Update notebooks

1 files changed, 142 insertions, 130 deletions

diff --git a/notebooks/03-look-at-iam-paragraphs.ipynb b/notebooks/03-look-at-iam-paragraphs.ipynb
index 0f805f6..f57d491 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": 2,
+   "execution_count": 1,
    "id": "6ce2519f",
    "metadata": {},
    "outputs": [],
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 35,
    "id": "e9386367-2b49-4633-9936-57081132e59e",
    "metadata": {},
    "outputs": [
@@ -69,34 +69,51 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "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: false\n",
+      "ignore_warnings: true\n",
+      "summary:\n",
+      "- - 1\n",
+      "  - 1\n",
+      "  - 576\n",
+      "  - 640\n",
+      "- - 1\n",
+      "  - 682\n",
       "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.UploadConfigAsArtifact\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",
-      "    log_train: false\n",
+      "  lightning:\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",
+      "  wandb:\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.UploadConfigAsArtifact\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",
       "  stochastic_weight_averaging:\n",
       "    _target_: pytorch_lightning.callbacks.StochasticWeightAveraging\n",
       "    swa_epoch_start: 0.75\n",
@@ -104,6 +121,20 @@
       "    annealing_epochs: 10\n",
       "    annealing_strategy: cos\n",
       "    device: null\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",
+      "  pin_memory: true\n",
+      "  transform: transform/paragraphs.yaml\n",
+      "  test_transform: test_transform/paragraphs.yaml\n",
+      "  mapping:\n",
+      "    _target_: text_recognizer.data.mappings.emnist_mapping.EmnistMapping\n",
+      "    extra_symbols:\n",
+      "    - '\n",
+      "\n",
+      "      '\n",
       "logger:\n",
       "  wandb:\n",
       "    _target_: pytorch_lightning.loggers.wandb.WandbLogger\n",
@@ -122,36 +153,20 @@
       "  stochastic_weight_avg: true\n",
       "  auto_scale_batch_size: binsearch\n",
       "  auto_lr_find: false\n",
-      "  gradient_clip_val: 0.5\n",
+      "  gradient_clip_val: 0.0\n",
       "  fast_dev_run: false\n",
       "  gpus: 1\n",
       "  precision: 16\n",
-      "  max_epochs: 512\n",
+      "  max_epochs: 1000\n",
       "  terminate_on_nan: true\n",
       "  weights_summary: null\n",
       "  limit_train_batches: 1.0\n",
       "  limit_val_batches: 1.0\n",
       "  limit_test_batches: 1.0\n",
       "  resume_from_checkpoint: null\n",
-      "  accumulate_grad_batches: 32\n",
+      "  accumulate_grad_batches: 16\n",
       "  overfit_batches: 0\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: false\n",
-      "ignore_warnings: true\n",
-      "summary:\n",
-      "- - 1\n",
-      "  - 1\n",
-      "  - 576\n",
-      "  - 640\n",
-      "- - 1\n",
-      "  - 682\n",
-      "epochs: 512\n",
+      "epochs: 1000\n",
       "ignore_index: 3\n",
       "num_classes: 58\n",
       "max_output_len: 682\n",
@@ -159,17 +174,18 @@
       "  _target_: torch.nn.CrossEntropyLoss\n",
       "  ignore_index: 3\n",
       "mapping:\n",
-      "  _target_: text_recognizer.data.emnist_mapping.EmnistMapping\n",
-      "  extra_symbols:\n",
-      "  - '\n",
+      "  mapping:\n",
+      "    _target_: text_recognizer.data.mappings.emnist_mapping.EmnistMapping\n",
+      "    extra_symbols:\n",
+      "    - '\n",
       "\n",
-      "    '\n",
+      "      '\n",
       "optimizers:\n",
       "  madgrad:\n",
       "    _target_: madgrad.MADGRAD\n",
       "    lr: 0.0003\n",
       "    momentum: 0.9\n",
-      "    weight_decay: 0\n",
+      "    weight_decay: 5.0e-06\n",
       "    eps: 1.0e-06\n",
       "    parameters: network\n",
       "lr_schedulers:\n",
@@ -177,9 +193,9 @@
       "    _target_: torch.optim.lr_scheduler.OneCycleLR\n",
       "    max_lr: 0.0003\n",
       "    total_steps: null\n",
-      "    epochs: 512\n",
-      "    steps_per_epoch: 90\n",
-      "    pct_start: 0.1\n",
+      "    epochs: 1000\n",
+      "    steps_per_epoch: 632\n",
+      "    pct_start: 0.03\n",
       "    anneal_strategy: cos\n",
       "    cycle_momentum: true\n",
       "    base_momentum: 0.85\n",
@@ -191,21 +207,12 @@
       "    verbose: false\n",
       "    interval: step\n",
       "    monitor: val/loss\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: false\n",
-      "  resize: null\n",
       "network:\n",
       "  _target_: text_recognizer.networks.conv_transformer.ConvTransformer\n",
       "  input_dims:\n",
       "  - 1\n",
-      "  - 56\n",
-      "  - 1024\n",
+      "  - 576\n",
+      "  - 640\n",
       "  hidden_dim: 128\n",
       "  encoder_dim: 1280\n",
       "  dropout_rate: 0.2\n",
@@ -250,6 +257,12 @@
       "    dropout_rate: 0.2\n",
       "    max_len: 682\n",
       "model:\n",
+      "  mapping:\n",
+      "    _target_: text_recognizer.data.mappings.emnist_mapping.EmnistMapping\n",
+      "    extra_symbols:\n",
+      "    - '\n",
+      "\n",
+      "      '\n",
       "  _target_: text_recognizer.models.transformer.TransformerLitModel\n",
       "  max_output_len: 682\n",
       "  start_token: <s>\n",
@@ -257,26 +270,18 @@
       "  pad_token: <p>\n",
       "\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/aktersnurra/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/hydra/_internal/defaults_list.py:251: UserWarning: In 'config': Defaults list is missing `_self_`. See https://hydra.cc/docs/upgrades/1.0_to_1.1/default_composition_order for more information\n",
-      "  warnings.warn(msg, UserWarning)\n"
-     ]
     }
    ],
    "source": [
     "# context initialization\n",
-    "with initialize(config_path=\"../training/conf/\", job_name=\"test_app\"):\n",
+    "with initialize(config_path=\"../training/conf/\"):\n",
     "    cfg = compose(config_name=\"config\", overrides=[\"+experiment=cnn_transformer_paragraphs\"])\n",
     "    print(OmegaConf.to_yaml(cfg))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 36,
    "id": "1c4624d1-6de5-41ab-9208-0988fcdba76d",
    "metadata": {},
    "outputs": [
@@ -284,8 +289,11 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2021-10-02 23:26:26.439 | INFO     | text_recognizer.data.iam_paragraphs:setup:103 - Loading IAM paragraph regions and lines for None...\n",
-      "2021-10-02 23:26:49.529 | INFO     | text_recognizer.data.iam_synthetic_paragraphs:setup:68 - IAM Synthetic dataset steup for stage None...\n"
+      "2021-10-11 22:01:56.908 | INFO     | text_recognizer.data.iam_paragraphs:setup:92 - Loading IAM paragraph regions and lines for None...\n",
+      "2021-10-11 22:02:16.771 | DEBUG    | text_recognizer.data.transforms.load_transform:_load_config:17 - Loading transforms from config: transform/paragraphs.yaml\n",
+      "2021-10-11 22:02:19.953 | DEBUG    | text_recognizer.data.transforms.load_transform:_load_config:17 - Loading transforms from config: test_transform/paragraphs.yaml\n",
+      "2021-10-11 22:02:19.957 | INFO     | text_recognizer.data.iam_synthetic_paragraphs:setup:67 - IAM Synthetic dataset steup for stage None...\n",
+      "2021-10-11 22:02:32.207 | DEBUG    | text_recognizer.data.transforms.load_transform:_load_config:17 - Loading transforms from config: transform/paragraphs.yaml\n"
      ]
     },
     {
@@ -296,8 +304,8 @@
       "Num classes: 58\n",
       "Dims: (1, 576, 640)\n",
       "Output dims: (682, 1)\n",
-      "Train/val/test sizes: 19981, 262, 231\n",
-      "Train Batch x stats: (torch.Size([4, 1, 576, 640]), torch.float32, tensor(0.), tensor(0.0134), tensor(0.0650), tensor(1.))\n",
+      "Train/val/test sizes: 19882, 262, 231\n",
+      "Train Batch x stats: (torch.Size([4, 1, 576, 640]), torch.float32, tensor(0.), tensor(0.0316), tensor(0.0974), tensor(1.))\n",
       "Train Batch y stats: (torch.Size([4, 682]), torch.int64, tensor(1), tensor(57))\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(57))\n",
@@ -306,7 +314,7 @@
     }
    ],
    "source": [
-    "datamodule = instantiate(cfg.datamodule, mapping=cfg.mapping)\n",
+    "datamodule = instantiate(cfg.datamodule)\n",
     "datamodule.prepare_data()\n",
     "datamodule.setup()\n",
     "print(datamodule)"
@@ -466,112 +474,116 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "8e667fb0-3ff4-45c7-9de3-8f712407d12f",
+   "execution_count": 23,
+   "id": "6f53950a-6858-40b6-ad4a-2857227d9d59",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import torch"
+    "a = torch.randn(2, 1, 576, 640), torch.randn(2, 1, 576, 640)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "c4b7e5c5-62a9-4415-954d-65d59ddd82c4",
+   "execution_count": 3,
+   "id": "580b9bbc-b213-4ef8-aefb-cfb453d08a44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from text_recognizer.data.transforms.load_transform import load_transforms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "15744542-0880-45f2-881b-d81e04668305",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "torch.Size([2, 92160])"
+       "Compose(\n",
+       "    <text_recognizer.data.transforms.embed_crop.EmbedCrop object at 0x7f830edf5df0>\n",
+       "    ColorJitter(brightness=[0.8, 1.6], contrast=None, saturation=None, hue=None)\n",
+       "    RandomAffine(degrees=[-1.0, 1.0], shear=[-30.0, 20.0], interpolation=bilinear)\n",
+       "    ToTensor()\n",
+       ")"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "torch.randn(2, 256, 18, 20).flatten(start_dim=1).shape"
+    "load_transforms(\"iam_lines.yaml\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "bf820d79-2ce1-4c15-b006-6cb4407ccbba",
+   "execution_count": 16,
+   "id": "5d7eab42-c407-4b88-9492-e9279a38232a",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[autoreload of text_recognizer.networks.barlow_twins.projector failed: Traceback (most recent call last):\n",
-      "  File \"/home/aktersnurra/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/IPython/extensions/autoreload.py\", line 245, in check\n",
-      "    superreload(m, reload, self.old_objects)\n",
-      "  File \"/home/aktersnurra/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/IPython/extensions/autoreload.py\", line 410, in superreload\n",
-      "    update_generic(old_obj, new_obj)\n",
-      "  File \"/home/aktersnurra/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/IPython/extensions/autoreload.py\", line 347, in update_generic\n",
-      "    update(a, b)\n",
-      "  File \"/home/aktersnurra/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/IPython/extensions/autoreload.py\", line 302, in update_class\n",
-      "    if update_generic(old_obj, new_obj): continue\n",
-      "  File \"/home/aktersnurra/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/IPython/extensions/autoreload.py\", line 347, in update_generic\n",
-      "    update(a, b)\n",
-      "  File \"/home/aktersnurra/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/IPython/extensions/autoreload.py\", line 266, in update_function\n",
-      "    setattr(old, name, getattr(new, name))\n",
-      "ValueError: __init__() requires a code object with 0 free vars, not 1\n",
-      "]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from text_recognizer.networks.barlow_twins.projector import Projector"
+    "from torchvision.transforms import ColorJitter"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "cda38d24-b69a-4033-b07c-2e7558889cf7",
+   "execution_count": 17,
+   "id": "3d02a3fe-1128-416f-80e1-84c9287e613d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ColorJitter(brightness=[0.5, 1.0], contrast=None, saturation=None, hue=None)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "net = Projector([92160, 4096, 256])"
+    "ColorJitter(brightness=[0.5, 1.0])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "ed706880-8556-418d-85a6-616ac16a3334",
+   "execution_count": 45,
+   "id": "f0ead6d1-3093-4a42-a3b2-b3cdea75fc21",
    "metadata": {},
    "outputs": [],
    "source": [
-    "z = torch.randn(2, 256, 18, 20).flatten(start_dim=1)\n"
+    "import torchvision"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "58e6fddc-81be-44c1-bbe8-05401aa05287",
+   "execution_count": 46,
+   "id": "f4c1606d-a063-465d-bf22-61a1cbc14ab9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "torch.Size([2, 256])"
+       "<InterpolationMode.BILINEAR: 'bilinear'>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "net(z).shape"
+    "getattr(torchvision.transforms.functional.InterpolationMode, \"BILINEAR\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "6f53950a-6858-40b6-ad4a-2857227d9d59",
+   "id": "617568a7-fde1-4f60-80c5-922d764f0c52",
    "metadata": {},
    "outputs": [],
    "source": []