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 accumulate(self):
"""
Accumulate metric results and calculate mAP
"""
mAP = 0.
valid_cnt = 0
eval_results = []
for score_pos, count in zip(self.class_score_poss,
self.class_gt_counts):
if count == 0: continue
... |
Accumulate metric results and calculate mAP
| accumulate | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/metrics/map_utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/metrics/map_utils.py | Apache-2.0 |
def _get_tp_fp_accum(self, score_pos_list):
"""
Calculate accumulating true/false positive results from
[score, pos] records
"""
sorted_list = sorted(score_pos_list, key=lambda s: s[0], reverse=True)
accum_tp = 0
accum_fp = 0
accum_tp_list = []
acc... |
Calculate accumulating true/false positive results from
[score, pos] records
| _get_tp_fp_accum | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/metrics/map_utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/metrics/map_utils.py | Apache-2.0 |
def ap_per_class(tp, conf, pred_cls, target_cls):
"""
Computes the average precision, given the recall and precision curves.
Method originally from https://github.com/rafaelpadilla/Object-Detection-Metrics.
Args:
tp (list): True positives.
conf (list): Objectness value from 0-1.
... |
Computes the average precision, given the recall and precision curves.
Method originally from https://github.com/rafaelpadilla/Object-Detection-Metrics.
Args:
tp (list): True positives.
conf (list): Objectness value from 0-1.
pred_cls (list): Predicted object classes.
t... | ap_per_class | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/metrics/map_utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/metrics/map_utils.py | Apache-2.0 |
def compute_ap(recall, precision):
"""
Computes the average precision, given the recall and precision curves.
Code originally from https://github.com/rbgirshick/py-faster-rcnn.
Args:
recall (list): The recall curve.
precision (list): The precision curve.
Returns:
The av... |
Computes the average precision, given the recall and precision curves.
Code originally from https://github.com/rbgirshick/py-faster-rcnn.
Args:
recall (list): The recall curve.
precision (list): The precision curve.
Returns:
The average precision as computed in py-faster-r... | compute_ap | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/metrics/map_utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/metrics/map_utils.py | Apache-2.0 |
def __init__(self,
width,
num_joints,
backbone='HRNet',
loss='KeyPointMSELoss',
post_process='HRNetPostProcess',
flip_perm=None,
flip=True,
shift_heatmap=True,
use_dar... |
HRNet network, see https://arxiv.org/abs/1902.09212
Args:
backbone (nn.Layer): backbone instance
post_process (object): `HRNetPostProcess` instance
flip_perm (list): The left-right joints exchange order list
use_dark(bool): Whether to use DARK in post pr... | __init__ | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/models/keypoint_hrnet.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/models/keypoint_hrnet.py | Apache-2.0 |
def get_max_preds(self, heatmaps):
'''get predictions from score maps
Args:
heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
Returns:
preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
maxvals: numpy.ndarray([batch_size, num_... | get predictions from score maps
Args:
heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
Returns:
preds: numpy.ndarray([batch_size, num_joints, 2]), keypoints coords
maxvals: numpy.ndarray([batch_size, num_joints, 2]), the maximum confidence of the key... | get_max_preds | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/models/keypoint_hrnet.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/models/keypoint_hrnet.py | Apache-2.0 |
def dark_postprocess(self, hm, coords, kernelsize):
'''DARK postpocessing, Zhang et al. Distribution-Aware Coordinate
Representation for Human Pose Estimation (CVPR 2020).
'''
hm = self.gaussian_blur(hm, kernelsize)
hm = np.maximum(hm, 1e-10)
hm = np.log(hm)
for ... | DARK postpocessing, Zhang et al. Distribution-Aware Coordinate
Representation for Human Pose Estimation (CVPR 2020).
| dark_postprocess | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/models/keypoint_hrnet.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/models/keypoint_hrnet.py | Apache-2.0 |
def get_final_preds(self, heatmaps, center, scale, kernelsize=3):
"""the highest heatvalue location with a quarter offset in the
direction from the highest response to the second highest response.
Args:
heatmaps (numpy.ndarray): The predicted heatmaps
center (numpy.ndarr... | the highest heatvalue location with a quarter offset in the
direction from the highest response to the second highest response.
Args:
heatmaps (numpy.ndarray): The predicted heatmaps
center (numpy.ndarray): The boxes center
scale (numpy.ndarray): The scale factor
... | get_final_preds | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/models/keypoint_hrnet.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/models/keypoint_hrnet.py | Apache-2.0 |
def __init__(self, use_target_weight=True, loss_scale=0.5):
"""
KeyPointMSELoss layer
Args:
use_target_weight (bool): whether to use target weight
"""
super(KeyPointMSELoss, self).__init__()
self.criterion = nn.MSELoss(reduction='mean')
self.use_targe... |
KeyPointMSELoss layer
Args:
use_target_weight (bool): whether to use target weight
| __init__ | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/models/loss.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/models/loss.py | Apache-2.0 |
def check_gpu(use_gpu):
"""
Log error and exit when set use_gpu=true in paddlepaddle
cpu version.
"""
err = "Config use_gpu cannot be set as true while you are " \
"using paddlepaddle cpu version ! \nPlease try: \n" \
"\t1. Install paddlepaddle-gpu to run model on GPU \n" \
... |
Log error and exit when set use_gpu=true in paddlepaddle
cpu version.
| check_gpu | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/check.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/check.py | Apache-2.0 |
def check_version(version='2.0'):
"""
Log error and exit when the installed version of paddlepaddle is
not satisfied.
"""
err = "PaddlePaddle version {} or higher is required, " \
"or a suitable develop version is satisfied as well. \n" \
"Please make sure the version is good wit... |
Log error and exit when the installed version of paddlepaddle is
not satisfied.
| check_version | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/check.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/check.py | Apache-2.0 |
def check_config(cfg):
"""
Check the correctness of the configuration file. Log error and exit
when Config is not compliant.
"""
err = "'{}' not specified in config file. Please set it in config file."
check_list = ['architecture', 'num_classes']
try:
for var in check_list:
... |
Check the correctness of the configuration file. Log error and exit
when Config is not compliant.
| check_config | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/check.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/check.py | Apache-2.0 |
def is_url(path):
"""
Whether path is URL.
Args:
path (string): URL string or not.
"""
return path.startswith('http://') \
or path.startswith('https://') \
or path.startswith('ppdet://') |
Whether path is URL.
Args:
path (string): URL string or not.
| is_url | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/checkpoint.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/checkpoint.py | Apache-2.0 |
def match_state_dict(model_state_dict, weight_state_dict):
"""
Match between the model state dict and pretrained weight state dict.
Return the matched state dict.
The method supposes that all the names in pretrained weight state dict are
subclass of the names in models`, if the prefix 'backbone.' i... |
Match between the model state dict and pretrained weight state dict.
Return the matched state dict.
The method supposes that all the names in pretrained weight state dict are
subclass of the names in models`, if the prefix 'backbone.' in pretrained weight
keys is stripped. And we could get the can... | match_state_dict | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/checkpoint.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/checkpoint.py | Apache-2.0 |
def save_model(model, optimizer, save_dir, save_name, last_epoch):
"""
save model into disk.
Args:
model (paddle.nn.Layer): the Layer instalce to save parameters.
optimizer (paddle.optimizer.Optimizer): the Optimizer instance to
save optimizer states.
save_dir (str): the... |
save model into disk.
Args:
model (paddle.nn.Layer): the Layer instalce to save parameters.
optimizer (paddle.optimizer.Optimizer): the Optimizer instance to
save optimizer states.
save_dir (str): the directory to be saved.
save_name (str): the path to be saved.
... | save_model | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/checkpoint.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/checkpoint.py | Apache-2.0 |
def get_weights_path(url):
"""Get weights path from WEIGHTS_HOME, if not exists,
download it from url.
"""
url = parse_url(url)
path, _ = get_path(url, WEIGHTS_HOME)
return path | Get weights path from WEIGHTS_HOME, if not exists,
download it from url.
| get_weights_path | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/download.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/download.py | Apache-2.0 |
def get_config_path(url):
"""Get weights path from CONFIGS_HOME, if not exists,
download it from url.
"""
url = parse_url(url)
path = map_path(url, CONFIGS_HOME, path_depth=2)
if os.path.isfile(path):
return path
# config file not found, try download
# 1. clear configs directory... | Get weights path from CONFIGS_HOME, if not exists,
download it from url.
| get_config_path | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/download.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/download.py | Apache-2.0 |
def get_dataset_path(path, annotation, image_dir):
"""
If path exists, return path.
Otherwise, get dataset path from DATASET_HOME, if not exists,
download it.
"""
if _dataset_exists(path, annotation, image_dir):
return path
logger.info(
"Dataset {} is not valid for reason ab... |
If path exists, return path.
Otherwise, get dataset path from DATASET_HOME, if not exists,
download it.
| get_dataset_path | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/download.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/download.py | Apache-2.0 |
def get_path(url, root_dir, md5sum=None, check_exist=True):
""" Download from given url to root_dir.
if file or directory specified by url is exists under
root_dir, return the path directly, otherwise download
from url and decompress it, return the path.
url (str): download url
root_dir (str): ... | Download from given url to root_dir.
if file or directory specified by url is exists under
root_dir, return the path directly, otherwise download
from url and decompress it, return the path.
url (str): download url
root_dir (str): root dir for downloading, it should be
WEIGHTS_... | get_path | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/download.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/download.py | Apache-2.0 |
def _dataset_exists(path, annotation, image_dir):
"""
Check if user define dataset exists
"""
if not osp.exists(path):
logger.warning("Config dataset_dir {} is not exits, "
"dataset config is not valid".format(path))
return False
if annotation:
annotat... |
Check if user define dataset exists
| _dataset_exists | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/download.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/download.py | Apache-2.0 |
def _download(url, path, md5sum=None):
"""
Download from url, save to path.
url (str): download url
path (str): download to given path
"""
if not osp.exists(path):
os.makedirs(path)
fname = osp.split(url)[-1]
fullname = osp.join(path, fname)
retry_cnt = 0
while not (os... |
Download from url, save to path.
url (str): download url
path (str): download to given path
| _download | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/download.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/download.py | Apache-2.0 |
def _decompress(fname):
"""
Decompress for zip and tar file
"""
logger.info("Decompressing {}...".format(fname))
# For protecting decompressing interupted,
# decompress to fpath_tmp directory firstly, if decompress
# successed, move decompress files to fpath and delete
# fpath_tmp and r... |
Decompress for zip and tar file
| _decompress | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/download.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/download.py | Apache-2.0 |
def _move_and_merge_tree(src, dst):
"""
Move src directory to dst, if dst is already exists,
merge src to dst
"""
if not osp.exists(dst):
shutil.move(src, dst)
elif osp.isfile(src):
shutil.move(src, dst)
else:
for fp in os.listdir(src):
src_fp = osp.join(s... |
Move src directory to dst, if dst is already exists,
merge src to dst
| _move_and_merge_tree | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/download.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/download.py | Apache-2.0 |
def get_affine_transform(center,
input_size,
rot,
output_size,
shift=(0., 0.),
inv=False):
"""Get the affine transform matrix, given the center/scale/rot/output_size.
Args:
cente... | Get the affine transform matrix, given the center/scale/rot/output_size.
Args:
center (np.ndarray[2, ]): Center of the bounding box (x, y).
input_size (np.ndarray[2, ]): Size of input feature (width, height).
rot (float): Rotation angle (degree).
output_size (np.ndarray[2, ]): Size ... | get_affine_transform | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/keypoint_utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/keypoint_utils.py | Apache-2.0 |
def get_warp_matrix(theta, size_input, size_dst, size_target):
"""Calculate the transformation matrix under the constraint of unbiased.
Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased
Data Processing for Human Pose Estimation (CVPR 2020).
Args:
theta (float): Rotation... | Calculate the transformation matrix under the constraint of unbiased.
Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased
Data Processing for Human Pose Estimation (CVPR 2020).
Args:
theta (float): Rotation angle in degrees.
size_input (np.ndarray): Size of input imag... | get_warp_matrix | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/keypoint_utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/keypoint_utils.py | Apache-2.0 |
def _get_3rd_point(a, b):
"""To calculate the affine matrix, three pairs of points are required. This
function is used to get the 3rd point, given 2D points a & b.
The 3rd point is defined by rotating vector `a - b` by 90 degrees
anticlockwise, using b as the rotation center.
Args:
a (np.n... | To calculate the affine matrix, three pairs of points are required. This
function is used to get the 3rd point, given 2D points a & b.
The 3rd point is defined by rotating vector `a - b` by 90 degrees
anticlockwise, using b as the rotation center.
Args:
a (np.ndarray): point(x,y)
b (np... | _get_3rd_point | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/keypoint_utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/keypoint_utils.py | Apache-2.0 |
def rotate_point(pt, angle_rad):
"""Rotate a point by an angle.
Args:
pt (list[float]): 2 dimensional point to be rotated
angle_rad (float): rotation angle by radian
Returns:
list[float]: Rotated point.
"""
assert len(pt) == 2
sn, cs = np.sin(angle_rad), np.cos(angle_ra... | Rotate a point by an angle.
Args:
pt (list[float]): 2 dimensional point to be rotated
angle_rad (float): rotation angle by radian
Returns:
list[float]: Rotated point.
| rotate_point | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/keypoint_utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/keypoint_utils.py | Apache-2.0 |
def warp_affine_joints(joints, mat):
"""Apply affine transformation defined by the transform matrix on the
joints.
Args:
joints (np.ndarray[..., 2]): Origin coordinate of joints.
mat (np.ndarray[3, 2]): The affine matrix.
Returns:
matrix (np.ndarray[..., 2]): Result coordinate ... | Apply affine transformation defined by the transform matrix on the
joints.
Args:
joints (np.ndarray[..., 2]): Origin coordinate of joints.
mat (np.ndarray[3, 2]): The affine matrix.
Returns:
matrix (np.ndarray[..., 2]): Result coordinate of joints.
| warp_affine_joints | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/keypoint_utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/keypoint_utils.py | Apache-2.0 |
def oks_nms(kpts_db, thresh, sigmas=None, in_vis_thre=None):
"""greedily select boxes with high confidence and overlap with current maximum <= thresh
rule out overlap >= thresh
Args:
kpts_db (list): The predicted keypoints within the image
thresh (float): The threshold to select the boxes
... | greedily select boxes with high confidence and overlap with current maximum <= thresh
rule out overlap >= thresh
Args:
kpts_db (list): The predicted keypoints within the image
thresh (float): The threshold to select the boxes
sigmas (np.array): The variance to calculate the oks iou
... | oks_nms | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/keypoint_utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/keypoint_utils.py | Apache-2.0 |
def soft_oks_nms(kpts_db, thresh, sigmas=None, in_vis_thre=None):
"""greedily select boxes with high confidence and overlap with current maximum <= thresh
rule out overlap >= thresh
Args:
kpts_db (list): The predicted keypoints within the image
thresh (float): The threshold to select the bo... | greedily select boxes with high confidence and overlap with current maximum <= thresh
rule out overlap >= thresh
Args:
kpts_db (list): The predicted keypoints within the image
thresh (float): The threshold to select the boxes
sigmas (np.array): The variance to calculate the oks iou
... | soft_oks_nms | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/keypoint_utils.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/keypoint_utils.py | Apache-2.0 |
def setup_logger(name="ppdet", output=None):
"""
Initialize logger and set its verbosity level to INFO.
Args:
output (str): a file name or a directory to save log. If None, will not save log file.
If ends with ".txt" or ".log", assumed to be a file name.
Otherwise, logs will ... |
Initialize logger and set its verbosity level to INFO.
Args:
output (str): a file name or a directory to save log. If None, will not save log file.
If ends with ".txt" or ".log", assumed to be a file name.
Otherwise, logs will be saved to `output/log.txt`.
name (str): th... | setup_logger | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/logger.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/logger.py | Apache-2.0 |
def colormap(rgb=False):
"""
Get colormap
The code of this function is copied from https://github.com/facebookresearch/Detectron/blob/main/detectron/utils/colormap.py
"""
color_list = np.array([
0.000, 0.447, 0.741, 0.850, 0.325, 0.098, 0.929, 0.694, 0.125, 0.494,
0.184, 0.556, 0.46... |
Get colormap
The code of this function is copied from https://github.com/facebookresearch/Detectron/blob/main/detectron/utils/colormap.py
| colormap | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/visualizer.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/visualizer.py | Apache-2.0 |
def load_config(file_path):
"""
Load config from file.
Args:
file_path (str): Path of the config file to be loaded.
Returns: global config
"""
_, ext = os.path.splitext(file_path)
assert ext in ['.yml', '.yaml'], "only support yaml files for now"
# load config from file and me... |
Load config from file.
Args:
file_path (str): Path of the config file to be loaded.
Returns: global config
| load_config | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/workspace.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/workspace.py | Apache-2.0 |
def dict_merge(dct, merge_dct):
""" Recursive dict merge. Inspired by :meth:``dict.update()``, instead of
updating only top-level keys, dict_merge recurses down into dicts nested
to an arbitrary depth, updating keys. The ``merge_dct`` is merged into
``dct``.
Args:
dct: dict onto which the m... | Recursive dict merge. Inspired by :meth:``dict.update()``, instead of
updating only top-level keys, dict_merge recurses down into dicts nested
to an arbitrary depth, updating keys. The ``merge_dct`` is merged into
``dct``.
Args:
dct: dict onto which the merge is executed
merge_dct: dct... | dict_merge | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/workspace.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/workspace.py | Apache-2.0 |
def merge_config(config, another_cfg=None):
"""
Merge config into global config or another_cfg.
Args:
config (dict): Config to be merged.
Returns: global config
"""
global global_config
dct = another_cfg or global_config
return dict_merge(dct, config) |
Merge config into global config or another_cfg.
Args:
config (dict): Config to be merged.
Returns: global config
| merge_config | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/workspace.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/workspace.py | Apache-2.0 |
def register(cls):
"""
Register a given module class.
Args:
cls (type): Module class to be registered.
Returns: cls
"""
if cls.__name__ in global_config:
raise ValueError("Module class already registered: {}".format(
cls.__name__))
if hasattr(cls, '__op__'):
... |
Register a given module class.
Args:
cls (type): Module class to be registered.
Returns: cls
| register | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/workspace.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/workspace.py | Apache-2.0 |
def create(cls_or_name, **kwargs):
"""
Create an instance of given module class.
Args:
cls_or_name (type or str): Class of which to create instance.
Returns: instance of type `cls_or_name`
"""
assert type(cls_or_name) in [type, str
], "should be a class... |
Create an instance of given module class.
Args:
cls_or_name (type or str): Class of which to create instance.
Returns: instance of type `cls_or_name`
| create | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/workspace.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/workspace.py | Apache-2.0 |
def extract_schema(cls):
"""
Extract schema from a given class
Args:
cls (type): Class from which to extract.
Returns:
schema (SchemaDict): Extracted schema.
"""
ctor = cls.__init__
# python 2 compatibility
if hasattr(inspect, 'getfullargspec'):
argspec = inspec... |
Extract schema from a given class
Args:
cls (type): Class from which to extract.
Returns:
schema (SchemaDict): Extracted schema.
| extract_schema | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/config/schema.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/config/schema.py | Apache-2.0 |
def serializable(cls):
"""
Add loader and dumper for given class, which must be
"trivially serializable"
Args:
cls: class to be serialized
Returns: cls
"""
yaml.add_constructor(u'!{}'.format(cls.__name__),
_make_python_constructor(cls))
yaml.add_represe... |
Add loader and dumper for given class, which must be
"trivially serializable"
Args:
cls: class to be serialized
Returns: cls
| serializable | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/lib/utils/config/yaml_helpers.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/lib/utils/config/yaml_helpers.py | Apache-2.0 |
def get_test_images(infer_dir, infer_img):
"""
Get image path list in TEST mode
"""
assert infer_img is not None or infer_dir is not None, \
"--infer_img or --infer_dir should be set"
assert infer_img is None or os.path.isfile(infer_img), \
"{} is not a file".format(infer_img)
... |
Get image path list in TEST mode
| get_test_images | python | PaddlePaddle/models | tutorials/pp-series/HRNet-Keypoint/tools/infer.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/pp-series/HRNet-Keypoint/tools/infer.py | Apache-2.0 |
def get_args(add_help=True):
"""get_args
Parse all args using argparse lib
Args:
add_help: Whether to add -h option on args
Returns:
An object which contains many parameters used for inference.
"""
import argparse
parser = argparse.ArgumentParser(
description='Padd... | get_args
Parse all args using argparse lib
Args:
add_help: Whether to add -h option on args
Returns:
An object which contains many parameters used for inference.
| get_args | python | PaddlePaddle/models | tutorials/tipc/train_infer_python/template/code/export_model.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/tipc/train_infer_python/template/code/export_model.py | Apache-2.0 |
def export(args):
"""export
export inference model using jit.save
Args:
args: Parameters generated using argparser.
Returns: None
"""
model = build_model(args)
# decorate model with jit.save
model = paddle.jit.to_static(
model,
input_spec=[
InputSp... | export
export inference model using jit.save
Args:
args: Parameters generated using argparser.
Returns: None
| export | python | PaddlePaddle/models | tutorials/tipc/train_infer_python/template/code/export_model.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/tipc/train_infer_python/template/code/export_model.py | Apache-2.0 |
def infer_main(args):
"""infer_main
Main inference function.
Args:
args: Parameters generated using argparser.
Returns:
class_id: Class index of the input.
prob: : Probability of the input.
"""
# init inference engine
inference_engine = InferenceEngine(args)
#... | infer_main
Main inference function.
Args:
args: Parameters generated using argparser.
Returns:
class_id: Class index of the input.
prob: : Probability of the input.
| infer_main | python | PaddlePaddle/models | tutorials/tipc/train_infer_python/template/code/infer.py | https://github.com/PaddlePaddle/models/blob/master/tutorials/tipc/train_infer_python/template/code/infer.py | Apache-2.0 |
def pytest_configure(config):
"""Pytest configuration hook to help reproduce test segfaults
Sets and outputs rng seeds.
The segfault-debug procedure on a module called test_module.py is:
1. run "pytest --verbose test_module.py". A seg-faulting output might be:
[INFO] np, mx and python random... | Pytest configuration hook to help reproduce test segfaults
Sets and outputs rng seeds.
The segfault-debug procedure on a module called test_module.py is:
1. run "pytest --verbose test_module.py". A seg-faulting output might be:
[INFO] np, mx and python random seeds = 4018804151
test_modul... | pytest_configure | python | dmlc/gluon-nlp | conftest.py | https://github.com/dmlc/gluon-nlp/blob/master/conftest.py | Apache-2.0 |
def pytest_runtest_makereport(item, call):
"""Make test outcome available to fixture.
https://docs.pytest.org/en/latest/example/simple.html#making-test-result-information-available-in-fixtures
"""
# execute all other hooks to obtain the report object
outcome = yield
rep = outcome.get_result()
... | Make test outcome available to fixture.
https://docs.pytest.org/en/latest/example/simple.html#making-test-result-information-available-in-fixtures
| pytest_runtest_makereport | python | dmlc/gluon-nlp | conftest.py | https://github.com/dmlc/gluon-nlp/blob/master/conftest.py | Apache-2.0 |
def function_scope_seed(request):
"""A function scope fixture that manages rng seeds.
This fixture automatically initializes the python, numpy and mxnet random
number generators randomly on every test run.
def test_ok_with_random_data():
...
To fix the seed used for a test case mark the t... | A function scope fixture that manages rng seeds.
This fixture automatically initializes the python, numpy and mxnet random
number generators randomly on every test run.
def test_ok_with_random_data():
...
To fix the seed used for a test case mark the test function with the
desired seed:
... | function_scope_seed | python | dmlc/gluon-nlp | conftest.py | https://github.com/dmlc/gluon-nlp/blob/master/conftest.py | Apache-2.0 |
def predict_extended(original_feature,
chunked_features,
results,
n_best_size,
max_answer_length=64,
start_top_n=5,
end_top_n=5):
"""Get prediction results for SQuAD.
Start Logits: (B, ... | Get prediction results for SQuAD.
Start Logits: (B, N_start)
End Logits: (B, N_start, N_end)
Parameters
----------
original_feature:
The original SquadFeature before chunked
chunked_features
List of ChunkFeatures
results
List of model predictions for span start and ... | predict_extended | python | dmlc/gluon-nlp | docs/tutorials/question_answering/squad_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/docs/tutorials/question_answering/squad_utils.py | Apache-2.0 |
def get_end_logits(self, contextual_embedding, start_positions, p_mask):
"""
Parameters
----------
contextual_embedding
Shape (batch_size, sequence_length, C)
start_positions
Shape (batch_size, N)
We process multiple candidates simultaneously
... |
Parameters
----------
contextual_embedding
Shape (batch_size, sequence_length, C)
start_positions
Shape (batch_size, N)
We process multiple candidates simultaneously
p_mask
Shape (batch_size, sequence_length)
Returns
... | get_end_logits | python | dmlc/gluon-nlp | docs/tutorials/question_answering/squad_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/docs/tutorials/question_answering/squad_utils.py | Apache-2.0 |
def get_answerable_logits(self, contextual_embedding, p_mask):
"""Get the answerable logits.
Parameters
----------
contextual_embedding
Shape (batch_size, sequence_length, C)
p_mask
Shape (batch_size, sequence_length)
Mask the sequence.
... | Get the answerable logits.
Parameters
----------
contextual_embedding
Shape (batch_size, sequence_length, C)
p_mask
Shape (batch_size, sequence_length)
Mask the sequence.
0 --> Denote that the element is masked,
1 --> Denote th... | get_answerable_logits | python | dmlc/gluon-nlp | docs/tutorials/question_answering/squad_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/docs/tutorials/question_answering/squad_utils.py | Apache-2.0 |
def forward(self, tokens, token_types, valid_length, p_mask, start_position):
"""
Parameters
----------
tokens
Shape (batch_size, sequence_length)
token_types
Shape (batch_size, sequence_length)
valid_length
Shape (batch_size,)
... |
Parameters
----------
tokens
Shape (batch_size, sequence_length)
token_types
Shape (batch_size, sequence_length)
valid_length
Shape (batch_size,)
p_mask
Shape (batch_size, sequence_length)
start_position
... | forward | python | dmlc/gluon-nlp | docs/tutorials/question_answering/squad_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/docs/tutorials/question_answering/squad_utils.py | Apache-2.0 |
def inference(self, tokens, token_types, valid_length, p_mask,
start_top_n: int = 5, end_top_n: int = 5):
"""Get the inference result with beam search
Parameters
----------
tokens
The input tokens. Shape (batch_size, sequence_length)
token_types
... | Get the inference result with beam search
Parameters
----------
tokens
The input tokens. Shape (batch_size, sequence_length)
token_types
The input token types. Shape (batch_size, sequence_length)
valid_length
The valid length of the tokens. Sh... | inference | python | dmlc/gluon-nlp | docs/tutorials/question_answering/squad_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/docs/tutorials/question_answering/squad_utils.py | Apache-2.0 |
def get_chunks(self, doc_stride, max_chunk_length=None):
"""Get a sequence of chunks for the squad feature.
In reality, the document will be too long for the NLP model, and we will split it into
multiple chunks.
For example, consider the following
Doc: the man went to the store... | Get a sequence of chunks for the squad feature.
In reality, the document will be too long for the NLP model, and we will split it into
multiple chunks.
For example, consider the following
Doc: the man went to the store and bought a gallon of milk
We may divide it into four chu... | get_chunks | python | dmlc/gluon-nlp | docs/tutorials/question_answering/squad_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/docs/tutorials/question_answering/squad_utils.py | Apache-2.0 |
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace.
This is from the official evaluate-v2.0.py in SQuAD.
"""
def remove_articles(text):
regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
return re.sub(regex, ' ', text)
def white_space_fix(te... | Lower text and remove punctuation, articles and extra whitespace.
This is from the official evaluate-v2.0.py in SQuAD.
| normalize_answer | python | dmlc/gluon-nlp | docs/tutorials/question_answering/squad_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/docs/tutorials/question_answering/squad_utils.py | Apache-2.0 |
def get_squad_examples_from_json(json_file: str, is_training: bool) -> List[SquadExample]:
"""
Read the whole entry of raw json file and convert it to examples.
Parameters
----------
json_file
The path to the json file
is_training
Whether or not training
Returns
-------... |
Read the whole entry of raw json file and convert it to examples.
Parameters
----------
json_file
The path to the json file
is_training
Whether or not training
Returns
-------
ret
List of SquadExample objects
| get_squad_examples_from_json | python | dmlc/gluon-nlp | docs/tutorials/question_answering/squad_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/docs/tutorials/question_answering/squad_utils.py | Apache-2.0 |
def convert_squad_example_to_feature(example: SquadExample,
tokenizer: BaseTokenizerWithVocab,
is_training: bool):
"""
Convert a SquadExample object to a SquadFeature object with the designated tokenizer.
There are accually few examp... |
Convert a SquadExample object to a SquadFeature object with the designated tokenizer.
There are accually few examples can not be converted properly with token level tokenization,
due to the ground-truth are given by the start position and the answer text, and some examples
are annotated with wrong lab... | convert_squad_example_to_feature | python | dmlc/gluon-nlp | docs/tutorials/question_answering/squad_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/docs/tutorials/question_answering/squad_utils.py | Apache-2.0 |
def __init__(self, tokenizer, doc_stride, max_seq_length, max_query_length):
"""
Parameters
----------
tokenizer
The tokenizer
doc_stride
The stride to chunk the document
max_seq_length
Maximum length of the merged data
max_que... |
Parameters
----------
tokenizer
The tokenizer
doc_stride
The stride to chunk the document
max_seq_length
Maximum length of the merged data
max_query_length
Maximum query length
| __init__ | python | dmlc/gluon-nlp | docs/tutorials/question_answering/squad_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/docs/tutorials/question_answering/squad_utils.py | Apache-2.0 |
def process_sample(self, feature: SquadFeature):
"""Process the data to the following format.
Note that we mask all the special tokens except the CLS token. The reason for not masking
the CLS token is that if the question is not answerable, we will set the start and end to
be 0.
... | Process the data to the following format.
Note that we mask all the special tokens except the CLS token. The reason for not masking
the CLS token is that if the question is not answerable, we will set the start and end to
be 0.
Merged: <CLS> Question <SEP> Context <SEP>
S... | process_sample | python | dmlc/gluon-nlp | docs/tutorials/question_answering/squad_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/docs/tutorials/question_answering/squad_utils.py | Apache-2.0 |
def get_train(self, features, skip_unreliable=True):
"""Get the training dataset
Parameters
----------
features
skip_unreliable
Whether to skip the unreliable spans in the training set
Returns
-------
train_dataset
num_token_answer_mi... | Get the training dataset
Parameters
----------
features
skip_unreliable
Whether to skip the unreliable spans in the training set
Returns
-------
train_dataset
num_token_answer_mismatch
num_unreliable
| get_train | python | dmlc/gluon-nlp | docs/tutorials/question_answering/squad_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/docs/tutorials/question_answering/squad_utils.py | Apache-2.0 |
def separate_process_wrapper_fn(func: Callable[[], None], do_multi_processing: bool) -> Callable[[], None]:
"""
This function wraps another function into its own separated process.
In order to ensure accurate memory measurements it is important that the function
is executed in a separate pro... |
This function wraps another function into its own separated process.
In order to ensure accurate memory measurements it is important that the function
is executed in a separate process
Args:
- `func`: (`callable`): function() -> ...
generic function which wi... | separate_process_wrapper_fn | python | dmlc/gluon-nlp | scripts/benchmarks/benchmark_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/benchmarks/benchmark_utils.py | Apache-2.0 |
def get_cpu_memory(process_id: int) -> int:
"""
measures current cpu memory usage of a given `process_id`
Args:
- `process_id`: (`int`)
process_id for which to measure memory
Returns
- `memory`: (`int`)
cosumed memory in Bytes
"""... |
measures current cpu memory usage of a given `process_id`
Args:
- `process_id`: (`int`)
process_id for which to measure memory
Returns
- `memory`: (`int`)
cosumed memory in Bytes
| get_cpu_memory | python | dmlc/gluon-nlp | scripts/benchmarks/benchmark_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/benchmarks/benchmark_utils.py | Apache-2.0 |
def measure_peak_memory_cpu(function: Callable[[], None], interval=0.5, device_idx=None) -> int:
"""
measures peak cpu memory consumption of a given `function`
running the function for at least interval seconds
and at most 20 * interval seconds.
This function is heavily inspired by: ... |
measures peak cpu memory consumption of a given `function`
running the function for at least interval seconds
and at most 20 * interval seconds.
This function is heavily inspired by: `memory_usage`
of the package `memory_profiler`: https://github.com/pythonprofilers/memory_profi... | measure_peak_memory_cpu | python | dmlc/gluon-nlp | scripts/benchmarks/benchmark_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/benchmarks/benchmark_utils.py | Apache-2.0 |
def traceit(frame, event, args):
""" Tracing method executed before running each line in a module or sub-module
Record memory allocated in a list with debugging information
"""
global _is_memory_tracing_enabled
if not _is_memory_tracing_enabled:
return traceit
... | Tracing method executed before running each line in a module or sub-module
Record memory allocated in a list with debugging information
| traceit | python | dmlc/gluon-nlp | scripts/benchmarks/benchmark_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/benchmarks/benchmark_utils.py | Apache-2.0 |
def stop_memory_tracing(
memory_trace: Optional[MemoryTrace] = None, ignore_released_memory: bool = True
) -> Optional[MemorySummary]:
""" Stop memory tracing cleanly and return a summary of the memory trace if a trace is given.
Args:
- `memory_trace` (optional output of start_memory_tracin... | Stop memory tracing cleanly and return a summary of the memory trace if a trace is given.
Args:
- `memory_trace` (optional output of start_memory_tracing, default: None): memory trace to convert in summary
- `ignore_released_memory` (boolean, default: None): if True we only sum memory ... | stop_memory_tracing | python | dmlc/gluon-nlp | scripts/benchmarks/benchmark_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/benchmarks/benchmark_utils.py | Apache-2.0 |
def __init__(self, workloads, model_names, use_fp16=False,
repeat=3, use_gpu=True,
device_idx=0,
profile_inference=True,
profile_train=True,
env_print=True,
to_csv=False,
use_tvm=False,
... |
Parameters
----------
workloads
List of workloads to profile
model_names
List of model names to profile
use_fp16
Whether to use fp16
repeat
The number of repeat
use_gpu
Whether to use GPU
device... | __init__ | python | dmlc/gluon-nlp | scripts/benchmarks/benchmark_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/benchmarks/benchmark_utils.py | Apache-2.0 |
def get_network(model_name,
ctx_l,
checkpoint_path=None,
backbone_path=None,
task=None):
"""
Get the network that fine-tune the Question Answering Task
"""
use_segmentation = 'roberta' not in model_name and 'xlmr' not in model_name
Mod... |
Get the network that fine-tune the Question Answering Task
| get_network | python | dmlc/gluon-nlp | scripts/classification/train_classification.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/classification/train_classification.py | Apache-2.0 |
def convert_tf_assets(tf_assets_dir, model_size, electra_path):
"""Convert the assets file including config, vocab and tokenizer model"""
file_names = os.listdir(tf_assets_dir)
vocab_path = None
for ele in file_names:
if ele.endswith('.txt'):
assert vocab_path is None
voc... | Convert the assets file including config, vocab and tokenizer model | convert_tf_assets | python | dmlc/gluon-nlp | scripts/conversion_toolkits/convert_electra.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/conversion_toolkits/convert_electra.py | Apache-2.0 |
def get_name_map(tf_names, convert_type='backbone'):
"""
Get the converting mapping between tensor names and mxnet names.
The above mapping CONVERT_MAP is effectively adaptive to Bert and Albert,
but there is no guarantee that it can match to other tf models in case of
some sepecial variable_scope (... |
Get the converting mapping between tensor names and mxnet names.
The above mapping CONVERT_MAP is effectively adaptive to Bert and Albert,
but there is no guarantee that it can match to other tf models in case of
some sepecial variable_scope (tensorflow) and prefix (mxnet).
Redefined mapping is en... | get_name_map | python | dmlc/gluon-nlp | scripts/conversion_toolkits/convert_electra.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/conversion_toolkits/convert_electra.py | Apache-2.0 |
def convert_qkv_weights(tf_prefix, mx_prefix):
"""
To convert the qkv weights with different prefix.
In tensorflow framework, the prefix of query/key/value for the albert model is
'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/query/kernel',
... |
To convert the qkv weights with different prefix.
In tensorflow framework, the prefix of query/key/value for the albert model is
'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/query/kernel',
and that for the bert model is 'bert/encoder/layer_{}/attenti... | convert_qkv_weights | python | dmlc/gluon-nlp | scripts/conversion_toolkits/convert_electra.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/conversion_toolkits/convert_electra.py | Apache-2.0 |
def convert_tf_assets(tf_assets_dir):
"""Convert the assets file including config, vocab and tokenizer model"""
file_names = os.listdir(tf_assets_dir)
vocab_path = None
json_cfg_path = None
for ele in file_names:
if ele.endswith('.txt'):
assert vocab_path is None
voca... | Convert the assets file including config, vocab and tokenizer model | convert_tf_assets | python | dmlc/gluon-nlp | scripts/conversion_toolkits/convert_mobilebert.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/conversion_toolkits/convert_mobilebert.py | Apache-2.0 |
def get_name_map(tf_names, num_stacked_ffn):
"""
Get the converting mapping between tensor names and mxnet names.
The above mapping CONVERT_MAP is effectively adaptive to Bert and Albert,
but there is no guarantee that it can match to other tf models in case of
some sepecial variable_scope (tensorfl... |
Get the converting mapping between tensor names and mxnet names.
The above mapping CONVERT_MAP is effectively adaptive to Bert and Albert,
but there is no guarantee that it can match to other tf models in case of
some sepecial variable_scope (tensorflow) and prefix (mxnet).
Redefined mapping is en... | get_name_map | python | dmlc/gluon-nlp | scripts/conversion_toolkits/convert_mobilebert.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/conversion_toolkits/convert_mobilebert.py | Apache-2.0 |
def convert_tf_assets(tf_assets_dir, model_type):
"""Convert the assets file including config, vocab and tokenizer model"""
file_names = os.listdir(tf_assets_dir)
json_cfg_path = None
spm_model_path = None
vocab_path = None
for ele in file_names:
if ele.endswith('.model'):
as... | Convert the assets file including config, vocab and tokenizer model | convert_tf_assets | python | dmlc/gluon-nlp | scripts/conversion_toolkits/convert_tf_hub_model.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/conversion_toolkits/convert_tf_hub_model.py | Apache-2.0 |
def get_name_map(tf_names, is_TF1=True):
"""
Get the converting mapping between TF names and mxnet names.
The above mapping CONVERT_MAP is effectively adaptive to Bert and Albert,
but there is no guarantee that it can match to other tf models in case of
some special variable_scope (tensorflow) and p... |
Get the converting mapping between TF names and mxnet names.
The above mapping CONVERT_MAP is effectively adaptive to Bert and Albert,
but there is no guarantee that it can match to other tf models in case of
some special variable_scope (tensorflow) and prefix (mxnet).
Redefined mapping is encoura... | get_name_map | python | dmlc/gluon-nlp | scripts/conversion_toolkits/convert_tf_hub_model.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/conversion_toolkits/convert_tf_hub_model.py | Apache-2.0 |
def convert_qkv_weights(tf_prefix, prefix, is_mlm):
"""
To convert the qkv weights with different prefix.
In tensorflow framework, the prefix of query/key/value for the albert model is
'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/query/kernel',
and that for t... |
To convert the qkv weights with different prefix.
In tensorflow framework, the prefix of query/key/value for the albert model is
'bert/encoder/transformer/group_0/inner_group_0/attention_1/self/query/kernel',
and that for the bert model is 'bert/encoder/layer_{}/attention/self/key/bias... | convert_qkv_weights | python | dmlc/gluon-nlp | scripts/conversion_toolkits/convert_tf_hub_model.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/conversion_toolkits/convert_tf_hub_model.py | Apache-2.0 |
def get_hash_and_size(obj, retries=5, algorithm='sha1', cache=None, save_path=None,
verify_ssl=True):
"""Fetch sha1 hash of all urls in the input obj"""
def _get_hash_and_size(obj, retries, algorithm, cache=None, save_path=None):
if isinstance(obj, str):
if obj.startswi... | Fetch sha1 hash of all urls in the input obj | get_hash_and_size | python | dmlc/gluon-nlp | scripts/datasets/update_download_stats.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/datasets/update_download_stats.py | Apache-2.0 |
def build_vocab(corpus_path_l: List, eos_token: Optional[str] = '<eos>') -> Vocab:
"""Build the default vocabulary used in datasets like
- wikitext2
- wikitext103
- text8
- enwiki8
The strategy is to split with white-space and store all appeared tokens.
Also, the tokens wil... | Build the default vocabulary used in datasets like
- wikitext2
- wikitext103
- text8
- enwiki8
The strategy is to split with white-space and store all appeared tokens.
Also, the tokens will be sorted with a descending order of their frequency.
Parameters
----------
... | build_vocab | python | dmlc/gluon-nlp | scripts/datasets/language_modeling/prepare_lm.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/datasets/language_modeling/prepare_lm.py | Apache-2.0 |
def parse_sgm(path_or_buffer: Union[str, IO[AnyStr]],
out_path_or_buffer: Optional[Union[str, IO[AnyStr]]] = None,
return_sentences=False,
clean_space=True) -> Optional[List[str]]:
"""Returns sentences from a single SGML file. This is compatible to the behavior of
`inpu... | Returns sentences from a single SGML file. This is compatible to the behavior of
`input-from-sgm.perl` in
https://github.com/moses-smt/mosesdecoder/blob/a89691fee395bb7eb6dfd51e368825f0578f437d/scripts/ems/support/input-from-sgm.perl
Parameters
----------
path_or_buffer
The source path to p... | parse_sgm | python | dmlc/gluon-nlp | scripts/datasets/machine_translation/prepare_wmt.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/datasets/machine_translation/prepare_wmt.py | Apache-2.0 |
def concatenate_files(fname_l: List[str],
out_fname: Optional[str] = None,
chunk_size: int = 128 * 1024) -> str:
"""Concatenate multiple files into a single file. This is used to recover a large file that has
been split into multiple parts. E.g.,
UNv1.0.en-zh.tar... | Concatenate multiple files into a single file. This is used to recover a large file that has
been split into multiple parts. E.g.,
UNv1.0.en-zh.tar.gz.00, UNv1.0.en-zh.tar.gz.01 --> UNv1.0.en-zh.tar.gz
Parameters
----------
fname_l
out_fname
chunk_size
Returns
-------
ret
| concatenate_files | python | dmlc/gluon-nlp | scripts/datasets/machine_translation/prepare_wmt.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/datasets/machine_translation/prepare_wmt.py | Apache-2.0 |
def fetch_mono_dataset(selection: Union[str, List[str], List[List[str]]],
lang: str = 'de',
path: Optional[str] = _BASE_DATASET_PATH,
overwrite: bool = False) -> List[str]:
"""Fetch the monolingual dataset provided by WMT
Parameters
-----... | Fetch the monolingual dataset provided by WMT
Parameters
----------
selection
The selected datasets
lang
Language of the monolingual corpus
path
overwrite
Whether to overwrite the downloaded dataset
Returns
-------
src_corpus_paths
| fetch_mono_dataset | python | dmlc/gluon-nlp | scripts/datasets/machine_translation/prepare_wmt.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/datasets/machine_translation/prepare_wmt.py | Apache-2.0 |
def download_mono_newscrawl(lang: str = 'de', path: str = _BASE_DATASET_PATH)\
-> List[str]:
"""Download the train dataset used for WMT2014
Parameters
----------
lang
path
Returns
-------
train_src_paths
"""
if lang == 'de':
train_src_paths =\
fetch_... | Download the train dataset used for WMT2014
Parameters
----------
lang
path
Returns
-------
train_src_paths
| download_mono_newscrawl | python | dmlc/gluon-nlp | scripts/datasets/machine_translation/prepare_wmt.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/datasets/machine_translation/prepare_wmt.py | Apache-2.0 |
def download_wmt14_train(lang_pair: str = 'en-de', path: str = _BASE_DATASET_PATH)\
-> Tuple[List[str], List[str]]:
"""Download the train dataset used for WMT2014
Parameters
----------
lang_pair
path
Returns
-------
train_src_paths
train_tgt_paths
"""
if lang_pair =... | Download the train dataset used for WMT2014
Parameters
----------
lang_pair
path
Returns
-------
train_src_paths
train_tgt_paths
| download_wmt14_train | python | dmlc/gluon-nlp | scripts/datasets/machine_translation/prepare_wmt.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/datasets/machine_translation/prepare_wmt.py | Apache-2.0 |
def download_wmt16_train(lang_pair: str = 'en-de', path: str = _BASE_DATASET_PATH)\
-> Tuple[List[str], List[str]]:
"""Download the train dataset used for WMT2016
Parameters
----------
lang_pair
path
Returns
-------
train_src_paths
train_tgt_paths
"""
if lang_pair ... | Download the train dataset used for WMT2016
Parameters
----------
lang_pair
path
Returns
-------
train_src_paths
train_tgt_paths
| download_wmt16_train | python | dmlc/gluon-nlp | scripts/datasets/machine_translation/prepare_wmt.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/datasets/machine_translation/prepare_wmt.py | Apache-2.0 |
def download_wmt17_train(lang_pair: str = 'en-de', path: str = _BASE_DATASET_PATH)\
-> Tuple[List[str], List[str]]:
"""Download the train dataset used for WMT2017
Parameters
----------
lang_pair
path
Returns
-------
train_src_paths
train_tgt_paths
"""
if lang_pair ... | Download the train dataset used for WMT2017
Parameters
----------
lang_pair
path
Returns
-------
train_src_paths
train_tgt_paths
| download_wmt17_train | python | dmlc/gluon-nlp | scripts/datasets/machine_translation/prepare_wmt.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/datasets/machine_translation/prepare_wmt.py | Apache-2.0 |
def extract_files(full_name, output_dir, shuffle=False):
"""
Extract the file and concatenate all the TXT files it archives
"""
if not full_name.endswith(".xz"):
return
file_prefix = re.split(r'\.|/', full_name)[-2]
file_prefix = file_prefix.replace('urlsf_subset', 'openwebtext-prepared-... |
Extract the file and concatenate all the TXT files it archives
| extract_files | python | dmlc/gluon-nlp | scripts/datasets/pretrain_corpus/prepare_openwebtext.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/datasets/pretrain_corpus/prepare_openwebtext.py | Apache-2.0 |
def get_formatting_list(wiki_path, recursive=False):
"""
get formatting list of file names from extracted content
"""
filenames = []
for dirname in glob.glob(os.path.join(wiki_path, '*'), recursive=False):
for filename in glob.glob(os.path.join(dirname, 'wiki_*'), recursive=recursive):
... |
get formatting list of file names from extracted content
| get_formatting_list | python | dmlc/gluon-nlp | scripts/datasets/pretrain_corpus/prepare_wikipedia.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/datasets/pretrain_corpus/prepare_wikipedia.py | Apache-2.0 |
def download_wikicorpus(lang, date, output):
"""
lang: the language code such as en, zh
date: string, the date of the Wikipedia with format of YYYYMMDD, or 'latest'.
"""
if not os.path.exists(output):
os.makedirs(output)
if lang not in __LANGUAGES_BANK:
raise ValueError('Unsuppor... |
lang: the language code such as en, zh
date: string, the date of the Wikipedia with format of YYYYMMDD, or 'latest'.
| download_wikicorpus | python | dmlc/gluon-nlp | scripts/datasets/pretrain_corpus/prepare_wikipedia.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/datasets/pretrain_corpus/prepare_wikipedia.py | Apache-2.0 |
def calculate_self_bleu4(sample_strs, num_bleu_samples):
"""Self-BLEU is calculated by computing the BLEU score of each generated document
using all other generations in the evaluation set as references.
"""
pool = Pool(processes=os.cpu_count())
return sum(tqdm(
pool.imap_unordered(
... | Self-BLEU is calculated by computing the BLEU score of each generated document
using all other generations in the evaluation set as references.
| calculate_self_bleu4 | python | dmlc/gluon-nlp | scripts/generation/calculate_metrics.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/generation/calculate_metrics.py | Apache-2.0 |
def calculate_zipf_coefficient(sample_ids, tokenizer):
"""The Zipfian coefficient (R-squared) can be used to compare the distribution in a given
text to a theoretically perfect exponential curve.
"""
cnt = Counter()
for sample_id in sample_ids:
cnt.update(sample_id)
xs = np.arange(1, mi... | The Zipfian coefficient (R-squared) can be used to compare the distribution in a given
text to a theoretically perfect exponential curve.
| calculate_zipf_coefficient | python | dmlc/gluon-nlp | scripts/generation/calculate_metrics.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/generation/calculate_metrics.py | Apache-2.0 |
def calculate_repetition(sample_ids):
"""The repetition rate in generated samples.
"""
max_n = 90
n_repeated_examples = 0
for sample_id in sample_ids:
rev = list(reversed(sample_id))
last_n_repeats = [0 for _ in range(max_n)]
for n in range(1, max_n + 1):
n_repeat... | The repetition rate in generated samples.
| calculate_repetition | python | dmlc/gluon-nlp | scripts/generation/calculate_metrics.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/generation/calculate_metrics.py | Apache-2.0 |
def get_base_tokenizer(method, lang):
"""The base tokenization method
Parameters
----------
method
lang
Returns
-------
"""
if method == 'moses':
return tokenizers.create('moses', lang)
elif method == 'whitespace':
return tokenizers.create('whitespace')
el... | The base tokenization method
Parameters
----------
method
lang
Returns
-------
| get_base_tokenizer | python | dmlc/gluon-nlp | scripts/machine_translation/evaluate_transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/machine_translation/evaluate_transformer.py | Apache-2.0 |
def validation(model, data_loader, inference_model, sequence_sampler,
tgt_tokenizer, ctx_l):
"""Validate the model on the dataset
Parameters
----------
model : TransformerModel
The transformer model
data_loader : DataLoader
DataLoader
inference_model
The m... | Validate the model on the dataset
Parameters
----------
model : TransformerModel
The transformer model
data_loader : DataLoader
DataLoader
inference_model
The model for inference
sequence_sampler:
The sequence sampler for doing beam search
tgt_tokenizer
... | validation | python | dmlc/gluon-nlp | scripts/machine_translation/train_transformer.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/machine_translation/train_transformer.py | Apache-2.0 |
def tokenize_lines_to_ids(lines, tokenizer):
"""
Worker function to tokenize lines based on the tokenizer, and perform vocabulary lookup.
Parameters
----------
lines
Lines to be tokenized of the whole file
tokenizer
The trained tokenizer
Returns
-------
results
... |
Worker function to tokenize lines based on the tokenizer, and perform vocabulary lookup.
Parameters
----------
lines
Lines to be tokenized of the whole file
tokenizer
The trained tokenizer
Returns
-------
results
A list storing the valid tokenized lines
| tokenize_lines_to_ids | python | dmlc/gluon-nlp | scripts/pretraining/pretraining_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/pretraining/pretraining_utils.py | Apache-2.0 |
def get_all_features(x):
"""
Get the feature data in numpy form.
Parameters
----------
x
List/tuple that contains:
- file_list
A list of text files
- output_file
The path to a output file that store the np_features
- tokenizer
Th... |
Get the feature data in numpy form.
Parameters
----------
x
List/tuple that contains:
- file_list
A list of text files
- output_file
The path to a output file that store the np_features
- tokenizer
The trained tokenizer
- ma... | get_all_features | python | dmlc/gluon-nlp | scripts/pretraining/pretraining_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/pretraining/pretraining_utils.py | Apache-2.0 |
def process_a_text(text_file, tokenizer, max_seq_length, short_seq_prob=0.05):
"""
Create features from a single raw text file, in which one line is treated
as a sentence, and double blank lines represent document separators.
In this process, mxnet-unrelated features are generated, to easily convert
... |
Create features from a single raw text file, in which one line is treated
as a sentence, and double blank lines represent document separators.
In this process, mxnet-unrelated features are generated, to easily convert
to features of a particular deep learning framework in subsequent steps
Parame... | process_a_text | python | dmlc/gluon-nlp | scripts/pretraining/pretraining_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/pretraining/pretraining_utils.py | Apache-2.0 |
def convert_to_npz(all_features, output_file=None):
"""
Convert features to numpy array and store if output_file provided
Parameters
----------
all_features
A list of processed features.
output_file
The path to a output file that store the np_features.
Returns
-------
... |
Convert features to numpy array and store if output_file provided
Parameters
----------
all_features
A list of processed features.
output_file
The path to a output file that store the np_features.
Returns
-------
input_ids
A tuple of features
segment_ids
... | convert_to_npz | python | dmlc/gluon-nlp | scripts/pretraining/pretraining_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/pretraining/pretraining_utils.py | Apache-2.0 |
def sentenceize(current_sentences, max_seq_length, target_seq_length):
"""
Generate a pair of sentences based on a segmentation strategy
cloned from official electra model.
Parameters
----------
current_sentences
max_seq_length
Maximum sequence length of the training features
ta... |
Generate a pair of sentences based on a segmentation strategy
cloned from official electra model.
Parameters
----------
current_sentences
max_seq_length
Maximum sequence length of the training features
target_seq_length
Target sequence length of the training features
Re... | sentenceize | python | dmlc/gluon-nlp | scripts/pretraining/pretraining_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/pretraining/pretraining_utils.py | Apache-2.0 |
def prepare_pretrain_npz_dataset(filename, allow_pickle=False):
"""Create dataset based on the numpy npz file"""
if isinstance(filename, (list, tuple)):
assert len(filename) == 1, \
'When .npy/.npz data file is loaded, len(filename) must be 1.' \
' Received len(filename)={}.'.for... | Create dataset based on the numpy npz file | prepare_pretrain_npz_dataset | python | dmlc/gluon-nlp | scripts/pretraining/pretraining_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/pretraining/pretraining_utils.py | Apache-2.0 |
def prepare_pretrain_text_dataset(
filenames,
tokenizer,
max_seq_length,
short_seq_prob,
cached_file_path):
"""Create dataset based on the raw text files"""
if not isinstance(filenames, (list, tuple)):
filenames = [filenames]
if cached_file_path:
# gen... | Create dataset based on the raw text files | prepare_pretrain_text_dataset | python | dmlc/gluon-nlp | scripts/pretraining/pretraining_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/pretraining/pretraining_utils.py | Apache-2.0 |
def prepare_pretrain_bucket_sampler(dataset, batch_size, shuffle=False, num_buckets=1):
"""Create data sampler based on the dataset"""
if isinstance(dataset, NumpyDataset):
lengths = dataset.get_field('valid_lengths')
else:
lengths = dataset.transform(lambda input_ids, segment_ids,
... | Create data sampler based on the dataset | prepare_pretrain_bucket_sampler | python | dmlc/gluon-nlp | scripts/pretraining/pretraining_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/pretraining/pretraining_utils.py | Apache-2.0 |
def get_pretrain_data_npz(data, batch_size, shuffle, num_buckets,
vocab, num_parts=1, part_idx=0,
num_dataset_workers=1, num_batch_workers=1,
circle_length=1, repeat=1,
dataset_cached=False,
... | Get a data iterator from pre-processed npz files.
Parameters
----------
data: str
The path to the dataset directory
batch_size : int
The batch size per GPU.
shuffle : bool
Whether to shuffle the data.
num_buckets : int
The number of buckets for the FixedBucketSam... | get_pretrain_data_npz | python | dmlc/gluon-nlp | scripts/pretraining/pretraining_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/pretraining/pretraining_utils.py | Apache-2.0 |
def dynamic_masking(self, input_ids, valid_lengths):
# TODO(zheyuye), two additional flag `disallow_from_mask` and `already_masked`
# that control the masking status for each positions in the sequence.
"""
Generate masking positions on-the-fly instead of during preprocessing
Para... |
Generate masking positions on-the-fly instead of during preprocessing
Parameters
----------
input_ids
The batchified input_ids with shape (batch_size, max_seq_length)
valid_lengths
The batchified valid_lengths with shape (batch_size, )
Returns
... | dynamic_masking | python | dmlc/gluon-nlp | scripts/pretraining/pretraining_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/pretraining/pretraining_utils.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.