diff options
Diffstat (limited to 'notebooks/03-look-at-iam-paragraphs.ipynb')
| -rw-r--r-- | notebooks/03-look-at-iam-paragraphs.ipynb | 425 |
1 files changed, 410 insertions, 15 deletions
diff --git a/notebooks/03-look-at-iam-paragraphs.ipynb b/notebooks/03-look-at-iam-paragraphs.ipynb index ed67e9c..b56e2f6 100644 --- a/notebooks/03-look-at-iam-paragraphs.ipynb +++ b/notebooks/03-look-at-iam-paragraphs.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "6ce2519f", "metadata": {}, "outputs": [], @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "id": "726ac25b", "metadata": {}, "outputs": [], @@ -43,7 +43,240 @@ " plt.imshow(image, cmap='gray', vmin=vmin, vmax=vmax)\n", "\n", "def convert_y_label_to_string(y, mapping, padding_index=3):\n", - " return ''.join([mapping[i] for i in y if i != padding_index])" + " return ''.join([mapping[int(i)] for i in y if i != padding_index])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ec16e41f-3d12-4da2-bf02-7429b41cf98e", + "metadata": {}, + "outputs": [], + "source": [ + "from hydra import compose, initialize\n", + "from omegaconf import OmegaConf\n", + "from hydra.utils import instantiate" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "e9386367-2b49-4633-9936-57081132e59e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "callbacks:\n", + " model_checkpoint:\n", + " _target_: pytorch_lightning.callbacks.ModelCheckpoint\n", + " monitor: val/loss\n", + " save_top_k: 1\n", + " save_last: true\n", + " mode: min\n", + " verbose: false\n", + " dirpath: checkpoints/\n", + " filename: '{epoch:02d}'\n", + " learning_rate_monitor:\n", + " _target_: pytorch_lightning.callbacks.LearningRateMonitor\n", + " logging_interval: step\n", + " log_momentum: false\n", + " watch_model:\n", + " _target_: callbacks.wandb_callbacks.WatchModel\n", + " log: all\n", + " log_freq: 100\n", + " upload_code_as_artifact:\n", + " _target_: callbacks.wandb_callbacks.UploadCodeAsArtifact\n", + " project_dir: ${work_dir}/text_recognizer\n", + " upload_ckpts_as_artifact:\n", + " _target_: callbacks.wandb_callbacks.UploadCheckpointsAsArtifact\n", + " ckpt_dir: checkpoints/\n", + " upload_best_only: true\n", + " log_text_predictions:\n", + " _target_: callbacks.wandb_callbacks.LogTextPredictions\n", + " num_samples: 8\n", + "criterion:\n", + " _target_: text_recognizer.criterions.label_smoothing.LabelSmoothingLoss\n", + " smoothing: 0.1\n", + " ignore_index: 1000\n", + "datamodule:\n", + " _target_: text_recognizer.data.iam_extended_paragraphs.IAMExtendedParagraphs\n", + " batch_size: 4\n", + " num_workers: 12\n", + " train_fraction: 0.8\n", + " augment: true\n", + " pin_memory: false\n", + " word_pieces: true\n", + "logger:\n", + " wandb:\n", + " _target_: pytorch_lightning.loggers.wandb.WandbLogger\n", + " project: text-recognizer\n", + " name: null\n", + " save_dir: .\n", + " offline: false\n", + " id: null\n", + " log_model: false\n", + " prefix: ''\n", + " job_type: train\n", + " group: ''\n", + " tags: []\n", + "lr_scheduler:\n", + " _target_: torch.optim.lr_scheduler.OneCycleLR\n", + " max_lr: 0.001\n", + " total_steps: null\n", + " epochs: 512\n", + " steps_per_epoch: 4992\n", + " pct_start: 0.3\n", + " anneal_strategy: cos\n", + " cycle_momentum: true\n", + " base_momentum: 0.85\n", + " max_momentum: 0.95\n", + " div_factor: 25.0\n", + " final_div_factor: 10000.0\n", + " three_phase: true\n", + " last_epoch: -1\n", + " verbose: false\n", + "mapping:\n", + " _target_: text_recognizer.data.emnist_mapping.EmnistMapping\n", + " extra_symbols:\n", + " - '\n", + "\n", + " '\n", + "model:\n", + " _target_: text_recognizer.models.transformer.TransformerLitModel\n", + " interval: step\n", + " monitor: val/loss\n", + " max_output_len: 451\n", + " start_token: <s>\n", + " end_token: <e>\n", + " pad_token: <p>\n", + "network:\n", + " encoder:\n", + " _target_: text_recognizer.networks.encoders.efficientnet.EfficientNet\n", + " arch: b0\n", + " out_channels: 1280\n", + " stochastic_dropout_rate: 0.2\n", + " bn_momentum: 0.99\n", + " bn_eps: 0.001\n", + " decoder:\n", + " _target_: text_recognizer.networks.transformer.Decoder\n", + " dim: 96\n", + " depth: 2\n", + " num_heads: 8\n", + " attn_fn: text_recognizer.networks.transformer.attention.Attention\n", + " attn_kwargs:\n", + " dim_head: 16\n", + " dropout_rate: 0.2\n", + " norm_fn: torch.nn.LayerNorm\n", + " ff_fn: text_recognizer.networks.transformer.mlp.FeedForward\n", + " ff_kwargs:\n", + " dim_out: null\n", + " expansion_factor: 4\n", + " glu: true\n", + " dropout_rate: 0.2\n", + " cross_attend: true\n", + " pre_norm: true\n", + " rotary_emb: null\n", + " _target_: text_recognizer.networks.conv_transformer.ConvTransformer\n", + " input_dims:\n", + " - 1\n", + " - 576\n", + " - 640\n", + " hidden_dim: 96\n", + " dropout_rate: 0.2\n", + " num_classes: 1006\n", + " pad_index: 1002\n", + "optimizer:\n", + " _target_: madgrad.MADGRAD\n", + " lr: 0.001\n", + " momentum: 0.9\n", + " weight_decay: 0\n", + " eps: 1.0e-06\n", + "trainer:\n", + " _target_: pytorch_lightning.Trainer\n", + " stochastic_weight_avg: false\n", + " auto_scale_batch_size: binsearch\n", + " auto_lr_find: false\n", + " gradient_clip_val: 0\n", + " fast_dev_run: false\n", + " gpus: 1\n", + " precision: 16\n", + " max_epochs: 512\n", + " terminate_on_nan: true\n", + " weights_summary: top\n", + " limit_train_batches: 1.0\n", + " limit_val_batches: 1.0\n", + " limit_test_batches: 1.0\n", + " resume_from_checkpoint: null\n", + "seed: 4711\n", + "tune: false\n", + "train: true\n", + "test: true\n", + "logging: INFO\n", + "work_dir: ${hydra:runtime.cwd}\n", + "debug: false\n", + "print_config: true\n", + "ignore_warnings: true\n", + "\n", + "{'callbacks': {'model_checkpoint': {'_target_': 'pytorch_lightning.callbacks.ModelCheckpoint', 'monitor': 'val/loss', 'save_top_k': 1, 'save_last': True, 'mode': 'min', 'verbose': False, 'dirpath': 'checkpoints/', 'filename': '{epoch:02d}'}, 'learning_rate_monitor': {'_target_': 'pytorch_lightning.callbacks.LearningRateMonitor', 'logging_interval': 'step', 'log_momentum': False}, 'watch_model': {'_target_': 'callbacks.wandb_callbacks.WatchModel', 'log': 'all', 'log_freq': 100}, 'upload_code_as_artifact': {'_target_': 'callbacks.wandb_callbacks.UploadCodeAsArtifact', 'project_dir': '${work_dir}/text_recognizer'}, 'upload_ckpts_as_artifact': {'_target_': 'callbacks.wandb_callbacks.UploadCheckpointsAsArtifact', 'ckpt_dir': 'checkpoints/', 'upload_best_only': True}, 'log_text_predictions': {'_target_': 'callbacks.wandb_callbacks.LogTextPredictions', 'num_samples': 8}}, 'criterion': {'_target_': 'text_recognizer.criterions.label_smoothing.LabelSmoothingLoss', 'smoothing': 0.1, 'ignore_index': 1000}, 'datamodule': {'_target_': 'text_recognizer.data.iam_extended_paragraphs.IAMExtendedParagraphs', 'batch_size': 4, 'num_workers': 12, 'train_fraction': 0.8, 'augment': True, 'pin_memory': False, 'word_pieces': True}, 'logger': {'wandb': {'_target_': 'pytorch_lightning.loggers.wandb.WandbLogger', 'project': 'text-recognizer', 'name': None, 'save_dir': '.', 'offline': False, 'id': None, 'log_model': False, 'prefix': '', 'job_type': 'train', 'group': '', 'tags': []}}, 'lr_scheduler': {'_target_': 'torch.optim.lr_scheduler.OneCycleLR', 'max_lr': 0.001, 'total_steps': None, 'epochs': 512, 'steps_per_epoch': 4992, 'pct_start': 0.3, 'anneal_strategy': 'cos', 'cycle_momentum': True, 'base_momentum': 0.85, 'max_momentum': 0.95, 'div_factor': 25.0, 'final_div_factor': 10000.0, 'three_phase': True, 'last_epoch': -1, 'verbose': False}, 'mapping': {'_target_': 'text_recognizer.data.emnist_mapping.EmnistMapping', 'extra_symbols': ['\\n']}, 'model': {'_target_': 'text_recognizer.models.transformer.TransformerLitModel', 'interval': 'step', 'monitor': 'val/loss', 'max_output_len': 451, 'start_token': '<s>', 'end_token': '<e>', 'pad_token': '<p>'}, 'network': {'encoder': {'_target_': 'text_recognizer.networks.encoders.efficientnet.EfficientNet', 'arch': 'b0', 'out_channels': 1280, 'stochastic_dropout_rate': 0.2, 'bn_momentum': 0.99, 'bn_eps': 0.001}, 'decoder': {'_target_': 'text_recognizer.networks.transformer.Decoder', 'dim': 96, 'depth': 2, 'num_heads': 8, 'attn_fn': 'text_recognizer.networks.transformer.attention.Attention', 'attn_kwargs': {'dim_head': 16, 'dropout_rate': 0.2}, 'norm_fn': 'torch.nn.LayerNorm', 'ff_fn': 'text_recognizer.networks.transformer.mlp.FeedForward', 'ff_kwargs': {'dim_out': None, 'expansion_factor': 4, 'glu': True, 'dropout_rate': 0.2}, 'cross_attend': True, 'pre_norm': True, 'rotary_emb': None}, '_target_': 'text_recognizer.networks.conv_transformer.ConvTransformer', 'input_dims': [1, 576, 640], 'hidden_dim': 96, 'dropout_rate': 0.2, 'num_classes': 1006, 'pad_index': 1002}, 'optimizer': {'_target_': 'madgrad.MADGRAD', 'lr': 0.001, 'momentum': 0.9, 'weight_decay': 0, 'eps': 1e-06}, 'trainer': {'_target_': 'pytorch_lightning.Trainer', 'stochastic_weight_avg': False, 'auto_scale_batch_size': 'binsearch', 'auto_lr_find': False, 'gradient_clip_val': 0, 'fast_dev_run': False, 'gpus': 1, 'precision': 16, 'max_epochs': 512, 'terminate_on_nan': True, 'weights_summary': 'top', 'limit_train_batches': 1.0, 'limit_val_batches': 1.0, 'limit_test_batches': 1.0, 'resume_from_checkpoint': None}, 'seed': 4711, 'tune': False, 'train': True, 'test': True, 'logging': 'INFO', 'work_dir': '${hydra:runtime.cwd}', 'debug': False, 'print_config': True, 'ignore_warnings': True}\n" + ] + } + ], + "source": [ + "# context initialization\n", + "with initialize(config_path=\"../training/conf/\", job_name=\"test_app\"):\n", + " cfg = compose(config_name=\"config\", overrides=[\"mapping=emnist\"])\n", + " print(OmegaConf.to_yaml(cfg))\n", + " print(cfg)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "60b1b9a7-a504-47d5-948a-4f3bd0ce7e1d", + "metadata": {}, + "outputs": [], + "source": [ + "cfg.datamodule.word_pieces = False" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "1c4624d1-6de5-41ab-9208-0988fcdba76d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-08-03 19:11:47.244 | INFO | text_recognizer.data.iam_paragraphs:setup:97 - Loading IAM paragraph regions and lines for None...\n", + "2021-08-03 19:12:09.949 | INFO | text_recognizer.data.iam_synthetic_paragraphs:setup:68 - IAM Synthetic dataset steup for stage None...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "IAM Original and Synthetic Paragraphs Dataset\n", + "Num classes: 84\n", + "Dims: (1, 576, 640)\n", + "Output dims: (682, 1)\n", + "Train/val/test sizes: 19958, 262, 231\n", + "Train Batch x stats: (torch.Size([4, 1, 576, 640]), torch.float32, tensor(0.), tensor(0.0114), tensor(0.0515), tensor(0.9961))\n", + "Train Batch y stats: (torch.Size([4, 682]), torch.int64, tensor(1), tensor(83))\n", + "Test Batch x stats: (torch.Size([4, 1, 576, 640]), torch.float32, tensor(0.), tensor(0.0321), tensor(0.0744), tensor(0.8118))\n", + "Test Batch y stats: (torch.Size([4, 682]), torch.int64, tensor(1), tensor(83))\n", + "\n" + ] + } + ], + "source": [ + "datamodule = instantiate(cfg.datamodule, mapping=cfg.mapping)\n", + "datamodule.prepare_data()\n", + "datamodule.setup()\n", + "print(datamodule)" ] }, { @@ -63,12 +296,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "55b26b5d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1006" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "len(dataset.mapping)" + "len(datamodule.mapping)" ] }, { @@ -86,12 +330,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "id": "e6e8c05b", "metadata": {}, "outputs": [], "source": [ - "x, y = next(iter(dataset.test_dataloader()))" + "x, y = next(iter(datamodule.test_dataloader()))" ] }, { @@ -131,7 +375,7 @@ "metadata": {}, "outputs": [], "source": [ - "y" + "y[" ] }, { @@ -146,6 +390,48 @@ }, { "cell_type": "code", + "execution_count": 45, + "id": "bcfc61cc-e6cc-4fb0-91ca-eca02168c6e1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([3])" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "datamodule.mapping.get_index(\"<p>\")" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "1e657891-45bb-479e-95ba-bdefe3a84ae9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'<s>He rose from his breakfast-nook bench\\nand came into the livingroom, where\\nHeather and Steve stood aghast at\\nhis entrance. He came, almost falling\\nforward in an ungainly shuffle, neck\\nthrust out, arms dangling loosely.\\nThen, abruptly, he drew himself up\\nand walked on the very tips of\\nhis toes. He stretched his arms\\nover his head and yawned agape,\\ndrawing-in great breaths that\\nbecame great sighs of ecstacy.<e>'" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "convert_y_label_to_string(y[0], datamodule.mapping, padding_index=3)" + ] + }, + { + "cell_type": "code", "execution_count": null, "id": "7aa8c021", "metadata": { @@ -158,22 +444,131 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "7ef93252", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n", + " 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n", + " 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,\n", + " 1000, 1000, 1000, 1000, 1000, 1000, 1000])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, "id": "6c62572f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "TypeError", + "evalue": "'int' object is not iterable", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m/tmp/ipykernel_9271/3685142356.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0m_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_y_label_to_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatamodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmapping\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[0m", + "\u001b[0;32m/tmp/ipykernel_9271/1895060558.py\u001b[0m in \u001b[0;36mconvert_y_label_to_string\u001b[0;34m(y, mapping, padding_index)\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconvert_y_label_to_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\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;32m----> 8\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mpadding_index\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[0m", + "\u001b[0;32m/tmp/ipykernel_9271/1895060558.py\u001b[0m in 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\u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\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: 'int' object is not iterable" + ] + } + ], "source": [ - "_plot(x[0, 0], vmax=1, title=convert_y_label_to_string(y[0], dataset.mapping))" + "_plot(x[0, 0], vmax=1, title=convert_y_label_to_string(y[0], datamodule.mapping))" ] }, { |