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 get_kinetics_frames(kinetics_anotation_file: str) -> dict:
"""Given the AVA-kinetics anotation file, return a lookup to map the video
id and the the set of timestamps involved of this video id.
Args:
kinetics_anotation_file (str): Path to the AVA-like anotation file for
the kinetics... | Given the AVA-kinetics anotation file, return a lookup to map the video
id and the the set of timestamps involved of this video id.
Args:
kinetics_anotation_file (str): Path to the AVA-like anotation file for
the kinetics subset.
Returns:
dict: the dict keys are the kinetics vid... | get_kinetics_frames | python | open-mmlab/mmaction2 | tools/data/ava_kinetics/cut_kinetics.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/data/ava_kinetics/cut_kinetics.py | Apache-2.0 |
def filter_missing_videos(kinetics_list: str, frame_lookup: dict) -> dict:
"""Given the kinetics700 dataset list, remove the video ids from the lookup
that are missing videos or frames.
Args:
kinetics_list (str): Path to the kinetics700 dataset list.
The content of the list should be:
... | Given the kinetics700 dataset list, remove the video ids from the lookup
that are missing videos or frames.
Args:
kinetics_list (str): Path to the kinetics700 dataset list.
The content of the list should be:
```
Path_to_video1 label_1
Path_to... | filter_missing_videos | python | open-mmlab/mmaction2 | tools/data/ava_kinetics/cut_kinetics.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/data/ava_kinetics/cut_kinetics.py | Apache-2.0 |
def remove_failed_video(video_path: str) -> None:
"""Given the path to the video, delete the video if it cannot be read or if
the actual length of the video is 0.75 seconds shorter than expected."""
try:
v = decord.VideoReader(video_path)
fps = v.get_avg_fps()
num_frames = len(v)
... | Given the path to the video, delete the video if it cannot be read or if
the actual length of the video is 0.75 seconds shorter than expected. | remove_failed_video | python | open-mmlab/mmaction2 | tools/data/ava_kinetics/cut_kinetics.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/data/ava_kinetics/cut_kinetics.py | Apache-2.0 |
def download(video_identifier,
output_filename,
num_attempts=5,
url_base='https://www.youtube.com/watch?v='):
"""Download a video from youtube if exists and is not blocked.
arguments:
---------
video_identifier: str
Unique YouTube video identifier (11 chara... | Download a video from youtube if exists and is not blocked.
arguments:
---------
video_identifier: str
Unique YouTube video identifier (11 characters)
output_filename: str
File path where the video will be stored.
| download | python | open-mmlab/mmaction2 | tools/data/gym/download.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/data/gym/download.py | Apache-2.0 |
def construct_video_filename(item, trim_format, output_dir):
"""Given a dataset row, this function constructs the output filename for a
given video."""
youtube_id, start_time, end_time = item
start_time, end_time = int(start_time * 10), int(end_time * 10)
basename = '%s_%s_%s.mp4' % (youtube_id, tri... | Given a dataset row, this function constructs the output filename for a
given video. | construct_video_filename | python | open-mmlab/mmaction2 | tools/data/hvu/download.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/data/hvu/download.py | Apache-2.0 |
def download_clip(video_identifier,
output_filename,
start_time,
end_time,
tmp_dir='/tmp/hvu/.tmp_dir',
num_attempts=5,
url_base='https://www.youtube.com/watch?v='):
"""Download a video from youtube if exists... | Download a video from youtube if exists and is not blocked.
arguments:
---------
video_identifier: str
Unique YouTube video identifier (11 characters)
output_filename: str
File path where the video will be stored.
start_time: float
Indicates the beginning time in seconds from... | download_clip | python | open-mmlab/mmaction2 | tools/data/hvu/download.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/data/hvu/download.py | Apache-2.0 |
def parse_hvu_annotations(input_csv):
"""Returns a parsed DataFrame.
arguments:
---------
input_csv: str
Path to CSV file containing the following columns:
'Tags, youtube_id, time_start, time_end'
returns:
-------
dataset: List of tuples. Each tuple consists of
(you... | Returns a parsed DataFrame.
arguments:
---------
input_csv: str
Path to CSV file containing the following columns:
'Tags, youtube_id, time_start, time_end'
returns:
-------
dataset: List of tuples. Each tuple consists of
(youtube_id, time_start, time_end). The type of t... | parse_hvu_annotations | python | open-mmlab/mmaction2 | tools/data/hvu/download.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/data/hvu/download.py | Apache-2.0 |
def parse_directory(path,
rgb_prefix='img_',
flow_x_prefix='flow_x_',
flow_y_prefix='flow_y_',
level=1):
"""Parse directories holding extracted frames from standard benchmarks.
Args:
path (str): Directory path to parse fram... | Parse directories holding extracted frames from standard benchmarks.
Args:
path (str): Directory path to parse frames.
rgb_prefix (str): Prefix of generated rgb frames name.
default: 'img_'.
flow_x_prefix (str): Prefix of generated flow x name.
default: `flow_x_`.
... | parse_directory | python | open-mmlab/mmaction2 | tools/data/hvu/generate_file_list.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/data/hvu/generate_file_list.py | Apache-2.0 |
def count_files(directory, prefix_list):
"""Count file number with a given directory and prefix.
Args:
directory (str): Data directory to be search.
prefix_list (list): List or prefix.
Returns:
list (int): Number list of the file with the prefix.
"""... | Count file number with a given directory and prefix.
Args:
directory (str): Data directory to be search.
prefix_list (list): List or prefix.
Returns:
list (int): Number list of the file with the prefix.
| count_files | python | open-mmlab/mmaction2 | tools/data/hvu/generate_file_list.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/data/hvu/generate_file_list.py | Apache-2.0 |
def create_video_folders(dataset, output_dir, tmp_dir):
"""Creates a directory for each label name in the dataset."""
if 'label-name' not in dataset.columns:
this_dir = os.path.join(output_dir, 'test')
if not os.path.exists(this_dir):
os.makedirs(this_dir)
# I should return a... | Creates a directory for each label name in the dataset. | create_video_folders | python | open-mmlab/mmaction2 | tools/data/kinetics/download.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/data/kinetics/download.py | Apache-2.0 |
def construct_video_filename(row, label_to_dir, trim_format='%06d'):
"""Given a dataset row, this function constructs the output filename for a
given video."""
basename = '%s_%s_%s.mp4' % (row['video-id'],
trim_format % row['start-time'],
tri... | Given a dataset row, this function constructs the output filename for a
given video. | construct_video_filename | python | open-mmlab/mmaction2 | tools/data/kinetics/download.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/data/kinetics/download.py | Apache-2.0 |
def download_clip(video_identifier,
output_filename,
start_time,
end_time,
tmp_dir='/tmp/kinetics/.tmp_dir',
num_attempts=5,
url_base='https://www.youtube.com/watch?v='):
"""Download a video from youtube if e... | Download a video from youtube if exists and is not blocked.
arguments:
---------
video_identifier: str
Unique YouTube video identifier (11 characters)
output_filename: str
File path where the video will be stored.
start_time: float
Indicates the beginning time in seconds from... | download_clip | python | open-mmlab/mmaction2 | tools/data/kinetics/download.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/data/kinetics/download.py | Apache-2.0 |
def parse_kinetics_annotations(input_csv, ignore_is_cc=False):
"""Returns a parsed DataFrame.
arguments:
---------
input_csv: str
Path to CSV file containing the following columns:
'YouTube Identifier,Start time,End time,Class label'
returns:
-------
dataset: DataFrame
... | Returns a parsed DataFrame.
arguments:
---------
input_csv: str
Path to CSV file containing the following columns:
'YouTube Identifier,Start time,End time,Class label'
returns:
-------
dataset: DataFrame
Pandas with the following columns:
'video-id', 'start-... | parse_kinetics_annotations | python | open-mmlab/mmaction2 | tools/data/kinetics/download.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/data/kinetics/download.py | Apache-2.0 |
def _compress_images(input_output_pair, size=224):
"""Scale and downsample an input image to a given fps and size (shorter
side size).
This also removes the audio from the image.
"""
input_image_path, output_image_path = input_output_pair
try:
resize_image(input_image_path, output_image... | Scale and downsample an input image to a given fps and size (shorter
side size).
This also removes the audio from the image.
| _compress_images | python | open-mmlab/mmaction2 | tools/data/msrvtt/compress.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/data/msrvtt/compress.py | Apache-2.0 |
def _compress_videos(input_output_pair, size=224, fps=3):
"""Scale and downsample an input video to a given fps and size (shorter
side size).
This also removes the audio from the video.
"""
input_file_path, output_file_path = input_output_pair
try:
command = [
'ffmpeg',
... | Scale and downsample an input video to a given fps and size (shorter
side size).
This also removes the audio from the video.
| _compress_videos | python | open-mmlab/mmaction2 | tools/data/msrvtt/compress.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/data/msrvtt/compress.py | Apache-2.0 |
def format_det_result():
"""convert test results to specified format in MultiSports competition."""
test_results = load(args.test_result)
annos = load(args.anno_path)
test_videos = annos['test_videos'][0]
resolutions = annos['resolution']
frm_dets = []
for pred in track(test_results, descrip... | convert test results to specified format in MultiSports competition. | format_det_result | python | open-mmlab/mmaction2 | tools/data/multisports/format_det_result.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/data/multisports/format_det_result.py | Apache-2.0 |
def mmaction2torchserve(
config_file: str,
checkpoint_file: str,
output_folder: str,
model_name: str,
label_file: str,
model_version: str = '1.0',
force: bool = False,
):
"""Converts MMAction2 model (config + checkpoint) to TorchServe `.mar`.
Args:
config_file (str): In MMAc... | Converts MMAction2 model (config + checkpoint) to TorchServe `.mar`.
Args:
config_file (str): In MMAction2 config format.
checkpoint_file (str): In MMAction2 checkpoint format.
output_folder (str): Folder where `{model_name}.mar` will be created.
The file created will be in Torc... | mmaction2torchserve | python | open-mmlab/mmaction2 | tools/deployment/mmaction2torchserve.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/deployment/mmaction2torchserve.py | Apache-2.0 |
def load_video_infos(ann_file):
"""Load the video annotations.
Args:
ann_file (str): A json file path of the annotation file.
Returns:
list[dict]: A list containing annotations for videos.
"""
video_infos = []
anno_database = mmengine.load(ann_file)
for video_name in anno_d... | Load the video annotations.
Args:
ann_file (str): A json file path of the annotation file.
Returns:
list[dict]: A list containing annotations for videos.
| load_video_infos | python | open-mmlab/mmaction2 | tools/misc/bsn_proposal_generation.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/misc/bsn_proposal_generation.py | Apache-2.0 |
def generate_proposals(ann_file, tem_results_dir, pgm_proposals_dir,
pgm_proposals_thread, **kwargs):
"""Generate proposals using multi-process.
Args:
ann_file (str): A json file path of the annotation file for
all videos to be processed.
tem_results_dir (str)... | Generate proposals using multi-process.
Args:
ann_file (str): A json file path of the annotation file for
all videos to be processed.
tem_results_dir (str): Directory to read tem results
pgm_proposals_dir (str): Directory to save generated proposals.
pgm_proposals_thread... | generate_proposals | python | open-mmlab/mmaction2 | tools/misc/bsn_proposal_generation.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/misc/bsn_proposal_generation.py | Apache-2.0 |
def generate_features(ann_file, tem_results_dir, pgm_proposals_dir,
pgm_features_dir, pgm_features_thread, **kwargs):
"""Generate proposals features using multi-process.
Args:
ann_file (str): A json file path of the annotation file for
all videos to be processed.
... | Generate proposals features using multi-process.
Args:
ann_file (str): A json file path of the annotation file for
all videos to be processed.
tem_results_dir (str): Directory to read tem results.
pgm_proposals_dir (str): Directory to read generated proposals.
pgm_featur... | generate_features | python | open-mmlab/mmaction2 | tools/misc/bsn_proposal_generation.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/misc/bsn_proposal_generation.py | Apache-2.0 |
def flow_to_img(raw_flow, bound=20.):
"""Convert flow to gray image.
Args:
raw_flow (np.ndarray[float]): Estimated flow with the shape (w, h).
bound (float): Bound for the flow-to-image normalization. Default: 20.
Returns:
np.ndarray[uint8]: The result list of np.ndarray[uint8], wi... | Convert flow to gray image.
Args:
raw_flow (np.ndarray[float]): Estimated flow with the shape (w, h).
bound (float): Bound for the flow-to-image normalization. Default: 20.
Returns:
np.ndarray[uint8]: The result list of np.ndarray[uint8], with shape
(w, h).
| flow_to_img | python | open-mmlab/mmaction2 | tools/misc/flow_extraction.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/misc/flow_extraction.py | Apache-2.0 |
def generate_flow(frames, method='tvl1'):
"""Estimate flow with given frames.
Args:
frames (list[np.ndarray[uint8]]): List of rgb frames, with shape
(w, h, 3).
method (str): Use which method to generate flow. Options are 'tvl1'
and 'fa... | Estimate flow with given frames.
Args:
frames (list[np.ndarray[uint8]]): List of rgb frames, with shape
(w, h, 3).
method (str): Use which method to generate flow. Options are 'tvl1'
and 'farneback'. Default: 'tvl1'.
Returns:
... | generate_flow | python | open-mmlab/mmaction2 | tools/misc/flow_extraction.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/misc/flow_extraction.py | Apache-2.0 |
def extract_dense_flow(path,
dest,
bound=20.,
save_rgb=False,
start_idx=0,
rgb_tmpl='img_{:05d}.jpg',
flow_tmpl='{}_{:05d}.jpg',
method='tvl1'):
"""Extract... | Extract dense flow given video or frames, save them as gray-scale
images.
Args:
path (str): Location of the input video.
dest (str): The directory to store the extracted flow images.
bound (float): Bound for the flow-to-image normalization. Default: 20.
save_rgb (bool): Save ext... | extract_dense_flow | python | open-mmlab/mmaction2 | tools/misc/flow_extraction.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/misc/flow_extraction.py | Apache-2.0 |
def make_grid(videos, names, rescale_factor=None):
"""Concat list of pictures into a single big picture, align height here."""
vis = Visualizer()
ori_shapes = [vid[0].shape[:2] for vid in videos]
if rescale_factor is not None:
videos = [[mmcv.imrescale(img, rescale_factor) for img in video]
... | Concat list of pictures into a single big picture, align height here. | make_grid | python | open-mmlab/mmaction2 | tools/visualizations/browse_dataset.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/visualizations/browse_dataset.py | Apache-2.0 |
def build_inputs(model: nn.Module,
video_path: str,
use_frames: bool = False) -> Dict:
"""build inputs for GradCAM.
Note that, building inputs for GradCAM is exactly the same as building
inputs for Recognizer test stage. Codes from `inference_recognizer`.
Args:
... | build inputs for GradCAM.
Note that, building inputs for GradCAM is exactly the same as building
inputs for Recognizer test stage. Codes from `inference_recognizer`.
Args:
model (nn.Module): Recognizer model.
video_path (str): video file/url or rawframes directory.
use_frames (bool... | build_inputs | python | open-mmlab/mmaction2 | tools/visualizations/vis_cam.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/visualizations/vis_cam.py | Apache-2.0 |
def _resize_frames(frame_list: List[np.ndarray],
scale: Optional[Tuple[int]] = None,
keep_ratio: bool = True,
interpolation: str = 'bilinear') -> List[np.ndarray]:
"""Resize frames according to given scale.
Codes are modified from `mmaction/datasets/tran... | Resize frames according to given scale.
Codes are modified from `mmaction/datasets/transforms/processing.py`,
`Resize` class.
Args:
frame_list (list[np.ndarray]): Frames to be resized.
scale (tuple[int]): If keep_ratio is True, it serves as scaling
factor or maximum size: the i... | _resize_frames | python | open-mmlab/mmaction2 | tools/visualizations/vis_cam.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/visualizations/vis_cam.py | Apache-2.0 |
def plot_curve(lr_list, args, param_name, iters_per_epoch, by_epoch=True):
"""Plot learning rate vs iter graph."""
try:
import seaborn as sns
sns.set_style(args.style)
except ImportError:
pass
wind_w, wind_h = args.window_size.split('*')
wind_w, wind_h = int(wind_w), int(win... | Plot learning rate vs iter graph. | plot_curve | python | open-mmlab/mmaction2 | tools/visualizations/vis_scheduler.py | https://github.com/open-mmlab/mmaction2/blob/master/tools/visualizations/vis_scheduler.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.
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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.
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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.