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Diffstat (limited to 'notebooks/00-scratch-pad.ipynb')
| -rw-r--r-- | notebooks/00-scratch-pad.ipynb | 644 |
1 files changed, 588 insertions, 56 deletions
diff --git a/notebooks/00-scratch-pad.ipynb b/notebooks/00-scratch-pad.ipynb index 2ade2bb..16c6533 100644 --- a/notebooks/00-scratch-pad.ipynb +++ b/notebooks/00-scratch-pad.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -30,106 +30,244 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ - "from text_recognizer.networks.encoders.efficientnet.efficientnet import EfficientNet" + "from pathlib import Path" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ - "en = EfficientNet(\"b0\")" + "import attr" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ - "summary(en, (1, 224, 224));" + "@attr.s\n", + "class B:\n", + " batch_size = attr.ib()\n", + " num_workers = attr.ib()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "torch.cuda.is_available()" + "@attr.s\n", + "class T(B):\n", + "\n", + " def __attrs_post_init__(self) -> None:\n", + " super().__init__(self.batch_size, self.num_workers)\n", + " self.hej = None\n", + " \n", + " batch_size = attr.ib()\n", + " num_workers = attr.ib()\n", + " h: Path = attr.ib(converter=Path)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ - "decoder = Decoder(dim=128, depth=2, num_heads=8, ff_kwargs={}, attn_kwargs={}, cross_attend=True)" + "t = T(batch_size=16, num_workers=2, h=\"hej\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "PosixPath('hej')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "decoder.cuda()" + "t.h" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "16" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "transformer_decoder = Transformer(num_tokens=1000, max_seq_len=690, attn_layers=decoder, emb_dim=128, emb_dropout=0.1)" + "t.batch_size" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "PosixPath('hej')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "transformer_decoder.cuda()" + "t.h" ] }, { "cell_type": "code", - "execution_count": null, + "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, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<generator object Path.glob at 0x7ff8bb9ce5f0>" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Path(\"..\").glob(\"**/*.py\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "efficient_transformer = Nystromer(\n", - " dim = 64,\n", - " depth = 4,\n", - " num_heads = 8,\n", - " num_landmarks = 64\n", - ")" + "from text_recognizer.networks.encoders.efficientnet.efficientnet import EfficientNet" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "en = EfficientNet(\"b0\")" ] }, { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [], "source": [ - "v = ViT(\n", - " dim = 64,\n", - " image_size = (576, 640),\n", - " patch_size = (32, 32),\n", - " transformer = efficient_transformer\n", - ").cuda()" + "summary(en, (1, 224, 224));" ] }, { @@ -138,7 +276,7 @@ "metadata": {}, "outputs": [], "source": [ - "t = torch.randn(8, 1, 576, 640).cuda()" + "torch.cuda.is_available()" ] }, { @@ -147,7 +285,7 @@ "metadata": {}, "outputs": [], "source": [ - "en.cuda()" + "decoder = Decoder(dim=128, depth=2, num_heads=8, ff_kwargs={}, attn_kwargs={}, cross_attend=True)" ] }, { @@ -156,7 +294,7 @@ "metadata": {}, "outputs": [], "source": [ - "en(t)" + "decoder.cuda()" ] }, { @@ -165,7 +303,7 @@ "metadata": {}, "outputs": [], "source": [ - "o = v(t)" + "transformer_decoder = Transformer(num_tokens=1003, max_seq_len=451, attn_layers=decoder, emb_dim=128, emb_dropout=0.1)" ] }, { @@ -174,7 +312,7 @@ "metadata": {}, "outputs": [], "source": [ - "caption = torch.randint(0, 90, (16, 690)).cuda()" + "transformer_decoder.cuda()" ] }, { @@ -183,7 +321,12 @@ "metadata": {}, "outputs": [], "source": [ - "o.shape" + "efficient_transformer = Nystromer(\n", + " dim = 64,\n", + " depth = 4,\n", + " num_heads = 8,\n", + " num_landmarks = 64\n", + ")" ] }, { @@ -192,16 +335,405 @@ "metadata": {}, "outputs": [], "source": [ - "caption.shape" + "v = ViT(\n", + " dim = 64,\n", + " image_size = (576, 640),\n", + " patch_size = (32, 32),\n", + " transformer = efficient_transformer\n", + ").cuda()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "o = torch.randn(16, 20 * 18, 128).cuda()" + "t = torch.randn(4, 1, 576, 640).cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "EfficientNet(\n", + " (_conv_stem): Sequential(\n", + " (0): ZeroPad2d(padding=(0, 1, 0, 1), value=0.0)\n", + " (1): Conv2d(1, 32, kernel_size=(3, 3), stride=(2, 2), bias=False)\n", + " (2): BatchNorm2d(32, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (3): Mish(inplace=True)\n", + " )\n", + " (_blocks): ModuleList(\n", + " (0): MBConvBlock(\n", + " (_depthwise): Sequential(\n", + " (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), groups=32, bias=False)\n", + " (1): BatchNorm2d(32, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (2): Mish(inplace=True)\n", + " )\n", + " (_squeeze_excite): Sequential(\n", + " (0): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))\n", + " (1): Mish(inplace=True)\n", + " (2): Conv2d(8, 32, kernel_size=(1, 1), stride=(1, 1))\n", + " )\n", + " (_pointwise): Sequential(\n", + " (0): Conv2d(32, 16, 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track_running_stats=True)\n", + " )\n", + " )\n", + " (10): MBConvBlock(\n", + " (_inverted_bottleneck): Sequential(\n", + " (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(672, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (2): Mish(inplace=True)\n", + " )\n", + " (_depthwise): Sequential(\n", + " (0): Conv2d(672, 672, kernel_size=(5, 5), stride=(1, 1), groups=672, bias=False)\n", + " (1): BatchNorm2d(672, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (2): Mish(inplace=True)\n", + " )\n", + " (_squeeze_excite): Sequential(\n", + " (0): Conv2d(672, 168, kernel_size=(1, 1), stride=(1, 1))\n", + " (1): Mish(inplace=True)\n", + " (2): Conv2d(168, 672, kernel_size=(1, 1), stride=(1, 1))\n", + " )\n", + " (_pointwise): Sequential(\n", + " (0): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(112, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (11): MBConvBlock(\n", + " (_inverted_bottleneck): Sequential(\n", + " (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(672, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (2): Mish(inplace=True)\n", + " )\n", + " (_depthwise): Sequential(\n", + " (0): Conv2d(672, 672, kernel_size=(5, 5), stride=(2, 2), groups=672, bias=False)\n", + " (1): BatchNorm2d(672, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (2): Mish(inplace=True)\n", + " )\n", + " (_squeeze_excite): Sequential(\n", + " (0): Conv2d(672, 168, kernel_size=(1, 1), stride=(1, 1))\n", + " (1): Mish(inplace=True)\n", + " (2): Conv2d(168, 672, kernel_size=(1, 1), stride=(1, 1))\n", + " )\n", + " (_pointwise): Sequential(\n", + " (0): Conv2d(672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(192, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (12): MBConvBlock(\n", + " (_inverted_bottleneck): Sequential(\n", + " (0): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(1152, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (2): Mish(inplace=True)\n", + " )\n", + " (_depthwise): Sequential(\n", + " (0): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), groups=1152, bias=False)\n", + " (1): BatchNorm2d(1152, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (2): Mish(inplace=True)\n", + " )\n", + " (_squeeze_excite): Sequential(\n", + " (0): Conv2d(1152, 288, kernel_size=(1, 1), stride=(1, 1))\n", + " (1): Mish(inplace=True)\n", + " (2): Conv2d(288, 1152, kernel_size=(1, 1), stride=(1, 1))\n", + " )\n", + " (_pointwise): Sequential(\n", + " (0): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(192, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (13): MBConvBlock(\n", + " (_inverted_bottleneck): Sequential(\n", + " (0): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(1152, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (2): Mish(inplace=True)\n", + " )\n", + " (_depthwise): Sequential(\n", + " (0): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), groups=1152, bias=False)\n", + " (1): BatchNorm2d(1152, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (2): Mish(inplace=True)\n", + " )\n", + " (_squeeze_excite): Sequential(\n", + " (0): Conv2d(1152, 288, kernel_size=(1, 1), stride=(1, 1))\n", + " (1): Mish(inplace=True)\n", + " (2): Conv2d(288, 1152, kernel_size=(1, 1), stride=(1, 1))\n", + " )\n", + " (_pointwise): Sequential(\n", + " (0): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(192, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (14): MBConvBlock(\n", + " (_inverted_bottleneck): Sequential(\n", + " (0): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(1152, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (2): Mish(inplace=True)\n", + " )\n", + " (_depthwise): Sequential(\n", + " (0): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), groups=1152, bias=False)\n", + " (1): BatchNorm2d(1152, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (2): Mish(inplace=True)\n", + " )\n", + " (_squeeze_excite): Sequential(\n", + " (0): Conv2d(1152, 288, kernel_size=(1, 1), stride=(1, 1))\n", + " (1): Mish(inplace=True)\n", + " (2): Conv2d(288, 1152, kernel_size=(1, 1), stride=(1, 1))\n", + " )\n", + " (_pointwise): Sequential(\n", + " (0): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(192, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (15): MBConvBlock(\n", + " (_inverted_bottleneck): Sequential(\n", + " (0): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(1152, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (2): Mish(inplace=True)\n", + " )\n", + " (_depthwise): Sequential(\n", + " (0): Conv2d(1152, 1152, kernel_size=(3, 3), stride=(1, 1), groups=1152, bias=False)\n", + " (1): BatchNorm2d(1152, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " (2): Mish(inplace=True)\n", + " )\n", + " (_squeeze_excite): Sequential(\n", + " (0): Conv2d(1152, 288, kernel_size=(1, 1), stride=(1, 1))\n", + " (1): Mish(inplace=True)\n", + " (2): Conv2d(288, 1152, kernel_size=(1, 1), stride=(1, 1))\n", + " )\n", + " (_pointwise): Sequential(\n", + " (0): Conv2d(1152, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(320, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " )\n", + " (_conv_head): Sequential(\n", + " (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(1280, eps=0.001, momentum=0.99, affine=True, track_running_stats=True)\n", + " )\n", + ")" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "en.cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([4, 1280, 18, 20])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "en(t).shape" ] }, { @@ -210,7 +742,7 @@ "metadata": {}, "outputs": [], "source": [ - "caption = torch.randint(0, 1000, (16, 200)).cuda()" + "o = v(t)" ] }, { @@ -219,7 +751,7 @@ "metadata": {}, "outputs": [], "source": [ - "transformer_decoder(caption, context = o).shape # (1, 1024, 20000)" + "caption = torch.randint(0, 90, (16, 690)).cuda()" ] }, { @@ -228,7 +760,7 @@ "metadata": {}, "outputs": [], "source": [ - "from text_recognizer.networks.encoders.efficientnet.efficientnet import EfficientNet" + "o.shape" ] }, { @@ -237,7 +769,7 @@ "metadata": {}, "outputs": [], "source": [ - "en = EfficientNet()" + "caption.shape" ] }, { @@ -246,7 +778,7 @@ "metadata": {}, "outputs": [], "source": [ - "en.cuda()" + "o = torch.randn(16, 20 * 18, 128).cuda()" ] }, { @@ -255,7 +787,7 @@ "metadata": {}, "outputs": [], "source": [ - "summary(en, (1, 576, 640))" + "caption = torch.randint(0, 1000, (16, 200)).cuda()" ] }, { @@ -264,7 +796,7 @@ "metadata": {}, "outputs": [], "source": [ - "type(efficient_transformer)" + "transformer_decoder(caption, context = o).shape # (1, 1024, 20000)" ] }, { |