trackers/strongsort/deep/models/xception.py

from __future__ import division, absolute_import
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo

__all__ = ['xception']

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)


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