Source code for torchvision.models.video.swin_transformer
# Modified from 2d Swin Transformers in torchvision:
# https://github.com/pytorch/vision/blob/main/torchvision/models/swin_transformer.py
from functools import partial
from typing import Any, Callable, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import nn, Tensor
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
from ..swin_transformer import PatchMerging, SwinTransformerBlock
__all__ = [
"SwinTransformer3d",
"Swin3D_T_Weights",
"Swin3D_S_Weights",
"Swin3D_B_Weights",
"swin3d_t",
"swin3d_s",
"swin3d_b",
]
def _get_window_and_shift_size(
shift_size: List[int], size_dhw: List[int], window_size: List[int]
) -> Tuple[List[int], List[int]]:
for i in range(3):
if size_dhw[i] <= window_size[i]:
# In this case, window_size will adapt to the input size, and no need to shift
window_size[i] = size_dhw[i]
shift_size[i] = 0
return window_size, shift_size
torch.fx.wrap("_get_window_and_shift_size")
def _get_relative_position_bias(
relative_position_bias_table: torch.Tensor, relative_position_index: torch.Tensor, window_size: List[int]
) -> Tensor:
window_vol = window_size[0] * window_size[1] * window_size[2]
# In 3d case we flatten the relative_position_bias
relative_position_bias = relative_position_bias_table[
relative_position_index[:window_vol, :window_vol].flatten() # type: ignore[index]
]
relative_position_bias = relative_position_bias.view(window_vol, window_vol, -1)
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous().unsqueeze(0)
return relative_position_bias
torch.fx.wrap("_get_relative_position_bias")
def _compute_pad_size_3d(size_dhw: Tuple[int, int, int], patch_size: Tuple[int, int, int]) -> Tuple[int, int, int]:
pad_size = [(patch_size[i] - size_dhw[i] % patch_size[i]) % patch_size[i] for i in range(3)]
return pad_size[0], pad_size[1], pad_size[2]
torch.fx.wrap("_compute_pad_size_3d")
def _compute_attention_mask_3d(
x: Tensor,
size_dhw: Tuple[int, int, int],
window_size: Tuple[int, int, int],
shift_size: Tuple[int, int, int],
) -> Tensor:
# generate attention mask
attn_mask = x.new_zeros(*size_dhw)
num_windows = (size_dhw[0] // window_size[0]) * (size_dhw[1] // window_size[1]) * (size_dhw[2] // window_size[2])
slices = [
(
(0, -window_size[i]),
(-window_size[i], -shift_size[i]),
(-shift_size[i], None),
)
for i in range(3)
]
count = 0
for d in slices[0]:
for h in slices[1]:
for w in slices[2]:
attn_mask[d[0] : d[1], h[0] : h[1], w[0] : w[1]] = count
count += 1
# Partition window on attn_mask
attn_mask = attn_mask.view(
size_dhw[0] // window_size[0],
window_size[0],
size_dhw[1] // window_size[1],
window_size[1],
size_dhw[2] // window_size[2],
window_size[2],
)
attn_mask = attn_mask.permute(0, 2, 4, 1, 3, 5).reshape(
num_windows, window_size[0] * window_size[1] * window_size[2]
)
attn_mask = attn_mask.unsqueeze(1) - attn_mask.unsqueeze(2)
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
return attn_mask
torch.fx.wrap("_compute_attention_mask_3d")
def shifted_window_attention_3d(
input: Tensor,
qkv_weight: Tensor,
proj_weight: Tensor,
relative_position_bias: Tensor,
window_size: List[int],
num_heads: int,
shift_size: List[int],
attention_dropout: float = 0.0,
dropout: float = 0.0,
qkv_bias: Optional[Tensor] = None,
proj_bias: Optional[Tensor] = None,
training: bool = True,
) -> Tensor:
"""
Window based multi-head self attention (W-MSA) module with relative position bias.
It supports both of shifted and non-shifted window.
Args:
input (Tensor[B, T, H, W, C]): The input tensor, 5-dimensions.
qkv_weight (Tensor[in_dim, out_dim]): The weight tensor of query, key, value.
proj_weight (Tensor[out_dim, out_dim]): The weight tensor of projection.
relative_position_bias (Tensor): The learned relative position bias added to attention.
window_size (List[int]): 3-dimensions window size, T, H, W .
num_heads (int): Number of attention heads.
shift_size (List[int]): Shift size for shifted window attention (T, H, W).
attention_dropout (float): Dropout ratio of attention weight. Default: 0.0.
dropout (float): Dropout ratio of output. Default: 0.0.
qkv_bias (Tensor[out_dim], optional): The bias tensor of query, key, value. Default: None.
proj_bias (Tensor[out_dim], optional): The bias tensor of projection. Default: None.
training (bool, optional): Training flag used by the dropout parameters. Default: True.
Returns:
Tensor[B, T, H, W, C]: The output tensor after shifted window attention.
"""
b, t, h, w, c = input.shape
# pad feature maps to multiples of window size
pad_size = _compute_pad_size_3d((t, h, w), (window_size[0], window_size[1], window_size[2]))
x = F.pad(input, (0, 0, 0, pad_size[2], 0, pad_size[1], 0, pad_size[0]))
_, tp, hp, wp, _ = x.shape
padded_size = (tp, hp, wp)
# cyclic shift
if sum(shift_size) > 0:
x = torch.roll(x, shifts=(-shift_size[0], -shift_size[1], -shift_size[2]), dims=(1, 2, 3))
# partition windows
num_windows = (
(padded_size[0] // window_size[0]) * (padded_size[1] // window_size[1]) * (padded_size[2] // window_size[2])
)
x = x.view(
b,
padded_size[0] // window_size[0],
window_size[0],
padded_size[1] // window_size[1],
window_size[1],
padded_size[2] // window_size[2],
window_size[2],
c,
)
x = x.permute(0, 1, 3, 5, 2, 4, 6, 7).reshape(
b * num_windows, window_size[0] * window_size[1] * window_size[2], c
) # B*nW, Wd*Wh*Ww, C
# multi-head attention
qkv = F.linear(x, qkv_weight, qkv_bias)
qkv = qkv.reshape(x.size(0), x.size(1), 3, num_heads, c // num_heads).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
q = q * (c // num_heads) ** -0.5
attn = q.matmul(k.transpose(-2, -1))
# add relative position bias
attn = attn + relative_position_bias
if sum(shift_size) > 0:
# generate attention mask to handle shifted windows with varying size
attn_mask = _compute_attention_mask_3d(
x,
(padded_size[0], padded_size[1], padded_size[2]),
(window_size[0], window_size[1], window_size[2]),
(shift_size[0], shift_size[1], shift_size[2]),
)
attn = attn.view(x.size(0) // num_windows, num_windows, num_heads, x.size(1), x.size(1))
attn = attn + attn_mask.unsqueeze(1).unsqueeze(0)
attn = attn.view(-1, num_heads, x.size(1), x.size(1))
attn = F.softmax(attn, dim=-1)
attn = F.dropout(attn, p=attention_dropout, training=training)
x = attn.matmul(v).transpose(1, 2).reshape(x.size(0), x.size(1), c)
x = F.linear(x, proj_weight, proj_bias)
x = F.dropout(x, p=dropout, training=training)
# reverse windows
x = x.view(
b,
padded_size[0] // window_size[0],
padded_size[1] // window_size[1],
padded_size[2] // window_size[2],
window_size[0],
window_size[1],
window_size[2],
c,
)
x = x.permute(0, 1, 4, 2, 5, 3, 6, 7).reshape(b, tp, hp, wp, c)
# reverse cyclic shift
if sum(shift_size) > 0:
x = torch.roll(x, shifts=(shift_size[0], shift_size[1], shift_size[2]), dims=(1, 2, 3))
# unpad features
x = x[:, :t, :h, :w, :].contiguous()
return x
torch.fx.wrap("shifted_window_attention_3d")
class ShiftedWindowAttention3d(nn.Module):
"""
See :func:`shifted_window_attention_3d`.
"""
def __init__(
self,
dim: int,
window_size: List[int],
shift_size: List[int],
num_heads: int,
qkv_bias: bool = True,
proj_bias: bool = True,
attention_dropout: float = 0.0,
dropout: float = 0.0,
) -> None:
super().__init__()
if len(window_size) != 3 or len(shift_size) != 3:
raise ValueError("window_size and shift_size must be of length 2")
self.window_size = window_size # Wd, Wh, Ww
self.shift_size = shift_size
self.num_heads = num_heads
self.attention_dropout = attention_dropout
self.dropout = dropout
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.proj = nn.Linear(dim, dim, bias=proj_bias)
self.define_relative_position_bias_table()
self.define_relative_position_index()
def define_relative_position_bias_table(self) -> None:
# define a parameter table of relative position bias
self.relative_position_bias_table = nn.Parameter(
torch.zeros(
(2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1) * (2 * self.window_size[2] - 1),
self.num_heads,
)
) # 2*Wd-1 * 2*Wh-1 * 2*Ww-1, nH
nn.init.trunc_normal_(self.relative_position_bias_table, std=0.02)
def define_relative_position_index(self) -> None:
# get pair-wise relative position index for each token inside the window
coords_dhw = [torch.arange(self.window_size[i]) for i in range(3)]
coords = torch.stack(
torch.meshgrid(coords_dhw[0], coords_dhw[1], coords_dhw[2], indexing="ij")
) # 3, Wd, Wh, Ww
coords_flatten = torch.flatten(coords, 1) # 3, Wd*Wh*Ww
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 3, Wd*Wh*Ww, Wd*Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wd*Wh*Ww, Wd*Wh*Ww, 3
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += self.window_size[1] - 1
relative_coords[:, :, 2] += self.window_size[2] - 1
relative_coords[:, :, 0] *= (2 * self.window_size[1] - 1) * (2 * self.window_size[2] - 1)
relative_coords[:, :, 1] *= 2 * self.window_size[2] - 1
# We don't flatten the relative_position_index here in 3d case.
relative_position_index = relative_coords.sum(-1) # Wd*Wh*Ww, Wd*Wh*Ww
self.register_buffer("relative_position_index", relative_position_index)
def get_relative_position_bias(self, window_size: List[int]) -> torch.Tensor:
return _get_relative_position_bias(self.relative_position_bias_table, self.relative_position_index, window_size) # type: ignore
def forward(self, x: Tensor) -> Tensor:
_, t, h, w, _ = x.shape
size_dhw = [t, h, w]
window_size, shift_size = self.window_size.copy(), self.shift_size.copy()
# Handle case where window_size is larger than the input tensor
window_size, shift_size = _get_window_and_shift_size(shift_size, size_dhw, window_size)
relative_position_bias = self.get_relative_position_bias(window_size)
return shifted_window_attention_3d(
x,
self.qkv.weight,
self.proj.weight,
relative_position_bias,
window_size,
self.num_heads,
shift_size=shift_size,
attention_dropout=self.attention_dropout,
dropout=self.dropout,
qkv_bias=self.qkv.bias,
proj_bias=self.proj.bias,
training=self.training,
)
# Modified from:
# https://github.com/SwinTransformer/Video-Swin-Transformer/blob/master/mmaction/models/backbones/swin_transformer.py
class PatchEmbed3d(nn.Module):
"""Video to Patch Embedding.
Args:
patch_size (List[int]): Patch token size.
in_channels (int): Number of input channels. Default: 3
embed_dim (int): Number of linear projection output channels. Default: 96.
norm_layer (nn.Module, optional): Normalization layer. Default: None
"""
def __init__(
self,
patch_size: List[int],
in_channels: int = 3,
embed_dim: int = 96,
norm_layer: Optional[Callable[..., nn.Module]] = None,
) -> None:
super().__init__()
_log_api_usage_once(self)
self.tuple_patch_size = (patch_size[0], patch_size[1], patch_size[2])
self.proj = nn.Conv3d(
in_channels,
embed_dim,
kernel_size=self.tuple_patch_size,
stride=self.tuple_patch_size,
)
if norm_layer is not None:
self.norm = norm_layer(embed_dim)
else:
self.norm = nn.Identity()
def forward(self, x: Tensor) -> Tensor:
"""Forward function."""
# padding
_, _, t, h, w = x.size()
pad_size = _compute_pad_size_3d((t, h, w), self.tuple_patch_size)
x = F.pad(x, (0, pad_size[2], 0, pad_size[1], 0, pad_size[0]))
x = self.proj(x) # B C T Wh Ww
x = x.permute(0, 2, 3, 4, 1) # B T Wh Ww C
if self.norm is not None:
x = self.norm(x)
return x
class SwinTransformer3d(nn.Module):
"""
Implements 3D Swin Transformer from the `"Video Swin Transformer" <https://arxiv.org/abs/2106.13230>`_ paper.
Args:
patch_size (List[int]): Patch size.
embed_dim (int): Patch embedding dimension.
depths (List(int)): Depth of each Swin Transformer layer.
num_heads (List(int)): Number of attention heads in different layers.
window_size (List[int]): Window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0.
dropout (float): Dropout rate. Default: 0.0.
attention_dropout (float): Attention dropout rate. Default: 0.0.
stochastic_depth_prob (float): Stochastic depth rate. Default: 0.1.
num_classes (int): Number of classes for classification head. Default: 400.
norm_layer (nn.Module, optional): Normalization layer. Default: None.
block (nn.Module, optional): SwinTransformer Block. Default: None.
downsample_layer (nn.Module): Downsample layer (patch merging). Default: PatchMerging.
patch_embed (nn.Module, optional): Patch Embedding layer. Default: None.
"""
def __init__(
self,
patch_size: List[int],
embed_dim: int,
depths: List[int],
num_heads: List[int],
window_size: List[int],
mlp_ratio: float = 4.0,
dropout: float = 0.0,
attention_dropout: float = 0.0,
stochastic_depth_prob: float = 0.1,
num_classes: int = 400,
norm_layer: Optional[Callable[..., nn.Module]] = None,
block: Optional[Callable[..., nn.Module]] = None,
downsample_layer: Callable[..., nn.Module] = PatchMerging,
patch_embed: Optional[Callable[..., nn.Module]] = None,
) -> None:
super().__init__()
_log_api_usage_once(self)
self.num_classes = num_classes
if block is None:
block = partial(SwinTransformerBlock, attn_layer=ShiftedWindowAttention3d)
if norm_layer is None:
norm_layer = partial(nn.LayerNorm, eps=1e-5)
if patch_embed is None:
patch_embed = PatchEmbed3d
# split image into non-overlapping patches
self.patch_embed = patch_embed(patch_size=patch_size, embed_dim=embed_dim, norm_layer=norm_layer)
self.pos_drop = nn.Dropout(p=dropout)
layers: List[nn.Module] = []
total_stage_blocks = sum(depths)
stage_block_id = 0
# build SwinTransformer blocks
for i_stage in range(len(depths)):
stage: List[nn.Module] = []
dim = embed_dim * 2**i_stage
for i_layer in range(depths[i_stage]):
# adjust stochastic depth probability based on the depth of the stage block
sd_prob = stochastic_depth_prob * float(stage_block_id) / (total_stage_blocks - 1)
stage.append(
block(
dim,
num_heads[i_stage],
window_size=window_size,
shift_size=[0 if i_layer % 2 == 0 else w // 2 for w in window_size],
mlp_ratio=mlp_ratio,
dropout=dropout,
attention_dropout=attention_dropout,
stochastic_depth_prob=sd_prob,
norm_layer=norm_layer,
attn_layer=ShiftedWindowAttention3d,
)
)
stage_block_id += 1
layers.append(nn.Sequential(*stage))
# add patch merging layer
if i_stage < (len(depths) - 1):
layers.append(downsample_layer(dim, norm_layer))
self.features = nn.Sequential(*layers)
self.num_features = embed_dim * 2 ** (len(depths) - 1)
self.norm = norm_layer(self.num_features)
self.avgpool = nn.AdaptiveAvgPool3d(1)
self.head = nn.Linear(self.num_features, num_classes)
for m in self.modules():
if isinstance(m, nn.Linear):
nn.init.trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
nn.init.zeros_(m.bias)
def forward(self, x: Tensor) -> Tensor:
# x: B C T H W
x = self.patch_embed(x) # B _T _H _W C
x = self.pos_drop(x)
x = self.features(x) # B _T _H _W C
x = self.norm(x)
x = x.permute(0, 4, 1, 2, 3) # B, C, _T, _H, _W
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.head(x)
return x
def _swin_transformer3d(
patch_size: List[int],
embed_dim: int,
depths: List[int],
num_heads: List[int],
window_size: List[int],
stochastic_depth_prob: float,
weights: Optional[WeightsEnum],
progress: bool,
**kwargs: Any,
) -> SwinTransformer3d:
if weights is not None:
_ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))
model = SwinTransformer3d(
patch_size=patch_size,
embed_dim=embed_dim,
depths=depths,
num_heads=num_heads,
window_size=window_size,
stochastic_depth_prob=stochastic_depth_prob,
**kwargs,
)
if weights is not None:
model.load_state_dict(weights.get_state_dict(progress=progress, check_hash=True))
return model
_COMMON_META = {
"categories": _KINETICS400_CATEGORIES,
"min_size": (1, 1),
"min_temporal_size": 1,
}
[docs]class Swin3D_T_Weights(WeightsEnum):
KINETICS400_V1 = Weights(
url="https://download.pytorch.org/models/swin3d_t-7615ae03.pth",
transforms=partial(
VideoClassification,
crop_size=(224, 224),
resize_size=(256,),
mean=(0.4850, 0.4560, 0.4060),
std=(0.2290, 0.2240, 0.2250),
),
meta={
**_COMMON_META,
"recipe": "https://github.com/SwinTransformer/Video-Swin-Transformer#kinetics-400",
"_docs": (
"The weights were ported from the paper. The accuracies are estimated on video-level "
"with parameters `frame_rate=15`, `clips_per_video=12`, and `clip_len=32`"
),
"num_params": 28158070,
"_metrics": {
"Kinetics-400": {
"acc@1": 77.715,
"acc@5": 93.519,
}
},
"_ops": 43.882,
"_file_size": 121.543,
},
)
DEFAULT = KINETICS400_V1
[docs]class Swin3D_S_Weights(WeightsEnum):
KINETICS400_V1 = Weights(
url="https://download.pytorch.org/models/swin3d_s-da41c237.pth",
transforms=partial(
VideoClassification,
crop_size=(224, 224),
resize_size=(256,),
mean=(0.4850, 0.4560, 0.4060),
std=(0.2290, 0.2240, 0.2250),
),
meta={
**_COMMON_META,
"recipe": "https://github.com/SwinTransformer/Video-Swin-Transformer#kinetics-400",
"_docs": (
"The weights were ported from the paper. The accuracies are estimated on video-level "
"with parameters `frame_rate=15`, `clips_per_video=12`, and `clip_len=32`"
),
"num_params": 49816678,
"_metrics": {
"Kinetics-400": {
"acc@1": 79.521,
"acc@5": 94.158,
}
},
"_ops": 82.841,
"_file_size": 218.288,
},
)
DEFAULT = KINETICS400_V1
[docs]class Swin3D_B_Weights(WeightsEnum):
KINETICS400_V1 = Weights(
url="https://download.pytorch.org/models/swin3d_b_1k-24f7c7c6.pth",
transforms=partial(
VideoClassification,
crop_size=(224, 224),
resize_size=(256,),
mean=(0.4850, 0.4560, 0.4060),
std=(0.2290, 0.2240, 0.2250),
),
meta={
**_COMMON_META,
"recipe": "https://github.com/SwinTransformer/Video-Swin-Transformer#kinetics-400",
"_docs": (
"The weights were ported from the paper. The accuracies are estimated on video-level "
"with parameters `frame_rate=15`, `clips_per_video=12`, and `clip_len=32`"
),
"num_params": 88048984,
"_metrics": {
"Kinetics-400": {
"acc@1": 79.427,
"acc@5": 94.386,
}
},
"_ops": 140.667,
"_file_size": 364.134,
},
)
KINETICS400_IMAGENET22K_V1 = Weights(
url="https://download.pytorch.org/models/swin3d_b_22k-7c6ae6fa.pth",
transforms=partial(
VideoClassification,
crop_size=(224, 224),
resize_size=(256,),
mean=(0.4850, 0.4560, 0.4060),
std=(0.2290, 0.2240, 0.2250),
),
meta={
**_COMMON_META,
"recipe": "https://github.com/SwinTransformer/Video-Swin-Transformer#kinetics-400",
"_docs": (
"The weights were ported from the paper. The accuracies are estimated on video-level "
"with parameters `frame_rate=15`, `clips_per_video=12`, and `clip_len=32`"
),
"num_params": 88048984,
"_metrics": {
"Kinetics-400": {
"acc@1": 81.643,
"acc@5": 95.574,
}
},
"_ops": 140.667,
"_file_size": 364.134,
},
)
DEFAULT = KINETICS400_V1
[docs]@register_model()
@handle_legacy_interface(weights=("pretrained", Swin3D_T_Weights.KINETICS400_V1))
def swin3d_t(*, weights: Optional[Swin3D_T_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer3d:
"""
Constructs a swin_tiny architecture from
`Video Swin Transformer <https://arxiv.org/abs/2106.13230>`_.
Args:
weights (:class:`~torchvision.models.video.Swin3D_T_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.video.Swin3D_T_Weights` below for
more details, and possible values. By default, no pre-trained
weights are used.
progress (bool, optional): If True, displays a progress bar of the
download to stderr. Default is True.
**kwargs: parameters passed to the ``torchvision.models.video.swin_transformer.SwinTransformer``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/video/swin_transformer.py>`_
for more details about this class.
.. autoclass:: torchvision.models.video.Swin3D_T_Weights
:members:
"""
weights = Swin3D_T_Weights.verify(weights)
return _swin_transformer3d(
patch_size=[2, 4, 4],
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=[8, 7, 7],
stochastic_depth_prob=0.1,
weights=weights,
progress=progress,
**kwargs,
)
[docs]@register_model()
@handle_legacy_interface(weights=("pretrained", Swin3D_S_Weights.KINETICS400_V1))
def swin3d_s(*, weights: Optional[Swin3D_S_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer3d:
"""
Constructs a swin_small architecture from
`Video Swin Transformer <https://arxiv.org/abs/2106.13230>`_.
Args:
weights (:class:`~torchvision.models.video.Swin3D_S_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.video.Swin3D_S_Weights` below for
more details, and possible values. By default, no pre-trained
weights are used.
progress (bool, optional): If True, displays a progress bar of the
download to stderr. Default is True.
**kwargs: parameters passed to the ``torchvision.models.video.swin_transformer.SwinTransformer``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/video/swin_transformer.py>`_
for more details about this class.
.. autoclass:: torchvision.models.video.Swin3D_S_Weights
:members:
"""
weights = Swin3D_S_Weights.verify(weights)
return _swin_transformer3d(
patch_size=[2, 4, 4],
embed_dim=96,
depths=[2, 2, 18, 2],
num_heads=[3, 6, 12, 24],
window_size=[8, 7, 7],
stochastic_depth_prob=0.1,
weights=weights,
progress=progress,
**kwargs,
)
[docs]@register_model()
@handle_legacy_interface(weights=("pretrained", Swin3D_B_Weights.KINETICS400_V1))
def swin3d_b(*, weights: Optional[Swin3D_B_Weights] = None, progress: bool = True, **kwargs: Any) -> SwinTransformer3d:
"""
Constructs a swin_base architecture from
`Video Swin Transformer <https://arxiv.org/abs/2106.13230>`_.
Args:
weights (:class:`~torchvision.models.video.Swin3D_B_Weights`, optional): The
pretrained weights to use. See
:class:`~torchvision.models.video.Swin3D_B_Weights` below for
more details, and possible values. By default, no pre-trained
weights are used.
progress (bool, optional): If True, displays a progress bar of the
download to stderr. Default is True.
**kwargs: parameters passed to the ``torchvision.models.video.swin_transformer.SwinTransformer``
base class. Please refer to the `source code
<https://github.com/pytorch/vision/blob/main/torchvision/models/video/swin_transformer.py>`_
for more details about this class.
.. autoclass:: torchvision.models.video.Swin3D_B_Weights
:members:
"""
weights = Swin3D_B_Weights.verify(weights)
return _swin_transformer3d(
patch_size=[2, 4, 4],
embed_dim=128,
depths=[2, 2, 18, 2],
num_heads=[4, 8, 16, 32],
window_size=[8, 7, 7],
stochastic_depth_prob=0.1,
weights=weights,
progress=progress,
**kwargs,
)
