forked from mrq/DL-Art-School
Remover fixup code from arch_util
Going into it's own arch.
This commit is contained in:
James Betker
parent
5b8a77f02c
commit
a5188bb7ca
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@ -2,8 +2,10 @@
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<module type="PYTHON_MODULE" version="4">
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$">
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<sourceFolder url="file://$MODULE_DIR$/codes" isTestSource="false" />
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<excludeFolder url="file://$MODULE_DIR$/datasets" />
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<excludeFolder url="file://$MODULE_DIR$/experiments" />
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<excludeFolder url="file://$MODULE_DIR$/results" />
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<excludeFolder url="file://$MODULE_DIR$/tb_logger" />
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</content>
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<orderEntry type="jdk" jdkName="Python 3.7 (python37-torch)" jdkType="Python SDK" />
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@ -1,85 +1,195 @@
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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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import numpy as np
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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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__all__ = ['FixupResNet', 'fixup_resnet18', 'fixup_resnet34', 'fixup_resnet50', 'fixup_resnet101', 'fixup_resnet152']
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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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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.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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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, 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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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.lrelu(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.lrelu(out)
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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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class FixupBottleneck(nn.Module):
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expansion = 4
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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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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.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
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self.downsample = downsample
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self.stride = stride
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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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def forward(self, x):
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identity = x
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# activation function
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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out = self.conv1(x + self.bias1a)
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out = self.lrelu(out + self.bias1b)
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arch_util.initialize_weights([self.dimensionalize, self.linear1, self.linear2], 1)
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out = self.conv2(out + self.bias2a)
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out = self.lrelu(out + self.bias2b)
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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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out = self.conv3(out + self.bias3a)
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out = out * self.scale + self.bias3b
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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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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.lrelu(out)
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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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class FixupResNet(nn.Module):
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def __init__(self, block, layers, num_classes=1000):
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super(FixupResNet, self).__init__()
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self.num_layers = sum(layers)
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self.inplanes = 64
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
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bias=False)
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self.bias1 = nn.Parameter(torch.zeros(1))
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self.relu = nn.ReLU(inplace=True)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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self.bias2 = nn.Parameter(torch.zeros(1))
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self.fc = nn.Linear(512 * block.expansion, num_classes)
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for m in self.modules():
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if isinstance(m, FixupBasicBlock):
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nn.init.normal_(m.conv1.weight, mean=0, std=np.sqrt(2 / (m.conv1.weight.shape[0] * np.prod(m.conv1.weight.shape[2:]))) * self.num_layers ** (-0.5))
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nn.init.constant_(m.conv2.weight, 0)
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if m.downsample is not None:
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nn.init.normal_(m.downsample.weight, mean=0, std=np.sqrt(2 / (m.downsample.weight.shape[0] * np.prod(m.downsample.weight.shape[2:]))))
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elif isinstance(m, FixupBottleneck):
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nn.init.normal_(m.conv1.weight, mean=0, std=np.sqrt(2 / (m.conv1.weight.shape[0] * np.prod(m.conv1.weight.shape[2:]))) * self.num_layers ** (-0.25))
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nn.init.normal_(m.conv2.weight, mean=0, std=np.sqrt(2 / (m.conv2.weight.shape[0] * np.prod(m.conv2.weight.shape[2:]))) * self.num_layers ** (-0.25))
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nn.init.constant_(m.conv3.weight, 0)
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if m.downsample is not None:
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nn.init.normal_(m.downsample.weight, mean=0, std=np.sqrt(2 / (m.downsample.weight.shape[0] * np.prod(m.downsample.weight.shape[2:]))))
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elif isinstance(m, nn.Linear):
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nn.init.constant_(m.weight, 0)
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nn.init.constant_(m.bias, 0)
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def _make_layer(self, block, planes, blocks, stride=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = conv1x1(self.inplanes, planes * block.expansion, stride)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample))
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self.inplanes = planes * block.expansion
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for _ in range(1, blocks):
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layers.append(block(self.inplanes, planes))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv1(x)
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x = self.relu(x + self.bias1)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.avgpool(x)
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x = x.view(x.size(0), -1)
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x = self.fc(x + self.bias2)
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return x
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def fixup_resnet18(**kwargs):
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"""Constructs a Fixup-ResNet-18 model.2
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"""
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model = FixupResNet(FixupBasicBlock, [2, 2, 2, 2], **kwargs)
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return model
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def fixup_resnet34(**kwargs):
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"""Constructs a Fixup-ResNet-34 model.
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"""
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model = FixupResNet(FixupBasicBlock, [3, 4, 6, 3], **kwargs)
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return model
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def fixup_resnet50(**kwargs):
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"""Constructs a Fixup-ResNet-50 model.
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"""
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model = FixupResNet(FixupBottleneck, [3, 4, 6, 3], **kwargs)
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return model
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def fixup_resnet101(**kwargs):
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"""Constructs a Fixup-ResNet-101 model.
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"""
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model = FixupResNet(FixupBottleneck, [3, 4, 23, 3], **kwargs)
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return model
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def fixup_resnet152(**kwargs):
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"""Constructs a Fixup-ResNet-152 model.
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"""
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model = FixupResNet(FixupBottleneck, [3, 8, 36, 3], **kwargs)
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return model
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__all__ = ['FixupResNet', 'fixup_resnet18', 'fixup_resnet34', 'fixup_resnet50', 'fixup_resnet101', 'fixup_resnet152']
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@ -5,13 +5,8 @@ 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 pixel_norm(x, epsilon=1e-8):
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return x * torch.rsqrt(torch.mean(torch.pow(x, 2), dim=1, keepdims=True) + epsilon)
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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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@ -39,89 +34,6 @@ 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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@ -157,11 +69,7 @@ class ResidualBlockSpectralNorm(nn.Module):
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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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|
|
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Loading…
Reference in New Issue
Block a user