James Betker
parent
5b8a77f02c
commit
a5188bb7ca
@ -1,85 +1,195 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torchvision
|
||||
import models.archs.arch_util as arch_util
|
||||
import functools
|
||||
import torch.nn.functional as F
|
||||
import torch.nn.utils.spectral_norm as SpectralNorm
|
||||
import numpy as np
|
||||
|
||||
# Class that halfs the image size (x4 complexity reduction) and doubles the filter size. Substantial resnet
|
||||
# processing is also performed.
|
||||
class ResnetDownsampleLayer(nn.Module):
|
||||
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):
|
||||
super(ResnetDownsampleLayer, self).__init__()
|
||||
|
||||
self.skip_image_reducer = SpectralNorm(nn.Conv2d(starting_channels, number_filters, 3, stride=1, padding=1, bias=True))
|
||||
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)
|
||||
__all__ = ['FixupResNet', 'fixup_resnet18', 'fixup_resnet34', 'fixup_resnet50', 'fixup_resnet101', 'fixup_resnet152']
|
||||
|
||||
self.input_reducer = SpectralNorm(nn.Conv2d(number_filters, number_filters*filter_multiplier, 3, stride=2, padding=1, bias=True))
|
||||
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)
|
||||
|
||||
def conv3x3(in_planes, out_planes, stride=1):
|
||||
"""3x3 convolution with padding"""
|
||||
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
|
||||
padding=1, bias=False)
|
||||
|
||||
|
||||
def conv1x1(in_planes, out_planes, stride=1):
|
||||
"""1x1 convolution"""
|
||||
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
|
||||
|
||||
|
||||
class FixupBasicBlock(nn.Module):
|
||||
expansion = 1
|
||||
|
||||
def __init__(self, inplanes, planes, stride=1, downsample=None):
|
||||
super(FixupBasicBlock, self).__init__()
|
||||
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
|
||||
self.bias1a = nn.Parameter(torch.zeros(1))
|
||||
self.conv1 = conv3x3(inplanes, planes, stride)
|
||||
self.bias1b = nn.Parameter(torch.zeros(1))
|
||||
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
|
||||
arch_util.initialize_weights([self.input_reducer, self.skip_image_reducer], 1)
|
||||
self.bias2a = nn.Parameter(torch.zeros(1))
|
||||
self.conv2 = conv3x3(planes, planes)
|
||||
self.scale = nn.Parameter(torch.ones(1))
|
||||
self.bias2b = nn.Parameter(torch.zeros(1))
|
||||
self.downsample = downsample
|
||||
self.stride = stride
|
||||
|
||||
def forward(self, x):
|
||||
identity = x
|
||||
|
||||
def forward(self, x, skip_image):
|
||||
# Process the skip image first.
|
||||
skip = self.lrelu(self.skip_image_reducer(skip_image))
|
||||
skip = self.skip_image_res_trunk(skip)
|
||||
out = self.conv1(x + self.bias1a)
|
||||
out = self.lrelu(out + self.bias1b)
|
||||
|
||||
out = self.conv2(out + self.bias2a)
|
||||
out = out * self.scale + self.bias2b
|
||||
|
||||
if self.downsample is not None:
|
||||
identity = self.downsample(x + self.bias1a)
|
||||
|
||||
out += identity
|
||||
out = self.lrelu(out)
|
||||
|
||||
# Concat the processed skip image onto the input and perform processing.
|
||||
out = (x + skip) / 2
|
||||
out = self.lrelu(self.input_reducer(out))
|
||||
out = self.res_trunk(out)
|
||||
return out
|
||||
|
||||
class DiscriminatorResnet(nn.Module):
|
||||
# Discriminator that downsamples 5 times with resnet blocks at each layer. On each downsample, the filter size is
|
||||
# increased by a factor of 2. Feeds the output of the convs into a dense for prediction at the logits. Scales the
|
||||
# final dense based on the input image size. Intended for use with input images which are multiples of 32.
|
||||
#
|
||||
# This discriminator also includes provisions to pass an image at various downsample steps in directly. When this
|
||||
# is done with a generator, it will allow much shorter gradient paths between the generator and discriminator. When
|
||||
# no downsampled images are passed into the forward() pass, they will be automatically generated from the source
|
||||
# image using interpolation.
|
||||
#
|
||||
# Uses spectral normalization rather than batch normalization.
|
||||
def __init__(self, in_nc: int, nf: int, input_img_size: int, trunk_resblocks: int, skip_resblocks: int):
|
||||
super(DiscriminatorResnet, self).__init__()
|
||||
self.dimensionalize = nn.Conv2d(in_nc, nf, kernel_size=3, stride=1, padding=1, bias=True)
|
||||
|
||||
# Trunk resblocks are the important things to get right, so use those. 5=number of downsample layers.
|
||||
total_resblocks = trunk_resblocks * 5
|
||||
self.downsample1 = ResnetDownsampleLayer(in_nc, nf, 2, trunk_resblocks, skip_resblocks, total_resblocks)
|
||||
self.downsample2 = ResnetDownsampleLayer(in_nc, nf*2, 2, trunk_resblocks, skip_resblocks, total_resblocks)
|
||||
self.downsample3 = ResnetDownsampleLayer(in_nc, nf*4, 2, trunk_resblocks, skip_resblocks, total_resblocks)
|
||||
# At the bottom layers, we cap the filter multiplier. We want this particular network to focus as much on the
|
||||
# macro-details at higher image dimensionality as it does to the feature details.
|
||||
self.downsample4 = ResnetDownsampleLayer(in_nc, nf*8, 1, trunk_resblocks, skip_resblocks, total_resblocks)
|
||||
self.downsample5 = ResnetDownsampleLayer(in_nc, nf*8, 1, trunk_resblocks, skip_resblocks, total_resblocks)
|
||||
self.downsamplers = [self.downsample1, self.downsample2, self.downsample3, self.downsample4, self.downsample5]
|
||||
|
||||
downsampled_image_size = input_img_size / 32
|
||||
self.linear1 = nn.Linear(int(nf * 8 * downsampled_image_size * downsampled_image_size), 100)
|
||||
self.linear2 = nn.Linear(100, 1)
|
||||
|
||||
# activation function
|
||||
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
|
||||
|
||||
arch_util.initialize_weights([self.dimensionalize, self.linear1, self.linear2], 1)
|
||||
|
||||
def forward(self, x, skip_images=None):
|
||||
if skip_images is None:
|
||||
# Sythesize them from x.
|
||||
skip_images = []
|
||||
for i in range(len(self.downsamplers)):
|
||||
m = 2 ** i
|
||||
skip_images.append(F.interpolate(x, scale_factor=1 / m, mode='bilinear', align_corners=False))
|
||||
|
||||
fea = self.dimensionalize(x)
|
||||
for skip, d in zip(skip_images, self.downsamplers):
|
||||
fea = d(fea, skip)
|
||||
|
||||
fea = fea.view(fea.size(0), -1)
|
||||
fea = self.lrelu(self.linear1(fea))
|
||||
out = self.linear2(fea)
|
||||
class FixupBottleneck(nn.Module):
|
||||
expansion = 4
|
||||
|
||||
def __init__(self, inplanes, planes, stride=1, downsample=None):
|
||||
super(FixupBottleneck, self).__init__()
|
||||
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
|
||||
self.bias1a = nn.Parameter(torch.zeros(1))
|
||||
self.conv1 = conv1x1(inplanes, planes)
|
||||
self.bias1b = nn.Parameter(torch.zeros(1))
|
||||
self.bias2a = nn.Parameter(torch.zeros(1))
|
||||
self.conv2 = conv3x3(planes, planes, stride)
|
||||
self.bias2b = nn.Parameter(torch.zeros(1))
|
||||
self.bias3a = nn.Parameter(torch.zeros(1))
|
||||
self.conv3 = conv1x1(planes, planes * self.expansion)
|
||||
self.scale = nn.Parameter(torch.ones(1))
|
||||
self.bias3b = nn.Parameter(torch.zeros(1))
|
||||
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
|
||||
self.downsample = downsample
|
||||
self.stride = stride
|
||||
|
||||
def forward(self, x):
|
||||
identity = x
|
||||
|
||||
out = self.conv1(x + self.bias1a)
|
||||
out = self.lrelu(out + self.bias1b)
|
||||
|
||||
out = self.conv2(out + self.bias2a)
|
||||
out = self.lrelu(out + self.bias2b)
|
||||
|
||||
out = self.conv3(out + self.bias3a)
|
||||
out = out * self.scale + self.bias3b
|
||||
|
||||
if self.downsample is not None:
|
||||
identity = self.downsample(x + self.bias1a)
|
||||
|
||||
out += identity
|
||||
out = self.lrelu(out)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
class FixupResNet(nn.Module):
|
||||
|
||||
def __init__(self, block, layers, num_classes=1000):
|
||||
super(FixupResNet, self).__init__()
|
||||
self.num_layers = sum(layers)
|
||||
self.inplanes = 64
|
||||
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
|
||||
bias=False)
|
||||
self.bias1 = nn.Parameter(torch.zeros(1))
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
||||
self.layer1 = self._make_layer(block, 64, layers[0])
|
||||
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
|
||||
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
|
||||
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
|
||||
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
|
||||
self.bias2 = nn.Parameter(torch.zeros(1))
|
||||
self.fc = nn.Linear(512 * block.expansion, num_classes)
|
||||
|
||||
for m in self.modules():
|
||||
if isinstance(m, FixupBasicBlock):
|
||||
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))
|
||||
nn.init.constant_(m.conv2.weight, 0)
|
||||
if m.downsample is not None:
|
||||
nn.init.normal_(m.downsample.weight, mean=0, std=np.sqrt(2 / (m.downsample.weight.shape[0] * np.prod(m.downsample.weight.shape[2:]))))
|
||||
elif isinstance(m, FixupBottleneck):
|
||||
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))
|
||||
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))
|
||||
nn.init.constant_(m.conv3.weight, 0)
|
||||
if m.downsample is not None:
|
||||
nn.init.normal_(m.downsample.weight, mean=0, std=np.sqrt(2 / (m.downsample.weight.shape[0] * np.prod(m.downsample.weight.shape[2:]))))
|
||||
elif isinstance(m, nn.Linear):
|
||||
nn.init.constant_(m.weight, 0)
|
||||
nn.init.constant_(m.bias, 0)
|
||||
|
||||
def _make_layer(self, block, planes, blocks, stride=1):
|
||||
downsample = None
|
||||
if stride != 1 or self.inplanes != planes * block.expansion:
|
||||
downsample = conv1x1(self.inplanes, planes * block.expansion, stride)
|
||||
|
||||
layers = []
|
||||
layers.append(block(self.inplanes, planes, stride, downsample))
|
||||
self.inplanes = planes * block.expansion
|
||||
for _ in range(1, blocks):
|
||||
layers.append(block(self.inplanes, planes))
|
||||
|
||||
return nn.Sequential(*layers)
|
||||
|
||||
def forward(self, x):
|
||||
x = self.conv1(x)
|
||||
x = self.relu(x + self.bias1)
|
||||
x = self.maxpool(x)
|
||||
|
||||
x = self.layer1(x)
|
||||
x = self.layer2(x)
|
||||
x = self.layer3(x)
|
||||
x = self.layer4(x)
|
||||
|
||||
x = self.avgpool(x)
|
||||
x = x.view(x.size(0), -1)
|
||||
x = self.fc(x + self.bias2)
|
||||
|
||||
return x
|
||||
|
||||
|
||||
def fixup_resnet18(**kwargs):
|
||||
"""Constructs a Fixup-ResNet-18 model.2
|
||||
"""
|
||||
model = FixupResNet(FixupBasicBlock, [2, 2, 2, 2], **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def fixup_resnet34(**kwargs):
|
||||
"""Constructs a Fixup-ResNet-34 model.
|
||||
"""
|
||||
model = FixupResNet(FixupBasicBlock, [3, 4, 6, 3], **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def fixup_resnet50(**kwargs):
|
||||
"""Constructs a Fixup-ResNet-50 model.
|
||||
"""
|
||||
model = FixupResNet(FixupBottleneck, [3, 4, 6, 3], **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def fixup_resnet101(**kwargs):
|
||||
"""Constructs a Fixup-ResNet-101 model.
|
||||
"""
|
||||
model = FixupResNet(FixupBottleneck, [3, 4, 23, 3], **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
def fixup_resnet152(**kwargs):
|
||||
"""Constructs a Fixup-ResNet-152 model.
|
||||
"""
|
||||
model = FixupResNet(FixupBottleneck, [3, 8, 36, 3], **kwargs)
|
||||
return model
|
||||
|
||||
|
||||
__all__ = ['FixupResNet', 'fixup_resnet18', 'fixup_resnet34', 'fixup_resnet50', 'fixup_resnet101', 'fixup_resnet152']
|
||||
Loading…
Reference in New Issue