code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def load_original_weights(self, logger):
"""Load weights from original checkpoint, which required converting
keys."""
state_dict_torchvision = _load_checkpoint(
self.pretrained, map_location='cpu')
if 'state_dict' in state_dict_torchvision:
state_dict_torchvision ... | Load weights from original checkpoint, which required converting
keys. | load_original_weights | python | open-mmlab/mmaction2 | mmaction/models/backbones/resnet_tsm.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/resnet_tsm.py | Apache-2.0 |
def init_weights(self):
"""Initiate the parameters either from existing checkpoint or from
scratch."""
if self.pretrained2d:
logger = MMLogger.get_current_instance()
self.load_original_weights(logger)
else:
if self.pretrained:
self.init... | Initiate the parameters either from existing checkpoint or from
scratch. | init_weights | python | open-mmlab/mmaction2 | mmaction/models/backbones/resnet_tsm.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/resnet_tsm.py | Apache-2.0 |
def init_weights(self) -> None:
"""Initiate the parameters either from existing checkpoint or from
scratch."""
for m in self.modules():
if isinstance(m, nn.Conv3d):
kaiming_init(m)
elif isinstance(m, _BatchNorm):
constant_init(m, 1)
... | Initiate the parameters either from existing checkpoint or from
scratch. | init_weights | python | open-mmlab/mmaction2 | mmaction/models/backbones/rgbposeconv3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/rgbposeconv3d.py | Apache-2.0 |
def forward(self, imgs: torch.Tensor, heatmap_imgs: torch.Tensor) -> tuple:
"""Defines the computation performed at every call.
Args:
imgs (torch.Tensor): The input data.
heatmap_imgs (torch.Tensor): The input data.
Returns:
tuple[torch.Tensor]: The feature ... | Defines the computation performed at every call.
Args:
imgs (torch.Tensor): The input data.
heatmap_imgs (torch.Tensor): The input data.
Returns:
tuple[torch.Tensor]: The feature of the input
samples extracted by the backbone.
| forward | python | open-mmlab/mmaction2 | mmaction/models/backbones/rgbposeconv3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/rgbposeconv3d.py | Apache-2.0 |
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Defines the computation performed at every call."""
res = self.residual(x)
x = self.tcn(self.gcn(x)) + res
return self.relu(x) | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/backbones/stgcn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/stgcn.py | Apache-2.0 |
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Defines the computation performed at every call."""
N, M, T, V, C = x.size()
x = x.permute(0, 1, 3, 4, 2).contiguous()
if self.data_bn_type == 'MVC':
x = self.data_bn(x.view(N, M * V * C, T))
else:
x =... | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/backbones/stgcn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/stgcn.py | Apache-2.0 |
def window_partition(x: torch.Tensor,
window_size: Sequence[int]) -> torch.Tensor:
"""
Args:
x (torch.Tensor): The input features of shape :math:`(B, D, H, W, C)`.
window_size (Sequence[int]): The window size, :math:`(w_d, w_h, w_w)`.
Returns:
torch.Tensor: The ... |
Args:
x (torch.Tensor): The input features of shape :math:`(B, D, H, W, C)`.
window_size (Sequence[int]): The window size, :math:`(w_d, w_h, w_w)`.
Returns:
torch.Tensor: The partitioned windows of shape
:math:`(B*num_windows, w_d*w_h*w_w, C)`.
| window_partition | python | open-mmlab/mmaction2 | mmaction/models/backbones/swin.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/swin.py | Apache-2.0 |
def window_reverse(windows: torch.Tensor, window_size: Sequence[int], B: int,
D: int, H: int, W: int) -> torch.Tensor:
"""
Args:
windows (torch.Tensor): Input windows of shape
:meth:`(B*num_windows, w_d, w_h, w_w, C)`.
window_size (Sequence[int]): The window size, ... |
Args:
windows (torch.Tensor): Input windows of shape
:meth:`(B*num_windows, w_d, w_h, w_w, C)`.
window_size (Sequence[int]): The window size, :meth:`(w_d, w_h, w_w)`.
B (int): Batch size of feature maps.
D (int): Temporal length of feature maps.
H (int): Height o... | window_reverse | python | open-mmlab/mmaction2 | mmaction/models/backbones/swin.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/swin.py | Apache-2.0 |
def get_window_size(
x_size: Sequence[int],
window_size: Sequence[int],
shift_size: Optional[Sequence[int]] = None
) -> Union[Tuple[int], Tuple[Tuple[int]]]:
"""Calculate window size and shift size according to the input size.
Args:
x_size (Sequence[int]): The input size.
window_siz... | Calculate window size and shift size according to the input size.
Args:
x_size (Sequence[int]): The input size.
window_size (Sequence[int]): The expected window size.
shift_size (Sequence[int], optional): The expected shift size.
Defaults to None.
Returns:
tuple: Th... | get_window_size | python | open-mmlab/mmaction2 | mmaction/models/backbones/swin.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/swin.py | Apache-2.0 |
def compute_mask(D: int, H: int, W: int, window_size: Sequence[int],
shift_size: Sequence[int],
device: Union[str, torch.device]) -> torch.Tensor:
"""Compute attention mask.
Args:
D (int): Temporal length of feature maps.
H (int): Height of feature maps.
... | Compute attention mask.
Args:
D (int): Temporal length of feature maps.
H (int): Height of feature maps.
W (int): Width of feature maps.
window_size (Sequence[int]): The window size.
shift_size (Sequence[int]): The shift size.
device (str or :obj:`torch.device`): The... | compute_mask | python | open-mmlab/mmaction2 | mmaction/models/backbones/swin.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/swin.py | Apache-2.0 |
def forward(self,
x: torch.Tensor,
mask: Optional[torch.Tensor] = None) -> torch.Tensor:
"""Forward function.
Args:
x (torch.Tensor): Input feature maps of shape
:meth:`(B*num_windows, N, C)`.
mask (torch.Tensor, optional): (0/-inf... | Forward function.
Args:
x (torch.Tensor): Input feature maps of shape
:meth:`(B*num_windows, N, C)`.
mask (torch.Tensor, optional): (0/-inf) mask of shape
:meth:`(num_windows, N, N)`. Defaults to None.
| forward | python | open-mmlab/mmaction2 | mmaction/models/backbones/swin.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/swin.py | Apache-2.0 |
def forward(self, x: torch.Tensor,
mask_matrix: torch.Tensor) -> torch.Tensor:
"""
Args:
x (torch.Tensor): Input features of shape :math:`(B, D, H, W, C)`.
mask_matrix (torch.Tensor): Attention mask for cyclic shift.
"""
shortcut = x
if se... |
Args:
x (torch.Tensor): Input features of shape :math:`(B, D, H, W, C)`.
mask_matrix (torch.Tensor): Attention mask for cyclic shift.
| forward | python | open-mmlab/mmaction2 | mmaction/models/backbones/swin.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/swin.py | Apache-2.0 |
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Perform patch merging.
Args:
x (torch.Tensor): Input feature maps of shape
:math:`(B, D, H, W, C)`.
Returns:
torch.Tensor: The merged feature maps of shape
:math:`(B, D, H/2, W/2, 2*C... | Perform patch merging.
Args:
x (torch.Tensor): Input feature maps of shape
:math:`(B, D, H, W, C)`.
Returns:
torch.Tensor: The merged feature maps of shape
:math:`(B, D, H/2, W/2, 2*C)`.
| forward | python | open-mmlab/mmaction2 | mmaction/models/backbones/swin.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/swin.py | Apache-2.0 |
def forward(self,
x: torch.Tensor,
do_downsample: bool = True) -> torch.Tensor:
"""Forward function.
Args:
x (torch.Tensor): Input feature maps of shape
:math:`(B, C, D, H, W)`.
do_downsample (bool): Whether to downsample the outpu... | Forward function.
Args:
x (torch.Tensor): Input feature maps of shape
:math:`(B, C, D, H, W)`.
do_downsample (bool): Whether to downsample the output of
the current layer. Defaults to True.
| forward | python | open-mmlab/mmaction2 | mmaction/models/backbones/swin.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/swin.py | Apache-2.0 |
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Perform video to patch embedding.
Args:
x (torch.Tensor): The input videos of shape
:math:`(B, C, D, H, W)`. In most cases, C is 3.
Returns:
torch.Tensor: The video patches of shape
:... | Perform video to patch embedding.
Args:
x (torch.Tensor): The input videos of shape
:math:`(B, C, D, H, W)`. In most cases, C is 3.
Returns:
torch.Tensor: The video patches of shape
:math:`(B, embed_dims, Dp, Hp, Wp)`.
| forward | python | open-mmlab/mmaction2 | mmaction/models/backbones/swin.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/swin.py | Apache-2.0 |
def _freeze_stages(self) -> None:
"""Prevent all the parameters from being optimized before
``self.frozen_stages``."""
if self.frozen_stages >= 0:
self.patch_embed.eval()
for param in self.patch_embed.parameters():
param.requires_grad = False
if s... | Prevent all the parameters from being optimized before
``self.frozen_stages``. | _freeze_stages | python | open-mmlab/mmaction2 | mmaction/models/backbones/swin.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/swin.py | Apache-2.0 |
def inflate_weights(self, logger: MMLogger) -> None:
"""Inflate the swin2d parameters to swin3d.
The differences between swin3d and swin2d mainly lie in an extra
axis. To utilize the pretrained parameters in 2d model, the weight
of swin2d models should be inflated to fit in the shapes o... | Inflate the swin2d parameters to swin3d.
The differences between swin3d and swin2d mainly lie in an extra
axis. To utilize the pretrained parameters in 2d model, the weight
of swin2d models should be inflated to fit in the shapes of the
3d counterpart.
Args:
logger ... | inflate_weights | python | open-mmlab/mmaction2 | mmaction/models/backbones/swin.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/swin.py | Apache-2.0 |
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Defines the computation performed at every call."""
assert isinstance(self.block, Bottleneck)
def _inner_forward(x):
"""Forward wrapper for utilizing checkpoint."""
identity = x
out = self.block.conv1(x)... | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/backbones/tanet.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/tanet.py | Apache-2.0 |
def make_tam_modeling(self):
"""Replace ResNet-Block with TA-Block."""
def make_tam_block(stage, num_segments, tam_cfg=dict()):
blocks = list(stage.children())
for i, block in enumerate(blocks):
blocks[i] = TABlock(block, num_segments, deepcopy(tam_cfg))
... | Replace ResNet-Block with TA-Block. | make_tam_modeling | python | open-mmlab/mmaction2 | mmaction/models/backbones/tanet.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/tanet.py | Apache-2.0 |
def forward(self, x):
"""Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The output of the module.
"""
x = rearrange(x, 'b c t h w -> (b t) c h w')
x = self.projection(x).flatten(2).transpose(1,... | Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The output of the module.
| forward | python | open-mmlab/mmaction2 | mmaction/models/backbones/timesformer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/timesformer.py | Apache-2.0 |
def init_weights(self, pretrained=None):
"""Initiate the parameters either from existing checkpoint or from
scratch."""
trunc_normal_(self.pos_embed, std=.02)
trunc_normal_(self.cls_token, std=.02)
if pretrained:
self.pretrained = pretrained
if isinstance(sel... | Initiate the parameters either from existing checkpoint or from
scratch. | init_weights | python | open-mmlab/mmaction2 | mmaction/models/backbones/timesformer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/timesformer.py | Apache-2.0 |
def forward(self, x):
"""Defines the computation performed at every call."""
# x [batch_size * num_frames, num_patches, embed_dims]
batches = x.shape[0]
x = self.patch_embed(x)
# x [batch_size * num_frames, num_patches + 1, embed_dims]
cls_tokens = self.cls_token.expand(... | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/backbones/timesformer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/timesformer.py | Apache-2.0 |
def conv_3xnxn(inp: int,
oup: int,
kernel_size: int = 3,
stride: int = 3,
groups: int = 1):
"""3D convolution with kernel size of 3xnxn.
Args:
inp (int): Dimension of input features.
oup (int): Dimension of output features.
ker... | 3D convolution with kernel size of 3xnxn.
Args:
inp (int): Dimension of input features.
oup (int): Dimension of output features.
kernel_size (int): The spatial kernel size (i.e., n).
Defaults to 3.
stride (int): The spatial stride.
Defaults to 3.
grou... | conv_3xnxn | python | open-mmlab/mmaction2 | mmaction/models/backbones/uniformer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/uniformer.py | Apache-2.0 |
def conv_1xnxn(inp: int,
oup: int,
kernel_size: int = 3,
stride: int = 3,
groups: int = 1):
"""3D convolution with kernel size of 1xnxn.
Args:
inp (int): Dimension of input features.
oup (int): Dimension of output features.
ker... | 3D convolution with kernel size of 1xnxn.
Args:
inp (int): Dimension of input features.
oup (int): Dimension of output features.
kernel_size (int): The spatial kernel size (i.e., n).
Defaults to 3.
stride (int): The spatial stride.
Defaults to 3.
grou... | conv_1xnxn | python | open-mmlab/mmaction2 | mmaction/models/backbones/uniformer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/uniformer.py | Apache-2.0 |
def _load_pretrained(self, pretrained: str = None) -> None:
"""Load ImageNet-1K pretrained model.
The model is pretrained with ImageNet-1K.
https://github.com/Sense-X/UniFormer
Args:
pretrained (str): Model name of ImageNet-1K pretrained model.
Defaults to N... | Load ImageNet-1K pretrained model.
The model is pretrained with ImageNet-1K.
https://github.com/Sense-X/UniFormer
Args:
pretrained (str): Model name of ImageNet-1K pretrained model.
Defaults to None.
| _load_pretrained | python | open-mmlab/mmaction2 | mmaction/models/backbones/uniformer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/uniformer.py | Apache-2.0 |
def _load_pretrained(self, pretrained: str = None) -> None:
"""Load CLIP pretrained visual encoder.
The visual encoder is extracted from CLIP.
https://github.com/openai/CLIP
Args:
pretrained (str): Model name of pretrained CLIP visual encoder.
Defaults to No... | Load CLIP pretrained visual encoder.
The visual encoder is extracted from CLIP.
https://github.com/openai/CLIP
Args:
pretrained (str): Model name of pretrained CLIP visual encoder.
Defaults to None.
| _load_pretrained | python | open-mmlab/mmaction2 | mmaction/models/backbones/uniformerv2.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/uniformerv2.py | Apache-2.0 |
def forward(self, x: Tensor) -> Tensor:
"""Defines the computation performed at every call.
Args:
x (Tensor): The input data with size of (B, N, C).
Returns:
Tensor: The output of the attention block, same size as inputs.
"""
B, N, C = x.shape
if... | Defines the computation performed at every call.
Args:
x (Tensor): The input data with size of (B, N, C).
Returns:
Tensor: The output of the attention block, same size as inputs.
| forward | python | open-mmlab/mmaction2 | mmaction/models/backbones/vit_mae.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/vit_mae.py | Apache-2.0 |
def forward(self, x: Tensor) -> Tensor:
"""Defines the computation performed at every call.
Args:
x (Tensor): The input data with size of (B, N, C).
Returns:
Tensor: The output of the transformer block, same size as inputs.
"""
if hasattr(self, 'gamma_1')... | Defines the computation performed at every call.
Args:
x (Tensor): The input data with size of (B, N, C).
Returns:
Tensor: The output of the transformer block, same size as inputs.
| forward | python | open-mmlab/mmaction2 | mmaction/models/backbones/vit_mae.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/vit_mae.py | Apache-2.0 |
def get_sinusoid_encoding(n_position: int, embed_dims: int) -> Tensor:
"""Generate sinusoid encoding table.
Sinusoid encoding is a kind of relative position encoding method came from
`Attention Is All You Need<https://arxiv.org/abs/1706.03762>`_.
Args:
n_position (int): The length of the input ... | Generate sinusoid encoding table.
Sinusoid encoding is a kind of relative position encoding method came from
`Attention Is All You Need<https://arxiv.org/abs/1706.03762>`_.
Args:
n_position (int): The length of the input token.
embed_dims (int): The position embedding dimension.
Returns... | get_sinusoid_encoding | python | open-mmlab/mmaction2 | mmaction/models/backbones/vit_mae.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/vit_mae.py | Apache-2.0 |
def forward(self, x: Tensor) -> Tensor:
"""Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The feature of the input
samples extracted by the backbone.
"""
b, _, _, h, w = x.shape
... | Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The feature of the input
samples extracted by the backbone.
| forward | python | open-mmlab/mmaction2 | mmaction/models/backbones/vit_mae.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/vit_mae.py | Apache-2.0 |
def _round_width(width, multiplier, min_width=8, divisor=8):
"""Round width of filters based on width multiplier."""
width *= multiplier
min_width = min_width or divisor
width_out = max(min_width,
int(width + divisor / 2) // divisor * divisor)
if width_out... | Round width of filters based on width multiplier. | _round_width | python | open-mmlab/mmaction2 | mmaction/models/backbones/x3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/x3d.py | Apache-2.0 |
def forward(self, x):
"""Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The output of the module.
"""
module_input = x
x = self.avg_pool(x)
x = self.fc1(x)
x = self.relu(x)
... | Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The output of the module.
| forward | python | open-mmlab/mmaction2 | mmaction/models/backbones/x3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/x3d.py | Apache-2.0 |
def forward(self, x):
"""Defines the computation performed at every call."""
def _inner_forward(x):
"""Forward wrapper for utilizing checkpoint."""
identity = x
out = self.conv1(x)
out = self.conv2(out)
if self.se_ratio is not None:
... | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/backbones/x3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/x3d.py | Apache-2.0 |
def _round_width(width, multiplier, min_depth=8, divisor=8):
"""Round width of filters based on width multiplier."""
if not multiplier:
return width
width *= multiplier
min_depth = min_depth or divisor
new_filters = max(min_depth,
int(width ... | Round width of filters based on width multiplier. | _round_width | python | open-mmlab/mmaction2 | mmaction/models/backbones/x3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/x3d.py | Apache-2.0 |
def _round_repeats(repeats, multiplier):
"""Round number of layers based on depth multiplier."""
if not multiplier:
return repeats
return int(math.ceil(multiplier * repeats)) | Round number of layers based on depth multiplier. | _round_repeats | python | open-mmlab/mmaction2 | mmaction/models/backbones/x3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/x3d.py | Apache-2.0 |
def make_res_layer(self,
block,
layer_inplanes,
inplanes,
planes,
blocks,
spatial_stride=1,
se_style='half',
se_ratio=None,
... | Build residual layer for ResNet3D.
Args:
block (nn.Module): Residual module to be built.
layer_inplanes (int): Number of channels for the input feature
of the res layer.
inplanes (int): Number of channels for the input feature in each
block, w... | make_res_layer | python | open-mmlab/mmaction2 | mmaction/models/backbones/x3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/x3d.py | Apache-2.0 |
def _make_stem_layer(self):
"""Construct the stem layers consists of a conv+norm+act module and a
pooling layer."""
self.conv1_s = ConvModule(
self.in_channels,
self.base_channels,
kernel_size=(1, 3, 3),
stride=(1, 2, 2),
padding=(0, 1,... | Construct the stem layers consists of a conv+norm+act module and a
pooling layer. | _make_stem_layer | python | open-mmlab/mmaction2 | mmaction/models/backbones/x3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/x3d.py | Apache-2.0 |
def _freeze_stages(self):
"""Prevent all the parameters from being optimized before
``self.frozen_stages``."""
if self.frozen_stages >= 0:
self.conv1_s.eval()
self.conv1_t.eval()
for param in self.conv1_s.parameters():
param.requires_grad = Fal... | Prevent all the parameters from being optimized before
``self.frozen_stages``. | _freeze_stages | python | open-mmlab/mmaction2 | mmaction/models/backbones/x3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/x3d.py | Apache-2.0 |
def init_weights(self):
"""Initiate the parameters either from existing checkpoint or from
scratch."""
if isinstance(self.pretrained, str):
logger = MMLogger.get_current_instance()
logger.info(f'load model from: {self.pretrained}')
load_checkpoint(self, self.... | Initiate the parameters either from existing checkpoint or from
scratch. | init_weights | python | open-mmlab/mmaction2 | mmaction/models/backbones/x3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/x3d.py | Apache-2.0 |
def forward(self, x):
"""Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The feature of the input
samples extracted by the backbone.
"""
x = self.conv1_s(x)
x = self.conv... | Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The feature of the input
samples extracted by the backbone.
| forward | python | open-mmlab/mmaction2 | mmaction/models/backbones/x3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/x3d.py | Apache-2.0 |
def train(self, mode=True):
"""Set the optimization status when training."""
super().train(mode)
self._freeze_stages()
if mode and self.norm_eval:
for m in self.modules():
if isinstance(m, _BatchNorm):
m.eval() | Set the optimization status when training. | train | python | open-mmlab/mmaction2 | mmaction/models/backbones/x3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/backbones/x3d.py | Apache-2.0 |
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The output of the module.
"""
x = self.conv_s(x)
x = self.bn_s(x)
x = s... | Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The output of the module.
| forward | python | open-mmlab/mmaction2 | mmaction/models/common/conv2plus1d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/common/conv2plus1d.py | Apache-2.0 |
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The output of the module.
"""
x_1 = self.conv_1(x)
x_2 = self.conv_2(x)
... | Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The output of the module.
| forward | python | open-mmlab/mmaction2 | mmaction/models/common/conv_audio.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/common/conv_audio.py | Apache-2.0 |
def aggregate_stats(self):
"""Synchronize running_mean, and running_var to self.bn.
Call this before eval, then call model.eval(); When eval, forward
function will call self.bn instead of self.split_bn, During this time
the running_mean, and running_var of self.bn has been obtained from... | Synchronize running_mean, and running_var to self.bn.
Call this before eval, then call model.eval(); When eval, forward
function will call self.bn instead of self.split_bn, During this time
the running_mean, and running_var of self.bn has been obtained from
self.split_bn.
| aggregate_stats | python | open-mmlab/mmaction2 | mmaction/models/common/sub_batchnorm3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/common/sub_batchnorm3d.py | Apache-2.0 |
def forward(self, x):
"""Defines the computation performed at every call."""
if self.training:
n, c, t, h, w = x.shape
assert n % self.num_splits == 0
x = x.view(n // self.num_splits, c * self.num_splits, t, h, w)
x = self.split_bn(x)
x = x.vie... | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/common/sub_batchnorm3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/common/sub_batchnorm3d.py | Apache-2.0 |
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The output of the module.
"""
# [n, c, h, w]
n, c, h, w = x.size()
num_... | Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The output of the module.
| forward | python | open-mmlab/mmaction2 | mmaction/models/common/tam.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/common/tam.py | Apache-2.0 |
def forward(self, query, key=None, value=None, residual=None, **kwargs):
"""Defines the computation performed at every call."""
assert residual is None, (
'Always adding the shortcut in the forward function')
init_cls_token = query[:, 0, :].unsqueeze(1)
identity = query_t = ... | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/common/transformer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/common/transformer.py | Apache-2.0 |
def forward(self, query, key=None, value=None, residual=None, **kwargs):
"""Defines the computation performed at every call."""
assert residual is None, (
'Always adding the shortcut in the forward function')
identity = query
init_cls_token = query[:, 0, :].unsqueeze(1)
... | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/common/transformer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/common/transformer.py | Apache-2.0 |
def forward(self, x, residual=None):
"""Defines the computation performed at every call."""
assert residual is None, ('Cannot apply pre-norm with FFNWithNorm')
return super().forward(self.norm(x), x) | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/common/transformer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/common/transformer.py | Apache-2.0 |
def forward(self,
data: Union[dict, Tuple[dict]],
training: bool = False) -> Union[dict, Tuple[dict]]:
"""Perform normalization, padding, bgr2rgb conversion and batch
augmentation based on ``BaseDataPreprocessor``.
Args:
data (dict or Tuple[dict]): da... | Perform normalization, padding, bgr2rgb conversion and batch
augmentation based on ``BaseDataPreprocessor``.
Args:
data (dict or Tuple[dict]): data sampled from dataloader.
training (bool): Whether to enable training time augmentation.
Returns:
dict or Tuple... | forward | python | open-mmlab/mmaction2 | mmaction/models/data_preprocessors/data_preprocessor.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/data_preprocessors/data_preprocessor.py | Apache-2.0 |
def forward_onesample(self, data, training: bool = False) -> dict:
"""Perform normalization, padding, bgr2rgb conversion and batch
augmentation on one data sample.
Args:
data (dict): data sampled from dataloader.
training (bool): Whether to enable training time augmentat... | Perform normalization, padding, bgr2rgb conversion and batch
augmentation on one data sample.
Args:
data (dict): data sampled from dataloader.
training (bool): Whether to enable training time augmentation.
Returns:
dict: Data in the same format as the model ... | forward_onesample | python | open-mmlab/mmaction2 | mmaction/models/data_preprocessors/data_preprocessor.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/data_preprocessors/data_preprocessor.py | Apache-2.0 |
def forward(self, data: Dict, training: bool = False) -> Dict:
"""Preprocesses the data into the model input format.
Args:
data (dict): Data returned by dataloader.
training (bool): Whether to enable training time augmentation.
Returns:
dict: Data in the sam... | Preprocesses the data into the model input format.
Args:
data (dict): Data returned by dataloader.
training (bool): Whether to enable training time augmentation.
Returns:
dict: Data in the same format as the model input.
| forward | python | open-mmlab/mmaction2 | mmaction/models/data_preprocessors/multimodal_data_preprocessor.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/data_preprocessors/multimodal_data_preprocessor.py | Apache-2.0 |
def loss(self, feats: Union[torch.Tensor, Tuple[torch.Tensor]],
data_samples: SampleList, **kwargs) -> Dict:
"""Perform forward propagation of head and loss calculation on the
features of the upstream network.
Args:
feats (torch.Tensor | tuple[torch.Tensor]): Features f... | Perform forward propagation of head and loss calculation on the
features of the upstream network.
Args:
feats (torch.Tensor | tuple[torch.Tensor]): Features from
upstream network.
data_samples (list[:obj:`ActionDataSample`]): The batch
data sample... | loss | python | open-mmlab/mmaction2 | mmaction/models/heads/base.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/base.py | Apache-2.0 |
def loss_by_feat(self, cls_scores: torch.Tensor,
data_samples: SampleList) -> Dict:
"""Calculate the loss based on the features extracted by the head.
Args:
cls_scores (torch.Tensor): Classification prediction results of
all class, has shape (batch_size,... | Calculate the loss based on the features extracted by the head.
Args:
cls_scores (torch.Tensor): Classification prediction results of
all class, has shape (batch_size, num_classes).
data_samples (list[:obj:`ActionDataSample`]): The batch
data samples.
... | loss_by_feat | python | open-mmlab/mmaction2 | mmaction/models/heads/base.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/base.py | Apache-2.0 |
def predict(self, feats: Union[torch.Tensor, Tuple[torch.Tensor]],
data_samples: SampleList, **kwargs) -> SampleList:
"""Perform forward propagation of head and predict recognition results
on the features of the upstream network.
Args:
feats (torch.Tensor | tuple[tor... | Perform forward propagation of head and predict recognition results
on the features of the upstream network.
Args:
feats (torch.Tensor | tuple[torch.Tensor]): Features from
upstream network.
data_samples (list[:obj:`ActionDataSample`]): The batch
... | predict | python | open-mmlab/mmaction2 | mmaction/models/heads/base.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/base.py | Apache-2.0 |
def predict_by_feat(self, cls_scores: torch.Tensor,
data_samples: SampleList) -> SampleList:
"""Transform a batch of output features extracted from the head into
prediction results.
Args:
cls_scores (torch.Tensor): Classification scores, has a shape
... | Transform a batch of output features extracted from the head into
prediction results.
Args:
cls_scores (torch.Tensor): Classification scores, has a shape
(B*num_segs, num_classes)
data_samples (list[:obj:`ActionDataSample`]): The
annotation data o... | predict_by_feat | python | open-mmlab/mmaction2 | mmaction/models/heads/base.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/base.py | Apache-2.0 |
def average_clip(self,
cls_scores: torch.Tensor,
num_segs: int = 1) -> torch.Tensor:
"""Averaging class scores over multiple clips.
Using different averaging types ('score' or 'prob' or None,
which defined in test_cfg) to computed the final averaged
... | Averaging class scores over multiple clips.
Using different averaging types ('score' or 'prob' or None,
which defined in test_cfg) to computed the final averaged
class score. Only called in test mode.
Args:
cls_scores (torch.Tensor): Class scores to be averaged.
... | average_clip | python | open-mmlab/mmaction2 | mmaction/models/heads/base.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/base.py | Apache-2.0 |
def forward(self,
x: Tensor,
num_segs: Optional[int] = None,
**kwargs) -> Tensor:
"""Defines the computation performed at every call.
Args:
x (Tensor): The input data.
num_segs (int): For 2D backbone. Number of segments into which
... | Defines the computation performed at every call.
Args:
x (Tensor): The input data.
num_segs (int): For 2D backbone. Number of segments into which
a video is divided. Defaults to None.
Returns:
Tensor: The output features after pooling.
| forward | python | open-mmlab/mmaction2 | mmaction/models/heads/feature_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/feature_head.py | Apache-2.0 |
def predict_by_feat(self, feats: Union[Tensor, Tuple[Tensor]],
data_samples) -> Tensor:
"""Integrate multi-view features into one tensor.
Args:
feats (torch.Tensor | tuple[torch.Tensor]): Features from
upstream network.
data_samples (list[... | Integrate multi-view features into one tensor.
Args:
feats (torch.Tensor | tuple[torch.Tensor]): Features from
upstream network.
data_samples (list[:obj:`ActionDataSample`]): The batch
data samples.
Returns:
Tensor: The integrated mul... | predict_by_feat | python | open-mmlab/mmaction2 | mmaction/models/heads/feature_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/feature_head.py | Apache-2.0 |
def forward(self, x: torch.Tensor, **kwargs) -> torch.Tensor:
"""Forward features from the upstream network.
Args:
x (torch.Tensor): Features from the upstream network.
Returns:
torch.Tensor: Classification scores with shape (B, num_classes).
"""
N, M, ... | Forward features from the upstream network.
Args:
x (torch.Tensor): Features from the upstream network.
Returns:
torch.Tensor: Classification scores with shape (B, num_classes).
| forward | python | open-mmlab/mmaction2 | mmaction/models/heads/gcn_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/gcn_head.py | Apache-2.0 |
def forward(self, x: Tensor, **kwargs) -> Tensor:
"""Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The classification scores for input samples.
"""
# [N, in_channels, 4, 7, 7]
if self.avg_pool... | Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The classification scores for input samples.
| forward | python | open-mmlab/mmaction2 | mmaction/models/heads/i3d_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/i3d_head.py | Apache-2.0 |
def pre_logits(self, feats: Tuple[List[Tensor]]) -> Tensor:
"""The process before the final classification head.
The input ``feats`` is a tuple of list of tensor, and each tensor is
the feature of a backbone stage.
"""
if self.with_cls_token:
_, cls_token = feats[-1]... | The process before the final classification head.
The input ``feats`` is a tuple of list of tensor, and each tensor is
the feature of a backbone stage.
| pre_logits | python | open-mmlab/mmaction2 | mmaction/models/heads/mvit_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/mvit_head.py | Apache-2.0 |
def forward(self, x: Tuple[List[Tensor]], **kwargs) -> Tensor:
"""Defines the computation performed at every call.
Args:
x (Tuple[List[Tensor]]): The input data.
Returns:
Tensor: The classification scores for input samples.
"""
x = self.pre_logits(x)
... | Defines the computation performed at every call.
Args:
x (Tuple[List[Tensor]]): The input data.
Returns:
Tensor: The classification scores for input samples.
| forward | python | open-mmlab/mmaction2 | mmaction/models/heads/mvit_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/mvit_head.py | Apache-2.0 |
def forward(self, x: Tensor, **kwargs) -> Tensor:
"""Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The classification scores for input samples.
"""
if len(x.shape) == 4:
cls_score = self.f... | Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The classification scores for input samples.
| forward | python | open-mmlab/mmaction2 | mmaction/models/heads/omni_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/omni_head.py | Apache-2.0 |
def loss_by_feat(self, cls_scores: Union[Tensor, Tuple[Tensor]],
data_samples: SampleList) -> dict:
"""Calculate the loss based on the features extracted by the head.
Args:
cls_scores (Tensor): Classification prediction results of
all class, has shape (b... | Calculate the loss based on the features extracted by the head.
Args:
cls_scores (Tensor): Classification prediction results of
all class, has shape (batch_size, num_classes).
data_samples (List[:obj:`ActionDataSample`]): The batch
data samples.
... | loss_by_feat | python | open-mmlab/mmaction2 | mmaction/models/heads/omni_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/omni_head.py | Apache-2.0 |
def forward(self, x: Tuple[torch.Tensor]) -> Dict:
"""Defines the computation performed at every call."""
x_rgb, x_pose = self.avg_pool(x[0]), self.avg_pool(x[1])
x_rgb = x_rgb.view(x_rgb.size(0), -1)
x_pose = x_pose.view(x_pose.size(0), -1)
x_rgb = self.dropout_rgb(x_rgb)
... | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/heads/rgbpose_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/rgbpose_head.py | Apache-2.0 |
def loss(self, feats: Tuple[torch.Tensor], data_samples: SampleList,
**kwargs) -> Dict:
"""Perform forward propagation of head and loss calculation on the
features of the upstream network.
Args:
feats (tuple[torch.Tensor]): Features from upstream network.
da... | Perform forward propagation of head and loss calculation on the
features of the upstream network.
Args:
feats (tuple[torch.Tensor]): Features from upstream network.
data_samples (list[:obj:`ActionDataSample`]): The batch
data samples.
Returns:
... | loss | python | open-mmlab/mmaction2 | mmaction/models/heads/rgbpose_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/rgbpose_head.py | Apache-2.0 |
def loss_by_feat(self, cls_scores: Dict[str, torch.Tensor],
data_samples: SampleList) -> Dict:
"""Calculate the loss based on the features extracted by the head.
Args:
cls_scores (dict[str, torch.Tensor]): The dict of
classification scores,
d... | Calculate the loss based on the features extracted by the head.
Args:
cls_scores (dict[str, torch.Tensor]): The dict of
classification scores,
data_samples (list[:obj:`ActionDataSample`]): The batch
data samples.
Returns:
dict: A dict... | loss_by_feat | python | open-mmlab/mmaction2 | mmaction/models/heads/rgbpose_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/rgbpose_head.py | Apache-2.0 |
def loss_by_scores(self, cls_scores: torch.Tensor,
labels: torch.Tensor) -> Dict:
"""Calculate the loss based on the features extracted by the head.
Args:
cls_scores (torch.Tensor): Classification prediction
results of all class, has shape (batch_size,... | Calculate the loss based on the features extracted by the head.
Args:
cls_scores (torch.Tensor): Classification prediction
results of all class, has shape (batch_size, num_classes).
labels (torch.Tensor): The labels used to calculate the loss.
Returns:
... | loss_by_scores | python | open-mmlab/mmaction2 | mmaction/models/heads/rgbpose_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/rgbpose_head.py | Apache-2.0 |
def predict(self, feats: Tuple[torch.Tensor], data_samples: SampleList,
**kwargs) -> SampleList:
"""Perform forward propagation of head and predict recognition results
on the features of the upstream network.
Args:
feats (tuple[torch.Tensor]): Features from upstream ... | Perform forward propagation of head and predict recognition results
on the features of the upstream network.
Args:
feats (tuple[torch.Tensor]): Features from upstream network.
data_samples (list[:obj:`ActionDataSample`]): The batch
data samples.
Returns:... | predict | python | open-mmlab/mmaction2 | mmaction/models/heads/rgbpose_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/rgbpose_head.py | Apache-2.0 |
def predict_by_feat(self, cls_scores: Dict[str, torch.Tensor],
data_samples: SampleList) -> SampleList:
"""Transform a batch of output features extracted from the head into
prediction results.
Args:
cls_scores (dict[str, torch.Tensor]): The dict of
... | Transform a batch of output features extracted from the head into
prediction results.
Args:
cls_scores (dict[str, torch.Tensor]): The dict of
classification scores,
data_samples (list[:obj:`ActionDataSample`]): The
annotation data of every samples... | predict_by_feat | python | open-mmlab/mmaction2 | mmaction/models/heads/rgbpose_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/rgbpose_head.py | Apache-2.0 |
def predict_by_scores(self, cls_scores: torch.Tensor,
data_samples: SampleList) -> torch.Tensor:
"""Transform a batch of output features extracted from the head into
prediction results.
Args:
cls_scores (torch.Tensor): Classification scores, has a shape
... | Transform a batch of output features extracted from the head into
prediction results.
Args:
cls_scores (torch.Tensor): Classification scores, has a shape
(B*num_segs, num_classes)
data_samples (list[:obj:`ActionDataSample`]): The annotation
data o... | predict_by_scores | python | open-mmlab/mmaction2 | mmaction/models/heads/rgbpose_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/rgbpose_head.py | Apache-2.0 |
def forward(self, x: Tuple[Tensor], **kwargs) -> None:
"""Defines the computation performed at every call.
Args:
x (tuple[torch.Tensor]): The input data.
Returns:
Tensor: The classification scores for input samples.
"""
# ([N, channel_slow, T1, H, W], [(... | Defines the computation performed at every call.
Args:
x (tuple[torch.Tensor]): The input data.
Returns:
Tensor: The classification scores for input samples.
| forward | python | open-mmlab/mmaction2 | mmaction/models/heads/slowfast_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/slowfast_head.py | Apache-2.0 |
def forward(self, x: Tensor, **kwargs) -> Tensor:
"""Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The classification scores for input samples.
"""
# [N, in_channels]
if self.dropout is not No... | Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The classification scores for input samples.
| forward | python | open-mmlab/mmaction2 | mmaction/models/heads/timesformer_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/timesformer_head.py | Apache-2.0 |
def forward(self,
x,
num_segs: Optional[int] = None,
fcn_test: bool = False,
**kwargs) -> Tensor:
"""Defines the computation performed at every call.
Args:
x (Tensor): The input data.
num_segs (int, optional): Numbe... | Defines the computation performed at every call.
Args:
x (Tensor): The input data.
num_segs (int, optional): Number of segments into which a video
is divided. Defaults to None.
fcn_test (bool): Whether to apply full convolution (fcn) testing.
... | forward | python | open-mmlab/mmaction2 | mmaction/models/heads/tpn_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/tpn_head.py | Apache-2.0 |
def forward(self, x):
"""Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The classification scores for input samples.
"""
# [N, num_segs * hidden_dim]
x = x.view(x.size(0), -1)
x = self.c... | Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The classification scores for input samples.
| forward | python | open-mmlab/mmaction2 | mmaction/models/heads/trn_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/trn_head.py | Apache-2.0 |
def forward(self, x, num_segs, **kwargs):
"""Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
num_segs (int): Useless in TRNHead. By default, `num_segs`
is equal to `clip_len * num_clips * num_crops`, which is
... | Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
num_segs (int): Useless in TRNHead. By default, `num_segs`
is equal to `clip_len * num_clips * num_crops`, which is
automatically generated in Recognizer forward phas... | forward | python | open-mmlab/mmaction2 | mmaction/models/heads/trn_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/trn_head.py | Apache-2.0 |
def forward(self, x: Tensor, num_segs: int, **kwargs) -> Tensor:
"""Defines the computation performed at every call.
Args:
x (Tensor): The input data.
num_segs (int): Useless in TSMHead. By default, `num_segs`
is equal to `clip_len * num_clips * num_crops`, which... | Defines the computation performed at every call.
Args:
x (Tensor): The input data.
num_segs (int): Useless in TSMHead. By default, `num_segs`
is equal to `clip_len * num_clips * num_crops`, which is
automatically generated in Recognizer forward phase and
... | forward | python | open-mmlab/mmaction2 | mmaction/models/heads/tsm_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/tsm_head.py | Apache-2.0 |
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The classification scores for input samples.
"""
# [N * num_segs, in_channels, h, w]
... | Defines the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The classification scores for input samples.
| forward | python | open-mmlab/mmaction2 | mmaction/models/heads/tsn_audio_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/tsn_audio_head.py | Apache-2.0 |
def forward(self, x: Tensor, num_segs: int, **kwargs) -> Tensor:
"""Defines the computation performed at every call.
Args:
x (Tensor): The input data.
num_segs (int): Number of segments into which a video
is divided.
Returns:
Tensor: The class... | Defines the computation performed at every call.
Args:
x (Tensor): The input data.
num_segs (int): Number of segments into which a video
is divided.
Returns:
Tensor: The classification scores for input samples.
| forward | python | open-mmlab/mmaction2 | mmaction/models/heads/tsn_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/tsn_head.py | Apache-2.0 |
def forward(self, x: Tensor, **kwargs) -> Tensor:
"""Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The classification scores for input samples.
"""
# [N, in_channels]
if self.dropout is not No... | Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The classification scores for input samples.
| forward | python | open-mmlab/mmaction2 | mmaction/models/heads/uniformer_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/uniformer_head.py | Apache-2.0 |
def forward(self, x: Tensor, **kwargs) -> Tensor:
"""Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The classification scores for input samples.
"""
# [N, in_channels, T, H, W]
assert self.pool... | Defines the computation performed at every call.
Args:
x (Tensor): The input data.
Returns:
Tensor: The classification scores for input samples.
| forward | python | open-mmlab/mmaction2 | mmaction/models/heads/x3d_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/heads/x3d_head.py | Apache-2.0 |
def forward(self, inputs, data_samples, mode, **kwargs):
"""The unified entry for a forward process in both training and test.
The method should accept three modes:
- ``tensor``: Forward the whole network and return tensor or tuple of
tensor without any post-processing, same as a commo... | The unified entry for a forward process in both training and test.
The method should accept three modes:
- ``tensor``: Forward the whole network and return tensor or tuple of
tensor without any post-processing, same as a common nn.Module.
- ``predict``: Forward and return the predictio... | forward | python | open-mmlab/mmaction2 | mmaction/models/localizers/bmn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bmn.py | Apache-2.0 |
def loss(self, batch_inputs, batch_data_samples, **kwargs):
"""Calculate losses from a batch of inputs and data samples.
Args:
batch_inputs (Tensor): Raw Inputs of the recognizer.
These should usually be mean centered and std scaled.
batch_data_samples (List[:obj... | Calculate losses from a batch of inputs and data samples.
Args:
batch_inputs (Tensor): Raw Inputs of the recognizer.
These should usually be mean centered and std scaled.
batch_data_samples (List[:obj:`ActionDataSample`]): The batch
data samples. It usual... | loss | python | open-mmlab/mmaction2 | mmaction/models/localizers/bmn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bmn.py | Apache-2.0 |
def predict(self, batch_inputs, batch_data_samples, **kwargs):
"""Define the computation performed at every call when testing."""
confidence_map, start, end = self._forward(batch_inputs)
start_scores = start[0].cpu().numpy()
end_scores = end[0].cpu().numpy()
cls_confidence = (con... | Define the computation performed at every call when testing. | predict | python | open-mmlab/mmaction2 | mmaction/models/localizers/bmn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bmn.py | Apache-2.0 |
def _get_interp1d_bin_mask(seg_tmin, seg_tmax, tscale, num_samples,
num_samples_per_bin):
"""Generate sample mask for a boundary-matching pair."""
plen = float(seg_tmax - seg_tmin)
plen_sample = plen / (num_samples * num_samples_per_bin - 1.0)
total_samples... | Generate sample mask for a boundary-matching pair. | _get_interp1d_bin_mask | python | open-mmlab/mmaction2 | mmaction/models/localizers/bmn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bmn.py | Apache-2.0 |
def _get_interp1d_mask(self):
"""Generate sample mask for each point in Boundary-Matching Map."""
mask_mat = []
for start_index in range(self.tscale):
mask_mat_vector = []
for duration_index in range(self.tscale):
if start_index + duration_index < self.tsc... | Generate sample mask for each point in Boundary-Matching Map. | _get_interp1d_mask | python | open-mmlab/mmaction2 | mmaction/models/localizers/bmn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bmn.py | Apache-2.0 |
def _temporal_anchors(self, tmin_offset=0., tmax_offset=1.):
"""Generate temporal anchors.
Args:
tmin_offset (int): Offset for the minimum value of temporal anchor.
Default: 0.
tmax_offset (int): Offset for the maximum value of temporal anchor.
De... | Generate temporal anchors.
Args:
tmin_offset (int): Offset for the minimum value of temporal anchor.
Default: 0.
tmax_offset (int): Offset for the maximum value of temporal anchor.
Default: 1.
Returns:
tuple[Sequence[float]]: The minim... | _temporal_anchors | python | open-mmlab/mmaction2 | mmaction/models/localizers/bmn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bmn.py | Apache-2.0 |
def _forward(self, x):
"""Define the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The output of the module.
"""
# x.shape [batch_size, self.feat_dim, self.tscale]
base_feature = self.x_1d_b(x)... | Define the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The output of the module.
| _forward | python | open-mmlab/mmaction2 | mmaction/models/localizers/bmn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bmn.py | Apache-2.0 |
def init_weights(self) -> None:
"""Initiate the parameters either from existing checkpoint or from
scratch."""
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, nn.BatchNorm2d):
constant_init(m, 1) | Initiate the parameters either from existing checkpoint or from
scratch. | init_weights | python | open-mmlab/mmaction2 | mmaction/models/localizers/bsn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bsn.py | Apache-2.0 |
def _temporal_anchors(self, tmin_offset=0., tmax_offset=1.):
"""Generate temporal anchors.
Args:
tmin_offset (int): Offset for the minimum value of temporal anchor.
Default: 0.
tmax_offset (int): Offset for the maximum value of temporal anchor.
De... | Generate temporal anchors.
Args:
tmin_offset (int): Offset for the minimum value of temporal anchor.
Default: 0.
tmax_offset (int): Offset for the maximum value of temporal anchor.
Default: 1.
Returns:
tuple[Sequence[float]]: The minim... | _temporal_anchors | python | open-mmlab/mmaction2 | mmaction/models/localizers/bsn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bsn.py | Apache-2.0 |
def _forward(self, x):
"""Define the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The output of the module.
"""
x = F.relu(self.conv1_ratio * self.conv1(x))
x = F.relu(self.conv2_ratio * self.... | Define the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The output of the module.
| _forward | python | open-mmlab/mmaction2 | mmaction/models/localizers/bsn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bsn.py | Apache-2.0 |
def loss(self, batch_inputs, batch_data_samples, **kwargs):
"""Calculate losses from a batch of inputs and data samples.
Args:
batch_inputs (Tensor): Raw Inputs of the recognizer.
These should usually be mean centered and std scaled.
batch_data_samples (List[:obj... | Calculate losses from a batch of inputs and data samples.
Args:
batch_inputs (Tensor): Raw Inputs of the recognizer.
These should usually be mean centered and std scaled.
batch_data_samples (List[:obj:`ActionDataSample`]): The batch
data samples. It usual... | loss | python | open-mmlab/mmaction2 | mmaction/models/localizers/bsn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bsn.py | Apache-2.0 |
def predict(self, batch_inputs, batch_data_samples, **kwargs):
"""Define the computation performed at every call when testing."""
tem_output = self._forward(batch_inputs).cpu().numpy()
batch_action = tem_output[:, 0, :]
batch_start = tem_output[:, 1, :]
batch_end = tem_output[:, ... | Define the computation performed at every call when testing. | predict | python | open-mmlab/mmaction2 | mmaction/models/localizers/bsn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bsn.py | Apache-2.0 |
def forward(self, inputs, data_samples, mode, **kwargs):
"""The unified entry for a forward process in both training and test.
The method should accept three modes:
- ``tensor``: Forward the whole network and return tensor or tuple of
tensor without any post-processing, same as a commo... | The unified entry for a forward process in both training and test.
The method should accept three modes:
- ``tensor``: Forward the whole network and return tensor or tuple of
tensor without any post-processing, same as a common nn.Module.
- ``predict``: Forward and return the predictio... | forward | python | open-mmlab/mmaction2 | mmaction/models/localizers/bsn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bsn.py | Apache-2.0 |
def init_weights(self) -> None:
"""Initiate the parameters either from existing checkpoint or from
scratch."""
for m in self.modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
elif isinstance(m, nn.BatchNorm2d):
constant_init(m, 1) | Initiate the parameters either from existing checkpoint or from
scratch. | init_weights | python | open-mmlab/mmaction2 | mmaction/models/localizers/bsn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bsn.py | Apache-2.0 |
def _forward(self, x):
"""Define the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The output of the module.
"""
x = F.relu(self.fc1_ratio * self.fc1(x))
x = torch.sigmoid(self.fc2_ratio * self... | Define the computation performed at every call.
Args:
x (torch.Tensor): The input data.
Returns:
torch.Tensor: The output of the module.
| _forward | python | open-mmlab/mmaction2 | mmaction/models/localizers/bsn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bsn.py | Apache-2.0 |
def loss(self, batch_inputs, batch_data_samples, **kwargs):
"""Calculate losses from a batch of inputs and data samples.
Args:
batch_inputs (Tensor): Raw Inputs of the recognizer.
These should usually be mean centered and std scaled.
batch_data_samples (List[:obj... | Calculate losses from a batch of inputs and data samples.
Args:
batch_inputs (Tensor): Raw Inputs of the recognizer.
These should usually be mean centered and std scaled.
batch_data_samples (List[:obj:`ActionDataSample`]): The batch
data samples. It usual... | loss | python | open-mmlab/mmaction2 | mmaction/models/localizers/bsn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bsn.py | Apache-2.0 |
def predict(self, batch_inputs, batch_data_samples, **kwargs):
"""Define the computation performed at every call when testing."""
device = self.fc1.weight.device
bsp_feature = torch.cat([
sample.gt_instances['bsp_feature'] for sample in batch_data_samples
]).to(device)
... | Define the computation performed at every call when testing. | predict | python | open-mmlab/mmaction2 | mmaction/models/localizers/bsn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bsn.py | Apache-2.0 |
def forward(self, inputs, data_samples, mode, **kwargs):
"""The unified entry for a forward process in both training and test.
The method should accept three modes:
- ``tensor``: Forward the whole network and return tensor or tuple of
tensor without any post-processing, same as a commo... | The unified entry for a forward process in both training and test.
The method should accept three modes:
- ``tensor``: Forward the whole network and return tensor or tuple of
tensor without any post-processing, same as a common nn.Module.
- ``predict``: Forward and return the predictio... | forward | python | open-mmlab/mmaction2 | mmaction/models/localizers/bsn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/bsn.py | Apache-2.0 |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.