{ "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\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 }