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-rw-r--r--notebooks/03-look-at-iam-paragraphs.ipynb425
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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+ "text/plain": [
+ "<Figure size 864x864 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
"source": [
- "_plot(x[0], vmax=1, title=dataset.mapping.get_text(y))"
+ "_plot(x[0, 0], vmax=1, title=datamodule.mapping.get_text(y[0]))"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 21,
+ "id": "edd0e44b-b383-4117-83ca-0bfd7e5235aa",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([1000])"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "datamodule.mapping[\"<p>\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "3480ae5f-9cec-4814-98fe-02082a139add",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "tensor([1002, 25, 147, 233, 88, 16, 45, 1, 61, 54, 7, 20,\n",
+ " 95, 71, 20, 2, 15, 30, 21, 24, 24, 95, 18, 21,\n",
+ " 78, 1001, 14, 779, 7, 1, 218, 3, 1, 36, 23, 64,\n",
+ " 23, 21, 46, 54, 24, 24, 16, 4, 1, 542, 1001, 1,\n",
+ " 47, 7, 20, 15, 47, 7, 54, 14, 1, 2, 15, 7,\n",
+ " 64, 7, 99, 281, 1, 20, 46, 47, 20, 2, 15, 80,\n",
+ " 1001, 45, 1, 7, 21, 15, 54, 20, 21, 31, 7, 33,\n",
+ " 25, 1, 31, 20, 16, 7, 4, 1, 28, 744, 489, 12,\n",
+ " 1001, 35, 362, 11, 67, 1, 41, 21, 46, 20, 23, 21,\n",
+ " 36, 13, 1, 2, 47, 41, 71, 71, 36, 7, 4, 1,\n",
+ " 120, 155, 1001, 22, 54, 41, 66, 1, 24, 41, 15, 4,\n",
+ " 673, 2, 1, 17, 20, 21, 46, 36, 23, 21, 46, 1,\n",
+ " 36, 24, 24, 2, 7, 36, 13, 33, 1001, 1, 15, 47,\n",
+ " 7, 21, 4, 1, 20, 61, 54, 41, 26, 15, 36, 13,\n",
+ " 4, 25, 172, 7, 84, 162, 237, 121, 1001, 14, 34, 28,\n",
+ " 95, 9, 42, 3, 1, 351, 1, 15, 23, 26, 2, 10,\n",
+ " 1001, 45, 1, 15, 24, 7, 2, 33, 25, 1, 2, 15,\n",
+ " 54, 7, 15, 31, 47, 7, 17, 45, 673, 2, 1001, 223,\n",
+ " 45, 534, 14, 1, 13, 20, 84, 21, 7, 17, 1, 20,\n",
+ " 46, 20, 26, 7, 4, 1001, 1, 17, 54, 20, 84, 23,\n",
+ " 21, 46, 30, 23, 21, 229, 1, 61, 54, 7, 20, 15,\n",
+ " 47, 2, 22, 19, 1001, 18, 31, 20, 197, 229, 1, 2,\n",
+ " 193, 47, 2, 10, 1, 7, 31, 2, 15, 20, 31, 13,\n",
+ " 33, 1003, 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, 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, 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, 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, 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 \u001b[0;36m<listcomp>\u001b[0;34m(.0)\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~/projects/text-recognizer/text_recognizer/data/word_piece_mapping.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 93\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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~/projects/text-recognizer/text_recognizer/data/word_piece_mapping.py\u001b[0m in \u001b[0;36mget_text\u001b[0;34m(self, indices)\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTensor\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 77\u001b[0m \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\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---> 78\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwordpiece_processor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindices\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 79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_indices\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;32m~/projects/text-recognizer/text_recognizer/data/iam_preprocessor.py\u001b[0m in \u001b[0;36mto_text\u001b[0;34m(self, indices)\u001b[0m\n\u001b[1;32m 150\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlexicon\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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 151\u001b[0m \u001b[0mencoding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtokens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_post_process\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mencoding\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[0mindices\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 153\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtokens_to_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m:\u001b[0m \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))"
]
},
{