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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": 7,
"id": "3cf50475-39f2-4642-a7d1-5bcbc0a036f7",
"metadata": {},
"outputs": [],
"source": [
"path = \"../training/conf/network/conv_transformer.yaml\""
]
},
{
"cell_type": "code",
"execution_count": 35,
"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": 36,
"id": "f939aa37-7b1d-45cc-885c-323c4540bda1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'_target_': 'text_recognizer.networks.ConvTransformer', 'input_dims': [1, 1, 576, 640], 'hidden_dim': 144, 'num_classes': 58, 'pad_index': 3, 'encoder': {'_target_': 'text_recognizer.networks.EfficientNet', 'arch': 'b0', 'stochastic_dropout_rate': 0.2, 'bn_momentum': 0.99, 'bn_eps': 0.001, 'depth': 5, 'out_channels': 144}, 'decoder': {'_target_': 'text_recognizer.networks.transformer.Decoder', 'depth': 6, 'block': {'_target_': 'text_recognizer.networks.transformer.DecoderBlock', 'self_attn': {'_target_': 'text_recognizer.networks.transformer.Attention', 'dim': 144, 'num_heads': 12, 'dim_head': 64, 'dropout_rate': 0.2, 'causal': True, 'rotary_embedding': {'_target_': 'text_recognizer.networks.transformer.RotaryEmbedding', 'dim': 64}}, 'cross_attn': {'_target_': 'text_recognizer.networks.transformer.Attention', 'dim': 144, 'num_heads': 8, 'dim_head': 64, 'dropout_rate': 0.2, 'causal': False}, 'norm': {'_target_': 'text_recognizer.networks.transformer.RMSNorm', 'dim': 144}, 'ff': {'_target_': 'text_recognizer.networks.transformer.FeedForward', 'dim': 144, 'dim_out': None, 'expansion_factor': 2, 'glu': True, 'dropout_rate': 0.2}}}, 'pixel_embedding': {'_target_': 'text_recognizer.networks.transformer.embeddings.axial.AxialPositionalEmbeddingImage', 'dim': 144, 'axial_shape': [3, 63], 'axial_dims': [72, 72]}, 'token_pos_embedding': {'_target_': 'text_recognizer.networks.transformer.embeddings.fourier.PositionalEncoding', 'dim': 144, 'dropout_rate': 0.1, 'max_len': 89}}"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cfg"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "aaeab329-aeb0-4a1b-aa35-5a2aab81b1d0",
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"net = instantiate(cfg)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "618b997c-e6a6-4487-b70c-9d260cb556d3",
"metadata": {},
"outputs": [],
"source": [
"from torchinfo import summary"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "7daf1f49",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"torch.Size([1, 144, 3, 63])\n"
]
},
{
"data": {
"text/plain": [
"==============================================================================================================\n",
"Layer (type:depth-idx) Output Shape Param #\n",
"==============================================================================================================\n",
"ConvTransformer [1, 58, 89] --\n",
"├─EfficientNet: 1-1 [1, 144, 3, 63] 850,880\n",
"│ └─Sequential: 2-1 [1, 32, 26, 510] --\n",
"│ │ └─ZeroPad2d: 3-1 [1, 1, 57, 1025] --\n",
"│ │ └─Conv2d: 3-2 [1, 32, 26, 510] 1,568\n",
"│ │ └─BatchNorm2d: 3-3 [1, 32, 26, 510] 64\n",
"│ │ └─Mish: 3-4 [1, 32, 26, 510] --\n",
"│ └─ModuleList: 2 -- --\n",
"│ │ └─MBConvBlock: 3-5 [1, 16, 26, 510] --\n",
"│ │ │ └─Depthwise: 4-1 [1, 32, 26, 510] 352\n",
"│ │ │ └─SqueezeAndExcite: 4-2 [1, 32, 26, 510] 552\n",
"│ │ │ └─Pointwise: 4-3 [1, 16, 26, 510] 544\n",
"│ │ └─MBConvBlock: 3-6 [1, 24, 13, 255] --\n",
"│ │ │ └─InvertedBottleneck: 4-4 [1, 96, 26, 510] 1,728\n",
"│ │ │ └─Depthwise: 4-5 [1, 96, 13, 255] 1,056\n",
"│ │ │ └─SqueezeAndExcite: 4-6 [1, 96, 13, 255] 868\n",
"│ │ │ └─Pointwise: 4-7 [1, 24, 13, 255] 2,352\n",
"│ │ └─MBConvBlock: 3-7 [1, 24, 13, 255] --\n",
"│ │ │ └─InvertedBottleneck: 4-8 [1, 144, 13, 255] 3,744\n",
"│ │ │ └─Depthwise: 4-9 [1, 144, 13, 255] 1,584\n",
"│ │ │ └─SqueezeAndExcite: 4-10 [1, 144, 13, 255] 1,878\n",
"│ │ │ └─Pointwise: 4-11 [1, 24, 13, 255] 3,504\n",
"│ │ └─MBConvBlock: 3-8 [1, 40, 6, 127] --\n",
"│ │ │ └─InvertedBottleneck: 4-12 [1, 144, 13, 255] 3,744\n",
"│ │ │ └─Depthwise: 4-13 [1, 144, 6, 127] 3,888\n",
"│ │ │ └─SqueezeAndExcite: 4-14 [1, 144, 6, 127] 1,878\n",
"│ │ │ └─Pointwise: 4-15 [1, 40, 6, 127] 5,840\n",
"│ │ └─MBConvBlock: 3-9 [1, 40, 6, 127] --\n",
"│ │ │ └─InvertedBottleneck: 4-16 [1, 240, 6, 127] 10,080\n",
"│ │ │ └─Depthwise: 4-17 [1, 240, 6, 127] 6,480\n",
"│ │ │ └─SqueezeAndExcite: 4-18 [1, 240, 6, 127] 5,050\n",
"│ │ │ └─Pointwise: 4-19 [1, 40, 6, 127] 9,680\n",
"│ │ └─MBConvBlock: 3-10 [1, 80, 3, 63] --\n",
"│ │ │ └─InvertedBottleneck: 4-20 [1, 240, 6, 127] 10,080\n",
"│ │ │ └─Depthwise: 4-21 [1, 240, 3, 63] 2,640\n",
"│ │ │ └─SqueezeAndExcite: 4-22 [1, 240, 3, 63] 5,050\n",
"│ │ │ └─Pointwise: 4-23 [1, 80, 3, 63] 19,360\n",
"│ │ └─MBConvBlock: 3-11 [1, 80, 3, 63] --\n",
"│ │ │ └─InvertedBottleneck: 4-24 [1, 480, 3, 63] 39,360\n",
"│ │ │ └─Depthwise: 4-25 [1, 480, 3, 63] 5,280\n",
"│ │ │ └─SqueezeAndExcite: 4-26 [1, 480, 3, 63] 19,700\n",
"│ │ │ └─Pointwise: 4-27 [1, 80, 3, 63] 38,560\n",
"│ │ └─MBConvBlock: 3-12 [1, 80, 3, 63] --\n",
"│ │ │ └─InvertedBottleneck: 4-28 [1, 480, 3, 63] 39,360\n",
"│ │ │ └─Depthwise: 4-29 [1, 480, 3, 63] 5,280\n",
"│ │ │ └─SqueezeAndExcite: 4-30 [1, 480, 3, 63] 19,700\n",
"│ │ │ └─Pointwise: 4-31 [1, 80, 3, 63] 38,560\n",
"│ │ └─MBConvBlock: 3-13 [1, 112, 3, 63] --\n",
"│ │ │ └─InvertedBottleneck: 4-32 [1, 480, 3, 63] 39,360\n",
"│ │ │ └─Depthwise: 4-33 [1, 480, 3, 63] 12,960\n",
"│ │ │ └─SqueezeAndExcite: 4-34 [1, 480, 3, 63] 19,700\n",
"│ │ │ └─Pointwise: 4-35 [1, 112, 3, 63] 53,984\n",
"│ │ └─MBConvBlock: 3-14 [1, 112, 3, 63] --\n",
"│ │ │ └─InvertedBottleneck: 4-36 [1, 672, 3, 63] 76,608\n",
"│ │ │ └─Depthwise: 4-37 [1, 672, 3, 63] 18,144\n",
"│ │ │ └─SqueezeAndExcite: 4-38 [1, 672, 3, 63] 38,332\n",
"│ │ │ └─Pointwise: 4-39 [1, 112, 3, 63] 75,488\n",
"│ │ └─MBConvBlock: 3-15 [1, 112, 3, 63] --\n",
"│ │ │ └─InvertedBottleneck: 4-40 [1, 672, 3, 63] 76,608\n",
"│ │ │ └─Depthwise: 4-41 [1, 672, 3, 63] 18,144\n",
"│ │ │ └─SqueezeAndExcite: 4-42 [1, 672, 3, 63] 38,332\n",
"│ │ │ └─Pointwise: 4-43 [1, 112, 3, 63] 75,488\n",
"│ └─Sequential: 2-2 [1, 144, 3, 63] --\n",
"│ │ └─Conv2d: 3-16 [1, 144, 3, 63] 16,128\n",
"│ │ └─BatchNorm2d: 3-17 [1, 144, 3, 63] 288\n",
"│ │ └─Dropout: 3-18 [1, 144, 3, 63] --\n",
"├─Conv2d: 1-2 [1, 144, 3, 63] 20,880\n",
"├─AxialPositionalEmbeddingImage: 1-3 [1, 144, 3, 63] --\n",
"│ └─AxialPositionalEmbedding: 2-3 [1, 189, 144] 4,752\n",
"├─Embedding: 1-4 [1, 89, 144] 8,352\n",
"├─PositionalEncoding: 1-5 [1, 89, 144] --\n",
"│ └─Dropout: 2-4 [1, 89, 144] --\n",
"├─Decoder: 1-6 [1, 89, 144] --\n",
"│ └─ModuleList: 2 -- --\n",
"│ │ └─DecoderBlock: 3-19 [1, 89, 144] --\n",
"│ │ └─DecoderBlock: 3-20 [1, 89, 144] --\n",
"│ │ └─DecoderBlock: 3-21 [1, 89, 144] --\n",
"│ │ └─DecoderBlock: 3-22 [1, 89, 144] --\n",
"│ │ └─DecoderBlock: 3-23 [1, 89, 144] --\n",
"│ │ └─DecoderBlock: 3-24 [1, 89, 144] --\n",
"├─Linear: 1-7 [1, 89, 58] 8,410\n",
"==============================================================================================================\n",
"Total params: 6,090,138\n",
"Trainable params: 6,090,138\n",
"Non-trainable params: 0\n",
"Total mult-adds (M): 313.64\n",
"==============================================================================================================\n",
"Input size (MB): 0.23\n",
"Forward/backward pass size (MB): 145.27\n",
"Params size (MB): 24.36\n",
"Estimated Total Size (MB): 169.86\n",
"=============================================================================================================="
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(net, ((1, 1, 56, 1024), (1, 89)), device=\"cpu\", depth=4)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "25759b7b-8deb-4163-b75d-a1357c9fe88f",
"metadata": {
"scrolled": false
},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "Failed to run torchinfo. See above stack traces for more details. Executed layers up to: [EfficientNet: 1, Sequential: 2, ZeroPad2d: 3, Conv2d: 3, BatchNorm2d: 3, Mish: 3, MBConvBlock: 3, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Sequential: 2, Conv2d: 3, BatchNorm2d: 3, Dropout: 3, Conv2d: 1]",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torchinfo/torchinfo.py:290\u001b[0m, in \u001b[0;36mforward_pass\u001b[0;34m(model, x, batch_dim, cache_forward_pass, device, mode, **kwargs)\u001b[0m\n\u001b[1;32m 289\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, (\u001b[38;5;28mlist\u001b[39m, \u001b[38;5;28mtuple\u001b[39m)):\n\u001b[0;32m--> 290\u001b[0m _ \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 291\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mdict\u001b[39m):\n",
"File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1148\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1146\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(\u001b[38;5;28minput\u001b[39m)\n\u001b[0;32m-> 1148\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1149\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n",
"File \u001b[0;32m~/projects/text-recognizer/text_recognizer/networks/conv_transformer.py:132\u001b[0m, in \u001b[0;36mConvTransformer.forward\u001b[0;34m(self, x, context)\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[38;5;124;03m\"\"\"Encodes images into word piece logtis.\u001b[39;00m\n\u001b[1;32m 117\u001b[0m \n\u001b[1;32m 118\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[38;5;124;03m Tensor: Sequence of logits.\u001b[39;00m\n\u001b[1;32m 131\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 132\u001b[0m z \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 133\u001b[0m logits \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecode(z, context)\n",
"File \u001b[0;32m~/projects/text-recognizer/text_recognizer/networks/conv_transformer.py:82\u001b[0m, in \u001b[0;36mConvTransformer.encode\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 81\u001b[0m z \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv(z)\n\u001b[0;32m---> 82\u001b[0m z \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpixel_embedding\u001b[49m\u001b[43m(\u001b[49m\u001b[43mz\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 83\u001b[0m z \u001b[38;5;241m=\u001b[39m z\u001b[38;5;241m.\u001b[39mflatten(start_dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n",
"File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torch/nn/modules/module.py:1148\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1146\u001b[0m \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m bw_hook\u001b[38;5;241m.\u001b[39msetup_input_hook(\u001b[38;5;28minput\u001b[39m)\n\u001b[0;32m-> 1148\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1149\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks:\n",
"File \u001b[0;32m~/projects/text-recognizer/text_recognizer/networks/transformer/embeddings/axial.py:40\u001b[0m, in \u001b[0;36mAxialPositionalEmbedding.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m---> 40\u001b[0m b, t, _ \u001b[38;5;241m=\u001b[39m x\u001b[38;5;241m.\u001b[39mshape\n\u001b[1;32m 41\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m (\n\u001b[1;32m 42\u001b[0m t \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_seq_len\n\u001b[1;32m 43\u001b[0m ), \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSequence length (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mt\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) must be less than the maximum sequence length allowed (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_seq_len\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
"\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 3)",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [22]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msummary\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m576\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m640\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m682\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcpu\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdepth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torchinfo/torchinfo.py:218\u001b[0m, in \u001b[0;36msummary\u001b[0;34m(model, input_size, input_data, batch_dim, cache_forward_pass, col_names, col_width, depth, device, dtypes, mode, row_settings, verbose, **kwargs)\u001b[0m\n\u001b[1;32m 211\u001b[0m validate_user_params(\n\u001b[1;32m 212\u001b[0m input_data, input_size, columns, col_width, device, dtypes, verbose\n\u001b[1;32m 213\u001b[0m )\n\u001b[1;32m 215\u001b[0m x, correct_input_size \u001b[38;5;241m=\u001b[39m process_input(\n\u001b[1;32m 216\u001b[0m input_data, input_size, batch_dim, device, dtypes\n\u001b[1;32m 217\u001b[0m )\n\u001b[0;32m--> 218\u001b[0m summary_list \u001b[38;5;241m=\u001b[39m \u001b[43mforward_pass\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 219\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_dim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache_forward_pass\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel_mode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 220\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 221\u001b[0m formatting \u001b[38;5;241m=\u001b[39m FormattingOptions(depth, verbose, columns, col_width, rows)\n\u001b[1;32m 222\u001b[0m results \u001b[38;5;241m=\u001b[39m ModelStatistics(\n\u001b[1;32m 223\u001b[0m summary_list, correct_input_size, get_total_memory_used(x), formatting\n\u001b[1;32m 224\u001b[0m )\n",
"File \u001b[0;32m~/.cache/pypoetry/virtualenvs/text-recognizer-ejNaVa9M-py3.9/lib/python3.9/site-packages/torchinfo/torchinfo.py:299\u001b[0m, in \u001b[0;36mforward_pass\u001b[0;34m(model, x, batch_dim, cache_forward_pass, device, mode, **kwargs)\u001b[0m\n\u001b[1;32m 297\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 298\u001b[0m executed_layers \u001b[38;5;241m=\u001b[39m [layer \u001b[38;5;28;01mfor\u001b[39;00m layer \u001b[38;5;129;01min\u001b[39;00m summary_list \u001b[38;5;28;01mif\u001b[39;00m layer\u001b[38;5;241m.\u001b[39mexecuted]\n\u001b[0;32m--> 299\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 300\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed to run torchinfo. See above stack traces for more details. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 301\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExecuted layers up to: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mexecuted_layers\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 302\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 303\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 304\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m hooks:\n",
"\u001b[0;31mRuntimeError\u001b[0m: Failed to run torchinfo. See above stack traces for more details. Executed layers up to: [EfficientNet: 1, Sequential: 2, ZeroPad2d: 3, Conv2d: 3, BatchNorm2d: 3, Mish: 3, MBConvBlock: 3, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, MBConvBlock: 3, InvertedBottleneck: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, Depthwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Mish: 6, SqueezeAndExcite: 4, Sequential: 5, Conv2d: 6, Mish: 6, Conv2d: 6, Pointwise: 4, Sequential: 5, Conv2d: 6, BatchNorm2d: 6, Sequential: 2, Conv2d: 3, BatchNorm2d: 3, Dropout: 3, Conv2d: 1]"
]
}
],
"source": [
"summary(net, ((1, 1, 576, 640), (1, 682)), device=\"cpu\", depth=4)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "248a0cb1",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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"name": "python3"
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