From a1d795bf02d14befc62cf600fb48842958148eba Mon Sep 17 00:00:00 2001 From: Gustaf Rydholm Date: Fri, 23 Jul 2021 14:55:31 +0200 Subject: Complete cnn-transformer network, not tested --- notebooks/00-scratch-pad.ipynb | 421 +++++++++-------------------------------- 1 file changed, 94 insertions(+), 327 deletions(-) (limited to 'notebooks') diff --git a/notebooks/00-scratch-pad.ipynb b/notebooks/00-scratch-pad.ipynb index 1e30038..2c98064 100644 --- a/notebooks/00-scratch-pad.ipynb +++ b/notebooks/00-scratch-pad.ipynb @@ -2,9 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "metadata": {}, - "outputs": [], + "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", @@ -30,472 +39,230 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path" - ] - }, - { - "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "import attr" + "from text_recognizer.networks.encoders.efficientnet.efficientnet import EfficientNet" ] }, { "cell_type": "code", - "execution_count": 44, - "metadata": {}, + "execution_count": 7, + "metadata": { + "scrolled": false + }, "outputs": [], "source": [ - "@attr.s\n", - "class B(nn.Module):\n", - " input_dim = attr.ib()\n", - " hidden = attr.ib()\n", - " xx = attr.ib(init=False, default=\"hek\")\n", - " \n", - " def __attrs_post_init__(self):\n", - " super().__init__()\n", - " self.fc = nn.Linear(self.input_dim, self.hidden)\n", - " self.xx = \"da\"\n", - " \n", - " def forward(self, x):\n", - " return self.fc(x)" + "en = EfficientNet(\"b0\")" ] }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "def f(x):\n", - " return 2\n", - "\n", - "@attr.s(auto_attribs=True)\n", - "class T(B):\n", - " \n", - " h: Path = attr.ib(converter=Path)\n", - " p: int = attr.ib(init=False, default=f(3))" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "__init__() missing 1 required positional argument: 'hidden'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_dim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"hej\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m: __init__() missing 1 required positional argument: 'hidden'" - ] - } - ], - "source": [ - "t = T(input_dim=16, h=\"hej\")" + "def generate_square_subsequent_mask(size: int) -> torch.Tensor:\n", + " \"\"\"Generate a triangular (size, size) mask.\"\"\"\n", + " mask = (torch.triu(torch.ones(size, size)) == 1).transpose(0, 1)\n", + " mask = mask.float().masked_fill(mask == 0, float(\"-inf\")).masked_fill(mask == 1, float(0.0))\n", + " return mask" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "'da'" + "tensor([[0., -inf, -inf, -inf],\n", + " [0., 0., -inf, -inf],\n", + " [0., 0., 0., -inf],\n", + " [0., 0., 0., 0.]])" ] }, - "execution_count": 51, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "t.xx" + "generate_square_subsequent_mask(4)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "from torch import Tensor" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "t.p" + "tgt = torch.randint(0, 4, (1, 4))\n", + "tgt_mask = torch.ones_like(tgt).bool()" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "16" + "tensor([[True, True, True, True]])" ] }, - "execution_count": 19, + "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "t.input_dim" + "tgt_mask" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ - "x = torch.rand(16, 16)" + "def target_padding_mask(trg: Tensor, pad_index: int) -> Tensor:\n", + " \"\"\"Returns causal target mask.\"\"\"\n", + " trg_pad_mask = (trg != pad_index)[:, None, None]\n", + " trg_len = trg.shape[1]\n", + " trg_sub_mask = torch.tril(torch.ones((trg_len, trg_len), device=trg.device)).bool()\n", + " trg_mask = trg_pad_mask & trg_sub_mask\n", + " return trg_mask" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 54, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "torch.Size([16, 16])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "x.shape" + "t = torch.randint(0, 6, (0, 4))" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 55, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "T(input_dim=16, hidden=24, h=PosixPath('hej'))" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "t.cuda()" + "t = torch.Tensor([[0, 0, 0, 3, 3, 3]])" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ - "x = x.cuda()" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[ 3.6047e-01, 1.0200e+00, 3.6786e-01, 1.6077e-01, 3.9281e-02,\n", - " 3.2830e-01, 1.3433e-01, -9.0334e-02, -3.8712e-01, 8.1547e-01,\n", - " -5.4483e-01, -9.7471e-01, 3.3706e-01, -9.5283e-01, -1.6271e-01,\n", - " 3.8504e-01, -5.0106e-01, -4.8638e-01, 3.7033e-01, -4.9557e-01,\n", - " 2.6555e-01, 5.1245e-01, 6.6751e-01, -2.6291e-01],\n", - " [ 1.3811e-01, 7.4522e-01, 4.9935e-01, 3.3878e-01, 1.8501e-01,\n", - " 2.2269e-02, -2.0328e-01, 1.4629e-01, -2.2957e-01, 4.1197e-01,\n", - " -1.9555e-01, -4.7609e-01, 9.0206e-02, -8.8568e-01, -2.1618e-01,\n", - " 2.8882e-01, -5.4335e-01, -6.6301e-01, 4.9990e-01, -4.0144e-01,\n", - " 3.6403e-01, 5.3901e-01, 8.6665e-01, -7.8312e-02],\n", - " [ 1.6493e-02, 4.6157e-01, 2.9500e-02, 2.4190e-01, 6.5753e-01,\n", - " 4.3770e-02, -5.3773e-02, 1.8183e-01, -2.5983e-02, 4.1634e-01,\n", - " -3.5218e-01, -5.6129e-01, 4.1452e-01, -1.2265e+00, -5.8544e-01,\n", - " 3.6382e-01, -6.4090e-01, -5.8679e-01, 4.3489e-02, -1.1233e-01,\n", - " 3.1175e-01, 4.2857e-01, 1.6501e-01, -2.4118e-01],\n", - " [ 9.2361e-02, 6.0196e-01, 1.3081e-02, -8.1091e-02, 4.2342e-01,\n", - 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" -9.8319e-03, 1.3403e-01, 1.8460e-02, -1.4025e-01, 5.9780e-01,\n", - " -3.7015e-01, -5.7865e-01, 4.9211e-01, -1.1262e+00, -2.1693e-01,\n", - " 3.2002e-01, -2.9313e-01, -3.1941e-01, 9.8446e-02, -6.2767e-02,\n", - " -9.8636e-03, 3.5712e-01, 2.8833e-01, -5.3506e-01]], device='cuda:0',\n", - " grad_fn=)" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "t(x)" + "tt = t != 3" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "PosixPath('hej')" + "tensor([[ True, True, True, False, False, False]])" ] }, - "execution_count": 13, + "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "t.h" + "tt" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 43, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "16" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "t.batch_size" + "t = torch.cat((t, t))" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "PosixPath('hej')" + "torch.Size([2, 6])" ] }, - "execution_count": 11, + "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "t.h" + "t.shape" ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "../text_recognizer/__init__.py\n", - "../text_recognizer/callbacks/__init__.py\n", - "../text_recognizer/callbacks/wandb_callbacks.py\n", - "../text_recognizer/data/image_utils.py\n", - "../text_recognizer/data/emnist.py\n", - "../text_recognizer/data/iam_lines.py\n", - "../text_recognizer/data/download_utils.py\n", - "../text_recognizer/data/mappings.py\n", - "../text_recognizer/data/iam_preprocessor.py\n", - "../text_recognizer/data/__init__.py\n", - "../text_recognizer/data/make_wordpieces.py\n", - "../text_recognizer/data/iam_paragraphs.py\n", - "../text_recognizer/data/sentence_generator.py\n", - "../text_recognizer/data/emnist_lines.py\n", - "../text_recognizer/data/build_transitions.py\n", - "../text_recognizer/data/base_dataset.py\n", - "../text_recognizer/data/base_data_module.py\n", - "../text_recognizer/data/iam.py\n", - "../text_recognizer/data/iam_synthetic_paragraphs.py\n", - "../text_recognizer/data/transforms.py\n", - "../text_recognizer/data/iam_extended_paragraphs.py\n", - "../text_recognizer/networks/__init__.py\n", - "../text_recognizer/networks/util.py\n", - "../text_recognizer/networks/cnn_tranformer.py\n", - "../text_recognizer/networks/encoders/__init__.py\n", - "../text_recognizer/networks/encoders/efficientnet/efficientnet.py\n", - "../text_recognizer/networks/encoders/efficientnet/__init__.py\n", - "../text_recognizer/networks/encoders/efficientnet/utils.py\n", - "../text_recognizer/networks/encoders/efficientnet/mbconv.py\n", - "../text_recognizer/networks/loss/__init__.py\n", - "../text_recognizer/networks/loss/label_smoothing_loss.py\n", - "../text_recognizer/networks/vqvae/__init__.py\n", - "../text_recognizer/networks/vqvae/decoder.py\n", - "../text_recognizer/networks/vqvae/vqvae.py\n", - "../text_recognizer/networks/vqvae/vector_quantizer.py\n", - "../text_recognizer/networks/vqvae/encoder.py\n", - "../text_recognizer/networks/transformer/__init__.py\n", - "../text_recognizer/networks/transformer/layers.py\n", - "../text_recognizer/networks/transformer/residual.py\n", - "../text_recognizer/networks/transformer/attention.py\n", - "../text_recognizer/networks/transformer/transformer.py\n", - "../text_recognizer/networks/transformer/vit.py\n", - "../text_recognizer/networks/transformer/mlp.py\n", - "../text_recognizer/networks/transformer/norm.py\n", - "../text_recognizer/networks/transformer/positional_encodings/positional_encoding.py\n", - "../text_recognizer/networks/transformer/positional_encodings/__init__.py\n", - "../text_recognizer/networks/transformer/positional_encodings/absolute_embedding.py\n", - "../text_recognizer/networks/transformer/positional_encodings/rotary_embedding.py\n", - "../text_recognizer/networks/transformer/nystromer/__init__.py\n", - "../text_recognizer/networks/transformer/nystromer/nystromer.py\n", - "../text_recognizer/networks/transformer/nystromer/attention.py\n", - "../text_recognizer/models/__init__.py\n", - "../text_recognizer/models/base.py\n", - "../text_recognizer/models/vqvae.py\n", - "../text_recognizer/models/transformer.py\n", - "../text_recognizer/models/dino.py\n", - "../text_recognizer/models/metrics.py\n" - ] - } - ], - "source": [ - "for f in Path(\"../text_recognizer\").glob(\"**/*.py\"):\n", - " print(f)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, + "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "tensor([[[[ True, False, False, False, False, False],\n", + " [ True, True, False, False, False, False],\n", + " [ True, True, True, False, False, False],\n", + " [ True, True, True, False, False, False],\n", + " [ True, True, True, False, False, False],\n", + " [ True, True, True, False, False, False]]],\n", + "\n", + "\n", + " [[[ True, False, False, False, False, False],\n", + " [ True, True, False, False, False, False],\n", + " [ True, True, True, False, False, False],\n", + " [ True, True, True, False, False, False],\n", + " [ True, True, True, False, False, False],\n", + " [ True, True, True, False, False, False]]]])" ] }, - "execution_count": 12, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "Path(\"..\").glob(\"**/*.py\")" + "target_padding_mask(t, 3)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "from text_recognizer.networks.encoders.efficientnet.efficientnet import EfficientNet" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "en = EfficientNet(\"b0\")" + "target_padding_mask()" ] }, { @@ -1404,7 +1171,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.5" + "version": "3.9.6" } }, "nbformat": 4, -- cgit v1.2.3-70-g09d2