# Source code for torchreid.models.pcb

    from __future__ import absolute_import
    from __future__ import division

    __all__ = ['pcb_p6', 'pcb_p4']

    import torch
    from torch import nn
    from torch.nn import functional as F
    import torchvision
    import torch.utils.model_zoo as model_zoo

    model_urls = {
        'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
        'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
        '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',
    }

    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)

    class BasicBlock(nn.Module):
        expansion = 1

        def __init__(self, inplanes, planes, stride=1, downsample=None):
            super(BasicBlock, self).__init__()
            self.conv1 = conv3x3(inplanes, planes, stride)
            self.bn1 = nn.BatchNorm2d(planes)
            self.relu = nn.ReLU(inplace=True)
            self.conv2 = conv3x3(planes, planes)
            self.bn2 = nn.BatchNorm2d(planes)
            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)

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

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

            return out

    class Bottleneck(nn.Module):
        expansion = 4

        def __init__(self, inplanes, planes, stride=1, downsample=None):
            super(Bottleneck, self).__init__()
            self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
            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 DimReduceLayer(nn.Module):

        def __init__(self, in_channels, out_channels, nonlinear):
            super(DimReduceLayer, self).__init__()
            layers = []
            layers.append(nn.Conv2d(in_channels, out_channels, 1, stride=1, padding=0, bias=False))
            layers.append(nn.BatchNorm2d(out_channels))

            if nonlinear == 'relu':
                layers.append(nn.ReLU(inplace=True))
            elif nonlinear == 'leakyrelu':
                layers.append(nn.LeakyReLU(0.1))

            self.layers = nn.Sequential(*layers)

        def forward(self, x):
            return self.layers(x)

    class PCB(nn.Module):
        """Part-based Convolutional Baseline.

        Reference:
            Sun et al. Beyond Part Models: Person Retrieval with Refined
            Part Pooling (and A Strong Convolutional Baseline). ECCV 2018.

        Public keys:
            - ``pcb_p4``: PCB with 4-part strips.
            - ``pcb_p6``: PCB with 6-part strips.
        """

        def __init__(self, num_classes, loss, block, layers,
                     parts=6,
                     reduced_dim=256,
                     nonlinear='relu',
                     **kwargs):
            self.inplanes = 64
            super(PCB, self).__init__()
            self.loss = loss
            self.parts = parts
            self.feature_dim = 512 * block.expansion

            # backbone network
            self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
            self.bn1 = nn.BatchNorm2d(64)
            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=1)

            # pcb layers
            self.parts_avgpool = nn.AdaptiveAvgPool2d((self.parts, 1))
            self.dropout = nn.Dropout(p=0.5)
            self.conv5 = DimReduceLayer(512 * block.expansion, reduced_dim, nonlinear=nonlinear)
            self.feature_dim = reduced_dim
            self.classifier = nn.ModuleList([nn.Linear(self.feature_dim, num_classes) for _ in range(self.parts)])

            self._init_params()

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

            return nn.Sequential(*layers)

        def _init_params(self):
            for m in self.modules():
                if isinstance(m, nn.Conv2d):
                    nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                    if m.bias is not None:
                        nn.init.constant_(m.bias, 0)
                elif isinstance(m, nn.BatchNorm2d):
                    nn.init.constant_(m.weight, 1)
                    nn.init.constant_(m.bias, 0)
                elif isinstance(m, nn.BatchNorm1d):
                    nn.init.constant_(m.weight, 1)
                    nn.init.constant_(m.bias, 0)
                elif isinstance(m, nn.Linear):
                    nn.init.normal_(m.weight, 0, 0.01)
                    if m.bias is not None:
                        nn.init.constant_(m.bias, 0)

        def featuremaps(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)
            return x

        def forward(self, x):
            f = self.featuremaps(x)
            v_g = self.parts_avgpool(f)

            if not self.training:
                v_g = F.normalize(v_g, p=2, dim=1)
                return v_g.view(v_g.size(0), -1)

            v_g = self.dropout(v_g)
            v_h = self.conv5(v_g)

            y = []
            for i in range(self.parts):
                v_h_i = v_h[:, :, i, :]
                v_h_i = v_h_i.view(v_h_i.size(0), -1)
                y_i = self.classifier[i](v_h_i)
                y.append(y_i)

            if self.loss == 'softmax':
                return y
            elif self.loss == 'triplet':
                v_g = F.normalize(v_g, p=2, dim=1)
                return y, v_g.view(v_g.size(0), -1)
            else:
                raise KeyError('Unsupported loss: {}'.format(self.loss))

    def init_pretrained_weights(model, model_url):
        """Initializes model with pretrained weights.

        Layers that don't match with pretrained layers in name or size are kept unchanged.
        """
        pretrain_dict = model_zoo.load_url(model_url)
        model_dict = model.state_dict()
        pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
        model_dict.update(pretrain_dict)
        model.load_state_dict(model_dict)
        print('Initialized model with pretrained weights from {}'.format(model_url))

    def pcb_p6(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = PCB(
            num_classes=num_classes,
            loss=loss,
            block=Bottleneck,
            layers=[3, 4, 6, 3],
            last_stride=1,
            parts=6,
            reduced_dim=256,
            nonlinear='relu',
            **kwargs
        )
        if pretrained:
            init_pretrained_weights(model, model_urls['resnet50'])
        return model

    def pcb_p4(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = PCB(
            num_classes=num_classes,
            loss=loss,
            block=Bottleneck,
            layers=[3, 4, 6, 3],
            last_stride=1,
            parts=4,
            reduced_dim=256,
            nonlinear='relu',
            **kwargs
        )
        if pretrained:
            init_pretrained_weights(model, model_urls['resnet50'])
        return model