Source code for torchvision.models.video.resnet
from torch import Tensor
import torch.nn as nn
from typing import Tuple, Optional, Callable, List, Type, Any, Union
from ..._internally_replaced_utils import load_state_dict_from_url
__all__ = ['r3d_18', 'mc3_18', 'r2plus1d_18']
model_urls = {
'r3d_18': 'https://download.pytorch.org/models/r3d_18-b3b3357e.pth',
'mc3_18': 'https://download.pytorch.org/models/mc3_18-a90a0ba3.pth',
'r2plus1d_18': 'https://download.pytorch.org/models/r2plus1d_18-91a641e6.pth',
}
class Conv3DSimple(nn.Conv3d):
def __init__(
self,
in_planes: int,
out_planes: int,
midplanes: Optional[int] = None,
stride: int = 1,
padding: int = 1
) -> None:
super(Conv3DSimple, self).__init__(
in_channels=in_planes,
out_channels=out_planes,
kernel_size=(3, 3, 3),
stride=stride,
padding=padding,
bias=False)
@staticmethod
def get_downsample_stride(stride: int) -> Tuple[int, int, int]:
return stride, stride, stride
class Conv2Plus1D(nn.Sequential):
def __init__(
self,
in_planes: int,
out_planes: int,
midplanes: int,
stride: int = 1,
padding: int = 1
) -> None:
super(Conv2Plus1D, self).__init__(
nn.Conv3d(in_planes, midplanes, kernel_size=(1, 3, 3),
stride=(1, stride, stride), padding=(0, padding, padding),
bias=False),
nn.BatchNorm3d(midplanes),
nn.ReLU(inplace=True),
nn.Conv3d(midplanes, out_planes, kernel_size=(3, 1, 1),
stride=(stride, 1, 1), padding=(padding, 0, 0),
bias=False))
@staticmethod
def get_downsample_stride(stride: int) -> Tuple[int, int, int]:
return stride, stride, stride
class Conv3DNoTemporal(nn.Conv3d):
def __init__(
self,
in_planes: int,
out_planes: int,
midplanes: Optional[int] = None,
stride: int = 1,
padding: int = 1
) -> None:
super(Conv3DNoTemporal, self).__init__(
in_channels=in_planes,
out_channels=out_planes,
kernel_size=(1, 3, 3),
stride=(1, stride, stride),
padding=(0, padding, padding),
bias=False)
@staticmethod
def get_downsample_stride(stride: int) -> Tuple[int, int, int]:
return 1, stride, stride
class BasicBlock(nn.Module):
expansion = 1
def __init__(
self,
inplanes: int,
planes: int,
conv_builder: Callable[..., nn.Module],
stride: int = 1,
downsample: Optional[nn.Module] = None,
) -> None:
midplanes = (inplanes * planes * 3 * 3 * 3) // (inplanes * 3 * 3 + 3 * planes)
super(BasicBlock, self).__init__()
self.conv1 = nn.Sequential(
conv_builder(inplanes, planes, midplanes, stride),
nn.BatchNorm3d(planes),
nn.ReLU(inplace=True)
)
self.conv2 = nn.Sequential(
conv_builder(planes, planes, midplanes),
nn.BatchNorm3d(planes)
)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x: Tensor) -> Tensor:
residual = x
out = self.conv1(x)
out = self.conv2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(
self,
inplanes: int,
planes: int,
conv_builder: Callable[..., nn.Module],
stride: int = 1,
downsample: Optional[nn.Module] = None,
) -> None:
super(Bottleneck, self).__init__()
midplanes = (inplanes * planes * 3 * 3 * 3) // (inplanes * 3 * 3 + 3 * planes)
# 1x1x1
self.conv1 = nn.Sequential(
nn.Conv3d(inplanes, planes, kernel_size=1, bias=False),
nn.BatchNorm3d(planes),
nn.ReLU(inplace=True)
)
# Second kernel
self.conv2 = nn.Sequential(
conv_builder(planes, planes, midplanes, stride),
nn.BatchNorm3d(planes),
nn.ReLU(inplace=True)
)
# 1x1x1
self.conv3 = nn.Sequential(
nn.Conv3d(planes, planes * self.expansion, kernel_size=1, bias=False),
nn.BatchNorm3d(planes * self.expansion)
)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x: Tensor) -> Tensor:
residual = x
out = self.conv1(x)
out = self.conv2(out)
out = self.conv3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class BasicStem(nn.Sequential):
"""The default conv-batchnorm-relu stem
"""
def __init__(self) -> None:
super(BasicStem, self).__init__(
nn.Conv3d(3, 64, kernel_size=(3, 7, 7), stride=(1, 2, 2),
padding=(1, 3, 3), bias=False),
nn.BatchNorm3d(64),
nn.ReLU(inplace=True))
class R2Plus1dStem(nn.Sequential):
"""R(2+1)D stem is different than the default one as it uses separated 3D convolution
"""
def __init__(self) -> None:
super(R2Plus1dStem, self).__init__(
nn.Conv3d(3, 45, kernel_size=(1, 7, 7),
stride=(1, 2, 2), padding=(0, 3, 3),
bias=False),
nn.BatchNorm3d(45),
nn.ReLU(inplace=True),
nn.Conv3d(45, 64, kernel_size=(3, 1, 1),
stride=(1, 1, 1), padding=(1, 0, 0),
bias=False),
nn.BatchNorm3d(64),
nn.ReLU(inplace=True))
class VideoResNet(nn.Module):
def __init__(
self,
block: Type[Union[BasicBlock, Bottleneck]],
conv_makers: List[Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]]],
layers: List[int],
stem: Callable[..., nn.Module],
num_classes: int = 400,
zero_init_residual: bool = False,
) -> None:
"""Generic resnet video generator.
Args:
block (Type[Union[BasicBlock, Bottleneck]]): resnet building block
conv_makers (List[Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]]]): generator
function for each layer
layers (List[int]): number of blocks per layer
stem (Callable[..., nn.Module]): module specifying the ResNet stem.
num_classes (int, optional): Dimension of the final FC layer. Defaults to 400.
zero_init_residual (bool, optional): Zero init bottleneck residual BN. Defaults to False.
"""
super(VideoResNet, self).__init__()
self.inplanes = 64
self.stem = stem()
self.layer1 = self._make_layer(block, conv_makers[0], 64, layers[0], stride=1)
self.layer2 = self._make_layer(block, conv_makers[1], 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, conv_makers[2], 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, conv_makers[3], 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool3d((1, 1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
# init weights
self._initialize_weights()
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0) # type: ignore[union-attr, arg-type]
def forward(self, x: Tensor) -> Tensor:
x = self.stem(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
# Flatten the layer to fc
x = x.flatten(1)
x = self.fc(x)
return x
def _make_layer(
self,
block: Type[Union[BasicBlock, Bottleneck]],
conv_builder: Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]],
planes: int,
blocks: int,
stride: int = 1
) -> nn.Sequential:
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
ds_stride = conv_builder.get_downsample_stride(stride)
downsample = nn.Sequential(
nn.Conv3d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=ds_stride, bias=False),
nn.BatchNorm3d(planes * block.expansion)
)
layers = []
layers.append(block(self.inplanes, planes, conv_builder, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, conv_builder))
return nn.Sequential(*layers)
def _initialize_weights(self) -> None:
for m in self.modules():
if isinstance(m, nn.Conv3d):
nn.init.kaiming_normal_(m.weight, mode='fan_out',
nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm3d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
nn.init.constant_(m.bias, 0)
def _video_resnet(arch: str, pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VideoResNet:
model = VideoResNet(**kwargs)
if pretrained:
state_dict = load_state_dict_from_url(model_urls[arch],
progress=progress)
model.load_state_dict(state_dict)
return model
[docs]def r3d_18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VideoResNet:
"""Construct 18 layer Resnet3D model as in
https://arxiv.org/abs/1711.11248
Args:
pretrained (bool): If True, returns a model pre-trained on Kinetics-400
progress (bool): If True, displays a progress bar of the download to stderr
Returns:
nn.Module: R3D-18 network
"""
return _video_resnet('r3d_18',
pretrained, progress,
block=BasicBlock,
conv_makers=[Conv3DSimple] * 4,
layers=[2, 2, 2, 2],
stem=BasicStem, **kwargs)
[docs]def mc3_18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VideoResNet:
"""Constructor for 18 layer Mixed Convolution network as in
https://arxiv.org/abs/1711.11248
Args:
pretrained (bool): If True, returns a model pre-trained on Kinetics-400
progress (bool): If True, displays a progress bar of the download to stderr
Returns:
nn.Module: MC3 Network definition
"""
return _video_resnet('mc3_18',
pretrained, progress,
block=BasicBlock,
conv_makers=[Conv3DSimple] + [Conv3DNoTemporal] * 3, # type: ignore[list-item]
layers=[2, 2, 2, 2],
stem=BasicStem, **kwargs)
[docs]def r2plus1d_18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VideoResNet:
"""Constructor for the 18 layer deep R(2+1)D network as in
https://arxiv.org/abs/1711.11248
Args:
pretrained (bool): If True, returns a model pre-trained on Kinetics-400
progress (bool): If True, displays a progress bar of the download to stderr
Returns:
nn.Module: R(2+1)D-18 network
"""
return _video_resnet('r2plus1d_18',
pretrained, progress,
block=BasicBlock,
conv_makers=[Conv2Plus1D] * 4,
layers=[2, 2, 2, 2],
stem=R2Plus1dStem, **kwargs)
