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stride為1時，input_channel應該是branch_features的兩倍# python中 ’<<’ 是位運算，可理解為計算×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

到此這篇關于Python深度學習之使用Pytorch搭建ShuffleNetv2的文章就介紹到這了,更多相關Python用Pytorch搭建ShuffleNetv2內容請搜索好吧啦網以前的文章或繼續瀏覽下面的相關文章希望大家以后多多支持好吧啦網！