229 lines
11 KiB
Plaintext
229 lines
11 KiB
Plaintext
{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "VGG_architecture.ipynb",
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"provenance": [],
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"authorship_tag": "ABX9TyPe/3ZXN0rsAfvPzmbfIYYW",
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/PacktPublishing/Modern-Computer-Vision-with-PyTorch/blob/master/Chapter05/VGG_architecture.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "ZGFMlprVNhM9",
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"outputId": "87d633f5-43a1-4f2b-b1f9-cd0c50d88553"
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},
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"source": [
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"import torchvision\n",
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"import torch.nn as nn\n",
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"import torch\n",
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"import torch.nn.functional as F\n",
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"from torchvision import transforms,models,datasets\n",
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"!pip install torch_summary\n",
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"from torchsummary import summary\n",
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"device = 'cuda' if torch.cuda.is_available() else 'cpu'"
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],
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: torch_summary in /usr/local/lib/python3.6/dist-packages (1.4.3)\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "3VRuWU7rNjNq"
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},
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"source": [
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"model = models.vgg16(pretrained=True).to(device)"
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],
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"execution_count": 2,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "lQ9BIH_QNkyY",
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"outputId": "f7f6af55-29b7-4df6-8974-3e80abd635f1"
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},
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"source": [
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"summary(model, torch.zeros(1,3,224,224));"
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],
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"execution_count": 3,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"==========================================================================================\n",
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"Layer (type:depth-idx) Output Shape Param #\n",
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"==========================================================================================\n",
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"├─Sequential: 1-1 [-1, 512, 7, 7] --\n",
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"| └─Conv2d: 2-1 [-1, 64, 224, 224] 1,792\n",
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"| └─ReLU: 2-2 [-1, 64, 224, 224] --\n",
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"| └─Conv2d: 2-3 [-1, 64, 224, 224] 36,928\n",
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"| └─ReLU: 2-4 [-1, 64, 224, 224] --\n",
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"| └─MaxPool2d: 2-5 [-1, 64, 112, 112] --\n",
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"| └─Conv2d: 2-6 [-1, 128, 112, 112] 73,856\n",
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"| └─ReLU: 2-7 [-1, 128, 112, 112] --\n",
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"| └─Conv2d: 2-8 [-1, 128, 112, 112] 147,584\n",
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"| └─ReLU: 2-9 [-1, 128, 112, 112] --\n",
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"| └─MaxPool2d: 2-10 [-1, 128, 56, 56] --\n",
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"| └─Conv2d: 2-11 [-1, 256, 56, 56] 295,168\n",
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"| └─ReLU: 2-12 [-1, 256, 56, 56] --\n",
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"| └─Conv2d: 2-13 [-1, 256, 56, 56] 590,080\n",
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"| └─ReLU: 2-14 [-1, 256, 56, 56] --\n",
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"| └─Conv2d: 2-15 [-1, 256, 56, 56] 590,080\n",
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"| └─ReLU: 2-16 [-1, 256, 56, 56] --\n",
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"| └─MaxPool2d: 2-17 [-1, 256, 28, 28] --\n",
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"| └─Conv2d: 2-18 [-1, 512, 28, 28] 1,180,160\n",
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"| └─ReLU: 2-19 [-1, 512, 28, 28] --\n",
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"| └─Conv2d: 2-20 [-1, 512, 28, 28] 2,359,808\n",
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"| └─ReLU: 2-21 [-1, 512, 28, 28] --\n",
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"| └─Conv2d: 2-22 [-1, 512, 28, 28] 2,359,808\n",
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"| └─ReLU: 2-23 [-1, 512, 28, 28] --\n",
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"| └─MaxPool2d: 2-24 [-1, 512, 14, 14] --\n",
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"| └─Conv2d: 2-25 [-1, 512, 14, 14] 2,359,808\n",
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"| └─ReLU: 2-26 [-1, 512, 14, 14] --\n",
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"| └─Conv2d: 2-27 [-1, 512, 14, 14] 2,359,808\n",
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"| └─ReLU: 2-28 [-1, 512, 14, 14] --\n",
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"| └─Conv2d: 2-29 [-1, 512, 14, 14] 2,359,808\n",
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"| └─ReLU: 2-30 [-1, 512, 14, 14] --\n",
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"| └─MaxPool2d: 2-31 [-1, 512, 7, 7] --\n",
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"├─AdaptiveAvgPool2d: 1-2 [-1, 512, 7, 7] --\n",
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"├─Sequential: 1-3 [-1, 1000] --\n",
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"| └─Linear: 2-32 [-1, 4096] 102,764,544\n",
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"| └─ReLU: 2-33 [-1, 4096] --\n",
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"| └─Dropout: 2-34 [-1, 4096] --\n",
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"| └─Linear: 2-35 [-1, 4096] 16,781,312\n",
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"| └─ReLU: 2-36 [-1, 4096] --\n",
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"| └─Dropout: 2-37 [-1, 4096] --\n",
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"| └─Linear: 2-38 [-1, 1000] 4,097,000\n",
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"==========================================================================================\n",
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"Total params: 138,357,544\n",
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"Trainable params: 138,357,544\n",
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"Non-trainable params: 0\n",
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"Total mult-adds (G): 15.61\n",
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"==========================================================================================\n",
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"Input size (MB): 0.57\n",
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"Forward/backward pass size (MB): 103.43\n",
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"Params size (MB): 527.79\n",
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"Estimated Total Size (MB): 631.80\n",
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"==========================================================================================\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "StQBD486NmM-",
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"outputId": "612481e0-7665-4b5c-9c5a-ebf76f335178"
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},
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"source": [
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"model"
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],
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"execution_count": 4,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"VGG(\n",
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" (features): Sequential(\n",
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" (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (1): ReLU(inplace=True)\n",
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" (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (3): ReLU(inplace=True)\n",
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" (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (6): ReLU(inplace=True)\n",
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" (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (8): ReLU(inplace=True)\n",
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" (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (11): ReLU(inplace=True)\n",
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" (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (13): ReLU(inplace=True)\n",
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" (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (15): ReLU(inplace=True)\n",
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" (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (18): ReLU(inplace=True)\n",
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" (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (20): ReLU(inplace=True)\n",
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" (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (22): ReLU(inplace=True)\n",
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" (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (25): ReLU(inplace=True)\n",
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" (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (27): ReLU(inplace=True)\n",
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" (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
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" (29): ReLU(inplace=True)\n",
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" (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" )\n",
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" (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
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" (classifier): Sequential(\n",
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" (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
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" (1): ReLU(inplace=True)\n",
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" (2): Dropout(p=0.5, inplace=False)\n",
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" (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
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" (4): ReLU(inplace=True)\n",
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" (5): Dropout(p=0.5, inplace=False)\n",
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" (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
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" )\n",
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")"
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]
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},
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"metadata": {
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"tags": []
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},
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"execution_count": 4
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "TBnhG2vTNr8R"
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},
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"source": [
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""
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],
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"execution_count": 4,
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"outputs": []
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}
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]
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} |