# torchreid.models.shufflenetv2 — torchreid 1.4.0 documentation

# Source code for torchreid.models.shufflenetv2

    """
    Code source: https://github.com/pytorch/vision
    """
    from __future__ import division, absolute_import
    import torch
    import torch.utils.model_zoo as model_zoo
    from torch import nn

    __all__ = [
        'shufflenet_v2_x0_5', 'shufflenet_v2_x1_0', 'shufflenet_v2_x1_5',
        'shufflenet_v2_x2_0'
    ]

    model_urls = {
        'shufflenetv2_x0.5':
        'https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth',
        'shufflenetv2_x1.0':
        'https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth',
        'shufflenetv2_x1.5': None,
        'shufflenetv2_x2.0': None,
    }

    def channel_shuffle(x, groups):
        batchsize, num_channels, height, width = x.data.size()
        channels_per_group = num_channels // groups

        # reshape
        x = x.view(batchsize, groups, channels_per_group, height, width)

        x = torch.transpose(x, 1, 2).contiguous()

        # flatten
        x = x.view(batchsize, -1, height, width)

        return x

    class InvertedResidual(nn.Module):

        def __init__(self, inp, oup, stride):
            super(InvertedResidual, self).__init__()

            if not (1 <= stride <= 3):
                raise ValueError('illegal stride value')
            self.stride = stride

            branch_features = oup // 2
            assert (self.stride != 1) or (inp == branch_features << 1)

            if self.stride > 1:
                self.branch1 = nn.Sequential(
                    self.depthwise_conv(
                        inp, inp, kernel_size=3, stride=self.stride, padding=1
                    ),
                    nn.BatchNorm2d(inp),
                    nn.Conv2d(
                        inp,
                        branch_features,
                        kernel_size=1,
                        stride=1,
                        padding=0,
                        bias=False
                    ),
                    nn.BatchNorm2d(branch_features),
                    nn.ReLU(inplace=True),
                )

            self.branch2 = nn.Sequential(
                nn.Conv2d(
                    inp if (self.stride > 1) else branch_features,
                    branch_features,
                    kernel_size=1,
                    stride=1,
                    padding=0,
                    bias=False
                ),
                nn.BatchNorm2d(branch_features),
                nn.ReLU(inplace=True),
                self.depthwise_conv(
                    branch_features,
                    branch_features,
                    kernel_size=3,
                    stride=self.stride,
                    padding=1
                ),
                nn.BatchNorm2d(branch_features),
                nn.Conv2d(
                    branch_features,
                    branch_features,
                    kernel_size=1,
                    stride=1,
                    padding=0,
                    bias=False
                ),
                nn.BatchNorm2d(branch_features),
                nn.ReLU(inplace=True),
            )

        @staticmethod
        def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
            return nn.Conv2d(
                i, o, kernel_size, stride, padding, bias=bias, groups=i
            )

        def forward(self, x):
            if self.stride == 1:
                x1, x2 = x.chunk(2, dim=1)
                out = torch.cat((x1, self.branch2(x2)), dim=1)
            else:
                out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)

            out = channel_shuffle(out, 2)

            return out

    class ShuffleNetV2(nn.Module):
        """ShuffleNetV2.

        Reference:
            Ma et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. ECCV 2018.

        Public keys:
            - ``shufflenet_v2_x0_5``: ShuffleNetV2 x0.5.
            - ``shufflenet_v2_x1_0``: ShuffleNetV2 x1.0.
            - ``shufflenet_v2_x1_5``: ShuffleNetV2 x1.5.
            - ``shufflenet_v2_x2_0``: ShuffleNetV2 x2.0.
        """

        def __init__(
            self, num_classes, loss, stages_repeats, stages_out_channels, **kwargs
        ):
            super(ShuffleNetV2, self).__init__()
            self.loss = loss

            if len(stages_repeats) != 3:
                raise ValueError(
                    'expected stages_repeats as list of 3 positive ints'
                )
            if len(stages_out_channels) != 5:
                raise ValueError(
                    'expected stages_out_channels as list of 5 positive ints'
                )
            self._stage_out_channels = stages_out_channels

            input_channels = 3
            output_channels = self._stage_out_channels[0]
            self.conv1 = nn.Sequential(
                nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),
                nn.BatchNorm2d(output_channels),
                nn.ReLU(inplace=True),
            )
            input_channels = output_channels

            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

            stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
            for name, repeats, output_channels in zip(
                stage_names, stages_repeats, self._stage_out_channels[1:]
            ):
                seq = [InvertedResidual(input_channels, output_channels, 2)]
                for i in range(repeats - 1):
                    seq.append(
                        InvertedResidual(output_channels, output_channels, 1)
                    )
                setattr(self, name, nn.Sequential(*seq))
                input_channels = output_channels

            output_channels = self._stage_out_channels[-1]
            self.conv5 = nn.Sequential(
                nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False),
                nn.BatchNorm2d(output_channels),
                nn.ReLU(inplace=True),
            )
            self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))

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

        def featuremaps(self, x):
            x = self.conv1(x)
            x = self.maxpool(x)
            x = self.stage2(x)
            x = self.stage3(x)
            x = self.stage4(x)
            x = self.conv5(x)
            return x

        def forward(self, x):
            f = self.featuremaps(x)
            v = self.global_avgpool(f)
            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.
        """
        if model_url is None:
            import warnings
            warnings.warn(
                'ImageNet pretrained weights are unavailable for this model'
            )
            return
        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)

    def shufflenet_v2_x0_5(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = ShuffleNetV2(
            num_classes, loss, [4, 8, 4], [24, 48, 96, 192, 1024], **kwargs
        )
        if pretrained:
            init_pretrained_weights(model, model_urls['shufflenetv2_x0.5'])
        return model

    def shufflenet_v2_x1_0(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = ShuffleNetV2(
            num_classes, loss, [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs
        )
        if pretrained:
            init_pretrained_weights(model, model_urls['shufflenetv2_x1.0'])
        return model

    def shufflenet_v2_x1_5(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = ShuffleNetV2(
            num_classes, loss, [4, 8, 4], [24, 176, 352, 704, 1024], **kwargs
        )
        if pretrained:
            init_pretrained_weights(model, model_urls['shufflenetv2_x1.5'])
        return model

    def shufflenet_v2_x2_0(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = ShuffleNetV2(
            num_classes, loss, [4, 8, 4], [24, 244, 488, 976, 2048], **kwargs
        )
        if pretrained:
            init_pretrained_weights(model, model_urls['shufflenetv2_x2.0'])
        return model