forked from mrq/DL-Art-School
Full resnet corrupt, no BN
And it works! Thanks fixup..
This commit is contained in:
James Betker
parent
03258445bc
commit
7eaabce48d
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@ -94,21 +94,20 @@ class FixupBottleneck(nn.Module):
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class FixupResNet(nn.Module):
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def __init__(self, block, layers, num_classes=1000):
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def __init__(self, block, layers, num_filters=64, 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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self.inplanes = num_filters
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self.conv1 = nn.Conv2d(3, num_filters, 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.lrelu = nn.LeakyReLU(negative_slope=0.2, 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.layer1 = self._make_layer(block, num_filters, layers[0], stride=2)
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self.layer2 = self._make_layer(block, num_filters*2, layers[1], stride=2)
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self.layer3 = self._make_layer(block, num_filters*4, layers[2], stride=2)
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self.layer4 = self._make_layer(block, num_filters*8, layers[3], stride=2)
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self.bias2 = nn.Parameter(torch.zeros(1))
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self.fc1 = nn.Linear(512 * 2 * 2, 100)
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self.fc1 = nn.Linear(num_filters * 8 * 2 * 2, 100)
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self.fc2 = nn.Linear(100, num_classes)
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for m in self.modules():
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@ -123,9 +122,10 @@ class FixupResNet(nn.Module):
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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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'''
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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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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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@ -143,7 +143,6 @@ class FixupResNet(nn.Module):
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def forward(self, x):
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x = self.conv1(x)
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x = self.lrelu(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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134
codes/models/archs/FlatProcessorNetNew_arch.py
Normal file
134
codes/models/archs/FlatProcessorNetNew_arch.py
Normal file
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@ -0,0 +1,134 @@
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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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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.bn1 = nn.BatchNorm2d(planes, affine=True)
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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 = conv3x3(planes, planes)
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self.bn2 = nn.BatchNorm2d(planes, affine=True)
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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, num_filters, 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.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.pixel_shuffle = nn.PixelShuffle(2)
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# 4 input channels, including the noise.
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self.conv1 = nn.Conv2d(4, num_filters, kernel_size=7, stride=2, padding=3,
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bias=False)
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self.inplanes = num_filters
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self.down_layer1 = self._make_layer(block, num_filters, layers[0])
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self.down_layer2 = self._make_layer(block, num_filters, layers[1], stride=2)
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self.down_layer3 = self._make_layer(block, num_filters * 4, layers[2], stride=2)
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self.down_layer4 = self._make_layer(block, num_filters * 16, layers[3], stride=2)
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self.inplanes = num_filters * 4
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self.up_layer1 = self._make_layer(block, num_filters * 4, layers[4], stride=1)
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self.inplanes = num_filters
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self.up_layer2 = self._make_layer(block, num_filters, layers[5], stride=1)
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self.defilter = nn.Conv2d(num_filters, 3, kernel_size=5, stride=1, padding=2, bias=False)
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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, 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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skip = x
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# Noise has the same shape as the input with only one channel.
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rand_feature = torch.randn((x.shape[0], 1) + x.shape[2:], device=x.device, dtype=x.dtype)
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x = torch.cat([x, rand_feature], dim=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.down_layer1(x)
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x = self.down_layer2(x)
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x = self.down_layer3(x)
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x = self.down_layer4(x)
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x = self.pixel_shuffle(x)
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x = self.up_layer1(x)
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x = self.pixel_shuffle(x)
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x = self.up_layer2(x)
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x = self.defilter(x)
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base = F.interpolate(skip, scale_factor=.25, mode='bilinear', align_corners=False)
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return x + base
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def fixup_resnet34(num_filters, **kwargs):
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"""Constructs a Fixup-ResNet-34 model.
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"""
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model = FixupResNet(FixupBasicBlock, num_filters, [3, 4, 6, 3, 2, 2], **kwargs)
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return model
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@ -3,6 +3,7 @@ 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.DiscriminatorResnetBN_arch as DiscriminatorResnetBN_arch
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import models.archs.FlatProcessorNetNew_arch as FlatProcessorNetNew_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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@ -30,9 +31,10 @@ def define_G(opt):
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netG = HighToLowResNet.HighToLowResNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
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nf=opt_net['nf'], nb=opt_net['nb'], downscale=opt_net['scale'])
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elif which_model == 'FlatProcessorNet':
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netG = FlatProcessorNet_arch.FlatProcessorNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
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'''netG = FlatProcessorNet_arch.FlatProcessorNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'],
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nf=opt_net['nf'], downscale=opt_net['scale'], reduce_anneal_blocks=opt_net['ra_blocks'],
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assembler_blocks=opt_net['assembler_blocks'])
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assembler_blocks=opt_net['assembler_blocks'])'''
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netG = FlatProcessorNetNew_arch.fixup_resnet34(num_filters=opt_net['nf'])
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# video restoration
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elif which_model == 'EDVR':
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netG = EDVR_arch.EDVR(nf=opt_net['nf'], nframes=opt_net['nframes'],
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@ -56,7 +58,7 @@ 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 = DiscriminatorResnetBN_arch.resnet32(num_filters=opt_net['nf'], num_classes=1)
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netD = DiscriminatorResnet_arch.fixup_resnet34(num_filters=opt_net['nf'], num_classes=1)
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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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@ -16,8 +16,8 @@ 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: 0 # per GPU
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batch_size: 16
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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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use_rot: false
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@ -34,11 +34,14 @@ network_G:
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which_model_G: FlatProcessorNet
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in_nc: 3
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out_nc: 3
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nf: 48
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ra_blocks: 4
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assembler_blocks: 3
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nf: 32
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ra_blocks: 6
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assembler_blocks: 4
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network_D:
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#which_model_D: discriminator_vgg_128
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#in_nc: 3
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#nf: 64
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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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@ -56,7 +59,7 @@ 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 1e-4
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lr_D: !!float 2e-4
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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 +74,11 @@ 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: 2
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D_init_iters: 1200
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D_init_iters: 0
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manual_seed: 10
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val_freq: !!float 5e2
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