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