pytorch_vgg.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "import torchvision.datasets as datasets\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "VGG_types = {\n",
    "    'VGG11': [64, 'M', 128, 'M', 256,256, 'M', 512,512, 'M', 512,512, 'M'],\n",
    "    'VGG13': [64,64, 'M', 128, 128, 'M', 256,256, 'M', 512,512, 'M', 512,512, 'M'],\n",
    "    'VGG16': [64,64, 'M', 128, 128, 'M', 256,256,256, 'M', 512,512,512, 'M', 512,512,512, 'M'], #GOOD\n",
    "    'VGG19': [64,64, 'M', 128, 128, 'M', 256,256,256,256, 'M', 512,512,512,512, 'M', 512,512,512,512, 'M'],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#처음 224 이미지를 Maxpool 할때만 이미지 크기 1/2\n",
    "224/(2**5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VGG_net(nn.Module):\n",
    "    def __init__(self, in_channels=3, num_classes=1000):\n",
    "        super(VGG_net, self).__init__()\n",
    "        self.in_channels = in_channels\n",
    "        self.conv_layers = self.create_conv_layers(VGG_types['VGG16'])\n",
    "        \n",
    "        self.fcs = nn.Sequential(\n",
    "            nn.Linear(512*7*7, 4096),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.5),\n",
    "            nn.Linear(4096,4096),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.5),\n",
    "            nn.Linear(4096, num_classes)\n",
    "            )\n",
    "        \n",
    "    def forward(self,x):\n",
    "        x = self.conv_layers(x)\n",
    "        x = x.reshape(x.shape[0],-1)\n",
    "        x = self.fcs(x)\n",
    "        return x\n",
    "    \n",
    "    def create_conv_layers(self, architecture):\n",
    "        layers = []\n",
    "        in_channels = self.in_channels\n",
    "        \n",
    "        for x in architecture:\n",
    "            if type(x) == int:\n",
    "                out_channels = x\n",
    "                \n",
    "                layers += [nn.Conv2d(in_channels=in_channels, out_channels=out_channels,\n",
    "                                    kernel_size=(3,3), stride=(1,1), padding=(1,1)),\n",
    "                                    nn.BatchNorm2d(x),\n",
    "                                    nn.ReLU()]\n",
    "                in_channels = x\n",
    "            elif x == 'M':\n",
    "                layers += [nn.MaxPool2d(kernel_size=(2,2), stride=(2,2))]\n",
    "                \n",
    "        return nn.Sequential(*layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 1000])\n"
     ]
    }
   ],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "model = VGG_net(in_channels=3, num_classes=1000).to(device)\n",
    "x = torch.randn(1,3,224,224).to(device)\n",
    "print(model(x).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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   "name": "pytorch_1.5"
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