# torchreid.models.resnet — torchreid 1.4.0 documentation

# Source code for torchreid.models.resnet

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

    __all__ = [
        'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
        'resnext50_32x4d', 'resnext101_32x8d', 'resnet50_fc512'
    ]

    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',
        'resnext50_32x4d':
        'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
        'resnext101_32x8d':
        'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
    }

    def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
        """3x3 convolution with padding"""
        return nn.Conv2d(
            in_planes,
            out_planes,
            kernel_size=3,
            stride=stride,
            padding=dilation,
            groups=groups,
            bias=False,
            dilation=dilation
        )

    def conv1x1(in_planes, out_planes, stride=1):
        """1x1 convolution"""
        return nn.Conv2d(
            in_planes, out_planes, kernel_size=1, stride=stride, bias=False
        )

    class BasicBlock(nn.Module):
        expansion = 1

        def __init__(
            self,
            inplanes,
            planes,
            stride=1,
            downsample=None,
            groups=1,
            base_width=64,
            dilation=1,
            norm_layer=None
        ):
            super(BasicBlock, self).__init__()
            if norm_layer is None:
                norm_layer = nn.BatchNorm2d
            if groups != 1 or base_width != 64:
                raise ValueError(
                    'BasicBlock only supports groups=1 and base_width=64'
                )
            if dilation > 1:
                raise NotImplementedError(
                    "Dilation > 1 not supported in BasicBlock"
                )
            # Both self.conv1 and self.downsample layers downsample the input when stride != 1
            self.conv1 = conv3x3(inplanes, planes, stride)
            self.bn1 = norm_layer(planes)
            self.relu = nn.ReLU(inplace=True)
            self.conv2 = conv3x3(planes, planes)
            self.bn2 = norm_layer(planes)
            self.downsample = downsample
            self.stride = stride

        def forward(self, x):
            identity = 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:
                identity = self.downsample(x)

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

            return out

    class Bottleneck(nn.Module):
        expansion = 4

        def __init__(
            self,
            inplanes,
            planes,
            stride=1,
            downsample=None,
            groups=1,
            base_width=64,
            dilation=1,
            norm_layer=None
        ):
            super(Bottleneck, self).__init__()
            if norm_layer is None:
                norm_layer = nn.BatchNorm2d
            width = int(planes * (base_width/64.)) * groups
            # Both self.conv2 and self.downsample layers downsample the input when stride != 1
            self.conv1 = conv1x1(inplanes, width)
            self.bn1 = norm_layer(width)
            self.conv2 = conv3x3(width, width, stride, groups, dilation)
            self.bn2 = norm_layer(width)
            self.conv3 = conv1x1(width, planes * self.expansion)
            self.bn3 = norm_layer(planes * self.expansion)
            self.relu = nn.ReLU(inplace=True)
            self.downsample = downsample
            self.stride = stride

        def forward(self, x):
            identity = 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:
                identity = self.downsample(x)

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

            return out

    class ResNet(nn.Module):
        """Residual network.

        Reference:
            - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
            - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.

        Public keys:
            - ``resnet18``: ResNet18.
            - ``resnet34``: ResNet34.
            - ``resnet50``: ResNet50.
            - ``resnet101``: ResNet101.
            - ``resnet152``: ResNet152.
            - ``resnext50_32x4d``: ResNeXt50.
            - ``resnext101_32x8d``: ResNeXt101.
            - ``resnet50_fc512``: ResNet50 + FC.
        """

        def __init__(
            self,
            num_classes,
            loss,
            block,
            layers,
            zero_init_residual=False,
            groups=1,
            width_per_group=64,
            replace_stride_with_dilation=None,
            norm_layer=None,
            last_stride=2,
            fc_dims=None,
            dropout_p=None,
            **kwargs
        ):
            super(ResNet, self).__init__()
            if norm_layer is None:
                norm_layer = nn.BatchNorm2d
            self._norm_layer = norm_layer
            self.loss = loss
            self.feature_dim = 512 * block.expansion
            self.inplanes = 64
            self.dilation = 1
            if replace_stride_with_dilation is None:
                # each element in the tuple indicates if we should replace
                # the 2x2 stride with a dilated convolution instead
                replace_stride_with_dilation = [False, False, False]
            if len(replace_stride_with_dilation) != 3:
                raise ValueError(
                    "replace_stride_with_dilation should be None "
                    "or a 3-element tuple, got {}".
                    format(replace_stride_with_dilation)
                )
            self.groups = groups
            self.base_width = width_per_group
            self.conv1 = nn.Conv2d(
                3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
            )
            self.bn1 = norm_layer(self.inplanes)
            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,
                dilate=replace_stride_with_dilation[0]
            )
            self.layer3 = self._make_layer(
                block,
                256,
                layers[2],
                stride=2,
                dilate=replace_stride_with_dilation[1]
            )
            self.layer4 = self._make_layer(
                block,
                512,
                layers[3],
                stride=last_stride,
                dilate=replace_stride_with_dilation[2]
            )
            self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))
            self.fc = self._construct_fc_layer(
                fc_dims, 512 * block.expansion, dropout_p
            )
            self.classifier = nn.Linear(self.feature_dim, num_classes)

            self._init_params()

            # Zero-initialize the last BN in each residual branch,
            # so that the residual branch starts with zeros, and each residual block behaves like an identity.
            # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
            if zero_init_residual:
                for m in self.modules():
                    if isinstance(m, Bottleneck):
                        nn.init.constant_(m.bn3.weight, 0)
                    elif isinstance(m, BasicBlock):
                        nn.init.constant_(m.bn2.weight, 0)

        def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
            norm_layer = self._norm_layer
            downsample = None
            previous_dilation = self.dilation
            if dilate:
                self.dilation *= stride
                stride = 1
            if stride != 1 or self.inplanes != planes * block.expansion:
                downsample = nn.Sequential(
                    conv1x1(self.inplanes, planes * block.expansion, stride),
                    norm_layer(planes * block.expansion),
                )

            layers = []
            layers.append(
                block(
                    self.inplanes, planes, stride, downsample, self.groups,
                    self.base_width, previous_dilation, norm_layer
                )
            )
            self.inplanes = planes * block.expansion
            for _ in range(1, blocks):
                layers.append(
                    block(
                        self.inplanes,
                        planes,
                        groups=self.groups,
                        base_width=self.base_width,
                        dilation=self.dilation,
                        norm_layer=norm_layer
                    )
                )

            return nn.Sequential(*layers)

        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, 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 = 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):
        """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)

    """ResNet"""

    def resnet18(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = ResNet(
            num_classes=num_classes,
            loss=loss,
            block=BasicBlock,
            layers=[2, 2, 2, 2],
            last_stride=2,
            fc_dims=None,
            dropout_p=None,
            **kwargs
        )
        if pretrained:
            init_pretrained_weights(model, model_urls['resnet18'])
        return model

    def resnet34(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = ResNet(
            num_classes=num_classes,
            loss=loss,
            block=BasicBlock,
            layers=[3, 4, 6, 3],
            last_stride=2,
            fc_dims=None,
            dropout_p=None,
            **kwargs
        )
        if pretrained:
            init_pretrained_weights(model, model_urls['resnet34'])
        return model

    def resnet50(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = ResNet(
            num_classes=num_classes,
            loss=loss,
            block=Bottleneck,
            layers=[3, 4, 6, 3],
            last_stride=2,
            fc_dims=None,
            dropout_p=None,
            **kwargs
        )
        if pretrained:
            init_pretrained_weights(model, model_urls['resnet50'])
        return model

    def resnet101(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = ResNet(
            num_classes=num_classes,
            loss=loss,
            block=Bottleneck,
            layers=[3, 4, 23, 3],
            last_stride=2,
            fc_dims=None,
            dropout_p=None,
            **kwargs
        )
        if pretrained:
            init_pretrained_weights(model, model_urls['resnet101'])
        return model

    def resnet152(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = ResNet(
            num_classes=num_classes,
            loss=loss,
            block=Bottleneck,
            layers=[3, 8, 36, 3],
            last_stride=2,
            fc_dims=None,
            dropout_p=None,
            **kwargs
        )
        if pretrained:
            init_pretrained_weights(model, model_urls['resnet152'])
        return model

    """ResNeXt"""

    def resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = ResNet(
            num_classes=num_classes,
            loss=loss,
            block=Bottleneck,
            layers=[3, 4, 6, 3],
            last_stride=2,
            fc_dims=None,
            dropout_p=None,
            groups=32,
            width_per_group=4,
            **kwargs
        )
        if pretrained:
            init_pretrained_weights(model, model_urls['resnext50_32x4d'])
        return model

    def resnext101_32x8d(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = ResNet(
            num_classes=num_classes,
            loss=loss,
            block=Bottleneck,
            layers=[3, 4, 23, 3],
            last_stride=2,
            fc_dims=None,
            dropout_p=None,
            groups=32,
            width_per_group=8,
            **kwargs
        )
        if pretrained:
            init_pretrained_weights(model, model_urls['resnext101_32x8d'])
        return model

    """
    ResNet + FC
    """

    def resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
        model = ResNet(
            num_classes=num_classes,
            loss=loss,
            block=Bottleneck,
            layers=[3, 4, 6, 3],
            last_stride=1,
            fc_dims=[512],
            dropout_p=None,
            **kwargs
        )
        if pretrained:
            init_pretrained_weights(model, model_urls['resnet50'])
        return model