# Source code for torchreid.models.xception

    from __future__ import absolute_import
    from __future__ import division

    __all__ = ['xception']

    import math
    import torch
    import torch.nn as nn
    import torch.nn.functional as F
    import torch.utils.model_zoo as model_zoo
    from torch.nn import init

    pretrained_settings = {
        'xception': {
            'imagenet': {
                'url': 'http://data.lip6.fr/cadene/pretrainedmodels/xception-43020ad28.pth',
                'input_space': 'RGB',
                'input_size': [3, 299, 299],
                'input_range': [0, 1],
                'mean': [0.5, 0.5, 0.5],
                'std': [0.5, 0.5, 0.5],
                'num_classes': 1000,
                'scale': 0.8975 # The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
            }
        }
    }

    class SeparableConv2d(nn.Module):

        def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, dilation=1, bias=False):
            super(SeparableConv2d,self).__init__()

            self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size,
                                   stride, padding, dilation,
                                   groups=in_channels, bias=bias)
            self.pointwise = nn.Conv2d(in_channels, out_channels, 1, 1, 0, 1, 1, bias=bias)

        def forward(self,x):
            x = self.conv1(x)
            x = self.pointwise(x)
            return x

    class Block(nn.Module):

        def __init__(self, in_filters, out_filters, reps, strides=1, start_with_relu=True, grow_first=True):
            super(Block, self).__init__()

            if out_filters != in_filters or strides != 1:
                self.skip = nn.Conv2d(in_filters, out_filters, 1, stride=strides, bias=False)
                self.skipbn = nn.BatchNorm2d(out_filters)
            else:
                self.skip = None

            self.relu = nn.ReLU(inplace=True)
            rep=[]

            filters = in_filters
            if grow_first:
                rep.append(self.relu)
                rep.append(SeparableConv2d(in_filters, out_filters, 3, stride=1, padding=1, bias=False))
                rep.append(nn.BatchNorm2d(out_filters))
                filters = out_filters

            for i in range(reps - 1):
                rep.append(self.relu)
                rep.append(SeparableConv2d(filters, filters, 3, stride=1, padding=1, bias=False))
                rep.append(nn.BatchNorm2d(filters))

            if not grow_first:
                rep.append(self.relu)
                rep.append(SeparableConv2d(in_filters, out_filters, 3, stride=1, padding=1, bias=False))
                rep.append(nn.BatchNorm2d(out_filters))

            if not start_with_relu:
                rep = rep[1:]
            else:
                rep[0] = nn.ReLU(inplace=False)

            if strides != 1:
                rep.append(nn.MaxPool2d(3, strides, 1))
            self.rep = nn.Sequential(*rep)

        def forward(self,inp):
            x = self.rep(inp)

            if self.skip is not None:
                skip = self.skip(inp)
                skip = self.skipbn(skip)
            else:
                skip = inp

            x += skip
            return x

    class Xception(nn.Module):
        """Xception.

        Reference:
            Chollet. Xception: Deep Learning with Depthwise
            Separable Convolutions. CVPR 2017.

        Public keys:
            - ``xception``: Xception.
        """

        def __init__(self, num_classes, loss, fc_dims=None, dropout_p=None, **kwargs):
            super(Xception, self).__init__()
            self.loss = loss

            self.conv1 = nn.Conv2d(3, 32, 3,2, 0, bias=False)
            self.bn1 = nn.BatchNorm2d(32)

            self.conv2 = nn.Conv2d(32, 64, 3, bias=False)
            self.bn2 = nn.BatchNorm2d(64)

            self.block1 = Block(64, 128, 2, 2, start_with_relu=False, grow_first=True)
            self.block2 = Block(128, 256, 2, 2, start_with_relu=True, grow_first=True)
            self.block3 = Block(256, 728, 2, 2, start_with_relu=True, grow_first=True)

            self.block4 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
            self.block5 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
            self.block6 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
            self.block7 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)

            self.block8 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
            self.block9 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
            self.block10 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)
            self.block11 = Block(728, 728, 3, 1, start_with_relu=True, grow_first=True)

            self.block12 = Block(728, 1024, 2, 2, start_with_relu=True, grow_first=False)

            self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1)
            self.bn3 = nn.BatchNorm2d(1536)

            self.conv4 = SeparableConv2d(1536, 2048, 3, 1, 1)
            self.bn4 = nn.BatchNorm2d(2048)

            self.global_avgpool = nn.AdaptiveAvgPool2d(1)
            self.feature_dim = 2048
            self.fc = self._construct_fc_layer(fc_dims, 2048, dropout_p)
            self.classifier = nn.Linear(self.feature_dim, num_classes)

            self._init_params()

        def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
            """Constructs fully connected layer.

            Args:
                fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
                input_dim (int): input dimension
                dropout_p (float): dropout probability, if None, dropout is unused
            """
            if fc_dims is None:
                self.feature_dim = input_dim
                return None

            assert isinstance(fc_dims, (list, tuple)), 'fc_dims must be either list or tuple, but got {}'.format(type(fc_dims))

            layers = []
            for dim in fc_dims:
                layers.append(nn.Linear(input_dim, dim))
                layers.append(nn.BatchNorm1d(dim))
                layers.append(nn.ReLU(inplace=True))
                if dropout_p is not None:
                    layers.append(nn.Dropout(p=dropout_p))
                input_dim = dim

            self.feature_dim = fc_dims[-1]

            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, input):
            x = self.conv1(input)
            x = self.bn1(x)
            x = F.relu(x, inplace=True)

            x = self.conv2(x)
            x = self.bn2(x)
            x = F.relu(x, inplace=True)

            x = self.block1(x)
            x = self.block2(x)
            x = self.block3(x)
            x = self.block4(x)
            x = self.block5(x)
            x = self.block6(x)
            x = self.block7(x)
            x = self.block8(x)
            x = self.block9(x)
            x = self.block10(x)
            x = self.block11(x)
            x = self.block12(x)

            x = self.conv3(x)
            x = self.bn3(x)
            x = F.relu(x, inplace=True)

            x = self.conv4(x)
            x = self.bn4(x)
            x = F.relu(x, inplace=True)
            return x

        def forward(self, x):
            f = self.featuremaps(x)
            v = self.global_avgpool(f)
            v = v.view(v.size(0), -1)

            if self.fc is not None:
                v = self.fc(v)

            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):
        """Initialize models 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 xception(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = Xception(
            num_classes,
            loss,
            fc_dims=None,
            dropout_p=None,
            **kwargs
        )
        if pretrained:
            model_url = pretrained_settings['xception']['imagenet']['url']
            init_pretrained_weights(model, model_url)
        return model