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
values |
|---|---|---|---|---|---|---|---|
def text_to_sequence(text):
'''Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
The text can optionally have ARPAbet sequences enclosed in curly braces embedded
in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
Args:
text: string to convert to ... | Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
The text can optionally have ARPAbet sequences enclosed in curly braces embedded
in it. For example, "Turn left on {HH AW1 S S T AH0 N} Street."
Args:
text: string to convert to a sequence
cleaner_names: nam... | text_to_sequence | python | openvinotoolkit/open_model_zoo | demos/text_to_speech_demo/python/utils/text_preprocessing.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/text_to_speech_demo/python/utils/text_preprocessing.py | Apache-2.0 |
def fold_with_overlap(x, target, overlap):
''' Fold the tensor with overlap for quick batched inference.
Overlap will be used for crossfading in xfade_and_unfold()
Args:
x (tensor) : Upsampled conditioning features.
shape=(1, timesteps, features)
target (int) ... | Fold the tensor with overlap for quick batched inference.
Overlap will be used for crossfading in xfade_and_unfold()
Args:
x (tensor) : Upsampled conditioning features.
shape=(1, timesteps, features)
target (int) : Target timesteps for each index of batch
... | fold_with_overlap | python | openvinotoolkit/open_model_zoo | demos/text_to_speech_demo/python/utils/wav_processing.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/text_to_speech_demo/python/utils/wav_processing.py | Apache-2.0 |
def xfade_and_unfold(y, overlap):
''' Applies a crossfade and unfolds into a 1d array.
Args:
y (ndarry) : Batched sequences of audio samples
shape=(num_folds, target + 2 * overlap)
dtype=float
overlap (int) : Timesteps for both xfade and rnn wa... | Applies a crossfade and unfolds into a 1d array.
Args:
y (ndarry) : Batched sequences of audio samples
shape=(num_folds, target + 2 * overlap)
dtype=float
overlap (int) : Timesteps for both xfade and rnn warmup
Return:
(ndarry) : audi... | xfade_and_unfold | python | openvinotoolkit/open_model_zoo | demos/text_to_speech_demo/python/utils/wav_processing.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/text_to_speech_demo/python/utils/wav_processing.py | Apache-2.0 |
def infer_from_discretized_mix_logistic(params):
"""
Sample from discretized mixture of logistic distributions
Args:
params (Tensor): B x C x T, [C/3,C/3,C/3] = [logit probs, means, log scales]
Returns:
Tensor: sample in range of [-1, 1].
"""
log_scale_min = float(np.log(1e-14))
... |
Sample from discretized mixture of logistic distributions
Args:
params (Tensor): B x C x T, [C/3,C/3,C/3] = [logit probs, means, log scales]
Returns:
Tensor: sample in range of [-1, 1].
| infer_from_discretized_mix_logistic | python | openvinotoolkit/open_model_zoo | demos/text_to_speech_demo/python/utils/wav_processing.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/text_to_speech_demo/python/utils/wav_processing.py | Apache-2.0 |
def __call__(self, inputs):
""" Main forecasting method.
Arguments:
inputs (np.array): input data.
Returns:
preds (dict): predicted quantiles.
"""
self.infer_request.infer(inputs={self.input_tensor_name: inputs})
preds = self.infer_request.get_te... | Main forecasting method.
Arguments:
inputs (np.array): input data.
Returns:
preds (dict): predicted quantiles.
| __call__ | python | openvinotoolkit/open_model_zoo | demos/time_series_forecasting_demo/python/time_series_forecasting_demo.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/time_series_forecasting_demo/python/time_series_forecasting_demo.py | Apache-2.0 |
def __getitem__(self, idx):
""" Get sample.
Arguments:
idx (idx): idx of the sample.
Returns:
sample: get annotation object.
"""
try:
return self.data[idx]
except KeyError:
raise IndexError | Get sample.
Arguments:
idx (idx): idx of the sample.
Returns:
sample: get annotation object.
| __getitem__ | python | openvinotoolkit/open_model_zoo | demos/time_series_forecasting_demo/python/time_series_forecasting_demo.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/time_series_forecasting_demo/python/time_series_forecasting_demo.py | Apache-2.0 |
def update_curves(self, preds, gt):
""" Update curves.
Arguments:
preds (dict<np.array>): dict of predicted quantiles.
gt (Annotation): annotation object.
"""
self.curves["gt"].set_ydata(gt.inorm(gt.outputs.flatten()))
for key, val in preds.items():
... | Update curves.
Arguments:
preds (dict<np.array>): dict of predicted quantiles.
gt (Annotation): annotation object.
| update_curves | python | openvinotoolkit/open_model_zoo | demos/time_series_forecasting_demo/python/time_series_forecasting_demo.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/demos/time_series_forecasting_demo/python/time_series_forecasting_demo.py | Apache-2.0 |
def main():
"""
Preprocess GNHK dataset into text line images
"""
os.makedirs(os.path.dirname(TEST_FOLDER), exist_ok=True)
open(TEST_TXT, 'w').close() # clear the file
images = glob.glob('eng*.jpg')
for img_idx, image in enumerate(images):
img_id = Path(image).stem
print(img... |
Preprocess GNHK dataset into text line images
| main | python | openvinotoolkit/open_model_zoo | models/intel/handwritten-english-recognition-0001/preprocess_gnhk.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/intel/handwritten-english-recognition-0001/preprocess_gnhk.py | Apache-2.0 |
def infer(self, m_t, a1_t, a2_t, a3_t, a4_t, h1, h2, x):
"""
implement one step forward pass from WaveRNN fatchord version
:return:
"""
x = torch.cat([x, m_t, a1_t], dim=1)
x = self.model.I(x)
h1 = self.rnn1(x, h1)
x = x + h1
inp = torch.cat([x, a... |
implement one step forward pass from WaveRNN fatchord version
:return:
| infer | python | openvinotoolkit/open_model_zoo | models/public/wavernn/wavernn_to_onnx.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/wavernn/wavernn_to_onnx.py | Apache-2.0 |
def forward(self, images):
"""
Args:
inputs[Tensor]: [BxCxHxW]
Returns:
outputs[Tensor]: [Bx(H*W*A=6)x(K(classes)+4(boxes))]
"""
h, w = images.shape[2:]
features = self.backbone(images)
features = [features[f] for f in self.in_features]
... |
Args:
inputs[Tensor]: [BxCxHxW]
Returns:
outputs[Tensor]: [Bx(H*W*A=6)x(K(classes)+4(boxes))]
| forward | python | openvinotoolkit/open_model_zoo | models/public/yolof/model.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/yolof/model.py | Apache-2.0 |
def register(linter):
"""
Required method to auto register this checker.
"""
linter.register_checker(BackslashChecker(linter))
linter.register_checker(AbsoluteImportsChecker(linter))
linter.register_checker(StringFormatChecker(linter))
linter.register_checker(BadFunctionChecker(linter)) |
Required method to auto register this checker.
| register | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/pylint_checkers.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/pylint_checkers.py | Apache-2.0 |
def get_opts(options):
"""
Args:
options: options object.
Returns:
args (tuple): positional options.
kwargs (map): keyword arguments.
"""
if isinstance(options, tuple):
if len(options) == 2 and isinstance(options[-1], dict):
args, kwargs = options
... |
Args:
options: options object.
Returns:
args (tuple): positional options.
kwargs (map): keyword arguments.
| get_opts | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/dependency.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/dependency.py | Apache-2.0 |
def process(self, raw, identifiers, frame_meta):
"""
Args:
identifiers: list of input data identifiers
raw: output of model
frame_meta: list of meta information about each frame
Returns:
list of ContainerPrediction objects
"""
r... |
Args:
identifiers: list of input data identifiers
raw: output of model
frame_meta: list of meta information about each frame
Returns:
list of ContainerPrediction objects
| process | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/attributes_recognition.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/attributes_recognition.py | Apache-2.0 |
def process(self, raw, identifiers, frame_meta):
"""
Args:
identifiers: list of input data identifiers
raw: output of model
frame_meta: list of meta information about each frame
Returns:
list of ContainerPrediction objects
"""
resul... |
Args:
identifiers: list of input data identifiers
raw: output of model
frame_meta: list of meta information about each frame
Returns:
list of ContainerPrediction objects
| process | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/attribute_classification.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/attribute_classification.py | Apache-2.0 |
def _ctc_decoder_prediction(prediction, labels):
"""
Decodes a sequence of labels to words
"""
blank_id = len(labels)
hypotheses = []
# CTC decoding procedure
for batch_elem in prediction:
decoded_prediction = []
previous = blank_id
... |
Decodes a sequence of labels to words
| _ctc_decoder_prediction | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | Apache-2.0 |
def _extract_predictions(self, outputs_list, meta):
"""
Extract the value of network's output identified by the provided name.
The result is returned as list(numpy.ndarray), arrays are to be
concatenated along the first axis.
"""
is_multi_infer = meta[-1].get('multi_infer... |
Extract the value of network's output identified by the provided name.
The result is returned as list(numpy.ndarray), arrays are to be
concatenated along the first axis.
| _extract_predictions | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | Apache-2.0 |
def __init__(self, alphabet, beam_width, sep=' ', lm=None, start_with_bos=True,
alpha=1.0, beta=0.0, oov_score=None, allowed_prefixes=None):
"""
Some args:
alphabet (list of str), alphabet, including the blank symbol as ''
sep (str), word separator char. Use '' for c... |
Some args:
alphabet (list of str), alphabet, including the blank symbol as ''
sep (str), word separator char. Use '' for character-based LM without
word separator (e.g. Chinese).
| __init__ | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | Apache-2.0 |
def finalize_scores(self):
"""
This essentially adds a word space at the end to score the last word.
The resulting score is in candidates[...].logp_blank, the higher the better.
"""
if self.candidates_finalized:
return
self.candidates_finalized = True
... |
This essentially adds a word space at the end to score the last word.
The resulting score is in candidates[...].logp_blank, the higher the better.
| finalize_scores | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | Apache-2.0 |
def _get_extended_candidate(self, old_cand, new_char, new_char_index):
"""
Find existing or create new CtcBeamSearchCandidate with text
extended with the given character.
"""
new_text_state, new_word = old_cand.text_state.extended(new_char, new_char_index, sep=self.sep)
i... |
Find existing or create new CtcBeamSearchCandidate with text
extended with the given character.
| _get_extended_candidate | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | Apache-2.0 |
def _prune_candidates(self, beam_width=None):
"""
Keep top beam_width candidates by score. Candidates are taken from
self.candidates, and updated in place. The score is computed from
cand.logp_blank and cand.logp_non_blank. Candidates are not sorted.
Side effects:
... |
Keep top beam_width candidates by score. Candidates are taken from
self.candidates, and updated in place. The score is computed from
cand.logp_blank and cand.logp_non_blank. Candidates are not sorted.
Side effects:
self.candidates is updated
| _prune_candidates | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | Apache-2.0 |
def __init__(self, other=None):
"""
Without args: make an uninitialized CtcBeamSearchCandidate.
With args: shallow copy words and lm_state to a new CtcBeamSearchCandidate,
set logp_* fields to -np.inf.
"""
if other is None:
return
self.text_state = o... |
Without args: make an uninitialized CtcBeamSearchCandidate.
With args: shallow copy words and lm_state to a new CtcBeamSearchCandidate,
set logp_* fields to -np.inf.
| __init__ | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | Apache-2.0 |
def empty(lm=None, start_with_bos=True):
"""
Create an empty initial candidate
Args:
with_lm (bool), if LM state should be used
start_with_bos (bool), True = initialize LM state with start-of-sentence
False = initialize LM state with empty context (anywhere in the ... |
Create an empty initial candidate
Args:
with_lm (bool), if LM state should be used
start_with_bos (bool), True = initialize LM state with start-of-sentence
False = initialize LM state with empty context (anywhere in the sentence)
| empty | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | Apache-2.0 |
def extended(self, new_char, new_char_index, sep=' '):
"""
Return a copy of self extended with a new character. Use sep='' for
character-based LMs.
Return pair:
TextState, new state with the string value extended by new_char
str or None, the new word if word space e... |
Return a copy of self extended with a new character. Use sep='' for
character-based LMs.
Return pair:
TextState, new state with the string value extended by new_char
str or None, the new word if word space encountered, or None otherwise
| extended | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | Apache-2.0 |
def log_sum_exp(a, b):
"""
Logically equivalent to:
return math.log(math.exp(a) + math.exp(b))
but avoids the possible over/underflow in math.exp().
"""
# Checking for -inf is here to only silence runtime warning.
if a == -np.inf:
return b
if b == -np.inf:
return a
... |
Logically equivalent to:
return math.log(math.exp(a) + math.exp(b))
but avoids the possible over/underflow in math.exp().
| log_sum_exp | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | Apache-2.0 |
def read_vocabulary_prefixes(lm_filename, vocab_offset, vocab_length):
"""
Extract the set of all possible prefixes of vocabulary words from LM file
in kenlm binary format.
Args:
lm_filename (pathlib.Path)
vocab_offset (int)
vocab_length (int or None), None defaults to spanning until th... |
Extract the set of all possible prefixes of vocabulary words from LM file
in kenlm binary format.
Args:
lm_filename (pathlib.Path)
vocab_offset (int)
vocab_length (int or None), None defaults to spanning until the end of file
Return:
set of str with all possible prefixes of th... | read_vocabulary_prefixes | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/audio_recognition.py | Apache-2.0 |
def process(self, raw, identifiers, frame_meta):
"""
Args:
identifiers: list of input data identifiers
raw: output of model
frame_meta: list of meta information about each frame
Returns:
list of ClassificationPrediction objects
"""
... |
Args:
identifiers: list of input data identifiers
raw: output of model
frame_meta: list of meta information about each frame
Returns:
list of ClassificationPrediction objects
| process | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/classification.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/classification.py | Apache-2.0 |
def get_proposals(self, rpn_cls_prob_reshape, bbox_deltas, im_info, _feat_stride=16):
"""
Parameters
rpn_cls_prob_reshape: (H , W , Ax2) probabilities for predicted regions
bbox_deltas: (H , W , Ax4), predicted regions
im_info: a list of [image_height, image_width, scale_ratios]
... |
Parameters
rpn_cls_prob_reshape: (H , W , Ax2) probabilities for predicted regions
bbox_deltas: (H , W , Ax4), predicted regions
im_info: a list of [image_height, image_width, scale_ratios]
_feat_stride: the downsampling ratio of feature map to the original input image
A... | get_proposals | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/ctpn.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/ctpn.py | Apache-2.0 |
def _filter_boxes(boxes, min_size):
"""Remove all boxes with any side smaller than min_size."""
ws = boxes[:, 2] - boxes[:, 0] + 1
hs = boxes[:, 3] - boxes[:, 1] + 1
keep = np.where((ws >= min_size) & (hs >= min_size))[0]
return keep | Remove all boxes with any side smaller than min_size. | _filter_boxes | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/ctpn.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/ctpn.py | Apache-2.0 |
def process(self, raw, identifiers=None, frame_meta=None):
"""
Args:
identifiers: list of input data identifiers
raw: output of model
Returns:
list of DetectionPrediction objects
"""
prediction_batch = self._extract_predictions(raw, frame_meta)... |
Args:
identifiers: list of input data identifiers
raw: output of model
Returns:
list of DetectionPrediction objects
| process | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/detection.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/detection.py | Apache-2.0 |
def process(self, raw, identifiers, frame_meta):
"""
Args:
identifiers: list of input data identifiers
raw: output of model
frame_meta: image metadata
Returns:
list of DetectionPrediction objects
"""
predictions = self._extract_pred... |
Args:
identifiers: list of input data identifiers
raw: output of model
frame_meta: image metadata
Returns:
list of DetectionPrediction objects
| process | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/detection.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/detection.py | Apache-2.0 |
def process(self, raw, identifiers, frame_meta):
"""
Args:
identifiers: list of input data identifiers
raw: output of model
frame_meta: images metadata
Returns:
list of DetectionPrediction objects
"""
raw_outputs = self._extract_pre... |
Args:
identifiers: list of input data identifiers
raw: output of model
frame_meta: images metadata
Returns:
list of DetectionPrediction objects
| process | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/detection.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/detection.py | Apache-2.0 |
def generate_output_layer_info(self):
"""
Generates face detection layer information,
which is referenced in process function
"""
output_layers = []
for i in range(len(self.layer_info['layer_names'])):
start = 1.5
anchor_size = self.layer_info['an... |
Generates face detection layer information,
which is referenced in process function
| generate_output_layer_info | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/detection.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/detection.py | Apache-2.0 |
def process(self, raw, identifiers, frame_meta):
"""
Args:
raw: output of model.
identifiers: list of input data identifiers.
frame_meta: metadata for frame.
Returns:
list of HitRatioPrediction objects.
"""
raw_prediction = self._e... |
Args:
raw: output of model.
identifiers: list of input data identifiers.
frame_meta: metadata for frame.
Returns:
list of HitRatioPrediction objects.
| process | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/hit_ratio.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/hit_ratio.py | Apache-2.0 |
def is_result_valid(result: dict):
'''
this method check whether values of result dict have the same length
'''
list_len = -1
for val in result.values():
if list_len < 0:
list_len = len(val)
else:
if list_len != len(val):
... |
this method check whether values of result dict have the same length
| is_result_valid | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/mixed_adapter.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/mixed_adapter.py | Apache-2.0 |
def nms(boxes, threshold, overlap_type):
"""
Args:
boxes: [:,0:5]
threshold: 0.5 like
overlap_type: 'Min' or 'Union'
Returns:
indexes of passed boxes
"""
if boxes.shape[0] == 0:
return np.array([])
x1 = boxes[:, 0]
... |
Args:
boxes: [:,0:5]
threshold: 0.5 like
overlap_type: 'Min' or 'Union'
Returns:
indexes of passed boxes
| nms | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/mtcnn.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/mtcnn.py | Apache-2.0 |
def process(self, raw, identifiers, frame_meta):
"""
Args:
identifiers: list of input data identifiers
raw: output of model
frame_meta: list of meta information about each frame
Returns:
list of RegressionPrediction objects
"""
pred... |
Args:
identifiers: list of input data identifiers
raw: output of model
frame_meta: list of meta information about each frame
Returns:
list of RegressionPrediction objects
| process | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/regression.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/regression.py | Apache-2.0 |
def process(self, raw, identifiers, frame_meta):
"""
Args:
identifiers: list of input data identifiers
raw: output of model
Returns:
list of ReIdentificationPrediction objects
"""
self.select_output_blob(raw if not isinstance(raw, list) else ra... |
Args:
identifiers: list of input data identifiers
raw: output of model
Returns:
list of ReIdentificationPrediction objects
| process | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/reidentification.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/reidentification.py | Apache-2.0 |
def delta2box(deltas, anchors, size, stride):
'Convert deltas from anchors to boxes'
anchors_wh = anchors[:, 2:] - anchors[:, :2] + 1
ctr = anchors[:, :2] + 0.5 * anchors_wh
pred_ctr = deltas[:, :2] * anchors_wh + ctr
pred_wh = np.exp(deltas[:, 2:]) * anchors... | Convert deltas from anchors to boxes | delta2box | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/retinanet.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/retinanet.py | Apache-2.0 |
def _generate_anchors(base_size=16):
"""
Generate anchor (reference) windows by enumerating aspect ratios X
scales w.r.t. a reference window.
"""
num_anchors = len(self.ratios) * len(self.scales)
# initialize output anchors
anchors = np... |
Generate anchor (reference) windows by enumerating aspect ratios X
scales w.r.t. a reference window.
| _generate_anchors | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/retinanet.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/retinanet.py | Apache-2.0 |
def process(self, raw, identifiers, frame_meta):
"""
Args:
identifiers: list of input data identifiers
raw: output of model which is ndarray of shape (batch, det_count, 6),
each detection is defined by 6 values: class_id, prob, x_min, y_min, x_max, y_max
... |
Args:
identifiers: list of input data identifiers
raw: output of model which is ndarray of shape (batch, det_count, 6),
each detection is defined by 6 values: class_id, prob, x_min, y_min, x_max, y_max
Returns:
list of DetectionPrediction objects
... | process | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/ssd.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/ssd.py | Apache-2.0 |
def to_boxes(self, image_shape, segm_pos_scores, link_pos_scores):
""" Returns boxes for each image in batch. """
mask = self.decode_image(segm_pos_scores, link_pos_scores)
mask = np.asarray(mask, np.int32)[...]
bboxes = self.mask_to_bboxes(mask, image_shape)
return bboxes | Returns boxes for each image in batch. | to_boxes | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/text_detection.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/text_detection.py | Apache-2.0 |
def get_neighbours(x_coord, y_coord):
""" Returns 8-point neighbourhood of given point. """
return [
(x_coord - 1, y_coord - 1), (x_coord, y_coord - 1), (x_coord + 1, y_coord - 1),
(x_coord - 1, y_coord), (x_coord + 1, y_coord),
(x_coord - 1, y_co... | Returns 8-point neighbourhood of given point. | get_neighbours | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/text_detection.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/text_detection.py | Apache-2.0 |
def nms_locality(polys, thres=0.3):
'''
locality aware nms of EAST
:param polys: a N*9 numpy array. first 8 coordinates, then prob
:return: boxes after nms
'''
def intersection(g, p):
g = Polygon(g[:8].reshape((4, 2)))
p = Polygon(p[:8].reshape((4,... |
locality aware nms of EAST
:param polys: a N*9 numpy array. first 8 coordinates, then prob
:return: boxes after nms
| nms_locality | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/text_detection.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/text_detection.py | Apache-2.0 |
def decode(probabilities, beam_size=10, blank_id=None):
"""
Decode given output probabilities to sequence of labels.
Arguments:
probabilities: The output log probabilities for each time step.
Should be an array of shape (time x output dim).
beam_size (int): S... |
Decode given output probabilities to sequence of labels.
Arguments:
probabilities: The output log probabilities for each time step.
Should be an array of shape (time x output dim).
beam_size (int): Size of the beam to use during decoding.
blank_id (int):... | decode | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/text_recognition.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/text_recognition.py | Apache-2.0 |
def decode(prob_index, blank_id):
"""
Decode given output probabilities to sequence of labels.
Arguments:
prob_index: The max index along the probabilities dimension.
blank_id (int): Index of the CTC blank label.
Returns the output label sequence.
"""
... |
Decode given output probabilities to sequence of labels.
Arguments:
prob_index: The max index along the probabilities dimension.
blank_id (int): Index of the CTC blank label.
Returns the output label sequence.
| decode | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/text_recognition.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/text_recognition.py | Apache-2.0 |
def sanitize_coordinates(_x1, _x2, img_size, padding=0):
"""
Sanitizes the input coordinates so that x1 < x2, x1 != x2, x1 >= 0, and x2 <= image_size.
Also converts from relative to absolute coordinates.
"""
_x1 = _x1 * img_size
_x2 = _x2 * img_size
x1 = np.clip(_x1 - padding, 0, img_size)
... |
Sanitizes the input coordinates so that x1 < x2, x1 != x2, x1 >= 0, and x2 <= image_size.
Also converts from relative to absolute coordinates.
| sanitize_coordinates | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/yolact.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/yolact.py | Apache-2.0 |
def process(self, raw, identifiers, frame_meta):
"""
Args:
identifiers: list of input data identifiers
raw: output of model
frame_meta: meta info about prediction
Returns:
list of DetectionPrediction objects
"""
prediction = self._... |
Args:
identifiers: list of input data identifiers
raw: output of model
frame_meta: meta info about prediction
Returns:
list of DetectionPrediction objects
| process | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/yolo.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/yolo.py | Apache-2.0 |
def permute_to_HWA_K(tensor, K):
"""
Transpose/reshape a tensor from ((A x K), H, W) to ((HxWxA), K)
"""
assert tensor.ndim == 3, tensor.shape
_, H, W = tensor.shape
tensor = tensor.reshape(-1, K, H, W)
tensor = tensor.transpose(2, 3, 0, 1)
tensor = tensor.reshape(-1, K)
return tenso... |
Transpose/reshape a tensor from ((A x K), H, W) to ((HxWxA), K)
| permute_to_HWA_K | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/yolo.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/yolo.py | Apache-2.0 |
def process(self, raw, identifiers, frame_meta):
"""
Args:
identifiers: list of input data identifiers
raw: output of model
frame_meta: meta info about data processing
Returns:
list of DetectionPrediction objects
"""
predictions = s... |
Args:
identifiers: list of input data identifiers
raw: output of model
frame_meta: meta info about data processing
Returns:
list of DetectionPrediction objects
| process | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/yolo.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/yolo.py | Apache-2.0 |
def parameters(cls):
"""
parameters declares additional configuration parameters for adapter.
It is classmethod for extraction parameters without object creation
"""
# get parent parameters if any
params = super().parameters()
# update configuration for specific a... |
parameters declares additional configuration parameters for adapter.
It is classmethod for extraction parameters without object creation
| parameters | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/yolo.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/yolo.py | Apache-2.0 |
def configure(self):
"""
configure method used for parsing and initialization configuration parameters.
It called in __init__
"""
self.conf_threshold = self.get_value_from_config("conf_threshold")
self.multi_label = self.get_value_from_config("multi_label") |
configure method used for parsing and initialization configuration parameters.
It called in __init__
| configure | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/yolo.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/yolo.py | Apache-2.0 |
def process(self, raw, identifiers, frame_meta):
"""
the main processing method for adapters.
Parameters:
raw: dict or list of dicts with raw model outputs from launcher
identifiers: unique identifier which input data this prediction belongs (usually file name)
fram... |
the main processing method for adapters.
Parameters:
raw: dict or list of dicts with raw model outputs from launcher
identifiers: unique identifier which input data this prediction belongs (usually file name)
frame_meta: list of metadata for input data per input frame in b... | process | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/adapters/yolo.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/adapters/yolo.py | Apache-2.0 |
def convert(self, check_content=False, progress_callback=None, progress_interval=100, **kwargs):
"""Reads data from disk and returns dataset in converted for AC format
Args:
check_content (bool, optional): Check if content is valid. Defaults to False.
progress_callback (bool, op... | Reads data from disk and returns dataset in converted for AC format
Args:
check_content (bool, optional): Check if content is valid. Defaults to False.
progress_callback (bool, optional): Display progress. Defaults to None.
progress_interval (int, optional): Units to display... | convert | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/annotation_converters/antispoofing.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/annotation_converters/antispoofing.py | Apache-2.0 |
def generate_annotations(self):
''' read json file with images paths and return
list of the items'''
annotation_store = []
dataset_annotations = read_json(self.annotations)
for index in dataset_annotations:
path = dataset_annotations[index]['path']
target_... | read json file with images paths and return
list of the items | generate_annotations | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/annotation_converters/antispoofing.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/annotation_converters/antispoofing.py | Apache-2.0 |
def configure(self):
"""
This method is responsible for obtaining the necessary parameters
for converting from the command line or config.
"""
self.data_batch_file = self.get_value_from_config('data_batch_file')
self.batch_meta_file = self.get_value_from_config('batch_met... |
This method is responsible for obtaining the necessary parameters
for converting from the command line or config.
| configure | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/annotation_converters/cifar.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/annotation_converters/cifar.py | Apache-2.0 |
def convert(self, check_content=False, progress_callback=None, progress_interval=100, **kwargs):
"""
This method is executed automatically when convert.py is started.
All arguments are automatically got from command line arguments or config file in method configure
Returns:
... |
This method is executed automatically when convert.py is started.
All arguments are automatically got from command line arguments or config file in method configure
Returns:
annotations: list of annotation representation objects.
meta: dictionary with additional dataset... | convert | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/annotation_converters/cifar.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/annotation_converters/cifar.py | Apache-2.0 |
def get_single_col_by_input_type(input_type, column_definition):
"""Returns name of single column.
Args:
input_type: Input type of column to extract
column_definition: Column definition list for experiment
"""
cols = [tup[0] for tup in column_definition if tup[2] == input_type]
if len(cols... | Returns name of single column.
Args:
input_type: Input type of column to extract
column_definition: Column definition list for experiment
| get_single_col_by_input_type | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/annotation_converters/electricity_time_series_forecasting.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/annotation_converters/electricity_time_series_forecasting.py | Apache-2.0 |
def extract_cols_from_data_type(data_type, column_definition,
excluded_input_types):
"""Extracts the names of columns that correspond to a define data_type.
Args:
data_type: DataType of columns to extract.
column_definition: Column definition to use.
excluded_input_ty... | Extracts the names of columns that correspond to a define data_type.
Args:
data_type: DataType of columns to extract.
column_definition: Column definition to use.
excluded_input_types: Set of input types to exclude
Returns:
List of names for columns with data type specified.
| extract_cols_from_data_type | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/annotation_converters/electricity_time_series_forecasting.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/annotation_converters/electricity_time_series_forecasting.py | Apache-2.0 |
def split_data(self, df, valid_boundary=1315, test_boundary=1339):
"""Splits data frame into training-validation-test data frames.
This also calibrates scaling object, and transforms data for each split.
Args:
df: Source data frame to split.
valid_boundary: Starting year for ... | Splits data frame into training-validation-test data frames.
This also calibrates scaling object, and transforms data for each split.
Args:
df: Source data frame to split.
valid_boundary: Starting year for validation data
test_boundary: Starting year for test data
R... | split_data | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/annotation_converters/electricity_time_series_forecasting.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/annotation_converters/electricity_time_series_forecasting.py | Apache-2.0 |
def set_scalers(self, df):
"""Calibrates scalers using the data supplied.
Args:
df: Data to use to calibrate scalers.
"""
print_info('Setting scalers with training data...')
column_definitions = self.getcolumn_definition()
id_column = get_single_col_by_... | Calibrates scalers using the data supplied.
Args:
df: Data to use to calibrate scalers.
| set_scalers | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/annotation_converters/electricity_time_series_forecasting.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/annotation_converters/electricity_time_series_forecasting.py | Apache-2.0 |
def transform_inputs(self, df):
"""Performs feature transformations.
This includes both feature engineering, preprocessing and normalisation.
Args:
df: Data frame to transform.
Returns:
Transformed data frame.
"""
if self.real_scalers is None and self... | Performs feature transformations.
This includes both feature engineering, preprocessing and normalisation.
Args:
df: Data frame to transform.
Returns:
Transformed data frame.
| transform_inputs | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/annotation_converters/electricity_time_series_forecasting.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/annotation_converters/electricity_time_series_forecasting.py | Apache-2.0 |
def getcolumn_definition(self):
""""Returns formatted column definition in order expected by the TFT."""
column_definition = self.column_definition
# Sanity checks first.
# Ensure only one ID and time column exist
def _check_single_column(input_type):
length = len(... | "Returns formatted column definition in order expected by the TFT. | getcolumn_definition | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/annotation_converters/electricity_time_series_forecasting.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/annotation_converters/electricity_time_series_forecasting.py | Apache-2.0 |
def read_vocab(vocab_path):
"""Reads vocab file from disk as .pkl or .json
Args:
vocab_path (str): path to vocab file
Raises:
ValueError: If wrong extension of the file
Returns:
Vocab: Vocab object with sign2id and id2sign dictinaries
"""
if vocab_path.suffix == '.pkl'... | Reads vocab file from disk as .pkl or .json
Args:
vocab_path (str): path to vocab file
Raises:
ValueError: If wrong extension of the file
Returns:
Vocab: Vocab object with sign2id and id2sign dictinaries
| read_vocab | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/annotation_converters/im2latex.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/annotation_converters/im2latex.py | Apache-2.0 |
def convert(self, check_content=False, progress_callback=None, progress_interval=100, **kwargs):
"""
This method is executed automatically when convert.py is started.
All arguments are automatically got from command line arguments or config file in method configure
Returns:
... |
This method is executed automatically when convert.py is started.
All arguments are automatically got from command line arguments or config file in method configure
Returns:
annotations: list of annotation representation objects.
meta: dictionary with additional dataset... | convert | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/annotation_converters/mrlEyes_2018_01.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/annotation_converters/mrlEyes_2018_01.py | Apache-2.0 |
def all_special_ids(self):
""" List the vocabulary indices of the special tokens ('<unk>', '<cls>'...) mapped to
class attributes (cls_token, unk_token...).
"""
all_toks = self.all_special_tokens
all_ids = self.convert_tokens_to_ids(all_toks)
return all_ids | List the vocabulary indices of the special tokens ('<unk>', '<cls>'...) mapped to
class attributes (cls_token, unk_token...).
| all_special_ids | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/annotation_converters/_nlp_common.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/annotation_converters/_nlp_common.py | Apache-2.0 |
def all_special_tokens(self):
""" List all the special tokens ('<unk>', '<cls>'...) mapped to class attributes
(cls_token, unk_token...).
"""
all_toks = []
set_attr = self.special_tokens_map
for attr_value in set_attr.values():
all_toks = all_toks + (list(... | List all the special tokens ('<unk>', '<cls>'...) mapped to class attributes
(cls_token, unk_token...).
| all_special_tokens | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/annotation_converters/_nlp_common.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/annotation_converters/_nlp_common.py | Apache-2.0 |
def convert_ids_to_tokens(self, ids, skip_special_tokens=False):
""" Converts a single index or a sequence of indices (integers) in a token "
(resp.) a sequence of tokens (str/unicode), using the vocabulary and added tokens.
Args:
skip_special_tokens: Don't decode special... | Converts a single index or a sequence of indices (integers) in a token "
(resp.) a sequence of tokens (str/unicode), using the vocabulary and added tokens.
Args:
skip_special_tokens: Don't decode special tokens (self.all_special_tokens). Default: False
| convert_ids_to_tokens | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/annotation_converters/_nlp_common.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/annotation_converters/_nlp_common.py | Apache-2.0 |
def truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tokens from each, since i... | Truncates a sequence pair in place to the maximum length. | truncate_seq_pair | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/annotation_converters/_nlp_common.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/annotation_converters/_nlp_common.py | Apache-2.0 |
def merge(arguments):
"""
Args:
arguments: command-line arguments.
Returns:
dictionary containing configuration.
"""
global_config, local_config = ConfigReader._read_configs(arguments)
if not local_config:
raise ConfigError('Missing lo... |
Args:
arguments: command-line arguments.
Returns:
dictionary containing configuration.
| merge | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/config/config_reader.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/config/config_reader.py | Apache-2.0 |
def layer_norm(input_array, gamma, beta, epsilon=1e-5):
"""
Input array layer normalization (aka torch.nn.LayerNorm).
"""
mean = np.mean(input_array, axis=-1, keepdims=True)
std = np.std(input_array, axis=-1, keepdims=True)
normalized = (input_array - mean) / np.sqrt(std ... |
Input array layer normalization (aka torch.nn.LayerNorm).
| layer_norm | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/evaluators/custom_evaluators/openvino_clip_evaluator.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/evaluators/custom_evaluators/openvino_clip_evaluator.py | Apache-2.0 |
def normalize(input_array, p=2, axis=-1, epsilon=1e-12):
"""
Input array normalization using the p-norm (aka torch.nn.functional.normalize).
"""
norm = np.linalg.norm(input_array, ord=p, axis=axis, keepdims=True)
norm = np.maximum(norm, epsilon)
normalized = input_array /... |
Input array normalization using the p-norm (aka torch.nn.functional.normalize).
| normalize | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/evaluators/custom_evaluators/openvino_clip_evaluator.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/evaluators/custom_evaluators/openvino_clip_evaluator.py | Apache-2.0 |
def get_w_embedding(w, embedding_dim=512, dtype=torch.float32):
"""
see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
Args:
timesteps: torch.Tensor: generate embedding vectors at these timesteps
embedding_dim: int: dimensio... |
see https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298
Args:
timesteps: torch.Tensor: generate embedding vectors at these timesteps
embedding_dim: int: dimension of the embeddings to generate
dtype: data type of the generated e... | get_w_embedding | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/evaluators/custom_evaluators/stable_diffusion_evaluator.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/evaluators/custom_evaluators/stable_diffusion_evaluator.py | Apache-2.0 |
def predict(self, inputs, metadata=None, **kwargs):
"""
Args:
inputs: dictionary where keys are input layers names and values are data for them.
metadata: metadata of input representations
Returns:
raw data from network.
"""
results = []
... |
Args:
inputs: dictionary where keys are input layers names and values are data for them.
metadata: metadata of input representations
Returns:
raw data from network.
| predict | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/launcher/caffe_launcher.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/launcher/caffe_launcher.py | Apache-2.0 |
def validate(self, entry, field_uri=None, ie_core=None, fetch_only=False, validation_scheme=None):
"""
Validate that launcher entry meets all configuration structure requirements.
Args:
entry: launcher configuration file entry.
field_uri: id of launcher entry.
... |
Validate that launcher entry meets all configuration structure requirements.
Args:
entry: launcher configuration file entry.
field_uri: id of launcher entry.
ie_core: IECore instance.
fetch_only: only fetch possible error without raising
valid... | validate | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/launcher/dlsdk_launcher_config.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/launcher/dlsdk_launcher_config.py | Apache-2.0 |
def create_launcher(launcher_config, model_name='', delayed_model_loading=False, **kwargs):
"""
Args:
launcher_config: launcher configuration file entry.
model_name: evaluation model name
delayed_model_loading: allows postpone model loading to the launcher
Returns:
framework-... |
Args:
launcher_config: launcher configuration file entry.
model_name: evaluation model name
delayed_model_loading: allows postpone model loading to the launcher
Returns:
framework-specific launcher object.
| create_launcher | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/launcher/launcher.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/launcher/launcher.py | Apache-2.0 |
def set_tensors(self, dataset_input):
"""
Set input tensors:
:param dataset_input: dict {"input_layer_name": input_data}
:return: None
"""
for layer, data in dataset_input.items():
self._interpreter.set_tensor(
self._inputs[layer]['index'], dat... |
Set input tensors:
:param dataset_input: dict {"input_layer_name": input_data}
:return: None
| set_tensors | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/launcher/tf_lite_launcher.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/launcher/tf_lite_launcher.py | Apache-2.0 |
def bbox_match(annotation: List[DetectionAnnotation], prediction: List[DetectionPrediction], label, overlap_evaluator,
overlap_thresh=0.5, ignore_difficult=True, allow_multiple_matches_per_ignored=True,
include_boundaries=True, use_filtered_tp=False):
"""
Args:
annotation: ... |
Args:
annotation: ground truth bounding boxes.
prediction: predicted bounding boxes.
label: class for which bounding boxes are matched.
overlap_evaluator: evaluator of overlap.
overlap_thresh: bounding box IoU threshold.
ignore_difficult: ignores difficult bounding b... | bbox_match | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/metrics/detection.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/metrics/detection.py | Apache-2.0 |
def score_calc(self, annotations, predictions):
"""
Calculate IS metric for prediction labels.
"""
mean_probabilities_of_classes = np.expand_dims(np.mean(predictions, axis=0), axis=0)
KL_d = predictions * (np.log(predictions + self.eps) - np.log(mean_probabilities_of_classes + s... |
Calculate IS metric for prediction labels.
| score_calc | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/metrics/gan_metrics.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/metrics/gan_metrics.py | Apache-2.0 |
def score_calc(self, annotations, predictions):
"""
Calculate FID between feature vector of the real and generated images.
"""
if isinstance(sqrtm, UnsupportedPackage):
sqrtm.raise_error(self.__provider__)
assert annotations.shape[1] == predictions.shape[1], "Expecte... |
Calculate FID between feature vector of the real and generated images.
| score_calc | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/metrics/gan_metrics.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/metrics/gan_metrics.py | Apache-2.0 |
def preprocess_formula(formula):
"""Formula preprocessing
Args:
l (str): Input formula
Returns:
str: Preprocessed formula
"""
formula = formula.strip()
formula = formula.replace(r'\pmatrix', r'\mypmatrix')
formula = formula.replace(r'\matrix', r'\mymatrix')
# remove lea... | Formula preprocessing
Args:
l (str): Input formula
Returns:
str: Preprocessed formula
| preprocess_formula | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/metrics/im2latex_images_match.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/metrics/im2latex_images_match.py | Apache-2.0 |
def render_routine(line):
"""Function for rendering single formula
Args:
line (tuple): formula idx, formula string, path to store rendered image
"""
formula, file_idx, folder_path = line
output_path = folder_path / file_idx
pre_name = os.path.normcase(output_path).replace('/', '_').repl... | Function for rendering single formula
Args:
line (tuple): formula idx, formula string, path to store rendered image
| render_routine | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/metrics/im2latex_images_match.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/metrics/im2latex_images_match.py | Apache-2.0 |
def check_differ(diff):
"""Checks if difference of two images has a substring
of blue or red pixels with length >= MAX_PX_ROW_DIFF
In other words, if one image is shifted from another less then
MAX_PX_ROW_DIFF, images are equal
Args:
diff (np.array): Difference of two images
Returns:
... | Checks if difference of two images has a substring
of blue or red pixels with length >= MAX_PX_ROW_DIFF
In other words, if one image is shifted from another less then
MAX_PX_ROW_DIFF, images are equal
Args:
diff (np.array): Difference of two images
Returns:
bool: Images match
| check_differ | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/metrics/im2latex_images_match.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/metrics/im2latex_images_match.py | Apache-2.0 |
def preprocess(img):
"""Preprocessing of the image (transpostion and binarization)
Args:
im1 (np.array): input image
Returns:
np.array: preprocessed image
"""
# transpose for more convenient work
img = np.transpose(img)
img = (img >= 160).astype(np.uint8)
return img | Preprocessing of the image (transpostion and binarization)
Args:
im1 (np.array): input image
Returns:
np.array: preprocessed image
| preprocess | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/metrics/im2latex_images_match.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/metrics/im2latex_images_match.py | Apache-2.0 |
def match_images(params):
"""Function for single comparing two images
Args:
im1 (str): path to image1
im2 (str): path to image2
out_path (str, optional): Path to store diff of two images. Defaults to None.
max_pixel_column_diff (int, optional): Maximum number of black pixels in ... | Function for single comparing two images
Args:
im1 (str): path to image1
im2 (str): path to image2
out_path (str, optional): Path to store diff of two images. Defaults to None.
max_pixel_column_diff (int, optional): Maximum number of black pixels in column
to treat it as whi... | match_images | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/metrics/im2latex_images_match.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/metrics/im2latex_images_match.py | Apache-2.0 |
def compare_pics(self, images_dir):
"""
Function reads images and compares them, first, as is
second, deletes all whitespaces and compares again.
This step helps to escape the situation when two pics
are not equal because of different length of spaces
"""
total_nu... |
Function reads images and compares them, first, as is
second, deletes all whitespaces and compares again.
This step helps to escape the situation when two pics
are not equal because of different length of spaces
| compare_pics | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/metrics/im2latex_images_match.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/metrics/im2latex_images_match.py | Apache-2.0 |
def render_images(self, annotations, predictions, images_dir):
"""Runs render script to render images and store them into images_dir
Args:
annotations (str): Ground-truth formula
predictions (str): Predicted formula
"""
out_path_gold = Path(images_dir, 'images_go... | Runs render script to render images and store them into images_dir
Args:
annotations (str): Ground-truth formula
predictions (str): Predicted formula
| render_images | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/metrics/im2latex_images_match.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/metrics/im2latex_images_match.py | Apache-2.0 |
def __call__(self, line):
"""Common post-processing tokenizer for `13a` and `zh` tokenizers.
:param line: a segment to tokenize
:return: the tokenized line
"""
for (_re, repl) in self._re:
line = _re.sub(repl, line)
# no leading or trailing spaces
ret... | Common post-processing tokenizer for `13a` and `zh` tokenizers.
:param line: a segment to tokenize
:return: the tokenized line
| __call__ | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/metrics/machine_translation.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/metrics/machine_translation.py | Apache-2.0 |
def __call__(self, line):
"""Tokenizes an input line using a relatively minimal tokenization
that is however equivalent to mteval-v13a, used by WMT.
:param line: a segment to tokenize
:return: the tokenized line
"""
if self.lower_case:
line = line.lower()
... | Tokenizes an input line using a relatively minimal tokenization
that is however equivalent to mteval-v13a, used by WMT.
:param line: a segment to tokenize
:return: the tokenized line
| __call__ | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/metrics/machine_translation.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/metrics/machine_translation.py | Apache-2.0 |
def to_homogeneous(points):
"""Convert N-dimensional points to homogeneous coordinates.
Args:
points: torch.Tensor or numpy.ndarray with size (..., N).
Returns:
A torch.Tensor or numpy.ndarray with size (..., N+1).
"""
if isinstance(points, torch.Tensor):
pad = points.new_one... | Convert N-dimensional points to homogeneous coordinates.
Args:
points: torch.Tensor or numpy.ndarray with size (..., N).
Returns:
A torch.Tensor or numpy.ndarray with size (..., N+1).
| to_homogeneous | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/metrics/matches_homography.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/metrics/matches_homography.py | Apache-2.0 |
def validate_config(cls, config, fetch_only=False, uri_prefix=''):
"""
Validate that metric entry meets all configuration structure requirements.
"""
errors = []
if cls.__name__ == Metric.__name__:
metric_provider = config.get('type')
if not metric_provide... |
Validate that metric entry meets all configuration structure requirements.
| validate_config | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/metrics/metric.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/metrics/metric.py | Apache-2.0 |
def update_metrics_on_object(self, annotation, prediction):
"""
Updates metric value corresponding given annotation and prediction objects.
"""
metric_results = []
for metric in self.metrics:
metric_results.append(metric.metric_fn.submit(annotation, prediction))
... |
Updates metric value corresponding given annotation and prediction objects.
| update_metrics_on_object | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/metrics/metric_executor.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/metrics/metric_executor.py | Apache-2.0 |
def update_metrics_on_batch(self, batch_ids, annotation, prediction,
profile=False, deprocessed_annotation=None, deprocessed_prediction=None):
"""
Updates metric value corresponding given batch.
Args:
annotation: list of batch number of annotation obj... |
Updates metric value corresponding given batch.
Args:
annotation: list of batch number of annotation objects.
prediction: list of batch number of prediction objects.
| update_metrics_on_batch | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/metrics/metric_executor.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/metrics/metric_executor.py | Apache-2.0 |
def frame(x, frame_length=2048, hop_length=512):
"""Slice a data array into (overlapping) frames."""
n_frames = (x.shape[-1] - frame_length) // hop_length
strides = np.asarray(x.strides)
new_stride = np.prod(strides[strides > 0] // x.itemsize) * x.itemsize
s... | Slice a data array into (overlapping) frames. | frame | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/preprocessor/audio_preprocessing.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/preprocessor/audio_preprocessing.py | Apache-2.0 |
def _convert_samples_to_float32(samples):
"""Convert sample type to float32.
Audio sample type is usually integer or float-point.
Integers will be scaled to [-1, 1] in float32.
"""
float32_samples = samples.astype('float32')
if samples.dtype in np.sctypes['int']:
... | Convert sample type to float32.
Audio sample type is usually integer or float-point.
Integers will be scaled to [-1, 1] in float32.
| _convert_samples_to_float32 | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/preprocessor/audio_preprocessing_ext.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/preprocessor/audio_preprocessing_ext.py | Apache-2.0 |
def __init__(self, identifier, path_to_gt, gt_loader=GTLoader.OPENCV):
"""
Args:
identifier: object identifier (e.g. image name).
path_to_gt: path where ground truth images should be loaded from.
gt_loader: back-end, used to load ground truth images.
"""
... |
Args:
identifier: object identifier (e.g. image name).
path_to_gt: path where ground truth images should be loaded from.
gt_loader: back-end, used to load ground truth images.
| __init__ | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/representation/image_inpainting.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/representation/image_inpainting.py | Apache-2.0 |
def __init__(self, identifiers, prediction):
"""
Args:
identifiers: object identifier (e.g. image name).
prediction: array with shape (height, width) contained result image.
"""
super().__init__(identifiers)
self.value = prediction |
Args:
identifiers: object identifier (e.g. image name).
prediction: array with shape (height, width) contained result image.
| __init__ | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/representation/image_inpainting.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/representation/image_inpainting.py | Apache-2.0 |
def __init__(self, identifier, path_to_gt, gt_loader=GTLoader.PILLOW):
"""
Args:
identifier: object identifier (e.g. image name).
path_to_gt: path where gt image should be loaded from. The path is relative to data source.
gt_loader: back-end, used to load segmentation... |
Args:
identifier: object identifier (e.g. image name).
path_to_gt: path where gt image should be loaded from. The path is relative to data source.
gt_loader: back-end, used to load segmentation masks.
| __init__ | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/representation/image_processing.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/representation/image_processing.py | Apache-2.0 |
def __init__(self, identifier, path_to_mask, mask_loader=GTMaskLoader.PILLOW):
"""
Args:
identifier: object identifier (e.g. image name).
path_to_mask: path where segmentation mask should be loaded from. The path is relative to data source.
mask_loader: back-end, used... |
Args:
identifier: object identifier (e.g. image name).
path_to_mask: path where segmentation mask should be loaded from. The path is relative to data source.
mask_loader: back-end, used to load segmentation masks.
| __init__ | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/representation/segmentation_representation.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/representation/segmentation_representation.py | Apache-2.0 |
def __init__(self, identifiers, mask):
"""
Args:
identifiers: object identifier (e.g. image name).
mask: array with shape (n_classes, height, width) of probabilities at each location.
"""
super().__init__(identifiers)
self.mask = mask |
Args:
identifiers: object identifier (e.g. image name).
mask: array with shape (n_classes, height, width) of probabilities at each location.
| __init__ | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/accuracy_checker/representation/segmentation_representation.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/accuracy_checker/representation/segmentation_representation.py | Apache-2.0 |
def make_representation(bounding_boxes, is_ground_truth=False, score=None, meta=None):
"""
Args:
bounding_boxes: string or list of strings `score label x0 y0 x1 y1; label score x0 y0 x1 y1; ...`.
is_ground_truth: True if bbs are annotation boxes.
score: value in [0, 1], if not None, all ... |
Args:
bounding_boxes: string or list of strings `score label x0 y0 x1 y1; label score x0 y0 x1 y1; ...`.
is_ground_truth: True if bbs are annotation boxes.
score: value in [0, 1], if not None, all prediction boxes are considered with the given score.
meta: metadata for representatio... | make_representation | python | openvinotoolkit/open_model_zoo | tools/accuracy_checker/tests/common.py | https://github.com/openvinotoolkit/open_model_zoo/blob/master/tools/accuracy_checker/tests/common.py | Apache-2.0 |
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