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Diffstat (limited to 'notebooks/04-efficientnet-transformer.ipynb')
| -rw-r--r-- | notebooks/04-efficientnet-transformer.ipynb | 219 |
1 files changed, 219 insertions, 0 deletions
diff --git a/notebooks/04-efficientnet-transformer.ipynb b/notebooks/04-efficientnet-transformer.ipynb new file mode 100644 index 0000000..427c98c --- /dev/null +++ b/notebooks/04-efficientnet-transformer.ipynb @@ -0,0 +1,219 @@ +{ + "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 +} |