reid-strong-baseline/modeling/backbones/resnet_ibn_a.py

import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo


__all__ = ['ResNet_IBN', 'resnet50_ibn_a', 'resnet101_ibn_a',
           'resnet152_ibn_a']


model_urls = {
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}


class IBN(nn.Module):
    def __init__(self, planes):
        super(IBN, self).__init__()
        half1 = int(planes/2)
        self.half = half1
        half2 = planes - half1
        self.IN = nn.InstanceNorm2d(half1, affine=True)
        self.BN = nn.BatchNorm2d(half2)
    
    def forward(self, x):
        split = torch.split(x, self.half, 1)
        out1 = self.IN(split[0].contiguous())
        out2 = self.BN(split[1].contiguous())
        out = torch.cat((out1, out2), 1)
        return out


class Bottleneck_IBN(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, ibn=False, stride=1, downsample=None):
        super(Bottleneck_IBN, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        if ibn:
            self.bn1 = IBN(planes)
        else:
            self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x
        
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet_IBN(nn.Module):

    def __init__(self, last_stride, block, layers, num_classes=1000):
        scale = 64
        self.inplanes = scale
        super(ResNet_IBN, self).__init__()
        self.conv1 = nn.Conv2d(3, scale, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(scale)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, scale, layers[0])
        self.layer2 = self._make_layer(block, scale*2, layers[1], stride=2)
        self.layer3 = self._make_layer(block, scale*4, layers[2], stride=2)
        self.layer4 = self._make_layer(block, scale*8, layers[3], stride=last_stride)
        self.avgpool = nn.AvgPool2d(7)
        self.fc = nn.Linear(scale * 8 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.InstanceNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        ibn = True
        if planes == 512:
            ibn = False
        layers.append(block(self.inplanes, planes, ibn, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, ibn))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        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)

        return x

    def load_param(self, model_path):
        param_dict = torch.load(model_path)
        for i in param_dict:
            if 'fc' in i:
                continue
            self.state_dict()[i].copy_(param_dict[i])


def resnet50_ibn_a(last_stride, pretrained=False, **kwargs):
    """Constructs a ResNet-50 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet_IBN(last_stride, Bottleneck_IBN, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
    return model


def resnet101_ibn_a(last_stride, pretrained=False, **kwargs):
    """Constructs a ResNet-101 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet_IBN(last_stride, Bottleneck_IBN, [3, 4, 23, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
    return model


def resnet152_ibn_a(last_stride, pretrained=False, **kwargs):
    """Constructs a ResNet-152 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet_IBN(last_stride, Bottleneck_IBN, [3, 8, 36, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
    return model