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


[docs]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