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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/network/encoder/efficientnet.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": [
"{'_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}"
]
},
"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)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "618b997c-e6a6-4487-b70c-9d260cb556d3",
"metadata": {},
"outputs": [],
"source": [
"from torchinfo import summary"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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, 288, 320] --\n",
"│ └─ZeroPad2d: 2-1 [2, 1, 577, 641] --\n",
"│ └─Conv2d: 2-2 [2, 32, 288, 320] 288\n",
"│ └─BatchNorm2d: 2-3 [2, 32, 288, 320] 64\n",
"│ └─Mish: 2-4 [2, 32, 288, 320] --\n",
"├─ModuleList: 1-1 -- --\n",
"│ └─MBConvBlock: 2-5 [2, 16, 288, 320] --\n",
"│ │ └─Sequential: 3-1 [2, 32, 288, 320] 352\n",
"│ │ └─Sequential: 3-2 [2, 32, 288, 320] 552\n",
"│ │ └─Sequential: 3-3 [2, 16, 288, 320] 544\n",
"│ └─MBConvBlock: 2-6 [2, 24, 144, 160] --\n",
"│ │ └─Sequential: 3-4 [2, 96, 288, 320] 1,728\n",
"│ │ └─Sequential: 3-5 [2, 96, 144, 160] 1,056\n",
"│ │ └─Sequential: 3-6 [2, 96, 144, 160] 4,728\n",
"│ │ └─Sequential: 3-7 [2, 24, 144, 160] 2,352\n",
"│ └─MBConvBlock: 2-7 [2, 24, 144, 160] --\n",
"│ │ └─Sequential: 3-8 [2, 144, 144, 160] 3,744\n",
"│ │ └─Sequential: 3-9 [2, 144, 144, 160] 1,584\n",
"│ │ └─Sequential: 3-10 [2, 144, 144, 160] 10,548\n",
"│ │ └─Sequential: 3-11 [2, 24, 144, 160] 3,504\n",
"│ └─MBConvBlock: 2-8 [2, 40, 72, 80] --\n",
"│ │ └─Sequential: 3-12 [2, 144, 144, 160] 3,744\n",
"│ │ └─Sequential: 3-13 [2, 144, 72, 80] 3,888\n",
"│ │ └─Sequential: 3-14 [2, 144, 72, 80] 10,548\n",
"│ │ └─Sequential: 3-15 [2, 40, 72, 80] 5,840\n",
"│ └─MBConvBlock: 2-9 [2, 40, 72, 80] --\n",
"│ │ └─Sequential: 3-16 [2, 240, 72, 80] 10,080\n",
"│ │ └─Sequential: 3-17 [2, 240, 72, 80] 6,480\n",
"│ │ └─Sequential: 3-18 [2, 240, 72, 80] 29,100\n",
"│ │ └─Sequential: 3-19 [2, 40, 72, 80] 9,680\n",
"│ └─MBConvBlock: 2-10 [2, 80, 36, 40] --\n",
"│ │ └─Sequential: 3-20 [2, 240, 72, 80] 10,080\n",
"│ │ └─Sequential: 3-21 [2, 240, 36, 40] 2,640\n",
"│ │ └─Sequential: 3-22 [2, 240, 36, 40] 29,100\n",
"│ │ └─Sequential: 3-23 [2, 80, 36, 40] 19,360\n",
"│ └─MBConvBlock: 2-11 [2, 80, 36, 40] --\n",
"│ │ └─Sequential: 3-24 [2, 480, 36, 40] 39,360\n",
"│ │ └─Sequential: 3-25 [2, 480, 36, 40] 5,280\n",
"│ │ └─Sequential: 3-26 [2, 480, 36, 40] 115,800\n",
"│ │ └─Sequential: 3-27 [2, 80, 36, 40] 38,560\n",
"│ └─MBConvBlock: 2-12 [2, 80, 36, 40] --\n",
"│ │ └─Sequential: 3-28 [2, 480, 36, 40] 39,360\n",
"│ │ └─Sequential: 3-29 [2, 480, 36, 40] 5,280\n",
"│ │ └─Sequential: 3-30 [2, 480, 36, 40] 115,800\n",
"│ │ └─Sequential: 3-31 [2, 80, 36, 40] 38,560\n",
"│ └─MBConvBlock: 2-13 [2, 112, 36, 40] --\n",
"│ │ └─Sequential: 3-32 [2, 480, 36, 40] 39,360\n",
"│ │ └─Sequential: 3-33 [2, 480, 36, 40] 12,960\n",
"│ │ └─Sequential: 3-34 [2, 480, 36, 40] 115,800\n",
"│ │ └─Sequential: 3-35 [2, 112, 36, 40] 53,984\n",
"│ └─MBConvBlock: 2-14 [2, 112, 36, 40] --\n",
"│ │ └─Sequential: 3-36 [2, 672, 36, 40] 76,608\n",
"│ │ └─Sequential: 3-37 [2, 672, 36, 40] 18,144\n",
"│ │ └─Sequential: 3-38 [2, 672, 36, 40] 226,632\n",
"│ │ └─Sequential: 3-39 [2, 112, 36, 40] 75,488\n",
"│ └─MBConvBlock: 2-15 [2, 112, 36, 40] --\n",
"│ │ └─Sequential: 3-40 [2, 672, 36, 40] 76,608\n",
"│ │ └─Sequential: 3-41 [2, 672, 36, 40] 18,144\n",
"│ │ └─Sequential: 3-42 [2, 672, 36, 40] 226,632\n",
"│ │ └─Sequential: 3-43 [2, 112, 36, 40] 75,488\n",
"│ └─MBConvBlock: 2-16 [2, 192, 18, 20] --\n",
"│ │ └─Sequential: 3-44 [2, 672, 36, 40] 76,608\n",
"│ │ └─Sequential: 3-45 [2, 672, 18, 20] 18,144\n",
"│ │ └─Sequential: 3-46 [2, 672, 18, 20] 226,632\n",
"│ │ └─Sequential: 3-47 [2, 192, 18, 20] 129,408\n",
"│ └─MBConvBlock: 2-17 [2, 192, 18, 20] --\n",
"│ │ └─Sequential: 3-48 [2, 1152, 18, 20] 223,488\n",
"│ │ └─Sequential: 3-49 [2, 1152, 18, 20] 31,104\n",
"│ │ └─Sequential: 3-50 [2, 1152, 18, 20] 664,992\n",
"│ │ └─Sequential: 3-51 [2, 192, 18, 20] 221,568\n",
"│ └─MBConvBlock: 2-18 [2, 192, 18, 20] --\n",
"│ │ └─Sequential: 3-52 [2, 1152, 18, 20] 223,488\n",
"│ │ └─Sequential: 3-53 [2, 1152, 18, 20] 31,104\n",
"│ │ └─Sequential: 3-54 [2, 1152, 18, 20] 664,992\n",
"│ │ └─Sequential: 3-55 [2, 192, 18, 20] 221,568\n",
"│ └─MBConvBlock: 2-19 [2, 192, 18, 20] --\n",
"│ │ └─Sequential: 3-56 [2, 1152, 18, 20] 223,488\n",
"│ │ └─Sequential: 3-57 [2, 1152, 18, 20] 31,104\n",
"│ │ └─Sequential: 3-58 [2, 1152, 18, 20] 664,992\n",
"│ │ └─Sequential: 3-59 [2, 192, 18, 20] 221,568\n",
"│ └─MBConvBlock: 2-20 [2, 320, 18, 20] --\n",
"│ │ └─Sequential: 3-60 [2, 1152, 18, 20] 223,488\n",
"│ │ └─Sequential: 3-61 [2, 1152, 18, 20] 12,672\n",
"│ │ └─Sequential: 3-62 [2, 1152, 18, 20] 664,992\n",
"│ │ └─Sequential: 3-63 [2, 320, 18, 20] 369,280\n",
"├─Sequential: 1-3 [2, 1280, 18, 20] --\n",
"│ └─Conv2d: 2-21 [2, 1280, 18, 20] 409,600\n",
"│ └─BatchNorm2d: 2-22 [2, 1280, 18, 20] 2,560\n",
"==========================================================================================\n",
"Total params: 7,142,272\n",
"Trainable params: 7,142,272\n",
"Non-trainable params: 0\n",
"Total mult-adds (G): 11.27\n",
"==========================================================================================\n",
"Input size (MB): 2.95\n",
"Forward/backward pass size (MB): 1922.96\n",
"Params size (MB): 28.57\n",
"Estimated Total Size (MB): 1954.48\n",
"=========================================================================================="
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(net, (2, 1, 576, 640), device=\"cpu\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "3ef95a63-7044-45bf-a085-faf5ea0c03ec",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'EfficientNet' object is not subscriptable",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipykernel_2800/4064962505.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnet\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: 'EfficientNet' object is not subscriptable"
]
}
],
"source": [
"net[:-2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62ca0d97-625c-474b-8d6c-d0caba79e198",
"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"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|
