option ${flag}" ;; - esac -done - - -if [ -z ${experiment_config} ]; -then - echo "experiment_config not specified!" - usage - exit 1 -fi - -experiments_filename="training/experiments/${experiment_config}" -train_command=$(bash tasks/prepare_experiments.sh $experiments_filename) - -if [ ${checkpoint} ]; -then - train_command="${train_command} --checkpoint $checkpoint" -fi - -if [ ${pretrained_weights} ]; -then - train_command="${train_command} --pretrained_weights $pretrained_weights" -fi - -if [ ${verbose} ]; -then - train_command="${train_command} -$verbose" -fi - -train_command="${train_command} $test $notrain" -echo $train_command -eval $train_command diff --git a/src/text_recognizer/__init__.py b/src/text_recognizer/__init__.py deleted file mode 100644 index 3dc1f76..0000000 --- a/src/text_recognizer/__init__.py +++ /dev/null @@ -1 +0,0 @@ -__version__ = "0.1.0" diff --git a/src/text_recognizer/character_predictor.py b/src/text_recognizer/character_predictor.py deleted file mode 100644 index ad71289..0000000 --- a/src/text_recognizer/character_predictor.py +++ /dev/null @@ -1,29 +0,0 @@ -"""CharacterPredictor class.""" -from typing import Dict, Tuple, Type, Union - -import numpy as np -from torch import nn - -from text_recognizer import datasets, networks -from text_recognizer.models import CharacterModel -from text_recognizer.util import read_image - - -class CharacterPredictor: - """Recognizes the character in handwritten character images.""" - - def __init__(self, network_fn: str, dataset: str) -> None: - """Intializes the CharacterModel and load the pretrained weights.""" - network_fn = getattr(networks, network_fn) - dataset = getattr(datasets, dataset) - self.model = CharacterModel(network_fn=network_fn, dataset=dataset) - self.model.eval() - self.model.use_swa_model() - - def predict(self, image_or_filename: Union[np.ndarray, str]) -> Tuple[str, float]: - """Predict on a single images contianing a handwritten character.""" - if isinstance(image_or_filename, str): - image = read_image(image_or_filename, grayscale=True) - else: - image = image_or_filename - return self.model.predict_on_image(image) diff --git a/src/text_recognizer/datasets/__init__.py b/src/text_recognizer/datasets/__init__.py deleted file mode 100644 index a6c1c59..0000000 --- a/src/text_recognizer/datasets/__init__.py +++ /dev/null @@ -1,39 +0,0 @@ -"""Dataset modules.""" -from .emnist_dataset import EmnistDataset -from .emnist_lines_dataset import ( - construct_image_from_string, - EmnistLinesDataset, - get_samples_by_character, -) -from .iam_dataset import IamDataset -from .iam_lines_dataset import IamLinesDataset -from .iam_paragraphs_dataset import IamParagraphsDataset -from .iam_preprocessor import load_metadata, Preprocessor -from .transforms import AddTokens, Transpose -from .util import ( - _download_raw_dataset, - compute_sha256, - DATA_DIRNAME, - download_url, - EmnistMapper, - ESSENTIALS_FILENAME, -) - -__all__ = [ - "_download_raw_dataset", - "AddTokens", - "compute_sha256", - "construct_image_from_string", - "DATA_DIRNAME", - "download_url", - "EmnistDataset", - "EmnistMapper", - "EmnistLinesDataset", - "get_samples_by_character", - "load_metadata", - "IamDataset", - "IamLinesDataset", - "IamParagraphsDataset", - "Preprocessor", - "Transpose", -] diff --git a/src/text_recognizer/datasets/dataset.py b/src/text_recognizer/datasets/dataset.py deleted file mode 100644 index e794605..0000000 --- a/src/text_recognizer/datasets/dataset.py +++ /dev/null @@ -1,152 +0,0 @@ -"""Abstract dataset class.""" -from typing import Callable, Dict, List, Optional, Tuple, Union - -import torch -from torch import Tensor -from torch.utils import data -from torchvision.transforms import ToTensor - -import text_recognizer.datasets.transforms as transforms -from text_recognizer.datasets.util import EmnistMapper - - -class Dataset(data.Dataset): - """Abstract class for with common methods for all datasets.""" - - def __init__( - self, - train: bool, - subsample_fraction: float = None, - transform: Optional[List[Dict]] = None, - target_transform: Optional[List[Dict]] = None, - init_token: Optional[str] = None, - pad_token: Optional[str] = None, - eos_token: Optional[str] = None, - lower: bool = False, - ) -> None: - """Initialization of Dataset class. - - Args: - train (bool): If True, loads the training set, otherwise the validation set is loaded. Defaults to False. - subsample_fraction (float): The fraction of the dataset to use for training. Defaults to None. - transform (Optional[List[Dict]]): List of Transform types and args for input data. Defaults to None. - target_transform (Optional[List[Dict]]): List of Transform types and args for output data. Defaults to None. - init_token (Optional[str]): String representing the start of sequence token. Defaults to None. - pad_token (Optional[str]): String representing the pad token. Defaults to None. - eos_token (Optional[str]): String representing the end of sequence token. Defaults to None. - lower (bool): Only use lower case letters. Defaults to False. - - Raises: - ValueError: If subsample_fraction is not None and outside the range (0, 1). - - """ - self.train = train - self.split = "train" if self.train else "test" - - if subsample_fraction is not None: - if not 0.0 < subsample_fraction < 1.0: - raise ValueError("The subsample fraction must be in (0, 1).") - self.subsample_fraction = subsample_fraction - - self._mapper = EmnistMapper( - init_token=init_token, eos_token=eos_token, pad_token=pad_token, lower=lower - ) - self._input_shape = self._mapper.input_shape - self._output_shape = self._mapper._num_classes - self.num_classes = self.mapper.num_classes - - # Set transforms. - self.transform = self._configure_transform(transform) - self.target_transform = self._configure_target_transform(target_transform) - - self._data = None - self._targets = None - - def _configure_transform(self, transform: List[Dict]) -> transforms.Compose: - transform_list = [] - if transform is not None: - for t in transform: - t_type = t["type"] - t_args = t["args"] or {} - transform_list.append(getattr(transforms, t_type)(**t_args)) - else: - transform_list.append(ToTensor()) - return transforms.Compose(transform_list) - - def _configure_target_transform( - self, target_transform: List[Dict] - ) -> transforms.Compose: - target_transform_list = [torch.tensor] - if target_transform is not None: - for t in target_transform: - t_type = t["type"] - t_args = t["args"] or {} - target_transform_list.append(getattr(transforms, t_type)(**t_args)) - return transforms.Compose(target_transform_list) - - @property - def data(self) -> Tensor: - """The input data.""" - return self._data - - @property - def targets(self) -> Tensor: - """The target data.""" - return self._targets - - @property - def input_shape(self) -> Tuple: - """Input shape of the data.""" - return self._input_shape - - @property - def output_shape(self) -> Tuple: - """Output shape of the data.""" - return self._output_shape - - @property - def mapper(self) -> EmnistMapper: - """Returns the EmnistMapper.""" - return self._mapper - - @property - def mapping(self) -> Dict: - """Return EMNIST mapping from index to character.""" - return self._mapper.mapping - - @property - def inverse_mapping(self) -> Dict: - """Returns the inverse mapping from character to index.""" - return self.mapper.inverse_mapping - - def _subsample(self) -> None: - """Only this fraction of the data will be loaded.""" - if self.subsample_fraction is None: - return - num_subsample = int(self.data.shape[0] * self.subsample_fraction) - self._data = self.data[:num_subsample] - self._targets = self.targets[:num_subsample] - - def __len__(self) -> int: - """Returns the length of the dataset.""" - return len(self.data) - - def load_or_generate_data(self) -> None: - """Load or generate dataset data.""" - raise NotImplementedError - - def __getitem__(self, index: Union[int, Tensor]) -> Tuple[Tensor, Tensor]: - """Fetches samples from the dataset. - - Args: - index (Union[int, torch.Tensor]): The indices of the samples to fetch. - - Raises: - NotImplementedError: If the method is not implemented in child class. - - """ - raise NotImplementedError - - def __repr__(self) -> str: - """Returns information about the dataset.""" - raise NotImplementedError diff --git a/src/text_recognizer/datasets/emnist_dataset.py b/src/text_recognizer/datasets/emnist_dataset.py deleted file mode 100644 index 9884fdf..0000000 --- a/src/text_recognizer/datasets/emnist_dataset.py +++ /dev/null @@ -1,131 +0,0 @@ -"""Emnist dataset: black and white images of handwritten characters (Aa-Zz) and digits (0-9).""" - -import json -from pathlib import Path -from typing import Callable, Optional, Tuple, Union - -from loguru import logger -import numpy as np -from PIL import Image -import torch -from torch import Tensor -from torchvision.datasets import EMNIST -from torchvision.transforms import Compose, ToTensor - -from text_recognizer.datasets.dataset import Dataset -from text_recognizer.datasets.transforms import Transpose -from text_recognizer.datasets.util import DATA_DIRNAME - - -class EmnistDataset(Dataset): - """This is a class for resampling and subsampling the PyTorch EMNIST dataset.""" - - def __init__( - self, - pad_token: str = None, - train: bool = False, - sample_to_balance: bool = False, - subsample_fraction: float = None, - transform: Optional[Callable] = None, - target_transform: Optional[Callable] = None, - seed: int = 4711, - ) -> None: - """Loads the dataset and the mappings. - - Args: - pad_token (str): The pad token symbol. Defaults to _. - train (bool): If True, loads the training set, otherwise the validation set is loaded. Defaults to False. - sample_to_balance (bool): Resamples the dataset to make it balanced. Defaults to False. - subsample_fraction (float): Description of parameter `subsample_fraction`. Defaults to None. - transform (Optional[Callable]): Transform(s) for input data. Defaults to None. - target_transform (Optional[Callable]): Transform(s) for output data. Defaults to None. - seed (int): Seed number. Defaults to 4711. - - """ - super().__init__( - train=train, - subsample_fraction=subsample_fraction, - transform=transform, - target_transform=target_transform, - pad_token=pad_token, - ) - - self.sample_to_balance = sample_to_balance - - # Have to transpose the emnist characters, ToTensor norms input between [0,1]. - if transform is None: - self.transform = Compose([Transpose(), ToTensor()]) - - self.target_transform = None - - self.seed = seed - - def __getitem__(self, index: Union[int, Tensor]) -> Tuple[Tensor, Tensor]: - """Fetches samples from the dataset. - - Args: - index (Union[int, Tensor]): The indices of the samples to fetch. - - Returns: - Tuple[Tensor, Tensor]: Data target tuple. - - """ - if torch.is_tensor(index): - index = index.tolist() - - data = self.data[index] - targets = self.targets[index] - - if self.transform: - data = self.transform(data) - - if self.target_transform: - targets = self.target_transform(targets) - - return data, targets - - def __repr__(self) -> str: - """Returns information about the dataset.""" - return ( - "EMNIST Dataset\n" - f"Num classes: {self.num_classes}\n" - f"Input shape: {self.input_shape}\n" - f"Mapping: {self.mapper.mapping}\n" - ) - - def _sample_to_balance(self) -> None: - """Because the dataset is not balanced, we take at most the mean number of instances per class.""" - np.random.seed(self.seed) - x = self._data - y = self._targets - num_to_sample = int(np.bincount(y.flatten()).mean()) - all_sampled_indices = [] - for label in np.unique(y.flatten()): - inds = np.where(y == label)[0] - sampled_indices = np.unique(np.random.choice(inds, num_to_sample)) - all_sampled_indices.append(sampled_indices) - indices = np.concatenate(all_sampled_indices) - x_sampled = x[indices] - y_sampled = y[indices] - self._data = x_sampled - self._targets = y_sampled - - def load_or_generate_data(self) -> None: - """Fetch the EMNIST dataset.""" - dataset = EMNIST( - root=DATA_DIRNAME, - split="byclass", - train=self.train, - download=False, - transform=None, - target_transform=None, - ) - - self._data = dataset.data - self._targets = dataset.targets - - if self.sample_to_balance: - self._sample_to_balance() - - if self.subsample_fraction is not None: - self._subsample() diff --git a/src/text_recognizer/datasets/emnist_essentials.json b/src/text_recognizer/datasets/emnist_essentials.json deleted file mode 100644 index 2a0648a..0000000 --- a/src/text_recognizer/datasets/emnist_essentials.json +++ /dev/null @@ -1 +0,0 @@ -{"mapping": [[0, "0"], [1, "1"], [2, "2"], [3, "3"], [4, "4"], [5, "5"], [6, "6"], [7, "7"], [8, "8"], [9, "9"], [10, "A"], [11, "B"], [12, "C"], [13, "D"], [14, "E"], [15, "F"], [16, "G"], [17, "H"], [18, "I"], [19, "J"], [20, "K"], [21, "L"], [22, "M"], [23, "N"], [24, "O"], [25, "P"], [26, "Q"], [27, "R"], [28, "S"], [29, "T"], [30, "U"], [31, "V"], [32, "W"], [33, "X"], [34, "Y"], [35, "Z"], [36, "a"], [37, "b"], [38, "c"], [39, "d"], [40, "e"], [41, "f"], [42, "g"], [43, "h"], [44, "i"], [45, "j"], [46, "k"], [47, "l"], [48, "m"], [49, "n"], [50, "o"], [51, "p"], [52, "q"], [53, "r"], [54, "s"], [55, "t"], [56, "u"], [57, "v"], [58, "w"], [59, "x"], [60, "y"], [61, "z"]], "input_shape": [28, 28]} diff --git a/src/text_recognizer/datasets/emnist_lines_dataset.py b/src/text_recognizer/datasets/emnist_lines_dataset.py deleted file mode 100644 index 1992446..0000000 --- a/src/text_recognizer/datasets/emnist_lines_dataset.py +++ /dev/null @@ -1,359 +0,0 @@ -"""Emnist Lines dataset: synthetic handwritten lines dataset made from Emnist characters.""" - -from collections import defaultdict -from pathlib import Path -from typing import Callable, Dict, List, Optional, Tuple, Union - -import click -import h5py -from loguru import logger -import numpy as np -import torch -from torch import Tensor -import torch.nn.functional as F -from torchvision.transforms import ToTensor - -from text_recognizer.datasets.dataset import Dataset -from text_recognizer.datasets.emnist_dataset import EmnistDataset, Transpose -from text_recognizer.datasets.sentence_generator import SentenceGenerator -from text_recognizer.datasets.util import ( - DATA_DIRNAME, - EmnistMapper, - ESSENTIALS_FILENAME, -) - -DATA_DIRNAME = DATA_DIRNAME / "processed" / "emnist_lines" - -MAX_WIDTH = 952 - - -class EmnistLinesDataset(Dataset): - """Synthetic dataset of lines from the Brown corpus with Emnist characters.""" - - def __init__( - self, - train: bool = False, - transform: Optional[Callable] = None, - target_transform: Optional[Callable] = None, - subsample_fraction: float = None, - max_length: int = 34, - min_overlap: float = 0, - max_overlap: float = 0.33, - num_samples: int = 10000, - seed: int = 4711, - init_token: Optional[str] = None, - pad_token: Optional[str] = None, - eos_token: Optional[str] = None, - lower: bool = False, - ) -> None: - """Set attributes and loads the dataset. - - Args: - train (bool): Flag for the filename. Defaults to False. Defaults to None. - transform (Optional[Callable]): The transform of the data. Defaults to None. - target_transform (Optional[Callable]): The transform of the target. Defaults to None. - subsample_fraction (float): The fraction of the dataset to use for training. Defaults to None. - max_length (int): The maximum number of characters. Defaults to 34. - min_overlap (float): The minimum overlap between concatenated images. Defaults to 0. - max_overlap (float): The maximum overlap between concatenated images. Defaults to 0.33. - num_samples (int): Number of samples to generate. Defaults to 10000. - seed (int): Seed number. Defaults to 4711. - init_token (Optional[str]): String representing the start of sequence token. Defaults to None. - pad_token (Optional[str]): String representing the pad token. Defaults to None. - eos_token (Optional[str]): String representing the end of sequence token. Defaults to None. - lower (bool): If True, convert uppercase letters to lowercase. Otherwise, use both upper and lowercase. - - """ - self.pad_token = "_" if pad_token is None else pad_token - - super().__init__( - train=train, - transform=transform, - target_transform=target_transform, - subsample_fraction=subsample_fraction, - init_token=init_token, - pad_token=self.pad_token, - eos_token=eos_token, - lower=lower, - ) - - # Extract dataset information. - self._input_shape = self._mapper.input_shape - self.num_classes = self._mapper.num_classes - - self.max_length = max_length - self.min_overlap = min_overlap - self.max_overlap = max_overlap - self.num_samples = num_samples - self._input_shape = ( - self.input_shape[0], - self.input_shape[1] * self.max_length, - ) - self._output_shape = (self.max_length, self.num_classes) - self.seed = seed - - # Placeholders for the dataset. - self._data = None - self._target = None - - def __getitem__(self, index: Union[int, Tensor]) -> Tuple[Tensor, Tensor]: - """Fetches data, target pair of the dataset for a given and index or indices. - - Args: - index (Union[int, Tensor]): Either a list or int of indices/index. - - Returns: - Tuple[Tensor, Tensor]: Data target pair. - - """ - if torch.is_tensor(index): - index = index.tolist() - - data = self.data[index] - targets = self.targets[index] - - if self.transform: - data = self.transform(data) - - if self.target_transform: - targets = self.target_transform(targets) - - return data, targets - - def __repr__(self) -> str: - """Returns information about the dataset.""" - return ( - "EMNIST Lines Dataset\n" # pylint: disable=no-member - f"Max length: {self.max_length}\n" - f"Min overlap: {self.min_overlap}\n" - f"Max overlap: {self.max_overlap}\n" - f"Num classes: {self.num_classes}\n" - f"Input shape: {self.input_shape}\n" - f"Data: {self.data.shape}\n" - f"Tagets: {self.targets.shape}\n" - ) - - @property - def data_filename(self) -> Path: - """Path to the h5 file.""" - filename = "train.pt" if self.train else "test.pt" - return DATA_DIRNAME / filename - - def load_or_generate_data(self) -> None: - """Loads the dataset, if it does not exist a new dataset is generated before loading it.""" - np.random.seed(self.seed) - - if not self.data_filename.exists(): - self._generate_data() - self._load_data() - self._subsample() - - def _load_data(self) -> None: - """Loads the dataset from the h5 file.""" - logger.debug("EmnistLinesDataset loading data from HDF5...") - with h5py.File(self.data_filename, "r") as f: - self._data = f["data"][()] - self._targets = f["targets"][()] - - def _generate_data(self) -> str: - """Generates a dataset with the Brown corpus and Emnist characters.""" - logger.debug("Generating data...") - - sentence_generator = SentenceGenerator(self.max_length) - - # Load emnist dataset. - emnist = EmnistDataset( - train=self.train, sample_to_balance=True, pad_token=self.pad_token - ) - emnist.load_or_generate_data() - - samples_by_character = get_samples_by_character( - emnist.data.numpy(), emnist.targets.numpy(), self.mapper.mapping, - ) - - DATA_DIRNAME.mkdir(parents=True, exist_ok=True) - with h5py.File(self.data_filename, "a") as f: - data, targets = create_dataset_of_images( - self.num_samples, - samples_by_character, - sentence_generator, - self.min_overlap, - self.max_overlap, - ) - - targets = convert_strings_to_categorical_labels( - targets, emnist.inverse_mapping - ) - - f.create_dataset("data", data=data, dtype="u1", compression="lzf") - f.create_dataset("targets", data=targets, dtype="u1", compression="lzf") - - -def get_samples_by_character( - samples: np.ndarray, labels: np.ndarray, mapping: Dict -) -> defaultdict: - """Creates a dictionary with character as key and value as the list of images of that character. - - Args: - samples (np.ndarray): Dataset of images of characters. - labels (np.ndarray): The labels for each image. - mapping (Dict): The Emnist mapping dictionary. - - Returns: - defaultdict: A dictionary with characters as keys and list of images as values. - - """ - samples_by_character = defaultdict(list) - for sample, label in zip(samples, labels.flatten()): - samples_by_character[mapping[label]].append(sample) - return samples_by_character - - -def select_letter_samples_for_string( - string: str, samples_by_character: Dict -) -> List[np.ndarray]: - """Randomly selects Emnist characters to use for the senetence. - - Args: - string (str): The word or sentence. - samples_by_character (Dict): The dictionary of emnist images of each character. - - Returns: - List[np.ndarray]: A list of emnist images of the string. - - """ - zero_image = np.zeros((28, 28), np.uint8) - sample_image_by_character = {} - for character in string: - if character in sample_image_by_character: - continue - samples = samples_by_character[character] - sample = samples[np.random.choice(len(samples))] if samples else zero_image - sample_image_by_character[character] = sample.reshape(28, 28).swapaxes(0, 1) - return [sample_image_by_character[character] for character in string] - - -def construct_image_from_string( - string: str, samples_by_character: Dict, min_overlap: float, max_overlap: float -) -> np.ndarray: - """Concatenates images of the characters in the string. - - The concatination is made with randomly selected overlap so that some portion of the character will overlap. - - Args: - string (str): The word or sentence. - samples_by_character (Dict): The dictionary of emnist images of each character. - min_overlap (float): Minimum amount of overlap between Emnist images. - max_overlap (float): Maximum amount of overlap between Emnist images. - - Returns: - np.ndarray: The Emnist image of the string. - - """ - overlap = np.random.uniform(min_overlap, max_overlap) - sampled_images = select_letter_samples_for_string(string, samples_by_character) - length = len(sampled_images) - height, width = sampled_images[0].shape - next_overlap_width = width - int(overlap * width) - concatenated_image = np.zeros((height, width * length), np.uint8) - x = 0 - for image in sampled_images: - concatenated_image[:, x : (x + width)] += image - x += next_overlap_width - - if concatenated_image.shape[-1] > MAX_WIDTH: - concatenated_image = Tensor(concatenated_image).unsqueeze(0) - concatenated_image = F.interpolate( - concatenated_image, size=MAX_WIDTH, mode="nearest" - ) - concatenated_image = concatenated_image.squeeze(0).numpy() - - return np.minimum(255, concatenated_image) - - -def create_dataset_of_images( - length: int, - samples_by_character: Dict, - sentence_generator: SentenceGenerator, - min_overlap: float, - max_overlap: float, -) -> Tuple[np.ndarray, List[str]]: - """Creates a dataset with images and labels from strings generated from the SentenceGenerator. - - Args: - length (int): The number of characters for each string. - samples_by_character (Dict): The dictionary of emnist images of each character. - sentence_generator (SentenceGenerator): A SentenceGenerator objest. - min_overlap (float): Minimum amount of overlap between Emnist images. - max_overlap (float): Maximum amount of overlap between Emnist images. - - Returns: - Tuple[np.ndarray, List[str]]: A list of Emnist images and a list of the strings (labels). - - Raises: - RuntimeError: If the sentence generator is not able to generate a string. - - """ - sample_label = sentence_generator.generate() - sample_image = construct_image_from_string(sample_label, samples_by_character, 0, 0) - images = np.zeros((length, sample_image.shape[0], sample_image.shape[1]), np.uint8) - labels = [] - for n in range(length): - label = None - # Try several times to generate before actually throwing an error. - for _ in range(10): - try: - label = sentence_generator.generate() - break - except Exception: # pylint: disable=broad-except - pass - if label is None: - raise RuntimeError("Was not able to generate a valid string.") - images[n] = construct_image_from_string( - label, samples_by_character, min_overlap, max_overlap - ) - labels.append(label) - return images, labels - - -def convert_strings_to_categorical_labels( - labels: List[str], mapping: Dict -) -> np.ndarray: - """Translates a string of characters in to a target array of class int.""" - return np.array([[mapping[c] for c in label] for label in labels]) - - -@click.command() -@click.option( - "--max_length", type=int, default=34, help="Number of characters in a sentence." -) -@click.option( - "--min_overlap", type=float, default=0.0, help="Min overlap between characters." -) -@click.option( - "--max_overlap", type=float, default=0.33, help="Max overlap between characters." -) -@click.option("--num_train", type=int, default=10_000, help="Number of train examples.") -@click.option("--num_test", type=int, default=1_000, help="Number of test examples.") -def create_datasets( - max_length: int = 34, - min_overlap: float = 0, - max_overlap: float = 0.33, - num_train: int = 10000, - num_test: int = 1000, -) -> None: - """Creates a training an validation dataset of Emnist lines.""" - num_samples = [num_train, num_test] - for num, train in zip(num_samples, [True, False]): - emnist_lines = EmnistLinesDataset( - train=train, - max_length=max_length, - min_overlap=min_overlap, - max_overlap=max_overlap, - num_samples=num, - ) - emnist_lines.load_or_generate_data() - - -if __name__ == "__main__": - create_datasets() diff --git a/src/text_recognizer/datasets/iam_dataset.py b/src/text_recognizer/datasets/iam_dataset.py deleted file mode 100644 index f4a869d..0000000 --- a/src/text_recognizer/datasets/iam_dataset.py +++ /dev/null @@ -1,132 +0,0 @@ -"""Class for loading the IAM dataset, which encompasses both paragraphs and lines, with associated utilities.""" -import os -from typing import Any, Dict, List -import zipfile - -from boltons.cacheutils import cachedproperty -import defusedxml.ElementTree as ET -from loguru import logger -import toml - -from text_recognizer.datasets.util import _download_raw_dataset, DATA_DIRNAME - -RAW_DATA_DIRNAME = DATA_DIRNAME / "raw" / "iam" -METADATA_FILENAME = RAW_DATA_DIRNAME / "metadata.toml" -EXTRACTED_DATASET_DIRNAME = RAW_DATA_DIRNAME / "iamdb" - -DOWNSAMPLE_FACTOR = 2 # If images were downsampled, the regions must also be. -LINE_REGION_PADDING = 0 # Add this many pixels around the exact coordinates. - - -class IamDataset: - """IAM dataset. - - "The IAM Lines dataset, first published at the ICDAR 1999, contains forms of unconstrained handwritten text, - which were scanned at a resolution of 300dpi and saved as PNG images with 256 gray levels." - From http://www.fki.inf.unibe.ch/databases/iam-handwriting-database - - The data split we will use is - IAM lines Large Writer Independent Text Line Recognition Task (lwitlrt): 9,862 text lines. - The validation set has been merged into the train set. - The train set has 7,101 lines from 326 writers. - The test set has 1,861 lines from 128 writers. - The text lines of all data sets are mutually exclusive, thus each writer has contributed to one set only. - - """ - - def __init__(self) -> None: - self.metadata = toml.load(METADATA_FILENAME) - - def load_or_generate_data(self) -> None: - """Downloads IAM dataset if xml files does not exist.""" - if not self.xml_filenames: - self._download_iam() - - @property - def xml_filenames(self) -> List: - """List of xml filenames.""" - return list((EXTRACTED_DATASET_DIRNAME / "xml").glob("*.xml")) - - @property - def form_filenames(self) -> List: - """List of forms filenames.""" - return list((EXTRACTED_DATASET_DIRNAME / "forms").glob("*.jpg")) - - def _download_iam(self) -> None: - curdir = os.getcwd() - os.chdir(RAW_DATA_DIRNAME) - _download_raw_dataset(self.metadata) - _extract_raw_dataset(self.metadata) - os.chdir(curdir) - - @property - def form_filenames_by_id(self) -> Dict: - """Creates a dictionary with filenames as keys and forms as values.""" - return {filename.stem: filename for filename in self.form_filenames} - - @cachedproperty - def line_strings_by_id(self) -> Dict: - """Return a dict from name of IAM form to a list of line texts in it.""" - return { - filename.stem: _get_line_strings_from_xml_file(filename) - for filename in self.xml_filenames - } - - @cachedproperty - def line_regions_by_id(self) -> Dict: - """Return a dict from name of IAM form to a list of (x1, x2, y1, y2) coordinates of all lines in it.""" - return { - filename.stem: _get_line_regions_from_xml_file(filename) - for filename in self.xml_filenames - } - - def __repr__(self) -> str: - """Print info about dataset.""" - return "IAM Dataset\n" f"Number of forms: {len(self.xml_filenames)}\n" - - -def _extract_raw_dataset(metadata: Dict) -> None: - logger.info("Extracting IAM data.") - with zipfile.ZipFile(metadata["filename"], "r") as zip_file: - zip_file.extractall() - - -def _get_line_strings_from_xml_file(filename: str) -> List[str]: - """Get the text content of each line. Note that we replace " with ".""" - xml_root_element = ET.parse(filename).getroot() # nosec - xml_line_elements = xml_root_element.findall("handwritten-part/line") - return [el.attrib["text"].replace(""", '"') for el in xml_line_elements] - - -def _get_line_regions_from_xml_file(filename: str) -> List[Dict[str, int]]: - """Get the line region dict for each line.""" - xml_root_element = ET.parse(filename).getroot() # nosec - xml_line_elements = xml_root_element.findall("handwritten-part/line") - return [_get_line_region_from_xml_element(el) for el in xml_line_elements] - - -def _get_line_region_from_xml_element(xml_line: Any) -> Dict[str, int]: - """Extracts coordinates for each line of text.""" - # TODO: fix input! - word_elements = xml_line.findall("word/cmp") - x1s = [int(el.attrib["x"]) for el in word_elements] - y1s = [int(el.attrib["y"]) for el in word_elements] - x2s = [int(el.attrib["x"]) + int(el.attrib["width"]) for el in word_elements] - y2s = [int(el.attrib["y"]) + int(el.attrib["height"]) for el in word_elements] - return { - "x1": min(x1s) // DOWNSAMPLE_FACTOR - LINE_REGION_PADDING, - "y1": min(y1s) // DOWNSAMPLE_FACTOR - LINE_REGION_PADDING, - "x2": max(x2s) // DOWNSAMPLE_FACTOR + LINE_REGION_PADDING, - "y2": max(y2s) // DOWNSAMPLE_FACTOR + LINE_REGION_PADDING, - } - - -def main() -> None: - """Initializes the dataset and print info about the dataset.""" - dataset = IamDataset() - dataset.load_or_generate_data() - print(dataset) - - -if __name__ == "__main__": - main() diff --git a/src/text_recognizer/datasets/iam_lines_dataset.py b/src/text_recognizer/datasets/iam_lines_dataset.py deleted file mode 100644 index 1cb84bd..0000000 --- a/src/text_recognizer/datasets/iam_lines_dataset.py +++ /dev/null @@ -1,110 +0,0 @@ -"""IamLinesDataset class.""" -from typing import Callable, Dict, List, Optional, Tuple, Union - -import h5py -from loguru import logger -import torch -from torch import Tensor -from torchvision.transforms import ToTensor - -from text_recognizer.datasets.dataset import Dataset -from text_recognizer.datasets.util import ( - compute_sha256, - DATA_DIRNAME, - download_url, - EmnistMapper, -) - - -PROCESSED_DATA_DIRNAME = DATA_DIRNAME / "processed" / "iam_lines" -PROCESSED_DATA_FILENAME = PROCESSED_DATA_DIRNAME / "iam_lines.h5" -PROCESSED_DATA_URL = ( - "https://s3-us-west-2.amazonaws.com/fsdl-public-assets/iam_lines.h5" -) - - -class IamLinesDataset(Dataset): - """IAM lines datasets for handwritten text lines.""" - - def __init__( - self, - train: bool = False, - subsample_fraction: float = None, - transform: Optional[Callable] = None, - target_transform: Optional[Callable] = None, - init_token: Optional[str] = None, - pad_token: Optional[str] = None, - eos_token: Optional[str] = None, - lower: bool = False, - ) -> None: - self.pad_token = "_" if pad_token is None else pad_token - - super().__init__( - train=train, - subsample_fraction=subsample_fraction, - transform=transform, - target_transform=target_transform, - init_token=init_token, - pad_token=pad_token, - eos_token=eos_token, - lower=lower, - ) - - @property - def input_shape(self) -> Tuple: - """Input shape of the data.""" - return self.data.shape[1:] if self.data is not None else None - - @property - def output_shape(self) -> Tuple: - """Output shape of the data.""" - return ( - self.targets.shape[1:] + (self.num_classes,) - if self.targets is not None - else None - ) - - def load_or_generate_data(self) -> None: - """Load or generate dataset data.""" - if not PROCESSED_DATA_FILENAME.exists(): - PROCESSED_DATA_DIRNAME.mkdir(parents=True, exist_ok=True) - logger.info("Downloading IAM lines...") - download_url(PROCESSED_DATA_URL, PROCESSED_DATA_FILENAME) - with h5py.File(PROCESSED_DATA_FILENAME, "r") as f: - self._data = f[f"x_{self.split}"][:] - self._targets = f[f"y_{self.split}"][:] - self._subsample() - - def __repr__(self) -> str: - """Print info about the dataset.""" - return ( - "IAM Lines Dataset\n" # pylint: disable=no-member - f"Number classes: {self.num_classes}\n" - f"Mapping: {self.mapper.mapping}\n" - f"Data: {self.data.shape}\n" - f"Targets: {self.targets.shape}\n" - ) - - def __getitem__(self, index: Union[Tensor, int]) -> Tuple[Tensor, Tensor]: - """Fetches data, target pair of the dataset for a given and index or indices. - - Args: - index (Union[int, Tensor]): Either a list or int of indices/index. - - Returns: - Tuple[Tensor, Tensor]: Data target pair. - - """ - if torch.is_tensor(index): - index = index.tolist() - - data = self.data[index] - targets = self.targets[index] - - if self.transform: - data = self.transform(data) - - if self.target_transform: - targets = self.target_transform(targets) - - return data, targets diff --git a/src/text_recognizer/datasets/iam_paragraphs_dataset.py b/src/text_recognizer/datasets/iam_paragraphs_dataset.py deleted file mode 100644 index 8ba5142..0000000 --- a/src/text_recognizer/datasets/iam_paragraphs_dataset.py +++ /dev/null @@ -1,291 +0,0 @@ -"""IamParagraphsDataset class and functions for data processing.""" -import random -from typing import Callable, Dict, List, Optional, Tuple, Union - -import click -import cv2 -import h5py -from loguru import logger -import numpy as np -import torch -from torch import Tensor -from torchvision.transforms import ToTensor - -from text_recognizer import util -from text_recognizer.datasets.dataset import Dataset -from text_recognizer.datasets.iam_dataset import IamDataset -from text_recognizer.datasets.util import ( - compute_sha256, - DATA_DIRNAME, - download_url, - EmnistMapper, -) - -INTERIM_DATA_DIRNAME = DATA_DIRNAME / "interim" / "iam_paragraphs" -DEBUG_CROPS_DIRNAME = INTERIM_DATA_DIRNAME / "debug_crops" -PROCESSED_DATA_DIRNAME = DATA_DIRNAME / "processed" / "iam_paragraphs" -CROPS_DIRNAME = PROCESSED_DATA_DIRNAME / "crops" -GT_DIRNAME = PROCESSED_DATA_DIRNAME / "gt" - -PARAGRAPH_BUFFER = 50 # Pixels in the IAM form images to leave around the lines. -TEST_FRACTION = 0.2 -SEED = 4711 - - -class IamParagraphsDataset(Dataset): - """IAM Paragraphs dataset for paragraphs of handwritten text.""" - - def __init__( - self, - train: bool = False, - subsample_fraction: float = None, - transform: Optional[Callable] = None, - target_transform: Optional[Callable] = None, - ) -> None: - super().__init__( - train=train, - subsample_fraction=subsample_fraction, - transform=transform, - target_transform=target_transform, - ) - # Load Iam dataset. - self.iam_dataset = IamDataset() - - self.num_classes = 3 - self._input_shape = (256, 256) - self._output_shape = self._input_shape + (self.num_classes,) - self._ids = None - - def __getitem__(self, index: Union[Tensor, int]) -> Tuple[Tensor, Tensor]: - """Fetches data, target pair of the dataset for a given and index or indices. - - Args: - index (Union[int, Tensor]): Either a list or int of indices/index. - - Returns: - Tuple[Tensor, Tensor]: Data target pair. - - """ - if torch.is_tensor(index): - index = index.tolist() - - data = self.data[index] - targets = self.targets[index] - - seed = np.random.randint(SEED) - random.seed(seed) # apply this seed to target tranfsorms - torch.manual_seed(seed) # needed for torchvision 0.7 - if self.transform: - data = self.transform(data) - - random.seed(seed) # apply this seed to target tranfsorms - torch.manual_seed(seed) # needed for torchvision 0.7 - if self.target_transform: - targets = self.target_transform(targets) - - return data, targets.long() - - @property - def ids(self) -> Tensor: - """Ids of the dataset.""" - return self._ids - - def get_data_and_target_from_id(self, id_: str) -> Tuple[Tensor, Tensor]: - """Get data target pair from id.""" - ind = self.ids.index(id_) - return self.data[ind], self.targets[ind] - - def load_or_generate_data(self) -> None: - """Load or generate dataset data.""" - num_actual = len(list(CROPS_DIRNAME.glob("*.jpg"))) - num_targets = len(self.iam_dataset.line_regions_by_id) - - if num_actual < num_targets - 2: - self._process_iam_paragraphs() - - self._data, self._targets, self._ids = _load_iam_paragraphs() - self._get_random_split() - self._subsample() - - def _get_random_split(self) -> None: - np.random.seed(SEED) - num_train = int((1 - TEST_FRACTION) * self.data.shape[0]) - indices = np.random.permutation(self.data.shape[0]) - train_indices, test_indices = indices[:num_train], indices[num_train:] - if self.train: - self._data = self.data[train_indices] - self._targets = self.targets[train_indices] - else: - self._data = self.data[test_indices] - self._targets = self.targets[test_indices] - - def _process_iam_paragraphs(self) -> None: - """Crop the part with the text. - - For each page, crop out the part of it that correspond to the paragraph of text, and make sure all crops are - self.input_shape. The ground truth data is the same size, with a one-hot vector at each pixel - corresponding to labels 0=background, 1=odd-numbered line, 2=even-numbered line - """ - crop_dims = self._decide_on_crop_dims() - CROPS_DIRNAME.mkdir(parents=True, exist_ok=True) - DEBUG_CROPS_DIRNAME.mkdir(parents=True, exist_ok=True) - GT_DIRNAME.mkdir(parents=True, exist_ok=True) - logger.info( - f"Cropping paragraphs, generating ground truth, and saving debugging images to {DEBUG_CROPS_DIRNAME}" - ) - for filename in self.iam_dataset.form_filenames: - id_ = filename.stem - line_region = self.iam_dataset.line_regions_by_id[id_] - _crop_paragraph_image(filename, line_region, crop_dims, self.input_shape) - - def _decide_on_crop_dims(self) -> Tuple[int, int]: - """Decide on the dimensions to crop out of the form image. - - Since image width is larger than a comfortable crop around the longest paragraph, - we will make the crop a square form factor. - And since the found dimensions 610x610 are pretty close to 512x512, - we might as well resize crops and make it exactly that, which lets us - do all kinds of power-of-2 pooling and upsampling should we choose to. - - Returns: - Tuple[int, int]: A tuple of crop dimensions. - - Raises: - RuntimeError: When max crop height is larger than max crop width. - - """ - - sample_form_filename = self.iam_dataset.form_filenames[0] - sample_image = util.read_image(sample_form_filename, grayscale=True) - max_crop_width = sample_image.shape[1] - max_crop_height = _get_max_paragraph_crop_height( - self.iam_dataset.line_regions_by_id - ) - if not max_crop_height <= max_crop_width: - raise RuntimeError( - f"Max crop height is larger then max crop width: {max_crop_height} >= {max_crop_width}" - ) - - crop_dims = (max_crop_width, max_crop_width) - logger.info( - f"Max crop width and height were found to be {max_crop_width}x{max_crop_height}." - ) - logger.info(f"Setting them to {max_crop_width}x{max_crop_width}") - return crop_dims - - def __repr__(self) -> str: - """Return info about the dataset.""" - return ( - "IAM Paragraph Dataset\n" # pylint: disable=no-member - f"Num classes: {self.num_classes}\n" - f"Data: {self.data.shape}\n" - f"Targets: {self.targets.shape}\n" - ) - - -def _get_max_paragraph_crop_height(line_regions_by_id: Dict) -> int: - heights = [] - for regions in line_regions_by_id.values(): - min_y1 = min(region["y1"] for region in regions) - PARAGRAPH_BUFFER - max_y2 = max(region["y2"] for region in regions) + PARAGRAPH_BUFFER - height = max_y2 - min_y1 - heights.append(height) - return max(heights) - - -def _crop_paragraph_image( - filename: str, line_regions: Dict, crop_dims: Tuple[int, int], final_dims: Tuple -) -> None: - image = util.read_image(filename, grayscale=True) - - min_y1 = min(region["y1"] for region in line_regions) - PARAGRAPH_BUFFER - max_y2 = max(region["y2"] for region in line_regions) + PARAGRAPH_BUFFER - height = max_y2 - min_y1 - crop_height = crop_dims[0] - buffer = (crop_height - height) // 2 - - # Generate image crop. - image_crop = 255 * np.ones(crop_dims, dtype=np.uint8) - try: - image_crop[buffer : buffer + height] = image[min_y1:max_y2] - except Exception as e: # pylint: disable=broad-except - logger.error(f"Rescued {filename}: {e}") - return - - # Generate ground truth. - gt_image = np.zeros_like(image_crop, dtype=np.uint8) - for index, region in enumerate(line_regions): - gt_image[ - (region["y1"] - min_y1 + buffer) : (region["y2"] - min_y1 + buffer), - region["x1"] : region["x2"], - ] = (index % 2 + 1) - - # Generate image for debugging. - import matplotlib.pyplot as plt - - cmap = plt.get_cmap("Set1") - image_crop_for_debug = np.dstack([image_crop, image_crop, image_crop]) - for index, region in enumerate(line_regions): - color = [255 * _ for _ in cmap(index)[:-1]] - cv2.rectangle( - image_crop_for_debug, - (region["x1"], region["y1"] - min_y1 + buffer), - (region["x2"], region["y2"] - min_y1 + buffer), - color, - 3, - ) - image_crop_for_debug = cv2.resize( - image_crop_for_debug, final_dims, interpolation=cv2.INTER_AREA - ) - util.write_image(image_crop_for_debug, DEBUG_CROPS_DIRNAME / f"{filename.stem}.jpg") - - image_crop = cv2.resize(image_crop, final_dims, interpolation=cv2.INTER_AREA) - util.write_image(image_crop, CROPS_DIRNAME / f"{filename.stem}.jpg") - - gt_image = cv2.resize(gt_image, final_dims, interpolation=cv2.INTER_NEAREST) - util.write_image(gt_image, GT_DIRNAME / f"{filename.stem}.png") - - -def _load_iam_paragraphs() -> None: - logger.info("Loading IAM paragraph crops and ground truth from image files...") - images = [] - gt_images = [] - ids = [] - for filename in CROPS_DIRNAME.glob("*.jpg"): - id_ = filename.stem - image = util.read_image(filename, grayscale=True) - image = 1.0 - image / 255 - - gt_filename = GT_DIRNAME / f"{id_}.png" - gt_image = util.read_image(gt_filename, grayscale=True) - - images.append(image) - gt_images.append(gt_image) - ids.append(id_) - images = np.array(images).astype(np.float32) - gt_images = np.array(gt_images).astype(np.uint8) - ids = np.array(ids) - return images, gt_images, ids - - -@click.command() -@click.option( - "--subsample_fraction", - type=float, - default=None, - help="The subsampling factor of the dataset.", -) -def main(subsample_fraction: float) -> None: - """Load dataset and print info.""" - logger.info("Creating train set...") - dataset = IamParagraphsDataset(train=True, subsample_fraction=subsample_fraction) - dataset.load_or_generate_data() - print(dataset) - logger.info("Creating test set...") - dataset = IamParagraphsDataset(subsample_fraction=subsample_fraction) - dataset.load_or_generate_data() - print(dataset) - - -if __name__ == "__main__": - main() diff --git a/src/text_recognizer/datasets/iam_preprocessor.py b/src/text_recognizer/datasets/iam_preprocessor.py deleted file mode 100644 index a93eb00..0000000 --- a/src/text_recognizer/datasets/iam_preprocessor.py +++ /dev/null @@ -1,196 +0,0 @@ -"""Preprocessor for extracting word letters from the IAM dataset. - -The code is mostly stolen from: - - https://github.com/facebookresearch/gtn_applications/blob/master/datasets/iamdb.py - -""" - -import collections -import itertools -from pathlib import Path -import re -from typing import List, Optional, Union - -import click -from loguru import logger -import torch - - -def load_metadata( - data_dir: Path, wordsep: str, use_words: bool = False -) -> collections.defaultdict: - """Loads IAM metadata and returns it as a dictionary.""" - forms = collections.defaultdict(list) - filename = "words.txt" if use_words else "lines.txt" - - with open(data_dir / "ascii" / filename, "r") as f: - lines = (line.strip().split() for line in f if line[0] != "#") - for line in lines: - # Skip word segmentation errors. - if use_words and line[1] == "err": - continue - text = " ".join(line[8:]) - - # Remove garbage tokens: - text = text.replace("#", "") - - # Swap word sep form | to wordsep - text = re.sub(r"\|+|\s", wordsep, text).strip(wordsep) - form_key = "-".join(line[0].split("-")[:2]) - line_key = "-".join(line[0].split("-")[:3]) - box_idx = 4 - use_words - box = tuple(int(val) for val in line[box_idx : box_idx + 4]) - forms[form_key].append({"key": line_key, "box": box, "text": text}) - return forms - - -class Preprocessor: - """A preprocessor for the IAM dataset.""" - - # TODO: add lower case only to when generating... - - def __init__( - self, - data_dir: Union[str, Path], - num_features: int, - tokens_path: Optional[Union[str, Path]] = None, - lexicon_path: Optional[Union[str, Path]] = None, - use_words: bool = False, - prepend_wordsep: bool = False, - ) -> None: - self.wordsep = "▁" - self._use_word = use_words - self._prepend_wordsep = prepend_wordsep - - self.data_dir = Path(data_dir) - - self.forms = load_metadata(self.data_dir, self.wordsep, use_words=use_words) - - # Load the set of graphemes: - graphemes = set() - for _, form in self.forms.items(): - for line in form: - graphemes.update(line["text"].lower()) - self.graphemes = sorted(graphemes) - - # Build the token-to-index and index-to-token maps. - if tokens_path is not None: - with open(tokens_path, "r") as f: - self.tokens = [line.strip() for line in f] - else: - self.tokens = self.graphemes - - if lexicon_path is not None: - with open(lexicon_path, "r") as f: - lexicon = (line.strip().split() for line in f) - lexicon = {line[0]: line[1:] for line in lexicon} - self.lexicon = lexicon - else: - self.lexicon = None - - self.graphemes_to_index = {t: i for i, t in enumerate(self.graphemes)} - self.tokens_to_index = {t: i for i, t in enumerate(self.tokens)} - self.num_features = num_features - self.text = [] - - @property - def num_tokens(self) -> int: - """Returns the number or tokens.""" - return len(self.tokens) - - @property - def use_words(self) -> bool: - """If words are used.""" - return self._use_word - - def extract_train_text(self) -> None: - """Extracts training text.""" - keys = [] - with open(self.data_dir / "task" / "trainset.txt") as f: - keys.extend((line.strip() for line in f)) - - for _, examples in self.forms.items(): - for example in examples: - if example["key"] not in keys: - continue - self.text.append(example["text"].lower()) - - def to_index(self, line: str) -> torch.LongTensor: - """Converts text to a tensor of indices.""" - token_to_index = self.graphemes_to_index - if self.lexicon is not None: - if len(line) > 0: - # If the word is not found in the lexicon, fall back to letters. - line = [ - t - for w in line.split(self.wordsep) - for t in self.lexicon.get(w, self.wordsep + w) - ] - token_to_index = self.tokens_to_index - if self._prepend_wordsep: - line = itertools.chain([self.wordsep], line) - return torch.LongTensor([token_to_index[t] for t in line]) - - def to_text(self, indices: List[int]) -> str: - """Converts indices to text.""" - # Roughly the inverse of `to_index` - encoding = self.graphemes - if self.lexicon is not None: - encoding = self.tokens - return self._post_process(encoding[i] for i in indices) - - def tokens_to_text(self, indices: List[int]) -> str: - """Converts tokens to text.""" - return self._post_process(self.tokens[i] for i in indices) - - def _post_process(self, indices: List[int]) -> str: - """A list join.""" - return "".join(indices).strip(self.wordsep) - - -@click.command() -@click.option("--data_dir", type=str, default=None, help="Path to iam dataset") -@click.option( - "--use_words", is_flag=True, help="Load word segmented dataset instead of lines" -) -@click.option( - "--save_text", type=str, default=None, help="Path to save parsed train text" -) -@click.option("--save_tokens", type=str, default=None, help="Path to save tokens") -def cli( - data_dir: Optional[str], - use_words: bool, - save_text: Optional[str], - save_tokens: Optional[str], -) -> None: - """CLI for extracting text data from the iam dataset.""" - if data_dir is None: - data_dir = ( - Path(__file__).resolve().parents[3] / "data" / "raw" / "iam" / "iamdb" - ) - logger.debug(f"Using data dir: {data_dir}") - if not data_dir.exists(): - raise RuntimeError(f"Could not locate iamdb directory at {data_dir}") - else: - data_dir = Path(data_dir) - - preprocessor = Preprocessor(data_dir, 64, use_words=use_words) - preprocessor.extract_train_text() - - processed_dir = data_dir.parents[2] / "processed" / "iam_lines" - logger.debug(f"Saving processed files at: {processed_dir}") - - if save_text is not None: - logger.info("Saving training text") - with open(processed_dir / save_text, "w") as f: - f.write("\n".join(t for t in preprocessor.text)) - - if save_tokens is not None: - logger.info("Saving tokens") - with open(processed_dir / save_tokens, "w") as f: - f.write("\n".join(preprocessor.tokens)) - - -if __name__ == "__main__": - cli() diff --git a/src/text_recognizer/datasets/sentence_generator.py b/src/text_recognizer/datasets/sentence_generator.py deleted file mode 100644 index dd76652..0000000 --- a/src/text_recognizer/datasets/sentence_generator.py +++ /dev/null @@ -1,81 +0,0 @@ -"""Downloading the Brown corpus with NLTK for sentence generating.""" - -import itertools -import re -import string -from typing import Optional - -import nltk -from nltk.corpus.reader.util import ConcatenatedCorpusView -import numpy as np - -from text_recognizer.datasets.util import DATA_DIRNAME - -NLTK_DATA_DIRNAME = DATA_DIRNAME / "raw" / "nltk" - - -class SentenceGenerator: - """Generates text sentences using the Brown corpus.""" - - def __init__(self, max_length: Optional[int] = None) -> None: - """Loads the corpus and sets word start indices.""" - self.corpus = brown_corpus() - self.word_start_indices = [0] + [ - _.start(0) + 1 for _ in re.finditer(" ", self.corpus) - ] - self.max_length = max_length - - def generate(self, max_length: Optional[int] = None) -> str: - """Generates a word or sentences from the Brown corpus. - - Sample a string from the Brown corpus of length at least one word and at most max_length, padding to - max_length with the '_' characters if sentence is shorter. - - Args: - max_length (Optional[int]): The maximum number of characters in the sentence. Defaults to None. - - Returns: - str: A sentence from the Brown corpus. - - Raises: - ValueError: If max_length was not specified at initialization and not given as an argument. - - """ - if max_length is None: - max_length = self.max_length - if max_length is None: - raise ValueError( - "Must provide max_length to this method or when making this object." - ) - - index = np.random.randint(0, len(self.word_start_indices) - 1) - start_index = self.word_start_indices[index] - end_index_candidates = [] - for index in range(index + 1, len(self.word_start_indices)): - if self.word_start_indices[index] - start_index > max_length: - break - end_index_candidates.append(self.word_start_indices[index]) - end_index = np.random.choice(end_index_candidates) - sampled_text = self.corpus[start_index:end_index].strip() - padding = "_" * (max_length - len(sampled_text)) - return sampled_text + padding - - -def brown_corpus() -> str: - """Returns a single string with the Brown corpus with all punctuations stripped.""" - sentences = load_nltk_brown_corpus() - corpus = " ".join(itertools.chain.from_iterable(sentences)) - corpus = corpus.translate({ord(c): None for c in string.punctuation}) - corpus = re.sub(" +", " ", corpus) - return corpus - - -def load_nltk_brown_corpus() -> ConcatenatedCorpusView: - """Load the Brown corpus using the NLTK library.""" - nltk.data.path.append(NLTK_DATA_DIRNAME) - try: - nltk.corpus.brown.sents() - except LookupError: - NLTK_DATA_DIRNAME.mkdir(parents=True, exist_ok=True) - nltk.download("brown", download_dir=NLTK_DATA_DIRNAME) - return nltk.corpus.brown.sents() diff --git a/src/text_recognizer/datasets/transforms.py b/src/text_recognizer/datasets/transforms.py deleted file mode 100644 index b6a48f5..0000000 --- a/src/text_recognizer/datasets/transforms.py +++ /dev/null @@ -1,266 +0,0 @@ -"""Transforms for PyTorch datasets.""" -from abc import abstractmethod -from pathlib import Path -import random -from typing import Any, Optional, Union - -from loguru import logger -import numpy as np -from PIL import Image -import torch -from torch import Tensor -import torch.nn.functional as F -from torchvision import transforms -from torchvision.transforms import ( - ColorJitter, - Compose, - Normalize, - RandomAffine, - RandomHorizontalFlip, - RandomRotation, - ToPILImage, - ToTensor, -) - -from text_recognizer.datasets.iam_preprocessor import Preprocessor -from text_recognizer.datasets.util import EmnistMapper - - -class RandomResizeCrop: - """Image transform with random resize and crop applied. - - Stolen from - - https://github.com/facebookresearch/gtn_applications/blob/master/datasets/iamdb.py - - """ - - def __init__(self, jitter: int = 10, ratio: float = 0.5) -> None: - self.jitter = jitter - self.ratio = ratio - - def __call__(self, img: np.ndarray) -> np.ndarray: - """Applies random crop and rotation to an image.""" - w, h = img.size - - # pad with white: - img = transforms.functional.pad(img, self.jitter, fill=255) - - # crop at random (x, y): - x = self.jitter + random.randint(-self.jitter, self.jitter) - y = self.jitter + random.randint(-self.jitter, self.jitter) - - # randomize aspect ratio: - size_w = w * random.uniform(1 - self.ratio, 1 + self.ratio) - size = (h, int(size_w)) - img = transforms.functional.resized_crop(img, y, x, h, w, size) - return img - - -class Transpose: - """Transposes the EMNIST image to the correct orientation.""" - - def __call__(self, image: Image) -> np.ndarray: - """Swaps axis.""" - return np.array(image).swapaxes(0, 1) - - -class Resize: - """Resizes a tensor to a specified width.""" - - def __init__(self, width: int = 952) -> None: - # The default is 952 because of the IAM dataset. - self.width = width - - def __call__(self, image: Tensor) -> Tensor: - """Resize tensor in the last dimension.""" - return F.interpolate(image, size=self.width, mode="nearest") - - -class AddTokens: - """Adds start of sequence and end of sequence tokens to target tensor.""" - - def __init__(self, pad_token: str, eos_token: str, init_token: str = None) -> None: - self.init_token = init_token - self.pad_token = pad_token - self.eos_token = eos_token - if self.init_token is not None: - self.emnist_mapper = EmnistMapper( - init_token=self.init_token, - pad_token=self.pad_token, - eos_token=self.eos_token, - ) - else: - self.emnist_mapper = EmnistMapper( - pad_token=self.pad_token, eos_token=self.eos_token, - ) - self.pad_value = self.emnist_mapper(self.pad_token) - self.eos_value = self.emnist_mapper(self.eos_token) - - def __call__(self, target: Tensor) -> Tensor: - """Adds a sos token to the begining and a eos token to the end of a target sequence.""" - dtype, device = target.dtype, target.device - - # Find the where padding starts. - pad_index = torch.nonzero(target == self.pad_value, as_tuple=False)[0].item() - - target[pad_index] = self.eos_value - - if self.init_token is not None: - self.sos_value = self.emnist_mapper(self.init_token) - sos = torch.tensor([self.sos_value], dtype=dtype, device=device) - target = torch.cat([sos, target], dim=0) - - return target - - -class ApplyContrast: - """Sets everything below a threshold to zero, i.e. increase contrast.""" - - def __init__(self, low: float = 0.0, high: float = 0.25) -> None: - self.low = low - self.high = high - - def __call__(self, x: Tensor) -> Tensor: - """Apply mask binary mask to input tensor.""" - mask = x > np.random.RandomState().uniform(low=self.low, high=self.high) - return x * mask - - -class Unsqueeze: - """Add a dimension to the tensor.""" - - def __call__(self, x: Tensor) -> Tensor: - """Adds dim.""" - return x.unsqueeze(0) - - -class Squeeze: - """Removes the first dimension of a tensor.""" - - def __call__(self, x: Tensor) -> Tensor: - """Removes first dim.""" - return x.squeeze(0) - - -class ToLower: - """Converts target to lower case.""" - - def __call__(self, target: Tensor) -> Tensor: - """Corrects index value in target tensor.""" - device = target.device - return torch.stack([x - 26 if x > 35 else x for x in target]).to(device) - - -class ToCharcters: - """Converts integers to characters.""" - - def __init__( - self, pad_token: str, eos_token: str, init_token: str = None, lower: bool = True - ) -> None: - self.init_token = init_token - self.pad_token = pad_token - self.eos_token = eos_token - if self.init_token is not None: - self.emnist_mapper = EmnistMapper( - init_token=self.init_token, - pad_token=self.pad_token, - eos_token=self.eos_token, - lower=lower, - ) - else: - self.emnist_mapper = EmnistMapper( - pad_token=self.pad_token, eos_token=self.eos_token, lower=lower - ) - - def __call__(self, y: Tensor) -> str: - """Converts a Tensor to a str.""" - return ( - "".join([self.emnist_mapper(int(i)) for i in y]) - .strip("_") - .replace(" ", "▁") - ) - - -class WordPieces: - """Abstract transform for word pieces.""" - - def __init__( - self, - num_features: int, - data_dir: Optional[Union[str, Path]] = None, - tokens: Optional[Union[str, Path]] = None, - lexicon: Optional[Union[str, Path]] = None, - use_words: bool = False, - prepend_wordsep: bool = False, - ) -> None: - if data_dir is None: - data_dir = ( - Path(__file__).resolve().parents[3] / "data" / "raw" / "iam" / "iamdb" - ) - logger.debug(f"Using data dir: {data_dir}") - if not data_dir.exists(): - raise RuntimeError(f"Could not locate iamdb directory at {data_dir}") - else: - data_dir = Path(data_dir) - processed_path = ( - Path(__file__).resolve().parents[3] / "data" / "processed" / "iam_lines" - ) - tokens_path = processed_path / tokens - lexicon_path = processed_path / lexicon - - self.preprocessor = Preprocessor( - data_dir, - num_features, - tokens_path, - lexicon_path, - use_words, - prepend_wordsep, - ) - - @abstractmethod - def __call__(self, *args, **kwargs) -> Any: - """Transforms input.""" - ... - - -class ToWordPieces(WordPieces): - """Transforms str to word pieces.""" - - def __init__( - self, - num_features: int, - data_dir: Optional[Union[str, Path]] = None, - tokens: Optional[Union[str, Path]] = None, - lexicon: Optional[Union[str, Path]] = None, - use_words: bool = False, - prepend_wordsep: bool = False, - ) -> None: - super().__init__( - num_features, data_dir, tokens, lexicon, use_words, prepend_wordsep - ) - - def __call__(self, line: str) -> Tensor: - """Transforms str to word pieces.""" - return self.preprocessor.to_index(line) - - -class ToText(WordPieces): - """Takes word pieces and converts them to text.""" - - def __init__( - self, - num_features: int, - data_dir: Optional[Union[str, Path]] = None, - tokens: Optional[Union[str, Path]] = None, - lexicon: Optional[Union[str, Path]] = None, - use_words: bool = False, - prepend_wordsep: bool = False, - ) -> None: - super().__init__( - num_features, data_dir, tokens, lexicon, use_words, prepend_wordsep - ) - - def __call__(self, x: Tensor) -> str: - """Converts tensor to text.""" - return self.preprocessor.to_text(x.tolist()) diff --git a/src/text_recognizer/datasets/util.py b/src/text_recognizer/datasets/util.py deleted file mode 100644 index da87756..0000000 --- a/src/text_recognizer/datasets/util.py +++ /dev/null @@ -1,209 +0,0 @@ -"""Util functions for datasets.""" -import hashlib -import json -import os -from pathlib import Path -import string -from typing import Dict, List, Optional, Union -from urllib.request import urlretrieve - -from loguru import logger -import numpy as np -import torch -from torch import Tensor -from torchvision.datasets import EMNIST -from tqdm import tqdm - -DATA_DIRNAME = Path(__file__).resolve().parents[3] / "data" -ESSENTIALS_FILENAME = Path(__file__).resolve().parents[0] / "emnist_essentials.json" - - -def save_emnist_essentials(emnsit_dataset: EMNIST = EMNIST) -> None: - """Extract and saves EMNIST essentials.""" - labels = emnsit_dataset.classes - labels.sort() - mapping = [(i, str(label)) for i, label in enumerate(labels)] - essentials = { - "mapping": mapping, - "input_shape": tuple(np.array(emnsit_dataset[0][0]).shape[:]), - } - logger.info("Saving emnist essentials...") - with open(ESSENTIALS_FILENAME, "w") as f: - json.dump(essentials, f) - - -def download_emnist() -> None: - """Download the EMNIST dataset via the PyTorch class.""" - logger.info(f"Data directory is: {DATA_DIRNAME}") - dataset = EMNIST(root=DATA_DIRNAME, split="byclass", download=True) - save_emnist_essentials(dataset) - - -class EmnistMapper: - """Mapper between network output to Emnist character.""" - - def __init__( - self, - pad_token: str, - init_token: Optional[str] = None, - eos_token: Optional[str] = None, - lower: bool = False, - ) -> None: - """Loads the emnist essentials file with the mapping and input shape.""" - self.init_token = init_token - self.pad_token = pad_token - self.eos_token = eos_token - self.lower = lower - - self.essentials = self._load_emnist_essentials() - # Load dataset information. - self._mapping = dict(self.essentials["mapping"]) - self._augment_emnist_mapping() - self._inverse_mapping = {v: k for k, v in self.mapping.items()} - self._num_classes = len(self.mapping) - self._input_shape = self.essentials["input_shape"] - - def __call__(self, token: Union[str, int, np.uint8, Tensor]) -> Union[str, int]: - """Maps the token to emnist character or character index. - - If the token is an integer (index), the method will return the Emnist character corresponding to that index. - If the token is a str (Emnist character), the method will return the corresponding index for that character. - - Args: - token (Union[str, int, np.uint8, Tensor]): Either a string or index (integer). - - Returns: - Union[str, int]: The mapping result. - - Raises: - KeyError: If the index or string does not exist in the mapping. - - """ - if ( - (isinstance(token, np.uint8) or isinstance(token, int)) - or torch.is_tensor(token) - and int(token) in self.mapping - ): - return self.mapping[int(token)] - elif isinstance(token, str) and token in self._inverse_mapping: - return self._inverse_mapping[token] - else: - raise KeyError(f"Token {token} does not exist in the mappings.") - - @property - def mapping(self) -> Dict: - """Returns the mapping between index and character.""" - return self._mapping - - @property - def inverse_mapping(self) -> Dict: - """Returns the mapping between character and index.""" - return self._inverse_mapping - - @property - def num_classes(self) -> int: - """Returns the number of classes in the dataset.""" - return self._num_classes - - @property - def input_shape(self) -> List[int]: - """Returns the input shape of the Emnist characters.""" - return self._input_shape - - def _load_emnist_essentials(self) -> Dict: - """Load the EMNIST mapping.""" - with open(str(ESSENTIALS_FILENAME)) as f: - essentials = json.load(f) - return essentials - - def _augment_emnist_mapping(self) -> None: - """Augment the mapping with extra symbols.""" - # Extra symbols in IAM dataset - if self.lower: - self._mapping = { - k: str(v) - for k, v in enumerate(list(range(10)) + list(string.ascii_lowercase)) - } - - extra_symbols = [ - " ", - "!", - '"', - "#", - "&", - "'", - "(", - ")", - "*", - "+", - ",", - "-", - ".", - "/", - ":", - ";", - "?", - ] - - # padding symbol, and acts as blank symbol as well. - extra_symbols.append(self.pad_token) - - if self.init_token is not None: - extra_symbols.append(self.init_token) - - if self.eos_token is not None: - extra_symbols.append(self.eos_token) - - max_key = max(self.mapping.keys()) - extra_mapping = {} - for i, symbol in enumerate(extra_symbols): - extra_mapping[max_key + 1 + i] = symbol - - self._mapping = {**self.mapping, **extra_mapping} - - -def compute_sha256(filename: Union[Path, str]) -> str: - """Returns the SHA256 checksum of a file.""" - with open(filename, "rb") as f: - return hashlib.sha256(f.read()).hexdigest() - - -class TqdmUpTo(tqdm): - """TQDM progress bar when downloading files. - - From https://github.com/tqdm/tqdm/blob/master/examples/tqdm_wget.py - - """ - - def update_to( - self, blocks: int = 1, block_size: int = 1, total_size: Optional[int] = None - ) -> None: - """Updates the progress bar. - - Args: - blocks (int): Number of blocks transferred so far. Defaults to 1. - block_size (int): Size of each block, in tqdm units. Defaults to 1. - total_size (Optional[int]): Total size in tqdm units. Defaults to None. - """ - if total_size is not None: - self.total = total_size # pylint: disable=attribute-defined-outside-init - self.update(blocks * block_size - self.n) - - -def download_url(url: str, filename: str) -> None: - """Downloads a file from url to filename, with a progress bar.""" - with TqdmUpTo(unit="B", unit_scale=True, unit_divisor=1024, miniters=1) as t: - urlretrieve(url, filename, reporthook=t.update_to, data=None) # nosec - - -def _download_raw_dataset(metadata: Dict) -> None: - if os.path.exists(metadata["filename"]): - return - logger.info(f"Downloading raw dataset from {metadata['url']}...") - download_url(metadata["url"], metadata["filename"]) - logger.info("Computing SHA-256...") - sha256 = compute_sha256(metadata["filename"]) - if sha256 != metadata["sha256"]: - raise ValueError( - "Downloaded data file SHA-256 does not match that listed in metadata document." - ) diff --git a/src/text_recognizer/line_predictor.py b/src/text_recognizer/line_predictor.py deleted file mode 100644 index 8e348fe..0000000 --- a/src/text_recognizer/line_predictor.py +++ /dev/null @@ -1,28 +0,0 @@ -"""LinePredictor class.""" -import importlib -from typing import Tuple, Union - -import numpy as np -from torch import nn - -from text_recognizer import datasets, networks -from text_recognizer.models import TransformerModel -from text_recognizer.util import read_image - - -class LinePredictor: - """Given an image of a line of handwritten text, recognizes the text content.""" - - def __init__(self, dataset: str, network_fn: str) -> None: - network_fn = getattr(networks, network_fn) - dataset = getattr(datasets, dataset) - self.model = TransformerModel(network_fn=network_fn, dataset=dataset) - self.model.eval() - - def predict(self, image_or_filename: Union[np.ndarray, str]) -> Tuple[str, float]: - """Predict on a single images contianing a handwritten character.""" - if isinstance(image_or_filename, str): - image = read_image(image_or_filename, grayscale=True) - else: - image = image_or_filename - return self.model.predict_on_image(image) diff --git a/src/text_recognizer/models/__init__.py b/src/text_recognizer/models/__init__.py deleted file mode 100644 index 7647d7e..0000000 --- a/src/text_recognizer/models/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -"""Model modules.""" -from .base import Model -from .character_model import CharacterModel -from .crnn_model import CRNNModel -from .ctc_transformer_model import CTCTransformerModel -from .segmentation_model import SegmentationModel -from .transformer_model import TransformerModel -from .vqvae_model import VQVAEModel - -__all__ = [ - "CharacterModel", - "CRNNModel", - "CTCTransformerModel", - "Model", - "SegmentationModel", - "TransformerModel", - "VQVAEModel", -] diff --git a/src/text_recognizer/models/base.py b/src/text_recognizer/models/base.py deleted file mode 100644 index 70f4cdb..0000000 --- a/src/text_recognizer/models/base.py +++ /dev/null @@ -1,455 +0,0 @@ -"""Abstract Model class for PyTorch neural networks.""" - -from abc import ABC, abstractmethod -from glob import glob -import importlib -from pathlib import Path -import re -import shutil -from typing import Callable, Dict, List, Optional, Tuple, Type, Union - -from loguru import logger -import torch -from torch import nn -from torch import Tensor -from torch.optim.swa_utils import AveragedModel, SWALR -from torch.utils.data import DataLoader, Dataset, random_split -from torchsummary import summary - -from text_recognizer import datasets -from text_recognizer import networks -from text_recognizer.datasets import EmnistMapper - -WEIGHT_DIRNAME = Path(__file__).parents[1].resolve() / "weights" - - -class Model(ABC): - """Abstract Model class with composition of different parts defining a PyTorch neural network.""" - - def __init__( - self, - network_fn: str, - dataset: str, - network_args: Optional[Dict] = None, - dataset_args: Optional[Dict] = None, - metrics: Optional[Dict] = None, - criterion: Optional[Callable] = None, - criterion_args: Optional[Dict] = None, - optimizer: Optional[Callable] = None, - optimizer_args: Optional[Dict] = None, - lr_scheduler: Optional[Callable] = None, - lr_scheduler_args: Optional[Dict] = None, - swa_args: Optional[Dict] = None, - device: Optional[str] = None, - ) -> None: - """Base class, to be inherited by model for specific type of data. - - Args: - network_fn (str): The name of network. - dataset (str): The name dataset class. - network_args (Optional[Dict]): Arguments for the network. Defaults to None. - dataset_args (Optional[Dict]): Arguments for the dataset. - metrics (Optional[Dict]): Metrics to evaluate the performance with. Defaults to None. - criterion (Optional[Callable]): The criterion to evaluate the performance of the network. - Defaults to None. - criterion_args (Optional[Dict]): Dict of arguments for criterion. Defaults to None. - optimizer (Optional[Callable]): The optimizer for updating the weights. Defaults to None. - optimizer_args (Optional[Dict]): Dict of arguments for optimizer. Defaults to None. - lr_scheduler (Optional[Callable]): A PyTorch learning rate scheduler. Defaults to None. - lr_scheduler_args (Optional[Dict]): Dict of arguments for learning rate scheduler. Defaults to - None. - swa_args (Optional[Dict]): Dict of arguments for stochastic weight averaging. Defaults to - None. - device (Optional[str]): Name of the device to train on. Defaults to None. - - """ - self._name = f"{self.__class__.__name__}_{dataset}_{network_fn}" - # Has to be set in subclass. - self._mapper = None - - # Placeholder. - self._input_shape = None - - self.dataset_name = dataset - self.dataset = None - self.dataset_args = dataset_args - - # Placeholders for datasets. - self.train_dataset = None - self.val_dataset = None - self.test_dataset = None - - # Stochastic Weight Averaging placeholders. - self.swa_args = swa_args - self._swa_scheduler = None - self._swa_network = None - self._use_swa_model = False - - # Experiment directory. - self.model_dir = None - - # Flag for configured model. - self.is_configured = False - self.data_prepared = False - - # Flag for stopping training. - self.stop_training = False - - self._metrics = metrics if metrics is not None else None - - # Set the device. - self._device = ( - torch.device("cuda" if torch.cuda.is_available() else "cpu") - if device is None - else device - ) - - # Configure network. - self._network = None - self._network_args = network_args - self._configure_network(network_fn) - - # Place network on device (GPU). - self.to_device() - - # Loss and Optimizer placeholders for before loading. - self._criterion = criterion - self.criterion_args = criterion_args - - self._optimizer = optimizer - self.optimizer_args = optimizer_args - - self._lr_scheduler = lr_scheduler - self.lr_scheduler_args = lr_scheduler_args - - def configure_model(self) -> None: - """Configures criterion and optimizers.""" - if not self.is_configured: - self._configure_criterion() - self._configure_optimizers() - - # Set this flag to true to prevent the model from configuring again. - self.is_configured = True - - def prepare_data(self) -> None: - """Prepare data for training.""" - # TODO add downloading. - if not self.data_prepared: - # Load dataset module. - self.dataset = getattr(datasets, self.dataset_name) - - # Load train dataset. - train_dataset = self.dataset(train=True, **self.dataset_args["args"]) - train_dataset.load_or_generate_data() - - # Set input shape. - self._input_shape = train_dataset.input_shape - - # Split train dataset into a training and validation partition. - dataset_len = len(train_dataset) - train_len = int( - self.dataset_args["train_args"]["train_fraction"] * dataset_len - ) - val_len = dataset_len - train_len - self.train_dataset, self.val_dataset = random_split( - train_dataset, lengths=[train_len, val_len] - ) - - # Load test dataset. - self.test_dataset = self.dataset(train=False, **self.dataset_args["args"]) - self.test_dataset.load_or_generate_data() - - # Set the flag to true to disable ability to load data again. - self.data_prepared = True - - def train_dataloader(self) -> DataLoader: - """Returns data loader for training set.""" - return DataLoader( - self.train_dataset, - batch_size=self.dataset_args["train_args"]["batch_size"], - num_workers=self.dataset_args["train_args"]["num_workers"], - shuffle=True, - pin_memory=True, - ) - - def val_dataloader(self) -> DataLoader: - """Returns data loader for validation set.""" - return DataLoader( - self.val_dataset, - batch_size=self.dataset_args["train_args"]["batch_size"], - num_workers=self.dataset_args["train_args"]["num_workers"], - shuffle=True, - pin_memory=True, - ) - - def test_dataloader(self) -> DataLoader: - """Returns data loader for test set.""" - return DataLoader( - self.test_dataset, - batch_size=self.dataset_args["train_args"]["batch_size"], - num_workers=self.dataset_args["train_args"]["num_workers"], - shuffle=False, - pin_memory=True, - ) - - def _configure_network(self, network_fn: Type[nn.Module]) -> None: - """Loads the network.""" - # If no network arguments are given, load pretrained weights if they exist. - # Load network module. - network_fn = getattr(networks, network_fn) - if self._network_args is None: - self.load_weights(network_fn) - else: - self._network = network_fn(**self._network_args) - - def _configure_criterion(self) -> None: - """Loads the criterion.""" - self._criterion = ( - self._criterion(**self.criterion_args) - if self._criterion is not None - else None - ) - - def _configure_optimizers(self,) -> None: - """Loads the optimizers.""" - if self._optimizer is not None: - self._optimizer = self._optimizer( - self._network.parameters(), **self.optimizer_args - ) - else: - self._optimizer = None - - if self._optimizer and self._lr_scheduler is not None: - if "steps_per_epoch" in self.lr_scheduler_args: - self.lr_scheduler_args["steps_per_epoch"] = len(self.train_dataloader()) - - # Assume lr scheduler should update at each epoch if not specified. - if "interval" not in self.lr_scheduler_args: - interval = "epoch" - else: - interval = self.lr_scheduler_args.pop("interval") - self._lr_scheduler = { - "lr_scheduler": self._lr_scheduler( - self._optimizer, **self.lr_scheduler_args - ), - "interval": interval, - } - - if self.swa_args is not None: - self._swa_scheduler = { - "swa_scheduler": SWALR(self._optimizer, swa_lr=self.swa_args["lr"]), - "swa_start": self.swa_args["start"], - } - self._swa_network = AveragedModel(self._network).to(self.device) - - @property - def name(self) -> str: - """Returns the name of the model.""" - return self._name - - @property - def input_shape(self) -> Tuple[int, ...]: - """The input shape.""" - return self._input_shape - - @property - def mapper(self) -> EmnistMapper: - """Returns the mapper that maps between ints and chars.""" - return self._mapper - - @property - def mapping(self) -> Dict: - """Returns the mapping between network output and Emnist character.""" - return self._mapper.mapping if self._mapper is not None else None - - def eval(self) -> None: - """Sets the network to evaluation mode.""" - self._network.eval() - - def train(self) -> None: - """Sets the network to train mode.""" - self._network.train() - - @property - def device(self) -> str: - """Device where the weights are stored, i.e. cpu or cuda.""" - return self._device - - @property - def metrics(self) -> Optional[Dict]: - """Metrics.""" - return self._metrics - - @property - def criterion(self) -> Optional[Callable]: - """Criterion.""" - return self._criterion - - @property - def optimizer(self) -> Optional[Callable]: - """Optimizer.""" - return self._optimizer - - @property - def lr_scheduler(self) -> Optional[Dict]: - """Returns a directory with the learning rate scheduler.""" - return self._lr_scheduler - - @property - def swa_scheduler(self) -> Optional[Dict]: - """Returns a directory with the stochastic weight averaging scheduler.""" - return self._swa_scheduler - - @property - def swa_network(self) -> Optional[Callable]: - """Returns the stochastic weight averaging network.""" - return self._swa_network - - @property - def network(self) -> Type[nn.Module]: - """Neural network.""" - # Returns the SWA network if available. - return self._network - - @property - def weights_filename(self) -> str: - """Filepath to the network weights.""" - WEIGHT_DIRNAME.mkdir(parents=True, exist_ok=True) - return str(WEIGHT_DIRNAME / f"{self._name}_weights.pt") - - def use_swa_model(self) -> None: - """Set to use predictions from SWA model.""" - if self.swa_network is not None: - self._use_swa_model = True - - def forward(self, x: Tensor) -> Tensor: - """Feedforward pass with the network.""" - if self._use_swa_model: - return self.swa_network(x) - else: - return self.network(x) - - def summary( - self, - input_shape: Optional[Union[List, Tuple]] = None, - depth: int = 3, - device: Optional[str] = None, - ) -> None: - """Prints a summary of the network architecture.""" - device = self.device if device is None else device - - if input_shape is not None: - summary(self.network, input_shape, depth=depth, device=device) - elif self._input_shape is not None: - input_shape = tuple(self._input_shape) - summary(self.network, input_shape, depth=depth, device=device) - else: - logger.warning("Could not print summary as input shape is not set.") - - def to_device(self) -> None: - """Places the network on the device (GPU).""" - self._network.to(self._device) - - def _get_state_dict(self) -> Dict: - """Get the state dict of the model.""" - state = {"model_state": self._network.state_dict()} - - if self._optimizer is not None: - state["optimizer_state"] = self._optimizer.state_dict() - - if self._lr_scheduler is not None: - state["scheduler_state"] = self._lr_scheduler["lr_scheduler"].state_dict() - state["scheduler_interval"] = self._lr_scheduler["interval"] - - if self._swa_network is not None: - state["swa_network"] = self._swa_network.state_dict() - - return state - - def load_from_checkpoint(self, checkpoint_path: Union[str, Path]) -> None: - """Load a previously saved checkpoint. - - Args: - checkpoint_path (Path): Path to the experiment with the checkpoint. - - """ - checkpoint_path = Path(checkpoint_path) - self.prepare_data() - self.configure_model() - logger.debug("Loading checkpoint...") - if not checkpoint_path.exists(): - logger.debug("File does not exist {str(checkpoint_path)}") - - checkpoint = torch.load(str(checkpoint_path), map_location=self.device) - self._network.load_state_dict(checkpoint["model_state"]) - - if self._optimizer is not None: - self._optimizer.load_state_dict(checkpoint["optimizer_state"]) - - if self._lr_scheduler is not None: - # Does not work when loading from previous checkpoint and trying to train beyond the last max epochs - # with OneCycleLR. - if self._lr_scheduler["lr_scheduler"].__class__.__name__ != "OneCycleLR": - self._lr_scheduler["lr_scheduler"].load_state_dict( - checkpoint["scheduler_state"] - ) - self._lr_scheduler["interval"] = checkpoint["scheduler_interval"] - - if self._swa_network is not None: - self._swa_network.load_state_dict(checkpoint["swa_network"]) - - def save_checkpoint( - self, checkpoint_path: Path, is_best: bool, epoch: int, val_metric: str - ) -> None: - """Saves a checkpoint of the model. - - Args: - checkpoint_path (Path): Path to the experiment with the checkpoint. - is_best (bool): If it is the currently best model. - epoch (int): The epoch of the checkpoint. - val_metric (str): Validation metric. - - """ - state = self._get_state_dict() - state["is_best"] = is_best - state["epoch"] = epoch - state["network_args"] = self._network_args - - checkpoint_path.mkdir(parents=True, exist_ok=True) - - logger.debug("Saving checkpoint...") - filepath = str(checkpoint_path / "last.pt") - torch.save(state, filepath) - - if is_best: - logger.debug( - f"Found a new best {val_metric}. Saving best checkpoint and weights." - ) - shutil.copyfile(filepath, str(checkpoint_path / "best.pt")) - - def load_weights(self, network_fn: Optional[Type[nn.Module]] = None) -> None: - """Load the network weights.""" - logger.debug("Loading network with pretrained weights.") - filename = glob(self.weights_filename)[0] - if not filename: - raise FileNotFoundError( - f"Could not find any pretrained weights at {self.weights_filename}" - ) - # Loading state directory. - state_dict = torch.load(filename, map_location=torch.device(self._device)) - self._network_args = state_dict["network_args"] - weights = state_dict["model_state"] - - # Initializes the network with trained weights. - if network_fn is not None: - self._network = network_fn(**self._network_args) - self._network.load_state_dict(weights) - - if "swa_network" in state_dict: - self._swa_network = AveragedModel(self._network).to(self.device) - self._swa_network.load_state_dict(state_dict["swa_network"]) - - def save_weights(self, path: Path) -> None: - """Save the network weights.""" - logger.debug("Saving the best network weights.") - shutil.copyfile(str(path / "best.pt"), self.weights_filename) diff --git a/src/text_recognizer/models/character_model.py b/src/text_recognizer/models/character_model.py deleted file mode 100644 index f9944f3..0000000 --- a/src/text_recognizer/models/character_model.py +++ /dev/null @@ -1,88 +0,0 @@ -"""Defines the CharacterModel class.""" -from typing import Callable, Dict, Optional, Tuple, Type, Union - -import numpy as np -import torch -from torch import nn -from torch.utils.data import Dataset -from torchvision.transforms import ToTensor - -from text_recognizer.datasets import EmnistMapper -from text_recognizer.models.base import Model - - -class CharacterModel(Model): - """Model for predicting characters from images.""" - - def __init__( - self, - network_fn: Type[nn.Module], - dataset: Type[Dataset], - network_args: Optional[Dict] = None, - dataset_args: Optional[Dict] = None, - metrics: Optional[Dict] = None, - criterion: Optional[Callable] = None, - criterion_args: Optional[Dict] = None, - optimizer: Optional[Callable] = None, - optimizer_args: Optional[Dict] = None, - lr_scheduler: Optional[Callable] = None, - lr_scheduler_args: Optional[Dict] = None, - swa_args: Optional[Dict] = None, - device: Optional[str] = None, - ) -> None: - """Initializes the CharacterModel.""" - - super().__init__( - network_fn, - dataset, - network_args, - dataset_args, - metrics, - criterion, - criterion_args, - optimizer, - optimizer_args, - lr_scheduler, - lr_scheduler_args, - swa_args, - device, - ) - self.pad_token = dataset_args["args"]["pad_token"] - if self._mapper is None: - self._mapper = EmnistMapper(pad_token=self.pad_token,) - self.tensor_transform = ToTensor() - self.softmax = nn.Softmax(dim=0) - - @torch.no_grad() - def predict_on_image( - self, image: Union[np.ndarray, torch.Tensor] - ) -> Tuple[str, float]: - """Character prediction on an image. - - Args: - image (Union[np.ndarray, torch.Tensor]): An image containing a character. - - Returns: - Tuple[str, float]: The predicted character and the confidence in the prediction. - - """ - self.eval() - - if image.dtype == np.uint8: - # Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1]. - image = self.tensor_transform(image) - if image.dtype == torch.uint8: - # If the image is an unscaled tensor. - image = image.type("torch.FloatTensor") / 255 - - # Put the image tensor on the device the model weights are on. - image = image.to(self.device) - logits = self.forward(image) - - prediction = self.softmax(logits.squeeze(0)) - - index = int(torch.argmax(prediction, dim=0)) - confidence_of_prediction = prediction[index] - predicted_character = self.mapper(index) - - return predicted_character, confidence_of_prediction diff --git a/src/text_recognizer/models/crnn_model.py b/src/text_recognizer/models/crnn_model.py deleted file mode 100644 index 1e01a83..0000000 --- a/src/text_recognizer/models/crnn_model.py +++ /dev/null @@ -1,119 +0,0 @@ -"""Defines the CRNNModel class.""" -from typing import Callable, Dict, Optional, Tuple, Type, Union - -import numpy as np -import torch -from torch import nn -from torch import Tensor -from torch.utils.data import Dataset -from torchvision.transforms import ToTensor - -from text_recognizer.datasets import EmnistMapper -from text_recognizer.models.base import Model -from text_recognizer.networks import greedy_decoder - - -class CRNNModel(Model): - """Model for predicting a sequence of characters from an image of a text line.""" - - def __init__( - self, - network_fn: Type[nn.Module], - dataset: Type[Dataset], - network_args: Optional[Dict] = None, - dataset_args: Optional[Dict] = None, - metrics: Optional[Dict] = None, - criterion: Optional[Callable] = None, - criterion_args: Optional[Dict] = None, - optimizer: Optional[Callable] = None, - optimizer_args: Optional[Dict] = None, - lr_scheduler: Optional[Callable] = None, - lr_scheduler_args: Optional[Dict] = None, - swa_args: Optional[Dict] = None, - device: Optional[str] = None, - ) -> None: - super().__init__( - network_fn, - dataset, - network_args, - dataset_args, - metrics, - criterion, - criterion_args, - optimizer, - optimizer_args, - lr_scheduler, - lr_scheduler_args, - swa_args, - device, - ) - - self.pad_token = dataset_args["args"]["pad_token"] - if self._mapper is None: - self._mapper = EmnistMapper(pad_token=self.pad_token,) - self.tensor_transform = ToTensor() - - def criterion(self, output: Tensor, targets: Tensor) -> Tensor: - """Computes the CTC loss. - - Args: - output (Tensor): Model predictions. - targets (Tensor): Correct output sequence. - - Returns: - Tensor: The CTC loss. - - """ - - # Input lengths on the form [T, B] - input_lengths = torch.full( - size=(output.shape[1],), fill_value=output.shape[0], dtype=torch.long, - ) - - # Configure target tensors for ctc loss. - targets_ = Tensor([]).to(self.device) - target_lengths = [] - for t in targets: - # Remove padding symbol as it acts as the blank symbol. - t = t[t < 79] - targets_ = torch.cat([targets_, t]) - target_lengths.append(len(t)) - - targets = targets_.type(dtype=torch.long) - target_lengths = ( - torch.Tensor(target_lengths).type(dtype=torch.long).to(self.device) - ) - - return self._criterion(output, targets, input_lengths, target_lengths) - - @torch.no_grad() - def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tuple[str, float]: - """Predict on a single input.""" - self.eval() - - if image.dtype == np.uint8: - # Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1]. - image = self.tensor_transform(image) - - # Rescale image between 0 and 1. - if image.dtype == torch.uint8: - # If the image is an unscaled tensor. - image = image.type("torch.FloatTensor") / 255 - - # Put the image tensor on the device the model weights are on. - image = image.to(self.device) - log_probs = self.forward(image) - - raw_pred, _ = greedy_decoder( - predictions=log_probs, - character_mapper=self.mapper, - blank_label=79, - collapse_repeated=True, - ) - - log_probs, _ = log_probs.max(dim=2) - - predicted_characters = "".join(raw_pred[0]) - confidence_of_prediction = log_probs.cumprod(dim=0)[-1].item() - - return predicted_characters, confidence_of_prediction diff --git a/src/text_recognizer/models/ctc_transformer_model.py b/src/text_recognizer/models/ctc_transformer_model.py deleted file mode 100644 index 25925f2..0000000 --- a/src/text_recognizer/models/ctc_transformer_model.py +++ /dev/null @@ -1,120 +0,0 @@ -"""Defines the CTC Transformer Model class.""" -from typing import Callable, Dict, Optional, Tuple, Type, Union - -import numpy as np -import torch -from torch import nn -from torch import Tensor -from torch.utils.data import Dataset -from torchvision.transforms import ToTensor - -from text_recognizer.datasets import EmnistMapper -from text_recognizer.models.base import Model -from text_recognizer.networks import greedy_decoder - - -class CTCTransformerModel(Model): - """Model for predicting a sequence of characters from an image of a text line.""" - - def __init__( - self, - network_fn: Type[nn.Module], - dataset: Type[Dataset], - network_args: Optional[Dict] = None, - dataset_args: Optional[Dict] = None, - metrics: Optional[Dict] = None, - criterion: Optional[Callable] = None, - criterion_args: Optional[Dict] = None, - optimizer: Optional[Callable] = None, - optimizer_args: Optional[Dict] = None, - lr_scheduler: Optional[Callable] = None, - lr_scheduler_args: Optional[Dict] = None, - swa_args: Optional[Dict] = None, - device: Optional[str] = None, - ) -> None: - super().__init__( - network_fn, - dataset, - network_args, - dataset_args, - metrics, - criterion, - criterion_args, - optimizer, - optimizer_args, - lr_scheduler, - lr_scheduler_args, - swa_args, - device, - ) - self.pad_token = dataset_args["args"]["pad_token"] - self.lower = dataset_args["args"]["lower"] - - if self._mapper is None: - self._mapper = EmnistMapper(pad_token=self.pad_token, lower=self.lower,) - - self.tensor_transform = ToTensor() - - def criterion(self, output: Tensor, targets: Tensor) -> Tensor: - """Computes the CTC loss. - - Args: - output (Tensor): Model predictions. - targets (Tensor): Correct output sequence. - - Returns: - Tensor: The CTC loss. - - """ - # Input lengths on the form [T, B] - input_lengths = torch.full( - size=(output.shape[1],), fill_value=output.shape[0], dtype=torch.long, - ) - - # Configure target tensors for ctc loss. - targets_ = Tensor([]).to(self.device) - target_lengths = [] - for t in targets: - # Remove padding symbol as it acts as the blank symbol. - t = t[t < 53] - targets_ = torch.cat([targets_, t]) - target_lengths.append(len(t)) - - targets = targets_.type(dtype=torch.long) - target_lengths = ( - torch.Tensor(target_lengths).type(dtype=torch.long).to(self.device) - ) - - return self._criterion(output, targets, input_lengths, target_lengths) - - @torch.no_grad() - def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tuple[str, float]: - """Predict on a single input.""" - self.eval() - - if image.dtype == np.uint8: - # Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1]. - image = self.tensor_transform(image) - - # Rescale image between 0 and 1. - if image.dtype == torch.uint8: - # If the image is an unscaled tensor. - image = image.type("torch.FloatTensor") / 255 - - # Put the image tensor on the device the model weights are on. - image = image.to(self.device) - log_probs = self.forward(image) - - raw_pred, _ = greedy_decoder( - predictions=log_probs, - character_mapper=self.mapper, - blank_label=53, - collapse_repeated=True, - ) - - log_probs, _ = log_probs.max(dim=2) - - predicted_characters = "".join(raw_pred[0]) - confidence_of_prediction = log_probs.cumprod(dim=0)[-1].item() - - return predicted_characters, confidence_of_prediction diff --git a/src/text_recognizer/models/segmentation_model.py b/src/text_recognizer/models/segmentation_model.py deleted file mode 100644 index 613108a..0000000 --- a/src/text_recognizer/models/segmentation_model.py +++ /dev/null @@ -1,75 +0,0 @@ -"""Segmentation model for detecting and segmenting lines.""" -from typing import Callable, Dict, Optional, Type, Union - -import numpy as np -import torch -from torch import nn -from torch import Tensor -from torch.utils.data import Dataset -from torchvision.transforms import ToTensor - -from text_recognizer.models.base import Model - - -class SegmentationModel(Model): - """Model for segmenting lines in an image.""" - - def __init__( - self, - network_fn: str, - dataset: str, - network_args: Optional[Dict] = None, - dataset_args: Optional[Dict] = None, - metrics: Optional[Dict] = None, - criterion: Optional[Callable] = None, - criterion_args: Optional[Dict] = None, - optimizer: Optional[Callable] = None, - optimizer_args: Optional[Dict] = None, - lr_scheduler: Optional[Callable] = None, - lr_scheduler_args: Optional[Dict] = None, - swa_args: Optional[Dict] = None, - device: Optional[str] = None, - ) -> None: - super().__init__( - network_fn, - dataset, - network_args, - dataset_args, - metrics, - criterion, - criterion_args, - optimizer, - optimizer_args, - lr_scheduler, - lr_scheduler_args, - swa_args, - device, - ) - self.tensor_transform = ToTensor() - self.softmax = nn.Softmax(dim=2) - - @torch.no_grad() - def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tensor: - """Predict on a single input.""" - self.eval() - - if image.dtype is np.uint8: - # Converts an image with range [0, 255] with to PyTorch Tensor with range [0, 1]. - image = self.tensor_transform(image) - - # Rescale image between 0 and 1. - if image.dtype is torch.uint8 or image.dtype is torch.int64: - # If the image is an unscaled tensor. - image = image.type("torch.FloatTensor") / 255 - - if not torch.is_tensor(image): - image = Tensor(image) - - # Put the image tensor on the device the model weights are on. - image = image.to(self.device) - - logits = self.forward(image) - - segmentation_mask = torch.argmax(logits, dim=1) - - return segmentation_mask diff --git a/src/text_recognizer/models/transformer_model.py b/src/text_recognizer/models/transformer_model.py deleted file mode 100644 index 3f63053..0000000 --- a/src/text_recognizer/models/transformer_model.py +++ /dev/null @@ -1,124 +0,0 @@ -"""Defines the CNN-Transformer class.""" -from typing import Callable, Dict, List, Optional, Tuple, Type, Union - -import numpy as np -import torch -from torch import nn -from torch import Tensor -from torch.utils.data import Dataset - -from text_recognizer.datasets import EmnistMapper -import text_recognizer.datasets.transforms as transforms -from text_recognizer.models.base import Model -from text_recognizer.networks import greedy_decoder - - -class TransformerModel(Model): - """Model for predicting a sequence of characters from an image of a text line with a cnn-transformer.""" - - def __init__( - self, - network_fn: str, - dataset: str, - network_args: Optional[Dict] = None, - dataset_args: Optional[Dict] = None, - metrics: Optional[Dict] = None, - criterion: Optional[Callable] = None, - criterion_args: Optional[Dict] = None, - optimizer: Optional[Callable] = None, - optimizer_args: Optional[Dict] = None, - lr_scheduler: Optional[Callable] = None, - lr_scheduler_args: Optional[Dict] = None, - swa_args: Optional[Dict] = None, - device: Optional[str] = None, - ) -> None: - super().__init__( - network_fn, - dataset, - network_args, - dataset_args, - metrics, - criterion, - criterion_args, - optimizer, - optimizer_args, - lr_scheduler, - lr_scheduler_args, - swa_args, - device, - ) - self.init_token = dataset_args["args"]["init_token"] - self.pad_token = dataset_args["args"]["pad_token"] - self.eos_token = dataset_args["args"]["eos_token"] - self.lower = dataset_args["args"]["lower"] - self.max_len = 100 - - if self._mapper is None: - self._mapper = EmnistMapper( - init_token=self.init_token, - pad_token=self.pad_token, - eos_token=self.eos_token, - lower=self.lower, - ) - self.tensor_transform = transforms.Compose( - [transforms.ToTensor(), transforms.Normalize(mean=[0.912], std=[0.168])] - ) - self.softmax = nn.Softmax(dim=2) - - @torch.no_grad() - def _generate_sentence(self, image: Tensor) -> Tuple[List, float]: - src = self.network.extract_image_features(image) - - # Added for vqvae transformer. - if isinstance(src, Tuple): - src = src[0] - - memory = self.network.encoder(src) - - confidence_of_predictions = [] - trg_indices = [self.mapper(self.init_token)] - - for _ in range(self.max_len - 1): - trg = torch.tensor(trg_indices, device=self.device)[None, :].long() - trg = self.network.target_embedding(trg) - logits = self.network.decoder(trg=trg, memory=memory, trg_mask=None) - - # Convert logits to probabilities. - probs = self.softmax(logits) - - pred_token = probs.argmax(2)[:, -1].item() - confidence = probs.max(2).values[:, -1].item() - - trg_indices.append(pred_token) - confidence_of_predictions.append(confidence) - - if pred_token == self.mapper(self.eos_token): - break - - confidence = np.min(confidence_of_predictions) - predicted_characters = "".join([self.mapper(x) for x in trg_indices[1:]]) - - return predicted_characters, confidence - - @torch.no_grad() - def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tuple[str, float]: - """Predict on a single input.""" - self.eval() - - if image.dtype == np.uint8: - # Converts an image with range [0, 255] with to PyTorch Tensor with range [0, 1]. - image = self.tensor_transform(image) - - # Rescale image between 0 and 1. - if image.dtype == torch.uint8: - # If the image is an unscaled tensor. - image = image.type("torch.FloatTensor") / 255 - - # Put the image tensor on the device the model weights are on. - image = image.to(self.device) - - (predicted_characters, confidence_of_prediction,) = self._generate_sentence( - image - ) - - return predicted_characters, confidence_of_prediction diff --git a/src/text_recognizer/models/vqvae_model.py b/src/text_recognizer/models/vqvae_model.py deleted file mode 100644 index 70f6f1f..0000000 --- a/src/text_recognizer/models/vqvae_model.py +++ /dev/null @@ -1,80 +0,0 @@ -"""Defines the VQVAEModel class.""" -from typing import Callable, Dict, Optional, Tuple, Type, Union - -import numpy as np -import torch -from torch import nn -from torch.utils.data import Dataset -from torchvision.transforms import ToTensor - -from text_recognizer.datasets import EmnistMapper -from text_recognizer.models.base import Model - - -class VQVAEModel(Model): - """Model for reconstructing images from codebook.""" - - def __init__( - self, - network_fn: Type[nn.Module], - dataset: Type[Dataset], - network_args: Optional[Dict] = None, - dataset_args: Optional[Dict] = None, - metrics: Optional[Dict] = None, - criterion: Optional[Callable] = None, - criterion_args: Optional[Dict] = None, - optimizer: Optional[Callable] = None, - optimizer_args: Optional[Dict] = None, - lr_scheduler: Optional[Callable] = None, - lr_scheduler_args: Optional[Dict] = None, - swa_args: Optional[Dict] = None, - device: Optional[str] = None, - ) -> None: - """Initializes the CharacterModel.""" - - super().__init__( - network_fn, - dataset, - network_args, - dataset_args, - metrics, - criterion, - criterion_args, - optimizer, - optimizer_args, - lr_scheduler, - lr_scheduler_args, - swa_args, - device, - ) - self.pad_token = dataset_args["args"]["pad_token"] - if self._mapper is None: - self._mapper = EmnistMapper(pad_token=self.pad_token,) - self.tensor_transform = ToTensor() - self.softmax = nn.Softmax(dim=0) - - @torch.no_grad() - def predict_on_image(self, image: Union[np.ndarray, torch.Tensor]) -> torch.Tensor: - """Reconstruction of image. - - Args: - image (Union[np.ndarray, torch.Tensor]): An image containing a character. - - Returns: - Tuple[str, float]: The predicted character and the confidence in the prediction. - - """ - self.eval() - - if image.dtype == np.uint8: - # Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1]. - image = self.tensor_transform(image) - if image.dtype == torch.uint8: - # If the image is an unscaled tensor. - image = image.type("torch.FloatTensor") / 255 - - # Put the image tensor on the device the model weights are on. - image = image.to(self.device) - image_reconstructed, _ = self.forward(image) - - return image_reconstructed diff --git a/src/text_recognizer/networks/__init__.py b/src/text_recognizer/networks/__init__.py deleted file mode 100644 index 1521355..0000000 --- a/src/text_recognizer/networks/__init__.py +++ /dev/null @@ -1,43 +0,0 @@ -"""Network modules.""" -from .cnn import CNN -from .cnn_transformer import CNNTransformer -from .crnn import ConvolutionalRecurrentNetwork -from .ctc import greedy_decoder -from .densenet import DenseNet -from .lenet import LeNet -from .metrics import accuracy, cer, wer -from .mlp import MLP -from .residual_network import ResidualNetwork, ResidualNetworkEncoder -from .transducer import load_transducer_loss, TDS2d -from .transformer import Transformer -from .unet import UNet -from .util import sliding_window -from .vit import ViT -from .vq_transformer import VQTransformer -from .vqvae import VQVAE -from .wide_resnet import WideResidualNetwork - -__all__ = [ - "accuracy", - "cer", - "CNN", - "CNNTransformer", - "ConvolutionalRecurrentNetwork", - "DenseNet", - "FCN", - "greedy_decoder", - "MLP", - "LeNet", - "load_transducer_loss", - "ResidualNetwork", - "ResidualNetworkEncoder", - "sliding_window", - "UNet", - "TDS2d", - "Transformer", - "ViT", - "VQTransformer", - "VQVAE", - "wer", - "WideResidualNetwork", -] diff --git a/src/text_recognizer/networks/beam.py b/src/text_recognizer/networks/beam.py deleted file mode 100644 index dccccdb..0000000 --- a/src/text_recognizer/networks/beam.py +++ /dev/null @@ -1,83 +0,0 @@ -"""Implementation of beam search decoder for a sequence to sequence network. - -Stolen from: https://github.com/budzianowski/PyTorch-Beam-Search-Decoding/blob/master/decode_beam.py - -""" -# from typing import List -# from Queue import PriorityQueue - -# from loguru import logger -# import torch -# from torch import nn -# from torch import Tensor -# import torch.nn.functional as F - - -# class Node: -# def __init__( -# self, parent: Node, target_index: int, log_prob: Tensor, length: int -# ) -> None: -# self.parent = parent -# self.target_index = target_index -# self.log_prob = log_prob -# self.length = length -# self.reward = 0.0 - -# def eval(self, alpha: float = 1.0) -> Tensor: -# return self.log_prob / (self.length - 1 + 1e-6) + alpha * self.reward - - -# @torch.no_grad() -# def beam_decoder( -# network, mapper, device, memory: Tensor = None, max_len: int = 97, -# ) -> Tensor: -# beam_width = 10 -# topk = 1 # How many sentences to generate. - -# trg_indices = [mapper(mapper.init_token)] - -# end_nodes = [] - -# node = Node(None, trg_indices, 0, 1) -# nodes = PriorityQueue() - -# nodes.put((node.eval(), node)) -# q_size = 1 - -# # Beam search -# for _ in range(max_len): -# if q_size > 2000: -# logger.warning("Could not decoder input") -# break - -# # Fetch the best node. -# score, n = nodes.get() -# decoder_input = n.target_index - -# if n.target_index == mapper(mapper.eos_token) and n.parent is not None: -# end_nodes.append((score, n)) - -# # If we reached the maximum number of sentences required. -# if len(end_nodes) >= 1: -# break -# else: -# continue - -# # Forward pass with transformer. -# trg = torch.tensor(trg_indices, device=device)[None, :].long() -# trg = network.target_embedding(trg) -# logits = network.decoder(trg=trg, memory=memory, trg_mask=None) -# log_prob = F.log_softmax(logits, dim=2) - -# log_prob, indices = torch.topk(log_prob, beam_width) - -# for new_k in range(beam_width): -# # TODO: continue from here -# token_index = indices[0][new_k].view(1, -1) -# log_p = log_prob[0][new_k].item() - -# node = Node() - -# pass - -# pass diff --git a/src/text_recognizer/networks/cnn.py b/src/text_recognizer/networks/cnn.py deleted file mode 100644 index 1807bb9..0000000 --- a/src/text_recognizer/networks/cnn.py +++ /dev/null @@ -1,101 +0,0 @@ -"""Implementation of a simple backbone cnn network.""" -from typing import Callable, Dict, Optional, Tuple - -from einops.layers.torch import Rearrange -import torch -from torch import nn - -from text_recognizer.networks.util import activation_function - - -class CNN(nn.Module): - """LeNet network for character prediction.""" - - def __init__( - self, - channels: Tuple[int, ...] = (1, 32, 64, 128), - kernel_sizes: Tuple[int, ...] = (4, 4, 4), - strides: Tuple[int, ...] = (2, 2, 2), - max_pool_kernel: int = 2, - dropout_rate: float = 0.2, - activation: Optional[str] = "relu", - ) -> None: - """Initialization of the LeNet network. - - Args: - channels (Tuple[int, ...]): Channels in the convolutional layers. Defaults to (1, 32, 64). - kernel_sizes (Tuple[int, ...]): Kernel sizes in the convolutional layers. Defaults to (3, 3, 2). - strides (Tuple[int, ...]): Stride length of the convolutional filter. Defaults to (2, 2, 2). - max_pool_kernel (int): 2D max pooling kernel. Defaults to 2. - dropout_rate (float): The dropout rate. Defaults to 0.2. - activation (Optional[str]): The name of non-linear activation function. Defaults to relu. - - Raises: - RuntimeError: if the number of hyperparameters does not match in length. - - """ - super().__init__() - - if len(channels) - 1 != len(kernel_sizes) and len(kernel_sizes) != len(strides): - raise RuntimeError("The number of the hyperparameters does not match.") - - self.cnn = self._build_network( - channels, kernel_sizes, strides, max_pool_kernel, dropout_rate, activation, - ) - - def _build_network( - self, - channels: Tuple[int, ...], - kernel_sizes: Tuple[int, ...], - strides: Tuple[int, ...], - max_pool_kernel: int, - dropout_rate: float, - activation: str, - ) -> nn.Sequential: - # Load activation function. - activation_fn = activation_function(activation) - - channels = list(channels) - in_channels = channels.pop(0) - configuration = zip(channels, kernel_sizes, strides) - - modules = nn.ModuleList([]) - - for i, (out_channels, kernel_size, stride) in enumerate(configuration): - # Add max pool to reduce output size. - if i == len(channels) // 2: - modules.append(nn.MaxPool2d(max_pool_kernel)) - if i == 0: - modules.append( - nn.Conv2d( - in_channels, out_channels, kernel_size, stride=stride, padding=1 - ) - ) - else: - modules.append( - nn.Sequential( - activation_fn, - nn.BatchNorm2d(in_channels), - nn.Conv2d( - in_channels, - out_channels, - kernel_size, - stride=stride, - padding=1, - ), - ) - ) - - if dropout_rate: - modules.append(nn.Dropout2d(p=dropout_rate)) - - in_channels = out_channels - - return nn.Sequential(*modules) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """The feedforward pass.""" - # If batch dimenstion is missing, it needs to be added. - if len(x.shape) < 4: - x = x[(None,) * (4 - len(x.shape))] - return self.cnn(x) diff --git a/src/text_recognizer/networks/cnn_transformer.py b/src/text_recognizer/networks/cnn_transformer.py deleted file mode 100644 index a2d7926..0000000 --- a/src/text_recognizer/networks/cnn_transformer.py +++ /dev/null @@ -1,158 +0,0 @@ -"""A CNN-Transformer for image to text recognition.""" -from typing import Dict, Optional, Tuple - -from einops import rearrange, repeat -import torch -from torch import nn -from torch import Tensor - -from text_recognizer.networks.transformer import PositionalEncoding, Transformer -from text_recognizer.networks.util import activation_function -from text_recognizer.networks.util import configure_backbone - - -class CNNTransformer(nn.Module): - """CNN+Transfomer for image to sequence prediction.""" - - def __init__( - self, - num_encoder_layers: int, - num_decoder_layers: int, - hidden_dim: int, - vocab_size: int, - num_heads: int, - adaptive_pool_dim: Tuple, - expansion_dim: int, - dropout_rate: float, - trg_pad_index: int, - max_len: int, - backbone: str, - backbone_args: Optional[Dict] = None, - activation: str = "gelu", - pool_kernel: Optional[Tuple[int, int]] = None, - ) -> None: - super().__init__() - self.trg_pad_index = trg_pad_index - self.vocab_size = vocab_size - self.backbone = configure_backbone(backbone, backbone_args) - - if pool_kernel is not None: - self.max_pool = nn.MaxPool2d(pool_kernel, stride=2) - else: - self.max_pool = None - - self.character_embedding = nn.Embedding(self.vocab_size, hidden_dim) - - self.src_position_embedding = nn.Parameter(torch.randn(1, max_len, hidden_dim)) - self.pos_dropout = nn.Dropout(p=dropout_rate) - self.trg_position_encoding = PositionalEncoding(hidden_dim, dropout_rate) - - nn.init.normal_(self.character_embedding.weight, std=0.02) - - self.adaptive_pool = ( - nn.AdaptiveAvgPool2d((adaptive_pool_dim)) if adaptive_pool_dim else None - ) - - self.transformer = Transformer( - num_encoder_layers, - num_decoder_layers, - hidden_dim, - num_heads, - expansion_dim, - dropout_rate, - activation, - ) - - self.head = nn.Sequential( - # nn.Linear(hidden_dim, hidden_dim * 2), - # activation_function(activation), - nn.Linear(hidden_dim, vocab_size), - ) - - def _create_trg_mask(self, trg: Tensor) -> Tensor: - # Move this outside the transformer. - trg_pad_mask = (trg != self.trg_pad_index)[:, None, None] - trg_len = trg.shape[1] - trg_sub_mask = torch.tril( - torch.ones((trg_len, trg_len), device=trg.device) - ).bool() - trg_mask = trg_pad_mask & trg_sub_mask - return trg_mask - - def encoder(self, src: Tensor) -> Tensor: - """Forward pass with the encoder of the transformer.""" - return self.transformer.encoder(src) - - def decoder(self, trg: Tensor, memory: Tensor, trg_mask: Tensor) -> Tensor: - """Forward pass with the decoder of the transformer + classification head.""" - return self.head( - self.transformer.decoder(trg=trg, memory=memory, trg_mask=trg_mask) - ) - - def extract_image_features(self, src: Tensor) -> Tensor: - """Extracts image features with a backbone neural network. - - It seem like the winning idea was to swap channels and width dimension and collapse - the height dimension. The transformer is learning like a baby with this implementation!!! :D - Ohhhh, the joy I am experiencing right now!! Bring in the beers! :D :D :D - - Args: - src (Tensor): Input tensor. - - Returns: - Tensor: A input src to the transformer. - - """ - # If batch dimension is missing, it needs to be added. - if len(src.shape) < 4: - src = src[(None,) * (4 - len(src.shape))] - - src = self.backbone(src) - - if self.max_pool is not None: - src = self.max_pool(src) - - if self.adaptive_pool is not None and len(src.shape) == 4: - src = rearrange(src, "b c h w -> b w c h") - src = self.adaptive_pool(src) - src = src.squeeze(3) - elif len(src.shape) == 4: - src = rearrange(src, "b c h w -> b (h w) c") - - b, t, _ = src.shape - - src += self.src_position_embedding[:, :t] - src = self.pos_dropout(src) - - return src - - def target_embedding(self, trg: Tensor) -> Tuple[Tensor, Tensor]: - """Encodes target tensor with embedding and postion. - - Args: - trg (Tensor): Target tensor. - - Returns: - Tuple[Tensor, Tensor]: Encoded target tensor and target mask. - - """ - trg = self.character_embedding(trg.long()) - trg = self.trg_position_encoding(trg) - return trg - - def decode_image_features( - self, image_features: Tensor, trg: Optional[Tensor] = None - ) -> Tensor: - """Takes images features from the backbone and decodes them with the transformer.""" - trg_mask = self._create_trg_mask(trg) - trg = self.target_embedding(trg) - out = self.transformer(image_features, trg, trg_mask=trg_mask) - - logits = self.head(out) - return logits - - def forward(self, x: Tensor, trg: Optional[Tensor] = None) -> Tensor: - """Forward pass with CNN transfomer.""" - image_features = self.extract_image_features(x) - logits = self.decode_image_features(image_features, trg) - return logits diff --git a/src/text_recognizer/networks/crnn.py b/src/text_recognizer/networks/crnn.py deleted file mode 100644 index 778e232..0000000 --- a/src/text_recognizer/networks/crnn.py +++ /dev/null @@ -1,110 +0,0 @@ -"""CRNN for handwritten text recognition.""" -from typing import Dict, Tuple - -from einops import rearrange, reduce -from einops.layers.torch import Rearrange -from loguru import logger -from torch import nn -from torch import Tensor - -from text_recognizer.networks.util import configure_backbone - - -class ConvolutionalRecurrentNetwork(nn.Module): - """Network that takes a image of a text line and predicts tokens that are in the image.""" - - def __init__( - self, - backbone: str, - backbone_args: Dict = None, - input_size: int = 128, - hidden_size: int = 128, - bidirectional: bool = False, - num_layers: int = 1, - num_classes: int = 80, - patch_size: Tuple[int, int] = (28, 28), - stride: Tuple[int, int] = (1, 14), - recurrent_cell: str = "lstm", - avg_pool: bool = False, - use_sliding_window: bool = True, - ) -> None: - super().__init__() - self.backbone_args = backbone_args or {} - self.patch_size = patch_size - self.stride = stride - self.sliding_window = ( - self._configure_sliding_window() if use_sliding_window else None - ) - self.input_size = input_size - self.hidden_size = hidden_size - self.backbone = configure_backbone(backbone, backbone_args) - self.bidirectional = bidirectional - self.avg_pool = avg_pool - - if recurrent_cell.upper() in ["LSTM", "GRU"]: - recurrent_cell = getattr(nn, recurrent_cell) - else: - logger.warning( - f"Option {recurrent_cell} not valid, defaulting to LSTM cell." - ) - recurrent_cell = nn.LSTM - - self.rnn = recurrent_cell( - input_size=self.input_size, - hidden_size=self.hidden_size, - bidirectional=bidirectional, - num_layers=num_layers, - ) - - decoder_size = self.hidden_size * 2 if self.bidirectional else self.hidden_size - - self.decoder = nn.Sequential( - nn.Linear(in_features=decoder_size, out_features=num_classes), - nn.LogSoftmax(dim=2), - ) - - def _configure_sliding_window(self) -> nn.Sequential: - return nn.Sequential( - nn.Unfold(kernel_size=self.patch_size, stride=self.stride), - Rearrange( - "b (c h w) t -> b t c h w", - h=self.patch_size[0], - w=self.patch_size[1], - c=1, - ), - ) - - def forward(self, x: Tensor) -> Tensor: - """Converts images to sequence of patches, feeds them to a CNN, then predictions are made with an LSTM.""" - if len(x.shape) < 4: - x = x[(None,) * (4 - len(x.shape))] - - if self.sliding_window is not None: - # Create image patches with a sliding window kernel. - x = self.sliding_window(x) - - # Rearrange from a sequence of patches for feedforward network. - b, t = x.shape[:2] - x = rearrange(x, "b t c h w -> (b t) c h w", b=b, t=t) - - x = self.backbone(x) - - # Average pooling. - if self.avg_pool: - x = reduce(x, "(b t) c h w -> t b c", "mean", b=b, t=t) - else: - x = rearrange(x, "(b t) h -> t b h", b=b, t=t) - else: - # Encode the entire image with a CNN, and use the channels as temporal dimension. - x = self.backbone(x) - x = rearrange(x, "b c h w -> b w c h") - if self.adaptive_pool is not None: - x = self.adaptive_pool(x) - x = x.squeeze(3) - - # Sequence predictions. - x, _ = self.rnn(x) - - # Sequence to classification layer. - x = self.decoder(x) - return x diff --git a/src/text_recognizer/networks/ctc.py b/src/text_recognizer/networks/ctc.py deleted file mode 100644 index af9b700..0000000 --- a/src/text_recognizer/networks/ctc.py +++ /dev/null @@ -1,58 +0,0 @@ -"""Decodes the CTC output.""" -from typing import Callable, List, Optional, Tuple - -from einops import rearrange -import torch -from torch import Tensor - -from text_recognizer.datasets.util import EmnistMapper - - -def greedy_decoder( - predictions: Tensor, - targets: Optional[Tensor] = None, - target_lengths: Optional[Tensor] = None, - character_mapper: Optional[Callable] = None, - blank_label: int = 79, - collapse_repeated: bool = True, -) -> Tuple[List[str], List[str]]: - """Greedy CTC decoder. - - Args: - predictions (Tensor): Tenor of network predictions, shape [time, batch, classes]. - targets (Optional[Tensor]): Target tensor, shape is [batch, targets]. Defaults to None. - target_lengths (Optional[Tensor]): Length of each target tensor. Defaults to None. - character_mapper (Optional[Callable]): A emnist/character mapper for mapping integers to characters. Defaults - to None. - blank_label (int): The blank character to be ignored. Defaults to 80. - collapse_repeated (bool): Collapase consecutive predictions of the same character. Defaults to True. - - Returns: - Tuple[List[str], List[str]]: Tuple of decoded predictions and decoded targets. - - """ - - if character_mapper is None: - character_mapper = EmnistMapper(pad_token="_") # noqa: S106 - - predictions = rearrange(torch.argmax(predictions, dim=2), "t b -> b t") - decoded_predictions = [] - decoded_targets = [] - for i, prediction in enumerate(predictions): - decoded_prediction = [] - decoded_target = [] - if targets is not None and target_lengths is not None: - for target_index in targets[i][: target_lengths[i]]: - if target_index == blank_label: - continue - decoded_target.append(character_mapper(int(target_index))) - decoded_targets.append(decoded_target) - for j, index in enumerate(prediction): - if index != blank_label: - if collapse_repeated and j != 0 and index == prediction[j - 1]: - continue - decoded_prediction.append(index.item()) - decoded_predictions.append( - [character_mapper(int(pred_index)) for pred_index in decoded_prediction] - ) - return decoded_predictions, decoded_targets diff --git a/src/text_recognizer/networks/densenet.py b/src/text_recognizer/networks/densenet.py deleted file mode 100644 index 7dc58d9..0000000 --- a/src/text_recognizer/networks/densenet.py +++ /dev/null @@ -1,225 +0,0 @@ -"""Defines a Densely Connected Convolutional Networks in PyTorch. - -Sources: -https://arxiv.org/abs/1608.06993 -https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py - -""" -from typing import List, Optional, Union - -from einops.layers.torch import Rearrange -import torch -from torch import nn -from torch import Tensor - -from text_recognizer.networks.util import activation_function - - -class _DenseLayer(nn.Module): - """A dense layer with pre-batch norm -> activation function -> Conv-layer x 2.""" - - def __init__( - self, - in_channels: int, - growth_rate: int, - bn_size: int, - dropout_rate: float, - activation: str = "relu", - ) -> None: - super().__init__() - activation_fn = activation_function(activation) - self.dense_layer = [ - nn.BatchNorm2d(in_channels), - activation_fn, - nn.Conv2d( - in_channels=in_channels, - out_channels=bn_size * growth_rate, - kernel_size=1, - stride=1, - bias=False, - ), - nn.BatchNorm2d(bn_size * growth_rate), - activation_fn, - nn.Conv2d( - in_channels=bn_size * growth_rate, - out_channels=growth_rate, - kernel_size=3, - stride=1, - padding=1, - bias=False, - ), - ] - if dropout_rate: - self.dense_layer.append(nn.Dropout(p=dropout_rate)) - - self.dense_layer = nn.Sequential(*self.dense_layer) - - def forward(self, x: Union[Tensor, List[Tensor]]) -> Tensor: - if isinstance(x, list): - x = torch.cat(x, 1) - return self.dense_layer(x) - - -class _DenseBlock(nn.Module): - def __init__( - self, - num_layers: int, - in_channels: int, - bn_size: int, - growth_rate: int, - dropout_rate: float, - activation: str = "relu", - ) -> None: - super().__init__() - self.dense_block = self._build_dense_blocks( - num_layers, in_channels, bn_size, growth_rate, dropout_rate, activation, - ) - - def _build_dense_blocks( - self, - num_layers: int, - in_channels: int, - bn_size: int, - growth_rate: int, - dropout_rate: float, - activation: str = "relu", - ) -> nn.ModuleList: - dense_block = [] - for i in range(num_layers): - dense_block.append( - _DenseLayer( - in_channels=in_channels + i * growth_rate, - growth_rate=growth_rate, - bn_size=bn_size, - dropout_rate=dropout_rate, - activation=activation, - ) - ) - return nn.ModuleList(dense_block) - - def forward(self, x: Tensor) -> Tensor: - feature_maps = [x] - for layer in self.dense_block: - x = layer(feature_maps) - feature_maps.append(x) - return torch.cat(feature_maps, 1) - - -class _Transition(nn.Module): - def __init__( - self, in_channels: int, out_channels: int, activation: str = "relu", - ) -> None: - super().__init__() - activation_fn = activation_function(activation) - self.transition = nn.Sequential( - nn.BatchNorm2d(in_channels), - activation_fn, - nn.Conv2d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=1, - stride=1, - bias=False, - ), - nn.AvgPool2d(kernel_size=2, stride=2), - ) - - def forward(self, x: Tensor) -> Tensor: - return self.transition(x) - - -class DenseNet(nn.Module): - """Implementation of Densenet, a network archtecture that concats previous layers for maximum infomation flow.""" - - def __init__( - self, - growth_rate: int = 32, - block_config: List[int] = (6, 12, 24, 16), - in_channels: int = 1, - base_channels: int = 64, - num_classes: int = 80, - bn_size: int = 4, - dropout_rate: float = 0, - classifier: bool = True, - activation: str = "relu", - ) -> None: - super().__init__() - self.densenet = self._configure_densenet( - in_channels, - base_channels, - num_classes, - growth_rate, - block_config, - bn_size, - dropout_rate, - classifier, - activation, - ) - - def _configure_densenet( - self, - in_channels: int, - base_channels: int, - num_classes: int, - growth_rate: int, - block_config: List[int], - bn_size: int, - dropout_rate: float, - classifier: bool, - activation: str, - ) -> nn.Sequential: - activation_fn = activation_function(activation) - densenet = [ - nn.Conv2d( - in_channels=in_channels, - out_channels=base_channels, - kernel_size=3, - stride=1, - padding=1, - bias=False, - ), - nn.BatchNorm2d(base_channels), - activation_fn, - ] - - num_features = base_channels - - for i, num_layers in enumerate(block_config): - densenet.append( - _DenseBlock( - num_layers=num_layers, - in_channels=num_features, - bn_size=bn_size, - growth_rate=growth_rate, - dropout_rate=dropout_rate, - activation=activation, - ) - ) - num_features = num_features + num_layers * growth_rate - if i != len(block_config) - 1: - densenet.append( - _Transition( - in_channels=num_features, - out_channels=num_features // 2, - activation=activation, - ) - ) - num_features = num_features // 2 - - densenet.append(activation_fn) - - if classifier: - densenet.append(nn.AdaptiveAvgPool2d((1, 1))) - densenet.append(Rearrange("b c h w -> b (c h w)")) - densenet.append( - nn.Linear(in_features=num_features, out_features=num_classes) - ) - - return nn.Sequential(*densenet) - - def forward(self, x: Tensor) -> Tensor: - """Forward pass of Densenet.""" - # If batch dimenstion is missing, it will be added. - if len(x.shape) < 4: - x = x[(None,) * (4 - len(x.shape))] - return self.densenet(x) diff --git a/src/text_recognizer/networks/lenet.py b/src/text_recognizer/networks/lenet.py deleted file mode 100644 index 527e1a0..0000000 --- a/src/text_recognizer/networks/lenet.py +++ /dev/null @@ -1,68 +0,0 @@ -"""Implementation of the LeNet network.""" -from typing import Callable, Dict, Optional, Tuple - -from einops.layers.torch import Rearrange -import torch -from torch import nn - -from text_recognizer.networks.util import activation_function - - -class LeNet(nn.Module): - """LeNet network for character prediction.""" - - def __init__( - self, - channels: Tuple[int, ...] = (1, 32, 64), - kernel_sizes: Tuple[int, ...] = (3, 3, 2), - hidden_size: Tuple[int, ...] = (9216, 128), - dropout_rate: float = 0.2, - num_classes: int = 10, - activation_fn: Optional[str] = "relu", - ) -> None: - """Initialization of the LeNet network. - - Args: - channels (Tuple[int, ...]): Channels in the convolutional layers. Defaults to (1, 32, 64). - kernel_sizes (Tuple[int, ...]): Kernel sizes in the convolutional layers. Defaults to (3, 3, 2). - hidden_size (Tuple[int, ...]): Size of the flattend output form the convolutional layers. - Defaults to (9216, 128). - dropout_rate (float): The dropout rate. Defaults to 0.2. - num_classes (int): Number of classes. Defaults to 10. - activation_fn (Optional[str]): The name of non-linear activation function. Defaults to relu. - - """ - super().__init__() - - activation_fn = activation_function(activation_fn) - - self.layers = [ - nn.Conv2d( - in_channels=channels[0], - out_channels=channels[1], - kernel_size=kernel_sizes[0], - ), - activation_fn, - nn.Conv2d( - in_channels=channels[1], - out_channels=channels[2], - kernel_size=kernel_sizes[1], - ), - activation_fn, - nn.MaxPool2d(kernel_sizes[2]), - nn.Dropout(p=dropout_rate), - Rearrange("b c h w -> b (c h w)"), - nn.Linear(in_features=hidden_size[0], out_features=hidden_size[1]), - activation_fn, - nn.Dropout(p=dropout_rate), - nn.Linear(in_features=hidden_size[1], out_features=num_classes), - ] - - self.layers = nn.Sequential(*self.layers) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """The feedforward pass.""" - # If batch dimenstion is missing, it needs to be added. - if len(x.shape) < 4: - x = x[(None,) * (4 - len(x.shape))] - return self.layers(x) diff --git a/src/text_recognizer/networks/loss/__init__.py b/src/text_recognizer/networks/loss/__init__.py deleted file mode 100644 index b489264..0000000 --- a/src/text_recognizer/networks/loss/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -"""Loss module.""" -from .loss import EmbeddingLoss, LabelSmoothingCrossEntropy diff --git a/src/text_recognizer/networks/loss/loss.py b/src/text_recognizer/networks/loss/loss.py deleted file mode 100644 index cf9fa0d..0000000 --- a/src/text_recognizer/networks/loss/loss.py +++ /dev/null @@ -1,69 +0,0 @@ -"""Implementations of custom loss functions.""" -from pytorch_metric_learning import distances, losses, miners, reducers -import torch -from torch import nn -from torch import Tensor -from torch.autograd import Variable -import torch.nn.functional as F - -__all__ = ["EmbeddingLoss", "LabelSmoothingCrossEntropy"] - - -class EmbeddingLoss: - """Metric loss for training encoders to produce information-rich latent embeddings.""" - - def __init__(self, margin: float = 0.2, type_of_triplets: str = "semihard") -> None: - self.distance = distances.CosineSimilarity() - self.reducer = reducers.ThresholdReducer(low=0) - self.loss_fn = losses.TripletMarginLoss( - margin=margin, distance=self.distance, reducer=self.reducer - ) - self.miner = miners.MultiSimilarityMiner(epsilon=margin, distance=self.distance) - - def __call__(self, embeddings: Tensor, labels: Tensor) -> Tensor: - """Computes the metric loss for the embeddings based on their labels. - - Args: - embeddings (Tensor): The laten vectors encoded by the network. - labels (Tensor): Labels of the embeddings. - - Returns: - Tensor: The metric loss for the embeddings. - - """ - hard_pairs = self.miner(embeddings, labels) - loss = self.loss_fn(embeddings, labels, hard_pairs) - return loss - - -class LabelSmoothingCrossEntropy(nn.Module): - """Label smoothing loss function.""" - - def __init__( - self, - classes: int, - smoothing: float = 0.0, - ignore_index: int = None, - dim: int = -1, - ) -> None: - super().__init__() - self.confidence = 1.0 - smoothing - self.smoothing = smoothing - self.ignore_index = ignore_index - self.cls = classes - self.dim = dim - - def forward(self, pred: Tensor, target: Tensor) -> Tensor: - """Calculates the loss.""" - pred = pred.log_softmax(dim=self.dim) - with torch.no_grad(): - # true_dist = pred.data.clone() - true_dist = torch.zeros_like(pred) - true_dist.fill_(self.smoothing / (self.cls - 1)) - true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence) - if self.ignore_index is not None: - true_dist[:, self.ignore_index] = 0 - mask = torch.nonzero(target == self.ignore_index, as_tuple=False) - if mask.dim() > 0: - true_dist.index_fill_(0, mask.squeeze(), 0.0) - return torch.mean(torch.sum(-true_dist * pred, dim=self.dim)) diff --git a/src/text_recognizer/networks/metrics.py b/src/text_recognizer/networks/metrics.py deleted file mode 100644 index 2605731..0000000 --- a/src/text_recognizer/networks/metrics.py +++ /dev/null @@ -1,123 +0,0 @@ -"""Utility functions for models.""" -from typing import Optional - -from einops import rearrange -import Levenshtein as Lev -import torch -from torch import Tensor - -from text_recognizer.networks import greedy_decoder - - -def accuracy(outputs: Tensor, labels: Tensor, pad_index: int = 53) -> float: - """Computes the accuracy. - - Args: - outputs (Tensor): The output from the network. - labels (Tensor): Ground truth labels. - pad_index (int): Padding index. - - Returns: - float: The accuracy for the batch. - - """ - - _, predicted = torch.max(outputs, dim=-1) - - # Mask out the pad tokens - mask = labels != pad_index - - predicted *= mask - labels *= mask - - acc = (predicted == labels).sum().float() / labels.shape[0] - acc = acc.item() - return acc - - -def cer( - outputs: Tensor, - targets: Tensor, - batch_size: Optional[int] = None, - blank_label: Optional[int] = int, -) -> float: - """Computes the character error rate. - - Args: - outputs (Tensor): The output from the network. - targets (Tensor): Ground truth labels. - batch_size (Optional[int]): Batch size if target and output has been flattend. - blank_label (Optional[int]): The blank character to be ignored. Defaults to 79. - - Returns: - float: The cer for the batch. - - """ - if len(outputs.shape) == 2 and len(targets.shape) == 1 and batch_size is not None: - targets = rearrange(targets, "(b t) -> b t", b=batch_size) - outputs = rearrange(outputs, "(b t) v -> t b v", b=batch_size) - - target_lengths = torch.full( - size=(outputs.shape[1],), fill_value=targets.shape[1], dtype=torch.long, - ) - decoded_predictions, decoded_targets = greedy_decoder( - outputs, targets, target_lengths, blank_label=blank_label, - ) - - lev_dist = 0 - - for prediction, target in zip(decoded_predictions, decoded_targets): - prediction = "".join(prediction) - target = "".join(target) - prediction, target = ( - prediction.replace(" ", ""), - target.replace(" ", ""), - ) - lev_dist += Lev.distance(prediction, target) - return lev_dist / len(decoded_predictions) - - -def wer( - outputs: Tensor, - targets: Tensor, - batch_size: Optional[int] = None, - blank_label: Optional[int] = int, -) -> float: - """Computes the Word error rate. - - Args: - outputs (Tensor): The output from the network. - targets (Tensor): Ground truth labels. - batch_size (optional[int]): Batch size if target and output has been flattend. - blank_label (Optional[int]): The blank character to be ignored. Defaults to 79. - - Returns: - float: The wer for the batch. - - """ - if len(outputs.shape) == 2 and len(targets.shape) == 1 and batch_size is not None: - targets = rearrange(targets, "(b t) -> b t", b=batch_size) - outputs = rearrange(outputs, "(b t) v -> t b v", b=batch_size) - - target_lengths = torch.full( - size=(outputs.shape[1],), fill_value=targets.shape[1], dtype=torch.long, - ) - decoded_predictions, decoded_targets = greedy_decoder( - outputs, targets, target_lengths, blank_label=blank_label, - ) - - lev_dist = 0 - - for prediction, target in zip(decoded_predictions, decoded_targets): - prediction = "".join(prediction) - target = "".join(target) - - b = set(prediction.split() + target.split()) - word2char = dict(zip(b, range(len(b)))) - - w1 = [chr(word2char[w]) for w in prediction.split()] - w2 = [chr(word2char[w]) for w in target.split()] - - lev_dist += Lev.distance("".join(w1), "".join(w2)) - - return lev_dist / len(decoded_predictions) diff --git a/src/text_recognizer/networks/mlp.py b/src/text_recognizer/networks/mlp.py deleted file mode 100644 index 1101912..0000000 --- a/src/text_recognizer/networks/mlp.py +++ /dev/null @@ -1,73 +0,0 @@ -"""Defines the MLP network.""" -from typing import Callable, Dict, List, Optional, Union - -from einops.layers.torch import Rearrange -import torch -from torch import nn - -from text_recognizer.networks.util import activation_function - - -class MLP(nn.Module): - """Multi layered perceptron network.""" - - def __init__( - self, - input_size: int = 784, - num_classes: int = 10, - hidden_size: Union[int, List] = 128, - num_layers: int = 3, - dropout_rate: float = 0.2, - activation_fn: str = "relu", - ) -> None: - """Initialization of the MLP network. - - Args: - input_size (int): The input shape of the network. Defaults to 784. - num_classes (int): Number of classes in the dataset. Defaults to 10. - hidden_size (Union[int, List]): The number of `neurons` in each hidden layer. Defaults to 128. - num_layers (int): The number of hidden layers. Defaults to 3. - dropout_rate (float): The dropout rate at each layer. Defaults to 0.2. - activation_fn (str): Name of the activation function in the hidden layers. Defaults to - relu. - - """ - super().__init__() - - activation_fn = activation_function(activation_fn) - - if isinstance(hidden_size, int): - hidden_size = [hidden_size] * num_layers - - self.layers = [ - Rearrange("b c h w -> b (c h w)"), - nn.Linear(in_features=input_size, out_features=hidden_size[0]), - activation_fn, - ] - - for i in range(num_layers - 1): - self.layers += [ - nn.Linear(in_features=hidden_size[i], out_features=hidden_size[i + 1]), - activation_fn, - ] - - if dropout_rate: - self.layers.append(nn.Dropout(p=dropout_rate)) - - self.layers.append( - nn.Linear(in_features=hidden_size[-1], out_features=num_classes) - ) - - self.layers = nn.Sequential(*self.layers) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """The feedforward pass.""" - # If batch dimenstion is missing, it needs to be added. - if len(x.shape) < 4: - x = x[(None,) * (4 - len(x.shape))] - return self.layers(x) - - @property - def __name__(self) -> str: - """Returns the name of the network.""" - return "mlp" diff --git a/src/text_recognizer/networks/residual_network.py b/src/text_recognizer/networks/residual_network.py deleted file mode 100644 index c33f419..0000000 --- a/src/text_recognizer/networks/residual_network.py +++ /dev/null @@ -1,310 +0,0 @@ -"""Residual CNN.""" -from functools import partial -from typing import Callable, Dict, List, Optional, Type, Union - -from einops.layers.torch import Rearrange, Reduce -import torch -from torch import nn -from torch import Tensor - -from text_recognizer.networks.util import activation_function - - -class Conv2dAuto(nn.Conv2d): - """Convolution with auto padding based on kernel size.""" - - def __init__(self, *args, **kwargs) -> None: - super().__init__(*args, **kwargs) - self.padding = (self.kernel_size[0] // 2, self.kernel_size[1] // 2) - - -def conv_bn(in_channels: int, out_channels: int, *args, **kwargs) -> nn.Sequential: - """3x3 convolution with batch norm.""" - conv3x3 = partial(Conv2dAuto, kernel_size=3, bias=False,) - return nn.Sequential( - conv3x3(in_channels, out_channels, *args, **kwargs), - nn.BatchNorm2d(out_channels), - ) - - -class IdentityBlock(nn.Module): - """Residual with identity block.""" - - def __init__( - self, in_channels: int, out_channels: int, activation: str = "relu" - ) -> None: - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.blocks = nn.Identity() - self.activation_fn = activation_function(activation) - self.shortcut = nn.Identity() - - def forward(self, x: Tensor) -> Tensor: - """Forward pass.""" - residual = x - if self.apply_shortcut: - residual = self.shortcut(x) - x = self.blocks(x) - x += residual - x = self.activation_fn(x) - return x - - @property - def apply_shortcut(self) -> bool: - """Check if shortcut should be applied.""" - return self.in_channels != self.out_channels - - -class ResidualBlock(IdentityBlock): - """Residual with nonlinear shortcut.""" - - def __init__( - self, - in_channels: int, - out_channels: int, - expansion: int = 1, - downsampling: int = 1, - *args, - **kwargs - ) -> None: - """Short summary. - - Args: - in_channels (int): Number of in channels. - out_channels (int): umber of out channels. - expansion (int): Expansion factor of the out channels. Defaults to 1. - downsampling (int): Downsampling factor used in stride. Defaults to 1. - *args (type): Extra arguments. - **kwargs (type): Extra key value arguments. - - """ - super().__init__(in_channels, out_channels, *args, **kwargs) - self.expansion = expansion - self.downsampling = downsampling - - self.shortcut = ( - nn.Sequential( - nn.Conv2d( - in_channels=self.in_channels, - out_channels=self.expanded_channels, - kernel_size=1, - stride=self.downsampling, - bias=False, - ), - nn.BatchNorm2d(self.expanded_channels), - ) - if self.apply_shortcut - else None - ) - - @property - def expanded_channels(self) -> int: - """Computes the expanded output channels.""" - return self.out_channels * self.expansion - - @property - def apply_shortcut(self) -> bool: - """Check if shortcut should be applied.""" - return self.in_channels != self.expanded_channels - - -class BasicBlock(ResidualBlock): - """Basic ResNet block.""" - - expansion = 1 - - def __init__(self, in_channels: int, out_channels: int, *args, **kwargs) -> None: - super().__init__(in_channels, out_channels, *args, **kwargs) - self.blocks = nn.Sequential( - conv_bn( - in_channels=self.in_channels, - out_channels=self.out_channels, - bias=False, - stride=self.downsampling, - ), - self.activation_fn, - conv_bn( - in_channels=self.out_channels, - out_channels=self.expanded_channels, - bias=False, - ), - ) - - -class BottleNeckBlock(ResidualBlock): - """Bottleneck block to increase depth while minimizing parameter size.""" - - expansion = 4 - - def __init__(self, in_channels: int, out_channels: int, *args, **kwargs) -> None: - super().__init__(in_channels, out_channels, *args, **kwargs) - self.blocks = nn.Sequential( - conv_bn( - in_channels=self.in_channels, - out_channels=self.out_channels, - kernel_size=1, - ), - self.activation_fn, - conv_bn( - in_channels=self.out_channels, - out_channels=self.out_channels, - kernel_size=3, - stride=self.downsampling, - ), - self.activation_fn, - conv_bn( - in_channels=self.out_channels, - out_channels=self.expanded_channels, - kernel_size=1, - ), - ) - - -class ResidualLayer(nn.Module): - """ResNet layer.""" - - def __init__( - self, - in_channels: int, - out_channels: int, - block: BasicBlock = BasicBlock, - num_blocks: int = 1, - *args, - **kwargs - ) -> None: - super().__init__() - downsampling = 2 if in_channels != out_channels else 1 - self.blocks = nn.Sequential( - block( - in_channels, out_channels, *args, **kwargs, downsampling=downsampling - ), - *[ - block( - out_channels * block.expansion, - out_channels, - downsampling=1, - *args, - **kwargs - ) - for _ in range(num_blocks - 1) - ] - ) - - def forward(self, x: Tensor) -> Tensor: - """Forward pass.""" - x = self.blocks(x) - return x - - -class ResidualNetworkEncoder(nn.Module): - """Encoder network.""" - - def __init__( - self, - in_channels: int = 1, - block_sizes: Union[int, List[int]] = (32, 64), - depths: Union[int, List[int]] = (2, 2), - activation: str = "relu", - block: Type[nn.Module] = BasicBlock, - levels: int = 1, - *args, - **kwargs - ) -> None: - super().__init__() - self.block_sizes = ( - block_sizes if isinstance(block_sizes, list) else [block_sizes] * levels - ) - self.depths = depths if isinstance(depths, list) else [depths] * levels - self.activation = activation - self.gate = nn.Sequential( - nn.Conv2d( - in_channels=in_channels, - out_channels=self.block_sizes[0], - kernel_size=7, - stride=2, - padding=1, - bias=False, - ), - nn.BatchNorm2d(self.block_sizes[0]), - activation_function(self.activation), - # nn.MaxPool2d(kernel_size=2, stride=2, padding=1), - ) - - self.blocks = self._configure_blocks(block) - - def _configure_blocks( - self, block: Type[nn.Module], *args, **kwargs - ) -> nn.Sequential: - channels = [self.block_sizes[0]] + list( - zip(self.block_sizes, self.block_sizes[1:]) - ) - blocks = [ - ResidualLayer( - in_channels=channels[0], - out_channels=channels[0], - num_blocks=self.depths[0], - block=block, - activation=self.activation, - *args, - **kwargs - ) - ] - blocks += [ - ResidualLayer( - in_channels=in_channels * block.expansion, - out_channels=out_channels, - num_blocks=num_blocks, - block=block, - activation=self.activation, - *args, - **kwargs - ) - for (in_channels, out_channels), num_blocks in zip( - channels[1:], self.depths[1:] - ) - ] - - return nn.Sequential(*blocks) - - def forward(self, x: Tensor) -> Tensor: - """Forward pass.""" - # If batch dimenstion is missing, it needs to be added. - if len(x.shape) == 3: - x = x.unsqueeze(0) - x = self.gate(x) - x = self.blocks(x) - return x - - -class ResidualNetworkDecoder(nn.Module): - """Classification head.""" - - def __init__(self, in_features: int, num_classes: int = 80) -> None: - super().__init__() - self.decoder = nn.Sequential( - Reduce("b c h w -> b c", "mean"), - nn.Linear(in_features=in_features, out_features=num_classes), - ) - - def forward(self, x: Tensor) -> Tensor: - """Forward pass.""" - return self.decoder(x) - - -class ResidualNetwork(nn.Module): - """Full residual network.""" - - def __init__(self, in_channels: int, num_classes: int, *args, **kwargs) -> None: - super().__init__() - self.encoder = ResidualNetworkEncoder(in_channels, *args, **kwargs) - self.decoder = ResidualNetworkDecoder( - in_features=self.encoder.blocks[-1].blocks[-1].expanded_channels, - num_classes=num_classes, - ) - - def forward(self, x: Tensor) -> Tensor: - """Forward pass.""" - x = self.encoder(x) - x = self.decoder(x) - return x diff --git a/src/text_recognizer/networks/stn.py b/src/text_recognizer/networks/stn.py deleted file mode 100644 index e9d216f..0000000 --- a/src/text_recognizer/networks/stn.py +++ /dev/null @@ -1,44 +0,0 @@ -"""Spatial Transformer Network.""" - -from einops.layers.torch import Rearrange -import torch -from torch import nn -from torch import Tensor -import torch.nn.functional as F - - -class SpatialTransformerNetwork(nn.Module): - """A network with differentiable attention. - - Network that learns how to perform spatial transformations on the input image in order to enhance the - geometric invariance of the model. - - # TODO: add arguments to make it more general. - - """ - - def __init__(self) -> None: - super().__init__() - # Initialize the identity transformation and its weights and biases. - linear = nn.Linear(32, 3 * 2) - linear.weight.data.zero_() - linear.bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float)) - - self.theta = nn.Sequential( - nn.Conv2d(in_channels=1, out_channels=8, kernel_size=7), - nn.MaxPool2d(kernel_size=2, stride=2), - nn.ReLU(inplace=True), - nn.Conv2d(in_channels=8, out_channels=10, kernel_size=5), - nn.MaxPool2d(kernel_size=2, stride=2), - nn.ReLU(inplace=True), - Rearrange("b c h w -> b (c h w)", h=3, w=3), - nn.Linear(in_features=10 * 3 * 3, out_features=32), - nn.ReLU(inplace=True), - linear, - Rearrange("b (row col) -> b row col", row=2, col=3), - ) - - def forward(self, x: Tensor) -> Tensor: - """The spatial transformation.""" - grid = F.affine_grid(self.theta(x), x.shape) - return F.grid_sample(x, grid, align_corners=False) diff --git a/src/text_recognizer/networks/transducer/__init__.py b/src/text_recognizer/networks/transducer/__init__.py deleted file mode 100644 index 8c19a01..0000000 --- a/src/text_recognizer/networks/transducer/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -"""Transducer modules.""" -from .tds_conv import TDS2d -from .transducer import load_transducer_loss, Transducer diff --git a/src/text_recognizer/networks/transducer/tds_conv.py b/src/text_recognizer/networks/transducer/tds_conv.py deleted file mode 100644 index 5fb8ba9..0000000 --- a/src/text_recognizer/networks/transducer/tds_conv.py +++ /dev/null @@ -1,208 +0,0 @@ -"""Time-Depth Separable Convolutions. - -References: - https://arxiv.org/abs/1904.02619 - https://arxiv.org/pdf/2010.01003.pdf - -Code stolen from: - https://github.com/facebookresearch/gtn_applications - - -""" -from typing import List, Tuple - -from einops import rearrange -import gtn -import numpy as np -import torch -from torch import nn -from torch import Tensor - - -class TDSBlock2d(nn.Module): - """Internal block of a 2D TDSC network.""" - - def __init__( - self, - in_channels: int, - img_depth: int, - kernel_size: Tuple[int], - dropout_rate: float, - ) -> None: - super().__init__() - - self.in_channels = in_channels - self.img_depth = img_depth - self.kernel_size = kernel_size - self.dropout_rate = dropout_rate - self.fc_dim = in_channels * img_depth - - # Network placeholders. - self.conv = None - self.mlp = None - self.instance_norm = None - - self._build_block() - - def _build_block(self) -> None: - # Convolutional block. - self.conv = nn.Sequential( - nn.Conv3d( - in_channels=self.in_channels, - out_channels=self.in_channels, - kernel_size=(1, self.kernel_size[0], self.kernel_size[1]), - padding=(0, self.kernel_size[0] // 2, self.kernel_size[1] // 2), - ), - nn.ReLU(inplace=True), - nn.Dropout(self.dropout_rate), - ) - - # MLP block. - self.mlp = nn.Sequential( - nn.Linear(self.fc_dim, self.fc_dim), - nn.ReLU(inplace=True), - nn.Dropout(self.dropout_rate), - nn.Linear(self.fc_dim, self.fc_dim), - nn.Dropout(self.dropout_rate), - ) - - # Instance norm. - self.instance_norm = nn.ModuleList( - [ - nn.InstanceNorm2d(self.fc_dim, affine=True), - nn.InstanceNorm2d(self.fc_dim, affine=True), - ] - ) - - def forward(self, x: Tensor) -> Tensor: - """Forward pass. - - Args: - x (Tensor): Input tensor. - - Shape: - - x: :math: `(B, CD, H, W)` - - Returns: - Tensor: Output tensor. - - """ - B, CD, H, W = x.shape - C, D = self.in_channels, self.img_depth - residual = x - x = rearrange(x, "b (c d) h w -> b c d h w", c=C, d=D) - x = self.conv(x) - x = rearrange(x, "b c d h w -> b (c d) h w") - x += residual - - x = self.instance_norm[0](x) - - x = self.mlp(x.transpose(1, 3)).transpose(1, 3) + x - x + self.instance_norm[1](x) - - # Output shape: [B, CD, H, W] - return x - - -class TDS2d(nn.Module): - """TDS Netowrk. - - Structure is the following: - Downsample layer -> TDS2d group -> ... -> Linear output layer - - - """ - - def __init__( - self, - input_dim: int, - output_dim: int, - depth: int, - tds_groups: Tuple[int], - kernel_size: Tuple[int], - dropout_rate: float, - in_channels: int = 1, - ) -> None: - super().__init__() - - self.in_channels = in_channels - self.input_dim = input_dim - self.output_dim = output_dim - self.depth = depth - self.tds_groups = tds_groups - self.kernel_size = kernel_size - self.dropout_rate = dropout_rate - - self.tds = None - self.fc = None - - self._build_network() - - def _build_network(self) -> None: - in_channels = self.in_channels - modules = [] - stride_h = np.prod([grp["stride"][0] for grp in self.tds_groups]) - if self.input_dim % stride_h: - raise RuntimeError( - f"Image height not divisible by total stride {stride_h}." - ) - - for tds_group in self.tds_groups: - # Add downsample layer. - out_channels = self.depth * tds_group["channels"] - modules.extend( - [ - nn.Conv2d( - in_channels=in_channels, - out_channels=out_channels, - kernel_size=self.kernel_size, - padding=(self.kernel_size[0] // 2, self.kernel_size[1] // 2), - stride=tds_group["stride"], - ), - nn.ReLU(inplace=True), - nn.Dropout(self.dropout_rate), - nn.InstanceNorm2d(out_channels, affine=True), - ] - ) - - for _ in range(tds_group["num_blocks"]): - modules.append( - TDSBlock2d( - tds_group["channels"], - self.depth, - self.kernel_size, - self.dropout_rate, - ) - ) - - in_channels = out_channels - - self.tds = nn.Sequential(*modules) - self.fc = nn.Linear(in_channels * self.input_dim // stride_h, self.output_dim) - - def forward(self, x: Tensor) -> Tensor: - """Forward pass. - - Args: - x (Tensor): Input tensor. - - Shape: - - x: :math: `(B, H, W)` - - Returns: - Tensor: Output tensor. - - """ - if len(x.shape) == 4: - x = x.squeeze(1) # Squeeze the channel dim away. - - B, H, W = x.shape - x = rearrange( - x, "b (h1 h2) w -> b h1 h2 w", h1=self.in_channels, h2=H // self.in_channels - ) - x = self.tds(x) - - # x shape: [B, C, H, W] - x = rearrange(x, "b c h w -> b w (c h)") - - return self.fc(x) diff --git a/src/text_recognizer/networks/transducer/test.py b/src/text_recognizer/networks/transducer/test.py deleted file mode 100644 index cadcecc..0000000 --- a/src/text_recognizer/networks/transducer/test.py +++ /dev/null @@ -1,60 +0,0 @@ -import torch -from torch import nn - -from text_recognizer.networks.transducer import load_transducer_loss, Transducer -import unittest - - -class TestTransducer(unittest.TestCase): - def test_viterbi(self): - T = 5 - N = 4 - B = 2 - - # fmt: off - emissions1 = torch.tensor(( - 0, 4, 0, 1, - 0, 2, 1, 1, - 0, 0, 0, 2, - 0, 0, 0, 2, - 8, 0, 0, 2, - ), - dtype=torch.float, - ).view(T, N) - emissions2 = torch.tensor(( - 0, 2, 1, 7, - 0, 2, 9, 1, - 0, 0, 0, 2, - 0, 0, 5, 2, - 1, 0, 0, 2, - ), - dtype=torch.float, - ).view(T, N) - # fmt: on - - # Test without blank: - labels = [[1, 3, 0], [3, 2, 3, 2, 3]] - transducer = Transducer( - tokens=["a", "b", "c", "d"], - graphemes_to_idx={"a": 0, "b": 1, "c": 2, "d": 3}, - blank="none", - ) - emissions = torch.stack([emissions1, emissions2], dim=0) - predictions = transducer.viterbi(emissions) - self.assertEqual([p.tolist() for p in predictions], labels) - - # Test with blank without repeats: - labels = [[1, 0], [2, 2]] - transducer = Transducer( - tokens=["a", "b", "c"], - graphemes_to_idx={"a": 0, "b": 1, "c": 2}, - blank="optional", - allow_repeats=False, - ) - emissions = torch.stack([emissions1, emissions2], dim=0) - predictions = transducer.viterbi(emissions) - self.assertEqual([p.tolist() for p in predictions], labels) - - -if __name__ == "__main__": - unittest.main() diff --git a/src/text_recognizer/networks/transducer/transducer.py b/src/text_recognizer/networks/transducer/transducer.py deleted file mode 100644 index d7e3d08..0000000 --- a/src/text_recognizer/networks/transducer/transducer.py +++ /dev/null @@ -1,410 +0,0 @@ -"""Transducer and the transducer loss function.py - -Stolen from: - https://github.com/facebookresearch/gtn_applications/blob/master/transducer.py - -""" -from pathlib import Path -import itertools -from typing import Dict, List, Optional, Union, Tuple - -from loguru import logger -import gtn -import torch -from torch import nn -from torch import Tensor - -from text_recognizer.datasets.iam_preprocessor import Preprocessor - - -def make_scalar_graph(weight) -> gtn.Graph: - scalar = gtn.Graph() - scalar.add_node(True) - scalar.add_node(False, True) - scalar.add_arc(0, 1, 0, 0, weight) - return scalar - - -def make_chain_graph(sequence) -> gtn.Graph: - graph = gtn.Graph(False) - graph.add_node(True) - for i, s in enumerate(sequence): - graph.add_node(False, i == (len(sequence) - 1)) - graph.add_arc(i, i + 1, s) - return graph - - -def make_transitions_graph( - ngram: int, num_tokens: int, calc_grad: bool = False -) -> gtn.Graph: - transitions = gtn.Graph(calc_grad) - transitions.add_node(True, ngram == 1) - - state_map = {(): 0} - - # First build transitions which include <s>: - for n in range(1, ngram): - for state in itertools.product(range(num_tokens), repeat=n): - in_idx = state_map[state[:-1]] - out_idx = transitions.add_node(False, ngram == 1) - state_map[state] = out_idx - transitions.add_arc(in_idx, out_idx, state[-1]) - - for state in itertools.product(range(num_tokens), repeat=ngram): - state_idx = state_map[state[:-1]] - new_state_idx = state_map[state[1:]] - # p(state[-1] | state[:-1]) - transitions.add_arc(state_idx, new_state_idx, state[-1]) - - if ngram > 1: - # Build transitions which include </s>: - end_idx = transitions.add_node(False, True) - for in_idx in range(end_idx): - transitions.add_arc(in_idx, end_idx, gtn.epsilon) - - return transitions - - -def make_lexicon_graph(word_pieces: List, graphemes_to_idx: Dict) -> gtn.Graph: - """Constructs a graph which transduces letters to word pieces.""" - graph = gtn.Graph(False) - graph.add_node(True, True) - for i, wp in enumerate(word_pieces): - prev = 0 - for l in wp[:-1]: - n = graph.add_node() - graph.add_arc(prev, n, graphemes_to_idx[l], gtn.epsilon) - prev = n - graph.add_arc(prev, 0, graphemes_to_idx[wp[-1]], i) - graph.arc_sort() - return graph - - -def make_token_graph( - token_list: List, blank: str = "none", allow_repeats: bool = True -) -> gtn.Graph: - """Constructs a graph with all the individual token transition models.""" - if not allow_repeats and blank != "optional": - raise ValueError("Must use blank='optional' if disallowing repeats.") - - ntoks = len(token_list) - graph = gtn.Graph(False) - - # Creating nodes - graph.add_node(True, True) - for i in range(ntoks): - # We can consume one or more consecutive word - # pieces for each emission: - # E.g. [ab, ab, ab] transduces to [ab] - graph.add_node(False, blank != "forced") - - if blank != "none": - graph.add_node() - - # Creating arcs - if blank != "none": - # Blank index is assumed to be last (ntoks) - graph.add_arc(0, ntoks + 1, ntoks, gtn.epsilon) - graph.add_arc(ntoks + 1, 0, gtn.epsilon) - - for i in range(ntoks): - graph.add_arc((ntoks + 1) if blank == "forced" else 0, i + 1, i) - graph.add_arc(i + 1, i + 1, i, gtn.epsilon) - - if allow_repeats: - if blank == "forced": - # Allow transitions from token to blank only - graph.add_arc(i + 1, ntoks + 1, ntoks, gtn.epsilon) - else: - # Allow transition from token to blank and all other tokens - graph.add_arc(i + 1, 0, gtn.epsilon) - - else: - # allow transitions to blank and all other tokens except the same token - graph.add_arc(i + 1, ntoks + 1, ntoks, gtn.epsilon) - for j in range(ntoks): - if i != j: - graph.add_arc(i + 1, j + 1, j, j) - - return graph - - -class TransducerLossFunction(torch.autograd.Function): - @staticmethod - def forward( - ctx, - inputs, - targets, - tokens, - lexicon, - transition_params=None, - transitions=None, - reduction="none", - ) -> Tensor: - B, T, C = inputs.shape - - losses = [None] * B - emissions_graphs = [None] * B - - if transitions is not None: - if transition_params is None: - raise ValueError("Specified transitions, but not transition params.") - - cpu_data = transition_params.cpu().contiguous() - transitions.set_weights(cpu_data.data_ptr()) - transitions.calc_grad = transition_params.requires_grad - transitions.zero_grad() - - def process(b: int) -> None: - # Create emission graph: - emissions = gtn.linear_graph(T, C, inputs.requires_grad) - cpu_data = inputs[b].cpu().contiguous() - emissions.set_weights(cpu_data.data_ptr()) - target = make_chain_graph(targets[b]) - target.arc_sort(True) - - # Create token tot grapheme decomposition graph - tokens_target = gtn.remove(gtn.project_output(gtn.compose(target, lexicon))) - tokens_target.arc_sort() - - # Create alignment graph: - aligments = gtn.project_input( - gtn.remove(gtn.compose(tokens, tokens_target)) - ) - aligments.arc_sort() - - # Add transitions scores: - if transitions is not None: - aligments = gtn.intersect(transitions, aligments) - aligments.arc_sort() - - loss = gtn.forward_score(gtn.intersect(emissions, aligments)) - - # Normalize if needed: - if transitions is not None: - norm = gtn.forward_score(gtn.intersect(emissions, transitions)) - loss = gtn.subtract(loss, norm) - - losses[b] = gtn.negate(loss) - - # Save for backward: - if emissions.calc_grad: - emissions_graphs[b] = emissions - - gtn.parallel_for(process, range(B)) - - ctx.graphs = (losses, emissions_graphs, transitions) - ctx.input_shape = inputs.shape - - # Optionally reduce by target length - if reduction == "mean": - scales = [(1 / len(t) if len(t) > 0 else 1.0) for t in targets] - else: - scales = [1.0] * B - - ctx.scales = scales - - loss = torch.tensor([l.item() * s for l, s in zip(losses, scales)]) - return torch.mean(loss.to(inputs.device)) - - @staticmethod - def backward(ctx, grad_output) -> Tuple: - losses, emissions_graphs, transitions = ctx.graphs - scales = ctx.scales - - B, T, C = ctx.input_shape - calc_emissions = ctx.needs_input_grad[0] - input_grad = torch.empty((B, T, C)) if calc_emissions else None - - def process(b: int) -> None: - scale = make_scalar_graph(scales[b]) - gtn.backward(losses[b], scale) - emissions = emissions_graphs[b] - if calc_emissions: - grad = emissions.grad().weights_to_numpy() - input_grad[b] = torch.tensor(grad).view(1, T, C) - - gtn.parallel_for(process, range(B)) - - if calc_emissions: - input_grad = input_grad.to(grad_output.device) - input_grad *= grad_output / B - - if ctx.needs_input_grad[4]: - grad = transitions.grad().weights_to_numpy() - transition_grad = torch.tensor(grad).to(grad_output.device) - transition_grad *= grad_output / B - else: - transition_grad = None - - return ( - input_grad, - None, # target - None, # tokens - None, # lexicon - transition_grad, # transition params - None, # transitions graph - None, - ) - - -TransducerLoss = TransducerLossFunction.apply - - -class Transducer(nn.Module): - def __init__( - self, - tokens: List, - graphemes_to_idx: Dict, - ngram: int = 0, - transitions: str = None, - blank: str = "none", - allow_repeats: bool = True, - reduction: str = "none", - ) -> None: - """A generic transducer loss function. - - Args: - tokens (List) : A list of iterable objects (e.g. strings, tuples, etc) - representing the output tokens of the model (e.g. letters, - word-pieces, words). For example ["a", "b", "ab", "ba", "aba"] - could be a list of sub-word tokens. - graphemes_to_idx (dict) : A dictionary mapping grapheme units (e.g. - "a", "b", ..) to their corresponding integer index. - ngram (int) : Order of the token-level transition model. If `ngram=0` - then no transition model is used. - blank (string) : Specifies the usage of blank token - 'none' - do not use blank token - 'optional' - allow an optional blank inbetween tokens - 'forced' - force a blank inbetween tokens (also referred to as garbage token) - allow_repeats (boolean) : If false, then we don't allow paths with - consecutive tokens in the alignment graph. This keeps the graph - unambiguous in the sense that the same input cannot transduce to - different outputs. - """ - super().__init__() - if blank not in ["optional", "forced", "none"]: - raise ValueError( - "Invalid value specified for blank. Must be in ['optional', 'forced', 'none']" - ) - self.tokens = make_token_graph(tokens, blank=blank, allow_repeats=allow_repeats) - self.lexicon = make_lexicon_graph(tokens, graphemes_to_idx) - self.ngram = ngram - if ngram > 0 and transitions is not None: - raise ValueError("Only one of ngram and transitions may be specified") - - if ngram > 0: - transitions = make_transitions_graph( - ngram, len(tokens) + int(blank != "none"), True - ) - - if transitions is not None: - self.transitions = transitions - self.transitions.arc_sort() - self.transitions_params = nn.Parameter( - torch.zeros(self.transitions.num_arcs()) - ) - else: - self.transitions = None - self.transitions_params = None - self.reduction = reduction - - def forward(self, inputs: Tensor, targets: Tensor) -> TransducerLoss: - TransducerLoss( - inputs, - targets, - self.tokens, - self.lexicon, - self.transitions_params, - self.transitions, - self.reduction, - ) - - def viterbi(self, outputs: Tensor) -> List[Tensor]: - B, T, C = outputs.shape - - if self.transitions is not None: - cpu_data = self.transition_params.cpu().contiguous() - self.transitions.set_weights(cpu_data.data_ptr()) - self.transitions.calc_grad = False - - self.tokens.arc_sort() - - paths = [None] * B - - def process(b: int) -> None: - emissions = gtn.linear_graph(T, C, False) - cpu_data = outputs[b].cpu().contiguous() - emissions.set_weights(cpu_data.data_ptr()) - - if self.transitions is not None: - full_graph = gtn.intersect(emissions, self.transitions) - else: - full_graph = emissions - - # Find the best path and remove back-off arcs: - path = gtn.remove(gtn.viterbi_path(full_graph)) - - # Left compose the viterbi path with the "aligment to token" - # transducer to get the outputs: - path = gtn.compose(path, self.tokens) - - # When there are ambiguous paths (allow_repeats is true), we take - # the shortest: - path = gtn.viterbi_path(path) - path = gtn.remove(gtn.project_output(path)) - paths[b] = path.labels_to_list() - - gtn.parallel_for(process, range(B)) - predictions = [torch.IntTensor(path) for path in paths] - return predictions - - -def load_transducer_loss( - num_features: int, - ngram: int, - tokens: str, - lexicon: str, - transitions: str, - blank: str, - allow_repeats: bool, - prepend_wordsep: bool = False, - use_words: bool = False, - data_dir: Optional[Union[str, Path]] = None, - reduction: str = "mean", -) -> Tuple[Transducer, int]: - if data_dir is None: - data_dir = ( - Path(__file__).resolve().parents[4] / "data" / "raw" / "iam" / "iamdb" - ) - logger.debug(f"Using data dir: {data_dir}") - if not data_dir.exists(): - raise RuntimeError(f"Could not locate iamdb directory at {data_dir}") - else: - data_dir = Path(data_dir) - processed_path = ( - Path(__file__).resolve().parents[4] / "data" / "processed" / "iam_lines" - ) - tokens_path = processed_path / tokens - lexicon_path = processed_path / lexicon - - if transitions is not None: - transitions = gtn.load(str(processed_path / transitions)) - - preprocessor = Preprocessor( - data_dir, num_features, tokens_path, lexicon_path, use_words, prepend_wordsep, - ) - - num_tokens = preprocessor.num_tokens - - criterion = Transducer( - preprocessor.tokens, - preprocessor.graphemes_to_index, - ngram=ngram, - transitions=transitions, - blank=blank, - allow_repeats=allow_repeats, - reduction=reduction, - ) - - return criterion, num_tokens + int(blank != "none") diff --git a/src/text_recognizer/networks/transformer/__init__.py b/src/text_recognizer/networks/transformer/__init__.py deleted file mode 100644 index 9febc88..0000000 --- a/src/text_recognizer/networks/transformer/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -"""Transformer modules.""" -from .positional_encoding import PositionalEncoding -from .transformer import Decoder, Encoder, EncoderLayer, Transformer diff --git a/src/text_recognizer/networks/transformer/attention.py b/src/text_recognizer/networks/transformer/attention.py deleted file mode 100644 index cce1ecc..0000000 --- a/src/text_recognizer/networks/transformer/attention.py +++ /dev/null @@ -1,93 +0,0 @@ -"""Implementes the attention module for the transformer.""" -from typing import Optional, Tuple - -from einops import rearrange -import numpy as np -import torch -from torch import nn -from torch import Tensor - - -class MultiHeadAttention(nn.Module): - """Implementation of multihead attention.""" - - def __init__( - self, hidden_dim: int, num_heads: int = 8, dropout_rate: float = 0.0 - ) -> None: - super().__init__() - self.hidden_dim = hidden_dim - self.num_heads = num_heads - self.fc_q = nn.Linear( - in_features=hidden_dim, out_features=hidden_dim, bias=False - ) - self.fc_k = nn.Linear( - in_features=hidden_dim, out_features=hidden_dim, bias=False - ) - self.fc_v = nn.Linear( - in_features=hidden_dim, out_features=hidden_dim, bias=False - ) - self.fc_out = nn.Linear(in_features=hidden_dim, out_features=hidden_dim) - - self._init_weights() - - self.dropout = nn.Dropout(p=dropout_rate) - - def _init_weights(self) -> None: - nn.init.normal_( - self.fc_q.weight, - mean=0, - std=np.sqrt(self.hidden_dim + int(self.hidden_dim / self.num_heads)), - ) - nn.init.normal_( - self.fc_k.weight, - mean=0, - std=np.sqrt(self.hidden_dim + int(self.hidden_dim / self.num_heads)), - ) - nn.init.normal_( - self.fc_v.weight, - mean=0, - std=np.sqrt(self.hidden_dim + int(self.hidden_dim / self.num_heads)), - ) - nn.init.xavier_normal_(self.fc_out.weight) - - def scaled_dot_product_attention( - self, query: Tensor, key: Tensor, value: Tensor, mask: Optional[Tensor] = None - ) -> Tensor: - """Calculates the scaled dot product attention.""" - - # Compute the energy. - energy = torch.einsum("bhlk,bhtk->bhlt", [query, key]) / np.sqrt( - query.shape[-1] - ) - - # If we have a mask for padding some inputs. - if mask is not None: - energy = energy.masked_fill(mask == 0, -np.inf) - - # Compute the attention from the energy. - attention = torch.softmax(energy, dim=3) - - out = torch.einsum("bhlt,bhtv->bhlv", [attention, value]) - out = rearrange(out, "b head l v -> b l (head v)") - return out, attention - - def forward( - self, query: Tensor, key: Tensor, value: Tensor, mask: Optional[Tensor] = None - ) -> Tuple[Tensor, Tensor]: - """Forward pass for computing the multihead attention.""" - # Get the query, key, and value tensor. - query = rearrange( - self.fc_q(query), "b l (head k) -> b head l k", head=self.num_heads - ) - key = rearrange( - self.fc_k(key), "b t (head k) -> b head t k", head=self.num_heads - ) - value = rearrange( - self.fc_v(value), "b t (head v) -> b head t v", head=self.num_heads - ) - - out, attention = self.scaled_dot_product_attention(query, key, value, mask) - - out = self.fc_out(out) - out = self.dropout(out) - return out, attention diff --git a/src/text_recognizer/networks/transformer/positional_encoding.py b/src/text_recognizer/networks/transformer/positional_encoding.py deleted file mode 100644 index 1ba5537..0000000 --- a/src/text_recognizer/networks/transformer/positional_encoding.py +++ /dev/null @@ -1,32 +0,0 @@ -"""A positional encoding for the image features, as the transformer has no notation of the order of the sequence.""" -import numpy as np -import torch -from torch import nn -from torch import Tensor - - -class PositionalEncoding(nn.Module): - """Encodes a sense of distance or time for transformer networks.""" - - def __init__( - self, hidden_dim: int, dropout_rate: float, max_len: int = 1000 - ) -> None: - super().__init__() - self.dropout = nn.Dropout(p=dropout_rate) - self.max_len = max_len - - pe = torch.zeros(max_len, hidden_dim) - position = torch.arange(0, max_len).unsqueeze(1) - div_term = torch.exp( - torch.arange(0, hidden_dim, 2) * -(np.log(10000.0) / hidden_dim) - ) - - pe[:, 0::2] = torch.sin(position * div_term) - pe[:, 1::2] = torch.cos(position * div_term) - pe = pe.unsqueeze(0) - self.register_buffer("pe", pe) - - def forward(self, x: Tensor) -> Tensor: - """Encodes the tensor with a postional embedding.""" - x = x + self.pe[:, : x.shape[1]] - return self.dropout(x) diff --git a/src/text_recognizer/networks/transformer/transformer.py b/src/text_recognizer/networks/transformer/transformer.py deleted file mode 100644 index dd180c4..0000000 --- a/src/text_recognizer/networks/transformer/transformer.py +++ /dev/null @@ -1,264 +0,0 @@ -"""Transfomer module.""" -import copy -from typing import Dict, Optional, Type, Union - -import numpy as np -import torch -from torch import nn -from torch import Tensor -import torch.nn.functional as F - -from text_recognizer.networks.transformer.attention import MultiHeadAttention -from text_recognizer.networks.util import activation_function - - -class GEGLU(nn.Module): - """GLU activation for improving feedforward activations.""" - - def __init__(self, dim_in: int, dim_out: int) -> None: - super().__init__() - self.proj = nn.Linear(dim_in, dim_out * 2) - - def forward(self, x: Tensor) -> Tensor: - """Forward propagation.""" - x, gate = self.proj(x).chunk(2, dim=-1) - return x * F.gelu(gate) - - -def _get_clones(module: Type[nn.Module], num_layers: int) -> nn.ModuleList: - return nn.ModuleList([copy.deepcopy(module) for _ in range(num_layers)]) - - -class _IntraLayerConnection(nn.Module): - """Preforms the residual connection inside the transfomer blocks and applies layernorm.""" - - def __init__(self, dropout_rate: float, hidden_dim: int) -> None: - super().__init__() - self.norm = nn.LayerNorm(normalized_shape=hidden_dim) - self.dropout = nn.Dropout(p=dropout_rate) - - def forward(self, src: Tensor, residual: Tensor) -> Tensor: - return self.norm(self.dropout(src) + residual) - - -class _ConvolutionalLayer(nn.Module): - def __init__( - self, - hidden_dim: int, - expansion_dim: int, - dropout_rate: float, - activation: str = "relu", - ) -> None: - super().__init__() - - in_projection = ( - nn.Sequential( - nn.Linear(hidden_dim, expansion_dim), activation_function(activation) - ) - if activation != "glu" - else GEGLU(hidden_dim, expansion_dim) - ) - - self.layer = nn.Sequential( - in_projection, - nn.Dropout(p=dropout_rate), - nn.Linear(in_features=expansion_dim, out_features=hidden_dim), - ) - - def forward(self, x: Tensor) -> Tensor: - return self.layer(x) - - -class EncoderLayer(nn.Module): - """Transfomer encoding layer.""" - - def __init__( - self, - hidden_dim: int, - num_heads: int, - expansion_dim: int, - dropout_rate: float, - activation: str = "relu", - ) -> None: - super().__init__() - self.self_attention = MultiHeadAttention(hidden_dim, num_heads, dropout_rate) - self.cnn = _ConvolutionalLayer( - hidden_dim, expansion_dim, dropout_rate, activation - ) - self.block1 = _IntraLayerConnection(dropout_rate, hidden_dim) - self.block2 = _IntraLayerConnection(dropout_rate, hidden_dim) - - def forward(self, src: Tensor, mask: Optional[Tensor] = None) -> Tensor: - """Forward pass through the encoder.""" - # First block. - # Multi head attention. - out, _ = self.self_attention(src, src, src, mask) - - # Add & norm. - out = self.block1(out, src) - - # Second block. - # Apply 1D-convolution. - cnn_out = self.cnn(out) - - # Add & norm. - out = self.block2(cnn_out, out) - - return out - - -class Encoder(nn.Module): - """Transfomer encoder module.""" - - def __init__( - self, - num_layers: int, - encoder_layer: Type[nn.Module], - norm: Optional[Type[nn.Module]] = None, - ) -> None: - super().__init__() - self.layers = _get_clones(encoder_layer, num_layers) - self.norm = norm - - def forward(self, src: Tensor, src_mask: Optional[Tensor] = None) -> Tensor: - """Forward pass through all encoder layers.""" - for layer in self.layers: - src = layer(src, src_mask) - - if self.norm is not None: - src = self.norm(src) - - return src - - -class DecoderLayer(nn.Module): - """Transfomer decoder layer.""" - - def __init__( - self, - hidden_dim: int, - num_heads: int, - expansion_dim: int, - dropout_rate: float = 0.0, - activation: str = "relu", - ) -> None: - super().__init__() - self.hidden_dim = hidden_dim - self.self_attention = MultiHeadAttention(hidden_dim, num_heads, dropout_rate) - self.multihead_attention = MultiHeadAttention( - hidden_dim, num_heads, dropout_rate - ) - self.cnn = _ConvolutionalLayer( - hidden_dim, expansion_dim, dropout_rate, activation - ) - self.block1 = _IntraLayerConnection(dropout_rate, hidden_dim) - self.block2 = _IntraLayerConnection(dropout_rate, hidden_dim) - self.block3 = _IntraLayerConnection(dropout_rate, hidden_dim) - - def forward( - self, - trg: Tensor, - memory: Tensor, - trg_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - ) -> Tensor: - """Forward pass of the layer.""" - out, _ = self.self_attention(trg, trg, trg, trg_mask) - trg = self.block1(out, trg) - - out, _ = self.multihead_attention(trg, memory, memory, memory_mask) - trg = self.block2(out, trg) - - out = self.cnn(trg) - out = self.block3(out, trg) - - return out - - -class Decoder(nn.Module): - """Transfomer decoder module.""" - - def __init__( - self, - decoder_layer: Type[nn.Module], - num_layers: int, - norm: Optional[Type[nn.Module]] = None, - ) -> None: - super().__init__() - self.layers = _get_clones(decoder_layer, num_layers) - self.num_layers = num_layers - self.norm = norm - - def forward( - self, - trg: Tensor, - memory: Tensor, - trg_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - ) -> Tensor: - """Forward pass through the decoder.""" - for layer in self.layers: - trg = layer(trg, memory, trg_mask, memory_mask) - - if self.norm is not None: - trg = self.norm(trg) - - return trg - - -class Transformer(nn.Module): - """Transformer network.""" - - def __init__( - self, - num_encoder_layers: int, - num_decoder_layers: int, - hidden_dim: int, - num_heads: int, - expansion_dim: int, - dropout_rate: float, - activation: str = "relu", - ) -> None: - super().__init__() - - # Configure encoder. - encoder_norm = nn.LayerNorm(hidden_dim) - encoder_layer = EncoderLayer( - hidden_dim, num_heads, expansion_dim, dropout_rate, activation - ) - self.encoder = Encoder(num_encoder_layers, encoder_layer, encoder_norm) - - # Configure decoder. - decoder_norm = nn.LayerNorm(hidden_dim) - decoder_layer = DecoderLayer( - hidden_dim, num_heads, expansion_dim, dropout_rate, activation - ) - self.decoder = Decoder(decoder_layer, num_decoder_layers, decoder_norm) - - self._reset_parameters() - - def _reset_parameters(self) -> None: - for p in self.parameters(): - if p.dim() > 1: - nn.init.xavier_uniform_(p) - - def forward( - self, - src: Tensor, - trg: Tensor, - src_mask: Optional[Tensor] = None, - trg_mask: Optional[Tensor] = None, - memory_mask: Optional[Tensor] = None, - ) -> Tensor: - """Forward pass through the transformer.""" - if src.shape[0] != trg.shape[0]: - print(trg.shape) - raise RuntimeError("The batch size of the src and trg must be the same.") - if src.shape[2] != trg.shape[2]: - raise RuntimeError( - "The number of features for the src and trg must be the same." - ) - - memory = self.encoder(src, src_mask) - output = self.decoder(trg, memory, trg_mask, memory_mask) - return output diff --git a/src/text_recognizer/networks/unet.py b/src/text_recognizer/networks/unet.py deleted file mode 100644 index 510910f..0000000 --- a/src/text_recognizer/networks/unet.py +++ /dev/null @@ -1,255 +0,0 @@ -"""UNet for segmentation.""" -from typing import List, Optional, Tuple, Union - -import torch -from torch import nn -from torch import Tensor - -from text_recognizer.networks.util import activation_function - - -class _ConvBlock(nn.Module): - """Modified UNet convolutional block with dilation.""" - - def __init__( - self, - channels: List[int], - activation: str, - num_groups: int, - dropout_rate: float = 0.1, - kernel_size: int = 3, - dilation: int = 1, - padding: int = 0, - ) -> None: - super().__init__() - self.channels = channels - self.dropout_rate = dropout_rate - self.kernel_size = kernel_size - self.dilation = dilation - self.padding = padding - self.num_groups = num_groups - self.activation = activation_function(activation) - self.block = self._configure_block() - self.residual_conv = nn.Sequential( - nn.Conv2d( - self.channels[0], self.channels[-1], kernel_size=3, stride=1, padding=1 - ), - self.activation, - ) - - def _configure_block(self) -> nn.Sequential: - block = [] - for i in range(len(self.channels) - 1): - block += [ - nn.Dropout(p=self.dropout_rate), - nn.GroupNorm(self.num_groups, self.channels[i]), - self.activation, - nn.Conv2d( - self.channels[i], - self.channels[i + 1], - kernel_size=self.kernel_size, - padding=self.padding, - stride=1, - dilation=self.dilation, - ), - ] - - return nn.Sequential(*block) - - def forward(self, x: Tensor) -> Tensor: - """Apply the convolutional block.""" - residual = self.residual_conv(x) - return self.block(x) + residual - - -class _DownSamplingBlock(nn.Module): - """Basic down sampling block.""" - - def __init__( - self, - channels: List[int], - activation: str, - num_groups: int, - pooling_kernel: Union[int, bool] = 2, - dropout_rate: float = 0.1, - kernel_size: int = 3, - dilation: int = 1, - padding: int = 0, - ) -> None: - super().__init__() - self.conv_block = _ConvBlock( - channels, - activation, - num_groups, - dropout_rate, - kernel_size, - dilation, - padding, - ) - self.down_sampling = nn.MaxPool2d(pooling_kernel) if pooling_kernel else None - - def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]: - """Return the convolutional block output and a down sampled tensor.""" - x = self.conv_block(x) - x_down = self.down_sampling(x) if self.down_sampling is not None else x - - return x_down, x - - -class _UpSamplingBlock(nn.Module): - """The upsampling block of the UNet.""" - - def __init__( - self, - channels: List[int], - activation: str, - num_groups: int, - scale_factor: int = 2, - dropout_rate: float = 0.1, - kernel_size: int = 3, - dilation: int = 1, - padding: int = 0, - ) -> None: - super().__init__() - self.conv_block = _ConvBlock( - channels, - activation, - num_groups, - dropout_rate, - kernel_size, - dilation, - padding, - ) - self.up_sampling = nn.Upsample( - scale_factor=scale_factor, mode="bilinear", align_corners=True - ) - - def forward(self, x: Tensor, x_skip: Optional[Tensor] = None) -> Tensor: - """Apply the up sampling and convolutional block.""" - x = self.up_sampling(x) - if x_skip is not None: - x = torch.cat((x, x_skip), dim=1) - return self.conv_block(x) - - -class UNet(nn.Module): - """UNet architecture.""" - - def __init__( - self, - in_channels: int = 1, - base_channels: int = 64, - num_classes: int = 3, - depth: int = 4, - activation: str = "relu", - num_groups: int = 8, - dropout_rate: float = 0.1, - pooling_kernel: int = 2, - scale_factor: int = 2, - kernel_size: Optional[List[int]] = None, - dilation: Optional[List[int]] = None, - padding: Optional[List[int]] = None, - ) -> None: - super().__init__() - self.depth = depth - self.num_groups = num_groups - - if kernel_size is not None and dilation is not None and padding is not None: - if ( - len(kernel_size) != depth - and len(dilation) != depth - and len(padding) != depth - ): - raise RuntimeError( - "Length of convolutional parameters does not match the depth." - ) - self.kernel_size = kernel_size - self.padding = padding - self.dilation = dilation - - else: - self.kernel_size = [3] * depth - self.padding = [1] * depth - self.dilation = [1] * depth - - self.dropout_rate = dropout_rate - self.conv = nn.Conv2d( - in_channels, base_channels, kernel_size=3, stride=1, padding=1 - ) - - channels = [base_channels] + [base_channels * 2 ** i for i in range(depth)] - self.encoder_blocks = self._configure_down_sampling_blocks( - channels, activation, pooling_kernel - ) - self.decoder_blocks = self._configure_up_sampling_blocks( - channels, activation, scale_factor - ) - - self.head = nn.Conv2d(base_channels, num_classes, kernel_size=1) - - def _configure_down_sampling_blocks( - self, channels: List[int], activation: str, pooling_kernel: int - ) -> nn.ModuleList: - blocks = nn.ModuleList([]) - for i in range(len(channels) - 1): - pooling_kernel = pooling_kernel if i < self.depth - 1 else False - dropout_rate = self.dropout_rate if i < 0 else 0 - blocks += [ - _DownSamplingBlock( - [channels[i], channels[i + 1], channels[i + 1]], - activation, - self.num_groups, - pooling_kernel, - dropout_rate, - self.kernel_size[i], - self.dilation[i], - self.padding[i], - ) - ] - - return blocks - - def _configure_up_sampling_blocks( - self, channels: List[int], activation: str, scale_factor: int, - ) -> nn.ModuleList: - channels.reverse() - self.kernel_size.reverse() - self.dilation.reverse() - self.padding.reverse() - return nn.ModuleList( - [ - _UpSamplingBlock( - [channels[i] + channels[i + 1], channels[i + 1], channels[i + 1]], - activation, - self.num_groups, - scale_factor, - self.dropout_rate, - self.kernel_size[i], - self.dilation[i], - self.padding[i], - ) - for i in range(len(channels) - 2) - ] - ) - - def _encode(self, x: Tensor) -> List[Tensor]: - x_skips = [] - for block in self.encoder_blocks: - x, x_skip = block(x) - x_skips.append(x_skip) - return x_skips - - def _decode(self, x_skips: List[Tensor]) -> Tensor: - x = x_skips[-1] - for i, block in enumerate(self.decoder_blocks): - x = block(x, x_skips[-(i + 2)]) - return x - - def forward(self, x: Tensor) -> Tensor: - """Forward pass with the UNet model.""" - if len(x.shape) < 4: - x = x[(None,) * (4 - len(x.shape))] - x = self.conv(x) - x_skips = self._encode(x) - x = self._decode(x_skips) - return self.head(x) diff --git a/src/text_recognizer/networks/util.py b/src/text_recognizer/networks/util.py deleted file mode 100644 index 131a6b4..0000000 --- a/src/text_recognizer/networks/util.py +++ /dev/null @@ -1,89 +0,0 @@ -"""Miscellaneous neural network functionality.""" -import importlib -from pathlib import Path -from typing import Dict, Tuple, Type - -from einops import rearrange -from loguru import logger -import torch -from torch import nn - - -def sliding_window( - images: torch.Tensor, patch_size: Tuple[int, int], stride: Tuple[int, int] -) -> torch.Tensor: - """Creates patches of an image. - - Args: - images (torch.Tensor): A Torch tensor of a 4D image(s), i.e. (batch, channel, height, width). - patch_size (Tuple[int, int]): The size of the patches to generate, e.g. 28x28 for EMNIST. - stride (Tuple[int, int]): The stride of the sliding window. - - Returns: - torch.Tensor: A tensor with the shape (batch, patches, height, width). - - """ - unfold = nn.Unfold(kernel_size=patch_size, stride=stride) - # Preform the sliding window, unsqueeze as the channel dimesion is lost. - c = images.shape[1] - patches = unfold(images) - patches = rearrange( - patches, "b (c h w) t -> b t c h w", c=c, h=patch_size[0], w=patch_size[1], - ) - return patches - - -def activation_function(activation: str) -> Type[nn.Module]: - """Returns the callable activation function.""" - activation_fns = nn.ModuleDict( - [ - ["elu", nn.ELU(inplace=True)], - ["gelu", nn.GELU()], - ["glu", nn.GLU()], - ["leaky_relu", nn.LeakyReLU(negative_slope=1.0e-2, inplace=True)], - ["none", nn.Identity()], - ["relu", nn.ReLU(inplace=True)], - ["selu", nn.SELU(inplace=True)], - ] - ) - return activation_fns[activation.lower()] - - -def configure_backbone(backbone: str, backbone_args: Dict) -> Type[nn.Module]: - """Loads a backbone network.""" - network_module = importlib.import_module("text_recognizer.networks") - backbone_ = getattr(network_module, backbone) - - if "pretrained" in backbone_args: - logger.info("Loading pretrained backbone.") - checkpoint_file = Path(__file__).resolve().parents[2] / backbone_args.pop( - "pretrained" - ) - - # Loading state directory. - state_dict = torch.load(checkpoint_file) - network_args = state_dict["network_args"] - weights = state_dict["model_state"] - - freeze = False - if "freeze" in backbone_args and backbone_args["freeze"] is True: - backbone_args.pop("freeze") - freeze = True - network_args = backbone_args - - # Initializes the network with trained weights. - backbone = backbone_(**network_args) - backbone.load_state_dict(weights) - if freeze: - for params in backbone.parameters(): - params.requires_grad = False - else: - backbone_ = getattr(network_module, backbone) - backbone = backbone_(**backbone_args) - - if "remove_layers" in backbone_args and backbone_args["remove_layers"] is not None: - backbone = nn.Sequential( - *list(backbone.children())[:][: -backbone_args["remove_layers"]] - ) - - return backbone diff --git a/src/text_recognizer/networks/vit.py b/src/text_recognizer/networks/vit.py deleted file mode 100644 index efb3701..0000000 --- a/src/text_recognizer/networks/vit.py +++ /dev/null @@ -1,150 +0,0 @@ -"""A Vision Transformer. - -Inspired by: -https://openreview.net/pdf?id=YicbFdNTTy - -""" -from typing import Optional, Tuple - -from einops import rearrange, repeat -import torch -from torch import nn -from torch import Tensor - -from text_recognizer.networks.transformer import Transformer - - -class ViT(nn.Module): - """Transfomer for image to sequence prediction.""" - - def __init__( - self, - num_encoder_layers: int, - num_decoder_layers: int, - hidden_dim: int, - vocab_size: int, - num_heads: int, - expansion_dim: int, - patch_dim: Tuple[int, int], - image_size: Tuple[int, int], - dropout_rate: float, - trg_pad_index: int, - max_len: int, - activation: str = "gelu", - ) -> None: - super().__init__() - - self.trg_pad_index = trg_pad_index - self.patch_dim = patch_dim - self.num_patches = image_size[-1] // self.patch_dim[1] - - # Encoder - self.patch_to_embedding = nn.Linear( - self.patch_dim[0] * self.patch_dim[1], hidden_dim - ) - self.cls_token = nn.Parameter(torch.randn(1, 1, hidden_dim)) - self.character_embedding = nn.Embedding(vocab_size, hidden_dim) - self.pos_embedding = nn.Parameter(torch.randn(1, max_len, hidden_dim)) - self.dropout = nn.Dropout(dropout_rate) - self._init() - - self.transformer = Transformer( - num_encoder_layers, - num_decoder_layers, - hidden_dim, - num_heads, - expansion_dim, - dropout_rate, - activation, - ) - - self.head = nn.Sequential(nn.Linear(hidden_dim, vocab_size),) - - def _init(self) -> None: - nn.init.normal_(self.character_embedding.weight, std=0.02) - # nn.init.normal_(self.pos_embedding.weight, std=0.02) - - def _create_trg_mask(self, trg: Tensor) -> Tensor: - # Move this outside the transformer. - trg_pad_mask = (trg != self.trg_pad_index)[:, None, None] - trg_len = trg.shape[1] - trg_sub_mask = torch.tril( - torch.ones((trg_len, trg_len), device=trg.device) - ).bool() - trg_mask = trg_pad_mask & trg_sub_mask - return trg_mask - - def encoder(self, src: Tensor) -> Tensor: - """Forward pass with the encoder of the transformer.""" - return self.transformer.encoder(src) - - def decoder(self, trg: Tensor, memory: Tensor, trg_mask: Tensor) -> Tensor: - """Forward pass with the decoder of the transformer + classification head.""" - return self.head( - self.transformer.decoder(trg=trg, memory=memory, trg_mask=trg_mask) - ) - - def extract_image_features(self, src: Tensor) -> Tensor: - """Extracts image features with a backbone neural network. - - It seem like the winning idea was to swap channels and width dimension and collapse - the height dimension. The transformer is learning like a baby with this implementation!!! :D - Ohhhh, the joy I am experiencing right now!! Bring in the beers! :D :D :D - - Args: - src (Tensor): Input tensor. - - Returns: - Tensor: A input src to the transformer. - - """ - # If batch dimension is missing, it needs to be added. - if len(src.shape) < 4: - src = src[(None,) * (4 - len(src.shape))] - - patches = rearrange( - src, - "b c (h p1) (w p2) -> b (h w) (p1 p2 c)", - p1=self.patch_dim[0], - p2=self.patch_dim[1], - ) - - # From patches to encoded sequence. - x = self.patch_to_embedding(patches) - b, n, _ = x.shape - cls_tokens = repeat(self.cls_token, "() n d -> b n d", b=b) - x = torch.cat((cls_tokens, x), dim=1) - x += self.pos_embedding[:, : (n + 1)] - x = self.dropout(x) - - return x - - def target_embedding(self, trg: Tensor) -> Tuple[Tensor, Tensor]: - """Encodes target tensor with embedding and postion. - - Args: - trg (Tensor): Target tensor. - - Returns: - Tuple[Tensor, Tensor]: Encoded target tensor and target mask. - - """ - _, n = trg.shape - trg = self.character_embedding(trg.long()) - trg += self.pos_embedding[:, :n] - return trg - - def decode_image_features(self, h: Tensor, trg: Optional[Tensor] = None) -> Tensor: - """Takes images features from the backbone and decodes them with the transformer.""" - trg_mask = self._create_trg_mask(trg) - trg = self.target_embedding(trg) - out = self.transformer(h, trg, trg_mask=trg_mask) - - logits = self.head(out) - return logits - - def forward(self, x: Tensor, trg: Optional[Tensor] = None) -> Tensor: - """Forward pass with CNN transfomer.""" - h = self.extract_image_features(x) - logits = self.decode_image_features(h, trg) - return logits diff --git a/src/text_recognizer/networks/vq_transformer.py b/src/text_recognizer/networks/vq_transformer.py deleted file mode 100644 index c673d96..0000000 --- a/src/text_recognizer/networks/vq_transformer.py +++ /dev/null @@ -1,150 +0,0 @@ -"""A VQ-Transformer for image to text recognition.""" -from typing import Dict, Optional, Tuple - -from einops import rearrange, repeat -import torch -from torch import nn -from torch import Tensor - -from text_recognizer.networks.transformer import PositionalEncoding, Transformer -from text_recognizer.networks.util import activation_function -from text_recognizer.networks.util import configure_backbone -from text_recognizer.networks.vqvae.encoder import _ResidualBlock - - -class VQTransformer(nn.Module): - """VQ+Transfomer for image to character sequence prediction.""" - - def __init__( - self, - num_encoder_layers: int, - num_decoder_layers: int, - hidden_dim: int, - vocab_size: int, - num_heads: int, - adaptive_pool_dim: Tuple, - expansion_dim: int, - dropout_rate: float, - trg_pad_index: int, - max_len: int, - backbone: str, - backbone_args: Optional[Dict] = None, - activation: str = "gelu", - ) -> None: - super().__init__() - - # Configure vector quantized backbone. - self.backbone = configure_backbone(backbone, backbone_args) - self.conv = nn.Sequential( - nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=2), - nn.ReLU(inplace=True), - ) - - # Configure embeddings for Transformer network. - self.trg_pad_index = trg_pad_index - self.vocab_size = vocab_size - self.character_embedding = nn.Embedding(self.vocab_size, hidden_dim) - self.src_position_embedding = nn.Parameter(torch.randn(1, max_len, hidden_dim)) - self.trg_position_encoding = PositionalEncoding(hidden_dim, dropout_rate) - nn.init.normal_(self.character_embedding.weight, std=0.02) - - self.adaptive_pool = ( - nn.AdaptiveAvgPool2d((adaptive_pool_dim)) if adaptive_pool_dim else None - ) - - self.transformer = Transformer( - num_encoder_layers, - num_decoder_layers, - hidden_dim, - num_heads, - expansion_dim, - dropout_rate, - activation, - ) - - self.head = nn.Sequential(nn.Linear(hidden_dim, vocab_size),) - - def _create_trg_mask(self, trg: Tensor) -> Tensor: - # Move this outside the transformer. - trg_pad_mask = (trg != self.trg_pad_index)[:, None, None] - trg_len = trg.shape[1] - trg_sub_mask = torch.tril( - torch.ones((trg_len, trg_len), device=trg.device) - ).bool() - trg_mask = trg_pad_mask & trg_sub_mask - return trg_mask - - def encoder(self, src: Tensor) -> Tensor: - """Forward pass with the encoder of the transformer.""" - return self.transformer.encoder(src) - - def decoder(self, trg: Tensor, memory: Tensor, trg_mask: Tensor) -> Tensor: - """Forward pass with the decoder of the transformer + classification head.""" - return self.head( - self.transformer.decoder(trg=trg, memory=memory, trg_mask=trg_mask) - ) - - def extract_image_features(self, src: Tensor) -> Tuple[Tensor, Tensor]: - """Extracts image features with a backbone neural network. - - It seem like the winning idea was to swap channels and width dimension and collapse - the height dimension. The transformer is learning like a baby with this implementation!!! :D - Ohhhh, the joy I am experiencing right now!! Bring in the beers! :D :D :D - - Args: - src (Tensor): Input tensor. - - Returns: - Tensor: The input src to the transformer and the vq loss. - - """ - # If batch dimension is missing, it needs to be added. - if len(src.shape) < 4: - src = src[(None,) * (4 - len(src.shape))] - src, vq_loss = self.backbone.encode(src) - # src = self.backbone.decoder.res_block(src) - src = self.conv(src) - - if self.adaptive_pool is not None: - src = rearrange(src, "b c h w -> b w c h") - src = self.adaptive_pool(src) - src = src.squeeze(3) - else: - src = rearrange(src, "b c h w -> b (w h) c") - - b, t, _ = src.shape - - src += self.src_position_embedding[:, :t] - - return src, vq_loss - - def target_embedding(self, trg: Tensor) -> Tensor: - """Encodes target tensor with embedding and postion. - - Args: - trg (Tensor): Target tensor. - - Returns: - Tensor: Encoded target tensor. - - """ - trg = self.character_embedding(trg.long()) - trg = self.trg_position_encoding(trg) - return trg - - def decode_image_features( - self, image_features: Tensor, trg: Optional[Tensor] = None - ) -> Tensor: - """Takes images features from the backbone and decodes them with the transformer.""" - trg_mask = self._create_trg_mask(trg) - trg = self.target_embedding(trg) - out = self.transformer(image_features, trg, trg_mask=trg_mask) - - logits = self.head(out) - return logits - - def forward(self, x: Tensor, trg: Optional[Tensor] = None) -> Tensor: - """Forward pass with CNN transfomer.""" - image_features, vq_loss = self.extract_image_features(x) - logits = self.decode_image_features(image_features, trg) - return logits, vq_loss diff --git a/src/text_recognizer/networks/vqvae/__init__.py b/src/text_recognizer/networks/vqvae/__init__.py deleted file mode 100644 index 763953c..0000000 --- a/src/text_recognizer/networks/vqvae/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -"""VQ-VAE module.""" -from .decoder import Decoder -from .encoder import Encoder -from .vector_quantizer import VectorQuantizer -from .vqvae import VQVAE diff --git a/src/text_recognizer/networks/vqvae/decoder.py b/src/text_recognizer/networks/vqvae/decoder.py deleted file mode 100644 index 8847aba..0000000 --- a/src/text_recognizer/networks/vqvae/decoder.py +++ /dev/null @@ -1,133 +0,0 @@ -"""CNN decoder for the VQ-VAE.""" - -from typing import List, Optional, Tuple, Type - -import torch -from torch import nn -from torch import Tensor - -from text_recognizer.networks.util import activation_function -from text_recognizer.networks.vqvae.encoder import _ResidualBlock - - -class Decoder(nn.Module): - """A CNN encoder network.""" - - def __init__( - self, - channels: List[int], - kernel_sizes: List[int], - strides: List[int], - num_residual_layers: int, - embedding_dim: int, - upsampling: Optional[List[List[int]]] = None, - activation: str = "leaky_relu", - dropout_rate: float = 0.0, - ) -> None: - super().__init__() - - if dropout_rate: - if activation == "selu": - dropout = nn.AlphaDropout(p=dropout_rate) - else: - dropout = nn.Dropout(p=dropout_rate) - else: - dropout = None - - self.upsampling = upsampling - - self.res_block = nn.ModuleList([]) - self.upsampling_block = nn.ModuleList([]) - - self.embedding_dim = embedding_dim - activation = activation_function(activation) - - # Configure encoder. - self.decoder = self._build_decoder( - channels, kernel_sizes, strides, num_residual_layers, activation, dropout, - ) - - def _build_decompression_block( - self, - in_channels: int, - channels: int, - kernel_sizes: List[int], - strides: List[int], - activation: Type[nn.Module], - dropout: Optional[Type[nn.Module]], - ) -> nn.ModuleList: - modules = nn.ModuleList([]) - configuration = zip(channels, kernel_sizes, strides) - for i, (out_channels, kernel_size, stride) in enumerate(configuration): - modules.append( - nn.Sequential( - nn.ConvTranspose2d( - in_channels, - out_channels, - kernel_size, - stride=stride, - padding=1, - ), - activation, - ) - ) - - if i < len(self.upsampling): - modules.append(nn.Upsample(size=self.upsampling[i]),) - - if dropout is not None: - modules.append(dropout) - - in_channels = out_channels - - modules.extend( - nn.Sequential( - nn.ConvTranspose2d( - in_channels, 1, kernel_size=kernel_size, stride=stride, padding=1 - ), - nn.Tanh(), - ) - ) - - return modules - - def _build_decoder( - self, - channels: int, - kernel_sizes: List[int], - strides: List[int], - num_residual_layers: int, - activation: Type[nn.Module], - dropout: Optional[Type[nn.Module]], - ) -> nn.Sequential: - - self.res_block.append( - nn.Conv2d(self.embedding_dim, channels[0], kernel_size=1, stride=1,) - ) - - # Bottleneck module. - self.res_block.extend( - nn.ModuleList( - [ - _ResidualBlock(channels[0], channels[0], dropout) - for i in range(num_residual_layers) - ] - ) - ) - - # Decompression module - self.upsampling_block.extend( - self._build_decompression_block( - channels[0], channels[1:], kernel_sizes, strides, activation, dropout - ) - ) - - self.res_block = nn.Sequential(*self.res_block) - self.upsampling_block = nn.Sequential(*self.upsampling_block) - - return nn.Sequential(self.res_block, self.upsampling_block) - - def forward(self, z_q: Tensor) -> Tensor: - """Reconstruct input from given codes.""" - x_reconstruction = self.decoder(z_q) - return x_reconstruction diff --git a/src/text_recognizer/networks/vqvae/encoder.py b/src/text_recognizer/networks/vqvae/encoder.py deleted file mode 100644 index d3adac5..0000000 --- a/src/text_recognizer/networks/vqvae/encoder.py +++ /dev/null @@ -1,147 +0,0 @@ -"""CNN encoder for the VQ-VAE.""" -from typing import List, Optional, Tuple, Type - -import torch -from torch import nn -from torch import Tensor - -from text_recognizer.networks.util import activation_function -from text_recognizer.networks.vqvae.vector_quantizer import VectorQuantizer - - -class _ResidualBlock(nn.Module): - def __init__( - self, in_channels: int, out_channels: int, dropout: Optional[Type[nn.Module]], - ) -> None: - super().__init__() - self.block = [ - nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False), - nn.ReLU(inplace=True), - nn.Conv2d(out_channels, out_channels, kernel_size=1, bias=False), - ] - - if dropout is not None: - self.block.append(dropout) - - self.block = nn.Sequential(*self.block) - - def forward(self, x: Tensor) -> Tensor: - """Apply the residual forward pass.""" - return x + self.block(x) - - -class Encoder(nn.Module): - """A CNN encoder network.""" - - def __init__( - self, - in_channels: int, - channels: List[int], - kernel_sizes: List[int], - strides: List[int], - num_residual_layers: int, - embedding_dim: int, - num_embeddings: int, - beta: float = 0.25, - activation: str = "leaky_relu", - dropout_rate: float = 0.0, - ) -> None: - super().__init__() - - if dropout_rate: - if activation == "selu": - dropout = nn.AlphaDropout(p=dropout_rate) - else: - dropout = nn.Dropout(p=dropout_rate) - else: - dropout = None - - self.embedding_dim = embedding_dim - self.num_embeddings = num_embeddings - self.beta = beta - activation = activation_function(activation) - - # Configure encoder. - self.encoder = self._build_encoder( - in_channels, - channels, - kernel_sizes, - strides, - num_residual_layers, - activation, - dropout, - ) - - # Configure Vector Quantizer. - self.vector_quantizer = VectorQuantizer( - self.num_embeddings, self.embedding_dim, self.beta - ) - - def _build_compression_block( - self, - in_channels: int, - channels: int, - kernel_sizes: List[int], - strides: List[int], - activation: Type[nn.Module], - dropout: Optional[Type[nn.Module]], - ) -> nn.ModuleList: - modules = nn.ModuleList([]) - configuration = zip(channels, kernel_sizes, strides) - for out_channels, kernel_size, stride in configuration: - modules.append( - nn.Sequential( - nn.Conv2d( - in_channels, out_channels, kernel_size, stride=stride, padding=1 - ), - activation, - ) - ) - - if dropout is not None: - modules.append(dropout) - - in_channels = out_channels - - return modules - - def _build_encoder( - self, - in_channels: int, - channels: int, - kernel_sizes: List[int], - strides: List[int], - num_residual_layers: int, - activation: Type[nn.Module], - dropout: Optional[Type[nn.Module]], - ) -> nn.Sequential: - encoder = nn.ModuleList([]) - - # compression module - encoder.extend( - self._build_compression_block( - in_channels, channels, kernel_sizes, strides, activation, dropout - ) - ) - - # Bottleneck module. - encoder.extend( - nn.ModuleList( - [ - _ResidualBlock(channels[-1], channels[-1], dropout) - for i in range(num_residual_layers) - ] - ) - ) - - encoder.append( - nn.Conv2d(channels[-1], self.embedding_dim, kernel_size=1, stride=1,) - ) - - return nn.Sequential(*encoder) - - def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]: - """Encodes input into a discrete representation.""" - z_e = self.encoder(x) - z_q, vq_loss = self.vector_quantizer(z_e) - return z_q, vq_loss diff --git a/src/text_recognizer/networks/vqvae/vector_quantizer.py b/src/text_recognizer/networks/vqvae/vector_quantizer.py deleted file mode 100644 index f92c7ee..0000000 --- a/src/text_recognizer/networks/vqvae/vector_quantizer.py +++ /dev/null @@ -1,119 +0,0 @@ -"""Implementation of a Vector Quantized Variational AutoEncoder. - -Reference: -https://github.com/AntixK/PyTorch-VAE/blob/master/models/vq_vae.py - -""" - -from einops import rearrange -import torch -from torch import nn -from torch import Tensor -from torch.nn import functional as F - - -class VectorQuantizer(nn.Module): - """The codebook that contains quantized vectors.""" - - def __init__( - self, num_embeddings: int, embedding_dim: int, beta: float = 0.25 - ) -> None: - super().__init__() - self.K = num_embeddings - self.D = embedding_dim - self.beta = beta - - self.embedding = nn.Embedding(self.K, self.D) - - # Initialize the codebook. - nn.init.uniform_(self.embedding.weight, -1 / self.K, 1 / self.K) - - def discretization_bottleneck(self, latent: Tensor) -> Tensor: - """Computes the code nearest to the latent representation. - - First we compute the posterior categorical distribution, and then map - the latent representation to the nearest element of the embedding. - - Args: - latent (Tensor): The latent representation. - - Shape: - - latent :math:`(B x H x W, D)` - - Returns: - Tensor: The quantized embedding vector. - - """ - # Store latent shape. - b, h, w, d = latent.shape - - # Flatten the latent representation to 2D. - latent = rearrange(latent, "b h w d -> (b h w) d") - - # Compute the L2 distance between the latents and the embeddings. - l2_distance = ( - torch.sum(latent ** 2, dim=1, keepdim=True) - + torch.sum(self.embedding.weight ** 2, dim=1) - - 2 * latent @ self.embedding.weight.t() - ) # [BHW x K] - - # Find the embedding k nearest to each latent. - encoding_indices = torch.argmin(l2_distance, dim=1).unsqueeze(1) # [BHW, 1] - - # Convert to one-hot encodings, aka discrete bottleneck. - one_hot_encoding = torch.zeros( - encoding_indices.shape[0], self.K, device=latent.device - ) - one_hot_encoding.scatter_(1, encoding_indices, 1) # [BHW x K] - - # Embedding quantization. - quantized_latent = one_hot_encoding @ self.embedding.weight # [BHW, D] - quantized_latent = rearrange( - quantized_latent, "(b h w) d -> b h w d", b=b, h=h, w=w - ) - - return quantized_latent - - def vq_loss(self, latent: Tensor, quantized_latent: Tensor) -> Tensor: - """Vector Quantization loss. - - The vector quantization algorithm allows us to create a codebook. The VQ - algorithm works by moving the embedding vectors towards the encoder outputs. - - The embedding loss moves the embedding vector towards the encoder outputs. The - .detach() works as the stop gradient (sg) described in the paper. - - Because the volume of the embedding space is dimensionless, it can arbitarily - grow if the embeddings are not trained as fast as the encoder parameters. To - mitigate this, a commitment loss is added in the second term which makes sure - that the encoder commits to an embedding and that its output does not grow. - - Args: - latent (Tensor): The encoder output. - quantized_latent (Tensor): The quantized latent. - - Returns: - Tensor: The combinded VQ loss. - - """ - embedding_loss = F.mse_loss(quantized_latent, latent.detach()) - commitment_loss = F.mse_loss(quantized_latent.detach(), latent) - return embedding_loss + self.beta * commitment_loss - - def forward(self, latent: Tensor) -> Tensor: - """Forward pass that returns the quantized vector and the vq loss.""" - # Rearrange latent representation s.t. the hidden dim is at the end. - latent = rearrange(latent, "b d h w -> b h w d") - - # Maps latent to the nearest code in the codebook. - quantized_latent = self.discretization_bottleneck(latent) - - loss = self.vq_loss(latent, quantized_latent) - - # Add residue to the quantized latent. - quantized_latent = latent + (quantized_latent - latent).detach() - - # Rearrange the quantized shape back to the original shape. - quantized_latent = rearrange(quantized_latent, "b h w d -> b d h w") - - return quantized_latent, loss diff --git a/src/text_recognizer/networks/vqvae/vqvae.py b/src/text_recognizer/networks/vqvae/vqvae.py deleted file mode 100644 index 50448b4..0000000 --- a/src/text_recognizer/networks/vqvae/vqvae.py +++ /dev/null @@ -1,74 +0,0 @@ -"""The VQ-VAE.""" - -from typing import List, Optional, Tuple, Type - -import torch -from torch import nn -from torch import Tensor - -from text_recognizer.networks.vqvae import Decoder, Encoder - - -class VQVAE(nn.Module): - """Vector Quantized Variational AutoEncoder.""" - - def __init__( - self, - in_channels: int, - channels: List[int], - kernel_sizes: List[int], - strides: List[int], - num_residual_layers: int, - embedding_dim: int, - num_embeddings: int, - upsampling: Optional[List[List[int]]] = None, - beta: float = 0.25, - activation: str = "leaky_relu", - dropout_rate: float = 0.0, - ) -> None: - super().__init__() - - # configure encoder. - self.encoder = Encoder( - in_channels, - channels, - kernel_sizes, - strides, - num_residual_layers, - embedding_dim, - num_embeddings, - beta, - activation, - dropout_rate, - ) - - # Configure decoder. - channels.reverse() - kernel_sizes.reverse() - strides.reverse() - self.decoder = Decoder( - channels, - kernel_sizes, - strides, - num_residual_layers, - embedding_dim, - upsampling, - activation, - dropout_rate, - ) - - def encode(self, x: Tensor) -> Tuple[Tensor, Tensor]: - """Encodes input to a latent code.""" - return self.encoder(x) - - def decode(self, z_q: Tensor) -> Tensor: - """Reconstructs input from latent codes.""" - return self.decoder(z_q) - - def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]: - """Compresses and decompresses input.""" - if len(x.shape) < 4: - x = x[(None,) * (4 - len(x.shape))] - z_q, vq_loss = self.encode(x) - x_reconstruction = self.decode(z_q) - return x_reconstruction, vq_loss diff --git a/src/text_recognizer/networks/wide_resnet.py b/src/text_recognizer/networks/wide_resnet.py deleted file mode 100644 index b767778..0000000 --- a/src/text_recognizer/networks/wide_resnet.py +++ /dev/null @@ -1,221 +0,0 @@ -"""Wide Residual CNN.""" -from functools import partial -from typing import Callable, Dict, List, Optional, Type, Union - -from einops.layers.torch import Reduce -import numpy as np -import torch -from torch import nn -from torch import Tensor - -from text_recognizer.networks.util import activation_function - - -def conv3x3(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d: - """Helper function for a 3x3 2d convolution.""" - return nn.Conv2d( - in_channels=in_planes, - out_channels=out_planes, - kernel_size=3, - stride=stride, - padding=1, - bias=False, - ) - - -def conv_init(module: Type[nn.Module]) -> None: - """Initializes the weights for convolution and batchnorms.""" - classname = module.__class__.__name__ - if classname.find("Conv") != -1: - nn.init.xavier_uniform_(module.weight, gain=np.sqrt(2)) - nn.init.constant_(module.bias, 0) - elif classname.find("BatchNorm") != -1: - nn.init.constant_(module.weight, 1) - nn.init.constant_(module.bias, 0) - - -class WideBlock(nn.Module): - """Block used in WideResNet.""" - - def __init__( - self, - in_planes: int, - out_planes: int, - dropout_rate: float, - stride: int = 1, - activation: str = "relu", - ) -> None: - super().__init__() - self.in_planes = in_planes - self.out_planes = out_planes - self.dropout_rate = dropout_rate - self.stride = stride - self.activation = activation_function(activation) - - # Build blocks. - self.blocks = nn.Sequential( - nn.BatchNorm2d(self.in_planes), - self.activation, - conv3x3(in_planes=self.in_planes, out_planes=self.out_planes), - nn.Dropout(p=self.dropout_rate), - nn.BatchNorm2d(self.out_planes), - self.activation, - conv3x3( - in_planes=self.out_planes, - out_planes=self.out_planes, - stride=self.stride, - ), - ) - - self.shortcut = ( - nn.Sequential( - nn.Conv2d( - in_channels=self.in_planes, - out_channels=self.out_planes, - kernel_size=1, - stride=self.stride, - bias=False, - ), - ) - if self._apply_shortcut - else None - ) - - @property - def _apply_shortcut(self) -> bool: - """If shortcut should be applied or not.""" - return self.stride != 1 or self.in_planes != self.out_planes - - def forward(self, x: Tensor) -> Tensor: - """Forward pass.""" - residual = x - if self._apply_shortcut: - residual = self.shortcut(x) - x = self.blocks(x) - x += residual - return x - - -class WideResidualNetwork(nn.Module): - """WideResNet for character predictions. - - Can be used for classification or encoding of images to a latent vector. - - """ - - def __init__( - self, - in_channels: int = 1, - in_planes: int = 16, - num_classes: int = 80, - depth: int = 16, - width_factor: int = 10, - dropout_rate: float = 0.0, - num_layers: int = 3, - block: Type[nn.Module] = WideBlock, - num_stages: Optional[List[int]] = None, - activation: str = "relu", - use_decoder: bool = True, - ) -> None: - """The initialization of the WideResNet. - - Args: - in_channels (int): Number of input channels. Defaults to 1. - in_planes (int): Number of channels to use in the first output kernel. Defaults to 16. - num_classes (int): Number of classes. Defaults to 80. - depth (int): Set the number of blocks to use. Defaults to 16. - width_factor (int): Factor for scaling the number of channels in the network. Defaults to 10. - dropout_rate (float): The dropout rate. Defaults to 0.0. - num_layers (int): Number of layers of blocks. Defaults to 3. - block (Type[nn.Module]): The default block is WideBlock. Defaults to WideBlock. - num_stages (List[int]): If given, will use these channel values. Defaults to None. - activation (str): Name of the activation to use. Defaults to "relu". - use_decoder (bool): If True, the network output character predictions, if False, the network outputs a - latent vector. Defaults to True. - - Raises: - RuntimeError: If the depth is not of the size `6n+4`. - - """ - - super().__init__() - if (depth - 4) % 6 != 0: - raise RuntimeError("Wide-resnet depth should be 6n+4") - self.in_channels = in_channels - self.in_planes = in_planes - self.num_classes = num_classes - self.num_blocks = (depth - 4) // 6 - self.width_factor = width_factor - self.num_layers = num_layers - self.block = block - self.dropout_rate = dropout_rate - self.activation = activation_function(activation) - - if num_stages is None: - self.num_stages = [self.in_planes] + [ - self.in_planes * 2 ** n * self.width_factor - for n in range(self.num_layers) - ] - else: - self.num_stages = [self.in_planes] + num_stages - - self.num_stages = list(zip(self.num_stages, self.num_stages[1:])) - self.strides = [1] + [2] * (self.num_layers - 1) - - self.encoder = nn.Sequential( - conv3x3(in_planes=self.in_channels, out_planes=self.in_planes), - *[ - self._configure_wide_layer( - in_planes=in_planes, - out_planes=out_planes, - stride=stride, - activation=activation, - ) - for (in_planes, out_planes), stride in zip( - self.num_stages, self.strides - ) - ], - ) - - self.decoder = ( - nn.Sequential( - nn.BatchNorm2d(self.num_stages[-1][-1], momentum=0.8), - self.activation, - Reduce("b c h w -> b c", "mean"), - nn.Linear( - in_features=self.num_stages[-1][-1], out_features=self.num_classes - ), - ) - if use_decoder - else None - ) - - # self.apply(conv_init) - - def _configure_wide_layer( - self, in_planes: int, out_planes: int, stride: int, activation: str - ) -> List: - strides = [stride] + [1] * (self.num_blocks - 1) - planes = [out_planes] * len(strides) - planes = [(in_planes, out_planes)] + list(zip(planes, planes[1:])) - return nn.Sequential( - *[ - self.block( - in_planes=in_planes, - out_planes=out_planes, - dropout_rate=self.dropout_rate, - stride=stride, - activation=activation, - ) - for (in_planes, out_planes), stride in zip(planes, strides) - ] - ) - - def forward(self, x: Tensor) -> Tensor: - """Feedforward pass.""" - if len(x.shape) < 4: - x = x[(None,) * int(4 - len(x.shape))] - x = self.encoder(x) - if self.decoder is not None: - x = self.decoder(x) - return x diff --git a/src/text_recognizer/paragraph_text_recognizer.py b/src/text_recognizer/paragraph_text_recognizer.py deleted file mode 100644 index aa39662..0000000 --- a/src/text_recognizer/paragraph_text_recognizer.py +++ /dev/null @@ -1,153 +0,0 @@ -"""Full model. - -Takes an image and returns the text in the image, by first segmenting the image with a LineDetector, then extracting the -each crop of the image corresponding to line regions, and feeding them to a LinePredictor model that outputs the text -in each region. -""" -from typing import Dict, List, Tuple, Union - -import cv2 -import numpy as np -import torch - -from text_recognizer.models import SegmentationModel, TransformerModel -from text_recognizer.util import read_image - - -class ParagraphTextRecognizor: - """Given an image of a single handwritten character, recognizes it.""" - - def __init__(self, line_predictor_args: Dict, line_detector_args: Dict) -> None: - self._line_predictor = TransformerModel(**line_predictor_args) - self._line_detector = SegmentationModel(**line_detector_args) - self._line_detector.eval() - self._line_predictor.eval() - - def predict(self, image_or_filename: Union[str, np.ndarray]) -> Tuple: - """Takes an image and returns all text within it.""" - image = ( - read_image(image_or_filename) - if isinstance(image_or_filename, str) - else image_or_filename - ) - - line_region_crops = self._get_line_region_crops(image) - processed_line_region_crops = [ - self._process_image_for_line_predictor(image=crop) - for crop in line_region_crops - ] - line_region_strings = [ - self.line_predictor_model.predict_on_image(crop)[0] - for crop in processed_line_region_crops - ] - - return " ".join(line_region_strings), line_region_crops - - def _get_line_region_crops( - self, image: np.ndarray, min_crop_len_factor: float = 0.02 - ) -> List[np.ndarray]: - """Returns all the crops of text lines in a square image.""" - processed_image, scale_down_factor = self._process_image_for_line_detector( - image - ) - line_segmentation = self._line_detector.predict_on_image(processed_image) - bounding_boxes = _find_line_bounding_boxes(line_segmentation) - - bounding_boxes = (bounding_boxes * scale_down_factor).astype(int) - - min_crop_len = int(min_crop_len_factor * min(image.shape[0], image.shape[1])) - line_region_crops = [ - image[y : y + h, x : x + w] - for x, y, w, h in bounding_boxes - if w >= min_crop_len and h >= min_crop_len - ] - return line_region_crops - - def _process_image_for_line_detector( - self, image: np.ndarray - ) -> Tuple[np.ndarray, float]: - """Convert uint8 image to float image with black background with shape self._line_detector.image_shape.""" - resized_image, scale_down_factor = _resize_image_for_line_detector( - image=image, max_shape=self._line_detector.image_shape - ) - resized_image = (1.0 - resized_image / 255).astype("float32") - return resized_image, scale_down_factor - - def _process_image_for_line_predictor(self, image: np.ndarray) -> np.ndarray: - """Preprocessing of image before feeding it to the LinePrediction model. - - Convert uint8 image to float image with black background with shape - self._line_predictor.image_shape while maintaining the image aspect ratio. - - Args: - image (np.ndarray): Crop of text line. - - Returns: - np.ndarray: Processed crop for feeding line predictor. - """ - expected_shape = self._line_detector.image_shape - scale_factor = (np.array(expected_shape) / np.array(image.shape)).min() - scaled_image = cv2.resize( - image, - dsize=None, - fx=scale_factor, - fy=scale_factor, - interpolation=cv2.INTER_AREA, - ) - - pad_with = ( - (0, expected_shape[0] - scaled_image.shape[0]), - (0, expected_shape[1] - scaled_image.shape[1]), - ) - - padded_image = np.pad( - scaled_image, pad_with=pad_with, mode="constant", constant_values=255 - ) - return 1 - padded_image / 255 - - -def _find_line_bounding_boxes(line_segmentation: np.ndarray) -> np.ndarray: - """Given a line segmentation, find bounding boxes for connected-component regions corresponding to non-0 labels.""" - - def _find_line_bounding_boxes_in_channel( - line_segmentation_channel: np.ndarray, - ) -> np.ndarray: - line_segmentation_image = cv2.dilate( - line_segmentation_channel, kernel=np.ones((3, 3)), iterations=1 - ) - line_activation_image = (line_segmentation_image * 255).astype("uint8") - line_activation_image = cv2.threshold( - line_activation_image, 0.5, 1, cv2.THRESH_BINARY | cv2.THRESH_OTSU - )[1] - - bounding_cnts, _ = cv2.findContours( - line_segmentation_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE - ) - return np.array([cv2.boundingRect(cnt) for cnt in bounding_cnts]) - - bounding_boxes = np.concatenate( - [ - _find_line_bounding_boxes_in_channel(line_segmentation[:, :, i]) - for i in [1, 2] - ], - axis=0, - ) - - return bounding_boxes[np.argsort(bounding_boxes[:, 1])] - - -def _resize_image_for_line_detector( - image: np.ndarray, max_shape: Tuple[int, int] -) -> Tuple[np.ndarray, float]: - """Resize the image to less than the max_shape while maintaining the aspect ratio.""" - scale_down_factor = max(np.ndarray(image.shape) / np.ndarray(max_shape)) - if scale_down_factor == 1: - return image.copy(), scale_down_factor - resize_image = cv2.resize( - image, - dsize=None, - fx=1 / scale_down_factor, - fy=1 / scale_down_factor, - interpolation=cv2.INTER_AREA, - ) - return resize_image, scale_down_factor diff --git a/src/text_recognizer/tests/__init__.py b/src/text_recognizer/tests/__init__.py deleted file mode 100644 index 18ff212..0000000 --- a/src/text_recognizer/tests/__init__.py +++ /dev/null @@ -1 +0,0 @@ -"""Test modules for the text text recognizer.""" diff --git a/src/text_recognizer/tests/support/__init__.py b/src/text_recognizer/tests/support/__init__.py deleted file mode 100644 index a265ede..0000000 --- a/src/text_recognizer/tests/support/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -"""Support file modules.""" -from .create_emnist_support_files import create_emnist_support_files diff --git a/src/text_recognizer/tests/support/create_emnist_lines_support_files.py b/src/text_recognizer/tests/support/create_emnist_lines_support_files.py deleted file mode 100644 index 9abe143..0000000 --- a/src/text_recognizer/tests/support/create_emnist_lines_support_files.py +++ /dev/null @@ -1,51 +0,0 @@ -"""Module for creating EMNIST Lines test support files.""" -# flake8: noqa: S106 - -from pathlib import Path -import shutil - -import numpy as np - -from text_recognizer.datasets import EmnistLinesDataset -import text_recognizer.util as util - - -SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "emnist_lines" - - -def create_emnist_lines_support_files() -> None: - """Create EMNIST Lines test images.""" - shutil.rmtree(SUPPORT_DIRNAME, ignore_errors=True) - SUPPORT_DIRNAME.mkdir() - - # TODO: maybe have to add args to dataset. - dataset = EmnistLinesDataset( - init_token="<sos>", - pad_token="_", - eos_token="<eos>", - transform=[{"type": "ToTensor", "args": {}}], - target_transform=[ - { - "type": "AddTokens", - "args": {"init_token": "<sos>", "pad_token": "_", "eos_token": "<eos>"}, - } - ], - ) # nosec: S106 - dataset.load_or_generate_data() - - for index in [5, 7, 9]: - image, target = dataset[index] - if len(image.shape) == 3: - image = image.squeeze(0) - print(image.sum(), image.dtype) - - label = "".join(dataset.mapper(label) for label in target[1:]).strip( - dataset.mapper.pad_token - ) - print(label) - image = image.numpy() - util.write_image(image, str(SUPPORT_DIRNAME / f"{label}.png")) - - -if __name__ == "__main__": - create_emnist_lines_support_files() diff --git a/src/text_recognizer/tests/support/create_emnist_support_files.py b/src/text_recognizer/tests/support/create_emnist_support_files.py deleted file mode 100644 index f9ff030..0000000 --- a/src/text_recognizer/tests/support/create_emnist_support_files.py +++ /dev/null @@ -1,30 +0,0 @@ -"""Module for creating EMNIST test support files.""" -from pathlib import Path -import shutil - -from text_recognizer.datasets import EmnistDataset -from text_recognizer.util import write_image - -SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "emnist" - - -def create_emnist_support_files() -> None: - """Create support images for test of CharacterPredictor class.""" - shutil.rmtree(SUPPORT_DIRNAME, ignore_errors=True) - SUPPORT_DIRNAME.mkdir() - - dataset = EmnistDataset(train=False) - dataset.load_or_generate_data() - - for index in [5, 7, 9]: - image, label = dataset[index] - if len(image.shape) == 3: - image = image.squeeze(0) - image = image.numpy() - label = dataset.mapper(int(label)) - print(index, label) - write_image(image, str(SUPPORT_DIRNAME / f"{label}.png")) - - -if __name__ == "__main__": - create_emnist_support_files() diff --git a/src/text_recognizer/tests/support/create_iam_lines_support_files.py b/src/text_recognizer/tests/support/create_iam_lines_support_files.py deleted file mode 100644 index 50f9e3d..0000000 --- a/src/text_recognizer/tests/support/create_iam_lines_support_files.py +++ /dev/null @@ -1,50 +0,0 @@ -"""Module for creating IAM Lines test support files.""" -# flake8: noqa -from pathlib import Path -import shutil - -import numpy as np - -from text_recognizer.datasets import IamLinesDataset -import text_recognizer.util as util - - -SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "iam_lines" - - -def create_emnist_lines_support_files() -> None: - """Create IAM Lines test images.""" - shutil.rmtree(SUPPORT_DIRNAME, ignore_errors=True) - SUPPORT_DIRNAME.mkdir() - - # TODO: maybe have to add args to dataset. - dataset = IamLinesDataset( - init_token="<sos>", - pad_token="_", - eos_token="<eos>", - transform=[{"type": "ToTensor", "args": {}}], - target_transform=[ - { - "type": "AddTokens", - "args": {"init_token": "<sos>", "pad_token": "_", "eos_token": "<eos>"}, - } - ], - ) - dataset.load_or_generate_data() - - for index in [0, 1, 3]: - image, target = dataset[index] - if len(image.shape) == 3: - image = image.squeeze(0) - print(image.sum(), image.dtype) - - label = "".join(dataset.mapper(label) for label in target[1:]).strip( - dataset.mapper.pad_token - ) - print(label) - image = image.numpy() - util.write_image(image, str(SUPPORT_DIRNAME / f"{label}.png")) - - -if __name__ == "__main__": - create_emnist_lines_support_files() diff --git a/src/text_recognizer/tests/support/emnist/8.png b/src/text_recognizer/tests/support/emnist/8.png Binary files differdeleted file mode 100644 index faa29aa..0000000 --- a/src/text_recognizer/tests/support/emnist/8.png +++ /dev/null diff --git a/src/text_recognizer/tests/support/emnist/U.png b/src/text_recognizer/tests/support/emnist/U.png Binary files differdeleted file mode 100644 index 304eaec..0000000 --- a/src/text_recognizer/tests/support/emnist/U.png +++ /dev/null diff --git a/src/text_recognizer/tests/support/emnist/e.png b/src/text_recognizer/tests/support/emnist/e.png Binary files differdeleted file mode 100644 index a03ecd4..0000000 --- a/src/text_recognizer/tests/support/emnist/e.png +++ /dev/null diff --git a/src/text_recognizer/tests/support/emnist_lines/Knox Ky<eos>.png b/src/text_recognizer/tests/support/emnist_lines/Knox Ky<eos>.png Binary files differdeleted file mode 100644 index b7d0618..0000000 --- a/src/text_recognizer/tests/support/emnist_lines/Knox Ky<eos>.png +++ /dev/null diff --git a/src/text_recognizer/tests/support/emnist_lines/ancillary beliefs and<eos>.png b/src/text_recognizer/tests/support/emnist_lines/ancillary beliefs and<eos>.png Binary files differdeleted file mode 100644 index 14a8cf3..0000000 --- a/src/text_recognizer/tests/support/emnist_lines/ancillary beliefs and<eos>.png +++ /dev/null diff --git a/src/text_recognizer/tests/support/emnist_lines/they<eos>.png b/src/text_recognizer/tests/support/emnist_lines/they<eos>.png Binary files differdeleted file mode 100644 index 7f05951..0000000 --- a/src/text_recognizer/tests/support/emnist_lines/they<eos>.png +++ /dev/null diff --git a/src/text_recognizer/tests/support/iam_lines/He rose from his breakfast-nook bench<eos>.png b/src/text_recognizer/tests/support/iam_lines/He rose from his breakfast-nook bench<eos>.png Binary files differdeleted file mode 100644 index 6eeb642..0000000 --- a/src/text_recognizer/tests/support/iam_lines/He rose from his breakfast-nook bench<eos>.png +++ /dev/null diff --git a/src/text_recognizer/tests/support/iam_lines/and came into the livingroom, where<eos>.png b/src/text_recognizer/tests/support/iam_lines/and came into the livingroom, where<eos>.png Binary files differdeleted file mode 100644 index 4974cf8..0000000 --- a/src/text_recognizer/tests/support/iam_lines/and came into the livingroom, where<eos>.png +++ /dev/null diff --git a/src/text_recognizer/tests/support/iam_lines/his entrance. He came, almost falling<eos>.png b/src/text_recognizer/tests/support/iam_lines/his entrance. He came, almost falling<eos>.png Binary files differdeleted file mode 100644 index a731245..0000000 --- a/src/text_recognizer/tests/support/iam_lines/his entrance. He came, almost falling<eos>.png +++ /dev/null diff --git a/src/text_recognizer/tests/support/iam_paragraphs/a01-000u.jpg b/src/text_recognizer/tests/support/iam_paragraphs/a01-000u.jpg Binary files differdeleted file mode 100644 index d9753b6..0000000 --- a/src/text_recognizer/tests/support/iam_paragraphs/a01-000u.jpg +++ /dev/null diff --git a/src/text_recognizer/tests/test_character_predictor.py b/src/text_recognizer/tests/test_character_predictor.py deleted file mode 100644 index 01bda78..0000000 --- a/src/text_recognizer/tests/test_character_predictor.py +++ /dev/null @@ -1,31 +0,0 @@ -"""Test for CharacterPredictor class.""" -import importlib -import os -from pathlib import Path -import unittest - -from loguru import logger - -from text_recognizer.character_predictor import CharacterPredictor -from text_recognizer.networks import MLP - -SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "support" / "emnist" - -os.environ["CUDA_VISIBLE_DEVICES"] = "" - - -class TestCharacterPredictor(unittest.TestCase): - """Tests for the CharacterPredictor class.""" - - def test_filename(self) -> None: - """Test that CharacterPredictor correctly predicts on a single image, for serveral test images.""" - network_fn_ = MLP - predictor = CharacterPredictor(network_fn=network_fn_) - - for filename in SUPPORT_DIRNAME.glob("*.png"): - pred, conf = predictor.predict(str(filename)) - logger.info( - f"Prediction: {pred} at confidence: {conf} for image with character {filename.stem}" - ) - self.assertEqual(pred, filename.stem) - self.assertGreater(conf, 0.7) diff --git a/src/text_recognizer/tests/test_line_predictor.py b/src/text_recognizer/tests/test_line_predictor.py deleted file mode 100644 index eede4d4..0000000 --- a/src/text_recognizer/tests/test_line_predictor.py +++ /dev/null @@ -1,35 +0,0 @@ -"""Tests for LinePredictor.""" -import os -from pathlib import Path -import unittest - - -import editdistance -import numpy as np - -from text_recognizer.datasets import IamLinesDataset -from text_recognizer.line_predictor import LinePredictor -import text_recognizer.util as util - -SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "support" - -os.environ["CUDA_VISIBLE_DEVICES"] = "" - - -class TestEmnistLinePredictor(unittest.TestCase): - """Test LinePredictor class on the EmnistLines dataset.""" - - def test_filename(self) -> None: - """Test that LinePredictor correctly predicts on single images, for several test images.""" - predictor = LinePredictor( - dataset="EmnistLineDataset", network_fn="CNNTransformer" - ) - - for filename in (SUPPORT_DIRNAME / "emnist_lines").glob("*.png"): - pred, conf = predictor.predict(str(filename)) - true = str(filename.stem) - edit_distance = editdistance.eval(pred, true) / len(pred) - print( - f'Pred: "{pred}" | Confidence: {conf} | True: {true} | Edit distance: {edit_distance}' - ) - self.assertLess(edit_distance, 0.2) diff --git a/src/text_recognizer/tests/test_paragraph_text_recognizer.py b/src/text_recognizer/tests/test_paragraph_text_recognizer.py deleted file mode 100644 index 3e280b9..0000000 --- a/src/text_recognizer/tests/test_paragraph_text_recognizer.py +++ /dev/null @@ -1,37 +0,0 @@ -"""Test for ParagraphTextRecognizer class.""" -import os -from pathlib import Path -import unittest - -from text_recognizer.paragraph_text_recognizer import ParagraphTextRecognizor -import text_recognizer.util as util - - -SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "support" / "iam_paragraph" - -# Prevent using GPU. -os.environ["CUDA_VISIBLE_DEVICES"] = "" - - -class TestParagraphTextRecognizor(unittest.TestCase): - """Test that it can take non-square images of max dimension larger than 256px.""" - - def test_filename(self) -> None: - """Test model on support image.""" - line_predictor_args = { - "dataset": "EmnistLineDataset", - "network_fn": "CNNTransformer", - } - line_detector_args = {"dataset": "EmnistLineDataset", "network_fn": "UNet"} - model = ParagraphTextRecognizor( - line_predictor_args=line_predictor_args, - line_detector_args=line_detector_args, - ) - num_text_lines_by_name = {"a01-000u-cropped": 7} - for filename in (SUPPORT_DIRNAME).glob("*.jpg"): - full_image = util.read_image(str(filename), grayscale=True) - predicted_text, line_region_crops = model.predict(full_image) - print(predicted_text) - self.assertTrue( - len(line_region_crops), num_text_lines_by_name[filename.stem] - ) diff --git a/src/text_recognizer/util.py b/src/text_recognizer/util.py deleted file mode 100644 index b431e22..0000000 --- a/src/text_recognizer/util.py +++ /dev/null @@ -1,52 +0,0 @@ -"""Utility functions for text_recognizer module.""" -import os -from pathlib import Path -from typing import Union -from urllib.request import urlopen - -import cv2 -import numpy as np - - -def read_image(image_uri: Union[Path, str], grayscale: bool = False) -> np.ndarray: - """Read image_uri.""" - - def read_image_from_filename(image_filename: str, imread_flag: int) -> np.ndarray: - return cv2.imread(str(image_filename), imread_flag) - - def read_image_from_url(image_url: str, imread_flag: int) -> np.ndarray: - if image_url.lower().startswith("http"): - url_response = urlopen(str(image_url)) - image_array = np.array(bytearray(url_response.read()), dtype=np.uint8) - return cv2.imdecode(image_array, imread_flag) - else: - raise ValueError( - "Url does not start with http, therefore not safe to open..." - ) from None - - imread_flag = cv2.IMREAD_GRAYSCALE if grayscale else cv2.IMREAD_COLOR - local_file = os.path.exists(image_uri) - image = None - - if local_file: - image = read_image_from_filename(image_uri, imread_flag) - else: - image = read_image_from_url(image_uri, imread_flag) - - if image is None: - raise ValueError(f"Could not load image at {image_uri}") - - return image - - -def rescale_image(image: np.ndarray) -> np.ndarray: - """Rescale image from [0, 1] to [0, 255].""" - if image.max() <= 1.0: - image = 255 * (image - image.min()) / (image.max() - image.min()) - return image - - -def write_image(image: np.ndarray, filename: Union[Path, str]) -> None: - """Write image to file.""" - image = rescale_image(image) - cv2.imwrite(str(filename), image) diff --git a/src/text_recognizer/weights/CRNNModel_IamLinesDataset_ConvolutionalRecurrentNetwork_weights.pt b/src/text_recognizer/weights/CRNNModel_IamLinesDataset_ConvolutionalRecurrentNetwork_weights.pt deleted file mode 100644 index 344e0a3..0000000 --- a/src/text_recognizer/weights/CRNNModel_IamLinesDataset_ConvolutionalRecurrentNetwork_weights.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:46d483950ef0876ba072d06cd94021e08d99c4fa14eeccf22aeae1cbb2066b4f -size 5628749 diff --git a/src/text_recognizer/weights/CharacterModel_EmnistDataset_DenseNet_weights.pt b/src/text_recognizer/weights/CharacterModel_EmnistDataset_DenseNet_weights.pt deleted file mode 100644 index f2dfd84..0000000 --- a/src/text_recognizer/weights/CharacterModel_EmnistDataset_DenseNet_weights.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:8a69e5efedea70c4c5cb8ccdcc8cd480400f6c73e3313423f4dbbfe615644f0a -size 4500617 diff --git a/src/text_recognizer/weights/CharacterModel_EmnistDataset_WideResidualNetwork_weights.pt b/src/text_recognizer/weights/CharacterModel_EmnistDataset_WideResidualNetwork_weights.pt deleted file mode 100644 index e1add8d..0000000 --- a/src/text_recognizer/weights/CharacterModel_EmnistDataset_WideResidualNetwork_weights.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:68dd5c98eedc8753546f88b4e6fd5fc38725dc0079b837c30fb3d48069ec412b -size 15002754 diff --git a/src/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_FCN_weights.pt b/src/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_FCN_weights.pt Binary files differdeleted file mode 100644 index d9ca01d..0000000 --- a/src/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_FCN_weights.pt +++ /dev/null diff --git a/src/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_UNet_weights.pt b/src/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_UNet_weights.pt Binary files differdeleted file mode 100644 index 0af0e57..0000000 --- a/src/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_UNet_weights.pt +++ /dev/null diff --git a/src/text_recognizer/weights/VQVAEModel_IamLinesDataset_VQVAE_weights.pt b/src/text_recognizer/weights/VQVAEModel_IamLinesDataset_VQVAE_weights.pt Binary files differdeleted file mode 100644 index b5295c2..0000000 --- a/src/text_recognizer/weights/VQVAEModel_IamLinesDataset_VQVAE_weights.pt +++ /dev/null diff --git a/src/training/experiments/default_config_emnist.yml b/src/training/experiments/default_config_emnist.yml deleted file mode 100644 index bf2ed0a..0000000 --- a/src/training/experiments/default_config_emnist.yml +++ /dev/null @@ -1,70 +0,0 @@ -dataset: EmnistDataset -dataset_args: - sample_to_balance: true - subsample_fraction: 0.33 - transform: null - target_transform: null - seed: 4711 - -data_loader_args: - splits: [train, val] - shuffle: true - num_workers: 8 - cuda: true - -model: CharacterModel -metrics: [accuracy] - -network_args: - in_channels: 1 - num_classes: 80 - depths: [2] - block_sizes: [256] - -train_args: - batch_size: 256 - epochs: 5 - -criterion: CrossEntropyLoss -criterion_args: - weight: null - ignore_index: -100 - reduction: mean - -optimizer: AdamW -optimizer_args: - lr: 1.e-03 - betas: [0.9, 0.999] - eps: 1.e-08 - # weight_decay: 5.e-4 - amsgrad: false - -lr_scheduler: OneCycleLR -lr_scheduler_args: - max_lr: 1.e-03 - epochs: 5 - anneal_strategy: linear - - -callbacks: [Checkpoint, ProgressBar, EarlyStopping, WandbCallback, WandbImageLogger, OneCycleLR] -callback_args: - Checkpoint: - monitor: val_accuracy - ProgressBar: - epochs: 5 - log_batch_frequency: 100 - EarlyStopping: - monitor: val_loss - min_delta: 0.0 - patience: 3 - mode: min - WandbCallback: - log_batch_frequency: 10 - WandbImageLogger: - num_examples: 4 - OneCycleLR: - null -verbosity: 1 # 0, 1, 2 -resume_experiment: null -train: true -validation_metric: val_accuracy diff --git a/src/training/experiments/embedding_experiment.yml b/src/training/experiments/embedding_experiment.yml deleted file mode 100644 index 1e5f941..0000000 --- a/src/training/experiments/embedding_experiment.yml +++ /dev/null @@ -1,64 +0,0 @@ -experiment_group: Embedding Experiments -experiments: - - train_args: - transformer_model: false - batch_size: &batch_size 256 - max_epochs: &max_epochs 32 - input_shape: [[1, 28, 28]] - dataset: - type: EmnistDataset - args: - sample_to_balance: true - subsample_fraction: null - transform: null - target_transform: null - seed: 4711 - train_args: - num_workers: 8 - train_fraction: 0.85 - batch_size: *batch_size - model: CharacterModel - metrics: [] - network: - type: DenseNet - args: - growth_rate: 4 - block_config: [4, 4] - in_channels: 1 - base_channels: 24 - num_classes: 128 - bn_size: 4 - dropout_rate: 0.1 - classifier: true - activation: elu - criterion: - type: EmbeddingLoss - args: - margin: 0.2 - type_of_triplets: semihard - optimizer: - type: AdamW - args: - lr: 1.e-02 - betas: [0.9, 0.999] - eps: 1.e-08 - weight_decay: 5.e-4 - amsgrad: false - lr_scheduler: - type: CosineAnnealingLR - args: - T_max: *max_epochs - callbacks: [Checkpoint, ProgressBar, WandbCallback] - callback_args: - Checkpoint: - monitor: val_loss - mode: min - ProgressBar: - epochs: *max_epochs - WandbCallback: - log_batch_frequency: 10 - verbosity: 1 # 0, 1, 2 - resume_experiment: null - train: true - test: true - test_metric: mean_average_precision_at_r diff --git a/src/training/experiments/sample_experiment.yml b/src/training/experiments/sample_experiment.yml deleted file mode 100644 index 8f94475..0000000 --- a/src/training/experiments/sample_experiment.yml +++ /dev/null @@ -1,99 +0,0 @@ -experiment_group: Sample Experiments -experiments: - - train_args: - batch_size: 256 - max_epochs: &max_epochs 32 - dataset: - type: EmnistDataset - args: - sample_to_balance: true - subsample_fraction: null - transform: null - target_transform: null - seed: 4711 - train_args: - num_workers: 6 - train_fraction: 0.8 - - model: CharacterModel - metrics: [accuracy] - # network: MLP - # network_args: - # input_size: 784 - # hidden_size: 512 - # output_size: 80 - # num_layers: 5 - # dropout_rate: 0.2 - # activation_fn: SELU - network: - type: ResidualNetwork - args: - in_channels: 1 - num_classes: 80 - depths: [2, 2] - block_sizes: [64, 64] - activation: leaky_relu - # network: - # type: WideResidualNetwork - # args: - # in_channels: 1 - # num_classes: 80 - # depth: 10 - # num_layers: 3 - # width_factor: 4 - # dropout_rate: 0.2 - # activation: SELU - # network: LeNet - # network_args: - # output_size: 62 - # activation_fn: GELU - criterion: - type: CrossEntropyLoss - args: - weight: null - ignore_index: -100 - reduction: mean - optimizer: - type: AdamW - args: - lr: 1.e-02 - betas: [0.9, 0.999] - eps: 1.e-08 - # weight_decay: 5.e-4 - amsgrad: false - # lr_scheduler: - # type: OneCycleLR - # args: - # max_lr: 1.e-03 - # epochs: *max_epochs - # anneal_strategy: linear - lr_scheduler: - type: CosineAnnealingLR - args: - T_max: *max_epochs - interval: epoch - swa_args: - start: 2 - lr: 5.e-2 - callbacks: [Checkpoint, ProgressBar, WandbCallback, WandbImageLogger, EarlyStopping] - callback_args: - Checkpoint: - monitor: val_accuracy - ProgressBar: - epochs: null - log_batch_frequency: 100 - EarlyStopping: - monitor: val_loss - min_delta: 0.0 - patience: 5 - mode: min - WandbCallback: - log_batch_frequency: 10 - WandbImageLogger: - num_examples: 4 - use_transpose: true - verbosity: 0 # 0, 1, 2 - resume_experiment: null - train: true - test: true - test_metric: test_accuracy diff --git a/src/training/gpu_manager.py b/src/training/gpu_manager.py deleted file mode 100644 index ce1b3dd..0000000 --- a/src/training/gpu_manager.py +++ /dev/null @@ -1,62 +0,0 @@ -"""GPUManager class.""" -import os -import time -from typing import Optional - -import gpustat -from loguru import logger -import numpy as np -from redlock import Redlock - - -GPU_LOCK_TIMEOUT = 5000 # ms - - -class GPUManager: - """Class for allocating GPUs.""" - - def __init__(self, verbose: bool = False) -> None: - """Initializes Redlock manager.""" - self.lock_manager = Redlock([{"host": "localhost", "port": 6379, "db": 0}]) - self.verbose = verbose - - def get_free_gpu(self) -> int: - """Gets a free GPU. - - If some GPUs are available, try reserving one by checking out an exclusive redis lock. - If none available or can not get lock, sleep and check again. - - Returns: - int: The gpu index. - - """ - while True: - gpu_index = self._get_free_gpu() - if gpu_index is not None: - return gpu_index - - if self.verbose: - logger.debug(f"pid {os.getpid()} sleeping") - time.sleep(GPU_LOCK_TIMEOUT / 1000) - - def _get_free_gpu(self) -> Optional[int]: - """Fetches an available GPU index.""" - try: - available_gpu_indices = [ - gpu.index - for gpu in gpustat.GPUStatCollection.new_query() - if gpu.memory_used < 0.5 * gpu.memory_total - ] - except Exception as e: - logger.debug(f"Got the following exception: {e}") - return None - - if available_gpu_indices: - gpu_index = np.random.choice(available_gpu_indices) - if self.verbose: - logger.debug(f"pid {os.getpid()} picking gpu {gpu_index}") - if self.lock_manager.lock(f"gpu_{gpu_index}", GPU_LOCK_TIMEOUT): - return int(gpu_index) - if self.verbose: - logger.debug(f"pid {os.getpid()} could not get lock.") - return None diff --git a/src/training/prepare_experiments.py b/src/training/prepare_experiments.py deleted file mode 100644 index 21997af..0000000 --- a/src/training/prepare_experiments.py +++ /dev/null @@ -1,34 +0,0 @@ -"""Run a experiment from a config file.""" -import json - -import click -import yaml - - -def run_experiments(experiments_filename: str) -> None: - """Run experiment from file.""" - with open(experiments_filename, "r") as f: - experiments_config = yaml.safe_load(f) - - num_experiments = len(experiments_config["experiments"]) - for index in range(num_experiments): - experiment_config = experiments_config["experiments"][index] - experiment_config["experiment_group"] = experiments_config["experiment_group"] - cmd = f"poetry run run-experiment --gpu=-1 --save '{json.dumps(experiment_config)}'" - print(cmd) - - -@click.command() -@click.option( - "--experiments_filename", - required=True, - type=str, - help="Filename of Yaml file of experiments to run.", -) -def run_cli(experiments_filename: str) -> None: - """Parse command-line arguments and run experiments from provided file.""" - run_experiments(experiments_filename) - - -if __name__ == "__main__": - run_cli() diff --git a/src/training/run_experiment.py b/src/training/run_experiment.py deleted file mode 100644 index faafea6..0000000 --- a/src/training/run_experiment.py +++ /dev/null @@ -1,382 +0,0 @@ -"""Script to run experiments.""" -from datetime import datetime -from glob import glob -import importlib -import json -import os -from pathlib import Path -import re -from typing import Callable, Dict, List, Optional, Tuple, Type -import warnings - -import click -from loguru import logger -import numpy as np -import torch -from torchsummary import summary -from tqdm import tqdm -from training.gpu_manager import GPUManager -from training.trainer.callbacks import CallbackList -from training.trainer.train import Trainer -import wandb -import yaml - -import text_recognizer.models -from text_recognizer.models import Model -import text_recognizer.networks -from text_recognizer.networks.loss import loss as custom_loss_module - -EXPERIMENTS_DIRNAME = Path(__file__).parents[0].resolve() / "experiments" - - -def _get_level(verbose: int) -> int: - """Sets the logger level.""" - levels = {0: 40, 1: 20, 2: 10} - verbose = verbose if verbose <= 2 else 2 - return levels[verbose] - - -def _create_experiment_dir( - experiment_config: Dict, checkpoint: Optional[str] = None -) -> Path: - """Create new experiment.""" - EXPERIMENTS_DIRNAME.mkdir(parents=True, exist_ok=True) - experiment_dir = EXPERIMENTS_DIRNAME / ( - f"{experiment_config['model']}_" - + f"{experiment_config['dataset']['type']}_" - + f"{experiment_config['network']['type']}" - ) - - if checkpoint is None: - experiment = datetime.now().strftime("%m%d_%H%M%S") - logger.debug(f"Creating a new experiment called {experiment}") - else: - available_experiments = glob(str(experiment_dir) + "/*") - available_experiments.sort() - if checkpoint == "last": - experiment = available_experiments[-1] - logger.debug(f"Resuming the latest experiment {experiment}") - else: - experiment = checkpoint - if not str(experiment_dir / experiment) in available_experiments: - raise FileNotFoundError("Experiment does not exist.") - logger.debug(f"Resuming the from experiment {checkpoint}") - - experiment_dir = experiment_dir / experiment - - # Create log and model directories. - log_dir = experiment_dir / "log" - model_dir = experiment_dir / "model" - - return experiment_dir, log_dir, model_dir - - -def _load_modules_and_arguments(experiment_config: Dict,) -> Tuple[Callable, Dict]: - """Loads all modules and arguments.""" - # Load the dataset module. - dataset_args = experiment_config.get("dataset", {}) - dataset_ = dataset_args["type"] - - # Import the model module and model arguments. - model_class_ = getattr(text_recognizer.models, experiment_config["model"]) - - # Import metrics. - metric_fns_ = ( - { - metric: getattr(text_recognizer.networks, metric) - for metric in experiment_config["metrics"] - } - if experiment_config["metrics"] is not None - else None - ) - - # Import network module and arguments. - network_fn_ = experiment_config["network"]["type"] - network_args = experiment_config["network"].get("args", {}) - - # Criterion - if experiment_config["criterion"]["type"] in custom_loss_module.__all__: - criterion_ = getattr(custom_loss_module, experiment_config["criterion"]["type"]) - else: - criterion_ = getattr(torch.nn, experiment_config["criterion"]["type"]) - criterion_args = experiment_config["criterion"].get("args", {}) or {} - - # Optimizers - optimizer_ = getattr(torch.optim, experiment_config["optimizer"]["type"]) - optimizer_args = experiment_config["optimizer"].get("args", {}) - - # Learning rate scheduler - lr_scheduler_ = None - lr_scheduler_args = None - if "lr_scheduler" in experiment_config: - lr_scheduler_ = getattr( - torch.optim.lr_scheduler, experiment_config["lr_scheduler"]["type"] - ) - lr_scheduler_args = experiment_config["lr_scheduler"].get("args", {}) or {} - - # SWA scheduler. - if "swa_args" in experiment_config: - swa_args = experiment_config.get("swa_args", {}) or {} - else: - swa_args = None - - model_args = { - "dataset": dataset_, - "dataset_args": dataset_args, - "metrics": metric_fns_, - "network_fn": network_fn_, - "network_args": network_args, - "criterion": criterion_, - "criterion_args": criterion_args, - "optimizer": optimizer_, - "optimizer_args": optimizer_args, - "lr_scheduler": lr_scheduler_, - "lr_scheduler_args": lr_scheduler_args, - "swa_args": swa_args, - } - - return model_class_, model_args - - -def _configure_callbacks(experiment_config: Dict, model_dir: Path) -> CallbackList: - """Configure a callback list for trainer.""" - if "Checkpoint" in experiment_config["callback_args"]: - experiment_config["callback_args"]["Checkpoint"]["checkpoint_path"] = str( - model_dir - ) - - # Initializes callbacks. - callback_modules = importlib.import_module("training.trainer.callbacks") - callbacks = [] - for callback in experiment_config["callbacks"]: - args = experiment_config["callback_args"][callback] or {} - callbacks.append(getattr(callback_modules, callback)(**args)) - - return callbacks - - -def _configure_logger(log_dir: Path, verbose: int = 0) -> None: - """Configure the loguru logger for output to terminal and disk.""" - # Have to remove default logger to get tqdm to work properly. - logger.remove() - - # Fetch verbosity level. - level = _get_level(verbose) - - logger.add(lambda msg: tqdm.write(msg, end=""), colorize=True, level=level) - logger.add( - str(log_dir / "train.log"), - format="{time:YYYY-MM-DD at HH:mm:ss} | {level} | {message}", - ) - - -def _save_config(experiment_dir: Path, experiment_config: Dict) -> None: - """Copy config to experiment directory.""" - config_path = experiment_dir / "config.yml" - with open(str(config_path), "w") as f: - yaml.dump(experiment_config, f) - - -def _load_from_checkpoint( - model: Type[Model], model_dir: Path, pretrained_weights: str = None, -) -> None: - """If checkpoint exists, load model weights and optimizers from checkpoint.""" - # Get checkpoint path. - if pretrained_weights is not None: - logger.info(f"Loading weights from {pretrained_weights}.") - checkpoint_path = ( - EXPERIMENTS_DIRNAME / Path(pretrained_weights) / "model" / "best.pt" - ) - else: - logger.info(f"Loading weights from {model_dir}.") - checkpoint_path = model_dir / "last.pt" - if checkpoint_path.exists(): - logger.info("Loading and resuming training from checkpoint.") - model.load_from_checkpoint(checkpoint_path) - - -def evaluate_embedding(model: Type[Model]) -> Dict: - """Evaluates the embedding space.""" - from pytorch_metric_learning import testers - from pytorch_metric_learning.utils.accuracy_calculator import AccuracyCalculator - - accuracy_calculator = AccuracyCalculator( - include=("mean_average_precision_at_r",), k=10 - ) - - def get_all_embeddings(model: Type[Model]) -> Tuple: - tester = testers.BaseTester() - return tester.get_all_embeddings(model.test_dataset, model.network) - - embeddings, labels = get_all_embeddings(model) - logger.info("Computing embedding accuracy") - accuracies = accuracy_calculator.get_accuracy( - embeddings, embeddings, np.squeeze(labels), np.squeeze(labels), True - ) - logger.info( - f"Test set accuracy (MAP@10) = {accuracies['mean_average_precision_at_r']}" - ) - return accuracies - - -def run_experiment( - experiment_config: Dict, - save_weights: bool, - device: str, - use_wandb: bool, - train: bool, - test: bool, - verbose: int = 0, - checkpoint: Optional[str] = None, - pretrained_weights: Optional[str] = None, -) -> None: - """Runs an experiment.""" - logger.info(f"Experiment config: {json.dumps(experiment_config)}") - - # Create new experiment. - experiment_dir, log_dir, model_dir = _create_experiment_dir( - experiment_config, checkpoint - ) - - # Make sure the log/model directory exists. - log_dir.mkdir(parents=True, exist_ok=True) - model_dir.mkdir(parents=True, exist_ok=True) - - # Load the modules and model arguments. - model_class_, model_args = _load_modules_and_arguments(experiment_config) - - # Initializes the model with experiment config. - model = model_class_(**model_args, device=device) - - callbacks = _configure_callbacks(experiment_config, model_dir) - - # Setup logger. - _configure_logger(log_dir, verbose) - - # Load from checkpoint if resuming an experiment. - resume = False - if checkpoint is not None or pretrained_weights is not None: - # resume = True - _load_from_checkpoint(model, model_dir, pretrained_weights) - - logger.info(f"The class mapping is {model.mapping}") - - # Initializes Weights & Biases - if use_wandb: - wandb.init(project="text-recognizer", config=experiment_config, resume=resume) - - # Lets W&B save the model and track the gradients and optional parameters. - wandb.watch(model.network) - - experiment_config["experiment_group"] = experiment_config.get( - "experiment_group", None - ) - - experiment_config["device"] = device - - # Save the config used in the experiment folder. - _save_config(experiment_dir, experiment_config) - - # Prints a summary of the network in terminal. - model.summary(experiment_config["train_args"]["input_shape"]) - - # Load trainer. - trainer = Trainer( - max_epochs=experiment_config["train_args"]["max_epochs"], - callbacks=callbacks, - transformer_model=experiment_config["train_args"]["transformer_model"], - max_norm=experiment_config["train_args"]["max_norm"], - freeze_backbone=experiment_config["train_args"]["freeze_backbone"], - ) - - # Train the model. - if train: - trainer.fit(model) - - # Run inference over test set. - if test: - logger.info("Loading checkpoint with the best weights.") - if "checkpoint" in experiment_config["train_args"]: - model.load_from_checkpoint( - model_dir / experiment_config["train_args"]["checkpoint"] - ) - else: - model.load_from_checkpoint(model_dir / "best.pt") - - logger.info("Running inference on test set.") - if experiment_config["criterion"]["type"] == "EmbeddingLoss": - logger.info("Evaluating embedding.") - score = evaluate_embedding(model) - else: - score = trainer.test(model) - - logger.info(f"Test set evaluation: {score}") - - if use_wandb: - wandb.log( - { - experiment_config["test_metric"]: score[ - experiment_config["test_metric"] - ] - } - ) - - if save_weights: - model.save_weights(model_dir) - - -@click.command() -@click.argument("experiment_config",) -@click.option("--gpu", type=int, default=0, help="Provide the index of the GPU to use.") -@click.option( - "--save", - is_flag=True, - help="If set, the final weights will be saved to a canonical, version-controlled location.", -) -@click.option( - "--nowandb", is_flag=False, help="If true, do not use wandb for this run." -) -@click.option("--test", is_flag=True, help="If true, test the model.") -@click.option("-v", "--verbose", count=True) -@click.option("--checkpoint", type=str, help="Path to the experiment.") -@click.option( - "--pretrained_weights", type=str, help="Path to pretrained model weights." -) -@click.option( - "--notrain", is_flag=False, help="Do not train the model.", -) -def run_cli( - experiment_config: str, - gpu: int, - save: bool, - nowandb: bool, - notrain: bool, - test: bool, - verbose: int, - checkpoint: Optional[str] = None, - pretrained_weights: Optional[str] = None, -) -> None: - """Run experiment.""" - if gpu < 0: - gpu_manager = GPUManager(True) - gpu = gpu_manager.get_free_gpu() - device = "cuda:" + str(gpu) - - experiment_config = json.loads(experiment_config) - os.environ["CUDA_VISIBLE_DEVICES"] = f"{gpu}" - - run_experiment( - experiment_config, - save, - device, - use_wandb=not nowandb, - train=not notrain, - test=test, - verbose=verbose, - checkpoint=checkpoint, - pretrained_weights=pretrained_weights, - ) - - -if __name__ == "__main__": - run_cli() diff --git a/src/training/run_sweep.py b/src/training/run_sweep.py deleted file mode 100644 index a578592..0000000 --- a/src/training/run_sweep.py +++ /dev/null @@ -1,92 +0,0 @@ -"""W&B Sweep Functionality.""" -from ast import literal_eval -import json -import os -from pathlib import Path -import signal -import subprocess # nosec -import sys -from typing import Dict, List, Tuple - -import click -import yaml - -EXPERIMENTS_DIRNAME = Path(__file__).parents[0].resolve() / "experiments" - - -def load_config() -> Dict: - """Load base hyperparameter config.""" - with open(str(EXPERIMENTS_DIRNAME / "default_config_emnist.yml"), "r") as f: - default_config = yaml.safe_load(f) - return default_config - - -def args_to_json( - default_config: dict, preserve_args: tuple = ("gpu", "save") -) -> Tuple[dict, list]: - """Convert command line arguments to nested config values. - - i.e. run_sweep.py --dataset_args.foo=1.7 - { - "dataset_args": { - "foo": 1.7 - } - } - - Args: - default_config (dict): The base config used for every experiment. - preserve_args (tuple): Arguments preserved for all runs. Defaults to ("gpu", "save"). - - Returns: - Tuple[dict, list]: Tuple of config dictionary and list of arguments. - - """ - - args = [] - config = default_config.copy() - key, val = None, None - for arg in sys.argv[1:]: - if "=" in arg: - key, val = arg.split("=") - elif key: - val = arg - else: - key = arg - if key and val: - parsed_key = key.lstrip("-").split(".") - if parsed_key[0] in preserve_args: - args.append("--{}={}".format(parsed_key[0], val)) - else: - nested = config - for level in parsed_key[:-1]: - nested[level] = config.get(level, {}) - nested = nested[level] - try: - # Convert numerics to floats / ints - val = literal_eval(val) - except ValueError: - pass - nested[parsed_key[-1]] = val - key, val = None, None - return config, args - - -def main() -> None: - """Runs a W&B sweep.""" - default_config = load_config() - config, args = args_to_json(default_config) - env = { - k: v for k, v in os.environ.items() if k not in ("WANDB_PROGRAM", "WANDB_ARGS") - } - # pylint: disable=subprocess-popen-preexec-fn - run = subprocess.Popen( - ["python", "training/run_experiment.py", *args, json.dumps(config)], - env=env, - preexec_fn=os.setsid, - ) # nosec - signal.signal(signal.SIGTERM, lambda *args: run.terminate()) - run.wait() - - -if __name__ == "__main__": - main() diff --git a/src/training/sweep_emnist.yml b/src/training/sweep_emnist.yml deleted file mode 100644 index 48d7261..0000000 --- a/src/training/sweep_emnist.yml +++ /dev/null @@ -1,26 +0,0 @@ -program: training/run_sweep.py -method: bayes -metric: - name: val_loss - goal: minimize -parameters: - dataset: - value: EmnistDataset - model: - value: CharacterModel - network: - value: MLP - network_args.hidden_size: - values: [128, 256] - network_args.dropout_rate: - values: [0.2, 0.4] - network_args.num_layers: - values: [3, 6] - optimizer_args.lr: - values: [1.e-1, 1.e-5] - lr_scheduler_args.max_lr: - values: [1.0e-1, 1.0e-5] - train_args.batch_size: - values: [64, 128] - train_args.epochs: - value: 5 diff --git a/src/training/sweep_emnist_resnet.yml b/src/training/sweep_emnist_resnet.yml deleted file mode 100644 index 19a3040..0000000 --- a/src/training/sweep_emnist_resnet.yml +++ /dev/null @@ -1,50 +0,0 @@ -program: training/run_sweep.py -method: bayes -metric: - name: val_accuracy - goal: maximize -parameters: - dataset: - value: EmnistDataset - model: - value: CharacterModel - network: - value: ResidualNetwork - network_args.block_sizes: - distribution: q_uniform - min: 16 - max: 256 - q: 8 - network_args.depths: - distribution: int_uniform - min: 1 - max: 3 - network_args.levels: - distribution: int_uniform - min: 1 - max: 2 - network_args.activation: - distribution: categorical - values: - - gelu - - leaky_relu - - relu - - selu - optimizer_args.lr: - distribution: uniform - min: 1.e-5 - max: 1.e-1 - lr_scheduler_args.max_lr: - distribution: uniform - min: 1.e-5 - max: 1.e-1 - train_args.batch_size: - distribution: q_uniform - min: 32 - max: 256 - q: 8 - train_args.epochs: - value: 5 -early_terminate: - type: hyperband - min_iter: 2 diff --git a/src/training/trainer/__init__.py b/src/training/trainer/__init__.py deleted file mode 100644 index de41bfb..0000000 --- a/src/training/trainer/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -"""Trainer modules.""" -from .train import Trainer diff --git a/src/training/trainer/callbacks/__init__.py b/src/training/trainer/callbacks/__init__.py deleted file mode 100644 index 80c4177..0000000 --- a/src/training/trainer/callbacks/__init__.py +++ /dev/null @@ -1,29 +0,0 @@ -"""The callback modules used in the training script.""" -from .base import Callback, CallbackList -from .checkpoint import Checkpoint -from .early_stopping import EarlyStopping -from .lr_schedulers import ( - LRScheduler, - SWA, -) -from .progress_bar import ProgressBar -from .wandb_callbacks import ( - WandbCallback, - WandbImageLogger, - WandbReconstructionLogger, - WandbSegmentationLogger, -) - -__all__ = [ - "Callback", - "CallbackList", - "Checkpoint", - "EarlyStopping", - "LRScheduler", - "WandbCallback", - "WandbImageLogger", - "WandbReconstructionLogger", - "WandbSegmentationLogger", - "ProgressBar", - "SWA", -] diff --git a/src/training/trainer/callbacks/base.py b/src/training/trainer/callbacks/base.py deleted file mode 100644 index 500b642..0000000 --- a/src/training/trainer/callbacks/base.py +++ /dev/null @@ -1,188 +0,0 @@ -"""Metaclass for callback functions.""" - -from enum import Enum -from typing import Callable, Dict, List, Optional, Type, Union - -from loguru import logger -import numpy as np -import torch - -from text_recognizer.models import Model - - -class ModeKeys: - """Mode keys for CallbackList.""" - - TRAIN = "train" - VALIDATION = "validation" - - -class Callback: - """Metaclass for callbacks used in training.""" - - def __init__(self) -> None: - """Initializes the Callback instance.""" - self.model = None - - def set_model(self, model: Type[Model]) -> None: - """Set the model.""" - self.model = model - - def on_fit_begin(self) -> None: - """Called when fit begins.""" - pass - - def on_fit_end(self) -> None: - """Called when fit ends.""" - pass - - def on_epoch_begin(self, epoch: int, logs: Optional[Dict] = None) -> None: - """Called at the beginning of an epoch. Only used in training mode.""" - pass - - def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None: - """Called at the end of an epoch. Only used in training mode.""" - pass - - def on_train_batch_begin(self, batch: int, logs: Optional[Dict] = None) -> None: - """Called at the beginning of an epoch.""" - pass - - def on_train_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None: - """Called at the end of an epoch.""" - pass - - def on_validation_batch_begin( - self, batch: int, logs: Optional[Dict] = None - ) -> None: - """Called at the beginning of an epoch.""" - pass - - def on_validation_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None: - """Called at the end of an epoch.""" - pass - - def on_test_begin(self) -> None: - """Called at the beginning of test.""" - pass - - def on_test_end(self) -> None: - """Called at the end of test.""" - pass - - -class CallbackList: - """Container for abstracting away callback calls.""" - - mode_keys = ModeKeys() - - def __init__(self, model: Type[Model], callbacks: List[Callback] = None) -> None: - """Container for `Callback` instances. - - This object wraps a list of `Callback` instances and allows them all to be - called via a single end point. - - Args: - model (Type[Model]): A `Model` instance. - callbacks (List[Callback]): List of `Callback` instances. Defaults to None. - - """ - - self._callbacks = callbacks or [] - if model: - self.set_model(model) - - def set_model(self, model: Type[Model]) -> None: - """Set the model for all callbacks.""" - self.model = model - for callback in self._callbacks: - callback.set_model(model=self.model) - - def append(self, callback: Type[Callback]) -> None: - """Append new callback to callback list.""" - self._callbacks.append(callback) - - def on_fit_begin(self) -> None: - """Called when fit begins.""" - for callback in self._callbacks: - callback.on_fit_begin() - - def on_fit_end(self) -> None: - """Called when fit ends.""" - for callback in self._callbacks: - callback.on_fit_end() - - def on_test_begin(self) -> None: - """Called when test begins.""" - for callback in self._callbacks: - callback.on_test_begin() - - def on_test_end(self) -> None: - """Called when test ends.""" - for callback in self._callbacks: - callback.on_test_end() - - def on_epoch_begin(self, epoch: int, logs: Optional[Dict] = None) -> None: - """Called at the beginning of an epoch.""" - for callback in self._callbacks: - callback.on_epoch_begin(epoch, logs) - - def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None: - """Called at the end of an epoch.""" - for callback in self._callbacks: - callback.on_epoch_end(epoch, logs) - - def _call_batch_hook( - self, mode: str, hook: str, batch: int, logs: Optional[Dict] = None - ) -> None: - """Helper function for all batch_{begin | end} methods.""" - if hook == "begin": - self._call_batch_begin_hook(mode, batch, logs) - elif hook == "end": - self._call_batch_end_hook(mode, batch, logs) - else: - raise ValueError(f"Unrecognized hook {hook}.") - - def _call_batch_begin_hook( - self, mode: str, batch: int, logs: Optional[Dict] = None - ) -> None: - """Helper function for all `on_*_batch_begin` methods.""" - hook_name = f"on_{mode}_batch_begin" - self._call_batch_hook_helper(hook_name, batch, logs) - - def _call_batch_end_hook( - self, mode: str, batch: int, logs: Optional[Dict] = None - ) -> None: - """Helper function for all `on_*_batch_end` methods.""" - hook_name = f"on_{mode}_batch_end" - self._call_batch_hook_helper(hook_name, batch, logs) - - def _call_batch_hook_helper( - self, hook_name: str, batch: int, logs: Optional[Dict] = None - ) -> None: - """Helper function for `on_*_batch_begin` methods.""" - for callback in self._callbacks: - hook = getattr(callback, hook_name) - hook(batch, logs) - - def on_train_batch_begin(self, batch: int, logs: Optional[Dict] = None) -> None: - """Called at the beginning of an epoch.""" - self._call_batch_hook(self.mode_keys.TRAIN, "begin", batch, logs) - - def on_train_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None: - """Called at the end of an epoch.""" - self._call_batch_hook(self.mode_keys.TRAIN, "end", batch, logs) - - def on_validation_batch_begin( - self, batch: int, logs: Optional[Dict] = None - ) -> None: - """Called at the beginning of an epoch.""" - self._call_batch_hook(self.mode_keys.VALIDATION, "begin", batch, logs) - - def on_validation_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None: - """Called at the end of an epoch.""" - self._call_batch_hook(self.mode_keys.VALIDATION, "end", batch, logs) - - def __iter__(self) -> iter: - """Iter function for callback list.""" - return iter(self._callbacks) diff --git a/src/training/trainer/callbacks/checkpoint.py b/src/training/trainer/callbacks/checkpoint.py deleted file mode 100644 index a54e0a9..0000000 --- a/src/training/trainer/callbacks/checkpoint.py +++ /dev/null @@ -1,95 +0,0 @@ -"""Callback checkpoint for training models.""" -from enum import Enum -from pathlib import Path -from typing import Callable, Dict, List, Optional, Type, Union - -from loguru import logger -import numpy as np -import torch -from training.trainer.callbacks import Callback - -from text_recognizer.models import Model - - -class Checkpoint(Callback): - """Saving model parameters at the end of each epoch.""" - - mode_dict = { - "min": torch.lt, - "max": torch.gt, - } - - def __init__( - self, - checkpoint_path: Union[str, Path], - monitor: str = "accuracy", - mode: str = "auto", - min_delta: float = 0.0, - ) -> None: - """Monitors a quantity that will allow us to determine the best model weights. - - Args: - checkpoint_path (Union[str, Path]): Path to the experiment with the checkpoint. - monitor (str): Name of the quantity to monitor. Defaults to "accuracy". - mode (str): Description of parameter `mode`. Defaults to "auto". - min_delta (float): Description of parameter `min_delta`. Defaults to 0.0. - - """ - super().__init__() - self.checkpoint_path = Path(checkpoint_path) - self.monitor = monitor - self.mode = mode - self.min_delta = torch.tensor(min_delta) - - if mode not in ["auto", "min", "max"]: - logger.warning(f"Checkpoint mode {mode} is unkown, fallback to auto mode.") - - self.mode = "auto" - - if self.mode == "auto": - if "accuracy" in self.monitor: - self.mode = "max" - else: - self.mode = "min" - logger.debug( - f"Checkpoint mode set to {self.mode} for monitoring {self.monitor}." - ) - - torch_inf = torch.tensor(np.inf) - self.min_delta *= 1 if self.monitor_op == torch.gt else -1 - self.best_score = torch_inf if self.monitor_op == torch.lt else -torch_inf - - @property - def monitor_op(self) -> float: - """Returns the comparison method.""" - return self.mode_dict[self.mode] - - def on_epoch_end(self, epoch: int, logs: Dict) -> None: - """Saves a checkpoint for the network parameters. - - Args: - epoch (int): The current epoch. - logs (Dict): The log containing the monitored metrics. - - """ - current = self.get_monitor_value(logs) - if current is None: - return - if self.monitor_op(current - self.min_delta, self.best_score): - self.best_score = current - is_best = True - else: - is_best = False - - self.model.save_checkpoint(self.checkpoint_path, is_best, epoch, self.monitor) - - def get_monitor_value(self, logs: Dict) -> Union[float, None]: - """Extracts the monitored value.""" - monitor_value = logs.get(self.monitor) - if monitor_value is None: - logger.warning( - f"Checkpoint is conditioned on metric {self.monitor} which is not available. Available" - + f" metrics are: {','.join(list(logs.keys()))}" - ) - return None - return monitor_value diff --git a/src/training/trainer/callbacks/early_stopping.py b/src/training/trainer/callbacks/early_stopping.py deleted file mode 100644 index 02b431f..0000000 --- a/src/training/trainer/callbacks/early_stopping.py +++ /dev/null @@ -1,108 +0,0 @@ -"""Implements Early stopping for PyTorch model.""" -from typing import Dict, Union - -from loguru import logger -import numpy as np -import torch -from torch import Tensor -from training.trainer.callbacks import Callback - - -class EarlyStopping(Callback): - """Stops training when a monitored metric stops improving.""" - - mode_dict = { - "min": torch.lt, - "max": torch.gt, - } - - def __init__( - self, - monitor: str = "val_loss", - min_delta: float = 0.0, - patience: int = 3, - mode: str = "auto", - ) -> None: - """Initializes the EarlyStopping callback. - - Args: - monitor (str): Description of parameter `monitor`. Defaults to "val_loss". - min_delta (float): Description of parameter `min_delta`. Defaults to 0.0. - patience (int): Description of parameter `patience`. Defaults to 3. - mode (str): Description of parameter `mode`. Defaults to "auto". - - """ - super().__init__() - self.monitor = monitor - self.patience = patience - self.min_delta = torch.tensor(min_delta) - self.mode = mode - self.wait_count = 0 - self.stopped_epoch = 0 - - if mode not in ["auto", "min", "max"]: - logger.warning( - f"EarlyStopping mode {mode} is unkown, fallback to auto mode." - ) - - self.mode = "auto" - - if self.mode == "auto": - if "accuracy" in self.monitor: - self.mode = "max" - else: - self.mode = "min" - logger.debug( - f"EarlyStopping mode set to {self.mode} for monitoring {self.monitor}." - ) - - self.torch_inf = torch.tensor(np.inf) - self.min_delta *= 1 if self.monitor_op == torch.gt else -1 - self.best_score = ( - self.torch_inf if self.monitor_op == torch.lt else -self.torch_inf - ) - - @property - def monitor_op(self) -> float: - """Returns the comparison method.""" - return self.mode_dict[self.mode] - - def on_fit_begin(self) -> Union[torch.lt, torch.gt]: - """Reset the early stopping variables for reuse.""" - self.wait_count = 0 - self.stopped_epoch = 0 - self.best_score = ( - self.torch_inf if self.monitor_op == torch.lt else -self.torch_inf - ) - - def on_epoch_end(self, epoch: int, logs: Dict) -> None: - """Computes the early stop criterion.""" - current = self.get_monitor_value(logs) - if current is None: - return - if self.monitor_op(current - self.min_delta, self.best_score): - self.best_score = current - self.wait_count = 0 - else: - self.wait_count += 1 - if self.wait_count >= self.patience: - self.stopped_epoch = epoch - self.model.stop_training = True - - def on_fit_end(self) -> None: - """Logs if early stopping was used.""" - if self.stopped_epoch > 0: - logger.info( - f"Stopped training at epoch {self.stopped_epoch + 1} with early stopping." - ) - - def get_monitor_value(self, logs: Dict) -> Union[Tensor, None]: - """Extracts the monitor value.""" - monitor_value = logs.get(self.monitor) - if monitor_value is None: - logger.warning( - f"Early stopping is conditioned on metric {self.monitor} which is not available. Available" - + f"metrics are: {','.join(list(logs.keys()))}" - ) - return None - return torch.tensor(monitor_value) diff --git a/src/training/trainer/callbacks/lr_schedulers.py b/src/training/trainer/callbacks/lr_schedulers.py deleted file mode 100644 index 630c434..0000000 --- a/src/training/trainer/callbacks/lr_schedulers.py +++ /dev/null @@ -1,77 +0,0 @@ -"""Callbacks for learning rate schedulers.""" -from typing import Callable, Dict, List, Optional, Type - -from torch.optim.swa_utils import update_bn -from training.trainer.callbacks import Callback - -from text_recognizer.models import Model - - -class LRScheduler(Callback): - """Generic learning rate scheduler callback.""" - - def __init__(self) -> None: - super().__init__() - - def set_model(self, model: Type[Model]) -> None: - """Sets the model and lr scheduler.""" - self.model = model - self.lr_scheduler = self.model.lr_scheduler["lr_scheduler"] - self.interval = self.model.lr_scheduler["interval"] - - def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None: - """Takes a step at the end of every epoch.""" - if self.interval == "epoch": - if "ReduceLROnPlateau" in self.lr_scheduler.__class__.__name__: - self.lr_scheduler.step(logs["val_loss"]) - else: - self.lr_scheduler.step() - - def on_train_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None: - """Takes a step at the end of every training batch.""" - if self.interval == "step": - self.lr_scheduler.step() - - -class SWA(Callback): - """Stochastic Weight Averaging callback.""" - - def __init__(self) -> None: - """Initializes the callback.""" - super().__init__() - self.lr_scheduler = None - self.interval = None - self.swa_scheduler = None - self.swa_start = None - self.current_epoch = 1 - - def set_model(self, model: Type[Model]) -> None: - """Sets the model and lr scheduler.""" - self.model = model - self.lr_scheduler = self.model.lr_scheduler["lr_scheduler"] - self.interval = self.model.lr_scheduler["interval"] - self.swa_scheduler = self.model.swa_scheduler["swa_scheduler"] - self.swa_start = self.model.swa_scheduler["swa_start"] - - def on_epoch_end(self, epoch: int, logs: Optional[Dict] = None) -> None: - """Takes a step at the end of every training batch.""" - if epoch > self.swa_start: - self.model.swa_network.update_parameters(self.model.network) - self.swa_scheduler.step() - elif self.interval == "epoch": - self.lr_scheduler.step() - self.current_epoch = epoch - - def on_train_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None: - """Takes a step at the end of every training batch.""" - if self.current_epoch < self.swa_start and self.interval == "step": - self.lr_scheduler.step() - - def on_fit_end(self) -> None: - """Update batch norm statistics for the swa model at the end of training.""" - if self.model.swa_network: - update_bn( - self.model.val_dataloader(), - self.model.swa_network, - device=self.model.device, - ) diff --git a/src/training/trainer/callbacks/progress_bar.py b/src/training/trainer/callbacks/progress_bar.py deleted file mode 100644 index 6c4305a..0000000 --- a/src/training/trainer/callbacks/progress_bar.py +++ /dev/null @@ -1,65 +0,0 @@ -"""Progress bar callback for the training loop.""" -from typing import Dict, Optional - -from tqdm import tqdm -from training.trainer.callbacks import Callback - - -class ProgressBar(Callback): - """A TQDM progress bar for the training loop.""" - - def __init__(self, epochs: int, log_batch_frequency: int = None) -> None: - """Initializes the tqdm callback.""" - self.epochs = epochs - print(epochs, type(epochs)) - self.log_batch_frequency = log_batch_frequency - self.progress_bar = None - self.val_metrics = {} - - def _configure_progress_bar(self) -> None: - """Configures the tqdm progress bar with custom bar format.""" - self.progress_bar = tqdm( - total=len(self.model.train_dataloader()), - leave=False, - unit="steps", - mininterval=self.log_batch_frequency, - bar_format="{desc} |{bar:32}| {n_fmt}/{total_fmt} ETA: {remaining} {rate_fmt}{postfix}", - ) - - def _key_abbreviations(self, logs: Dict) -> Dict: - """Changes the length of keys, so that the progress bar fits better.""" - - def rename(key: str) -> str: - """Renames accuracy to acc.""" - return key.replace("accuracy", "acc") - - return {rename(key): value for key, value in logs.items()} - - # def on_fit_begin(self) -> None: - # """Creates a tqdm progress bar.""" - # self._configure_progress_bar() - - def on_epoch_begin(self, epoch: int, logs: Optional[Dict]) -> None: - """Updates the description with the current epoch.""" - if epoch == 1: - self._configure_progress_bar() - else: - self.progress_bar.reset() - self.progress_bar.set_description(f"Epoch {epoch}/{self.epochs}") - - def on_epoch_end(self, epoch: int, logs: Dict) -> None: - """At the end of each epoch, the validation metrics are updated to the progress bar.""" - self.val_metrics = logs - self.progress_bar.set_postfix(**self._key_abbreviations(logs)) - self.progress_bar.update() - - def on_train_batch_end(self, batch: int, logs: Dict) -> None: - """Updates the progress bar for each training step.""" - if self.val_metrics: - logs.update(self.val_metrics) - self.progress_bar.set_postfix(**self._key_abbreviations(logs)) - self.progress_bar.update() - - def on_fit_end(self) -> None: - """Closes the tqdm progress bar.""" - self.progress_bar.close() diff --git a/src/training/trainer/callbacks/wandb_callbacks.py b/src/training/trainer/callbacks/wandb_callbacks.py deleted file mode 100644 index 552a4f4..0000000 --- a/src/training/trainer/callbacks/wandb_callbacks.py +++ /dev/null @@ -1,261 +0,0 @@ -"""Callback for W&B.""" -from typing import Callable, Dict, List, Optional, Type - -import numpy as np -from training.trainer.callbacks import Callback -import wandb - -import text_recognizer.datasets.transforms as transforms -from text_recognizer.models.base import Model - - -class WandbCallback(Callback): - """A custom W&B metric logger for the trainer.""" - - def __init__(self, log_batch_frequency: int = None) -> None: - """Short summary. - - Args: - log_batch_frequency (int): If None, metrics will be logged every epoch. - If set to an integer, callback will log every metrics every log_batch_frequency. - - """ - super().__init__() - self.log_batch_frequency = log_batch_frequency - - def _on_batch_end(self, batch: int, logs: Dict) -> None: - if self.log_batch_frequency and batch % self.log_batch_frequency == 0: - wandb.log(logs, commit=True) - - def on_train_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None: - """Logs training metrics.""" - if logs is not None: - logs["lr"] = self.model.optimizer.param_groups[0]["lr"] - self._on_batch_end(batch, logs) - - def on_validation_batch_end(self, batch: int, logs: Optional[Dict] = None) -> None: - """Logs validation metrics.""" - if logs is not None: - self._on_batch_end(batch, logs) - - def on_epoch_end(self, epoch: int, logs: Dict) -> None: - """Logs at epoch end.""" - wandb.log(logs, commit=True) - - -class WandbImageLogger(Callback): - """Custom W&B callback for image logging.""" - - def __init__( - self, - example_indices: Optional[List] = None, - num_examples: int = 4, - transform: Optional[bool] = None, - ) -> None: - """Initializes the WandbImageLogger with the model to train. - - Args: - example_indices (Optional[List]): Indices for validation images. Defaults to None. - num_examples (int): Number of random samples to take if example_indices are not specified. Defaults to 4. - transform (Optional[Dict]): Use transform on image or not. Defaults to None. - - """ - - super().__init__() - self.caption = None - self.example_indices = example_indices - self.test_sample_indices = None - self.num_examples = num_examples - self.transform = ( - self._configure_transform(transform) if transform is not None else None - ) - - def _configure_transform(self, transform: Dict) -> Callable: - args = transform["args"] or {} - return getattr(transforms, transform["type"])(**args) - - def set_model(self, model: Type[Model]) -> None: - """Sets the model and extracts validation images from the dataset.""" - self.model = model - self.caption = "Validation Examples" - if self.example_indices is None: - self.example_indices = np.random.randint( - 0, len(self.model.val_dataset), self.num_examples - ) - self.images = self.model.val_dataset.dataset.data[self.example_indices] - self.targets = self.model.val_dataset.dataset.targets[self.example_indices] - self.targets = self.targets.tolist() - - def on_test_begin(self) -> None: - """Get samples from test dataset.""" - self.caption = "Test Examples" - if self.test_sample_indices is None: - self.test_sample_indices = np.random.randint( - 0, len(self.model.test_dataset), self.num_examples - ) - self.images = self.model.test_dataset.data[self.test_sample_indices] - self.targets = self.model.test_dataset.targets[self.test_sample_indices] - self.targets = self.targets.tolist() - - def on_test_end(self) -> None: - """Log test images.""" - self.on_epoch_end(0, {}) - - def on_epoch_end(self, epoch: int, logs: Dict) -> None: - """Get network predictions on validation images.""" - images = [] - for i, image in enumerate(self.images): - image = self.transform(image) if self.transform is not None else image - pred, conf = self.model.predict_on_image(image) - if isinstance(self.targets[i], list): - ground_truth = "".join( - [ - self.model.mapper(int(target_index) - 26) - if target_index > 35 - else self.model.mapper(int(target_index)) - for target_index in self.targets[i] - ] - ).rstrip("_") - else: - ground_truth = self.model.mapper(int(self.targets[i])) - caption = f"Prediction: {pred} Confidence: {conf:.3f} Ground Truth: {ground_truth}" - images.append(wandb.Image(image, caption=caption)) - - wandb.log({f"{self.caption}": images}, commit=False) - - -class WandbSegmentationLogger(Callback): - """Custom W&B callback for image logging.""" - - def __init__( - self, - class_labels: Dict, - example_indices: Optional[List] = None, - num_examples: int = 4, - ) -> None: - """Initializes the WandbImageLogger with the model to train. - - Args: - class_labels (Dict): A dict with int as key and class string as value. - example_indices (Optional[List]): Indices for validation images. Defaults to None. - num_examples (int): Number of random samples to take if example_indices are not specified. Defaults to 4. - - """ - - super().__init__() - self.caption = None - self.class_labels = {int(k): v for k, v in class_labels.items()} - self.example_indices = example_indices - self.test_sample_indices = None - self.num_examples = num_examples - - def set_model(self, model: Type[Model]) -> None: - """Sets the model and extracts validation images from the dataset.""" - self.model = model - self.caption = "Validation Segmentation Examples" - if self.example_indices is None: - self.example_indices = np.random.randint( - 0, len(self.model.val_dataset), self.num_examples - ) - self.images = self.model.val_dataset.dataset.data[self.example_indices] - self.targets = self.model.val_dataset.dataset.targets[self.example_indices] - self.targets = self.targets.tolist() - - def on_test_begin(self) -> None: - """Get samples from test dataset.""" - self.caption = "Test Segmentation Examples" - if self.test_sample_indices is None: - self.test_sample_indices = np.random.randint( - 0, len(self.model.test_dataset), self.num_examples - ) - self.images = self.model.test_dataset.data[self.test_sample_indices] - self.targets = self.model.test_dataset.targets[self.test_sample_indices] - self.targets = self.targets.tolist() - - def on_test_end(self) -> None: - """Log test images.""" - self.on_epoch_end(0, {}) - - def on_epoch_end(self, epoch: int, logs: Dict) -> None: - """Get network predictions on validation images.""" - images = [] - for i, image in enumerate(self.images): - pred_mask = ( - self.model.predict_on_image(image).detach().squeeze(0).cpu().numpy() - ) - gt_mask = np.array(self.targets[i]) - images.append( - wandb.Image( - image, - masks={ - "predictions": { - "mask_data": pred_mask, - "class_labels": self.class_labels, - }, - "ground_truth": { - "mask_data": gt_mask, - "class_labels": self.class_labels, - }, - }, - ) - ) - - wandb.log({f"{self.caption}": images}, commit=False) - - -class WandbReconstructionLogger(Callback): - """Custom W&B callback for image reconstructions logging.""" - - def __init__( - self, example_indices: Optional[List] = None, num_examples: int = 4, - ) -> None: - """Initializes the WandbImageLogger with the model to train. - - Args: - example_indices (Optional[List]): Indices for validation images. Defaults to None. - num_examples (int): Number of random samples to take if example_indices are not specified. Defaults to 4. - - """ - - super().__init__() - self.caption = None - self.example_indices = example_indices - self.test_sample_indices = None - self.num_examples = num_examples - - def set_model(self, model: Type[Model]) -> None: - """Sets the model and extracts validation images from the dataset.""" - self.model = model - self.caption = "Validation Reconstructions Examples" - if self.example_indices is None: - self.example_indices = np.random.randint( - 0, len(self.model.val_dataset), self.num_examples - ) - self.images = self.model.val_dataset.dataset.data[self.example_indices] - - def on_test_begin(self) -> None: - """Get samples from test dataset.""" - self.caption = "Test Reconstructions Examples" - if self.test_sample_indices is None: - self.test_sample_indices = np.random.randint( - 0, len(self.model.test_dataset), self.num_examples - ) - self.images = self.model.test_dataset.data[self.test_sample_indices] - - def on_test_end(self) -> None: - """Log test images.""" - self.on_epoch_end(0, {}) - - def on_epoch_end(self, epoch: int, logs: Dict) -> None: - """Get network predictions on validation images.""" - images = [] - for image in self.images: - reconstructed_image = ( - self.model.predict_on_image(image).detach().squeeze(0).cpu().numpy() - ) - images.append(image) - images.append(reconstructed_image) - - wandb.log( - {f"{self.caption}": [wandb.Image(image) for image in images]}, commit=False, - ) diff --git a/src/training/trainer/train.py b/src/training/trainer/train.py deleted file mode 100644 index b770c94..0000000 --- a/src/training/trainer/train.py +++ /dev/null @@ -1,325 +0,0 @@ -"""Training script for PyTorch models.""" - -from pathlib import Path -import time -from typing import Dict, List, Optional, Tuple, Type -import warnings - -from einops import rearrange -from loguru import logger -import numpy as np -import torch -from torch import Tensor -from torch.optim.swa_utils import update_bn -from training.trainer.callbacks import Callback, CallbackList, LRScheduler, SWA -from training.trainer.util import log_val_metric -import wandb - -from text_recognizer.models import Model - - -torch.backends.cudnn.benchmark = True -np.random.seed(4711) -torch.manual_seed(4711) -torch.cuda.manual_seed(4711) - - -warnings.filterwarnings("ignore") - - -class Trainer: - """Trainer for training PyTorch models.""" - - def __init__( - self, - max_epochs: int, - callbacks: List[Type[Callback]], - transformer_model: bool = False, - max_norm: float = 0.0, - freeze_backbone: Optional[int] = None, - ) -> None: - """Initialization of the Trainer. - - Args: - max_epochs (int): The maximum number of epochs in the training loop. - callbacks (CallbackList): List of callbacks to be called. - transformer_model (bool): Transformer model flag, modifies the input to the model. Default is False. - max_norm (float): Max norm for gradient cl:ipping. Defaults to 0.0. - freeze_backbone (Optional[int]): How many epochs to freeze the backbone for. Used when training - Transformers. Default is None. - - """ - # Training arguments. - self.start_epoch = 1 - self.max_epochs = max_epochs - self.callbacks = callbacks - self.freeze_backbone = freeze_backbone - - # Flag for setting callbacks. - self.callbacks_configured = False - - self.transformer_model = transformer_model - - self.max_norm = max_norm - - # Model placeholders - self.model = None - - def _configure_callbacks(self) -> None: - """Instantiate the CallbackList.""" - if not self.callbacks_configured: - # If learning rate schedulers are present, they need to be added to the callbacks. - if self.model.swa_scheduler is not None: - self.callbacks.append(SWA()) - elif self.model.lr_scheduler is not None: - self.callbacks.append(LRScheduler()) - - self.callbacks = CallbackList(self.model, self.callbacks) - - def compute_metrics( - self, output: Tensor, targets: Tensor, loss: Tensor, batch_size: int - ) -> Dict: - """Computes metrics for output and target pairs.""" - # Compute metrics. - loss = loss.detach().float().item() - output = output.detach() - targets = targets.detach() - if self.model.metrics is not None: - metrics = {} - for metric in self.model.metrics: - if metric == "cer" or metric == "wer": - metrics[metric] = self.model.metrics[metric]( - output, - targets, - batch_size, - self.model.mapper(self.model.pad_token), - ) - else: - metrics[metric] = self.model.metrics[metric](output, targets) - else: - metrics = {} - metrics["loss"] = loss - - return metrics - - def training_step(self, batch: int, samples: Tuple[Tensor, Tensor],) -> Dict: - """Performs the training step.""" - # Pass the tensor to the device for computation. - data, targets = samples - data, targets = ( - data.to(self.model.device), - targets.to(self.model.device), - ) - - batch_size = data.shape[0] - - # Placeholder for uxiliary loss. - aux_loss = None - - # Forward pass. - # Get the network prediction. - if self.transformer_model: - if self.freeze_backbone is not None and batch < self.freeze_backbone: - with torch.no_grad(): - image_features = self.model.network.extract_image_features(data) - - if isinstance(image_features, Tuple): - image_features, _ = image_features - - output = self.model.network.decode_image_features( - image_features, targets[:, :-1] - ) - else: - output = self.model.network.forward(data, targets[:, :-1]) - if isinstance(output, Tuple): - output, aux_loss = output - output = rearrange(output, "b t v -> (b t) v") - targets = rearrange(targets[:, 1:], "b t -> (b t)").long() - else: - output = self.model.forward(data) - - if isinstance(output, Tuple): - output, aux_loss = output - targets = data - - # Compute the loss. - loss = self.model.criterion(output, targets) - - if aux_loss is not None: - loss += aux_loss - - # Backward pass. - # Clear the previous gradients. - for p in self.model.network.parameters(): - p.grad = None - - # Compute the gradients. - loss.backward() - - if self.max_norm > 0: - torch.nn.utils.clip_grad_norm_( - self.model.network.parameters(), self.max_norm - ) - - # Perform updates using calculated gradients. - self.model.optimizer.step() - - metrics = self.compute_metrics(output, targets, loss, batch_size) - - return metrics - - def train(self) -> None: - """Runs the training loop for one epoch.""" - # Set model to traning mode. - self.model.train() - - for batch, samples in enumerate(self.model.train_dataloader()): - self.callbacks.on_train_batch_begin(batch) - metrics = self.training_step(batch, samples) - self.callbacks.on_train_batch_end(batch, logs=metrics) - - @torch.no_grad() - def validation_step(self, batch: int, samples: Tuple[Tensor, Tensor],) -> Dict: - """Performs the validation step.""" - - # Pass the tensor to the device for computation. - data, targets = samples - data, targets = ( - data.to(self.model.device), - targets.to(self.model.device), - ) - - batch_size = data.shape[0] - - # Placeholder for uxiliary loss. - aux_loss = None - - # Forward pass. - # Get the network prediction. - # Use SWA if available and using test dataset. - if self.transformer_model: - output = self.model.network.forward(data, targets[:, :-1]) - if isinstance(output, Tuple): - output, aux_loss = output - output = rearrange(output, "b t v -> (b t) v") - targets = rearrange(targets[:, 1:], "b t -> (b t)").long() - else: - output = self.model.forward(data) - - if isinstance(output, Tuple): - output, aux_loss = output - targets = data - - # Compute the loss. - loss = self.model.criterion(output, targets) - - if aux_loss is not None: - loss += aux_loss - - # Compute metrics. - metrics = self.compute_metrics(output, targets, loss, batch_size) - - return metrics - - def validate(self) -> Dict: - """Runs the validation loop for one epoch.""" - # Set model to eval mode. - self.model.eval() - - # Summary for the current eval loop. - summary = [] - - for batch, samples in enumerate(self.model.val_dataloader()): - self.callbacks.on_validation_batch_begin(batch) - metrics = self.validation_step(batch, samples) - self.callbacks.on_validation_batch_end(batch, logs=metrics) - summary.append(metrics) - - # Compute mean of all metrics. - metrics_mean = { - "val_" + metric: np.mean([x[metric] for x in summary]) - for metric in summary[0] - } - - return metrics_mean - - def fit(self, model: Type[Model]) -> None: - """Runs the training and evaluation loop.""" - - # Sets model, loads the data, criterion, and optimizers. - self.model = model - self.model.prepare_data() - self.model.configure_model() - - # Configure callbacks. - self._configure_callbacks() - - # Set start time. - t_start = time.time() - - self.callbacks.on_fit_begin() - - # Run the training loop. - for epoch in range(self.start_epoch, self.max_epochs + 1): - self.callbacks.on_epoch_begin(epoch) - - # Perform one training pass over the training set. - self.train() - - # Evaluate the model on the validation set. - val_metrics = self.validate() - log_val_metric(val_metrics, epoch) - - self.callbacks.on_epoch_end(epoch, logs=val_metrics) - - if self.model.stop_training: - break - - # Calculate the total training time. - t_end = time.time() - t_training = t_end - t_start - - self.callbacks.on_fit_end() - - logger.info(f"Training took {t_training:.2f} s.") - - # "Teardown". - self.model = None - - def test(self, model: Type[Model]) -> Dict: - """Run inference on test data.""" - - # Sets model, loads the data, criterion, and optimizers. - self.model = model - self.model.prepare_data() - self.model.configure_model() - - # Configure callbacks. - self._configure_callbacks() - - self.callbacks.on_test_begin() - - self.model.eval() - - # Check if SWA network is available. - self.model.use_swa_model() - - # Summary for the current test loop. - summary = [] - - for batch, samples in enumerate(self.model.test_dataloader()): - metrics = self.validation_step(batch, samples) - summary.append(metrics) - - self.callbacks.on_test_end() - - # Compute mean of all test metrics. - metrics_mean = { - "test_" + metric: np.mean([x[metric] for x in summary]) - for metric in summary[0] - } - - # "Teardown". - self.model = None - - return metrics_mean diff --git a/src/training/trainer/util.py b/src/training/trainer/util.py deleted file mode 100644 index 7cf1b45..0000000 --- a/src/training/trainer/util.py +++ /dev/null @@ -1,28 +0,0 @@ -"""Utility functions for training neural networks.""" -from typing import Dict, Optional - -from loguru import logger - - -def log_val_metric(metrics_mean: Dict, epoch: Optional[int] = None) -> None: - """Logging of val metrics to file/terminal.""" - log_str = "Validation metrics " + (f"at epoch {epoch} - " if epoch else " - ") - logger.debug(log_str + " - ".join(f"{k}: {v:.4f}" for k, v in metrics_mean.items())) - - -class RunningAverage: - """Maintains a running average.""" - - def __init__(self) -> None: - """Initializes the parameters.""" - self.steps = 0 - self.total = 0 - - def update(self, val: float) -> None: - """Updates the parameters.""" - self.total += val - self.steps += 1 - - def __call__(self) -> float: - """Computes the running average.""" - return self.total / float(self.steps) diff --git a/src/wandb/settings b/src/wandb/settings deleted file mode 100644 index eafb083..0000000 --- a/src/wandb/settings +++ /dev/null @@ -1,4 +0,0 @@ -[default] -entity = aktersnurra -project = text-recognizer -base_url = https://api.wandb.ai |
