def channel_shuffle(x: Tensor, groups: int) -> Tensor: batch_size, num_channels, height, width = x.size() channels_per_group = num_channels // groups # reshape # [batch_size, num_channels, height, width] -> [batch_size, groups, channels_per_group, height, width] x = x.view(batch_size, groups, channels_per_group, height, width) x = torch.transpose(x, 1, 2).contiguous() # flatten x = x.view(batch_size, -1, height, width) return x1.2 block

class InvertedResidual(nn.Module): def __init__(self, input_c: int, output_c: int, stride: int):super(InvertedResidual, self).__init__()if stride not in [1, 2]: raise ValueError('illegal stride value.')self.stride = strideassert output_c % 2 == 0branch_features = output_c // 2# 當(dāng)stride為1時(shí)，input_channel應(yīng)該是branch_features的兩倍# python中 ’<<’ 是位運(yùn)算，可理解為計(jì)算×2的快速方法assert (self.stride != 1) or (input_c == branch_features << 1)if self.stride == 2: self.branch1 = nn.Sequential(self.depthwise_conv(input_c, input_c, kernel_s=3, stride=self.stride, padding=1),nn.BatchNorm2d(input_c),nn.Conv2d(input_c, branch_features, kernel_size=1, stride=1, padding=0, bias=False),nn.BatchNorm2d(branch_features),nn.ReLU(inplace=True) )else: self.branch1 = nn.Sequential()self.branch2 = nn.Sequential( nn.Conv2d(input_c 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_s=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(input_c: int, output_c: int, kernel_s: int, stride: int = 1, padding: int = 0, bias: bool = False) -> nn.Conv2d:return nn.Conv2d(in_channels=input_c, out_channels=output_c, kernel_size=kernel_s, stride=stride, padding=padding, bias=bias, groups=input_c) def forward(self, x: Tensor) -> Tensor: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 out1.3 shufflenet v2

class ShuffleNetV2(nn.Module): def __init__(self, stages_repeats: List[int], stages_out_channels: List[int], num_classes: int = 1000, inverted_residual: Callable[..., nn.Module] = InvertedResidual):super(ShuffleNetV2, self).__init__()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 RGB imageinput_channels = 3output_channels = self._stage_out_channels[0]self.conv1 = nn.Sequential( nn.Conv2d(input_channels, output_channels, kernel_size=3, stride=2, padding=1, bias=False), nn.BatchNorm2d(output_channels), nn.ReLU(inplace=True))input_channels = output_channelsself.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)# Static annotations for mypyself.stage2: nn.Sequentialself.stage3: nn.Sequentialself.stage4: nn.Sequentialstage_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 = [inverted_residual(input_channels, output_channels, 2)] for i in range(repeats - 1):seq.append(inverted_residual(output_channels, output_channels, 1)) setattr(self, name, nn.Sequential(*seq)) input_channels = output_channelsoutput_channels = self._stage_out_channels[-1]self.conv5 = nn.Sequential( nn.Conv2d(input_channels, output_channels, kernel_size=1, stride=1, padding=0, bias=False), nn.BatchNorm2d(output_channels), nn.ReLU(inplace=True))self.fc = nn.Linear(output_channels, num_classes) def _forward_impl(self, x: Tensor) -> Tensor:# See note [TorchScript super()]x = self.conv1(x)x = self.maxpool(x)x = self.stage2(x)x = self.stage3(x)x = self.stage4(x)x = self.conv5(x)x = x.mean([2, 3]) # global poolx = self.fc(x)return x def forward(self, x: Tensor) -> Tensor:return self._forward_impl(x)二、train.py

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