b8f67418d4
The receptive field of the original was *really* low. This new one has a receptive field of 36x36px patches. It also has some gradient issues that need to be worked out
87 lines
3.6 KiB
Python
87 lines
3.6 KiB
Python
import functools
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import torch.nn as nn
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import torch.nn.functional as F
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import models.archs.arch_util as arch_util
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import torch
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class HighToLowResNet(nn.Module):
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''' ResNet that applies a noise channel to the input, then downsamples it four times using strides. Finally, the
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input is upsampled to the desired downscale. Currently downscale=1,2,4 is supported.
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'''
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def __init__(self, in_nc=3, out_nc=3, nf=64, nb=16, downscale=4):
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super(HighToLowResNet, self).__init__()
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assert downscale in [1, 2, 4], "Requested downscale not supported; %i" % (downscale, )
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self.downscale = downscale
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# We will always apply a noise channel to the inputs, account for that here.
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in_nc += 1
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self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
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# All sub-modules must be explicit members. Make it so. Then add them to a list.
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self.trunk1 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf), 4)
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self.trunk2 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf*4), 8)
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self.trunk3 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf*8), 16)
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self.trunk4 = arch_util.make_layer(functools.partial(arch_util.ResidualBlock_noBN, nf=nf*16), 32)
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self.trunks = [self.trunk1, self.trunk2, self.trunk3, self.trunk4]
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self.trunkshapes = [4, 8, 16, 32]
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self.r1 = nn.Conv2d(nf, nf*4, 3, stride=2, padding=1, bias=True)
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self.r2 = nn.Conv2d(nf*4, nf*8, 3, stride=2, padding=1, bias=True)
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self.r3 = nn.Conv2d(nf*8, nf*16, 3, stride=2, padding=1, bias=True)
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self.reducers = [self.r1, self.r2, self.r3]
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self.pixel_shuffle = nn.PixelShuffle(2)
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self.a1 = nn.Conv2d(nf*4, nf*8, 3, stride=1, padding=1, bias=True)
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self.a2 = nn.Conv2d(nf*2, nf*4, 3, stride=1, padding=1, bias=True)
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self.a3 = nn.Conv2d(nf, nf, 3, stride=1, padding=1, bias=True)
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self.assemblers = [self.a1, self.a2, self.a3]
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if self.downscale == 1:
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nf_last = nf
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elif self.downscale == 2:
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nf_last = nf * 4
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elif self.downscale == 4:
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nf_last = nf * 8
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self.conv_last = nn.Conv2d(nf_last, out_nc, 3, stride=1, padding=1, bias=True)
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# activation function
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self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
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# initialization
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arch_util.initialize_weights([self.conv_first, self.conv_last] + self.reducers + self.assemblers,
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.1)
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def forward(self, x):
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# Noise has the same shape as the input with only one channel.
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rand_feature = torch.randn((x.shape[0], 1) + x.shape[2:], device=x.device, dtype=x.dtype)
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out = torch.cat([x, rand_feature], dim=1)
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out = self.lrelu(self.conv_first(out))
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skips = []
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for i in range(4):
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skips.append(out)
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out = self.trunks[i](out)
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if i < 3:
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out = self.lrelu(self.reducers[i](out))
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target_width = x.shape[-1] / self.downscale
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i = 0
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while out.shape[-1] != target_width:
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out = self.pixel_shuffle(out)
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out = self.lrelu(self.assemblers[i](out))
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out = out + skips[-i-2]
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i += 1
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# TODO: Figure out where this magic number '12' comes from and fix it.
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out = 12 * self.conv_last(out)
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if self.downscale == 1:
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base = x
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else:
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base = F.interpolate(x, scale_factor=1/self.downscale, mode='bilinear', align_corners=False)
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return out + base
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