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5106c86 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | import torch.nn as nn
from torchvision import models
from torchvision.models import VGG19_Weights
class VGG(nn.Module):
def __init__(self, content_layers, style_layers):
super(VGG, self).__init__()
self.model = models.vgg19(weights=VGG19_Weights.IMAGENET1K_V1).features
self.content_layers = content_layers.keys()
self.style_layers = style_layers.keys()
# 冻结模型的所有参数
for param in self.model.parameters():
param.requires_grad = False
def forward(self, x):
"""
对vgg19网络的包装，前向传播时保留了内容层和风格层的中间输出
:param x:
:return: 内容层和风格层的特征图
"""
content_features = {}
style_features = {}
for name, layer in self.model._modules.items():
x = layer(x)
if name in self.content_layers:
content_features[name] = x
if name in self.style_layers:
style_features[name] = x
return content_features, style_features
class ResBlock(nn.Module):
def __init__(self, c):
super(ResBlock, self).__init__()
self.layer = nn.Sequential(
nn.Conv2d(c, c, 3, 1, 1, bias=False),
nn.InstanceNorm2d(c),
nn.ReLU(True),
nn.Conv2d(c, c, 3, 1, 1, bias=False),
nn.InstanceNorm2d(c)
)
def forward(self, x):
return x + self.layer(x)
class TransNet(nn.Module):
def __init__(self, input_size):
"""
实时内容生成网络
"""
super(TransNet, self).__init__()
self.input_size = input_size
self.layer = nn.Sequential(
###################下采样层################
nn.Conv2d(in_channels=3, out_channels=32, kernel_size=9, stride=1, padding=4, bias=False),
nn.InstanceNorm2d(32),
nn.ReLU(True),
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=2, padding=1, bias=False),
nn.InstanceNorm2d(64),
nn.ReLU(True),
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=2, padding=1, bias=False),
nn.InstanceNorm2d(128),
nn.ReLU(True),
##################残差层##################
ResBlock(128),
ResBlock(128),
ResBlock(128),
ResBlock(128),
ResBlock(128),
################上采样层##################
nn.Upsample(scale_factor=2, mode='nearest'),
nn.Conv2d(in_channels=128, out_channels=64, kernel_size=3, stride=1, padding=1, bias=False),
nn.InstanceNorm2d(64),
nn.ReLU(True),
nn.Upsample(scale_factor=2, mode='nearest'),
nn.Conv2d(in_channels=64, out_channels=32, kernel_size=3, stride=1, padding=1, bias=False),
nn.InstanceNorm2d(32),
nn.ReLU(True),
###############输出层#####################
nn.Conv2d(in_channels=32, out_channels=3, kernel_size=9, stride=1, padding=4, bias=False),
nn.Sigmoid(),
nn.AdaptiveAvgPool2d(self.input_size)
)
def forward(self, x):
return self.layer(x)
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