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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/network/encoder/efficientnet.yaml\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"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": 5,
"id": "f939aa37-7b1d-45cc-885c-323c4540bda1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'_target_': 'text_recognizer.networks.encoders.efficientnet.EfficientNet', 'arch': 'b0', 'stochastic_dropout_rate': 0.2, 'bn_momentum': 0.99, 'bn_eps': 0.001}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cfg"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "42caaae5-7ee6-43fe-97eb-46c2a6915739",
"metadata": {},
"outputs": [],
"source": [
"cfg.depth = 6"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "aaeab329-aeb0-4a1b-aa35-5a2aab81b1d0",
"metadata": {},
"outputs": [],
"source": [
"net = instantiate(cfg)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "618b997c-e6a6-4487-b70c-9d260cb556d3",
"metadata": {},
"outputs": [],
"source": [
"from torchinfo import summary"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "25759b7b-8deb-4163-b75d-a1357c9fe88f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"===============================================================================================\n",
"Layer (type:depth-idx) Output Shape Param #\n",
"===============================================================================================\n",
"EfficientNet -- --\n",
"├─ModuleList: 1-1 -- --\n",
"├─Sequential: 1-2 [2, 32, 28, 512] --\n",
"│ └─ZeroPad2d: 2-1 [2, 1, 57, 1025] --\n",
"│ └─Conv2d: 2-2 [2, 32, 28, 512] 288\n",
"│ └─BatchNorm2d: 2-3 [2, 32, 28, 512] 64\n",
"│ └─Mish: 2-4 [2, 32, 28, 512] --\n",
"├─ModuleList: 1-1 -- --\n",
"│ └─MBConvBlock: 2-5 [2, 16, 28, 512] --\n",
"│ │ └─Depthwise: 3-1 [2, 32, 28, 512] --\n",
"│ │ │ └─Sequential: 4-1 [2, 32, 28, 512] 352\n",
"│ │ └─SqueezeAndExcite: 3-2 [2, 32, 28, 512] --\n",
"│ │ │ └─Sequential: 4-2 [2, 32, 28, 512] 552\n",
"│ │ └─Pointwise: 3-3 [2, 16, 28, 512] --\n",
"│ │ │ └─Sequential: 4-3 [2, 16, 28, 512] 544\n",
"│ └─MBConvBlock: 2-6 [2, 24, 14, 256] --\n",
"│ │ └─InvertedBottleneck: 3-4 [2, 96, 28, 512] --\n",
"│ │ │ └─Sequential: 4-4 [2, 96, 28, 512] 1,728\n",
"│ │ └─Depthwise: 3-5 [2, 96, 14, 256] --\n",
"│ │ │ └─Sequential: 4-5 [2, 96, 14, 256] 1,056\n",
"│ │ └─SqueezeAndExcite: 3-6 [2, 96, 14, 256] --\n",
"│ │ │ └─Sequential: 4-6 [2, 96, 14, 256] 868\n",
"│ │ └─Pointwise: 3-7 [2, 24, 14, 256] --\n",
"│ │ │ └─Sequential: 4-7 [2, 24, 14, 256] 2,352\n",
"│ └─MBConvBlock: 2-7 [2, 24, 14, 256] --\n",
"│ │ └─InvertedBottleneck: 3-8 [2, 144, 14, 256] --\n",
"│ │ │ └─Sequential: 4-8 [2, 144, 14, 256] 3,744\n",
"│ │ └─Depthwise: 3-9 [2, 144, 14, 256] --\n",
"│ │ │ └─Sequential: 4-9 [2, 144, 14, 256] 1,584\n",
"│ │ └─SqueezeAndExcite: 3-10 [2, 144, 14, 256] --\n",
"│ │ │ └─Sequential: 4-10 [2, 144, 14, 256] 1,878\n",
"│ │ └─Pointwise: 3-11 [2, 24, 14, 256] --\n",
"│ │ │ └─Sequential: 4-11 [2, 24, 14, 256] 3,504\n",
"│ └─MBConvBlock: 2-8 [2, 40, 7, 128] --\n",
"│ │ └─InvertedBottleneck: 3-12 [2, 144, 14, 256] --\n",
"│ │ │ └─Sequential: 4-12 [2, 144, 14, 256] 3,744\n",
"│ │ └─Depthwise: 3-13 [2, 144, 7, 128] --\n",
"│ │ │ └─Sequential: 4-13 [2, 144, 7, 128] 3,888\n",
"│ │ └─SqueezeAndExcite: 3-14 [2, 144, 7, 128] --\n",
"│ │ │ └─Sequential: 4-14 [2, 144, 7, 128] 1,878\n",
"│ │ └─Pointwise: 3-15 [2, 40, 7, 128] --\n",
"│ │ │ └─Sequential: 4-15 [2, 40, 7, 128] 5,840\n",
"│ └─MBConvBlock: 2-9 [2, 40, 7, 128] --\n",
"│ │ └─InvertedBottleneck: 3-16 [2, 240, 7, 128] --\n",
"│ │ │ └─Sequential: 4-16 [2, 240, 7, 128] 10,080\n",
"│ │ └─Depthwise: 3-17 [2, 240, 7, 128] --\n",
"│ │ │ └─Sequential: 4-17 [2, 240, 7, 128] 6,480\n",
"│ │ └─SqueezeAndExcite: 3-18 [2, 240, 7, 128] --\n",
"│ │ │ └─Sequential: 4-18 [2, 240, 7, 128] 5,050\n",
"│ │ └─Pointwise: 3-19 [2, 40, 7, 128] --\n",
"│ │ │ └─Sequential: 4-19 [2, 40, 7, 128] 9,680\n",
"│ └─MBConvBlock: 2-10 [2, 80, 3, 64] --\n",
"│ │ └─InvertedBottleneck: 3-20 [2, 240, 7, 128] --\n",
"│ │ │ └─Sequential: 4-20 [2, 240, 7, 128] 10,080\n",
"│ │ └─Depthwise: 3-21 [2, 240, 3, 64] --\n",
"│ │ │ └─Sequential: 4-21 [2, 240, 3, 64] 2,640\n",
"│ │ └─SqueezeAndExcite: 3-22 [2, 240, 3, 64] --\n",
"│ │ │ └─Sequential: 4-22 [2, 240, 3, 64] 5,050\n",
"│ │ └─Pointwise: 3-23 [2, 80, 3, 64] --\n",
"│ │ │ └─Sequential: 4-23 [2, 80, 3, 64] 19,360\n",
"│ └─MBConvBlock: 2-11 [2, 80, 3, 64] --\n",
"│ │ └─InvertedBottleneck: 3-24 [2, 480, 3, 64] --\n",
"│ │ │ └─Sequential: 4-24 [2, 480, 3, 64] 39,360\n",
"│ │ └─Depthwise: 3-25 [2, 480, 3, 64] --\n",
"│ │ │ └─Sequential: 4-25 [2, 480, 3, 64] 5,280\n",
"│ │ └─SqueezeAndExcite: 3-26 [2, 480, 3, 64] --\n",
"│ │ │ └─Sequential: 4-26 [2, 480, 3, 64] 19,700\n",
"│ │ └─Pointwise: 3-27 [2, 80, 3, 64] --\n",
"│ │ │ └─Sequential: 4-27 [2, 80, 3, 64] 38,560\n",
"│ └─MBConvBlock: 2-12 [2, 80, 3, 64] --\n",
"│ │ └─InvertedBottleneck: 3-28 [2, 480, 3, 64] --\n",
"│ │ │ └─Sequential: 4-28 [2, 480, 3, 64] 39,360\n",
"│ │ └─Depthwise: 3-29 [2, 480, 3, 64] --\n",
"│ │ │ └─Sequential: 4-29 [2, 480, 3, 64] 5,280\n",
"│ │ └─SqueezeAndExcite: 3-30 [2, 480, 3, 64] --\n",
"│ │ │ └─Sequential: 4-30 [2, 480, 3, 64] 19,700\n",
"│ │ └─Pointwise: 3-31 [2, 80, 3, 64] --\n",
"│ │ │ └─Sequential: 4-31 [2, 80, 3, 64] 38,560\n",
"│ └─MBConvBlock: 2-13 [2, 112, 3, 64] --\n",
"│ │ └─InvertedBottleneck: 3-32 [2, 480, 3, 64] --\n",
"│ │ │ └─Sequential: 4-32 [2, 480, 3, 64] 39,360\n",
"│ │ └─Depthwise: 3-33 [2, 480, 3, 64] --\n",
"│ │ │ └─Sequential: 4-33 [2, 480, 3, 64] 12,960\n",
"│ │ └─SqueezeAndExcite: 3-34 [2, 480, 3, 64] --\n",
"│ │ │ └─Sequential: 4-34 [2, 480, 3, 64] 19,700\n",
"│ │ └─Pointwise: 3-35 [2, 112, 3, 64] --\n",
"│ │ │ └─Sequential: 4-35 [2, 112, 3, 64] 53,984\n",
"│ └─MBConvBlock: 2-14 [2, 112, 3, 64] --\n",
"│ │ └─InvertedBottleneck: 3-36 [2, 672, 3, 64] --\n",
"│ │ │ └─Sequential: 4-36 [2, 672, 3, 64] 76,608\n",
"│ │ └─Depthwise: 3-37 [2, 672, 3, 64] --\n",
"│ │ │ └─Sequential: 4-37 [2, 672, 3, 64] 18,144\n",
"│ │ └─SqueezeAndExcite: 3-38 [2, 672, 3, 64] --\n",
"│ │ │ └─Sequential: 4-38 [2, 672, 3, 64] 38,332\n",
"│ │ └─Pointwise: 3-39 [2, 112, 3, 64] --\n",
"│ │ │ └─Sequential: 4-39 [2, 112, 3, 64] 75,488\n",
"│ └─MBConvBlock: 2-15 [2, 112, 3, 64] --\n",
"│ │ └─InvertedBottleneck: 3-40 [2, 672, 3, 64] --\n",
"│ │ │ └─Sequential: 4-40 [2, 672, 3, 64] 76,608\n",
"│ │ └─Depthwise: 3-41 [2, 672, 3, 64] --\n",
"│ │ │ └─Sequential: 4-41 [2, 672, 3, 64] 18,144\n",
"│ │ └─SqueezeAndExcite: 3-42 [2, 672, 3, 64] --\n",
"│ │ │ └─Sequential: 4-42 [2, 672, 3, 64] 38,332\n",
"│ │ └─Pointwise: 3-43 [2, 112, 3, 64] --\n",
"│ │ │ └─Sequential: 4-43 [2, 112, 3, 64] 75,488\n",
"│ └─MBConvBlock: 2-16 [2, 192, 1, 32] --\n",
"│ │ └─InvertedBottleneck: 3-44 [2, 672, 3, 64] --\n",
"│ │ │ └─Sequential: 4-44 [2, 672, 3, 64] 76,608\n",
"│ │ └─Depthwise: 3-45 [2, 672, 1, 32] --\n",
"│ │ │ └─Sequential: 4-45 [2, 672, 1, 32] 18,144\n",
"│ │ └─SqueezeAndExcite: 3-46 [2, 672, 1, 32] --\n",
"│ │ │ └─Sequential: 4-46 [2, 672, 1, 32] 38,332\n",
"│ │ └─Pointwise: 3-47 [2, 192, 1, 32] --\n",
"│ │ │ └─Sequential: 4-47 [2, 192, 1, 32] 129,408\n",
"│ └─MBConvBlock: 2-17 [2, 192, 1, 32] --\n",
"│ │ └─InvertedBottleneck: 3-48 [2, 1152, 1, 32] --\n",
"│ │ │ └─Sequential: 4-48 [2, 1152, 1, 32] 223,488\n",
"│ │ └─Depthwise: 3-49 [2, 1152, 1, 32] --\n",
"│ │ │ └─Sequential: 4-49 [2, 1152, 1, 32] 31,104\n",
"│ │ └─SqueezeAndExcite: 3-50 [2, 1152, 1, 32] --\n",
"│ │ │ └─Sequential: 4-50 [2, 1152, 1, 32] 111,792\n",
"│ │ └─Pointwise: 3-51 [2, 192, 1, 32] --\n",
"│ │ │ └─Sequential: 4-51 [2, 192, 1, 32] 221,568\n",
"│ └─MBConvBlock: 2-18 [2, 192, 1, 32] --\n",
"│ │ └─InvertedBottleneck: 3-52 [2, 1152, 1, 32] --\n",
"│ │ │ └─Sequential: 4-52 [2, 1152, 1, 32] 223,488\n",
"│ │ └─Depthwise: 3-53 [2, 1152, 1, 32] --\n",
"│ │ │ └─Sequential: 4-53 [2, 1152, 1, 32] 31,104\n",
"│ │ └─SqueezeAndExcite: 3-54 [2, 1152, 1, 32] --\n",
"│ │ │ └─Sequential: 4-54 [2, 1152, 1, 32] 111,792\n",
"│ │ └─Pointwise: 3-55 [2, 192, 1, 32] --\n",
"│ │ │ └─Sequential: 4-55 [2, 192, 1, 32] 221,568\n",
"│ └─MBConvBlock: 2-19 [2, 192, 1, 32] --\n",
"│ │ └─InvertedBottleneck: 3-56 [2, 1152, 1, 32] --\n",
"│ │ │ └─Sequential: 4-56 [2, 1152, 1, 32] 223,488\n",
"│ │ └─Depthwise: 3-57 [2, 1152, 1, 32] --\n",
"│ │ │ └─Sequential: 4-57 [2, 1152, 1, 32] 31,104\n",
"│ │ └─SqueezeAndExcite: 3-58 [2, 1152, 1, 32] --\n",
"│ │ │ └─Sequential: 4-58 [2, 1152, 1, 32] 111,792\n",
"│ │ └─Pointwise: 3-59 [2, 192, 1, 32] --\n",
"│ │ │ └─Sequential: 4-59 [2, 192, 1, 32] 221,568\n",
"├─Sequential: 1-3 [2, 1280, 1, 32] --\n",
"│ └─Conv2d: 2-20 [2, 1280, 1, 32] 245,760\n",
"│ └─BatchNorm2d: 2-21 [2, 1280, 1, 32] 2,560\n",
"│ └─Dropout: 2-22 [2, 1280, 1, 32] --\n",
"===============================================================================================\n",
"Total params: 3,125,900\n",
"Trainable params: 3,125,900\n",
"Non-trainable params: 0\n",
"Total mult-adds (M): 770.11\n",
"===============================================================================================\n",
"Input size (MB): 0.46\n",
"Forward/backward pass size (MB): 268.21\n",
"Params size (MB): 12.50\n",
"Estimated Total Size (MB): 281.17\n",
"==============================================================================================="
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(net, (2, 1, 56, 1024), device=\"cpu\", depth=4)"
]
}
],
"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": "3.9.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|
