Source code for torchvision.models.shufflenetv2
import torch
from torch import Tensor
import torch.nn as nn
from .utils import load_state_dict_from_url
from typing import Callable, Any, List
__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(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),
)
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(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_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 = ['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_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('pretrained {} is not supported as of now'.format(arch))
else:
state_dict = load_state_dict_from_url(model_url, progress=progress)
model.load_state_dict(state_dict)
return model
[docs]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"
<https://arxiv.org/abs/1807.11164>`_.
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)
[docs]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"
<https://arxiv.org/abs/1807.11164>`_.
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)
[docs]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"
<https://arxiv.org/abs/1807.11164>`_.
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)
[docs]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"
<https://arxiv.org/abs/1807.11164>`_.
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)
