773753073f
Move to a fully fixup residual network for the switch (no batch norms). Fix a bunch of other small bugs. Add in a temporary latent feed-forward from the bottom of the switch. Fix several initialization issues.
216 lines
8.6 KiB
Python
216 lines
8.6 KiB
Python
import torch
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import torch.nn as nn
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import numpy as np
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import torch.nn.functional as F
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__all__ = ['FixupResNet', 'fixup_resnet18', 'fixup_resnet34', 'fixup_resnet50', 'fixup_resnet101', 'fixup_resnet152']
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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 conv5x5(in_planes, out_planes, stride=1):
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"""5x5 convolution with padding"""
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return nn.Conv2d(in_planes, out_planes, kernel_size=5, stride=stride,
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padding=2, bias=False)
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def conv7x7(in_planes, out_planes, stride=1):
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"""7x7 convolution with padding"""
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return nn.Conv2d(in_planes, out_planes, kernel_size=7, stride=stride,
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padding=3, 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, conv_create=conv3x3):
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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 = conv_create(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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self.bias2a = nn.Parameter(torch.zeros(1))
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self.conv2 = conv_create(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.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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return out
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class FixupResNet(nn.Module):
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def __init__(self, block, layers, upscale_applications=2, num_filters=64, inject_noise=False):
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super(FixupResNet, self).__init__()
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self.inject_noise = inject_noise
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self.num_layers = sum(layers) + layers[-1] * (upscale_applications - 1) # The last layer is applied repeatedly to achieve high level SR.
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self.inplanes = num_filters
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self.upscale_applications = upscale_applications
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# Part 1 - Process raw input image. Most denoising should appear here and this should be the most complicated
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# part of the block.
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self.conv1 = nn.Conv2d(3, num_filters, kernel_size=5, stride=1, padding=2,
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bias=False)
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self.bias1 = nn.Parameter(torch.zeros(1))
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self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
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self.layer1 = self._make_layer(block, num_filters, layers[0], stride=1)
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self.skip1 = nn.Conv2d(num_filters, 3, kernel_size=5, stride=1, padding=2, bias=False)
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self.skip1_bias = nn.Parameter(torch.zeros(1))
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# Part 2 - This is the upsampler core. It consists of a normal multiplicative conv followed by several residual
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# convs which are intended to repair artifacts caused by 2x interpolation.
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# This core layer should by itself accomplish 2x super-resolution. We use it in repeat to do the
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# requested SR.
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self.nf2 = int(num_filters/4)
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# This part isn't repeated. It de-filters the output from the previous step to fit the filter size used in the
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# upsampler-conv.
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self.upsampler_conv = nn.Conv2d(num_filters, self.nf2, kernel_size=3, stride=1, padding=1, bias=False)
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self.uc_bias = nn.Parameter(torch.zeros(1))
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self.inplanes = self.nf2
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if layers[1] > 0:
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# This is the repeated part.
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self.layer2 = self._make_layer(block, int(self.nf2), layers[1], stride=1, conv_type=conv5x5)
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self.skip2 = nn.Conv2d(self.nf2, 3, kernel_size=5, stride=1, padding=2, bias=False)
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self.skip2_bias = nn.Parameter(torch.zeros(1))
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self.final_defilter = nn.Conv2d(self.nf2, 3, kernel_size=5, stride=1, padding=2, bias=True)
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self.bias2 = nn.Parameter(torch.zeros(1))
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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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def _make_layer(self, block, planes, blocks, stride=1, conv_type=conv3x3):
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defilter = None
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if self.inplanes != planes * block.expansion:
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defilter = conv1x1(self.inplanes, planes * block.expansion, stride)
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layers = []
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layers.append(block(self.inplanes, planes, stride, defilter))
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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, conv_create=conv_type))
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return nn.Sequential(*layers)
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def forward(self, x):
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if self.inject_noise:
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rand_feature = torch.randn_like(x)
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x = x + rand_feature * .1
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x = self.conv1(x)
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x = self.lrelu(x + self.bias1)
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x = self.layer1(x)
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skip_lo = self.skip1(x) + self.skip1_bias
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x = self.lrelu(self.upsampler_conv(x) + self.uc_bias)
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if self.upscale_applications > 0:
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x = F.interpolate(x, scale_factor=2.0, mode='nearest')
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x = self.layer2(x)
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skip_med = self.skip2(x) + self.skip2_bias
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else:
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skip_med = skip_lo
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if self.upscale_applications > 1:
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x = F.interpolate(x, scale_factor=2.0, mode='nearest')
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x = self.layer2(x)
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x = self.final_defilter(x) + self.bias2
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return x, skip_med, skip_lo
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class FixupResNetV2(FixupResNet):
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def __init__(self, **kwargs):
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super(FixupResNetV2, self).__init__(**kwargs)
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# Use one unified filter-to-image stack, not the previous skip stacks.
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self.skip1 = None
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self.skip1_bias = None
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self.skip2 = None
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self.skip2_bias = None
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# The new filter-to-image stack will be 2 conv layers deep, not 1.
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self.final_process = nn.Conv2d(self.nf2, self.nf2, kernel_size=5, stride=1, padding=2, bias=True)
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self.bias2 = nn.Parameter(torch.zeros(1))
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self.fp_bn = nn.BatchNorm2d(self.nf2)
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self.final_defilter = nn.Conv2d(self.nf2, 3, kernel_size=3, stride=1, padding=1, bias=True)
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self.bias3 = nn.Parameter(torch.zeros(1))
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def filter_to_image(self, filter):
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x = self.final_process(filter) + self.bias2
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x = self.lrelu(self.fp_bn(x))
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x = self.final_defilter(x) + self.bias3
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return x
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def forward(self, x):
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if self.inject_noise:
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rand_feature = torch.randn_like(x)
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x = x + rand_feature * .1
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x = self.conv1(x)
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x = self.lrelu(x + self.bias1)
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x = self.layer1(x)
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x = self.lrelu(self.upsampler_conv(x) + self.uc_bias)
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skip_lo = self.filter_to_image(x)
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if self.upscale_applications > 0:
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x = F.interpolate(x, scale_factor=2.0, mode='nearest')
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x = self.layer2(x)
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skip_med = self.filter_to_image(x)
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if self.upscale_applications > 1:
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x = F.interpolate(x, scale_factor=2.0, mode='nearest')
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x = self.layer2(x)
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if self.upscale_applications == 2:
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x = self.filter_to_image(x)
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elif self.upscale_applications == 1:
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x = skip_med
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skip_med = skip_lo
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skip_lo = None
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elif self.upscale_applications == 0:
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x = skip_lo
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skip_lo = None
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skip_med = None
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return x, skip_med, skip_lo
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def fixup_resnet34(nb_denoiser=20, nb_upsampler=10, **kwargs):
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"""Constructs a Fixup-ResNet-34 model.
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"""
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model = FixupResNet(FixupBasicBlock, [nb_denoiser, nb_upsampler], **kwargs)
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return model
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def fixup_resnet34_v2(nb_denoiser=20, nb_upsampler=10, **kwargs):
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"""Constructs a Fixup-ResNet-34 model.
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"""
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kwargs['block'] = FixupBasicBlock
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kwargs['layers'] = [nb_denoiser, nb_upsampler]
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model = FixupResNetV2(**kwargs)
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return model
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__all__ = ['FixupResNet', 'fixup_resnet34', 'fixup_resnet34_v2'] |