code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def draw_clip_range(self, frames, preds, bboxes, draw_range):
"""Draw a range of frames with the same bboxes and predictions."""
# no predictions to be draw
if bboxes is None or len(bboxes) == 0:
return frames
# draw frames in `draw_range`
left_frames = frames[:draw_... | Draw a range of frames with the same bboxes and predictions. | draw_clip_range | python | open-mmlab/mmaction2 | demo/webcam_demo_spatiotemporal_det.py | https://github.com/open-mmlab/mmaction2/blob/master/demo/webcam_demo_spatiotemporal_det.py | Apache-2.0 |
def abbrev(name):
"""Get the abbreviation of label name:
'take (an object) from (a person)' -> 'take ... from ...'
"""
while name.find('(') != -1:
st, ed = name.find('('), name.find(')')
name = name[:st] + '...' + name[ed + 1:]
return name | Get the abbreviation of label name:
'take (an object) from (a person)' -> 'take ... from ...'
| abbrev | python | open-mmlab/mmaction2 | demo/webcam_demo_spatiotemporal_det.py | https://github.com/open-mmlab/mmaction2/blob/master/demo/webcam_demo_spatiotemporal_det.py | Apache-2.0 |
def parse_version_info(version_str: str):
"""Parse a version string into a tuple.
Args:
version_str (str): The version string.
Returns:
tuple[int or str]: The version info, e.g., "1.3.0" is parsed into
(1, 3, 0), and "2.0.0rc1" is parsed into (2, 0, 0, 'rc1').
"""
versio... | Parse a version string into a tuple.
Args:
version_str (str): The version string.
Returns:
tuple[int or str]: The version info, e.g., "1.3.0" is parsed into
(1, 3, 0), and "2.0.0rc1" is parsed into (2, 0, 0, 'rc1').
| parse_version_info | python | open-mmlab/mmaction2 | mmaction/version.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/version.py | Apache-2.0 |
def init_recognizer(config: Union[str, Path, mmengine.Config],
checkpoint: Optional[str] = None,
device: Union[str, torch.device] = 'cuda:0') -> nn.Module:
"""Initialize a recognizer from config file.
Args:
config (str or :obj:`Path` or :obj:`mmengine.Config`): C... | Initialize a recognizer from config file.
Args:
config (str or :obj:`Path` or :obj:`mmengine.Config`): Config file
path, :obj:`Path` or the config object.
checkpoint (str, optional): Checkpoint path/url. If set to None,
the model will not load any weights. Defaults to None.
... | init_recognizer | python | open-mmlab/mmaction2 | mmaction/apis/inference.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/apis/inference.py | Apache-2.0 |
def inference_recognizer(model: nn.Module,
video: Union[str, dict],
test_pipeline: Optional[Compose] = None
) -> ActionDataSample:
"""Inference a video with the recognizer.
Args:
model (nn.Module): The loaded recognizer.
... | Inference a video with the recognizer.
Args:
model (nn.Module): The loaded recognizer.
video (Union[str, dict]): The video file path or the results
dictionary (the input of pipeline).
test_pipeline (:obj:`Compose`, optional): The test pipeline.
If not specified, the ... | inference_recognizer | python | open-mmlab/mmaction2 | mmaction/apis/inference.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/apis/inference.py | Apache-2.0 |
def inference_skeleton(model: nn.Module,
pose_results: List[dict],
img_shape: Tuple[int],
test_pipeline: Optional[Compose] = None
) -> ActionDataSample:
"""Inference a pose results with the skeleton recognizer.
Args:
... | Inference a pose results with the skeleton recognizer.
Args:
model (nn.Module): The loaded recognizer.
pose_results (List[dict]): The pose estimation results dictionary
(the results of `pose_inference`)
img_shape (Tuple[int]): The original image shape used for inference
... | inference_skeleton | python | open-mmlab/mmaction2 | mmaction/apis/inference.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/apis/inference.py | Apache-2.0 |
def detection_inference(det_config: Union[str, Path, mmengine.Config,
nn.Module],
det_checkpoint: str,
frame_paths: List[str],
det_score_thr: float = 0.9,
det_cat_id: int = 0,
... | Detect human boxes given frame paths.
Args:
det_config (Union[str, :obj:`Path`, :obj:`mmengine.Config`,
:obj:`torch.nn.Module`]):
Det config file path or Detection model object. It can be
a :obj:`Path`, a config object, or a module object.
det_checkpoint: Checkpo... | detection_inference | python | open-mmlab/mmaction2 | mmaction/apis/inference.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/apis/inference.py | Apache-2.0 |
def pose_inference(pose_config: Union[str, Path, mmengine.Config, nn.Module],
pose_checkpoint: str,
frame_paths: List[str],
det_results: List[np.ndarray],
device: Union[str, torch.device] = 'cuda:0') -> tuple:
"""Perform Top-Down pose estim... | Perform Top-Down pose estimation.
Args:
pose_config (Union[str, :obj:`Path`, :obj:`mmengine.Config`,
:obj:`torch.nn.Module`]): Pose config file path or
pose model object. It can be a :obj:`Path`, a config object,
or a module object.
pose_checkpoint: Checkpoint pa... | pose_inference | python | open-mmlab/mmaction2 | mmaction/apis/inference.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/apis/inference.py | Apache-2.0 |
def __call__(self,
inputs: InputsType,
return_datasamples: bool = False,
batch_size: int = 1,
return_vis: bool = False,
show: bool = False,
wait_time: int = 0,
draw_pred: bool = True,
... | Call the inferencer.
Args:
inputs (InputsType): Inputs for the inferencer.
return_datasamples (bool): Whether to return results as
:obj:`BaseDataElement`. Defaults to False.
batch_size (int): Inference batch size. Defaults to 1.
show (bool): Wheth... | __call__ | python | open-mmlab/mmaction2 | mmaction/apis/inferencers/actionrecog_inferencer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/apis/inferencers/actionrecog_inferencer.py | Apache-2.0 |
def _inputs_to_list(self, inputs: InputsType) -> list:
"""Preprocess the inputs to a list. The main difference from mmengine
version is that we don't list a directory cause input could be a frame
folder.
Preprocess inputs to a list according to its type:
- list or tuple: return... | Preprocess the inputs to a list. The main difference from mmengine
version is that we don't list a directory cause input could be a frame
folder.
Preprocess inputs to a list according to its type:
- list or tuple: return inputs
- str: return a list containing the string. The st... | _inputs_to_list | python | open-mmlab/mmaction2 | mmaction/apis/inferencers/actionrecog_inferencer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/apis/inferencers/actionrecog_inferencer.py | Apache-2.0 |
def visualize(
self,
inputs: InputsType,
preds: PredType,
return_vis: bool = False,
show: bool = False,
wait_time: int = 0,
draw_pred: bool = True,
fps: int = 30,
out_type: str = 'video',
target_resolution: Optional[Tuple[int]] = None,
... | Visualize predictions.
Args:
inputs (List[Union[str, np.ndarray]]): Inputs for the inferencer.
preds (List[Dict]): Predictions of the model.
return_vis (bool): Whether to return the visualization result.
Defaults to False.
show (bool): Whether to ... | visualize | python | open-mmlab/mmaction2 | mmaction/apis/inferencers/actionrecog_inferencer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/apis/inferencers/actionrecog_inferencer.py | Apache-2.0 |
def postprocess(
self,
preds: PredType,
visualization: Optional[List[np.ndarray]] = None,
return_datasample: bool = False,
print_result: bool = False,
pred_out_file: str = '',
) -> Union[ResType, Tuple[ResType, np.ndarray]]:
"""Process the predictions and visu... | Process the predictions and visualization results from ``forward``
and ``visualize``.
This method should be responsible for the following tasks:
1. Convert datasamples into a json-serializable dict if needed.
2. Pack the predictions and visualization results and return them.
3.... | postprocess | python | open-mmlab/mmaction2 | mmaction/apis/inferencers/actionrecog_inferencer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/apis/inferencers/actionrecog_inferencer.py | Apache-2.0 |
def pred2dict(self, data_sample: ActionDataSample) -> Dict:
"""Extract elements necessary to represent a prediction into a
dictionary. It's better to contain only basic data elements such as
strings and numbers in order to guarantee it's json-serializable.
Args:
data_sample ... | Extract elements necessary to represent a prediction into a
dictionary. It's better to contain only basic data elements such as
strings and numbers in order to guarantee it's json-serializable.
Args:
data_sample (ActionDataSample): The data sample to be converted.
Returns:
... | pred2dict | python | open-mmlab/mmaction2 | mmaction/apis/inferencers/actionrecog_inferencer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/apis/inferencers/actionrecog_inferencer.py | Apache-2.0 |
def forward(self, inputs: InputType, batch_size: int,
**forward_kwargs) -> PredType:
"""Forward the inputs to the model.
Args:
inputs (InputsType): The inputs to be forwarded.
batch_size (int): Batch size. Defaults to 1.
Returns:
Dict: The pr... | Forward the inputs to the model.
Args:
inputs (InputsType): The inputs to be forwarded.
batch_size (int): Batch size. Defaults to 1.
Returns:
Dict: The prediction results. Possibly with keys "rec".
| forward | python | open-mmlab/mmaction2 | mmaction/apis/inferencers/mmaction2_inferencer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/apis/inferencers/mmaction2_inferencer.py | Apache-2.0 |
def visualize(self, inputs: InputsType, preds: PredType,
**kwargs) -> List[np.ndarray]:
"""Visualize predictions.
Args:
inputs (List[Union[str, np.ndarray]]): Inputs for the inferencer.
preds (List[Dict]): Predictions of the model.
show (bool): Whet... | Visualize predictions.
Args:
inputs (List[Union[str, np.ndarray]]): Inputs for the inferencer.
preds (List[Dict]): Predictions of the model.
show (bool): Whether to display the image in a popup window.
Defaults to False.
wait_time (float): The int... | visualize | python | open-mmlab/mmaction2 | mmaction/apis/inferencers/mmaction2_inferencer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/apis/inferencers/mmaction2_inferencer.py | Apache-2.0 |
def __call__(
self,
inputs: InputsType,
batch_size: int = 1,
**kwargs,
) -> dict:
"""Call the inferencer.
Args:
inputs (InputsType): Inputs for the inferencer. It can be a path
to image / image directory, or an array, or a list of these.
... | Call the inferencer.
Args:
inputs (InputsType): Inputs for the inferencer. It can be a path
to image / image directory, or an array, or a list of these.
return_datasamples (bool): Whether to return results as
:obj:`BaseDataElement`. Defaults to False.
... | __call__ | python | open-mmlab/mmaction2 | mmaction/apis/inferencers/mmaction2_inferencer.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/apis/inferencers/mmaction2_inferencer.py | Apache-2.0 |
def load_data_list(self) -> List[Dict]:
"""Load annotation file to get audio information."""
check_file_exist(self.ann_file)
data_list = []
with open(self.ann_file, 'r') as fin:
for line in fin:
line_split = line.strip().split()
video_info = {}... | Load annotation file to get audio information. | load_data_list | python | open-mmlab/mmaction2 | mmaction/datasets/audio_dataset.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/audio_dataset.py | Apache-2.0 |
def parse_img_record(self, img_records: List[dict]) -> tuple:
"""Merge image records of the same entity at the same time.
Args:
img_records (List[dict]): List of img_records (lines in AVA
annotations).
Returns:
Tuple(list): A tuple consists of lists of b... | Merge image records of the same entity at the same time.
Args:
img_records (List[dict]): List of img_records (lines in AVA
annotations).
Returns:
Tuple(list): A tuple consists of lists of bboxes, action labels and
entity_ids.
| parse_img_record | python | open-mmlab/mmaction2 | mmaction/datasets/ava_dataset.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/ava_dataset.py | Apache-2.0 |
def load_data_list(self) -> List[Dict]:
"""Load annotation file to get skeleton information."""
assert self.ann_file.endswith('.pkl')
mmengine.exists(self.ann_file)
data_list = mmengine.load(self.ann_file)
if self.split is not None:
split, annos = data_list['split'],... | Load annotation file to get skeleton information. | load_data_list | python | open-mmlab/mmaction2 | mmaction/datasets/pose_dataset.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/pose_dataset.py | Apache-2.0 |
def get_type(transform: Union[dict, Callable]) -> str:
"""get the type of the transform."""
if isinstance(transform, dict) and 'type' in transform:
return transform['type']
elif callable(transform):
return transform.__repr__().split('(')[0]
else:
raise TypeError | get the type of the transform. | get_type | python | open-mmlab/mmaction2 | mmaction/datasets/repeat_aug_dataset.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/repeat_aug_dataset.py | Apache-2.0 |
def prepare_data(self, idx) -> List[dict]:
"""Get data processed by ``self.pipeline``.
Reduce the video loading and decompressing.
Args:
idx (int): The index of ``data_info``.
Returns:
List[dict]: A list of length num_repeats.
"""
transforms = sel... | Get data processed by ``self.pipeline``.
Reduce the video loading and decompressing.
Args:
idx (int): The index of ``data_info``.
Returns:
List[dict]: A list of length num_repeats.
| prepare_data | python | open-mmlab/mmaction2 | mmaction/datasets/repeat_aug_dataset.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/repeat_aug_dataset.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""The transform function of :class:`PackActionInputs`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
packed_results = dict()
if self.collect_keys is not None:
p... | The transform function of :class:`PackActionInputs`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/formatting.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/formatting.py | Apache-2.0 |
def transform(self, results):
"""Method to pack the input data.
Args:
results (dict): Result dict from the data pipeline.
Returns:
dict:
- 'inputs' (obj:`torch.Tensor`): The forward data of models.
- 'data_samples' (obj:`DetDataSample`): The ann... | Method to pack the input data.
Args:
results (dict): Result dict from the data pipeline.
Returns:
dict:
- 'inputs' (obj:`torch.Tensor`): The forward data of models.
- 'data_samples' (obj:`DetDataSample`): The annotation info of the
sampl... | transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/formatting.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/formatting.py | Apache-2.0 |
def transform(self, results):
"""Performs the Transpose formatting.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
for key in self.keys:
results[key] = results[key].transpose(self.order)
... | Performs the Transpose formatting.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/formatting.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/formatting.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""The transform function of :class:`FormatGCNInput`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
keypoint = results['keypoint']
if 'keypoint_score' in results:
... | The transform function of :class:`FormatGCNInput`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/formatting.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/formatting.py | Apache-2.0 |
def transform(self, results):
"""Convert the label dictionary to 3 tensors: "label", "mask" and
"category_mask".
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
if not self.hvu_initialized:
... | Convert the label dictionary to 3 tensors: "label", "mask" and
"category_mask".
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def _get_train_clips(self, num_frames: int,
ori_clip_len: float) -> np.array:
"""Get clip offsets in train mode.
It will calculate the average interval for selected frames,
and randomly shift them within offsets between [0, avg_interval].
If the total number of ... | Get clip offsets in train mode.
It will calculate the average interval for selected frames,
and randomly shift them within offsets between [0, avg_interval].
If the total number of frames is smaller than clips num or origin
frames length, it will return all zero indices.
Args:
... | _get_train_clips | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def _get_test_clips(self, num_frames: int,
ori_clip_len: float) -> np.array:
"""Get clip offsets in test mode.
If the total number of frames is
not enough, it will return all zero indices.
Args:
num_frames (int): Total number of frame in the video.
... | Get clip offsets in test mode.
If the total number of frames is
not enough, it will return all zero indices.
Args:
num_frames (int): Total number of frame in the video.
ori_clip_len (float): length of original sample clip.
Returns:
np.ndarray: Sampl... | _get_test_clips | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def _get_ori_clip_len(self, fps_scale_ratio: float) -> float:
"""calculate length of clip segment for different strategy.
Args:
fps_scale_ratio (float): Scale ratio to adjust fps.
"""
if self.target_fps is not None:
# align test sample strategy with `PySlowFast` ... | calculate length of clip segment for different strategy.
Args:
fps_scale_ratio (float): Scale ratio to adjust fps.
| _get_ori_clip_len | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def _get_sample_clips(self, num_frames: int) -> np.ndarray:
"""To sample an n-frame clip from the video. UniformSample basically
divides the video into n segments of equal length and randomly samples
one frame from each segment. When the duration of video frames is
shorter than the desir... | To sample an n-frame clip from the video. UniformSample basically
divides the video into n segments of equal length and randomly samples
one frame from each segment. When the duration of video frames is
shorter than the desired length of the target clip, this approach will
duplicate the ... | _get_sample_clips | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""Perform the Uniform Sampling.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
num_frames = results['total_frames']
inds = self._get_sample_clips(num_frames)
start_... | Perform the Uniform Sampling.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def _get_train_clips(self, num_frames: int) -> np.array:
"""Get clip offsets by dense sample strategy in train mode.
It will calculate a sample position and sample interval and set
start index 0 when sample_pos == 1 or randomly choose from
[0, sample_pos - 1]. Then it will shift the sta... | Get clip offsets by dense sample strategy in train mode.
It will calculate a sample position and sample interval and set
start index 0 when sample_pos == 1 or randomly choose from
[0, sample_pos - 1]. Then it will shift the start index by each
base offset.
Args:
num... | _get_train_clips | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def _get_test_clips(self, num_frames: int) -> np.array:
"""Get clip offsets by dense sample strategy in test mode.
It will calculate a sample position and sample interval and evenly
sample several start indexes as start positions between
[0, sample_position-1]. Then it will shift each s... | Get clip offsets by dense sample strategy in test mode.
It will calculate a sample position and sample interval and evenly
sample several start indexes as start positions between
[0, sample_position-1]. Then it will shift each start index by the
base offsets.
Args:
... | _get_test_clips | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results):
"""Perform the PyAV initialization.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
try:
import av
except ImportError:
raise ImportError('P... | Perform the PyAV initialization.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results):
"""Perform the PyAV decoding.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
container = results['video_reader']
imgs = list()
if self.multi_thread:
... | Perform the PyAV decoding.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results):
"""Perform the PIMS initialization.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
try:
import pims
except ImportError:
raise ImportError(... | Perform the PIMS initialization.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results):
"""Perform the PIMS decoding.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
container = results['video_reader']
if results['frame_inds'].ndim != 1:
... | Perform the PIMS decoding.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results):
"""Perform the PyAV motion vector decoding.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
container = results['video_reader']
imgs = list()
if self.mult... | Perform the PyAV motion vector decoding.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""Perform the Decord initialization.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
container = self._get_video_reader(results['filename'])
results['total_frames'] = len(con... | Perform the Decord initialization.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""Perform the Decord decoding.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
container = results['video_reader']
if results['frame_inds'].ndim != 1:
results['f... | Perform the Decord decoding.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results: dict) -> dict:
"""Perform the OpenCV initialization.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
if self.io_backend == 'disk':
new_path = results['filename'... | Perform the OpenCV initialization.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results: dict) -> dict:
"""Perform the OpenCV decoding.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
container = results['video_reader']
imgs = list()
if results... | Perform the OpenCV decoding.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results: dict) -> dict:
"""Perform the ``RawFrameDecode`` to pick frames given indices.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
mmcv.use_backend(self.decoding_backend)
... | Perform the ``RawFrameDecode`` to pick frames given indices.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results):
"""Perform the ``ImageDecode`` to load image given the file path.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
mmcv.use_backend(self.decoding_backend)
filename... | Perform the ``ImageDecode`` to load image given the file path.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""Perform the numpy loading.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
if osp.exists(results['audio_path']):
feature_map = np.load(results... | Perform the numpy loading.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results):
"""Perform the building of pseudo clips.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
# the input should be one single image
assert len(results['imgs']) == 1
... | Perform the building of pseudo clips.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""Perform the ``AudioFeatureSelector`` to pick audio feature clips.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
audio = results['audios']
frame_... | Perform the ``AudioFeatureSelector`` to pick audio feature clips.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results):
"""Perform the LoadLocalizationFeature loading.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
data_path = results['feature_path']
raw_feature = np.loadtxt(
... | Perform the LoadLocalizationFeature loading.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results):
"""Perform the GenerateLocalizationLabels loading.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
video_frame = results['duration_frame']
video_second = results['... | Perform the GenerateLocalizationLabels loading.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results):
"""Perform the LoadProposals loading.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
video_name = results['video_name']
proposal_path = osp.join(self.pgm_proposal... | Perform the LoadProposals loading.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/loading.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/loading.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""Perform the pose decoding.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
required_keys = ['total_frames', 'frame_inds', 'keypoint']
for k in req... | Perform the pose decoding.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def generate_a_heatmap(self, arr: np.ndarray, centers: np.ndarray,
max_values: np.ndarray) -> None:
"""Generate pseudo heatmap for one keypoint in one frame.
Args:
arr (np.ndarray): The array to store the generated heatmaps.
Shape: img_h * img_w.
... | Generate pseudo heatmap for one keypoint in one frame.
Args:
arr (np.ndarray): The array to store the generated heatmaps.
Shape: img_h * img_w.
centers (np.ndarray): The coordinates of corresponding keypoints
(of multiple persons). Shape: M * 2.
... | generate_a_heatmap | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def generate_a_limb_heatmap(self, arr: np.ndarray, starts: np.ndarray,
ends: np.ndarray, start_values: np.ndarray,
end_values: np.ndarray) -> None:
"""Generate pseudo heatmap for one limb in one frame.
Args:
arr (np.ndarray): T... | Generate pseudo heatmap for one limb in one frame.
Args:
arr (np.ndarray): The array to store the generated heatmaps.
Shape: img_h * img_w.
starts (np.ndarray): The coordinates of one keypoint in the
corresponding limbs. Shape: M * 2.
ends (np... | generate_a_limb_heatmap | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def generate_heatmap(self, arr: np.ndarray, kps: np.ndarray,
max_values: np.ndarray) -> None:
"""Generate pseudo heatmap for all keypoints and limbs in one frame (if
needed).
Args:
arr (np.ndarray): The array to store the generated heatmaps.
... | Generate pseudo heatmap for all keypoints and limbs in one frame (if
needed).
Args:
arr (np.ndarray): The array to store the generated heatmaps.
Shape: V * img_h * img_w.
kps (np.ndarray): The coordinates of keypoints in this frame.
Shape: M * V *... | generate_heatmap | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def gen_an_aug(self, results: Dict) -> np.ndarray:
"""Generate pseudo heatmaps for all frames.
Args:
results (dict): The dictionary that contains all info of a sample.
Returns:
np.ndarray: The generated pseudo heatmaps.
"""
all_kps = results['keypoint']... | Generate pseudo heatmaps for all frames.
Args:
results (dict): The dictionary that contains all info of a sample.
Returns:
np.ndarray: The generated pseudo heatmaps.
| gen_an_aug | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""Generate pseudo heatmaps based on joint coordinates and confidence.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
heatmap = self.gen_an_aug(results)
... | Generate pseudo heatmaps based on joint coordinates and confidence.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""Convert the coordinates of keypoints to make it more compact.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
img_shape = results['img_shape']
h, ... | Convert the coordinates of keypoints to make it more compact.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def angle_between(self, v1: np.ndarray, v2: np.ndarray) -> float:
"""Returns the angle in radians between vectors 'v1' and 'v2'."""
if np.abs(v1).sum() < 1e-6 or np.abs(v2).sum() < 1e-6:
return 0
v1_u = self.unit_vector(v1)
v2_u = self.unit_vector(v2)
return np.arccos... | Returns the angle in radians between vectors 'v1' and 'v2'. | angle_between | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def rotation_matrix(self, axis: np.ndarray, theta: float) -> np.ndarray:
"""Returns the rotation matrix associated with counterclockwise
rotation about the given axis by theta radians."""
if np.abs(axis).sum() < 1e-6 or np.abs(theta) < 1e-6:
return np.eye(3)
axis = np.asarray... | Returns the rotation matrix associated with counterclockwise
rotation about the given axis by theta radians. | rotation_matrix | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""The transform function of :class:`PreNormalize3D`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
skeleton = results['keypoint']
total_frames = results.get('total_frames',... | The transform function of :class:`PreNormalize3D`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""The transform function of :class:`PreNormalize2D`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
h, w = results.get('img_shape', self.img_shape)
results['keypoint'][..., ... | The transform function of :class:`PreNormalize2D`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""The transform function of :class:`JointToBone`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
keypoint = results['keypoint']
M, T, V, C = keypoint.shape
bone = np... | The transform function of :class:`JointToBone`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""The transform function of :class:`ToMotion`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
data = results[self.source]
M, T, V, C = data.shape
motion = np.zeros_l... | The transform function of :class:`ToMotion`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""The transform function of :class:`MergeSkeFeat`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
feats = []
for name in self.feat_list:
feats.append(results.pop... | The transform function of :class:`MergeSkeFeat`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""The transform function of :class:`GenSkeFeat`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
if 'keypoint_score' in results and 'keypoint' in results:
assert self.dat... | The transform function of :class:`GenSkeFeat`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def _get_train_clips(self, num_frames: int, clip_len: int) -> np.ndarray:
"""Uniformly sample indices for training clips.
Args:
num_frames (int): The number of frames.
clip_len (int): The length of the clip.
Returns:
np.ndarray: The sampled indices for train... | Uniformly sample indices for training clips.
Args:
num_frames (int): The number of frames.
clip_len (int): The length of the clip.
Returns:
np.ndarray: The sampled indices for training clips.
| _get_train_clips | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def _get_test_clips(self, num_frames: int, clip_len: int) -> np.ndarray:
"""Uniformly sample indices for testing clips.
Args:
num_frames (int): The number of frames.
clip_len (int): The length of the clip.
Returns:
np.ndarray: The sampled indices for testing... | Uniformly sample indices for testing clips.
Args:
num_frames (int): The number of frames.
clip_len (int): The length of the clip.
Returns:
np.ndarray: The sampled indices for testing clips.
| _get_test_clips | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""The transform function of :class:`UniformSampleFrames`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
num_frames = results['total_frames']
if self.test_mode:
... | The transform function of :class:`UniformSampleFrames`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""The transform function of :class:`PadTo`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
total_frames = results['total_frames']
assert total_frames <= self.length
... | The transform function of :class:`PadTo`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def _load_kpscore(kpscore: np.ndarray,
frame_inds: np.ndarray) -> np.ndarray:
"""Load keypoint scores according to sampled indexes."""
return kpscore[:, frame_inds].astype(np.float32) | Load keypoint scores according to sampled indexes. | _load_kpscore | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""The transform function of :class:`PoseDecode`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
if 'total_frames' not in results:
results['total_frames'] = results['keyp... | The transform function of :class:`PoseDecode`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""The transform function of :class:`MMUniformSampleFrames`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
num_frames = results['total_frames']
modalities = []
for m... | The transform function of :class:`MMUniformSampleFrames`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""The transform function of :class:`MMDecode`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
for mod in results['modality']:
if results[f'{mod}_inds'].ndim != 1:
... | The transform function of :class:`MMDecode`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def _get_box(self, keypoint: np.ndarray, img_shape: Tuple[int]) -> Tuple:
"""Calculate the bounding box surrounding all joints in the frames."""
h, w = img_shape
kp_x = keypoint[..., 0]
kp_y = keypoint[..., 1]
min_x = np.min(kp_x[kp_x != 0], initial=np.Inf)
min_y = np.m... | Calculate the bounding box surrounding all joints in the frames. | _get_box | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def _compact_images(self, imgs: List[np.ndarray], img_shape: Tuple[int],
box: Tuple[int]) -> List:
"""Crop the images acoordding the bounding box."""
h, w = img_shape
min_x, min_y, max_x, max_y = box
pad_l, pad_u, pad_r, pad_d = 0, 0, 0, 0
if min_x < 0:
... | Crop the images acoordding the bounding box. | _compact_images | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""The transform function of :class:`MMCompact`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
img_shape = results['img_shape']
kp = results['keypoint']
# Make NaN z... | The transform function of :class:`MMCompact`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/pose_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/pose_transforms.py | Apache-2.0 |
def transform(self, results):
"""Fuse lazy operations.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
if 'lazy' not in results:
raise ValueError('No lazy operation detected')
lazyo... | Fuse lazy operations.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/processing.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/processing.py | Apache-2.0 |
def transform(self, results):
"""Perform ColorJitter.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
imgs = results['imgs']
num_clips, clip_len = 1, len(imgs)
new_imgs = []
fo... | Perform ColorJitter.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/processing.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/processing.py | Apache-2.0 |
def transform(self, results: Dict) -> Dict:
"""The transform function of :class:`CLIPTokenize`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
"""
try:
import clip
except ImportError:
raise ImportE... | The transform function of :class:`CLIPTokenize`.
Args:
results (dict): The result dict.
Returns:
dict: The result dict.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/text_transforms.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/text_transforms.py | Apache-2.0 |
def transform(self, results):
"""Perform Torchvision augmentations.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
assert 'imgs' in results
imgs = [x.transpose(2, 0, 1) for x in results['imgs... | Perform Torchvision augmentations.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/wrappers.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/wrappers.py | Apache-2.0 |
def transform(self, results):
"""Perform PytorchVideoTrans augmentations.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
assert 'imgs' in results
assert 'gt_bboxes' not in results,\
... | Perform PytorchVideoTrans augmentations.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/wrappers.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/wrappers.py | Apache-2.0 |
def default_transforms():
"""Default transforms for imgaug.
Implement RandAugment by imgaug.
Please visit `https://arxiv.org/abs/1909.13719` for more information.
Augmenters and hyper parameters are borrowed from the following repo:
https://github.com/tensorflow/tpu/blob/master... | Default transforms for imgaug.
Implement RandAugment by imgaug.
Please visit `https://arxiv.org/abs/1909.13719` for more information.
Augmenters and hyper parameters are borrowed from the following repo:
https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaug... | default_transforms | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/wrappers.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/wrappers.py | Apache-2.0 |
def imgaug_builder(self, cfg):
"""Import a module from imgaug.
It follows the logic of :func:`build_from_cfg`. Use a dict object to
create an iaa.Augmenter object.
Args:
cfg (dict): Config dict. It should at least contain the key "type".
Returns:
obj:`i... | Import a module from imgaug.
It follows the logic of :func:`build_from_cfg`. Use a dict object to
create an iaa.Augmenter object.
Args:
cfg (dict): Config dict. It should at least contain the key "type".
Returns:
obj:`iaa.Augmenter`: The constructed imgaug augm... | imgaug_builder | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/wrappers.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/wrappers.py | Apache-2.0 |
def transform(self, results):
"""Perform Imgaug augmentations.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
"""
assert results['modality'] == 'RGB', 'Imgaug only support RGB images.'
in_type = r... | Perform Imgaug augmentations.
Args:
results (dict): The resulting dict to be modified and passed
to the next transform in pipeline.
| transform | python | open-mmlab/mmaction2 | mmaction/datasets/transforms/wrappers.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/datasets/transforms/wrappers.py | Apache-2.0 |
def _draw_samples(self,
batch_idx: int,
data_batch: dict,
data_samples: Sequence[ActionDataSample],
step: int = 0) -> None:
"""Visualize every ``self.interval`` samples from a data batch.
Args:
batch_idx... | Visualize every ``self.interval`` samples from a data batch.
Args:
batch_idx (int): The index of the current batch in the val loop.
data_batch (dict): Data from dataloader.
outputs (Sequence[:obj:`ActionDataSample`]): Outputs from model.
step (int): Global step v... | _draw_samples | python | open-mmlab/mmaction2 | mmaction/engine/hooks/visualization_hook.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/engine/hooks/visualization_hook.py | Apache-2.0 |
def after_val_iter(self, runner: Runner, batch_idx: int, data_batch: dict,
outputs: Sequence[ActionDataSample]) -> None:
"""Visualize every ``self.interval`` samples during validation.
Args:
runner (:obj:`Runner`): The runner of the validation process.
bat... | Visualize every ``self.interval`` samples during validation.
Args:
runner (:obj:`Runner`): The runner of the validation process.
batch_idx (int): The index of the current batch in the val loop.
data_batch (dict): Data from dataloader.
outputs (Sequence[:obj:`Acti... | after_val_iter | python | open-mmlab/mmaction2 | mmaction/engine/hooks/visualization_hook.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/engine/hooks/visualization_hook.py | Apache-2.0 |
def conv_branch_init(conv: nn.Module, branches: int) -> None:
"""Perform initialization for a conv branch.
Args:
conv (nn.Module): The conv module of a branch.
branches (int): The number of branches.
"""
weight = conv.weight
n = weight.size(0)
k1 = weight.size(1)
k2 = weigh... | Perform initialization for a conv branch.
Args:
conv (nn.Module): The conv module of a branch.
branches (int): The number of branches.
| conv_branch_init | python | open-mmlab/mmaction2 | mmaction/engine/model/weight_init.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/engine/model/weight_init.py | Apache-2.0 |
def add_params(self,
params: List[dict],
module: nn.Module,
prefix: str = 'base',
**kwargs) -> None:
"""Add all parameters of module to the params list.
The parameters of the given module will be added to the list of param
... | Add all parameters of module to the params list.
The parameters of the given module will be added to the list of param
groups, with specific rules defined by paramwise_cfg.
Args:
params (list[dict]): A list of param groups, it will be modified
in place.
... | add_params | python | open-mmlab/mmaction2 | mmaction/engine/optimizers/swin_optim_wrapper_constructor.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/engine/optimizers/swin_optim_wrapper_constructor.py | Apache-2.0 |
def add_params(self, params, model, **kwargs):
"""Add parameters and their corresponding lr and wd to the params.
Args:
params (list): The list to be modified, containing all parameter
groups and their corresponding lr and wd configurations.
model (nn.Module): Th... | Add parameters and their corresponding lr and wd to the params.
Args:
params (list): The list to be modified, containing all parameter
groups and their corresponding lr and wd configurations.
model (nn.Module): The model to be trained with the optimizer.
| add_params | python | open-mmlab/mmaction2 | mmaction/engine/optimizers/tsm_optim_wrapper_constructor.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/engine/optimizers/tsm_optim_wrapper_constructor.py | Apache-2.0 |
def confusion_matrix(y_pred, y_real, normalize=None):
"""Compute confusion matrix.
Args:
y_pred (list[int] | np.ndarray[int]): Prediction labels.
y_real (list[int] | np.ndarray[int]): Ground truth labels.
normalize (str | None): Normalizes confusion matrix over the true
(row... | Compute confusion matrix.
Args:
y_pred (list[int] | np.ndarray[int]): Prediction labels.
y_real (list[int] | np.ndarray[int]): Ground truth labels.
normalize (str | None): Normalizes confusion matrix over the true
(rows), predicted (columns) conditions or all the population.
... | confusion_matrix | python | open-mmlab/mmaction2 | mmaction/evaluation/functional/accuracy.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/evaluation/functional/accuracy.py | Apache-2.0 |
def mean_class_accuracy(scores, labels):
"""Calculate mean class accuracy.
Args:
scores (list[np.ndarray]): Prediction scores for each class.
labels (list[int]): Ground truth labels.
Returns:
np.ndarray: Mean class accuracy.
"""
pred = np.argmax(scores, axis=1)
cf_mat =... | Calculate mean class accuracy.
Args:
scores (list[np.ndarray]): Prediction scores for each class.
labels (list[int]): Ground truth labels.
Returns:
np.ndarray: Mean class accuracy.
| mean_class_accuracy | python | open-mmlab/mmaction2 | mmaction/evaluation/functional/accuracy.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/evaluation/functional/accuracy.py | Apache-2.0 |
def top_k_classes(scores, labels, k=10, mode='accurate'):
"""Calculate the most K accurate (inaccurate) classes.
Given the prediction scores, ground truth label and top-k value,
compute the top K accurate (inaccurate) classes.
Args:
scores (list[np.ndarray]): Prediction scores for each class.
... | Calculate the most K accurate (inaccurate) classes.
Given the prediction scores, ground truth label and top-k value,
compute the top K accurate (inaccurate) classes.
Args:
scores (list[np.ndarray]): Prediction scores for each class.
labels (list[int] | np.ndarray): Ground truth labels.
... | top_k_classes | python | open-mmlab/mmaction2 | mmaction/evaluation/functional/accuracy.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/evaluation/functional/accuracy.py | Apache-2.0 |
def top_k_accuracy(scores, labels, topk=(1, )):
"""Calculate top k accuracy score.
Args:
scores (list[np.ndarray]): Prediction scores for each class.
labels (list[int]): Ground truth labels.
topk (tuple[int]): K value for top_k_accuracy. Default: (1, ).
Returns:
list[float]... | Calculate top k accuracy score.
Args:
scores (list[np.ndarray]): Prediction scores for each class.
labels (list[int]): Ground truth labels.
topk (tuple[int]): K value for top_k_accuracy. Default: (1, ).
Returns:
list[float]: Top k accuracy score for each k.
| top_k_accuracy | python | open-mmlab/mmaction2 | mmaction/evaluation/functional/accuracy.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/evaluation/functional/accuracy.py | Apache-2.0 |
def mean_average_precision(scores, labels):
"""Mean average precision for multi-label recognition.
Args:
scores (list[np.ndarray]): Prediction scores of different classes for
each sample.
labels (list[np.ndarray]): Ground truth many-hot vector for each
sample.
Retur... | Mean average precision for multi-label recognition.
Args:
scores (list[np.ndarray]): Prediction scores of different classes for
each sample.
labels (list[np.ndarray]): Ground truth many-hot vector for each
sample.
Returns:
np.float64: The mean average precision.... | mean_average_precision | python | open-mmlab/mmaction2 | mmaction/evaluation/functional/accuracy.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/evaluation/functional/accuracy.py | Apache-2.0 |
def binary_precision_recall_curve(y_score, y_true):
"""Calculate the binary precision recall curve at step thresholds.
Args:
y_score (np.ndarray): Prediction scores for each class.
Shape should be (num_classes, ).
y_true (np.ndarray): Ground truth many-hot vector.
Shape ... | Calculate the binary precision recall curve at step thresholds.
Args:
y_score (np.ndarray): Prediction scores for each class.
Shape should be (num_classes, ).
y_true (np.ndarray): Ground truth many-hot vector.
Shape should be (num_classes, ).
Returns:
precision ... | binary_precision_recall_curve | python | open-mmlab/mmaction2 | mmaction/evaluation/functional/accuracy.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/evaluation/functional/accuracy.py | Apache-2.0 |
def pairwise_temporal_iou(candidate_segments,
target_segments,
calculate_overlap_self=False):
"""Compute intersection over union between segments.
Args:
candidate_segments (np.ndarray): 1-dim/2-dim array in format
``[init, end]/[m x 2:=[in... | Compute intersection over union between segments.
Args:
candidate_segments (np.ndarray): 1-dim/2-dim array in format
``[init, end]/[m x 2:=[init, end]]``.
target_segments (np.ndarray): 2-dim array in format
``[n x 2:=[init, end]]``.
calculate_overlap_self (bool): Whe... | pairwise_temporal_iou | python | open-mmlab/mmaction2 | mmaction/evaluation/functional/accuracy.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/evaluation/functional/accuracy.py | Apache-2.0 |
def average_recall_at_avg_proposals(ground_truth,
proposals,
total_num_proposals,
max_avg_proposals=None,
temporal_iou_thresholds=np.linspace(
... | Computes the average recall given an average number (percentile) of
proposals per video.
Args:
ground_truth (dict): Dict containing the ground truth instances.
proposals (dict): Dict containing the proposal instances.
total_num_proposals (int): Total number of proposals in the
... | average_recall_at_avg_proposals | python | open-mmlab/mmaction2 | mmaction/evaluation/functional/accuracy.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/evaluation/functional/accuracy.py | Apache-2.0 |
def get_weighted_score(score_list, coeff_list):
"""Get weighted score with given scores and coefficients.
Given n predictions by different classifier: [score_1, score_2, ...,
score_n] (score_list) and their coefficients: [coeff_1, coeff_2, ...,
coeff_n] (coeff_list), return weighted score: weighted_sco... | Get weighted score with given scores and coefficients.
Given n predictions by different classifier: [score_1, score_2, ...,
score_n] (score_list) and their coefficients: [coeff_1, coeff_2, ...,
coeff_n] (coeff_list), return weighted score: weighted_score =
score_1 * coeff_1 + score_2 * coeff_2 + ... + ... | get_weighted_score | python | open-mmlab/mmaction2 | mmaction/evaluation/functional/accuracy.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/evaluation/functional/accuracy.py | Apache-2.0 |
def average_precision_at_temporal_iou(ground_truth,
prediction,
temporal_iou_thresholds=(np.linspace(
0.5, 0.95, 10))):
"""Compute average precision (in detection task) between ground truth and
... | Compute average precision (in detection task) between ground truth and
predicted data frames. If multiple predictions match the same predicted
segment, only the one with highest score is matched as true positive. This
code is greatly inspired by Pascal VOC devkit.
Args:
ground_truth (dict): Dic... | average_precision_at_temporal_iou | python | open-mmlab/mmaction2 | mmaction/evaluation/functional/accuracy.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/evaluation/functional/accuracy.py | Apache-2.0 |
def det2csv(results, custom_classes):
"""Convert detection results to csv file."""
csv_results = []
for idx in range(len(results)):
video_id = results[idx]['video_id']
timestamp = results[idx]['timestamp']
result = results[idx]['outputs']
for label, _ in enumerate(result):
... | Convert detection results to csv file. | det2csv | python | open-mmlab/mmaction2 | mmaction/evaluation/functional/ava_utils.py | https://github.com/open-mmlab/mmaction2/blob/master/mmaction/evaluation/functional/ava_utils.py | Apache-2.0 |
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