code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
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def forward(self, x: Tensor) -> Tensor:
"""Forward call for LGTE.
Args:
x (torch.Tensor): The input tensor with shape (B, C, L)
"""
x = x.permute(2, 0, 1)
mask = self.mask.repeat(x.size(1), 1, 1, 1)
L = x.shape[0]
x = self.atten(x, attn_mask=mask.resh... | Forward call for LGTE.
Args:
x (torch.Tensor): The input tensor with shape (B, C, L)
| forward | python | open-mmlab/mmaction2 | mmaction/models/localizers/tcanet.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/tcanet.py | Apache-2.0 |
def StartEndRegressor(sample_num: int, feat_dim: int) -> nn.Module:
"""Start and End Regressor in the Temporal Boundary Regressor.
Args:
sample_num (int): number of samples for the start & end.
feat_dim (int): feature dimension.
Returns:
A pytorch module that works as the start and... | Start and End Regressor in the Temporal Boundary Regressor.
Args:
sample_num (int): number of samples for the start & end.
feat_dim (int): feature dimension.
Returns:
A pytorch module that works as the start and end regressor. The input
of the module should have a shape of (B, ... | StartEndRegressor | python | open-mmlab/mmaction2 | mmaction/models/localizers/tcanet.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/tcanet.py | Apache-2.0 |
def CenterWidthRegressor(temporal_len: int, feat_dim: int) -> nn.Module:
"""Center Width in the Temporal Boundary Regressor.
Args:
temporal_len (int): temporal dimension of the inputs.
feat_dim (int): feature dimension.
Returns:
A pytorch module that works as the start and end regr... | Center Width in the Temporal Boundary Regressor.
Args:
temporal_len (int): temporal dimension of the inputs.
feat_dim (int): feature dimension.
Returns:
A pytorch module that works as the start and end regressor. The input
of the module should have a shape of (B, feat_dim, temp... | CenterWidthRegressor | python | open-mmlab/mmaction2 | mmaction/models/localizers/tcanet.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/tcanet.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/tcanet.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/tcanet.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 = self.x_1d_b_f(x)
for layer in self.lgtes:
x = layer(x)
r... | 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/tcanet.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/tcanet.py | Apache-2.0 |
def __init__(self, pre_nms_thresh, pre_nms_top_n, nms_thresh,
fpn_post_nms_top_n, min_size, num_classes, is_first_stage):
"""
Arguments:
pre_nms_thresh (float)
pre_nms_top_n (int)
nms_thresh (float)
fpn_post_nms_top_n (int)
min... |
Arguments:
pre_nms_thresh (float)
pre_nms_top_n (int)
nms_thresh (float)
fpn_post_nms_top_n (int)
min_size (int)
num_classes (int)
box_coder (BoxCoder)
| __init__ | python | open-mmlab/mmaction2 | mmaction/models/localizers/drn/drn_utils/inference.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/drn/drn_utils/inference.py | Apache-2.0 |
def forward_for_single_feature_map(self, locations, box_cls,
box_regression, level, iou_scores):
"""
Arguments:
anchors: list[BoxList]
box_cls: tensor of size N, A * C, H, W
box_regression: tensor of size N, A * 4, H, W
"... |
Arguments:
anchors: list[BoxList]
box_cls: tensor of size N, A * C, H, W
box_regression: tensor of size N, A * 4, H, W
| forward_for_single_feature_map | python | open-mmlab/mmaction2 | mmaction/models/localizers/drn/drn_utils/inference.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/drn/drn_utils/inference.py | Apache-2.0 |
def forward(self, locations, box_cls, box_regression, iou_scores):
"""
Arguments:
anchors: list[list[BoxList]]
box_cls: list[tensor]
box_regression: list[tensor]
image_sizes: list[(h, w)]
Returns:
boxlists (list[BoxList]): the post-proc... |
Arguments:
anchors: list[list[BoxList]]
box_cls: list[tensor]
box_regression: list[tensor]
image_sizes: list[(h, w)]
Returns:
boxlists (list[BoxList]): the post-processed anchors, after
applying box decoding and NMS
| forward | python | open-mmlab/mmaction2 | mmaction/models/localizers/drn/drn_utils/inference.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/drn/drn_utils/inference.py | Apache-2.0 |
def generate_candidate_proposals(video_list,
video_infos,
tem_results_dir,
temporal_scale,
peak_threshold,
tem_results_ext='.csv',
... | Generate Candidate Proposals with given temporal evaluation results.
Each proposal file will contain:
'tmin,tmax,tmin_score,tmax_score,score,match_iou,match_ioa'.
Args:
video_list (list[int]): List of video indexes to generate proposals.
video_infos (list[dict]): List of video_info dict tha... | generate_candidate_proposals | python | open-mmlab/mmaction2 | mmaction/models/localizers/utils/bsn_utils.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/utils/bsn_utils.py | Apache-2.0 |
def generate_bsp_feature(video_list,
video_infos,
tem_results_dir,
pgm_proposals_dir,
top_k=1000,
bsp_boundary_ratio=0.2,
num_sample_start=8,
num... | Generate Boundary-Sensitive Proposal Feature with given proposals.
Args:
video_list (list[int]): List of video indexes to generate bsp_feature.
video_infos (list[dict]): List of video_info dict that contains
'video_name'.
tem_results_dir (str): Directory to load temporal evaluat... | generate_bsp_feature | python | open-mmlab/mmaction2 | mmaction/models/localizers/utils/bsn_utils.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/utils/bsn_utils.py | Apache-2.0 |
def temporal_iou(proposal_min, proposal_max, gt_min, gt_max):
"""Compute IoU score between a groundtruth bbox and the proposals.
Args:
proposal_min (list[float]): List of temporal anchor min.
proposal_max (list[float]): List of temporal anchor max.
gt_min (float): Groundtruth temporal b... | Compute IoU score between a groundtruth bbox and the proposals.
Args:
proposal_min (list[float]): List of temporal anchor min.
proposal_max (list[float]): List of temporal anchor max.
gt_min (float): Groundtruth temporal box min.
gt_max (float): Groundtruth temporal box max.
Ret... | temporal_iou | python | open-mmlab/mmaction2 | mmaction/models/localizers/utils/proposal_utils.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/utils/proposal_utils.py | Apache-2.0 |
def temporal_iop(proposal_min, proposal_max, gt_min, gt_max):
"""Compute IoP score between a groundtruth bbox and the proposals.
Compute the IoP which is defined as the overlap ratio with
groundtruth proportional to the duration of this proposal.
Args:
proposal_min (list[float]): List of tempor... | Compute IoP score between a groundtruth bbox and the proposals.
Compute the IoP which is defined as the overlap ratio with
groundtruth proportional to the duration of this proposal.
Args:
proposal_min (list[float]): List of temporal anchor min.
proposal_max (list[float]): List of temporal a... | temporal_iop | python | open-mmlab/mmaction2 | mmaction/models/localizers/utils/proposal_utils.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/utils/proposal_utils.py | Apache-2.0 |
def soft_nms(proposals, alpha, low_threshold, high_threshold, top_k):
"""Soft NMS for temporal proposals.
Args:
proposals (np.ndarray): Proposals generated by network.
alpha (float): Alpha value of Gaussian decaying function.
low_threshold (float): Low threshold for soft nms.
hi... | Soft NMS for temporal proposals.
Args:
proposals (np.ndarray): Proposals generated by network.
alpha (float): Alpha value of Gaussian decaying function.
low_threshold (float): Low threshold for soft nms.
high_threshold (float): High threshold for soft nms.
top_k (int): Top k... | soft_nms | python | open-mmlab/mmaction2 | mmaction/models/localizers/utils/proposal_utils.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/utils/proposal_utils.py | Apache-2.0 |
def post_processing(result, video_info, soft_nms_alpha, soft_nms_low_threshold,
soft_nms_high_threshold, post_process_top_k,
feature_extraction_interval):
"""Post process for temporal proposals generation.
Args:
result (np.ndarray): Proposals generated by network.... | Post process for temporal proposals generation.
Args:
result (np.ndarray): Proposals generated by network.
video_info (dict): Meta data of video. Required keys are
'duration_frame', 'duration_second'.
soft_nms_alpha (float): Alpha value of Gaussian decaying function.
soft... | post_processing | python | open-mmlab/mmaction2 | mmaction/models/localizers/utils/proposal_utils.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/localizers/utils/proposal_utils.py | Apache-2.0 |
def forward(self, *args, **kwargs):
"""Defines the computation performed at every call.
Args:
*args: The positional arguments for the corresponding
loss.
**kwargs: The keyword arguments for the corresponding
loss.
Returns:
tor... | Defines the computation performed at every call.
Args:
*args: The positional arguments for the corresponding
loss.
**kwargs: The keyword arguments for the corresponding
loss.
Returns:
torch.Tensor: The calculated loss.
| forward | python | open-mmlab/mmaction2 | mmaction/models/losses/base.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/losses/base.py | Apache-2.0 |
def forward(self,
reg_score,
label,
threshold=0.5,
ratio_range=(1.05, 21),
eps=1e-5):
"""Calculate Binary Logistic Regression Loss.
Args:
reg_score (torch.Tensor): Predicted score by model.
l... | Calculate Binary Logistic Regression Loss.
Args:
reg_score (torch.Tensor): Predicted score by model.
label (torch.Tensor): Groundtruth labels.
threshold (float): Threshold for positive instances.
Default: 0.5.
ratio_range (tupl... | forward | python | open-mmlab/mmaction2 | mmaction/models/losses/binary_logistic_regression_loss.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/losses/binary_logistic_regression_loss.py | Apache-2.0 |
def tem_loss(pred_start, pred_end, gt_start, gt_end):
"""Calculate Temporal Evaluation Module Loss.
This function calculate the binary_logistic_regression_loss for start
and end respectively and returns the sum of their losses.
Args:
pred_start (torch.Tensor): Predicted sta... | Calculate Temporal Evaluation Module Loss.
This function calculate the binary_logistic_regression_loss for start
and end respectively and returns the sum of their losses.
Args:
pred_start (torch.Tensor): Predicted start score by BMN model.
pred_end (torch.Tensor): Predi... | tem_loss | python | open-mmlab/mmaction2 | mmaction/models/losses/bmn_loss.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/losses/bmn_loss.py | Apache-2.0 |
def pem_reg_loss(pred_score,
gt_iou_map,
mask,
high_temporal_iou_threshold=0.7,
low_temporal_iou_threshold=0.3):
"""Calculate Proposal Evaluation Module Regression Loss.
Args:
pred_score (torch.Tensor): Pred... | Calculate Proposal Evaluation Module Regression Loss.
Args:
pred_score (torch.Tensor): Predicted temporal_iou score by BMN.
gt_iou_map (torch.Tensor): Groundtruth temporal_iou score.
mask (torch.Tensor): Boundary-Matching mask.
high_temporal_iou_threshold (float)... | pem_reg_loss | python | open-mmlab/mmaction2 | mmaction/models/losses/bmn_loss.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/losses/bmn_loss.py | Apache-2.0 |
def pem_cls_loss(pred_score,
gt_iou_map,
mask,
threshold=0.9,
ratio_range=(1.05, 21),
eps=1e-5):
"""Calculate Proposal Evaluation Module Classification Loss.
Args:
pred_score (torch.Tens... | Calculate Proposal Evaluation Module Classification Loss.
Args:
pred_score (torch.Tensor): Predicted temporal_iou score by BMN.
gt_iou_map (torch.Tensor): Groundtruth temporal_iou score.
mask (torch.Tensor): Boundary-Matching mask.
threshold (float): Threshold of... | pem_cls_loss | python | open-mmlab/mmaction2 | mmaction/models/losses/bmn_loss.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/losses/bmn_loss.py | Apache-2.0 |
def forward(self,
pred_bm,
pred_start,
pred_end,
gt_iou_map,
gt_start,
gt_end,
bm_mask,
weight_tem=1.0,
weight_pem_reg=10.0,
weight_pem_cls=1.0):
"""Cal... | Calculate Boundary Matching Network Loss.
Args:
pred_bm (torch.Tensor): Predicted confidence score for boundary
matching map.
pred_start (torch.Tensor): Predicted confidence score for start.
pred_end (torch.Tensor): Predicted confidence score for end.
... | forward | python | open-mmlab/mmaction2 | mmaction/models/losses/bmn_loss.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/losses/bmn_loss.py | Apache-2.0 |
def _forward(self, cls_score: torch.Tensor, label: torch.Tensor,
**kwargs) -> torch.Tensor:
"""Forward function.
Args:
cls_score (torch.Tensor): The class score.
label (torch.Tensor): The ground truth label.
kwargs: Any keyword argument to be used to... | Forward function.
Args:
cls_score (torch.Tensor): The class score.
label (torch.Tensor): The ground truth label.
kwargs: Any keyword argument to be used to calculate
CrossEntropy loss.
Returns:
torch.Tensor: The returned CrossEntropy loss... | _forward | python | open-mmlab/mmaction2 | mmaction/models/losses/cross_entropy_loss.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/losses/cross_entropy_loss.py | Apache-2.0 |
def _forward(self, cls_score: torch.Tensor, label: torch.Tensor,
**kwargs) -> torch.Tensor:
"""Forward function.
Args:
cls_score (torch.Tensor): The class score.
label (torch.Tensor): The ground truth label.
kwargs: Any keyword argument to be used to... | Forward function.
Args:
cls_score (torch.Tensor): The class score.
label (torch.Tensor): The ground truth label.
kwargs: Any keyword argument to be used to calculate
bce loss with logits.
Returns:
torch.Tensor: The returned bce loss with ... | _forward | python | open-mmlab/mmaction2 | mmaction/models/losses/cross_entropy_loss.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/losses/cross_entropy_loss.py | Apache-2.0 |
def _forward(self, cls_score: torch.Tensor, label: torch.Tensor,
**kwargs) -> torch.Tensor:
"""Forward function.
Args:
cls_score (torch.Tensor): The class score.
label (torch.Tensor): The ground truth label.
kwargs: Any keyword argument to be used to... | Forward function.
Args:
cls_score (torch.Tensor): The class score.
label (torch.Tensor): The ground truth label.
kwargs: Any keyword argument to be used to calculate
bce loss with logits.
Returns:
torch.Tensor: The returned bce loss with ... | _forward | python | open-mmlab/mmaction2 | mmaction/models/losses/cross_entropy_loss.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/losses/cross_entropy_loss.py | Apache-2.0 |
def _forward(self, cls_score, label, mask, category_mask):
"""Forward function.
Args:
cls_score (torch.Tensor): The class score.
label (torch.Tensor): The ground truth label.
mask (torch.Tensor): The mask of tags. 0 indicates that the
category of this... | Forward function.
Args:
cls_score (torch.Tensor): The class score.
label (torch.Tensor): The ground truth label.
mask (torch.Tensor): The mask of tags. 0 indicates that the
category of this tag is missing in the label of the video.
category_mask (... | _forward | python | open-mmlab/mmaction2 | mmaction/models/losses/hvu_loss.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/losses/hvu_loss.py | Apache-2.0 |
def forward(ctx, pred, labels, is_positive, ohem_ratio, group_size):
"""Calculate OHEM hinge loss.
Args:
pred (torch.Tensor): Predicted completeness score.
labels (torch.Tensor): Groundtruth class label.
is_positive (int): Set to 1 when proposals are positive and
... | Calculate OHEM hinge loss.
Args:
pred (torch.Tensor): Predicted completeness score.
labels (torch.Tensor): Groundtruth class label.
is_positive (int): Set to 1 when proposals are positive and
set to -1 when proposals are incomplete.
ohem_ratio (fl... | forward | python | open-mmlab/mmaction2 | mmaction/models/losses/ohem_hinge_loss.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/losses/ohem_hinge_loss.py | Apache-2.0 |
def backward(ctx, grad_output):
"""Defines a formula for differentiating the operation with backward
mode automatic differentiation."""
labels = ctx.labels
slopes = ctx.slopes
grad_in = torch.zeros(ctx.shape, device=ctx.slopes.device)
for group in range(ctx.num_groups):... | Defines a formula for differentiating the operation with backward
mode automatic differentiation. | backward | python | open-mmlab/mmaction2 | mmaction/models/losses/ohem_hinge_loss.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/losses/ohem_hinge_loss.py | Apache-2.0 |
def forward(self, activity_score, completeness_score, bbox_pred,
proposal_type, labels, bbox_targets, train_cfg):
"""Calculate Boundary Matching Network Loss.
Args:
activity_score (torch.Tensor): Predicted activity score.
completeness_score (torch.Tensor): Predic... | Calculate Boundary Matching Network Loss.
Args:
activity_score (torch.Tensor): Predicted activity score.
completeness_score (torch.Tensor): Predicted completeness score.
bbox_pred (torch.Tensor): Predicted interval center and span
of positive proposals.
... | forward | python | open-mmlab/mmaction2 | mmaction/models/losses/ssn_loss.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/losses/ssn_loss.py | Apache-2.0 |
def forward(self, hidden_states: torch.Tensor):
"""forward function.
Args:
hidden_states (torch.Tensor): The input. Shape: [b,t,l,c]
Returns: TODO
"""
b = hidden_states.shape[0]
output = einops.rearrange(hidden_states, 'b t l c -> (b l) t c')
output ... | forward function.
Args:
hidden_states (torch.Tensor): The input. Shape: [b,t,l,c]
Returns: TODO
| forward | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/beit3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/beit3d.py | Apache-2.0 |
def forward(self,
pixel_values: torch.Tensor,
bool_masked_pos: Optional[torch.BoolTensor] = None
) -> torch.Tensor:
"""
Args:
pixel_values (torch.Tensor): The input image patches.
Shape: [B, T, C, H, W].
"""
t ... |
Args:
pixel_values (torch.Tensor): The input image patches.
Shape: [B, T, C, H, W].
| forward | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/beit3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/beit3d.py | Apache-2.0 |
def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
"""Load tf checkpoints in a pytorch model."""
logger = MMLogger.get_current_instance()
try:
import re
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'Loading a Tenso... | Load tf checkpoints in a pytorch model. | load_tf_weights_in_bert | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/modeling_bert.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/modeling_bert.py | Apache-2.0 |
def get_extended_attention_mask(self, attention_mask: Tensor,
input_shape: Tuple[int], device: device,
is_decoder: bool) -> Tensor:
"""Makes broadcastable attention and causal masks so that future and
masked tokens are ignored.
... | Makes broadcastable attention and causal masks so that future and
masked tokens are ignored.
Arguments:
attention_mask (:obj:`torch.Tensor`):
Mask with ones indicating tokens to attend to, zeros for tokens to ignore.
input_shape (:obj:`Tuple[int]`):
... | get_extended_attention_mask | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/modeling_bert.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/modeling_bert.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
... |
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attent... | forward | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/modeling_bert.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/modeling_bert.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
next_sentence_label=None,
output_attentions=None,
output_hidden_states=None,
re... |
labels (:obj:`torch.LongTensor` of shape ``(batch_size, sequence_length)``, `optional`):
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
... | forward | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/modeling_bert.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/modeling_bert.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
labels=None,
past_key_values=None,
... |
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attent... | forward | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/modeling_bert.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/modeling_bert.py | Apache-2.0 |
def tie_aux_decoder_weights(self, module, aux_modules):
"""Tie decoder weights of all `aux_modules` to `module`, (not bias)"""
for m in aux_modules:
m.predictions.decoder.weight = module.predictions.decoder.weight | Tie decoder weights of all `aux_modules` to `module`, (not bias) | tie_aux_decoder_weights | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/modeling_bert.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/modeling_bert.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
labels=None,
o... |
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,
config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored
... | forward | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/modeling_bert.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/modeling_bert.py | Apache-2.0 |
def forward(self, x: torch.Tensor):
"""forward.
Args:
x (torch.Tensor): input features.
Shape: [bs, nframes, l, c]. l = 1 + h*w
Returns: features after adapter. The same shape as input.
"""
if x.shape[1] == 1: # for single frame, return itself.
... | forward.
Args:
x (torch.Tensor): input features.
Shape: [bs, nframes, l, c]. l = 1 + h*w
Returns: features after adapter. The same shape as input.
| forward | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/temporal_model.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/temporal_model.py | Apache-2.0 |
def __init__(self, input_dim=768, droppath_rate=0.1):
"""
Kwargs:
input_dim (int): The input feature dimension.
"""
super().__init__()
self._input_dim = input_dim
self.temporal_attn = MultiheadAttention(
input_dim, num_heads=input_dim // 64)
... |
Kwargs:
input_dim (int): The input feature dimension.
| __init__ | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/temporal_model.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/temporal_model.py | Apache-2.0 |
def forward(self, x: torch.Tensor):
"""forward.
Args:
x (torch.Tensor): input features.
Shape: [bs, nframes, l, c]. l = 1 + h*w
Returns: features after adapter. The same shape as input.
"""
if x.shape[1] == 1: # for single frame, return itself.
... | forward.
Args:
x (torch.Tensor): input features.
Shape: [bs, nframes, l, c]. l = 1 + h*w
Returns: features after adapter. The same shape as input.
| forward | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/temporal_model.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/temporal_model.py | Apache-2.0 |
def __init__(self, input_dim=768, droppath_rate=0.1, window_size=(2, 2)):
"""
Kwargs:
input_dim (int): The input feature dimension.
"""
super().__init__()
self._input_dim = input_dim
self.temporal_attn = MultiheadAttention(
input_dim, num_heads... |
Kwargs:
input_dim (int): The input feature dimension.
| __init__ | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/temporal_model.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/temporal_model.py | Apache-2.0 |
def forward(self, x: torch.Tensor):
"""forward.
Args:
x (torch.Tensor): input features.
Shape: [bs, nframes, l, c]. l = 1 + h*w
Returns: features after adapter. The same shape as input.
"""
if x.shape[1] == 1: # for single frame, return itself.
... | forward.
Args:
x (torch.Tensor): input features.
Shape: [bs, nframes, l, c]. l = 1 + h*w
Returns: features after adapter. The same shape as input.
| forward | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/temporal_model.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/temporal_model.py | Apache-2.0 |
def __init__(self, input_dim=768, droppath_rate=0.1, num_prompts=1):
"""
Kwargs:
input_dim (int): The input feature dimension.
"""
super().__init__()
d_model = input_dim
self.message_fc = nn.Linear(d_model, d_model)
self.message_ln = LayerNorm(d_m... |
Kwargs:
input_dim (int): The input feature dimension.
| __init__ | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/temporal_model.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/temporal_model.py | Apache-2.0 |
def forward(self, x: torch.Tensor):
"""forward.
Args:
x (torch.Tensor): input features.
Shape: [bs, nframes, l, c]. l = 1 + h*w
Returns: features after adapter. The same shape as input.
"""
if x.shape[1] == 1: # for single frame, return itself.
... | forward.
Args:
x (torch.Tensor): input features.
Shape: [bs, nframes, l, c]. l = 1 + h*w
Returns: features after adapter. The same shape as input.
| forward | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/temporal_model.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/temporal_model.py | Apache-2.0 |
def build_inputs_with_special_tokens(
self,
token_ids_0: List[int],
token_ids_1: Optional[List[int]] = None) -> List[int]:
"""Build model inputs from a sequence or a pair of sequence for
sequence classification tasks by concatenating and adding special
tokens.... | Build model inputs from a sequence or a pair of sequence for
sequence classification tasks by concatenating and adding special
tokens. A BERT sequence has the following format:
- single sequence: `[CLS] X`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids... | build_inputs_with_special_tokens | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/tokenizer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/tokenizer.py | Apache-2.0 |
def all_gather_concat(data: torch.Tensor) -> torch.Tensor:
"""Gather tensors with different first-dimension size and concat to one
tenosr.
Note:
Only the first dimension should be different.
Args:
data (Tensor): Tensor to be gathered.
Returns:
torch.Tensor: The concatenate... | Gather tensors with different first-dimension size and concat to one
tenosr.
Note:
Only the first dimension should be different.
Args:
data (Tensor): Tensor to be gathered.
Returns:
torch.Tensor: The concatenated tenosr.
| all_gather_concat | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/utils.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/utils.py | Apache-2.0 |
def interpolate_pos_embed_beit(state_dict, new_model):
"""interpolate the positional embeddings. The spatial pe is relative and
temporal pe is absolute. additional temporal pe is padded with 0.
Args:
state_dict (dict): The state_dict.
new_model (nn.Module): The created model.
Returns: ... | interpolate the positional embeddings. The spatial pe is relative and
temporal pe is absolute. additional temporal pe is padded with 0.
Args:
state_dict (dict): The state_dict.
new_model (nn.Module): The created model.
Returns: dict. The state_dict with updated positional embeddings.
| interpolate_pos_embed_beit | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/utils.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/utils.py | Apache-2.0 |
def load_temp_embed_with_mismatch(temp_embed_old,
temp_embed_new,
add_zero=True):
"""Add/Remove extra temporal_embeddings as needed.
https://arxiv.org/abs/2104.00650 shows adding zero paddings works.
temp_embed_old: (1, num_frames_old, 1, ... | Add/Remove extra temporal_embeddings as needed.
https://arxiv.org/abs/2104.00650 shows adding zero paddings works.
temp_embed_old: (1, num_frames_old, 1, d)
temp_embed_new: (1, num_frames_new, 1, d)
add_zero: bool, if True, add zero, else, interpolate trained embeddings.
| load_temp_embed_with_mismatch | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/utils.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/utils.py | Apache-2.0 |
def interpolate_pos_relative_bias_beit(state_dict_old, state_dict_new,
patch_shape_new):
"""
Args:
state_dict_old: loaded state dict
state_dict_new: state dict for model with new image size
patch_shape_new: new model patch_shape
ref: https://git... |
Args:
state_dict_old: loaded state dict
state_dict_new: state dict for model with new image size
patch_shape_new: new model patch_shape
ref: https://github.com/microsoft/unilm/blob/master/beit/run_class_finetuning.py # noqa: E501
| interpolate_pos_relative_bias_beit | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/utils.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/utils.py | Apache-2.0 |
def loss(self, inputs: torch.Tensor, data_samples: SampleList,
**kwargs) -> dict:
"""Calculate losses from a batch of inputs and data samples.""" | Calculate losses from a batch of inputs and data samples. | loss | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/vindlu.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/vindlu.py | Apache-2.0 |
def forward(self, inputs, data_samples, mode: str = 'loss'):
"""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 c... | 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/multimodal/vindlu/vindlu.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/vindlu.py | Apache-2.0 |
def encode_vision(self, image):
"""encode image / videos as features.
Args:
image (torch.Tensor): The input images.
Returns: tuple.
- vision_embeds (torch.Tensor): The features of all patches.
Shape: [B,T,L,C].
- pooled_vision_embeds (torch.T... | encode image / videos as features.
Args:
image (torch.Tensor): The input images.
Returns: tuple.
- vision_embeds (torch.Tensor): The features of all patches.
Shape: [B,T,L,C].
- pooled_vision_embeds (torch.Tensor): The pooled features.
... | encode_vision | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/vindlu.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/vindlu.py | Apache-2.0 |
def encode_text(self, text):
"""encode text.
Args:
text (dict): The output of huggingface's `PreTrainedTokenizer`.
contains keys:
- input_ids (torch.Tensor): Token ids to be fed to a model.
Shape: [B,L].
- attention_mask (to... | encode text.
Args:
text (dict): The output of huggingface's `PreTrainedTokenizer`.
contains keys:
- input_ids (torch.Tensor): Token ids to be fed to a model.
Shape: [B,L].
- attention_mask (torch.Tensor): The mask indicate padded to... | encode_text | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/vindlu.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/vindlu.py | Apache-2.0 |
def loss(
self,
inputs: torch.Tensor,
data_samples: Optional[List[ActionDataSample]] = None,
) -> Dict[str, torch.tensor]:
"""Calculate losses from a batch of inputs and data samples.
Args:
inputs (dict): A batch of inputs. The input tensor with of
... | Calculate losses from a batch of inputs and data samples.
Args:
inputs (dict): A batch of inputs. The input tensor with of
at least one modality. For image, the value is a tensor
of shape (N, C, ...) in general.
For text, the value is a dict of tokeni... | loss | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/vindlu_ret.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/vindlu_ret.py | Apache-2.0 |
def extract_feat(
self,
images: torch.Tensor = None,
data_samples: List[ActionDataSample] = None,
return_texts=True,
) -> Dict[str, torch.Tensor]:
"""Extract features from the input dict.
Args:
images (tensor, optional): The images to extract features.
... | Extract features from the input dict.
Args:
images (tensor, optional): The images to extract features.
Defaults to None.
data_samples (list, optional): The data samples containing texts
to extract features. Defaults to None.
return_texts (bool... | extract_feat | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/vindlu_ret.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/vindlu_ret.py | Apache-2.0 |
def compute_score_matrix_i2t(self, img_feats, img_embeds, text_feats,
text_embeds, text_atts):
"""Compare the score matrix for image-to-text retrieval. Every image
should compare to all the text features.
Args:
img_feats (torch.Tensor): The input img... | Compare the score matrix for image-to-text retrieval. Every image
should compare to all the text features.
Args:
img_feats (torch.Tensor): The input img feats tensor with shape
(M, C). M stands for numbers of samples on a single GPU.
img_embeds (torch.Tensor): Th... | compute_score_matrix_i2t | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/vindlu_ret.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/vindlu_ret.py | Apache-2.0 |
def compute_score_matrix_t2i(self, img_feats, img_embeds, text_feats,
text_embeds, text_atts):
"""Compare the score matrix for text-to-image retrieval. Every text
should compare to all the image features.
Args:
img_feats (torch.Tensor): The input img... | Compare the score matrix for text-to-image retrieval. Every text
should compare to all the image features.
Args:
img_feats (torch.Tensor): The input img feats tensor with shape
(M, C). M stands for numbers of samples on a single GPU.
img_embeds (torch.Tensor): Th... | compute_score_matrix_t2i | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/vindlu_ret.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/vindlu_ret.py | Apache-2.0 |
def _get_predictions(self,
result: torch.Tensor,
data_samples: List[ActionDataSample],
mode: str = 'i2t'):
"""Post-process the output of retriever.
Args:
result (torch.Tensor): Score matrix of single retrieve,
... | Post-process the output of retriever.
Args:
result (torch.Tensor): Score matrix of single retrieve,
either from image or text.
data_samples (List[ActionDataSample], optional): The annotation
data of every samples.
mode (str): Retrieve mode, ei... | _get_predictions | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/vindlu_ret.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/vindlu_ret.py | Apache-2.0 |
def predict(self, inputs, data_samples, **kwargs):
"""Predict captions from a batch of inputs.
Args:
images (torch.Tensor): The input images tensor with shape
(N, C, ...) in general.
data_samples (List[DataSample], optional): The annotation
data o... | Predict captions from a batch of inputs.
Args:
images (torch.Tensor): The input images tensor with shape
(N, C, ...) in general.
data_samples (List[DataSample], optional): The annotation
data of every samples. Defaults to None.
**kwargs: Other... | predict | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/vindlu_ret_mc.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/vindlu_ret_mc.py | Apache-2.0 |
def loss(self, inputs, data_samples):
"""Calculate losses from a batch of inputs and data samples.
Args:
inputs (dict): A batch of inputs. The input tensor with of
at least one modality. For image, the value is a tensor
of shape (N, C, ...) in general.
... | Calculate losses from a batch of inputs and data samples.
Args:
inputs (dict): A batch of inputs. The input tensor with of
at least one modality. For image, the value is a tensor
of shape (N, C, ...) in general.
For text, the value is a dict of tokeni... | loss | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/vindlu_vqa.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/vindlu_vqa.py | Apache-2.0 |
def rank_answer(self, question_states, question_atts, answer_ids,
answer_atts, k):
"""
question_states: (bsz, Lq, d)
answer_ids: answer input id after tokenization, (#answers, La)
"""
num_ques = question_states.size(0)
start_ids = answer_ids[0, 0].repe... |
question_states: (bsz, Lq, d)
answer_ids: answer input id after tokenization, (#answers, La)
| rank_answer | python | open-mmlab/mmaction2 | mmaction/models/multimodal/vindlu/vindlu_vqa.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/multimodal/vindlu/vindlu_vqa.py | Apache-2.0 |
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Defines the computation performed at every call."""
if self.downsample_position == 'before':
x = self.pool(x)
x = self.conv(x)
else:
x = self.conv(x)
x = self.pool(x)
return x | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/necks/tpn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/necks/tpn.py | Apache-2.0 |
def forward(self, x: Tuple[torch.Tensor]) -> torch.Tensor:
"""Defines the computation performed at every call."""
out = [self.downsamples[i](feature) for i, feature in enumerate(x)]
out = torch.cat(out, 1)
out = self.fusion_conv(out)
return out | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/necks/tpn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/necks/tpn.py | Apache-2.0 |
def forward(self, x: Tuple[torch.Tensor]) -> list:
"""Defines the computation performed at every call."""
out = []
for i, _ in enumerate(x):
if isinstance(self.spatial_modulation[i], nn.ModuleList):
out_ = x[i]
for op in self.spatial_modulation[i]:
... | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/necks/tpn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/necks/tpn.py | Apache-2.0 |
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Defines the computation performed at every call."""
x = self.conv(x)
x = self.pool(x)
return x | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/necks/tpn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/necks/tpn.py | Apache-2.0 |
def init_weights(self) -> None:
"""Default init_weights for conv(msra) and norm in ConvModule."""
for m in self.modules():
if isinstance(m, nn.Conv3d):
xavier_init(m, distribution='uniform')
if isinstance(m, nn.BatchNorm3d):
constant_init(m, 1)
... | Default init_weights for conv(msra) and norm in ConvModule. | init_weights | python | open-mmlab/mmaction2 | mmaction/models/necks/tpn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/necks/tpn.py | Apache-2.0 |
def forward(self,
x: Tuple[torch.Tensor],
data_samples: Optional[SampleList] = None) -> tuple:
"""Defines the computation performed at every call."""
loss_aux = dict()
# Calculate auxiliary loss if `self.aux_head`
# and `data_samples` are not None.
... | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/necks/tpn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/necks/tpn.py | Apache-2.0 |
def loss(self, inputs: torch.Tensor, data_samples: SampleList,
**kwargs) -> dict:
"""Calculate losses from a batch of inputs and data samples.
Args:
inputs (torch.Tensor): Raw Inputs of the recognizer.
These should usually be mean centered and std scaled.
... | Calculate losses from a batch of inputs and data samples.
Args:
inputs (torch.Tensor): Raw Inputs of the recognizer.
These should usually be mean centered and std scaled.
data_samples (List[``ActionDataSample``]): The batch
data samples. It usually includ... | loss | python | open-mmlab/mmaction2 | mmaction/models/recognizers/base.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/recognizers/base.py | Apache-2.0 |
def predict(self, inputs: torch.Tensor, data_samples: SampleList,
**kwargs) -> SampleList:
"""Predict results from a batch of inputs and data samples with post-
processing.
Args:
inputs (torch.Tensor): Raw Inputs of the recognizer.
These should usuall... | Predict results from a batch of inputs and data samples with post-
processing.
Args:
inputs (torch.Tensor): Raw Inputs of the recognizer.
These should usually be mean centered and std scaled.
data_samples (List[``ActionDataSample``]): The batch
da... | predict | python | open-mmlab/mmaction2 | mmaction/models/recognizers/base.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/recognizers/base.py | Apache-2.0 |
def _forward(self,
inputs: torch.Tensor,
stage: str = 'backbone',
**kwargs) -> ForwardResults:
"""Network forward process. Usually includes backbone, neck and head
forward without any post-processing.
Args:
inputs (torch.Tensor): Ra... | Network forward process. Usually includes backbone, neck and head
forward without any post-processing.
Args:
inputs (torch.Tensor): Raw Inputs of the recognizer.
stage (str): Which stage to output the features.
Returns:
Union[tuple, torch.Tensor]: Features f... | _forward | python | open-mmlab/mmaction2 | mmaction/models/recognizers/base.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/recognizers/base.py | Apache-2.0 |
def forward(self,
inputs: torch.Tensor,
data_samples: OptSampleList = None,
mode: str = 'tensor',
**kwargs) -> ForwardResults:
"""The unified entry for a forward process in both training and test.
The method should accept three modes:
... | 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/recognizers/base.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/recognizers/base.py | Apache-2.0 |
def extract_feat(self,
inputs: torch.Tensor,
stage: str = 'neck',
data_samples: SampleList = None,
test_mode: bool = False) -> tuple:
"""Extract features of different stages.
Args:
inputs (Tensor): The input... | Extract features of different stages.
Args:
inputs (Tensor): The input data.
stage (str): Which stage to output the feature.
Defaults to ``neck``.
data_samples (List[:obj:`ActionDataSample`]): Action data
samples, which are only needed in trai... | extract_feat | python | open-mmlab/mmaction2 | mmaction/models/recognizers/recognizer2d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/recognizers/recognizer2d.py | Apache-2.0 |
def extract_feat(self,
inputs: Tensor,
stage: str = 'neck',
data_samples: OptSampleList = None,
test_mode: bool = False) -> tuple:
"""Extract features of different stages.
Args:
inputs (torch.Tensor): The in... | Extract features of different stages.
Args:
inputs (torch.Tensor): The input data.
stage (str): Which stage to output the feature.
Defaults to ``'neck'``.
data_samples (list[:obj:`ActionDataSample`], optional): Action data
samples, which are o... | extract_feat | python | open-mmlab/mmaction2 | mmaction/models/recognizers/recognizer3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/recognizers/recognizer3d.py | Apache-2.0 |
def extract_feat(self,
inputs: Dict[str, torch.Tensor],
stage: str = 'backbone',
data_samples: OptSampleList = None,
test_mode: bool = False) -> Tuple:
"""Extract features.
Args:
inputs (dict[str, torch.Tens... | Extract features.
Args:
inputs (dict[str, torch.Tensor]): The multi-modal input data.
stage (str): Which stage to output the feature.
Defaults to ``'backbone'``.
data_samples (list[:obj:`ActionDataSample`], optional): Action data
samples, whic... | extract_feat | python | open-mmlab/mmaction2 | mmaction/models/recognizers/recognizer3d_mm.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/recognizers/recognizer3d_mm.py | Apache-2.0 |
def extract_feat(self,
batch_inputs: Tensor,
stage: str = 'backbone',
**kwargs) -> tuple:
"""Extract features of different stages.
Args:
batch_inputs (Tensor): The input data.
stage (str): Which stage to output the f... | Extract features of different stages.
Args:
batch_inputs (Tensor): The input data.
stage (str): Which stage to output the feature.
Defaults to ``backbone``.
Returns:
Tensor: The extracted features.
dict: A dict recording the kwargs for do... | extract_feat | python | open-mmlab/mmaction2 | mmaction/models/recognizers/recognizer_audio.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/recognizers/recognizer_audio.py | Apache-2.0 |
def extract_feat(self,
inputs: torch.Tensor,
stage: str = 'backbone',
**kwargs) -> Tuple:
"""Extract features at the given stage.
Args:
inputs (torch.Tensor): The input skeleton with shape of
`(B, num_clips, num_... | Extract features at the given stage.
Args:
inputs (torch.Tensor): The input skeleton with shape of
`(B, num_clips, num_person, clip_len, num_joints, 3 or 2)`.
stage (str): The stage to output the features.
Defaults to ``'backbone'``.
Returns:
... | extract_feat | python | open-mmlab/mmaction2 | mmaction/models/recognizers/recognizer_gcn.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/recognizers/recognizer_gcn.py | Apache-2.0 |
def forward(self, *data_samples, mode: str, **kwargs) -> ForwardResults:
"""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,... | 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/recognizers/recognizer_omni.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/recognizers/recognizer_omni.py | Apache-2.0 |
def loss(self, data_samples: Sequence[SampleList]) -> dict:
"""Calculate losses from a batch of inputs and data samples.
Args:
data_samples (Sequence[SampleList]): a sequence of SampleList. Each
SampleList contains data samples from the same data source.
Returns:
... | Calculate losses from a batch of inputs and data samples.
Args:
data_samples (Sequence[SampleList]): a sequence of SampleList. Each
SampleList contains data samples from the same data source.
Returns:
dict: A dictionary of loss components.
| loss | python | open-mmlab/mmaction2 | mmaction/models/recognizers/recognizer_omni.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/recognizers/recognizer_omni.py | Apache-2.0 |
def predict(self, data_samples: Sequence[SampleList]) -> SampleList:
"""Predict results from a batch of inputs and data samples with post-
processing.
Args:
data_samples (Sequence[SampleList]): a sequence of SampleList. Each
SampleList contains data samples from the ... | Predict results from a batch of inputs and data samples with post-
processing.
Args:
data_samples (Sequence[SampleList]): a sequence of SampleList. Each
SampleList contains data samples from the same data source.
Returns:
List[``ActionDataSample``]: Retu... | predict | python | open-mmlab/mmaction2 | mmaction/models/recognizers/recognizer_omni.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/recognizers/recognizer_omni.py | Apache-2.0 |
def _forward(self,
inputs: torch.Tensor,
stage: str = 'backbone',
**kwargs) -> ForwardResults:
"""Network forward process. Usually includes backbone, neck and head
forward without any post-processing.
Args:
inputs (torch.Tensor): Ra... | Network forward process. Usually includes backbone, neck and head
forward without any post-processing.
Args:
inputs (torch.Tensor): Raw Inputs of the recognizer.
stage (str): Which stage to output the features.
Returns:
Union[tuple, torch.Tensor]: Features f... | _forward | python | open-mmlab/mmaction2 | mmaction/models/recognizers/recognizer_omni.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/recognizers/recognizer_omni.py | Apache-2.0 |
def _run_forward(self, data: Union[dict, tuple, list],
mode: str) -> Union[Dict[str, torch.Tensor], list]:
"""Unpacks data for :meth:`forward`
Args:
data (dict or tuple or list): Data sampled from dataset.
mode (str): Mode of forward.
Returns:
... | Unpacks data for :meth:`forward`
Args:
data (dict or tuple or list): Data sampled from dataset.
mode (str): Mode of forward.
Returns:
dict or list: Results of training or testing mode.
| _run_forward | python | open-mmlab/mmaction2 | mmaction/models/recognizers/recognizer_omni.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/recognizers/recognizer_omni.py | Apache-2.0 |
def extract_feat(self,
inputs: torch.Tensor,
stage: str = 'backbone',
test_mode: bool = False) -> tuple:
"""Extract features of different stages.
Args:
inputs (torch.Tensor): The input data.
stage (str): Which stage ... | Extract features of different stages.
Args:
inputs (torch.Tensor): The input data.
stage (str): Which stage to output the feature.
Defaults to ``'backbone'``.
test_mode (bool): Whether in test mode. Defaults to False.
Returns:
torch.T... | extract_feat | python | open-mmlab/mmaction2 | mmaction/models/recognizers/recognizer_omni.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/recognizers/recognizer_omni.py | Apache-2.0 |
def loss(self, x: Union[Tensor,
Tuple[Tensor]], rpn_results_list: InstanceList,
data_samples: SampleList, **kwargs) -> dict:
"""Perform forward propagation and loss calculation of the detection
roi on the features of the upstream network.
Args:
... | Perform forward propagation and loss calculation of the detection
roi on the features of the upstream network.
Args:
x (Tensor or Tuple[Tensor]): The image features extracted by
the upstream network.
rpn_results_list (List[:obj:`InstanceData`]): List of region
... | loss | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/roi_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/roi_head.py | Apache-2.0 |
def _bbox_forward(self, x: Union[Tensor, Tuple[Tensor]], rois: Tensor,
batch_img_metas: List[dict], **kwargs) -> dict:
"""Box head forward function used in both training and testing.
Args:
x (Tensor or Tuple[Tensor]): The image features extracted by
the... | Box head forward function used in both training and testing.
Args:
x (Tensor or Tuple[Tensor]): The image features extracted by
the upstream network.
rois (Tensor): RoIs with the shape (n, 5) where the first
column indicates batch id of each RoI.
... | _bbox_forward | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/roi_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/roi_head.py | Apache-2.0 |
def bbox_loss(self, x: Union[Tensor, Tuple[Tensor]],
sampling_results: List[SamplingResult],
batch_img_metas: List[dict], **kwargs) -> dict:
"""Perform forward propagation and loss calculation of the bbox head on
the features of the upstream network.
Args:
... | Perform forward propagation and loss calculation of the bbox head on
the features of the upstream network.
Args:
x (Tensor or Tuple[Tensor]): The image features extracted by
the upstream network.
sampling_results (List[SamplingResult]): Sampling results.
... | bbox_loss | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/roi_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/roi_head.py | Apache-2.0 |
def predict(self, x: Union[Tensor,
Tuple[Tensor]], rpn_results_list: InstanceList,
data_samples: SampleList, **kwargs) -> InstanceList:
"""Perform forward propagation of the roi head and predict detection
results on the features of the upstream network.
... | Perform forward propagation of the roi head and predict detection
results on the features of the upstream network.
Args:
x (Tensor or Tuple[Tensor]): The image features extracted by
the upstream network.
rpn_results_list (List[:obj:`InstanceData`]): list of regio... | predict | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/roi_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/roi_head.py | Apache-2.0 |
def predict_bbox(self, x: Tuple[Tensor], batch_img_metas: List[dict],
rpn_results_list: InstanceList,
rcnn_test_cfg: ConfigType) -> InstanceList:
"""Perform forward propagation of the bbox head and predict detection
results on the features of the upstream networ... | Perform forward propagation of the bbox head and predict detection
results on the features of the upstream network.
Args:
x (tuple[Tensor]): Feature maps of all scale level.
batch_img_metas (list[dict]): List of image information.
rpn_results_list (list[:obj:`Instanc... | predict_bbox | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/roi_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/roi_head.py | Apache-2.0 |
def forward(self, x: Tensor) -> Tensor:
"""Computes the classification logits given ROI features."""
if self.dropout_before_pool and self.dropout_ratio > 0:
x = self.dropout(x)
x = self.temporal_pool(x)
x = self.spatial_pool(x)
if not self.dropout_before_pool and se... | Computes the classification logits given ROI features. | forward | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/bbox_heads/bbox_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/bbox_heads/bbox_head.py | Apache-2.0 |
def get_recall_prec(pred_vec: Tensor, target_vec: Tensor) -> tuple:
"""Computes the Recall/Precision for both multi-label and single label
scenarios.
Note that the computation calculates the micro average.
Note, that in both cases, the concept of correct/incorrect is the same.
... | Computes the Recall/Precision for both multi-label and single label
scenarios.
Note that the computation calculates the micro average.
Note, that in both cases, the concept of correct/incorrect is the same.
Args:
pred_vec (tensor[N x C]): each element is either 0 or 1
... | get_recall_prec | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/bbox_heads/bbox_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/bbox_heads/bbox_head.py | Apache-2.0 |
def topk_accuracy(self,
pred: Tensor,
target: Tensor,
thr: float = 0.5) -> tuple:
"""Computes the Top-K Accuracies for both single and multi-label
scenarios."""
# Define Target vector:
target_bool = target > 0.5
#... | Computes the Top-K Accuracies for both single and multi-label
scenarios. | topk_accuracy | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/bbox_heads/bbox_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/bbox_heads/bbox_head.py | Apache-2.0 |
def loss_and_target(self, cls_score: Tensor, rois: Tensor,
sampling_results: List[SamplingResult],
rcnn_train_cfg: ConfigDict, **kwargs) -> dict:
"""Calculate the loss based on the features extracted by the bbox head.
Args:
cls_score (Tensor):... | Calculate the loss based on the features extracted by the bbox head.
Args:
cls_score (Tensor): Classification prediction
results of all class, has shape
(batch_size * num_proposals_single_image, num_classes)
rois (Tensor): RoIs with the shape
... | loss_and_target | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/bbox_heads/bbox_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/bbox_heads/bbox_head.py | Apache-2.0 |
def predict_by_feat(self,
rois: Tuple[Tensor],
cls_scores: Tuple[Tensor],
batch_img_metas: List[dict],
rcnn_test_cfg: Optional[ConfigDict] = None,
**kwargs) -> InstanceList:
"""Transform a bat... | Transform a batch of output features extracted from the head into
bbox results.
Args:
rois (tuple[Tensor]): Tuple of boxes to be transformed.
Each has shape (num_boxes, 5). last dimension 5 arrange as
(batch_index, x1, y1, x2, y2).
cls_scores (tu... | predict_by_feat | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/bbox_heads/bbox_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/bbox_heads/bbox_head.py | Apache-2.0 |
def _predict_by_feat_single(self,
roi: Tensor,
cls_score: Tensor,
img_meta: dict,
rcnn_test_cfg: Optional[ConfigDict] = None,
**kwargs) -> InstanceData:
... | Transform a single image's features extracted from the head into
bbox results.
Args:
roi (Tensor): Boxes to be transformed. Has shape (num_boxes, 5).
last dimension 5 arrange as (batch_index, x1, y1, x2, y2).
cls_score (Tensor): Box scores, has shape
... | _predict_by_feat_single | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/bbox_heads/bbox_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/bbox_heads/bbox_head.py | Apache-2.0 |
def forward(self, feat: Union[Tensor, Tuple[Tensor]],
rois: Tensor) -> tuple:
"""Forward function for extract roi features.
Args:
feat (Tensor or Tuple[Tensor]): The image features extracted by
the upstream network. The shape of feat is N, C, T, H, W.
... | Forward function for extract roi features.
Args:
feat (Tensor or Tuple[Tensor]): The image features extracted by
the upstream network. The shape of feat is N, C, T, H, W.
rois (Tensor): Input RoIs, shape (k, 5).
Returns:
tuple: A tuple of roi feature... | forward | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/roi_extractors/single_straight3d.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/roi_extractors/single_straight3d.py | Apache-2.0 |
def forward(self, x, feat, rois, **kwargs):
"""Defines the computation performed at every call.
Args:
x (torch.Tensor): The extracted RoI feature.
feat (torch.Tensor): The context feature.
rois (torch.Tensor): The regions of interest.
Returns:
to... | Defines the computation performed at every call.
Args:
x (torch.Tensor): The extracted RoI feature.
feat (torch.Tensor): The context feature.
rois (torch.Tensor): The regions of interest.
Returns:
torch.Tensor: The RoI features that have interacted with ... | forward | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/shared_heads/acrn_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/shared_heads/acrn_head.py | Apache-2.0 |
def init_weights(self, pretrained=None):
"""Initiate the parameters either from existing checkpoint or from
scratch."""
if isinstance(pretrained, str):
logger = MMLogger.get_current_instance()
logger.info(f'load model from: {pretrained}')
load_checkpoint(self,... | Initiate the parameters either from existing checkpoint or from
scratch. | init_weights | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/shared_heads/fbo_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/shared_heads/fbo_head.py | Apache-2.0 |
def forward(self, st_feat, lt_feat):
"""Defines the computation performed at every call."""
n, c = st_feat.size(0), self.latent_channels
num_st_feat, num_lt_feat = self.num_st_feat, self.num_lt_feat
theta = self.st_feat_conv(st_feat)
theta = theta.view(n, c, num_st_feat)
... | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/shared_heads/fbo_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/shared_heads/fbo_head.py | Apache-2.0 |
def init_weights(self, pretrained=None):
"""Initiate the parameters either from existing checkpoint or from
scratch."""
if isinstance(pretrained, str):
logger = MMLogger.get_current_instance()
load_checkpoint(self, pretrained, strict=False, logger=logger)
elif pre... | Initiate the parameters either from existing checkpoint or from
scratch. | init_weights | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/shared_heads/fbo_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/shared_heads/fbo_head.py | Apache-2.0 |
def forward(self, st_feat, lt_feat):
"""Defines the computation performed at every call."""
# prepare st_feat
st_feat = self.st_feat_conv(st_feat)
if self.st_feat_dropout_ratio > 0:
st_feat = self.st_feat_dropout(st_feat)
# prepare lt_feat
lt_feat = self.lt_f... | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/shared_heads/fbo_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/shared_heads/fbo_head.py | Apache-2.0 |
def sample_lfb(self, rois, img_metas):
"""Sample long-term features for each ROI feature."""
inds = rois[:, 0].type(torch.int64)
lt_feat_list = []
for ind in inds:
lt_feat_list.append(self.lfb[img_metas[ind]['img_key']])
lt_feat = torch.stack(lt_feat_list, dim=0)
... | Sample long-term features for each ROI feature. | sample_lfb | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/shared_heads/fbo_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/shared_heads/fbo_head.py | Apache-2.0 |
def forward(self, x, rois, img_metas, **kwargs):
"""Defines the computation performed at every call."""
# [N, C, 1, 1, 1]
st_feat = self.temporal_pool(x)
st_feat = self.spatial_pool(st_feat)
identity = st_feat
# [N, C, window_size * num_feat_per_step, 1, 1]
lt_fe... | Defines the computation performed at every call. | forward | python | open-mmlab/mmaction2 | mmaction/models/roi_heads/shared_heads/fbo_head.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/models/roi_heads/shared_heads/fbo_head.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.