| import math
|
|
|
| import torch
|
| import torch.nn as nn
|
| import torch.nn.functional as F
|
|
|
| def default_conv(in_channels, out_channels, kernel_size,stride=1, bias=True):
|
| return nn.Conv2d(
|
| in_channels, out_channels, kernel_size,
|
| padding=(kernel_size//2),stride=stride, bias=bias)
|
|
|
| def spectral_conv(in_channels, out_channels, kernel_size,stride=1, bias=True):
|
| return nn.utils.spectral_norm(nn.Conv2d(
|
| in_channels, out_channels, kernel_size,
|
| padding=(kernel_size//2),stride=stride, bias=bias))
|
|
|
| class MeanShift(nn.Conv2d):
|
| def __init__(
|
| self, rgb_range,
|
| rgb_mean=(0.4488, 0.4371, 0.4040), rgb_std=(1.0, 1.0, 1.0), sign=-1):
|
|
|
| super(MeanShift, self).__init__(3, 3, kernel_size=1)
|
| std = torch.Tensor(rgb_std)
|
| self.weight.data = torch.eye(3).view(3, 3, 1, 1) / std.view(3, 1, 1, 1)
|
| self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean) / std
|
| for p in self.parameters():
|
| p.requires_grad = False
|
|
|
| class BasicBlock(nn.Sequential):
|
| def __init__(
|
| self, conv, in_channels, out_channels, kernel_size, stride=1, bias=True,
|
| bn=False, act=nn.PReLU()):
|
|
|
| m = [conv(in_channels, out_channels, kernel_size, bias=bias)]
|
| if bn:
|
| m.append(nn.BatchNorm2d(out_channels))
|
| if act is not None:
|
| m.append(act)
|
|
|
| super(BasicBlock, self).__init__(*m)
|
|
|
| class ResBlock(nn.Module):
|
| def __init__(
|
| self, conv, n_feats, kernel_size,
|
| bias=True, bn=False, act=nn.PReLU(), res_scale=1):
|
|
|
| super(ResBlock, self).__init__()
|
| m = []
|
| for i in range(2):
|
| m.append(conv(n_feats, n_feats, kernel_size, bias=bias))
|
| if bn:
|
| m.append(nn.BatchNorm2d(n_feats))
|
| if i == 0:
|
| m.append(act)
|
|
|
| self.body = nn.Sequential(*m)
|
| self.res_scale = res_scale
|
|
|
| def forward(self, x):
|
| res = self.body(x).mul(self.res_scale)
|
| res += x
|
|
|
| return res
|
|
|
| class Upsampler(nn.Sequential):
|
| def __init__(self, conv, scale, n_feats, bn=False, act=False, bias=True):
|
|
|
| m = []
|
| if (scale & (scale - 1)) == 0:
|
| for _ in range(int(math.log(scale, 2))):
|
| m.append(conv(n_feats, 4 * n_feats, 3, bias))
|
| m.append(nn.PixelShuffle(2))
|
| if bn:
|
| m.append(nn.BatchNorm2d(n_feats))
|
| if act == 'relu':
|
| m.append(nn.ReLU(True))
|
| elif act == 'prelu':
|
| m.append(nn.PReLU(n_feats))
|
|
|
| elif scale == 3:
|
| m.append(conv(n_feats, 9 * n_feats, 3, bias))
|
| m.append(nn.PixelShuffle(3))
|
| if bn:
|
| m.append(nn.BatchNorm2d(n_feats))
|
| if act == 'relu':
|
| m.append(nn.ReLU(True))
|
| elif act == 'prelu':
|
| m.append(nn.PReLU(n_feats))
|
| else:
|
| raise NotImplementedError
|
|
|
| super(Upsampler, self).__init__(*m)
|
|
|
|
|
