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Unverified Commit 11d36292 authored by F-G Fernandez's avatar F-G Fernandez Committed by GitHub
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Added typing annotations to models/video (#4229) 

* style: Added typing to models/video

* style: Fixed typing

* style: Fixed typing

* style: Fixed typing

* refactor: Removed default value for stem

* docs: Fixed docstring of VideoResNet

* style: Refactored typing

* docs: Fixed docstring

* style: Fixed typing

* docs: Specified docstring

* typing: Fixed tying

* docs: Fixed docstring

* Undoing change.
parent 185be3a9
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Showing with 78 additions and 42 deletions
torchvision/models/video/resnet.py
View file @ 11d36292
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
......@@ -13,12 +15,14 @@ model_urls = {
class Conv3DSimple(nn.Conv3d):
def __init__(self,
in_planes,
out_planes,
midplanes=None,
stride=1,
padding=1):
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,
......@@ -29,18 +33,20 @@ class Conv3DSimple(nn.Conv3d):
bias=False)
@staticmethod
def get_downsample_stride(stride):
def get_downsample_stride(stride: int) -> Tuple[int, int, int]:
return stride, stride, stride
class Conv2Plus1D(nn.Sequential):
def __init__(self,
in_planes,
out_planes,
midplanes,
stride=1,
padding=1):
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),
......@@ -52,18 +58,20 @@ class Conv2Plus1D(nn.Sequential):
bias=False))
@staticmethod
def get_downsample_stride(stride):
def get_downsample_stride(stride: int) -> Tuple[int, int, int]:
return stride, stride, stride
class Conv3DNoTemporal(nn.Conv3d):
def __init__(self,
in_planes,
out_planes,
midplanes=None,
stride=1,
padding=1):
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,
......@@ -74,7 +82,7 @@ class Conv3DNoTemporal(nn.Conv3d):
bias=False)
@staticmethod
def get_downsample_stride(stride):
def get_downsample_stride(stride: int) -> Tuple[int, int, int]:
return 1, stride, stride
......@@ -82,7 +90,14 @@ class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, conv_builder, stride=1, downsample=None):
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__()
......@@ -99,7 +114,7 @@ class BasicBlock(nn.Module):
self.downsample = downsample
self.stride = stride
def forward(self, x):
def forward(self, x: Tensor) -> Tensor:
residual = x
out = self.conv1(x)
......@@ -116,7 +131,14 @@ class BasicBlock(nn.Module):
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, conv_builder, stride=1, downsample=None):
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)
......@@ -143,7 +165,7 @@ class Bottleneck(nn.Module):
self.downsample = downsample
self.stride = stride
def forward(self, x):
def forward(self, x: Tensor) -> Tensor:
residual = x
out = self.conv1(x)
......@@ -162,7 +184,7 @@ class Bottleneck(nn.Module):
class BasicStem(nn.Sequential):
"""The default conv-batchnorm-relu stem
"""
def __init__(self):
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),
......@@ -173,7 +195,7 @@ class BasicStem(nn.Sequential):
class R2Plus1dStem(nn.Sequential):
"""R(2+1)D stem is different than the default one as it uses separated 3D convolution
"""
def __init__(self):
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),
......@@ -189,16 +211,23 @@ class R2Plus1dStem(nn.Sequential):
class VideoResNet(nn.Module):
def __init__(self, block, conv_makers, layers,
stem, num_classes=400,
zero_init_residual=False):
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 (nn.Module): resnet building block
conv_makers (list(functions)): generator function for each layer
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 (nn.Module, optional): Resnet stem, if None, defaults to conv-bn-relu. Defaults to None.
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.
"""
......@@ -221,9 +250,9 @@ class VideoResNet(nn.Module):
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
nn.init.constant_(m.bn3.weight, 0) # type: ignore[union-attr, arg-type]
def forward(self, x):
def forward(self, x: Tensor) -> Tensor:
x = self.stem(x)
x = self.layer1(x)
......@@ -238,7 +267,14 @@ class VideoResNet(nn.Module):
return x
def _make_layer(self, block, conv_builder, planes, blocks, stride=1):
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:
......@@ -257,7 +293,7 @@ class VideoResNet(nn.Module):
return nn.Sequential(*layers)
def _initialize_weights(self):
def _initialize_weights(self) -> None:
for m in self.modules():
if isinstance(m, nn.Conv3d):
nn.init.kaiming_normal_(m.weight, mode='fan_out',
......@@ -272,7 +308,7 @@ class VideoResNet(nn.Module):
nn.init.constant_(m.bias, 0)
def _video_resnet(arch, pretrained=False, progress=True, **kwargs):
def _video_resnet(arch: str, pretrained: bool = False, progress: bool = True, **kwargs: Any) -> VideoResNet:
model = VideoResNet(**kwargs)
if pretrained:
......@@ -282,7 +318,7 @@ def _video_resnet(arch, pretrained=False, progress=True, **kwargs):
return model
def r3d_18(pretrained=False, progress=True, **kwargs):
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
......@@ -302,7 +338,7 @@ def r3d_18(pretrained=False, progress=True, **kwargs):
stem=BasicStem, **kwargs)
def mc3_18(pretrained=False, progress=True, **kwargs):
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
......@@ -316,12 +352,12 @@ def mc3_18(pretrained=False, progress=True, **kwargs):
return _video_resnet('mc3_18',
pretrained, progress,
block=BasicBlock,
conv_makers=[Conv3DSimple] + [Conv3DNoTemporal] * 3,
conv_makers=[Conv3DSimple] + [Conv3DNoTemporal] * 3, # type: ignore[list-item]
layers=[2, 2, 2, 2],
stem=BasicStem, **kwargs)
def r2plus1d_18(pretrained=False, progress=True, **kwargs):
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
......
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