"""
Modified from https://github.com/pytorch/vision/blob/main/torchvision/models/shufflenetv2.py
"""
from typing import Callable, Any, List
import oneflow as flow
from oneflow import Tensor
import oneflow.nn as nn
from .utils import load_state_dict_from_url
from .registry import ModelCreator
__all__ = [
"ShuffleNetV2",
"shufflenet_v2_x0_5",
"shufflenet_v2_x1_0",
"shufflenet_v2_x1_5",
"shufflenet_v2_x2_0",
]
model_urls = {
"shufflenet_v2_x0_5": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ShuffleNetV2/shufflenet_v2_x0_5.zip",
"shufflenet_v2_x1_0": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ShuffleNetV2/shufflenet_v2_x1_0.zip",
"shufflenet_v2_x1_5": None,
"shufflenet_v2_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 = flow.reshape(x, [batchsize, groups, channels_per_group, height, width])
x = flow.transpose(x, 1, 2)
# flatten
x = flow.reshape(x, [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:
cnt_at_dim1 = int(x.shape[1] / 2)
x1 = x[:, 0:cnt_at_dim1, ::]
x2 = x[:, cnt_at_dim1:, ::]
out = flow.cat((x1, self.branch2(x2)), dim=1)
else:
out = flow.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__()
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:
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]@ModelCreator.register_model
def shufflenet_v2_x0_5(
pretrained: bool = False, progress: bool = True, **kwargs: Any
) -> ShuffleNetV2:
"""
Constructs the ShuffleNetV2(0.5x) model.
.. note::
ShuffleNetV2 with 0.5x output channels model architecture from the `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design <https://arxiv.org/abs/1807.11164>`_ paper.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> shufflenet_v2_x0_5 = flowvision.models.shufflenet_v2_x0_5(pretrained=False, progress=True)
"""
return _shufflenetv2(
"shufflenet_v2_x0_5",
pretrained,
progress,
[4, 8, 4],
[24, 48, 96, 192, 1024],
**kwargs
)
[docs]@ModelCreator.register_model
def shufflenet_v2_x1_0(
pretrained: bool = False, progress: bool = True, **kwargs: Any
) -> ShuffleNetV2:
"""
Constructs the ShuffleNetV2(1.0x) model.
.. note::
ShuffleNetV2 with 1.0x output channels model architecture from the `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design <https://arxiv.org/abs/1807.11164>`_ paper.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> shufflenet_v2_x1_0 = flowvision.models.shufflenet_v2_x1_0(pretrained=False, progress=True)
"""
return _shufflenetv2(
"shufflenet_v2_x1_0",
pretrained,
progress,
[4, 8, 4],
[24, 116, 232, 464, 1024],
**kwargs
)
[docs]@ModelCreator.register_model
def shufflenet_v2_x1_5(
pretrained: bool = False, progress: bool = True, **kwargs: Any
) -> ShuffleNetV2:
"""
Constructs the ShuffleNetV2(1.5x) model.
.. note::
ShuffleNetV2 with 1.5x output channels model architecture from the `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design <https://arxiv.org/abs/1807.11164>`_ paper.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> shufflenet_v2_x1_5 = flowvision.models.shufflenet_v2_x1_5(pretrained=False, progress=True)
"""
return _shufflenetv2(
"shufflenet_v2_x1_5",
pretrained,
progress,
[4, 8, 4],
[24, 176, 352, 704, 1024],
**kwargs
)
[docs]@ModelCreator.register_model
def shufflenet_v2_x2_0(
pretrained: bool = False, progress: bool = True, **kwargs: Any
) -> ShuffleNetV2:
"""
Constructs the ShuffleNetV2(2.0x) model.
.. note::
ShuffleNetV2 with 2.0x output channels model architecture from the `ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design <https://arxiv.org/abs/1807.11164>`_ paper.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> shufflenet_v2_x2_0 = flowvision.models.shufflenet_v2_x2_0(pretrained=False, progress=True)
"""
return _shufflenetv2(
"shufflenet_v2_x2_0",
pretrained,
progress,
[4, 8, 4],
[24, 244, 488, 976, 2048],
**kwargs
)