220 lines
7.6 KiB
Python
220 lines
7.6 KiB
Python
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from functools import partial
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from typing import Any, Callable, Optional
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import torch
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from torch import nn
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from torchvision.ops.misc import Conv3dNormActivation
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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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"S3D",
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"S3D_Weights",
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"s3d",
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]
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class TemporalSeparableConv(nn.Sequential):
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def __init__(
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self,
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in_planes: int,
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out_planes: int,
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kernel_size: int,
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stride: int,
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padding: int,
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norm_layer: Callable[..., nn.Module],
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):
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super().__init__(
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Conv3dNormActivation(
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in_planes,
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out_planes,
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kernel_size=(1, kernel_size, kernel_size),
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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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norm_layer=norm_layer,
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),
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Conv3dNormActivation(
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out_planes,
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out_planes,
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kernel_size=(kernel_size, 1, 1),
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stride=(stride, 1, 1),
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padding=(padding, 0, 0),
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bias=False,
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norm_layer=norm_layer,
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),
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)
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class SepInceptionBlock3D(nn.Module):
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def __init__(
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self,
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in_planes: int,
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b0_out: int,
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b1_mid: int,
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b1_out: int,
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b2_mid: int,
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b2_out: int,
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b3_out: int,
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norm_layer: Callable[..., nn.Module],
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):
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super().__init__()
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self.branch0 = Conv3dNormActivation(in_planes, b0_out, kernel_size=1, stride=1, norm_layer=norm_layer)
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self.branch1 = nn.Sequential(
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Conv3dNormActivation(in_planes, b1_mid, kernel_size=1, stride=1, norm_layer=norm_layer),
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TemporalSeparableConv(b1_mid, b1_out, kernel_size=3, stride=1, padding=1, norm_layer=norm_layer),
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)
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self.branch2 = nn.Sequential(
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Conv3dNormActivation(in_planes, b2_mid, kernel_size=1, stride=1, norm_layer=norm_layer),
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TemporalSeparableConv(b2_mid, b2_out, kernel_size=3, stride=1, padding=1, norm_layer=norm_layer),
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)
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self.branch3 = nn.Sequential(
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nn.MaxPool3d(kernel_size=(3, 3, 3), stride=1, padding=1),
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Conv3dNormActivation(in_planes, b3_out, kernel_size=1, stride=1, norm_layer=norm_layer),
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)
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def forward(self, x):
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x0 = self.branch0(x)
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x1 = self.branch1(x)
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x2 = self.branch2(x)
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x3 = self.branch3(x)
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out = torch.cat((x0, x1, x2, x3), 1)
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return out
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class S3D(nn.Module):
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"""S3D main class.
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Args:
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num_class (int): number of classes for the classification task.
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dropout (float): dropout probability.
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norm_layer (Optional[Callable]): Module specifying the normalization layer to use.
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Inputs:
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x (Tensor): batch of videos with dimensions (batch, channel, time, height, width)
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"""
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def __init__(
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self,
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num_classes: int = 400,
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dropout: float = 0.2,
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norm_layer: Optional[Callable[..., torch.nn.Module]] = None,
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) -> None:
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super().__init__()
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_log_api_usage_once(self)
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if norm_layer is None:
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norm_layer = partial(nn.BatchNorm3d, eps=0.001, momentum=0.001)
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self.features = nn.Sequential(
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TemporalSeparableConv(3, 64, 7, 2, 3, norm_layer),
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nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1)),
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Conv3dNormActivation(
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64,
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64,
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kernel_size=1,
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stride=1,
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norm_layer=norm_layer,
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),
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TemporalSeparableConv(64, 192, 3, 1, 1, norm_layer),
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nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1)),
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SepInceptionBlock3D(192, 64, 96, 128, 16, 32, 32, norm_layer),
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SepInceptionBlock3D(256, 128, 128, 192, 32, 96, 64, norm_layer),
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nn.MaxPool3d(kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1)),
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SepInceptionBlock3D(480, 192, 96, 208, 16, 48, 64, norm_layer),
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SepInceptionBlock3D(512, 160, 112, 224, 24, 64, 64, norm_layer),
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SepInceptionBlock3D(512, 128, 128, 256, 24, 64, 64, norm_layer),
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SepInceptionBlock3D(512, 112, 144, 288, 32, 64, 64, norm_layer),
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SepInceptionBlock3D(528, 256, 160, 320, 32, 128, 128, norm_layer),
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nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2), padding=(0, 0, 0)),
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SepInceptionBlock3D(832, 256, 160, 320, 32, 128, 128, norm_layer),
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SepInceptionBlock3D(832, 384, 192, 384, 48, 128, 128, norm_layer),
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)
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self.avgpool = nn.AvgPool3d(kernel_size=(2, 7, 7), stride=1)
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self.classifier = nn.Sequential(
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nn.Dropout(p=dropout),
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nn.Conv3d(1024, num_classes, kernel_size=1, stride=1, bias=True),
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)
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def forward(self, x):
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x = self.features(x)
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x = self.avgpool(x)
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x = self.classifier(x)
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x = torch.mean(x, dim=(2, 3, 4))
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return x
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class S3D_Weights(WeightsEnum):
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KINETICS400_V1 = Weights(
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url="https://download.pytorch.org/models/s3d-d76dad2f.pth",
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transforms=partial(
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VideoClassification,
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crop_size=(224, 224),
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resize_size=(256, 256),
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),
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meta={
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"min_size": (224, 224),
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"min_temporal_size": 14,
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"categories": _KINETICS400_CATEGORIES,
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"recipe": "https://github.com/pytorch/vision/tree/main/references/video_classification#s3d",
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"_docs": (
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"The weights aim to approximate the accuracy of the paper. The accuracies are estimated on clip-level "
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"with parameters `frame_rate=15`, `clips_per_video=1`, and `clip_len=128`."
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),
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"num_params": 8320048,
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"_metrics": {
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"Kinetics-400": {
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"acc@1": 68.368,
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"acc@5": 88.050,
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}
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},
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"_ops": 17.979,
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"_file_size": 31.972,
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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", S3D_Weights.KINETICS400_V1))
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def s3d(*, weights: Optional[S3D_Weights] = None, progress: bool = True, **kwargs: Any) -> S3D:
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"""Construct Separable 3D CNN model.
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Reference: `Rethinking Spatiotemporal Feature Learning <https://arxiv.org/abs/1712.04851>`__.
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.. betastatus:: video module
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Args:
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weights (:class:`~torchvision.models.video.S3D_Weights`, optional): The
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pretrained weights to use. See
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:class:`~torchvision.models.video.S3D_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.S3D`` 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/s3d.py>`_
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for more details about this class.
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.. autoclass:: torchvision.models.video.S3D_Weights
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:members:
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"""
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weights = S3D_Weights.verify(weights)
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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 = S3D(**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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