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504 lines
16 KiB
504 lines
16 KiB
from functools import partial
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from typing import Any, Callable, List, Optional, Sequence, Tuple, Type, Union
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import torch.nn as nn
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from torch import Tensor
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from ...transforms._presets import VideoClassification
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from ...utils import _log_api_usage_once
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from .._api import register_model, Weights, WeightsEnum
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from .._meta import _KINETICS400_CATEGORIES
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from .._utils import _ovewrite_named_param, handle_legacy_interface
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__all__ = [
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"VideoResNet",
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"R3D_18_Weights",
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"MC3_18_Weights",
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"R2Plus1D_18_Weights",
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"r3d_18",
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"mc3_18",
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"r2plus1d_18",
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]
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class Conv3DSimple(nn.Conv3d):
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def __init__(
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self, in_planes: int, out_planes: int, midplanes: Optional[int] = None, stride: int = 1, padding: int = 1
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) -> None:
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super().__init__(
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in_channels=in_planes,
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out_channels=out_planes,
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kernel_size=(3, 3, 3),
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stride=stride,
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padding=padding,
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bias=False,
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)
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@staticmethod
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def get_downsample_stride(stride: int) -> Tuple[int, int, int]:
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return stride, stride, stride
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class Conv2Plus1D(nn.Sequential):
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def __init__(self, in_planes: int, out_planes: int, midplanes: int, stride: int = 1, padding: int = 1) -> None:
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super().__init__(
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nn.Conv3d(
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in_planes,
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midplanes,
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kernel_size=(1, 3, 3),
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stride=(1, stride, stride),
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padding=(0, padding, padding),
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bias=False,
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),
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nn.BatchNorm3d(midplanes),
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nn.ReLU(inplace=True),
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nn.Conv3d(
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midplanes, out_planes, kernel_size=(3, 1, 1), stride=(stride, 1, 1), padding=(padding, 0, 0), bias=False
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),
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)
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@staticmethod
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def get_downsample_stride(stride: int) -> Tuple[int, int, int]:
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return stride, stride, stride
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class Conv3DNoTemporal(nn.Conv3d):
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def __init__(
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self, in_planes: int, out_planes: int, midplanes: Optional[int] = None, stride: int = 1, padding: int = 1
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) -> None:
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super().__init__(
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in_channels=in_planes,
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out_channels=out_planes,
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kernel_size=(1, 3, 3),
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stride=(1, stride, stride),
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padding=(0, padding, padding),
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bias=False,
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)
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@staticmethod
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def get_downsample_stride(stride: int) -> Tuple[int, int, int]:
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return 1, stride, stride
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(
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self,
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inplanes: int,
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planes: int,
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conv_builder: Callable[..., nn.Module],
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stride: int = 1,
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downsample: Optional[nn.Module] = None,
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) -> None:
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midplanes = (inplanes * planes * 3 * 3 * 3) // (inplanes * 3 * 3 + 3 * planes)
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super().__init__()
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self.conv1 = nn.Sequential(
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conv_builder(inplanes, planes, midplanes, stride), nn.BatchNorm3d(planes), nn.ReLU(inplace=True)
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)
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self.conv2 = nn.Sequential(conv_builder(planes, planes, midplanes), nn.BatchNorm3d(planes))
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x: Tensor) -> Tensor:
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residual = x
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out = self.conv1(x)
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out = self.conv2(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class Bottleneck(nn.Module):
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expansion = 4
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def __init__(
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self,
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inplanes: int,
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planes: int,
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conv_builder: Callable[..., nn.Module],
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stride: int = 1,
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downsample: Optional[nn.Module] = None,
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) -> None:
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super().__init__()
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midplanes = (inplanes * planes * 3 * 3 * 3) // (inplanes * 3 * 3 + 3 * planes)
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# 1x1x1
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self.conv1 = nn.Sequential(
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nn.Conv3d(inplanes, planes, kernel_size=1, bias=False), nn.BatchNorm3d(planes), nn.ReLU(inplace=True)
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)
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# Second kernel
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self.conv2 = nn.Sequential(
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conv_builder(planes, planes, midplanes, stride), nn.BatchNorm3d(planes), nn.ReLU(inplace=True)
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)
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# 1x1x1
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self.conv3 = nn.Sequential(
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nn.Conv3d(planes, planes * self.expansion, kernel_size=1, bias=False),
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nn.BatchNorm3d(planes * self.expansion),
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)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x: Tensor) -> Tensor:
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residual = x
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out = self.conv1(x)
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out = self.conv2(out)
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out = self.conv3(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class BasicStem(nn.Sequential):
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"""The default conv-batchnorm-relu stem"""
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def __init__(self) -> None:
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super().__init__(
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nn.Conv3d(3, 64, kernel_size=(3, 7, 7), stride=(1, 2, 2), padding=(1, 3, 3), bias=False),
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nn.BatchNorm3d(64),
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nn.ReLU(inplace=True),
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)
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class R2Plus1dStem(nn.Sequential):
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"""R(2+1)D stem is different than the default one as it uses separated 3D convolution"""
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def __init__(self) -> None:
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super().__init__(
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nn.Conv3d(3, 45, kernel_size=(1, 7, 7), stride=(1, 2, 2), padding=(0, 3, 3), bias=False),
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nn.BatchNorm3d(45),
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nn.ReLU(inplace=True),
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nn.Conv3d(45, 64, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False),
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nn.BatchNorm3d(64),
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nn.ReLU(inplace=True),
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)
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class VideoResNet(nn.Module):
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def __init__(
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self,
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block: Type[Union[BasicBlock, Bottleneck]],
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conv_makers: Sequence[Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]]],
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layers: List[int],
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stem: Callable[..., nn.Module],
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num_classes: int = 400,
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zero_init_residual: bool = False,
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) -> None:
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"""Generic resnet video generator.
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Args:
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block (Type[Union[BasicBlock, Bottleneck]]): resnet building block
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conv_makers (List[Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]]]): generator
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function for each layer
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layers (List[int]): number of blocks per layer
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stem (Callable[..., nn.Module]): module specifying the ResNet stem.
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num_classes (int, optional): Dimension of the final FC layer. Defaults to 400.
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zero_init_residual (bool, optional): Zero init bottleneck residual BN. Defaults to False.
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"""
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super().__init__()
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_log_api_usage_once(self)
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self.inplanes = 64
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self.stem = stem()
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self.layer1 = self._make_layer(block, conv_makers[0], 64, layers[0], stride=1)
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self.layer2 = self._make_layer(block, conv_makers[1], 128, layers[1], stride=2)
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self.layer3 = self._make_layer(block, conv_makers[2], 256, layers[2], stride=2)
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self.layer4 = self._make_layer(block, conv_makers[3], 512, layers[3], stride=2)
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self.avgpool = nn.AdaptiveAvgPool3d((1, 1, 1))
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self.fc = nn.Linear(512 * block.expansion, num_classes)
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# init weights
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for m in self.modules():
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if isinstance(m, nn.Conv3d):
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nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm3d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight, 0, 0.01)
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nn.init.constant_(m.bias, 0)
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if zero_init_residual:
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for m in self.modules():
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if isinstance(m, Bottleneck):
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nn.init.constant_(m.bn3.weight, 0) # type: ignore[union-attr, arg-type]
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def forward(self, x: Tensor) -> Tensor:
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x = self.stem(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.avgpool(x)
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# Flatten the layer to fc
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x = x.flatten(1)
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x = self.fc(x)
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return x
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def _make_layer(
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self,
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block: Type[Union[BasicBlock, Bottleneck]],
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conv_builder: Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]],
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planes: int,
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blocks: int,
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stride: int = 1,
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) -> nn.Sequential:
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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ds_stride = conv_builder.get_downsample_stride(stride)
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downsample = nn.Sequential(
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nn.Conv3d(self.inplanes, planes * block.expansion, kernel_size=1, stride=ds_stride, bias=False),
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nn.BatchNorm3d(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, conv_builder, stride, downsample))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes, conv_builder))
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return nn.Sequential(*layers)
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def _video_resnet(
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block: Type[Union[BasicBlock, Bottleneck]],
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conv_makers: Sequence[Type[Union[Conv3DSimple, Conv3DNoTemporal, Conv2Plus1D]]],
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layers: List[int],
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stem: Callable[..., nn.Module],
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weights: Optional[WeightsEnum],
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progress: bool,
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**kwargs: Any,
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) -> VideoResNet:
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if weights is not None:
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_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
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model = VideoResNet(block, conv_makers, layers, stem, **kwargs)
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if weights is not None:
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model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
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return model
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_COMMON_META = {
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"min_size": (1, 1),
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"categories": _KINETICS400_CATEGORIES,
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"recipe": "https://github.com/pytorch/vision/tree/main/references/video_classification",
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"_docs": (
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"The weights reproduce closely the accuracy of the paper. The accuracies are estimated on video-level "
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"with parameters `frame_rate=15`, `clips_per_video=5`, and `clip_len=16`."
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),
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}
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class R3D_18_Weights(WeightsEnum):
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KINETICS400_V1 = Weights(
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url="https://download.pytorch.org/models/r3d_18-b3b3357e.pth",
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transforms=partial(VideoClassification, crop_size=(112, 112), resize_size=(128, 171)),
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meta={
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**_COMMON_META,
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"num_params": 33371472,
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"_metrics": {
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"Kinetics-400": {
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"acc@1": 63.200,
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"acc@5": 83.479,
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}
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},
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"_ops": 40.697,
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"_file_size": 127.359,
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},
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)
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DEFAULT = KINETICS400_V1
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class MC3_18_Weights(WeightsEnum):
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KINETICS400_V1 = Weights(
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url="https://download.pytorch.org/models/mc3_18-a90a0ba3.pth",
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transforms=partial(VideoClassification, crop_size=(112, 112), resize_size=(128, 171)),
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meta={
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**_COMMON_META,
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"num_params": 11695440,
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"_metrics": {
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"Kinetics-400": {
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"acc@1": 63.960,
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"acc@5": 84.130,
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}
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},
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"_ops": 43.343,
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"_file_size": 44.672,
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},
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)
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DEFAULT = KINETICS400_V1
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class R2Plus1D_18_Weights(WeightsEnum):
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KINETICS400_V1 = Weights(
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url="https://download.pytorch.org/models/r2plus1d_18-91a641e6.pth",
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transforms=partial(VideoClassification, crop_size=(112, 112), resize_size=(128, 171)),
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meta={
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**_COMMON_META,
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"num_params": 31505325,
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"_metrics": {
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"Kinetics-400": {
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"acc@1": 67.463,
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"acc@5": 86.175,
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}
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},
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"_ops": 40.519,
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"_file_size": 120.318,
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},
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)
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DEFAULT = KINETICS400_V1
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@register_model()
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@handle_legacy_interface(weights=("pretrained", R3D_18_Weights.KINETICS400_V1))
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def r3d_18(*, weights: Optional[R3D_18_Weights] = None, progress: bool = True, **kwargs: Any) -> VideoResNet:
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"""Construct 18 layer Resnet3D model.
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.. betastatus:: video module
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Reference: `A Closer Look at Spatiotemporal Convolutions for Action Recognition <https://arxiv.org/abs/1711.11248>`__.
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Args:
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weights (:class:`~torchvision.models.video.R3D_18_Weights`, optional): The
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pretrained weights to use. See
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:class:`~torchvision.models.video.R3D_18_Weights`
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below for more details, and possible values. By default, no
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pre-trained weights are used.
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progress (bool): If True, displays a progress bar of the download to stderr. Default is True.
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**kwargs: parameters passed to the ``torchvision.models.video.resnet.VideoResNet`` base class.
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Please refer to the `source code
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<https://github.com/pytorch/vision/blob/main/torchvision/models/video/resnet.py>`_
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for more details about this class.
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.. autoclass:: torchvision.models.video.R3D_18_Weights
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:members:
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"""
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weights = R3D_18_Weights.verify(weights)
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return _video_resnet(
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BasicBlock,
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[Conv3DSimple] * 4,
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[2, 2, 2, 2],
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BasicStem,
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weights,
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progress,
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**kwargs,
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)
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@register_model()
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@handle_legacy_interface(weights=("pretrained", MC3_18_Weights.KINETICS400_V1))
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def mc3_18(*, weights: Optional[MC3_18_Weights] = None, progress: bool = True, **kwargs: Any) -> VideoResNet:
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"""Construct 18 layer Mixed Convolution network as in
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.. betastatus:: video module
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Reference: `A Closer Look at Spatiotemporal Convolutions for Action Recognition <https://arxiv.org/abs/1711.11248>`__.
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Args:
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weights (:class:`~torchvision.models.video.MC3_18_Weights`, optional): The
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pretrained weights to use. See
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:class:`~torchvision.models.video.MC3_18_Weights`
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below for more details, and possible values. By default, no
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pre-trained weights are used.
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progress (bool): If True, displays a progress bar of the download to stderr. Default is True.
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**kwargs: parameters passed to the ``torchvision.models.video.resnet.VideoResNet`` base class.
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Please refer to the `source code
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<https://github.com/pytorch/vision/blob/main/torchvision/models/video/resnet.py>`_
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for more details about this class.
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.. autoclass:: torchvision.models.video.MC3_18_Weights
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:members:
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"""
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weights = MC3_18_Weights.verify(weights)
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return _video_resnet(
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BasicBlock,
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[Conv3DSimple] + [Conv3DNoTemporal] * 3, # type: ignore[list-item]
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[2, 2, 2, 2],
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BasicStem,
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weights,
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progress,
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**kwargs,
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)
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@register_model()
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@handle_legacy_interface(weights=("pretrained", R2Plus1D_18_Weights.KINETICS400_V1))
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def r2plus1d_18(*, weights: Optional[R2Plus1D_18_Weights] = None, progress: bool = True, **kwargs: Any) -> VideoResNet:
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"""Construct 18 layer deep R(2+1)D network as in
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.. betastatus:: video module
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Reference: `A Closer Look at Spatiotemporal Convolutions for Action Recognition <https://arxiv.org/abs/1711.11248>`__.
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Args:
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weights (:class:`~torchvision.models.video.R2Plus1D_18_Weights`, optional): The
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pretrained weights to use. See
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:class:`~torchvision.models.video.R2Plus1D_18_Weights`
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below for more details, and possible values. By default, no
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pre-trained weights are used.
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progress (bool): If True, displays a progress bar of the download to stderr. Default is True.
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**kwargs: parameters passed to the ``torchvision.models.video.resnet.VideoResNet`` base class.
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Please refer to the `source code
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<https://github.com/pytorch/vision/blob/main/torchvision/models/video/resnet.py>`_
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for more details about this class.
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.. autoclass:: torchvision.models.video.R2Plus1D_18_Weights
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:members:
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"""
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weights = R2Plus1D_18_Weights.verify(weights)
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return _video_resnet(
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BasicBlock,
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[Conv2Plus1D] * 4,
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[2, 2, 2, 2],
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R2Plus1dStem,
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weights,
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progress,
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**kwargs,
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)
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# The dictionary below is internal implementation detail and will be removed in v0.15
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from .._utils import _ModelURLs
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model_urls = _ModelURLs(
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{
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"r3d_18": R3D_18_Weights.KINETICS400_V1.url,
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"mc3_18": MC3_18_Weights.KINETICS400_V1.url,
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"r2plus1d_18": R2Plus1D_18_Weights.KINETICS400_V1.url,
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}
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)
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