from typing import Callable, Any, List import torch import torch.nn as nn from torch import Tensor from .._internally_replaced_utils import load_state_dict_from_url from ..utils import _log_api_usage_once __all__ = ["ShuffleNetV2", "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: Tensor, groups: int) -> Tensor: batchsize, num_channels, height, width = x.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: int, oup: int, stride: int) -> None: super().__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), ) else: self.branch1 = nn.Sequential() 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: int, o: int, kernel_size: int, stride: int = 1, padding: int = 0, bias: bool = False ) -> nn.Conv2d: return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i) 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 out 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, ) -> None: super().__init__() _log_api_usage_once(self) 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) # Static annotations for mypy self.stage2: nn.Sequential self.stage3: nn.Sequential self.stage4: nn.Sequential stage_names = [f"stage{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_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.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]) # globalpool x = self.fc(x) return x def forward(self, x: Tensor) -> Tensor: return self._forward_impl(x) def _shufflenetv2(arch: str, pretrained: bool, progress: bool, *args: Any, **kwargs: Any) -> ShuffleNetV2: model = ShuffleNetV2(*args, **kwargs) if pretrained: model_url = model_urls[arch] if model_url is None: raise NotImplementedError(f"pretrained {arch} is not supported as of now") else: state_dict = load_state_dict_from_url(model_url, progress=progress) model.load_state_dict(state_dict) return model def shufflenet_v2_x0_5(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ShuffleNetV2: """ Constructs a ShuffleNetV2 with 0.5x output channels, as described in `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" `_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _shufflenetv2("shufflenetv2_x0.5", pretrained, progress, [4, 8, 4], [24, 48, 96, 192, 1024], **kwargs) def shufflenet_v2_x1_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ShuffleNetV2: """ Constructs a ShuffleNetV2 with 1.0x output channels, as described in `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" `_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _shufflenetv2("shufflenetv2_x1.0", pretrained, progress, [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs) def shufflenet_v2_x1_5(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ShuffleNetV2: """ Constructs a ShuffleNetV2 with 1.5x output channels, as described in `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" `_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _shufflenetv2("shufflenetv2_x1.5", pretrained, progress, [4, 8, 4], [24, 176, 352, 704, 1024], **kwargs) def shufflenet_v2_x2_0(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ShuffleNetV2: """ Constructs a ShuffleNetV2 with 2.0x output channels, as described in `"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design" `_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _shufflenetv2("shufflenetv2_x2.0", pretrained, progress, [4, 8, 4], [24, 244, 488, 976, 2048], **kwargs)