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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "7c02ae76-b540-4b16-9492-e9210b3b9249",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.environ['CUDA_VISIBLE_DEVICE'] = ''\n",
"import random\n",
"\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import numpy as np\n",
"from omegaconf import OmegaConf\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"from importlib.util import find_spec\n",
"if find_spec(\"text_recognizer\") is None:\n",
" import sys\n",
" sys.path.append('..')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ccdb6dde-47e5-429a-88f2-0764fb7e259a",
"metadata": {},
"outputs": [],
"source": [
"from hydra import compose, initialize\n",
"from omegaconf import OmegaConf\n",
"from hydra.utils import instantiate"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3cf50475-39f2-4642-a7d1-5bcbc0a036f7",
"metadata": {},
"outputs": [],
"source": [
"path = \"../training/conf/experiment/cnn_htr_char_lines.yaml\""
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e52ecb01-c975-4e55-925d-1182c7aea473",
"metadata": {},
"outputs": [],
"source": [
"with open(path, \"rb\") as f:\n",
" cfg = OmegaConf.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "f939aa37-7b1d-45cc-885c-323c4540bda1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'defaults': [{'override /mapping': None}, {'override /criterion': None}, {'override /datamodule': None}, {'override /network': None}, {'override /model': None}, {'override /lr_schedulers': None}, {'override /optimizers': None}], 'ignore_index': 3, 'num_classes': 58, 'max_output_len': 89, 'criterion': {'_target_': 'torch.nn.CrossEntropyLoss', 'ignore_index': 3}, 'mapping': {'_target_': 'text_recognizer.data.emnist_mapping.EmnistMapping'}, 'callbacks': {'stochastic_weight_averaging': {'_target_': 'pytorch_lightning.callbacks.StochasticWeightAveraging', 'swa_epoch_start': 0.8, 'swa_lrs': 0.05, 'annealing_epochs': 10, 'annealing_strategy': 'cos', 'device': None}}, 'optimizers': {'madgrad': {'_target_': 'madgrad.MADGRAD', 'lr': 0.0003, 'momentum': 0.9, 'weight_decay': 0, 'eps': 1e-06, 'parameters': 'network'}}, 'lr_schedulers': {'network': {'_target_': 'torch.optim.lr_scheduler.ReduceLROnPlateau', 'mode': 'min', 'factor': 0.5, 'patience': 10, 'threshold': 0.0001, 'threshold_mode': 'rel', 'cooldown': 0, 'min_lr': 1e-06, 'eps': 1e-08, 'interval': 'epoch', 'monitor': 'val/loss'}}, 'datamodule': {'_target_': 'text_recognizer.data.iam_lines.IAMLines', 'batch_size': 16, 'num_workers': 12, 'train_fraction': 0.8, 'augment': True, 'pin_memory': True, 'word_pieces': False}, 'network': {'_target_': 'text_recognizer.networks.conv_transformer.ConvTransformer', 'input_dims': [1, 56, 1024], 'hidden_dim': 128, 'encoder_dim': 1280, 'dropout_rate': 0.2, 'num_classes': 58, 'pad_index': 3, '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': 128, 'depth': 3, 'num_heads': 4, 'attn_fn': 'text_recognizer.networks.transformer.attention.Attention', 'attn_kwargs': {'dim_head': 32, 'dropout_rate': 0.2}, 'norm_fn': 'text_recognizer.networks.transformer.norm.ScaleNorm', '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': {'_target_': 'text_recognizer.networks.transformer.positional_encodings.rotary_embedding.RotaryEmbedding', 'dim': 32}}}, 'model': {'_target_': 'text_recognizer.models.transformer.TransformerLitModel', 'max_output_len': 89, 'start_token': '<s>', 'end_token': '<e>', 'pad_token': '<p>'}, 'trainer': {'_target_': 'pytorch_lightning.Trainer', 'stochastic_weight_avg': True, 'auto_scale_batch_size': 'binsearch', 'auto_lr_find': False, 'gradient_clip_val': 0.5, 'fast_dev_run': False, 'gpus': 1, 'precision': 16, 'max_epochs': 420, 'terminate_on_nan': True, 'weights_summary': None, 'limit_train_batches': 1.0, 'limit_val_batches': 1.0, 'limit_test_batches': 1.0, 'resume_from_checkpoint': None, 'accumulate_grad_batches': 8, 'overfit_batches': 0}, 'summary': [[1, 1, 56, 1024], [1, 89]]}"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cfg"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "aaeab329-aeb0-4a1b-aa35-5a2aab81b1d0",
"metadata": {},
"outputs": [],
"source": [
"net = instantiate(cfg.network)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "618b997c-e6a6-4487-b70c-9d260cb556d3",
"metadata": {},
"outputs": [],
"source": [
"from torchinfo import summary"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "25759b7b-8deb-4163-b75d-a1357c9fe88f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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" [ 0.7765, 0.0142, -0.2525, ..., -0.7171, 0.2236, 0.4911],\n",
" [ 0.3870, 0.5668, -0.2926, ..., -0.7172, 0.2239, 0.4913]]]])\n",
"tensor([[[[-0.4846, -0.0448, 0.7438, ..., -0.2732, 0.5203, -0.3579],\n",
" [-0.4858, -0.0525, 0.7421, ..., -0.2585, 0.5151, -0.3524],\n",
" [-0.4920, -0.0616, 0.7417, ..., -0.2429, 0.5112, -0.3484],\n",
" ...,\n",
" [-0.5254, -0.1285, 0.7435, ..., -0.2306, 0.5152, -0.3257],\n",
" [-0.5255, -0.1289, 0.7432, ..., -0.2314, 0.5157, -0.3253],\n",
" [-0.5254, -0.1291, 0.7429, ..., -0.2321, 0.5158, -0.3250]],\n",
"\n",
" [[-0.4301, 0.8671, 0.5170, ..., -1.1632, -0.3734, -1.1874],\n",
" [-0.4446, 0.8762, 0.5196, ..., -1.1528, -0.3724, -1.1955],\n",
" [-0.4582, 0.8844, 0.5180, ..., -1.1396, -0.3729, -1.1942],\n",
" ...,\n",
" [-0.4770, 0.8541, 0.5325, ..., -1.1392, -0.3525, -1.1223],\n",
" [-0.4766, 0.8535, 0.5316, ..., -1.1390, -0.3523, -1.1217],\n",
" [-0.4760, 0.8530, 0.5309, ..., -1.1390, -0.3519, -1.1214]],\n",
"\n",
" [[-0.6542, -0.1481, 0.0284, ..., 0.1807, 0.0668, -0.1159],\n",
" [-0.6490, -0.1293, 0.0320, ..., 0.1974, 0.0622, -0.1280],\n",
" [-0.6482, -0.1144, 0.0331, ..., 0.2133, 0.0589, -0.1325],\n",
" ...,\n",
" [-0.6662, -0.0985, 0.0627, ..., 0.1447, 0.0864, -0.0778],\n",
" [-0.6664, -0.0994, 0.0632, ..., 0.1434, 0.0864, -0.0774],\n",
" [-0.6662, -0.0997, 0.0639, ..., 0.1421, 0.0864, -0.0775]],\n",
"\n",
" [[-0.1255, 1.4525, -0.4119, ..., 0.6365, 0.6966, 0.2816],\n",
" [-0.1274, 1.4516, -0.4094, ..., 0.6396, 0.6962, 0.2907],\n",
" [-0.1325, 1.4417, -0.4089, ..., 0.6359, 0.6969, 0.3028],\n",
" ...,\n",
" [-0.1968, 1.3976, -0.3989, ..., 0.6016, 0.7498, 0.3160],\n",
" [-0.1972, 1.3966, -0.3989, ..., 0.6020, 0.7502, 0.3163],\n",
" [-0.1977, 1.3958, -0.3984, ..., 0.6025, 0.7504, 0.3163]]]])\n"
]
},
{
"data": {
"text/plain": [
"====================================================================================================\n",
"Layer (type:depth-idx) Output Shape Param #\n",
"====================================================================================================\n",
"ConvTransformer -- --\n",
"├─EfficientNet: 1-1 [1, 1280, 1, 32] 7,142,272\n",
"├─Sequential: 1-2 [1, 128, 32] 163,968\n",
"├─Embedding: 1-3 [1, 89, 128] 7,424\n",
"├─PositionalEncoding: 1-4 [1, 89, 128] --\n",
"├─Decoder: 1-5 [1, 89, 128] 13,176,969\n",
"├─Linear: 1-6 [1, 89, 58] 7,482\n",
"====================================================================================================\n",
"Total params: 20,498,115\n",
"Trainable params: 20,498,115\n",
"Non-trainable params: 0\n",
"Total mult-adds (M): 714.86\n",
"====================================================================================================\n",
"Input size (MB): 0.23\n",
"Forward/backward pass size (MB): 184.29\n",
"Params size (MB): 81.99\n",
"Estimated Total Size (MB): 266.51\n",
"===================================================================================================="
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(net, list(map(lambda x: list(x), cfg.summary)), device=\"cpu\", depth=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bf9d8d67-d7d2-4cf7-b166-377a79d5fd70",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
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|
