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2178 lines
469 KiB
2178 lines
469 KiB
3 years ago
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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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"source": [
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"# 9.11 样式迁移"
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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": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"cuda 1.1.0\n"
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]
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}
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],
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"source": [
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"%matplotlib inline\n",
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"import time\n",
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"import torch\n",
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"import torch.nn.functional as F\n",
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"import torchvision\n",
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"import numpy as np\n",
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"from PIL import Image\n",
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"\n",
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"import sys\n",
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"sys.path.append(\"..\") \n",
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"import d2lzh_pytorch as d2l\n",
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"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # 均已测试\n",
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"\n",
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"print(device, torch.__version__)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 9.11.2 读取内容图像和样式图像"
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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": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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" \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
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"<!-- Created with matplotlib (http://matplotlib.org/) -->\n",
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" <defs>\n",
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" <style type=\"text/css\">\n",
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]
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"metadata": {},
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}
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],
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"source": [
|
||
|
|
"d2l.set_figsize()\n",
|
||
|
|
"style_img = Image.open('../../data/autumn_oak.jpg')\n",
|
||
|
|
"d2l.plt.imshow(style_img);"
|
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|
]
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},
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{
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|
"cell_type": "markdown",
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"metadata": {},
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"source": [
|
||
|
|
"## 9.11.3. 预处理和后处理图像"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
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|
"cell_type": "code",
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|
"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"rgb_mean = np.array([0.485, 0.456, 0.406])\n",
|
||
|
|
"rgb_std = np.array([0.229, 0.224, 0.225])\n",
|
||
|
|
"\n",
|
||
|
|
"def preprocess(PIL_img, image_shape):\n",
|
||
|
|
" process = torchvision.transforms.Compose([\n",
|
||
|
|
" torchvision.transforms.Resize(image_shape),\n",
|
||
|
|
" torchvision.transforms.ToTensor(),\n",
|
||
|
|
" torchvision.transforms.Normalize(mean=rgb_mean, std=rgb_std)])\n",
|
||
|
|
"\n",
|
||
|
|
" return process(PIL_img).unsqueeze(dim = 0) # (batch_size, 3, H, W)\n",
|
||
|
|
"\n",
|
||
|
|
"def postprocess(img_tensor):\n",
|
||
|
|
" inv_normalize = torchvision.transforms.Normalize(\n",
|
||
|
|
" mean= -rgb_mean / rgb_std,\n",
|
||
|
|
" std= 1/rgb_std)\n",
|
||
|
|
" to_PIL_image = torchvision.transforms.ToPILImage()\n",
|
||
|
|
" return to_PIL_image(inv_normalize(img_tensor[0].cpu()).clamp(0, 1))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
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||
|
|
"metadata": {},
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||
|
|
"source": [
|
||
|
|
"## 9.11.4 抽取特征"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/data1/tangss/PyTorch_pretrainedmodels\r\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"!echo $TORCH_HOME # 将会把预训练好的模型下载到此处(没有输出的话默认是.cache/torch)\n",
|
||
|
|
"pretrained_net = torchvision.models.vgg19(pretrained=True, progress=True)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"VGG(\n",
|
||
|
|
" (features): Sequential(\n",
|
||
|
|
" (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (1): ReLU(inplace)\n",
|
||
|
|
" (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (3): ReLU(inplace)\n",
|
||
|
|
" (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
||
|
|
" (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (6): ReLU(inplace)\n",
|
||
|
|
" (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (8): ReLU(inplace)\n",
|
||
|
|
" (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
||
|
|
" (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (11): ReLU(inplace)\n",
|
||
|
|
" (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (13): ReLU(inplace)\n",
|
||
|
|
" (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (15): ReLU(inplace)\n",
|
||
|
|
" (16): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (17): ReLU(inplace)\n",
|
||
|
|
" (18): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
||
|
|
" (19): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (20): ReLU(inplace)\n",
|
||
|
|
" (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (22): ReLU(inplace)\n",
|
||
|
|
" (23): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (24): ReLU(inplace)\n",
|
||
|
|
" (25): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (26): ReLU(inplace)\n",
|
||
|
|
" (27): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
||
|
|
" (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (29): ReLU(inplace)\n",
|
||
|
|
" (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (31): ReLU(inplace)\n",
|
||
|
|
" (32): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (33): ReLU(inplace)\n",
|
||
|
|
" (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (35): ReLU(inplace)\n",
|
||
|
|
" (36): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
|
||
|
|
" )\n",
|
||
|
|
" (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))\n",
|
||
|
|
" (classifier): Sequential(\n",
|
||
|
|
" (0): Linear(in_features=25088, out_features=4096, bias=True)\n",
|
||
|
|
" (1): ReLU(inplace)\n",
|
||
|
|
" (2): Dropout(p=0.5)\n",
|
||
|
|
" (3): Linear(in_features=4096, out_features=4096, bias=True)\n",
|
||
|
|
" (4): ReLU(inplace)\n",
|
||
|
|
" (5): Dropout(p=0.5)\n",
|
||
|
|
" (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
|
||
|
|
" )\n",
|
||
|
|
")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"pretrained_net"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"style_layers, content_layers = [0, 5, 10, 19, 28], [25]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"net_list = []\n",
|
||
|
|
"for i in range(max(content_layers + style_layers) + 1):\n",
|
||
|
|
" net_list.append(pretrained_net.features[i])\n",
|
||
|
|
"net = torch.nn.Sequential(*net_list)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def extract_features(X, content_layers, style_layers):\n",
|
||
|
|
" contents = []\n",
|
||
|
|
" styles = []\n",
|
||
|
|
" for i in range(len(net)):\n",
|
||
|
|
" X = net[i](X)\n",
|
||
|
|
" if i in style_layers:\n",
|
||
|
|
" styles.append(X)\n",
|
||
|
|
" if i in content_layers:\n",
|
||
|
|
" contents.append(X)\n",
|
||
|
|
" return contents, styles"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def get_contents(image_shape, device):\n",
|
||
|
|
" content_X = preprocess(content_img, image_shape).to(device)\n",
|
||
|
|
" contents_Y, _ = extract_features(content_X, content_layers, style_layers)\n",
|
||
|
|
" return content_X, contents_Y\n",
|
||
|
|
"\n",
|
||
|
|
"def get_styles(image_shape, device):\n",
|
||
|
|
" style_X = preprocess(style_img, image_shape).to(device)\n",
|
||
|
|
" _, styles_Y = extract_features(style_X, content_layers, style_layers)\n",
|
||
|
|
" return style_X, styles_Y"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## 9.11.5 定义损失函数\n",
|
||
|
|
"### 9.11.5.1 内容损失"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def content_loss(Y_hat, Y):\n",
|
||
|
|
" return F.mse_loss(Y_hat, Y)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### 9.11.5.2 样式损失"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def gram(X):\n",
|
||
|
|
" num_channels, n = X.shape[1], X.shape[2] * X.shape[3]\n",
|
||
|
|
" X = X.view(num_channels, n)\n",
|
||
|
|
" return torch.matmul(X, X.t()) / (num_channels * n)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def style_loss(Y_hat, gram_Y):\n",
|
||
|
|
" return F.mse_loss(gram(Y_hat), gram_Y)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### 9.11.5.3 总变差损失"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def tv_loss(Y_hat):\n",
|
||
|
|
" return 0.5 * (F.l1_loss(Y_hat[:, :, 1:, :], Y_hat[:, :, :-1, :]) + \n",
|
||
|
|
" F.l1_loss(Y_hat[:, :, :, 1:], Y_hat[:, :, :, :-1]))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### 9.11.5.4 损失函数"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"content_weight, style_weight, tv_weight = 1, 1e3, 10\n",
|
||
|
|
"\n",
|
||
|
|
"def compute_loss(X, contents_Y_hat, styles_Y_hat, contents_Y, styles_Y_gram):\n",
|
||
|
|
" # 分别计算内容损失、样式损失和总变差损失\n",
|
||
|
|
" contents_l = [content_loss(Y_hat, Y) * content_weight for Y_hat, Y in zip(\n",
|
||
|
|
" contents_Y_hat, contents_Y)]\n",
|
||
|
|
" styles_l = [style_loss(Y_hat, Y) * style_weight for Y_hat, Y in zip(\n",
|
||
|
|
" styles_Y_hat, styles_Y_gram)]\n",
|
||
|
|
" tv_l = tv_loss(X) * tv_weight\n",
|
||
|
|
" # 对所有损失求和\n",
|
||
|
|
" l = sum(styles_l) + sum(contents_l) + tv_l\n",
|
||
|
|
" return contents_l, styles_l, tv_l, l"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## 9.11.6 创建和初始化合成图像"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"class GeneratedImage(torch.nn.Module):\n",
|
||
|
|
" def __init__(self, img_shape):\n",
|
||
|
|
" super(GeneratedImage, self).__init__()\n",
|
||
|
|
" self.weight = torch.nn.Parameter(torch.rand(*img_shape))\n",
|
||
|
|
"\n",
|
||
|
|
" def forward(self):\n",
|
||
|
|
" return self.weight"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def get_inits(X, device, lr, styles_Y):\n",
|
||
|
|
" gen_img = GeneratedImage(X.shape).to(device)\n",
|
||
|
|
" gen_img.weight.data = X.data\n",
|
||
|
|
" optimizer = torch.optim.Adam(gen_img.parameters(), lr=lr)\n",
|
||
|
|
" styles_Y_gram = [gram(Y) for Y in styles_Y]\n",
|
||
|
|
" return gen_img(), styles_Y_gram, optimizer"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## 9.11.7 训练"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def train(X, contents_Y, styles_Y, device, lr, max_epochs, lr_decay_epoch):\n",
|
||
|
|
" print(\"training on \", device)\n",
|
||
|
|
" X, styles_Y_gram, optimizer = get_inits(X, device, lr, styles_Y)\n",
|
||
|
|
" scheduler = torch.optim.lr_scheduler.StepLR(optimizer, lr_decay_epoch, gamma=0.1)\n",
|
||
|
|
" for i in range(max_epochs):\n",
|
||
|
|
" start = time.time()\n",
|
||
|
|
" \n",
|
||
|
|
" contents_Y_hat, styles_Y_hat = extract_features(\n",
|
||
|
|
" X, content_layers, style_layers)\n",
|
||
|
|
" contents_l, styles_l, tv_l, l = compute_loss(\n",
|
||
|
|
" X, contents_Y_hat, styles_Y_hat, contents_Y, styles_Y_gram)\n",
|
||
|
|
" \n",
|
||
|
|
" optimizer.zero_grad()\n",
|
||
|
|
" l.backward(retain_graph = True)\n",
|
||
|
|
" optimizer.step()\n",
|
||
|
|
" scheduler.step()\n",
|
||
|
|
" \n",
|
||
|
|
" if i % 50 == 0 and i != 0:\n",
|
||
|
|
" print('epoch %3d, content loss %.2f, style loss %.2f, '\n",
|
||
|
|
" 'TV loss %.2f, %.2f sec'\n",
|
||
|
|
" % (i, sum(contents_l).item(), sum(styles_l).item(), tv_l.item(),\n",
|
||
|
|
" time.time() - start))\n",
|
||
|
|
" return X.detach()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
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"text": [
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||
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"training on cuda\n",
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||
|
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"epoch 50, content loss 0.24, style loss 1.11, TV loss 1.33, 0.07 sec\n",
|
||
|
|
"epoch 100, content loss 0.24, style loss 0.81, TV loss 1.20, 0.07 sec\n",
|
||
|
|
"epoch 150, content loss 0.24, style loss 0.72, TV loss 1.12, 0.07 sec\n",
|
||
|
|
"epoch 200, content loss 0.24, style loss 0.68, TV loss 1.06, 0.07 sec\n",
|
||
|
|
"epoch 250, content loss 0.23, style loss 0.68, TV loss 1.05, 0.07 sec\n",
|
||
|
|
"epoch 300, content loss 0.23, style loss 0.67, TV loss 1.04, 0.07 sec\n",
|
||
|
|
"epoch 350, content loss 0.23, style loss 0.67, TV loss 1.04, 0.07 sec\n",
|
||
|
|
"epoch 400, content loss 0.23, style loss 0.67, TV loss 1.03, 0.07 sec\n",
|
||
|
|
"epoch 450, content loss 0.23, style loss 0.67, TV loss 1.03, 0.07 sec\n"
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|
|
]
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}
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],
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"source": [
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"image_shape = (150, 225)\n",
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"net = net.to(device)\n",
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"content_X, contents_Y = get_contents(image_shape, device)\n",
|
||
|
|
"style_X, styles_Y = get_styles(image_shape, device)\n",
|
||
|
|
"output = train(content_X, contents_Y, styles_Y, device, 0.01, 500, 200)"
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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": 20,
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"metadata": {},
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"outputs": [
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"training on cuda\n",
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"epoch 50, content loss 0.34, style loss 0.63, TV loss 0.79, 0.18 sec\n",
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|
|
"epoch 400, content loss 0.27, style loss 0.42, TV loss 0.69, 0.18 sec\n",
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|
|
"<matplotlib.figure.Figure at 0x7f0130079cc0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"d2l.set_figsize((7, 5))\n",
|
||
|
|
"d2l.plt.imshow(postprocess(big_output));"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": true
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python [default]",
|
||
|
|
"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": "3.6.2"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|