"""
Modified from https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py
"""
from typing import Type, Any, Callable, Union, List, Optional
import oneflow as flow
import oneflow.nn as nn
from oneflow import Tensor
from .registry import ModelCreator
from .utils import load_state_dict_from_url
__all__ = [
"ResNet",
"resnet18",
"resnet34",
"resnet50",
"resnet101",
"resnet152",
"resnext50_32x4d",
"resnext101_32x8d",
"wide_resnet50_2",
"wide_resnet101_2",
]
model_urls = {
"resnet18": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet18.zip",
"resnet34": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet34.zip",
"resnet50": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet50.zip",
"resnet101": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet101.zip",
"resnet152": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnet152.zip",
"resnext50_32x4d": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnext50_32x4d.zip",
"resnext101_32x8d": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/resnext101_32x8d.zip",
"wide_resnet50_2": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/wide_resnet50_2.zip",
"wide_resnet101_2": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/ResNet/wide_resnet101_2.zip",
}
def conv3x3(
in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1
) -> nn.Conv2d:
"""3x3 convolution with padding"""
return nn.Conv2d(
in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=dilation,
groups=groups,
bias=False,
dilation=dilation,
)
def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
expansion: int = 1
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
groups: int = 1,
base_width: int = 64,
dilation: int = 1,
norm_layer: Optional[Callable[..., nn.Module]] = None,
) -> None:
super(BasicBlock, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError("BasicBlock only supports groups=1 and base_width=64")
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU()
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x: Tensor) -> Tensor:
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion: int = 4
def __init__(
self,
inplanes: int,
planes: int,
stride: int = 1,
downsample: Optional[nn.Module] = None,
groups: int = 1,
base_width: int = 64,
dilation: int = 1,
norm_layer: Optional[Callable[..., nn.Module]] = None,
) -> None:
super(Bottleneck, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = int(planes * (base_width / 64.0)) * groups
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.conv2 = conv3x3(width, width, stride, groups, dilation)
self.bn2 = norm_layer(width)
self.conv3 = conv1x1(width, planes * self.expansion)
self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU()
self.downsample = downsample
self.stride = stride
def forward(self, x: Tensor) -> Tensor:
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(
self,
block: Type[Union[BasicBlock, Bottleneck]],
layers: List[int],
num_classes: int = 1000,
zero_init_residual: bool = False,
groups: int = 1,
width_per_group: int = 64,
replace_stride_with_dilation: Optional[List[bool]] = None,
norm_layer: Optional[Callable[..., nn.Module]] = None,
) -> None:
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError(
"replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation)
)
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(
3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False
)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(
block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]
)
self.layer3 = self._make_layer(
block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]
)
self.layer4 = self._make_layer(
block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]
)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type]
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type]
def _make_layer(
self,
block: Type[Union[BasicBlock, Bottleneck]],
planes: int,
blocks: int,
stride: int = 1,
dilate: bool = False,
) -> nn.Sequential:
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(
block(
self.inplanes,
planes,
stride,
downsample,
self.groups,
self.base_width,
previous_dilation,
norm_layer,
)
)
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(
block(
self.inplanes,
planes,
groups=self.groups,
base_width=self.base_width,
dilation=self.dilation,
norm_layer=norm_layer,
)
)
return nn.Sequential(*layers)
def _forward_impl(self, x: Tensor) -> Tensor:
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = flow.flatten(x, 1)
x = self.fc(x)
return x
def forward(self, x: Tensor) -> Tensor:
return self._forward_impl(x)
def _resnet(
arch: str,
block: Type[Union[BasicBlock, Bottleneck]],
layers: List[int],
pretrained: bool,
progress: bool,
**kwargs: Any
) -> ResNet:
model = ResNet(block, layers, **kwargs)
if pretrained:
state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
model.load_state_dict(state_dict)
return model
[docs]@ModelCreator.register_model
def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
"""
Constructs the ResNet-18 model.
.. note::
`Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`_.
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
>>> resnet18 = flowvision.models.resnet18(pretrained=False, progress=True)
"""
return _resnet("resnet18", BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs)
[docs]@ModelCreator.register_model
def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
"""
Constructs the ResNet-34 model.
.. note::
`Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`_.
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
>>> resnet34 = flowvision.models.resnet34(pretrained=False, progress=True)
"""
return _resnet("resnet34", BasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs)
[docs]@ModelCreator.register_model
def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
"""
Constructs the ResNet-50 model.
.. note::
`Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`_.
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
>>> resnet50 = flowvision.models.resnet50(pretrained=False, progress=True)
"""
return _resnet("resnet50", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs)
[docs]@ModelCreator.register_model
def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
"""
Constructs the ResNet-101 model.
.. note::
`Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`_.
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
>>> resnet101 = flowvision.models.resnet101(pretrained=False, progress=True)
"""
return _resnet(
"resnet101", Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs
)
[docs]@ModelCreator.register_model
def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
"""
Constructs the ResNet-152 model.
.. note::
`Deep Residual Learning for Image Recognition <https://arxiv.org/pdf/1512.03385.pdf>`_.
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
>>> resnet152 = flowvision.models.resnet152(pretrained=False, progress=True)
"""
return _resnet(
"resnet152", Bottleneck, [3, 8, 36, 3], pretrained, progress, **kwargs
)
[docs]@ModelCreator.register_model
def resnext50_32x4d(
pretrained: bool = False, progress: bool = True, **kwargs: Any
) -> ResNet:
"""
Constructs the ResNeXt-50 32x4d model.
.. note::
`Aggregated Residual Transformation for Deep Neural Networks <https://arxiv.org/pdf/1611.05431.pdf>`_.
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
>>> resnext50_32x4d = flowvision.models.resnext50_32x4d(pretrained=False, progress=True)
"""
kwargs["groups"] = 32
kwargs["width_per_group"] = 4
return _resnet(
"resnext50_32x4d", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs
)
[docs]@ModelCreator.register_model
def resnext101_32x8d(
pretrained: bool = False, progress: bool = True, **kwargs: Any
) -> ResNet:
"""
Constructs the ResNeXt-101 32x8d model.
.. note::
`Aggregated Residual Transformation for Deep Neural Networks <https://arxiv.org/pdf/1611.05431.pdf>`_.
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
>>> resnext101_32x8d = flowvision.models.resnext101_32x8d(pretrained=False, progress=True)
"""
kwargs["groups"] = 32
kwargs["width_per_group"] = 8
return _resnet(
"resnext101_32x8d", Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs
)
[docs]@ModelCreator.register_model
def wide_resnet50_2(
pretrained: bool = False, progress: bool = True, **kwargs: Any
) -> ResNet:
"""
Constructs the Wide ResNet-50-2 model.
.. note::
`Wide Residual Networks <https://arxiv.org/pdf/1605.07146.pdf>`_.
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
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
>>> wide_resnet50_2 = flowvision.models.wide_resnet50_2(pretrained=False, progress=True)
"""
kwargs["width_per_group"] = 64 * 2
return _resnet(
"wide_resnet50_2", Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs
)
[docs]@ModelCreator.register_model
def wide_resnet101_2(
pretrained: bool = False, progress: bool = True, **kwargs: Any
) -> ResNet:
"""
Constructs the Wide ResNet-101-2 model.
.. note::
`Wide Residual Networks <https://arxiv.org/pdf/1605.07146.pdf>`_.
The model is the same as ResNet except for the bottleneck number of channels
which is twice larger in every block. The number of channels in outer 1x1
convolutions is the same, e.g.
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
>>> wide_resnet101_2 = flowvision.models.wide_resnet101_2(pretrained=False, progress=True)
"""
kwargs["width_per_group"] = 64 * 2
return _resnet(
"wide_resnet101_2", Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs
)