Discriminator part 1
New discriminator. Includes spectral norming.
This commit is contained in:
James Betker
parent
2c145c39b6
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5b8a77f02c
85
codes/models/archs/DiscriminatorResnet_arch.py
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85
codes/models/archs/DiscriminatorResnet_arch.py
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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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@ -2,7 +2,16 @@ import torch
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import torch.nn as nn
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import torch.nn.init as init
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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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from math import sqrt
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def scale_conv_weights_fixup(conv, residual_block_count, m=2):
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k = conv.kernel_size[0]
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n = conv.out_channels
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scaling_factor = residual_block_count ** (-1.0 / (2 * m - 2))
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sigma = sqrt(2 / (k * k * n)) * scaling_factor
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conv.weight.data = conv.weight.data * sigma
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return conv
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def initialize_weights(net_l, scale=1):
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if not isinstance(net_l, list):
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@ -30,6 +39,89 @@ def make_layer(block, n_layers):
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layers.append(block())
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return nn.Sequential(*layers)
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def conv3x3(in_planes, out_planes, stride=1):
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"""3x3 convolution with padding"""
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
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padding=1, bias=False)
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def conv1x1(in_planes, out_planes, stride=1):
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"""1x1 convolution"""
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return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
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class FixupBasicBlock(nn.Module):
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expansion = 1
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super(FixupBasicBlock, self).__init__()
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# Both self.conv1 and self.downsample layers downsample the input when stride != 1
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self.bias1a = nn.Parameter(torch.zeros(1))
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self.conv1 = conv3x3(inplanes, planes, stride)
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self.bias1b = nn.Parameter(torch.zeros(1))
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self.relu = nn.ReLU(inplace=True)
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self.bias2a = nn.Parameter(torch.zeros(1))
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self.conv2 = conv3x3(planes, planes)
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self.scale = nn.Parameter(torch.ones(1))
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self.bias2b = nn.Parameter(torch.zeros(1))
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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identity = x
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out = self.conv1(x + self.bias1a)
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out = self.relu(out + self.bias1b)
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out = self.conv2(out + self.bias2a)
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out = out * self.scale + self.bias2b
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if self.downsample is not None:
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identity = self.downsample(x + self.bias1a)
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out += identity
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out = self.relu(out)
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return out
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class FixupBottleneck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, stride=1, downsample=None):
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super(FixupBottleneck, self).__init__()
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# Both self.conv2 and self.downsample layers downsample the input when stride != 1
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self.bias1a = nn.Parameter(torch.zeros(1))
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self.conv1 = conv1x1(inplanes, planes)
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self.bias1b = nn.Parameter(torch.zeros(1))
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self.bias2a = nn.Parameter(torch.zeros(1))
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self.conv2 = conv3x3(planes, planes, stride)
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self.bias2b = nn.Parameter(torch.zeros(1))
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self.bias3a = nn.Parameter(torch.zeros(1))
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self.conv3 = conv1x1(planes, planes * self.expansion)
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self.scale = nn.Parameter(torch.ones(1))
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self.bias3b = nn.Parameter(torch.zeros(1))
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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identity = x
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out = self.conv1(x + self.bias1a)
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out = self.relu(out + self.bias1b)
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out = self.conv2(out + self.bias2a)
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out = self.relu(out + self.bias2b)
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out = self.conv3(out + self.bias3a)
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out = out * self.scale + self.bias3b
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if self.downsample is not None:
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identity = self.downsample(x + self.bias1a)
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out += identity
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out = self.relu(out)
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return out
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class ResidualBlock(nn.Module):
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'''Residual block with BN
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---Conv-BN-ReLU-Conv-+-
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@ -38,6 +130,7 @@ class ResidualBlock(nn.Module):
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def __init__(self, nf=64):
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super(ResidualBlock, self).__init__()
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self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
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self.conv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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self.BN1 = nn.BatchNorm2d(nf)
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self.conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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@ -48,10 +141,33 @@ class ResidualBlock(nn.Module):
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def forward(self, x):
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identity = x
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out = F.relu(self.BN1(self.conv1(x)), inplace=True)
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out = self.lrelu(self.BN1(self.conv1(x)))
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out = self.BN2(self.conv2(out))
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return identity + out
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class ResidualBlockSpectralNorm(nn.Module):
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'''Residual block with Spectral Normalization.
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---SpecConv-ReLU-SpecConv-+-
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'''
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def __init__(self, nf, total_residual_blocks):
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super(ResidualBlockSpectralNorm, self).__init__()
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self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
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self.conv1 = SpectralNorm(nn.Conv2d(nf, nf, 3, 1, 1, bias=True))
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self.conv2 = SpectralNorm(nn.Conv2d(nf, nf, 3, 1, 1, bias=True))
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# Initialize first.
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initialize_weights([self.conv1, self.conv2], 1)
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# Then perform fixup scaling
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self.conv1 = scale_conv_weights_fixup(self.conv1, total_residual_blocks)
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self.conv2 = scale_conv_weights_fixup(self.conv2, total_residual_blocks)
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def forward(self, x):
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identity = x
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out = self.lrelu(self.conv1(x))
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out = self.conv2(out)
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return identity + out
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class ResidualBlock_noBN(nn.Module):
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'''Residual block w/o BN
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@ -61,6 +177,7 @@ class ResidualBlock_noBN(nn.Module):
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def __init__(self, nf=64):
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super(ResidualBlock_noBN, self).__init__()
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self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
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self.conv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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self.conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
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@ -69,7 +186,7 @@ class ResidualBlock_noBN(nn.Module):
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def forward(self, x):
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identity = x
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out = F.relu(self.conv1(x), inplace=True)
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out = self.lrelu(self.conv1(x))
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out = self.conv2(out)
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return identity + out
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@ -1,6 +1,7 @@
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import torch
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import models.archs.SRResNet_arch as SRResNet_arch
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import models.archs.discriminator_vgg_arch as SRGAN_arch
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import models.archs.DiscriminatorResnet_arch as DiscriminatorResnet_arch
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import models.archs.RRDBNet_arch as RRDBNet_arch
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import models.archs.EDVR_arch as EDVR_arch
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import models.archs.HighToLowResNet as HighToLowResNet
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@ -52,6 +53,9 @@ def define_D(opt):
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if which_model == 'discriminator_vgg_128':
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netD = SRGAN_arch.Discriminator_VGG_128(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_factor=img_sz / 128)
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elif which_model == 'discriminator_resnet':
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netD = DiscriminatorResnet_arch.DiscriminatorResnet(in_nc=opt_net['in_nc'], nf=opt_net['nf'], input_img_size=img_sz,
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trunk_resblocks=opt_net['trunk_resblocks'], skip_resblocks=opt_net['skip_resblocks'])
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else:
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raise NotImplementedError('Discriminator model [{:s}] not recognized'.format(which_model))
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return netD
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@ -16,7 +16,7 @@ datasets:
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dataroot_LQ: E:\\4k6k\\datasets\\ultra_lowq\\for_training
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mismatched_Data_OK: true
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use_shuffle: true
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n_workers: 4 # per GPU
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n_workers: 8 # per GPU
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batch_size: 32
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target_size: 64
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use_flip: false
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@ -35,19 +35,21 @@ network_G:
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in_nc: 3
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out_nc: 3
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nf: 32
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ra_blocks: 5
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assembler_blocks: 3
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ra_blocks: 3
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assembler_blocks: 2
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network_D:
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which_model_D: discriminator_vgg_128
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which_model_D: discriminator_resnet
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in_nc: 3
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nf: 64
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nf: 32
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trunk_resblocks: 3
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skip_resblocks: 2
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#### path
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path:
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pretrain_model_G: ../experiments/corrupt_flatnet_G.pth
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pretrain_model_D: ../experiments/corrupt_flatnet_D.pth
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resume_state: ../experiments/corruptGAN_4k_lqprn_closeup_flat_net/training_state/3000.state
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pretrain_model_G: ~
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pretrain_model_D: ~
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resume_state: ~
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strict_load: true
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#### training settings: learning rate scheme, loss
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@ -56,7 +58,7 @@ train:
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weight_decay_G: 0
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beta1_G: 0.9
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beta2_G: 0.99
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lr_D: !!float 4e-5
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lr_D: !!float 1e-5
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weight_decay_D: 0
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beta1_D: 0.9
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beta2_D: 0.99
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@ -71,11 +73,11 @@ train:
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pixel_weight: !!float 1e-2
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feature_criterion: l1
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feature_weight: 0
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gan_type: ragan # gan | ragan
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gan_type: gan # gan | ragan
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gan_weight: !!float 1e-1
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D_update_ratio: 1
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D_init_iters: 0
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D_init_iters: 1500
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manual_seed: 10
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val_freq: !!float 5e2
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