diff options
Diffstat (limited to 'src/notebooks/00-testing-stuff-out.ipynb')
| -rw-r--r-- | src/notebooks/00-testing-stuff-out.ipynb | 1059 |
1 files changed, 0 insertions, 1059 deletions
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 -} |