自适应patchgan代码
时间: 2023-11-12 19:13:09 浏览: 38
以下是一个自适应PatchGAN的代码示例,使用PyTorch框架:
```python
import torch
import torch.nn as nn
class AdaptivePatchDiscriminator(nn.Module):
def __init__(self, input_shape, num_filters=64, num_classes=1):
super(AdaptivePatchDiscriminator, self).__init__()
self.input_shape = input_shape
self.num_classes = num_classes
channels, height, width = self.input_shape
# Calculate output of image patch discriminator (PatchGAN)
assert height % 16 == 0 and width % 16 == 0, "Height and width must be a multiple of 16"
patch_h, patch_w = int(height / 16), int(width / 16)
self.output_shape = (num_classes, patch_h, patch_w)
# Build discriminator architecture
self.conv_blocks = nn.Sequential(
nn.Conv2d(channels, num_filters, kernel_size=4, stride=2, padding=1),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(num_filters, num_filters * 2, kernel_size=4, stride=2, padding=1),
nn.BatchNorm2d(num_filters * 2),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(num_filters * 2, num_filters * 4, kernel_size=4, stride=2, padding=1),
nn.BatchNorm2d(num_filters * 4),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(num_filters * 4, num_filters * 8, kernel_size=4, stride=2, padding=1),
nn.BatchNorm2d(num_filters * 8),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv2d(num_filters * 8, self.num_classes, kernel_size=4, stride=1, padding=0),
nn.Sigmoid()
)
def forward(self, x):
return self.conv_blocks(x)
```
这个自适应PatchGAN的实现与标准PatchGAN的唯一区别在于输出形状,它是根据输入图像的大小自动计算的。在这个示例中,我们将输入图像作为一个三元组 (channels, height, width) 传递给构造函数,并在 `__init__` 方法中计算了输出形状。在前向传递中,我们只需简单地通过卷积块传递输入,然后返回输出。注意,输出形状是 (num_classes, patch_h, patch_w),其中 num_classes 是 1 或更多类别之一,而 patch_h 和 patch_w 是根据输入图像大小计算出来的图像块数量。