DL-Art-School/codes/models/archs/DiscriminatorResnet_arch_passthrough.py

import torch
import torch.nn as nn
import numpy as np


__all__ = ['FixupResNet', 'fixup_resnet18', 'fixup_resnet34', 'fixup_resnet50', 'fixup_resnet101', 'fixup_resnet152']


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 conv5x5(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=5, stride=stride,
                     padding=2, 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, use_bn=False, conv_create=conv3x3):
        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 = conv_create(inplanes, planes, stride)
        self.bias1b = nn.Parameter(torch.zeros(1))
        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
        self.bias2a = nn.Parameter(torch.zeros(1))
        self.conv2 = conv_create(planes, planes)
        self.scale = nn.Parameter(torch.ones(1))
        self.bias2b = nn.Parameter(torch.zeros(1))
        self.downsample = downsample
        self.stride = stride
        self.use_bn = use_bn
        if use_bn:
            self.bn1 = nn.BatchNorm2d(planes)
            self.bn2 = nn.BatchNorm2d(planes)

    def forward(self, x):
        identity = x

        out = self.conv1(x + self.bias1a)
        if self.use_bn:
            out = self.bn1(out)
        out = self.lrelu(out + self.bias1b)

        out = self.conv2(out + self.bias2a)
        if self.use_bn:
            out = self.bn2(out)
        out = out * self.scale + self.bias2b

        if self.downsample is not None:
            identity = self.downsample(x + self.bias1a)

        out += identity
        out = self.lrelu(out)

        return out

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_filters=64, num_classes=1000, input_img_size=64, use_bn=False):
        super(FixupResNet, self).__init__()
        self.num_layers = sum(layers)
        self.inplanes = 3
        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
        self.layer0 = self._make_layer(block, num_filters*2, layers[0], stride=2, use_bn=use_bn, conv_type=conv5x5)
        self.inplanes = self.inplanes + 3  # Accomodate a skip connection from the generator.
        self.layer1 = self._make_layer(block, num_filters*4, layers[1], stride=2, use_bn=use_bn, conv_type=conv5x5)
        self.inplanes = self.inplanes + 3  # Accomodate a skip connection from the generator.
        self.layer2 = self._make_layer(block, num_filters*8, layers[2], stride=2, use_bn=use_bn)
        # SRGAN already has a feature loss tied to a separate VGG discriminator. We really don't care about features.
        # Therefore, level off the filter count from this block forwards.
        self.layer3 = self._make_layer(block, num_filters*8, layers[3], stride=2, use_bn=use_bn)
        self.layer4 = self._make_layer(block, num_filters*8, layers[4], stride=2, use_bn=use_bn)
        self.bias2 = nn.Parameter(torch.zeros(1))
        reduced_img_sz = int(input_img_size / 32)
        self.fc1 = nn.Linear(num_filters * 8 * reduced_img_sz * reduced_img_sz, 100)
        self.fc2 = nn.Linear(100, 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:]))))

    def _make_layer(self, block, outplanes, blocks, stride=1, use_bn=False, conv_type=conv3x3):
        layers = []
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, self.inplanes))

        downsample = None
        if stride != 1 or self.inplanes != outplanes * block.expansion:
            downsample = conv1x1(self.inplanes, outplanes * block.expansion, stride)
        layers.append(block(self.inplanes, outplanes, stride, downsample, use_bn=use_bn, conv_create=conv_type))
        self.inplanes = outplanes * block.expansion

        return nn.Sequential(*layers)

    def forward(self, x):
        # This class expects a medium skip (half-res) and low skip (quarter-res) provided as a tuple in the input.
        x, med_skip, lo_skip = x

        x = self.layer0(x)
        x = torch.cat([x, med_skip], dim=1)
        x = self.layer1(x)
        x = torch.cat([x, lo_skip], dim=1)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = x.view(x.size(0), -1)
        x = self.lrelu(self.fc1(x))
        x = self.fc2(x + self.bias2)

        return x


def fixup_resnet18(**kwargs):
    """Constructs a Fixup-ResNet-18 model.2
    """
    model = FixupResNet(FixupBasicBlock, [2, 2, 2, 2, 2], **kwargs)
    return model


def fixup_resnet34(**kwargs):
    """Constructs a Fixup-ResNet-34 model.
    """
    model = FixupResNet(FixupBasicBlock, [5, 5, 3, 3, 3], **kwargs)
    return model


def fixup_resnet50(**kwargs):
    """Constructs a Fixup-ResNet-50 model.
    """
    model = FixupResNet(FixupBottleneck, [3, 4, 6, 3, 2], **kwargs)
    return model


def fixup_resnet101(**kwargs):
    """Constructs a Fixup-ResNet-101 model.
    """
    model = FixupResNet(FixupBottleneck, [3, 4, 23, 3, 2], **kwargs)
    return model


def fixup_resnet152(**kwargs):
    """Constructs a Fixup-ResNet-152 model.
    """
    model = FixupResNet(FixupBottleneck, [3, 8, 36, 3, 2], **kwargs)
    return model


__all__ = ['FixupResNet', 'fixup_resnet18', 'fixup_resnet34', 'fixup_resnet50', 'fixup_resnet101', 'fixup_resnet152']