deep-person-reid/torchreid/models/shufflenet.py

from __future__ import absolute_import
from __future__ import division

import torch
from torch import nn
from torch.nn import functional as F
import torchvision
import torch.utils.model_zoo as model_zoo


__all__ = ['shufflenet']


model_urls = {
    # training epoch = 90, top1 = 61.8
    'imagenet': 'http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/imagenet-pretrained/shufflenet-bee1b265.pth.tar',
}


class ChannelShuffle(nn.Module):
    def __init__(self, num_groups):
        super(ChannelShuffle, self).__init__()
        self.g = num_groups

    def forward(self, x):
        b, c, h, w = x.size()
        n = c // self.g
        # reshape
        x = x.view(b, self.g, n, h, w)
        # transpose
        x = x.permute(0, 2, 1, 3, 4).contiguous()
        # flatten
        x = x.view(b, c, h, w)
        return x


class Bottleneck(nn.Module):
    def __init__(self, in_channels, out_channels, stride, num_groups, group_conv1x1=True):
        super(Bottleneck, self).__init__()
        assert stride in [1, 2], "Warning: stride must be either 1 or 2"
        self.stride = stride
        mid_channels = out_channels // 4
        if stride == 2: out_channels -= in_channels
        # group conv is not applied to first conv1x1 at stage 2
        num_groups_conv1x1 = num_groups if group_conv1x1 else 1
        self.conv1 = nn.Conv2d(in_channels, mid_channels, 1, groups=num_groups_conv1x1, bias=False)
        self.bn1 = nn.BatchNorm2d(mid_channels)
        self.shuffle1 = ChannelShuffle(num_groups)
        self.conv2 = nn.Conv2d(mid_channels, mid_channels, 3, stride=stride, padding=1, groups=mid_channels, bias=False)
        self.bn2 = nn.BatchNorm2d(mid_channels)
        self.conv3 = nn.Conv2d(mid_channels, out_channels, 1, groups=num_groups, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channels)
        if stride == 2: self.shortcut = nn.AvgPool2d(3, stride=2, padding=1)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.shuffle1(out)
        out = self.bn2(self.conv2(out))
        out = self.bn3(self.conv3(out))
        if self.stride == 2:
            res = self.shortcut(x)
            out = F.relu(torch.cat([res, out], 1))
        else:
            out = F.relu(x + out)
        return out


# configuration of (num_groups: #out_channels) based on Table 1 in the paper
cfg = {
    1: [144, 288, 576],
    2: [200, 400, 800],
    3: [240, 480, 960],
    4: [272, 544, 1088],
    8: [384, 768, 1536],
}


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

    Reference:
    Zhang et al. ShuffleNet: An Extremely Efficient Convolutional Neural
    Network for Mobile Devices. CVPR 2018.
    """
    def __init__(self, num_classes, loss={'xent'}, num_groups=3, **kwargs):
        super(ShuffleNet, self).__init__()
        self.loss = loss

        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 24, 3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(24),
            nn.ReLU(),
            nn.MaxPool2d(3, stride=2, padding=1),
        )

        self.stage2 = nn.Sequential(
            Bottleneck(24, cfg[num_groups][0], 2, num_groups, group_conv1x1=False),
            Bottleneck(cfg[num_groups][0], cfg[num_groups][0], 1, num_groups),
            Bottleneck(cfg[num_groups][0], cfg[num_groups][0], 1, num_groups),
            Bottleneck(cfg[num_groups][0], cfg[num_groups][0], 1, num_groups),
        )

        self.stage3 = nn.Sequential(
            Bottleneck(cfg[num_groups][0], cfg[num_groups][1], 2, num_groups),
            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
            Bottleneck(cfg[num_groups][1], cfg[num_groups][1], 1, num_groups),
        )

        self.stage4 = nn.Sequential(
            Bottleneck(cfg[num_groups][1], cfg[num_groups][2], 2, num_groups),
            Bottleneck(cfg[num_groups][2], cfg[num_groups][2], 1, num_groups),
            Bottleneck(cfg[num_groups][2], cfg[num_groups][2], 1, num_groups),
            Bottleneck(cfg[num_groups][2], cfg[num_groups][2], 1, num_groups),
        )

        self.classifier = nn.Linear(cfg[num_groups][2], num_classes)
        self.feat_dim = cfg[num_groups][2]

    def forward(self, x):
        x = self.conv1(x)
        x = self.stage2(x)
        x = self.stage3(x)
        x = self.stage4(x)
        x = F.avg_pool2d(x, x.size()[2:]).view(x.size(0), -1)

        if not self.training:
            return x

        y = self.classifier(x)

        if self.loss == {'xent'}:
            return y
        elif self.loss == {'xent', 'htri'}:
            return y, x
        else:
            raise KeyError("Unsupported loss: {}".format(self.loss))


def init_pretrained_weights(model, model_url):
    """
    Initialize 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 shufflenet(num_classes, loss, pretrained='imagenet', **kwargs):
    model = ShuffleNet(num_classes, loss, **kwargs)
    if pretrained == 'imagenet':
        init_pretrained_weights(model, model_urls['imagenet'])
    return model