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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": 10,
"id": "3cf50475-39f2-4642-a7d1-5bcbc0a036f7",
"metadata": {},
"outputs": [],
"source": [
"path = \"../training/conf/network/encoder/efficientnet.yaml\""
]
},
{
"cell_type": "code",
"execution_count": 11,
"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": 12,
"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": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cfg"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "42caaae5-7ee6-43fe-97eb-46c2a6915739",
"metadata": {},
"outputs": [],
"source": [
"cfg.arch = \"b2\""
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "aaeab329-aeb0-4a1b-aa35-5a2aab81b1d0",
"metadata": {},
"outputs": [],
"source": [
"net = instantiate(cfg)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "618b997c-e6a6-4487-b70c-9d260cb556d3",
"metadata": {},
"outputs": [],
"source": [
"from torchinfo import summary"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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, 16, 288, 320] --\n",
"│ │ └─Sequential: 3-4 [2, 16, 288, 320] 176\n",
"│ │ └─Sequential: 3-5 [2, 16, 288, 320] 148\n",
"│ │ └─Sequential: 3-6 [2, 16, 288, 320] 288\n",
"│ └─MBConvBlock: 2-7 [2, 24, 144, 160] --\n",
"│ │ └─Sequential: 3-7 [2, 96, 288, 320] 1,728\n",
"│ │ └─Sequential: 3-8 [2, 96, 144, 160] 1,056\n",
"│ │ └─Sequential: 3-9 [2, 96, 144, 160] 4,728\n",
"│ │ └─Sequential: 3-10 [2, 24, 144, 160] 2,352\n",
"│ └─MBConvBlock: 2-8 [2, 24, 144, 160] --\n",
"│ │ └─Sequential: 3-11 [2, 144, 144, 160] 3,744\n",
"│ │ └─Sequential: 3-12 [2, 144, 144, 160] 1,584\n",
"│ │ └─Sequential: 3-13 [2, 144, 144, 160] 10,548\n",
"│ │ └─Sequential: 3-14 [2, 24, 144, 160] 3,504\n",
"│ └─MBConvBlock: 2-9 [2, 24, 144, 160] --\n",
"│ │ └─Sequential: 3-15 [2, 144, 144, 160] 3,744\n",
"│ │ └─Sequential: 3-16 [2, 144, 144, 160] 1,584\n",
"│ │ └─Sequential: 3-17 [2, 144, 144, 160] 10,548\n",
"│ │ └─Sequential: 3-18 [2, 24, 144, 160] 3,504\n",
"│ └─MBConvBlock: 2-10 [2, 48, 72, 80] --\n",
"│ │ └─Sequential: 3-19 [2, 144, 144, 160] 3,744\n",
"│ │ └─Sequential: 3-20 [2, 144, 72, 80] 3,888\n",
"│ │ └─Sequential: 3-21 [2, 144, 72, 80] 10,548\n",
"│ │ └─Sequential: 3-22 [2, 48, 72, 80] 7,008\n",
"│ └─MBConvBlock: 2-11 [2, 48, 72, 80] --\n",
"│ │ └─Sequential: 3-23 [2, 288, 72, 80] 14,400\n",
"│ │ └─Sequential: 3-24 [2, 288, 72, 80] 7,776\n",
"│ │ └─Sequential: 3-25 [2, 288, 72, 80] 41,832\n",
"│ │ └─Sequential: 3-26 [2, 48, 72, 80] 13,920\n",
"│ └─MBConvBlock: 2-12 [2, 48, 72, 80] --\n",
"│ │ └─Sequential: 3-27 [2, 288, 72, 80] 14,400\n",
"│ │ └─Sequential: 3-28 [2, 288, 72, 80] 7,776\n",
"│ │ └─Sequential: 3-29 [2, 288, 72, 80] 41,832\n",
"│ │ └─Sequential: 3-30 [2, 48, 72, 80] 13,920\n",
"│ └─MBConvBlock: 2-13 [2, 88, 36, 40] --\n",
"│ │ └─Sequential: 3-31 [2, 288, 72, 80] 14,400\n",
"│ │ └─Sequential: 3-32 [2, 288, 36, 40] 3,168\n",
"│ │ └─Sequential: 3-33 [2, 288, 36, 40] 41,832\n",
"│ │ └─Sequential: 3-34 [2, 88, 36, 40] 25,520\n",
"│ └─MBConvBlock: 2-14 [2, 88, 36, 40] --\n",
"│ │ └─Sequential: 3-35 [2, 528, 36, 40] 47,520\n",
"│ │ └─Sequential: 3-36 [2, 528, 36, 40] 5,808\n",
"│ │ └─Sequential: 3-37 [2, 528, 36, 40] 140,052\n",
"│ │ └─Sequential: 3-38 [2, 88, 36, 40] 46,640\n",
"│ └─MBConvBlock: 2-15 [2, 88, 36, 40] --\n",
"│ │ └─Sequential: 3-39 [2, 528, 36, 40] 47,520\n",
"│ │ └─Sequential: 3-40 [2, 528, 36, 40] 5,808\n",
"│ │ └─Sequential: 3-41 [2, 528, 36, 40] 140,052\n",
"│ │ └─Sequential: 3-42 [2, 88, 36, 40] 46,640\n",
"│ └─MBConvBlock: 2-16 [2, 88, 36, 40] --\n",
"│ │ └─Sequential: 3-43 [2, 528, 36, 40] 47,520\n",
"│ │ └─Sequential: 3-44 [2, 528, 36, 40] 5,808\n",
"│ │ └─Sequential: 3-45 [2, 528, 36, 40] 140,052\n",
"│ │ └─Sequential: 3-46 [2, 88, 36, 40] 46,640\n",
"│ └─MBConvBlock: 2-17 [2, 120, 36, 40] --\n",
"│ │ └─Sequential: 3-47 [2, 528, 36, 40] 47,520\n",
"│ │ └─Sequential: 3-48 [2, 528, 36, 40] 14,256\n",
"│ │ └─Sequential: 3-49 [2, 528, 36, 40] 140,052\n",
"│ │ └─Sequential: 3-50 [2, 120, 36, 40] 63,600\n",
"│ └─MBConvBlock: 2-18 [2, 120, 36, 40] --\n",
"│ │ └─Sequential: 3-51 [2, 720, 36, 40] 87,840\n",
"│ │ └─Sequential: 3-52 [2, 720, 36, 40] 19,440\n",
"│ │ └─Sequential: 3-53 [2, 720, 36, 40] 260,100\n",
"│ │ └─Sequential: 3-54 [2, 120, 36, 40] 86,640\n",
"│ └─MBConvBlock: 2-19 [2, 120, 36, 40] --\n",
"│ │ └─Sequential: 3-55 [2, 720, 36, 40] 87,840\n",
"│ │ └─Sequential: 3-56 [2, 720, 36, 40] 19,440\n",
"│ │ └─Sequential: 3-57 [2, 720, 36, 40] 260,100\n",
"│ │ └─Sequential: 3-58 [2, 120, 36, 40] 86,640\n",
"│ └─MBConvBlock: 2-20 [2, 120, 36, 40] --\n",
"│ │ └─Sequential: 3-59 [2, 720, 36, 40] 87,840\n",
"│ │ └─Sequential: 3-60 [2, 720, 36, 40] 19,440\n",
"│ │ └─Sequential: 3-61 [2, 720, 36, 40] 260,100\n",
"│ │ └─Sequential: 3-62 [2, 120, 36, 40] 86,640\n",
"│ └─MBConvBlock: 2-21 [2, 208, 18, 20] --\n",
"│ │ └─Sequential: 3-63 [2, 720, 36, 40] 87,840\n",
"│ │ └─Sequential: 3-64 [2, 720, 18, 20] 19,440\n",
"│ │ └─Sequential: 3-65 [2, 720, 18, 20] 260,100\n",
"│ │ └─Sequential: 3-66 [2, 208, 18, 20] 150,176\n",
"│ └─MBConvBlock: 2-22 [2, 208, 18, 20] --\n",
"│ │ └─Sequential: 3-67 [2, 1248, 18, 20] 262,080\n",
"│ │ └─Sequential: 3-68 [2, 1248, 18, 20] 33,696\n",
"│ │ └─Sequential: 3-69 [2, 1248, 18, 20] 780,312\n",
"│ │ └─Sequential: 3-70 [2, 208, 18, 20] 260,000\n",
"│ └─MBConvBlock: 2-23 [2, 208, 18, 20] --\n",
"│ │ └─Sequential: 3-71 [2, 1248, 18, 20] 262,080\n",
"│ │ └─Sequential: 3-72 [2, 1248, 18, 20] 33,696\n",
"│ │ └─Sequential: 3-73 [2, 1248, 18, 20] 780,312\n",
"│ │ └─Sequential: 3-74 [2, 208, 18, 20] 260,000\n",
"│ └─MBConvBlock: 2-24 [2, 208, 18, 20] --\n",
"│ │ └─Sequential: 3-75 [2, 1248, 18, 20] 262,080\n",
"│ │ └─Sequential: 3-76 [2, 1248, 18, 20] 33,696\n",
"│ │ └─Sequential: 3-77 [2, 1248, 18, 20] 780,312\n",
"│ │ └─Sequential: 3-78 [2, 208, 18, 20] 260,000\n",
"│ └─MBConvBlock: 2-25 [2, 208, 18, 20] --\n",
"│ │ └─Sequential: 3-79 [2, 1248, 18, 20] 262,080\n",
"│ │ └─Sequential: 3-80 [2, 1248, 18, 20] 33,696\n",
"│ │ └─Sequential: 3-81 [2, 1248, 18, 20] 780,312\n",
"│ │ └─Sequential: 3-82 [2, 208, 18, 20] 260,000\n",
"│ └─MBConvBlock: 2-26 [2, 352, 18, 20] --\n",
"│ │ └─Sequential: 3-83 [2, 1248, 18, 20] 262,080\n",
"│ │ └─Sequential: 3-84 [2, 1248, 18, 20] 13,728\n",
"│ │ └─Sequential: 3-85 [2, 1248, 18, 20] 780,312\n",
"│ │ └─Sequential: 3-86 [2, 352, 18, 20] 440,000\n",
"│ └─MBConvBlock: 2-27 [2, 352, 18, 20] --\n",
"│ │ └─Sequential: 3-87 [2, 2112, 18, 20] 747,648\n",
"│ │ └─Sequential: 3-88 [2, 2112, 18, 20] 23,232\n",
"│ │ └─Sequential: 3-89 [2, 2112, 18, 20] 2,232,912\n",
"│ │ └─Sequential: 3-90 [2, 352, 18, 20] 744,128\n",
"├─Sequential: 1-3 [2, 1408, 18, 20] --\n",
"│ └─Conv2d: 2-28 [2, 1408, 18, 20] 495,616\n",
"│ └─BatchNorm2d: 2-29 [2, 1408, 18, 20] 2,816\n",
"==========================================================================================\n",
"Total params: 14,268,928\n",
"Trainable params: 14,268,928\n",
"Non-trainable params: 0\n",
"Total mult-adds (G): 20.53\n",
"==========================================================================================\n",
"Input size (MB): 2.95\n",
"Forward/backward pass size (MB): 2847.17\n",
"Params size (MB): 57.08\n",
"Estimated Total Size (MB): 2907.19\n",
"=========================================================================================="
]
},
"execution_count": 16,
"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
}
|
