""" 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