86 lines
4.9 KiB
Python
86 lines
4.9 KiB
Python
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import torch
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import torch.nn as nn
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import torchvision
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import models.archs.arch_util as arch_util
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import functools
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import torch.nn.functional as F
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import torch.nn.utils.spectral_norm as SpectralNorm
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# Class that halfs the image size (x4 complexity reduction) and doubles the filter size. Substantial resnet
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# processing is also performed.
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class ResnetDownsampleLayer(nn.Module):
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def __init__(self, starting_channels: int, number_filters: int, filter_multiplier: int, residual_blocks_input: int, residual_blocks_skip_image: int, total_residual_blocks: int):
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super(ResnetDownsampleLayer, self).__init__()
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self.skip_image_reducer = SpectralNorm(nn.Conv2d(starting_channels, number_filters, 3, stride=1, padding=1, bias=True))
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self.skip_image_res_trunk = arch_util.make_layer(functools.partial(arch_util.ResidualBlockSpectralNorm, nf=number_filters, total_residual_blocks=total_residual_blocks), residual_blocks_skip_image)
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self.input_reducer = SpectralNorm(nn.Conv2d(number_filters, number_filters*filter_multiplier, 3, stride=2, padding=1, bias=True))
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self.res_trunk = arch_util.make_layer(functools.partial(arch_util.ResidualBlockSpectralNorm, nf=number_filters*filter_multiplier, total_residual_blocks=total_residual_blocks), residual_blocks_input)
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self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
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arch_util.initialize_weights([self.input_reducer, self.skip_image_reducer], 1)
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def forward(self, x, skip_image):
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# Process the skip image first.
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skip = self.lrelu(self.skip_image_reducer(skip_image))
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skip = self.skip_image_res_trunk(skip)
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# Concat the processed skip image onto the input and perform processing.
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out = (x + skip) / 2
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out = self.lrelu(self.input_reducer(out))
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out = self.res_trunk(out)
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return out
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class DiscriminatorResnet(nn.Module):
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# Discriminator that downsamples 5 times with resnet blocks at each layer. On each downsample, the filter size is
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# increased by a factor of 2. Feeds the output of the convs into a dense for prediction at the logits. Scales the
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# final dense based on the input image size. Intended for use with input images which are multiples of 32.
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#
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# This discriminator also includes provisions to pass an image at various downsample steps in directly. When this
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# is done with a generator, it will allow much shorter gradient paths between the generator and discriminator. When
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# no downsampled images are passed into the forward() pass, they will be automatically generated from the source
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# image using interpolation.
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#
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# Uses spectral normalization rather than batch normalization.
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def __init__(self, in_nc: int, nf: int, input_img_size: int, trunk_resblocks: int, skip_resblocks: int):
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super(DiscriminatorResnet, self).__init__()
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self.dimensionalize = nn.Conv2d(in_nc, nf, kernel_size=3, stride=1, padding=1, bias=True)
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# Trunk resblocks are the important things to get right, so use those. 5=number of downsample layers.
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total_resblocks = trunk_resblocks * 5
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self.downsample1 = ResnetDownsampleLayer(in_nc, nf, 2, trunk_resblocks, skip_resblocks, total_resblocks)
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self.downsample2 = ResnetDownsampleLayer(in_nc, nf*2, 2, trunk_resblocks, skip_resblocks, total_resblocks)
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self.downsample3 = ResnetDownsampleLayer(in_nc, nf*4, 2, trunk_resblocks, skip_resblocks, total_resblocks)
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# At the bottom layers, we cap the filter multiplier. We want this particular network to focus as much on the
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# macro-details at higher image dimensionality as it does to the feature details.
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self.downsample4 = ResnetDownsampleLayer(in_nc, nf*8, 1, trunk_resblocks, skip_resblocks, total_resblocks)
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self.downsample5 = ResnetDownsampleLayer(in_nc, nf*8, 1, trunk_resblocks, skip_resblocks, total_resblocks)
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self.downsamplers = [self.downsample1, self.downsample2, self.downsample3, self.downsample4, self.downsample5]
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downsampled_image_size = input_img_size / 32
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self.linear1 = nn.Linear(int(nf * 8 * downsampled_image_size * downsampled_image_size), 100)
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self.linear2 = nn.Linear(100, 1)
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# activation function
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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arch_util.initialize_weights([self.dimensionalize, self.linear1, self.linear2], 1)
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def forward(self, x, skip_images=None):
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if skip_images is None:
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# Sythesize them from x.
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skip_images = []
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for i in range(len(self.downsamplers)):
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m = 2 ** i
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skip_images.append(F.interpolate(x, scale_factor=1 / m, mode='bilinear', align_corners=False))
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fea = self.dimensionalize(x)
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for skip, d in zip(skip_images, self.downsamplers):
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fea = d(fea, skip)
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fea = fea.view(fea.size(0), -1)
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fea = self.lrelu(self.linear1(fea))
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out = self.linear2(fea)
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return out
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