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- # 2021.05.07-Changed for IPT
- # Huawei Technologies Co., Ltd. <foss@huawei.com>
-
- import math
-
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
-
- def default_conv(in_channels, out_channels, kernel_size, bias=True):
- return nn.Conv2d(
- in_channels, out_channels, kernel_size,
- padding=(kernel_size//2), 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=False,
- bn=True, act=nn.ReLU(True)):
-
- 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.ReLU(True), 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: # Is scale = 2^n?
- 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)
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