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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"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": 3,
"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": 4,
"id": "3cf50475-39f2-4642-a7d1-5bcbc0a036f7",
"metadata": {},
"outputs": [],
"source": [
"path = \"../training/conf/experiment/cnn_htr_char_lines.yaml\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"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": 6,
"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}], 'criterion': {'_target_': 'torch.nn.CrossEntropyLoss', 'ignore_index': 3}, 'mapping': {'_target_': 'text_recognizer.data.emnist_mapping.EmnistMapping'}, 'optimizers': {'madgrad': {'_target_': 'madgrad.MADGRAD', 'lr': 0.0001, 'momentum': 0.9, 'weight_decay': 0, 'eps': 1e-06, 'parameters': 'network'}}, 'lr_schedulers': {'network': {'_target_': 'torch.optim.lr_scheduler.CosineAnnealingLR', 'T_max': 1024, 'eta_min': 4.5e-06, 'last_epoch': -1, 'interval': 'epoch', 'monitor': 'val/loss'}}, 'datamodule': {'_target_': 'text_recognizer.data.iam_lines.IAMLines', 'batch_size': 8, 'num_workers': 12, 'train_fraction': 0.8, 'augment': False, 'pin_memory': 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': 2, 'num_heads': 4, 'attn_fn': 'text_recognizer.networks.transformer.attention.Attention', 'attn_kwargs': {'dim_head': 32, '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}}, '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': False, 'auto_scale_batch_size': 'binsearch', 'auto_lr_find': False, 'gradient_clip_val': 0, 'fast_dev_run': False, 'gpus': 1, 'precision': 16, 'max_epochs': 1024, '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, 'accumulate_grad_batches': 4}}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cfg"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "aaeab329-aeb0-4a1b-aa35-5a2aab81b1d0",
"metadata": {},
"outputs": [],
"source": [
"net = instantiate(cfg.network)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "618b997c-e6a6-4487-b70c-9d260cb556d3",
"metadata": {},
"outputs": [],
"source": [
"from torchinfo import summary"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "25759b7b-8deb-4163-b75d-a1357c9fe88f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([4, 4, 89, 1024])\n",
"torch.Size([4, 4, 89, 1024])\n",
"torch.Size([4, 4, 89, 1024])\n",
"torch.Size([4, 4, 32, 1024])\n",
"torch.Size([4, 4, 89, 1024])\n",
"torch.Size([4, 4, 89, 1024])\n",
"torch.Size([4, 4, 89, 1024])\n",
"torch.Size([4, 4, 32, 1024])\n"
]
},
{
"data": {
"text/plain": [
"====================================================================================================\n",
"Layer (type:depth-idx) Output Shape Param #\n",
"====================================================================================================\n",
"ConvTransformer -- --\n",
"├─EfficientNet: 1 -- --\n",
"│ └─ModuleList: 2-1 -- --\n",
"├─Decoder: 1 -- --\n",
"│ └─ModuleList: 2-2 -- --\n",
"│ │ └─ModuleList: 3-1 -- 2,097,536\n",
"│ │ └─ModuleList: 3-2 -- 2,097,536\n",
"│ │ └─ModuleList: 3-3 -- 198,016\n",
"│ │ └─ModuleList: 3-4 -- 2,097,536\n",
"│ │ └─ModuleList: 3-5 -- 2,097,536\n",
"│ │ └─ModuleList: 3-6 -- 198,016\n",
"├─EfficientNet: 1-1 [4, 1280, 1, 32] --\n",
"│ └─Sequential: 2-3 [4, 32, 28, 512] --\n",
"│ │ └─ZeroPad2d: 3-7 [4, 1, 57, 1025] --\n",
"│ │ └─Conv2d: 3-8 [4, 32, 28, 512] 288\n",
"│ │ └─BatchNorm2d: 3-9 [4, 32, 28, 512] 64\n",
"│ │ └─Mish: 3-10 [4, 32, 28, 512] --\n",
"│ └─ModuleList: 2-1 -- --\n",
"│ │ └─MBConvBlock: 3-11 [4, 16, 28, 512] 1,448\n",
"│ │ └─MBConvBlock: 3-12 [4, 24, 14, 256] 9,864\n",
"│ │ └─MBConvBlock: 3-13 [4, 24, 14, 256] 19,380\n",
"│ │ └─MBConvBlock: 3-14 [4, 40, 7, 128] 24,020\n",
"│ │ └─MBConvBlock: 3-15 [4, 40, 7, 128] 55,340\n",
"│ │ └─MBConvBlock: 3-16 [4, 80, 3, 64] 61,180\n",
"│ │ └─MBConvBlock: 3-17 [4, 80, 3, 64] 199,000\n",
"│ │ └─MBConvBlock: 3-18 [4, 80, 3, 64] 199,000\n",
"│ │ └─MBConvBlock: 3-19 [4, 112, 3, 64] 222,104\n",
"│ │ └─MBConvBlock: 3-20 [4, 112, 3, 64] 396,872\n",
"│ │ └─MBConvBlock: 3-21 [4, 112, 3, 64] 396,872\n",
"│ │ └─MBConvBlock: 3-22 [4, 192, 1, 32] 450,792\n",
"│ │ └─MBConvBlock: 3-23 [4, 192, 1, 32] 1,141,152\n",
"│ │ └─MBConvBlock: 3-24 [4, 192, 1, 32] 1,141,152\n",
"│ │ └─MBConvBlock: 3-25 [4, 192, 1, 32] 1,141,152\n",
"│ │ └─MBConvBlock: 3-26 [4, 320, 1, 32] 1,270,432\n",
"│ └─Sequential: 2-4 [4, 1280, 1, 32] --\n",
"│ │ └─Conv2d: 3-27 [4, 1280, 1, 32] 409,600\n",
"│ │ └─BatchNorm2d: 3-28 [4, 1280, 1, 32] 2,560\n",
"├─Sequential: 1-2 [4, 128, 32] --\n",
"│ └─Conv2d: 2-5 [4, 128, 1, 32] 163,968\n",
"│ └─PositionalEncoding2D: 2-6 [4, 128, 1, 32] --\n",
"│ └─Flatten: 2-7 [4, 128, 32] --\n",
"├─Embedding: 1-3 [4, 89, 128] 7,424\n",
"├─PositionalEncoding: 1-4 [4, 89, 128] --\n",
"│ └─Dropout: 2-8 [4, 89, 128] --\n",
"├─Decoder: 1-5 [4, 89, 128] --\n",
"├─Linear: 1-6 [4, 89, 58] 7,482\n",
"====================================================================================================\n",
"Total params: 16,107,322\n",
"Trainable params: 16,107,322\n",
"Non-trainable params: 0\n",
"Total mult-adds (G): 2.84\n",
"====================================================================================================\n",
"Input size (MB): 0.92\n",
"Forward/backward pass size (MB): 677.01\n",
"Params size (MB): 64.43\n",
"Estimated Total Size (MB): 742.36\n",
"===================================================================================================="
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(net, ((4, 1, 56, 1024), (4, 89)), device=\"cpu\")"
]
}
],
"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"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|
