dcgan
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ritchie46
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generative-adversary-networks/dcgan/notebooks/ggan.ipynb
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448
generative-adversary-networks/dcgan/notebooks/ggan.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import torch\n",
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"import torch.nn as nn\n",
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"import torch.utils.data\n",
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"import torch.nn.functional as F\n",
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"import os\n",
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"from torchvision import datasets, transforms\n",
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"from tensorboardX import SummaryWriter\n",
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"from PIL import Image\n",
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"import logging"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"torch.cuda.is_available()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"torch.Size([1, 3, 256, 256])"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"class Generator(nn.Module):\n",
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" def __init__(self, input_size=200, alpha=0.2):\n",
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" super(Generator, self).__init__() \n",
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" kernel_size = 4\n",
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" padding = 1\n",
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" stride = 2\n",
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" \n",
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" self.input = nn.Linear(input_size, 4 * 4 * 1024)\n",
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" self.net = nn.Sequential(\n",
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" nn.BatchNorm2d(1024),\n",
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" nn.LeakyReLU(alpha),\n",
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" nn.ConvTranspose2d(1024, 512, kernel_size, stride, padding),\n",
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" nn.BatchNorm2d(512),\n",
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" nn.LeakyReLU(alpha),\n",
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" nn.ConvTranspose2d(512, 512, kernel_size, stride, padding),\n",
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" nn.BatchNorm2d(512),\n",
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" nn.LeakyReLU(alpha),\n",
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" nn.ConvTranspose2d(512, 512, kernel_size, stride, padding),\n",
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" nn.BatchNorm2d(512),\n",
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" nn.LeakyReLU(alpha),\n",
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" nn.ConvTranspose2d(512, 256, kernel_size, stride, padding),\n",
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" nn.BatchNorm2d(256),\n",
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" nn.LeakyReLU(alpha),\n",
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" nn.ConvTranspose2d(256, 128, kernel_size, stride, padding),\n",
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" nn.BatchNorm2d(128),\n",
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" nn.LeakyReLU(alpha),\n",
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" nn.ConvTranspose2d(128, 3, kernel_size, stride, padding),\n",
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" nn.Tanh()\n",
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" )\n",
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" \n",
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" def forward(self, z):\n",
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" x = self.input(z)\n",
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" return self.net(x.view(-1, 1024, 4, 4))\n",
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" \n",
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"gen = Generator()\n",
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"gen(torch.ones((1, 200))).shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[ 0.3871]])"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"class Discriminator(nn.Module):\n",
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" def __init__(self, alpha=0.2):\n",
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" super(Discriminator, self).__init__()\n",
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"\n",
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" kernel_size = 4\n",
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" padding = 1\n",
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" stride = 2\n",
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" \n",
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" self.net = nn.Sequential(\n",
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" nn.Conv2d(3, 128, kernel_size, stride, padding),\n",
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" nn.LeakyReLU(alpha),\n",
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" nn.Conv2d(128, 256, kernel_size, stride, padding),\n",
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" nn.BatchNorm2d(256),\n",
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" nn.LeakyReLU(alpha),\n",
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" nn.Conv2d(256, 512, kernel_size, stride, padding),\n",
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" nn.BatchNorm2d(512),\n",
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" nn.LeakyReLU(alpha),\n",
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" nn.Conv2d(512, 512, kernel_size, stride, padding),\n",
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" nn.BatchNorm2d(512),\n",
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" nn.LeakyReLU(alpha),\n",
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" nn.Conv2d(512, 512, kernel_size, stride, padding),\n",
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" nn.BatchNorm2d(512),\n",
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" nn.LeakyReLU(alpha),\n",
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" nn.Conv2d(512, 1024, kernel_size, stride, padding),\n",
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" nn.BatchNorm2d(1024),\n",
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" nn.LeakyReLU(alpha),\n",
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" )\n",
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" self.output = nn.Linear(4 * 4 * 1024, 1)\n",
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" \n",
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" \n",
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" def forward(self, x):\n",
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" x = self.net(x)\n",
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" x = torch.reshape(x, (-1, 4 * 4 * 1024))\n",
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" x = self.output(x)\n",
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" \n",
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" if self.training:\n",
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" return x\n",
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" \n",
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" return F.sigmoid(x)\n",
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" \n",
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"dis = Discriminator()\n",
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"dis(torch.ones(1, 3, 256, 256))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"class ImageFolderEX(datasets.ImageFolder):\n",
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" def __getitem__(self, index):\n",
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" def get_img(index):\n",
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" path, label = self.imgs[index]\n",
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" try:\n",
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" img = self.loader(os.path.join(self.root, path))\n",
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" except:\n",
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" img = get_img(index + 1)\n",
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" return img\n",
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" img = get_img(index)\n",
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" return self.transform(img) * 2 - 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"ename": "RuntimeError",
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"evalue": "Found 0 files in subfolders of: .\nSupported extensions are: .jpg,.jpeg,.png,.ppm,.bmp,.pgm,.tif",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-8-dc3c12e3e15a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m ])\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataLoader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImageFolderEX\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop_last\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_workers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/opt/miniconda3/lib/python3.6/site-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, root, transform, target_transform, loader)\u001b[0m\n\u001b[1;32m 176\u001b[0m super(ImageFolder, self).__init__(root, loader, IMG_EXTENSIONS,\n\u001b[1;32m 177\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m target_transform=target_transform)\n\u001b[0m\u001b[1;32m 179\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimgs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/opt/miniconda3/lib/python3.6/site-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, root, loader, extensions, transform, target_transform)\u001b[0m\n\u001b[1;32m 77\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 78\u001b[0m raise(RuntimeError(\"Found 0 files in subfolders of: \" + root + \"\\n\"\n\u001b[0;32m---> 79\u001b[0;31m \"Supported extensions are: \" + \",\".join(extensions)))\n\u001b[0m\u001b[1;32m 80\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mRuntimeError\u001b[0m: Found 0 files in subfolders of: .\nSupported extensions are: .jpg,.jpeg,.png,.ppm,.bmp,.pgm,.tif"
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]
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}
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],
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"source": [
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"trans = transforms.Compose([\n",
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" transforms.Resize((256, 256), interpolation=2),\n",
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" transforms.ToTensor(),\n",
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"])\n",
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"\n",
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"data = torch.utils.data.DataLoader(ImageFolderEX('.', trans), batch_size=1, shuffle=True, drop_last=True, num_workers=0)\n",
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"x = next(iter(data))\n",
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"print(x.min(), x.max())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"def train_dis(dis, gen, x):\n",
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" z = torch.tensor(np.random.normal(0, 1, (batch_size, 200)), dtype=torch.float32)\n",
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"\n",
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" if next(gen.parameters()).is_cuda:\n",
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" x = x.cuda()\n",
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" z = z.cuda()\n",
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"\n",
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" # train discriminator\n",
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" dis.zero_grad()\n",
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" y_real_pred = dis(x)\n",
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" \n",
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" idx = np.random.uniform(0, 1, y_real_pred.shape)\n",
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" idx = np.argwhere(idx < 0.03)\n",
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" \n",
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" # labels\n",
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" ones = np.ones(y_real_pred.shape) + np.random.uniform(-0.1, 0.1)\n",
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" ones[idx] = 0\n",
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" \n",
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" zeros = np.zeros(y_real_pred.shape) + np.random.uniform(0, 0.2)\n",
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" zeros[idx] = 1\n",
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" ones = torch.from_numpy(ones).float()\n",
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" zeros = torch.from_numpy(zeros).float()\n",
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"\n",
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" if next(gen.parameters()).is_cuda:\n",
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" ones = ones.cuda()\n",
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" zeros = zeros.cuda()\n",
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"\n",
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" loss_real = F.binary_cross_entropy_with_logits(y_real_pred, ones)\n",
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"\n",
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" generated = gen(z)\n",
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" y_fake_pred = dis(generated)\n",
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"\n",
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" loss_fake = F.binary_cross_entropy_with_logits(y_fake_pred, zeros)\n",
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" loss = loss_fake + loss_real\n",
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" loss.backward()\n",
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" optimizer_dis.step()\n",
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" return loss\n",
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"\n",
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" \n",
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"def train_gen(gen, batch_size):\n",
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" z = torch.tensor(np.random.normal(0, 1, (batch_size, 200)), dtype=torch.float32)\n",
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" \n",
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" if next(gen.parameters()).is_cuda:\n",
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" z = z.cuda()\n",
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" \n",
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" gen.zero_grad()\n",
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" generated = gen(z)\n",
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" y_fake = dis(generated)\n",
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"\n",
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" ones = torch.ones_like(y_fake)\n",
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" if next(gen.parameters()).is_cuda:\n",
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" ones = ones.cuda()\n",
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"\n",
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" loss = F.binary_cross_entropy_with_logits(y_fake, ones)\n",
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" loss.backward()\n",
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" optimizer_gen.step()\n",
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" return loss, generated"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"def show_img(x, real=True, title=None, figsize=(6, 6)):\n",
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" plt.figure(figsize=figsize)\n",
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" if title:\n",
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" plt.title(title)\n",
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" if isinstance(x, torch.Tensor):\n",
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" if next(gen.parameters()).is_cuda:\n",
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" x = x.cpu()\n",
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" x = x.data.numpy()\n",
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" \n",
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" x = np.transpose(np.squeeze(x), [1, 2, 0]) \n",
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" if not real:\n",
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" x = np.array((x + 1) / 2 * 255, int)\n",
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" plt.imshow(x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"logger = logging.getLogger()\n",
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"logger.addHandler(logging.FileHandler(\"256.log\"))\n",
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"logger.setLevel(logging.INFO)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"def save_checkpoint(state, filename='checkpoint.pth.tar'):\n",
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" torch.save(state, filename)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"dis = Discriminator().cuda()\n",
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"gen = Generator().cuda()\n",
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"gen = Generator()\n",
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"\n",
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"lr = 0.0002\n",
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"beta_1 = 0.5\n",
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"beta_2 = 0.999\n",
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"optimizer_gen = torch.optim.Adam(gen.parameters(), lr, betas=(beta_1, beta_2))\n",
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"optimizer_dis = torch.optim.Adam(dis.parameters(), lr, betas=(beta_1, beta_2))\n",
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"\n",
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"epochs = 20\n",
|
||||
"batch_size = 64\n",
|
||||
"data = torch.utils.data.DataLoader(ImageFolderEX('.', trans), batch_size=batch_size, shuffle=True, drop_last=True, num_workers=2)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"from_checkpoint = True\n",
|
||||
"if from_checkpoint:\n",
|
||||
" state = torch.load('gen5.pth')\n",
|
||||
" gen.load_state_dict(state['state_dict'])\n",
|
||||
" state = torch.load('backup/dis1.pth')\n",
|
||||
" dis.load_state_dict(state['state_dict'])\n",
|
||||
" total_step = state['total_step']\n",
|
||||
"else:\n",
|
||||
" total_step = 0\n",
|
||||
" "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def save():\n",
|
||||
" save_checkpoint({\n",
|
||||
" 'epoch': epoch,\n",
|
||||
" 'state_dict': dis.state_dict(),\n",
|
||||
" 'optimizer' : optimizer_dis.state_dict(),\n",
|
||||
" 'total_step': global_step,\n",
|
||||
" }, f'backup/dis{epoch}.pth')\n",
|
||||
"\n",
|
||||
" save_checkpoint({\n",
|
||||
" 'epoch': epoch,\n",
|
||||
" 'state_dict': gen.state_dict(),\n",
|
||||
" 'optimizer' : optimizer_gen.state_dict(),\n",
|
||||
" 'total_step': global_step,\n",
|
||||
" }, f'backup/gen{epoch}.pth')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/ritchie46/miniconda3/lib/python3.6/site-packages/PIL/Image.py:2514: DecompressionBombWarning: Image size (95799284 pixels) exceeds limit of 89478485 pixels, could be decompression bomb DOS attack.\n",
|
||||
" DecompressionBombWarning)\n",
|
||||
"/home/ritchie46/miniconda3/lib/python3.6/site-packages/PIL/Image.py:2514: DecompressionBombWarning: Image size (145486286 pixels) exceeds limit of 89478485 pixels, could be decompression bomb DOS attack.\n",
|
||||
" DecompressionBombWarning)\n",
|
||||
"/home/ritchie46/miniconda3/lib/python3.6/site-packages/PIL/Image.py:2514: DecompressionBombWarning: Image size (94435468 pixels) exceeds limit of 89478485 pixels, could be decompression bomb DOS attack.\n",
|
||||
" DecompressionBombWarning)\n",
|
||||
"/home/ritchie46/miniconda3/lib/python3.6/site-packages/PIL/Image.py:2514: DecompressionBombWarning: Image size (107327830 pixels) exceeds limit of 89478485 pixels, could be decompression bomb DOS attack.\n",
|
||||
" DecompressionBombWarning)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"n = len(data)\n",
|
||||
"for epoch in range(0, epochs):\n",
|
||||
" c = 0\n",
|
||||
" n = len(data) \n",
|
||||
" writer = SummaryWriter(log_dir=f'tb/epoch_{epoch}')\n",
|
||||
"\n",
|
||||
" for x in iter(data): \n",
|
||||
" c += 1\n",
|
||||
"\n",
|
||||
" loss_dis = train_dis(dis, gen, x)\n",
|
||||
" loss_gen, generated = train_gen(gen, batch_size)\n",
|
||||
" \n",
|
||||
" global_step = epoch * n + c\n",
|
||||
" if c % 1 == 0:\n",
|
||||
" writer.add_scalar('loss_dis', loss_dis.item(), total_step + global_step)\n",
|
||||
" writer.add_scalar('loss_gen', loss_gen.item(), total_step + global_step)\n",
|
||||
" if c % 4 == 0:\n",
|
||||
" msg = f'loss: {loss_dis.item()}, \\t {loss_gen.item()} \\t epoch: {epoch}, \\t {c}/{n}'\n",
|
||||
" logging.info(msg)\n",
|
||||
" \n",
|
||||
" if c % 2 == 0:\n",
|
||||
" writer.add_image('img', torch.sigmoid(generated[:5]), total_step + global_step)\n",
|
||||
" \n",
|
||||
" if c > (n // 2):\n",
|
||||
" save()\n",
|
||||
" \n",
|
||||
" print('finished epoch', epoch)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"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": "3.6.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user