# Source code for torchreid.models.mlfn

    from __future__ import absolute_import
    from __future__ import division

    __all__ = ['mlfn']

    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 = {
        # training epoch = 5, top1 = 51.6
        'imagenet': 'http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/imagenet-pretrained/mlfn-9cb5a267.pth.tar',
    }

    class MLFNBlock(nn.Module):

        def __init__(self, in_channels, out_channels, stride, fsm_channels, groups=32):
            super(MLFNBlock, self).__init__()
            self.groups = groups
            mid_channels = out_channels // 2

            # Factor Modules
            self.fm_conv1 = nn.Conv2d(in_channels, mid_channels, 1, bias=False)
            self.fm_bn1 = nn.BatchNorm2d(mid_channels)
            self.fm_conv2 = nn.Conv2d(mid_channels, mid_channels, 3, stride=stride, padding=1, bias=False, groups=self.groups)
            self.fm_bn2 = nn.BatchNorm2d(mid_channels)
            self.fm_conv3 = nn.Conv2d(mid_channels, out_channels, 1, bias=False)
            self.fm_bn3 = nn.BatchNorm2d(out_channels)

            # Factor Selection Module
            self.fsm = nn.Sequential(
                nn.AdaptiveAvgPool2d(1),
                nn.Conv2d(in_channels, fsm_channels[0], 1),
                nn.BatchNorm2d(fsm_channels[0]),
                nn.ReLU(inplace=True),
                nn.Conv2d(fsm_channels[0], fsm_channels[1], 1),
                nn.BatchNorm2d(fsm_channels[1]),
                nn.ReLU(inplace=True),
                nn.Conv2d(fsm_channels[1], self.groups, 1),
                nn.BatchNorm2d(self.groups),
                nn.Sigmoid(),
            )

            self.downsample = None
            if in_channels != out_channels or stride > 1:
                self.downsample = nn.Sequential(
                    nn.Conv2d(in_channels, out_channels, 1, stride=stride, bias=False),
                    nn.BatchNorm2d(out_channels),
                )

        def forward(self, x):
            residual = x
            s = self.fsm(x)

            # reduce dimension
            x = self.fm_conv1(x)
            x = self.fm_bn1(x)
            x = F.relu(x, inplace=True)

            # group convolution
            x = self.fm_conv2(x)
            x = self.fm_bn2(x)
            x = F.relu(x, inplace=True)

            # factor selection
            b, c = x.size(0), x.size(1)
            n = c // self.groups
            ss = s.repeat(1, n, 1, 1) # from (b, g, 1, 1) to (b, g*n=c, 1, 1)
            ss = ss.view(b, n, self.groups, 1, 1)
            ss = ss.permute(0, 2, 1, 3, 4).contiguous()
            ss = ss.view(b, c, 1, 1)
            x = ss * x

            # recover dimension
            x = self.fm_conv3(x)
            x = self.fm_bn3(x)
            x = F.relu(x, inplace=True)

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

            return F.relu(residual + x, inplace=True), s

    class MLFN(nn.Module):
        """Multi-Level Factorisation Net.

        Reference:
            Chang et al. Multi-Level Factorisation Net for
            Person Re-Identification. CVPR 2018.

        Public keys:
            - ``mlfn``: MLFN (Multi-Level Factorisation Net).
        """

        def __init__(self, num_classes, loss='softmax', groups=32, channels=[64, 256, 512, 1024, 2048], embed_dim=1024, **kwargs):
            super(MLFN, self).__init__()
            self.loss = loss
            self.groups = groups

            # first convolutional layer
            self.conv1 = nn.Conv2d(3, channels[0], 7, stride=2, padding=3)
            self.bn1 = nn.BatchNorm2d(channels[0])
            self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)

            # main body
            self.feature = nn.ModuleList([
                # layer 1-3
                MLFNBlock(channels[0], channels[1], 1, [128, 64], self.groups),
                MLFNBlock(channels[1], channels[1], 1, [128, 64], self.groups),
                MLFNBlock(channels[1], channels[1], 1, [128, 64], self.groups),
                # layer 4-7
                MLFNBlock(channels[1], channels[2], 2, [256, 128], self.groups),
                MLFNBlock(channels[2], channels[2], 1, [256, 128], self.groups),
                MLFNBlock(channels[2], channels[2], 1, [256, 128], self.groups),
                MLFNBlock(channels[2], channels[2], 1, [256, 128], self.groups),
                # layer 8-13
                MLFNBlock(channels[2], channels[3], 2, [512, 128], self.groups),
                MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
                MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
                MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
                MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
                MLFNBlock(channels[3], channels[3], 1, [512, 128], self.groups),
                # layer 14-16
                MLFNBlock(channels[3], channels[4], 2, [512, 128], self.groups),
                MLFNBlock(channels[4], channels[4], 1, [512, 128], self.groups),
                MLFNBlock(channels[4], channels[4], 1, [512, 128], self.groups),
            ])
            self.global_avgpool = nn.AdaptiveAvgPool2d(1)

            # projection functions
            self.fc_x = nn.Sequential(
                nn.Conv2d(channels[4], embed_dim, 1, bias=False),
                nn.BatchNorm2d(embed_dim),
                nn.ReLU(inplace=True),
            )
            self.fc_s = nn.Sequential(
                nn.Conv2d(self.groups * 16, embed_dim, 1, bias=False),
                nn.BatchNorm2d(embed_dim),
                nn.ReLU(inplace=True),
            )

            self.classifier = nn.Linear(embed_dim, num_classes)

            self.init_params()

        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.Linear):
                    nn.init.normal_(m.weight, 0, 0.01)
                    if m.bias is not None:
                        nn.init.constant_(m.bias, 0)

        def forward(self, x):
            x = self.conv1(x)
            x = self.bn1(x)
            x = F.relu(x, inplace=True)
            x = self.maxpool(x)

            s_hat = []
            for block in self.feature:
                x, s = block(x)
                s_hat.append(s)
            s_hat = torch.cat(s_hat, 1)

            x = self.global_avgpool(x)
            x = self.fc_x(x)
            s_hat = self.fc_s(s_hat)

            v = (x + s_hat) * 0.5
            v = v.view(v.size(0), -1)

            if not self.training:
                return v

            y = self.classifier(v)

            if self.loss == 'softmax':
                return y
            elif self.loss == 'triplet':
                return y, v
            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 mlfn(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = MLFN(num_classes, loss, **kwargs)
        if pretrained:
            init_pretrained_weights(model, model_urls['imagenet'])
        return model