Source code for torchvision.models.video.s3d
from functools import partial
from typing import Any, Callable, Optional
import torch
from torch import nn
from torchvision.ops.misc import Conv3dNormActivation
from ...transforms._presets import VideoClassification
from ...utils import _log_api_usage_once
from .._api import register_model, Weights, WeightsEnum
from .._meta import _KINETICS400_CATEGORIES
from .._utils import _ovewrite_named_param, handle_legacy_interface
__all__ = [
"S3D",
"S3D_Weights",
"s3d",
]
class TemporalSeparableConv(nn.Sequential):
def __init__(
self,
in_planes: int,
out_planes: int,
kernel_size: int,
stride: int,
padding: int,
norm_layer: Callable[..., nn.Module],
):
super().__init__(
Conv3dNormActivation(
in_planes,
out_planes,
kernel_size=(1, kernel_size, kernel_size),
stride=(1, stride, stride),
padding=(0, padding, padding),
bias=False,
norm_layer=norm_layer,
),
Conv3dNormActivation(
out_planes,
out_planes,
kernel_size=(kernel_size, 1, 1),
stride=(stride, 1, 1),
padding=(padding, 0, 0),
bias=False,
norm_layer=norm_layer,
),
)
class SepInceptionBlock3D(nn.Module):
def __init__(
self,
in_planes: int,
b0_out: int,
b1_mid: int,
b1_out: int,
b2_mid: int,
b2_out: int,
b3_out: int,
norm_layer: Callable[..., nn.Module],
):
super().__init__()
self.branch0 = Conv3dNormActivation(in_planes, b0_out, kernel_size=1, stride=1, norm_layer=norm_layer)
self.branch1 = nn.Sequential(
Conv3dNormActivation(in_planes, b1_mid, kernel_size=1, stride=1, norm_layer=norm_layer),
TemporalSeparableConv(b1_mid, b1_out, kernel_size=3, stride=1, padding=1, norm_layer=norm_layer),
)
self.branch2 = nn.Sequential(
Conv3dNormActivation(in_planes, b2_mid, kernel_size=1, stride=1, norm_layer=norm_layer),
TemporalSeparableConv(b2_mid, b2_out, kernel_size=3, stride=1, padding=1, norm_layer=norm_layer),
)
self.branch3 = nn.Sequential(
nn.MaxPool3d(kernel_size=(3, 3, 3), stride=1, padding=1),
Conv3dNormActivation(in_planes, b3_out, kernel_size=1, stride=1, norm_layer=norm_layer),
)
def forward(self, x):
x0 = self.branch0(x)
x1 = self.branch1(x)
x2 = self.branch2(x)
x3 = self.branch3(x)
out = torch.cat((x0, x1, x2, x3), 1)
return out
class S3D(nn.Module):
"""S3D main class.
Args:
num_class (int): number of classes for the classification task.
dropout (float): dropout probability.
norm_layer (Optional[Callable]): Module specifying the normalization layer to use.
Inputs:
x (Tensor): batch of videos with dimensions (batch, channel, time, height, width)
"""
def __init__(
self,
num_classes: int = 400,
dropout: float = 0.2,
norm_layer: Optional[Callable[..., torch.nn.Module]] = None,
) -> None:
super().__init__()
_log_api_usage_once(self)
if norm_layer is None:
norm_layer = partial(nn.BatchNorm3d, eps=0.001, momentum=0.001)
self.features = nn.Sequential(
TemporalSeparableConv(3, 64, 7, 2, 3, norm_layer),
nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1)),
Conv3dNormActivation(
64,
64,
kernel_size=1,
stride=1,
norm_layer=norm_layer,
),
TemporalSeparableConv(64, 192, 3, 1, 1, norm_layer),
nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1)),
SepInceptionBlock3D(192, 64, 96, 128, 16, 32, 32, norm_layer),
SepInceptionBlock3D(256, 128, 128, 192, 32, 96, 64, norm_layer),
nn.MaxPool3d(kernel_size=(3, 3, 3), stride=(2, 2, 2), padding=(1, 1, 1)),
SepInceptionBlock3D(480, 192, 96, 208, 16, 48, 64, norm_layer),
SepInceptionBlock3D(512, 160, 112, 224, 24, 64, 64, norm_layer),
SepInceptionBlock3D(512, 128, 128, 256, 24, 64, 64, norm_layer),
SepInceptionBlock3D(512, 112, 144, 288, 32, 64, 64, norm_layer),
SepInceptionBlock3D(528, 256, 160, 320, 32, 128, 128, norm_layer),
nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2), padding=(0, 0, 0)),
SepInceptionBlock3D(832, 256, 160, 320, 32, 128, 128, norm_layer),
SepInceptionBlock3D(832, 384, 192, 384, 48, 128, 128, norm_layer),
)
self.avgpool = nn.AvgPool3d(kernel_size=(2, 7, 7), stride=1)
self.classifier = nn.Sequential(
nn.Dropout(p=dropout),
nn.Conv3d(1024, num_classes, kernel_size=1, stride=1, bias=True),
)
def forward(self, x):
x = self.features(x)
x = self.avgpool(x)
x = self.classifier(x)
x = torch.mean(x, dim=(2, 3, 4))
return x
[docs]class S3D_Weights(WeightsEnum):
KINETICS400_V1 = Weights(
url="https://download.pytorch.org/models/s3d-d76dad2f.pth",
transforms=partial(
VideoClassification,
crop_size=(224, 224),
resize_size=(256, 256),
),
meta={
"min_size": (224, 224),
"min_temporal_size": 14,
"categories": _KINETICS400_CATEGORIES,
"recipe": "https://github.com/pytorch/vision/tree/main/references/video_classification#s3d",
"_docs": (
"The weights aim to approximate the accuracy of the paper. The accuracies are estimated on clip-level "
"with parameters `frame_rate=15`, `clips_per_video=1`, and `clip_len=128`."
),
"num_params": 8320048,
"_metrics": {
"Kinetics-400": {
"acc@1": 68.368,
"acc@5": 88.050,
}
},
"_ops": 17.979,
"_file_size": 31.972,
},
)
DEFAULT = KINETICS400_V1
[docs]@register_model()
@handle_legacy_interface(weights=("pretrained", S3D_Weights.KINETICS400_V1))
def s3d(*, weights: Optional[S3D_Weights] = None, progress: bool = True, **kwargs: Any) -> S3D:
"""Construct Separable 3D CNN model.
Reference: `Rethinking Spatiotemporal Feature Learning <https://arxiv.org/abs/1712.04851>`__.
.. betastatus:: video module
Args:
weights (:class:`~torchvision.models.video.S3D_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.video.S3D_Weights`
below for more details, and possible values. By default, no
pre-trained weights are used.
progress (bool): If True, displays a progress bar of the download to stderr. Default is True.
**kwargs: parameters passed to the ``torchvision.models.video.S3D`` base class.
Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/video/s3d.py>`_
for more details about this class.
.. autoclass:: torchvision.models.video.S3D_Weights
:members:
"""
weights = S3D_Weights.verify(weights)
if weights is not None:
_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
model = S3D(**kwargs)
if weights is not None:
model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
return model
