Shuu12121/python-treesitter-filtered-datasetsV2

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 logerror(logger: logging.Logger = logging.root): """A decorator that wraps the passed in function and logs exceptions. Parameters ---------- logger: logging.Logger The logger to which to log the error. """ def log_wrapper(function): @functools.wraps(function) def wra...	A decorator that wraps the passed in function and logs exceptions. Parameters ---------- logger: logging.Logger The logger to which to log the error.	logerror	python	dmlc/gluon-nlp	src/gluonnlp/utils/misc.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py	Apache-2.0
def grouper(iterable, n, fillvalue=None): """Collect data into fixed-length chunks or blocks""" # grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx args = [iter(iterable)] * n return itertools.zip_longest(*args, fillvalue=fillvalue)	Collect data into fixed-length chunks or blocks	grouper	python	dmlc/gluon-nlp	src/gluonnlp/utils/misc.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py	Apache-2.0
def repeat(iterable, count=None): """Repeat a basic iterator for multiple rounds Parameters ---------- iterable The basic iterable count Repeat the basic iterable for "count" times. If it is None, it will be an infinite iterator. Returns ------- new_iterable A n...	Repeat a basic iterator for multiple rounds Parameters ---------- iterable The basic iterable count Repeat the basic iterable for "count" times. If it is None, it will be an infinite iterator. Returns ------- new_iterable A new iterable in which the basic iterator h...	repeat	python	dmlc/gluon-nlp	src/gluonnlp/utils/misc.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py	Apache-2.0
def load_checksum_stats(path: str) -> dict: """ Parameters ---------- path Path to the stored checksum Returns ------- file_stats """ file_stats = dict() with open(path, 'r', encoding='utf-8') as f: for line in f: name, hex_hash, file_size = line.str...	Parameters ---------- path Path to the stored checksum Returns ------- file_stats	load_checksum_stats	python	dmlc/gluon-nlp	src/gluonnlp/utils/misc.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py	Apache-2.0
def download_file_from_google_drive(file_id, dest_path, overwrite=False, showsize=False): """Downloads a shared file from google drive into a given folder. Optionally unzips it. Parameters ---------- file_id: str the file identifier. You can obtain it fro...	Downloads a shared file from google drive into a given folder. Optionally unzips it. Parameters ---------- file_id: str the file identifier. You can obtain it from the sharable link. dest_path: str the destination where to save the downloaded ...	download_file_from_google_drive	python	dmlc/gluon-nlp	src/gluonnlp/utils/misc.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py	Apache-2.0
def download(url: str, path: Optional[str] = None, overwrite: Optional[bool] = False, sha1_hash: Optional[str] = None, retries: Optional[int] = 5, verify_ssl: Optional[bool] = True, anonymous_credential: Optional[bool] = True) -> str: """...	Download a given URL Parameters ---------- url URL to download path Destination path to store downloaded file. By default stores to the current directory with same name as in url. overwrite Whether to overwrite destination file if already exists. sha1_hash ...	download	python	dmlc/gluon-nlp	src/gluonnlp/utils/misc.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py	Apache-2.0
def check_version(min_version: str, warning_only: bool = False, library: Optional[ModuleType] = None): """Check the version of gluonnlp satisfies the provided minimum version. An exception is thrown if the check does not pass. Parameters ---------- min_version ...	Check the version of gluonnlp satisfies the provided minimum version. An exception is thrown if the check does not pass. Parameters ---------- min_version Minimum version warning_only Printing a warning instead of throwing an exception. library The target library for ver...	check_version	python	dmlc/gluon-nlp	src/gluonnlp/utils/misc.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py	Apache-2.0
def init_comm(backend, gpus): """Init communication backend Parameters ---------- backend The communication backend gpus Returns ------- store The kvstore num_workers The total number of workers rank local_rank is_master_node ctx_l """ ...	Init communication backend Parameters ---------- backend The communication backend gpus Returns ------- store The kvstore num_workers The total number of workers rank local_rank is_master_node ctx_l	init_comm	python	dmlc/gluon-nlp	src/gluonnlp/utils/misc.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py	Apache-2.0
def get_mxnet_visible_ctx(): """Get the visible contexts in MXNet. - If GPU is available it will return all the visible GPUs, which can be controlled via "CUDA_VISIBLE_DEVICES". - If no GPU is available it will return the cpu device. Returns ------- ctx_l The recommende...	Get the visible contexts in MXNet. - If GPU is available it will return all the visible GPUs, which can be controlled via "CUDA_VISIBLE_DEVICES". - If no GPU is available it will return the cpu device. Returns ------- ctx_l The recommended contexts to use for MXNet	get_mxnet_visible_ctx	python	dmlc/gluon-nlp	src/gluonnlp/utils/misc.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py	Apache-2.0
def __init__(self, params=None): """Maintain a set of shadow variables "v" that is calculated by v[:] = (1 - 1/t) v + 1/t \theta The t is the number of training steps. It is also known as "Polyak-Rupert averaging" applied to SGD and was rediscovered in "Towards Optimal One...	Maintain a set of shadow variables "v" that is calculated by v[:] = (1 - 1/t) v + 1/t heta The t is the number of training steps. It is also known as "Polyak-Rupert averaging" applied to SGD and was rediscovered in "Towards Optimal One Pass Large Scale Learning withAveraged Stoch...	__init__	python	dmlc/gluon-nlp	src/gluonnlp/utils/parameter.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py	Apache-2.0
def apply(self, params): """ Tell the moving average tracker which parameters we are going to track. Parameters ---------- params : ParameterDict The parameters that we are going to track and calculate the moving average. """ assert self._track_params is None...	Tell the moving average tracker which parameters we are going to track. Parameters ---------- params : ParameterDict The parameters that we are going to track and calculate the moving average.	apply	python	dmlc/gluon-nlp	src/gluonnlp/utils/parameter.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py	Apache-2.0
def copy_back(self, params=None): """ Copy the average parameters back to the given parameters Parameters ---------- params : ParameterDict The parameters that we will copy tha average params to. If it is not given, the tracked parameters will be updated ...	Copy the average parameters back to the given parameters Parameters ---------- params : ParameterDict The parameters that we will copy tha average params to. If it is not given, the tracked parameters will be updated	copy_back	python	dmlc/gluon-nlp	src/gluonnlp/utils/parameter.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py	Apache-2.0
def grad_global_norm(parameters: Iterable[Parameter]) -> float: """Calculate the 2-norm of gradients of parameters, and how much they should be scaled down such that their 2-norm does not exceed `max_norm`, if `max_norm` if provided. If gradients exist for more than one context for a parameter, user needs t...	Calculate the 2-norm of gradients of parameters, and how much they should be scaled down such that their 2-norm does not exceed `max_norm`, if `max_norm` if provided. If gradients exist for more than one context for a parameter, user needs to explicitly call ``trainer.allreduce_grads`` so that the gradients...	grad_global_norm	python	dmlc/gluon-nlp	src/gluonnlp/utils/parameter.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py	Apache-2.0
def clip_grad_global_norm(parameters: Iterable[Parameter], max_norm: float, check_isfinite: bool = True) -> Tuple[float, float, bool]: """Rescales gradients of parameters so that the sum of their 2-norm is smaller than `max_norm`. If gradients exist for more t...	Rescales gradients of parameters so that the sum of their 2-norm is smaller than `max_norm`. If gradients exist for more than one context for a parameter, user needs to explicitly call ``trainer.allreduce_grads`` so that the gradients are summed first before calculating the 2-norm. .. note:: T...	clip_grad_global_norm	python	dmlc/gluon-nlp	src/gluonnlp/utils/parameter.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py	Apache-2.0
def move_to_ctx(arr, ctx): """Move a nested structure of array to the given context Parameters ---------- arr The input array ctx The MXNet context Returns ------- new_arr The array that has been moved to context """ if isinstance(arr, tuple): re...	Move a nested structure of array to the given context Parameters ---------- arr The input array ctx The MXNet context Returns ------- new_arr The array that has been moved to context	move_to_ctx	python	dmlc/gluon-nlp	src/gluonnlp/utils/parameter.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py	Apache-2.0
def deduplicate_param_dict(param_dict): """Get a parameter dict that has been deduplicated Parameters ---------- param_dict The parameter dict returned by `model.collect_params()` Returns ------- dedup_param_dict """ dedup_param_dict = dict() param_uuid_set = set() ...	Get a parameter dict that has been deduplicated Parameters ---------- param_dict The parameter dict returned by `model.collect_params()` Returns ------- dedup_param_dict	deduplicate_param_dict	python	dmlc/gluon-nlp	src/gluonnlp/utils/parameter.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py	Apache-2.0
def count_parameters(params) -> Tuple[int, int]: """ Parameters ---------- params The input parameter dict Returns ------- num_params The number of parameters that requires gradient num_fixed_params The number of parameters that does not require gradient """...	Parameters ---------- params The input parameter dict Returns ------- num_params The number of parameters that requires gradient num_fixed_params The number of parameters that does not require gradient	count_parameters	python	dmlc/gluon-nlp	src/gluonnlp/utils/parameter.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py	Apache-2.0
def get_trimmed_lengths(lengths: List[int], max_length: int, do_merge: bool = False) -> np.ndarray: """Get the trimmed lengths of multiple text data. It will make sure that the trimmed length is smaller than or equal to the max_length - do_merge is True ...	Get the trimmed lengths of multiple text data. It will make sure that the trimmed length is smaller than or equal to the max_length - do_merge is True Make sure that sum(trimmed_lengths) <= max_length. The strategy is to always try to trim the longer lengths. - do_merge is False Mak...	get_trimmed_lengths	python	dmlc/gluon-nlp	src/gluonnlp/utils/preprocessing.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/preprocessing.py	Apache-2.0
def match_tokens_with_char_spans(token_offsets: np.ndarray, spans: np.ndarray) -> np.ndarray: """Match the span offsets with the character-level offsets. For each span, we perform the following: 1: Cutoff the boundary span[0] = max(span[0], token_offsets[0, 0]) ...	Match the span offsets with the character-level offsets. For each span, we perform the following: 1: Cutoff the boundary span[0] = max(span[0], token_offsets[0, 0]) span[1] = min(span[1], token_offsets[-1, 1]) 2: Find start + end We try to select the smallest number of tokens that c...	match_tokens_with_char_spans	python	dmlc/gluon-nlp	src/gluonnlp/utils/preprocessing.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/preprocessing.py	Apache-2.0
def register(self, *args): """ Register the given object under either the nickname or `obj.__name__`. It can be used as either a decorator or not. See docstring of this class for usage. """ if len(args) == 2: # Register an object with nick name by function call ...	Register the given object under either the nickname or `obj.__name__`. It can be used as either a decorator or not. See docstring of this class for usage.	register	python	dmlc/gluon-nlp	src/gluonnlp/utils/registry.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/registry.py	Apache-2.0
def create(self, name: str, args, *kwargs) -> object: """Create the class object with the given args and kwargs Parameters ---------- name The name in the registry args kwargs Returns ------- ret The created object ...	Create the class object with the given args and kwargs Parameters ---------- name The name in the registry args kwargs Returns ------- ret The created object	create	python	dmlc/gluon-nlp	src/gluonnlp/utils/registry.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/registry.py	Apache-2.0
def serialize(path, tbl): """Serialize tbl with out-of-band data to path for zero-copy shared memory usage. If the object to be serialized itself, or the objects it uses for data storage (such as numpy arrays) implement the the pickle protocol version 5 pickle.PickleBuffer type in __reduce_ex__, then t...	Serialize tbl with out-of-band data to path for zero-copy shared memory usage. If the object to be serialized itself, or the objects it uses for data storage (such as numpy arrays) implement the the pickle protocol version 5 pickle.PickleBuffer type in __reduce_ex__, then this function can store these ...	serialize	python	dmlc/gluon-nlp	src/gluonnlp/utils/shm.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/shm.py	Apache-2.0
def load(path): """Load serialized object with out-of-band data from path based on zero-copy shared memory. Parameters ---------- path : pathlib.Path Folder used to save serialized data with serialize(). Usually a folder /dev/shm """ num_buffers = len(list(path.iterdir())) - 1 # exclu...	Load serialized object with out-of-band data from path based on zero-copy shared memory. Parameters ---------- path : pathlib.Path Folder used to save serialized data with serialize(). Usually a folder /dev/shm	load	python	dmlc/gluon-nlp	src/gluonnlp/utils/shm.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/shm.py	Apache-2.0
def is_match_states_batch_size(states, states_batch_axis, batch_size) -> bool: """Test whether the generated states have the specified batch size Parameters ---------- states The states structure states_batch_axis The states batch axis structure batch_size The batch size...	Test whether the generated states have the specified batch size Parameters ---------- states The states structure states_batch_axis The states batch axis structure batch_size The batch size Returns ------- ret	is_match_states_batch_size	python	dmlc/gluon-nlp	src/gluonnlp/utils/testing.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/testing.py	Apache-2.0
def verify_nmt_model(model, batch_size: int = 4, src_seq_length: int = 5, tgt_seq_length: int = 10, atol: float = 1E-4, rtol: float = 1E-3): """Verify the correctness of an NMT model. Raise error message if it detects problems. ...	Verify the correctness of an NMT model. Raise error message if it detects problems. Parameters ---------- model The machine translation model batch_size The batch size to test the nmt model src_seq_length Length of the source sequence tgt_seq_length Length of the...	verify_nmt_model	python	dmlc/gluon-nlp	src/gluonnlp/utils/testing.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/testing.py	Apache-2.0
def verify_nmt_inference(train_model, inference_model, batch_size=4, src_seq_length=5, tgt_seq_length=10, atol=1E-4, rtol=1E-3): """Verify the correctness of an NMT inference model. Raise error message if it detects any problems. Parameters ---------- ...	Verify the correctness of an NMT inference model. Raise error message if it detects any problems. Parameters ---------- train_model The training model inference_model The inference model batch_size Batch size src_seq_length Length of the source sequence t...	verify_nmt_inference	python	dmlc/gluon-nlp	src/gluonnlp/utils/testing.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/testing.py	Apache-2.0
def _cast_nested_to_fp16(nested_dat): """Cast the nested input to fp16 Parameters ---------- dat The input nested data structure Returns ------- output The casted output data """ if isinstance(nested_dat, (mx.np.ndarray, np.ndarray)): if nested_dat.dtype == ...	Cast the nested input to fp16 Parameters ---------- dat The input nested data structure Returns ------- output The casted output data	_cast_nested_to_fp16	python	dmlc/gluon-nlp	src/gluonnlp/utils/testing.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/testing.py	Apache-2.0
def verify_backbone_fp16(model_cls, cfg, ctx, inputs, atol=1E-2, rtol=1E-2, check_amp=True): """Test whether the backbone model has the comparable parameter gradient + Parameters ---------- model_cls The modeling class cfg The configuration ctx T...	Test whether the backbone model has the comparable parameter gradient + Parameters ---------- model_cls The modeling class cfg The configuration ctx The context inputs The input tensors of the model. We will atol The absolute tolerance rtol ...	verify_backbone_fp16	python	dmlc/gluon-nlp	src/gluonnlp/utils/testing.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/testing.py	Apache-2.0
def get_ec2_tvm_flags() -> Dict[str, Dict]: r"""Return the recommended flags for TVM compilation in AWS EC2 instances. Including C4, C5, G4, P3. For more details about AWS EC2 instances, refer to https://aws.amazon.com/ec2/instance-types/. Returns ------- info_dict A dictionary that c...	Return the recommended flags for TVM compilation in AWS EC2 instances. Including C4, C5, G4, P3. For more details about AWS EC2 instances, refer to https://aws.amazon.com/ec2/instance-types/. Returns ------- info_dict A dictionary that contains the mapping between instance type and the ...	get_ec2_tvm_flags	python	dmlc/gluon-nlp	src/gluonnlp/utils/tvm_utils.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/tvm_utils.py	Apache-2.0
def update_tvm_convert_map() -> None: """A Monkey Patch to update convert map in tvm/relay/frontend/mxnet.py""" op = (('masked_softmax', _mx_masked_softmax),) _convert_map.update({key: value for key, value in op})	A Monkey Patch to update convert map in tvm/relay/frontend/mxnet.py	update_tvm_convert_map	python	dmlc/gluon-nlp	src/gluonnlp/utils/tvm_utils.py	https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/tvm_utils.py	Apache-2.0
def grouper(iterable, n, fillvalue=None): """Collect data into fixed-length chunks or blocks""" # grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx args = [iter(iterable)] * n return itertools.zip_longest(*args, fillvalue=fillvalue)	Collect data into fixed-length chunks or blocks	grouper	python	dmlc/gluon-nlp	tests/test_gluon_block.py	https://github.com/dmlc/gluon-nlp/blob/master/tests/test_gluon_block.py	Apache-2.0
def test_test(): """Test that fixing a random seed works.""" py_rnd = random.randint(0, 100) np_rnd = np.random.randint(0, 100) mx_rnd = mx.nd.random_uniform(shape=(1, )).asscalar() random.seed(1) mx.random.seed(1) np.random.seed(1) assert py_rnd == random.randint(0, 100) assert np...	Test that fixing a random seed works.	test_test	python	dmlc/gluon-nlp	tests/test_pytest.py	https://github.com/dmlc/gluon-nlp/blob/master/tests/test_pytest.py	Apache-2.0
def is_image_file(filename): """Checks if a file is an image. Args: filename (string): path to a file Returns: bool: True if the filename ends with a known image extension """ filename_lower = filename.lower() return any(filename_lower.endswith(ext) for ext in IMG_EXTENSIONS)	Checks if a file is an image. Args: filename (string): path to a file Returns: bool: True if the filename ends with a known image extension	is_image_file	python	ajbrock/BigGAN-PyTorch	datasets.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/datasets.py	MIT
def __getitem__(self, index): """ Args: index (int): Index Returns: tuple: (image, target) where target is class_index of the target class. """ if self.load_in_mem: img = self.data[index] target = self.labels[index] else: path, target = self.imgs[index] ...	Args: index (int): Index Returns: tuple: (image, target) where target is class_index of the target class.	__getitem__	python	ajbrock/BigGAN-PyTorch	datasets.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/datasets.py	MIT
def __getitem__(self, index): """ Args: index (int): Index Returns: tuple: (image, target) where target is class_index of the target class. """ # If loaded the entire dataset in RAM, get image from memory if self.load_in_mem: img = self.data[index] target = self.labe...	Args: index (int): Index Returns: tuple: (image, target) where target is class_index of the target class.	__getitem__	python	ajbrock/BigGAN-PyTorch	datasets.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/datasets.py	MIT
def __getitem__(self, index): """ Args: index (int): Index Returns: tuple: (image, target) where target is index of the target class. """ img, target = self.data[index], self.labels[index] # doing this so that it is consistent with all other datasets # to return a PIL Image ...	Args: index (int): Index Returns: tuple: (image, target) where target is index of the target class.	__getitem__	python	ajbrock/BigGAN-PyTorch	datasets.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/datasets.py	MIT
def torch_cov(m, rowvar=False): '''Estimate a covariance matrix given data. Covariance indicates the level to which two variables vary together. If we examine N-dimensional samples, `X = [x_1, x_2, ... x_N]^T`, then the covariance matrix element `C_{ij}` is the covariance of `x_i` and `x_j`. The el...	Estimate a covariance matrix given data. Covariance indicates the level to which two variables vary together. If we examine N-dimensional samples, `X = [x_1, x_2, ... x_N]^T`, then the covariance matrix element `C_{ij}` is the covariance of `x_i` and `x_j`. The element `C_{ii}` is the variance of `x_i`...	torch_cov	python	ajbrock/BigGAN-PyTorch	inception_utils.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/inception_utils.py	MIT
def numpy_calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): """Numpy implementation of the Frechet Distance. Taken from https://github.com/bioinf-jku/TTUR The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is d^2 = \|\|mu_1 - mu_2\|\|^2 + Tr(C_1...	Numpy implementation of the Frechet Distance. Taken from https://github.com/bioinf-jku/TTUR The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is d^2 = \|\|mu_1 - mu_2\|\|^2 + Tr(C_1 + C_2 - 2sqrt(C_1C_2)). Stable version by Dougal J. Sutherland. Params: ...	numpy_calculate_frechet_distance	python	ajbrock/BigGAN-PyTorch	inception_utils.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/inception_utils.py	MIT
def torch_calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): """Pytorch implementation of the Frechet Distance. Taken from https://github.com/bioinf-jku/TTUR The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is d^2 = \|\|mu_1 - mu_2\|\|^2 + Tr(C...	Pytorch implementation of the Frechet Distance. Taken from https://github.com/bioinf-jku/TTUR The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is d^2 = \|\|mu_1 - mu_2\|\|^2 + Tr(C_1 + C_2 - 2sqrt(C_1C_2)). Stable version by Dougal J. Sutherland. Params...	torch_calculate_frechet_distance	python	ajbrock/BigGAN-PyTorch	inception_utils.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/inception_utils.py	MIT
def __call__(self, img): """ Args: img (PIL Image): Image to be cropped. Returns: PIL Image: Cropped image. """ size = (min(img.size), min(img.size)) # Only step forward along this edge if it's the long edge i = (0 if size[0] == img.size[0] else np.random.randint(l...	Args: img (PIL Image): Image to be cropped. Returns: PIL Image: Cropped image.	__call__	python	ajbrock/BigGAN-PyTorch	utils.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/utils.py	MIT
def log(self, record=None, **kwargs): """ Assumption: no newlines in the input. """ if record is None: record = {} record.update(kwargs) record['_stamp'] = time.time() with open(self.fname, 'a') as f: f.write(json.dumps(record, ensure_ascii=True) + '\n')	Assumption: no newlines in the input.	log	python	ajbrock/BigGAN-PyTorch	utils.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/utils.py	MIT
def progress(items, desc='', total=None, min_delay=0.1, displaytype='s1k'): """ Returns a generator over `items`, printing the number and percentage of items processed and the estimated remaining processing time before yielding the next item. `total` gives the total number of items (required if `items` has no...	Returns a generator over `items`, printing the number and percentage of items processed and the estimated remaining processing time before yielding the next item. `total` gives the total number of items (required if `items` has no length), and `min_delay` gives the minimum time in seconds between subsequent ...	progress	python	ajbrock/BigGAN-PyTorch	utils.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/utils.py	MIT
def step(self, closure=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: loss = closure() for group in self.param_groups: for p in g...	Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss.	step	python	ajbrock/BigGAN-PyTorch	utils.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/utils.py	MIT
def _data_parallel_master(self, intermediates): """Reduce the sum and square-sum, compute the statistics, and broadcast it.""" # Always using same "device order" makes the ReduceAdd operation faster. # Thanks to:: Tete Xiao (http://tetexiao.com/) intermediates = sorted(intermediates, ke...	Reduce the sum and square-sum, compute the statistics, and broadcast it.	_data_parallel_master	python	ajbrock/BigGAN-PyTorch	sync_batchnorm/batchnorm.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/batchnorm.py	MIT
def _compute_mean_std(self, sum_, ssum, size): """Compute the mean and standard-deviation with sum and square-sum. This method also maintains the moving average on the master device.""" assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.' mean = sum...	Compute the mean and standard-deviation with sum and square-sum. This method also maintains the moving average on the master device.	_compute_mean_std	python	ajbrock/BigGAN-PyTorch	sync_batchnorm/batchnorm.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/batchnorm.py	MIT
def __init__(self, master_callback): """ Args: master_callback: a callback to be invoked after having collected messages from slave devices. """ self._master_callback = master_callback self._queue = queue.Queue() self._registry = collections.OrderedDict() ...	Args: master_callback: a callback to be invoked after having collected messages from slave devices.	__init__	python	ajbrock/BigGAN-PyTorch	sync_batchnorm/comm.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/comm.py	MIT
def register_slave(self, identifier): """ Register an slave device. Args: identifier: an identifier, usually is the device id. Returns: a `SlavePipe` object which can be used to communicate with the master device. """ if self._activated: assert ...	Register an slave device. Args: identifier: an identifier, usually is the device id. Returns: a `SlavePipe` object which can be used to communicate with the master device.	register_slave	python	ajbrock/BigGAN-PyTorch	sync_batchnorm/comm.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/comm.py	MIT
def run_master(self, master_msg): """ Main entry for the master device in each forward pass. The messages were first collected from each devices (including the master device), and then an callback will be invoked to compute the message to be sent back to each devices (including t...	Main entry for the master device in each forward pass. The messages were first collected from each devices (including the master device), and then an callback will be invoked to compute the message to be sent back to each devices (including the master device). Args: ...	run_master	python	ajbrock/BigGAN-PyTorch	sync_batchnorm/comm.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/comm.py	MIT
def execute_replication_callbacks(modules): """ Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` Note that, as all modules are isomorphism, we assign eac...	Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` Note that, as all modules are isomorphism, we assign each sub-module with a context (shared among multi...	execute_replication_callbacks	python	ajbrock/BigGAN-PyTorch	sync_batchnorm/replicate.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/replicate.py	MIT
def patch_replication_callback(data_parallel): """ Monkey-patch an existing `DataParallel` object. Add the replication callback. Useful when you have customized `DataParallel` implementation. Examples: > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) > sync_bn = DataParal...	Monkey-patch an existing `DataParallel` object. Add the replication callback. Useful when you have customized `DataParallel` implementation. Examples: > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) > patch_replic...	patch_replication_callback	python	ajbrock/BigGAN-PyTorch	sync_batchnorm/replicate.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/replicate.py	MIT
def dump_tfhub_to_hdf5(module_path, hdf5_path, redownload=False): """Loads TFHub weights and saves them to intermediate HDF5 file. Args: module_path ([Path-like]): Path to TFHub module. hdf5_path ([Path-like]): Path to output HDF5 file. Returns: [h5py.File]: Loaded hdf5 file containing module weight...	Loads TFHub weights and saves them to intermediate HDF5 file. Args: module_path ([Path-like]): Path to TFHub module. hdf5_path ([Path-like]): Path to output HDF5 file. Returns: [h5py.File]: Loaded hdf5 file containing module weights.	dump_tfhub_to_hdf5	python	ajbrock/BigGAN-PyTorch	TFHub/converter.py	https://github.com/ajbrock/BigGAN-PyTorch/blob/master/TFHub/converter.py	MIT
def read_img(t_imgfname, input_size, img_mean): # optional pre-processing arguments """Read one image and its corresponding mask with optional pre-processing. Args: input_queue: tf queue with paths to the image and its mask. input_size: a tuple with (height, width) values. If not given, return images of...	Read one image and its corresponding mask with optional pre-processing. Args: input_queue: tf queue with paths to the image and its mask. input_size: a tuple with (height, width) values. If not given, return images of original size. random_scale: whether to randomly scale the images prior to rand...	read_img	python	iyah4888/SIGGRAPH18SSS	main_hyper.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/main_hyper.py	MIT
def get_arguments(): """Parse all the arguments provided from the CLI. Returns: A list of parsed arguments. """ parser = argparse.ArgumentParser(description="DeepLab-ResNet Network") parser.add_argument("--batch-size", type=int, default=BATCH_SIZE, help="Number of ...	Parse all the arguments provided from the CLI. Returns: A list of parsed arguments.	get_arguments	python	iyah4888/SIGGRAPH18SSS	parse_opt.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/parse_opt.py	MIT
def __init__(self, sess, args): """Initialize the parameters. sess: tensorflow session """ self.sess = sess self.batch_size = args.batch_size self.args = args # parameters used to save a checkpoint self.dataset = "Hypcol" self.options = [] self._attrs = ['batch_size', 'dataset'] self.build_mo...	Initialize the parameters. sess: tensorflow session	__init__	python	iyah4888/SIGGRAPH18SSS	deeplab_resnet/hc_deeplab.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/hc_deeplab.py	MIT
def image_scaling(img, label): """ Randomly scales the images between 0.5 to 1.5 times the original size. Args: img: Training image to scale. label: Segmentation mask to scale. """ scale = tf.random_uniform([1], minval=0.5, maxval=1.5, dtype=tf.float32, seed=None) h_new = tf.to...	Randomly scales the images between 0.5 to 1.5 times the original size. Args: img: Training image to scale. label: Segmentation mask to scale.	image_scaling	python	iyah4888/SIGGRAPH18SSS	deeplab_resnet/image_reader.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py	MIT
def image_mirroring(img, label): """ Randomly mirrors the images. Args: img: Training image to mirror. label: Segmentation mask to mirror. """ distort_left_right_random = tf.random_uniform([1], 0, 1.0, dtype=tf.float32)[0] mirror = tf.less(tf.stack([1.0, distort_left_right_rand...	Randomly mirrors the images. Args: img: Training image to mirror. label: Segmentation mask to mirror.	image_mirroring	python	iyah4888/SIGGRAPH18SSS	deeplab_resnet/image_reader.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py	MIT
def random_crop_and_pad_image_and_labels(image, label, crop_h, crop_w, ignore_label=255): """ Randomly crop and pads the input images. Args: image: Training image to crop/ pad. label: Segmentation mask to crop/ pad. crop_h: Height of cropped segment. crop_w: Width of cropped segment...	Randomly crop and pads the input images. Args: image: Training image to crop/ pad. label: Segmentation mask to crop/ pad. crop_h: Height of cropped segment. crop_w: Width of cropped segment. ignore_label: Label to ignore during the training.	random_crop_and_pad_image_and_labels	python	iyah4888/SIGGRAPH18SSS	deeplab_resnet/image_reader.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py	MIT
def read_labeled_image_list(data_dir, data_list): """Reads txt file containing paths to images and ground truth masks. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/path/to/image /path/to/mask'. Returns: Two l...	Reads txt file containing paths to images and ground truth masks. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/path/to/image /path/to/mask'. Returns: Two lists with all file names for images and masks, respective...	read_labeled_image_list	python	iyah4888/SIGGRAPH18SSS	deeplab_resnet/image_reader.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py	MIT
def read_data_list(data_dir, data_list, ext): """Reads txt file containing paths to images and ground truth masks. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/path/to/image /path/to/mask'. Returns: Two lists...	Reads txt file containing paths to images and ground truth masks. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/path/to/image /path/to/mask'. Returns: Two lists with all file names for images and masks, respective...	read_data_list	python	iyah4888/SIGGRAPH18SSS	deeplab_resnet/image_reader.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py	MIT
def read_images_from_disk(input_queue, input_size, random_scale, random_mirror, ignore_label, img_mean): # optional pre-processing arguments """Read one image and its corresponding mask with optional pre-processing. Args: input_queue: tf queue with paths to the image and its mask. input_size: a...	Read one image and its corresponding mask with optional pre-processing. Args: input_queue: tf queue with paths to the image and its mask. input_size: a tuple with (height, width) values. If not given, return images of original size. random_scale: whether to randomly scale th...	read_images_from_disk	python	iyah4888/SIGGRAPH18SSS	deeplab_resnet/image_reader.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py	MIT
def __init__(self, data_dir, data_list, input_size, random_scale, random_mirror, ignore_label, img_mean, coord): '''Initialise an ImageReader. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/...	Initialise an ImageReader. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/path/to/image /path/to/mask'. input_size: a tuple with (height, width) values, to which all the images will be resized. ra...	__init__	python	iyah4888/SIGGRAPH18SSS	deeplab_resnet/image_reader.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py	MIT
def dequeue(self, num_elements): '''Pack images and labels into a batch. Args: num_elements: the batch size. Returns: Two tensors of size (batch_size, h, w, {3, 1}) for images and masks.''' image_batch, label_batch = tf.train.batch([self.image, sel...	Pack images and labels into a batch. Args: num_elements: the batch size. Returns: Two tensors of size (batch_size, h, w, {3, 1}) for images and masks.	dequeue	python	iyah4888/SIGGRAPH18SSS	deeplab_resnet/image_reader.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py	MIT
def read_an_image_from_disk(t_imgfname, t_labelfname, input_size, random_scale, random_mirror, ignore_label, img_mean): # optional pre-processing arguments """Read one image and its corresponding mask with optional pre-processing. Args: input_queue: tf queue with paths to the image and its mask. ...	Read one image and its corresponding mask with optional pre-processing. Args: input_queue: tf queue with paths to the image and its mask. input_size: a tuple with (height, width) values. If not given, return images of original size. random_scale: whether to randomly scale th...	read_an_image_from_disk	python	iyah4888/SIGGRAPH18SSS	deeplab_resnet/image_reader.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py	MIT
def setup(self, is_training, num_classes): '''Network definition. Args: is_training: whether to update the running mean and variance of the batch normalisation layer. If the batch size is small, it is better to keep the running mean and variance of ...	Network definition. Args: is_training: whether to update the running mean and variance of the batch normalisation layer. If the batch size is small, it is better to keep the running mean and variance of the-pretrained model frozen. num_...	setup	python	iyah4888/SIGGRAPH18SSS	deeplab_resnet/model.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/model.py	MIT
def decode_labels(mask, num_images=1, num_classes=21): """Decode batch of segmentation masks. Args: mask: result of inference after taking argmax. num_images: number of images to decode from the batch. num_classes: number of classes to predict (including background). Returns: ...	Decode batch of segmentation masks. Args: mask: result of inference after taking argmax. num_images: number of images to decode from the batch. num_classes: number of classes to predict (including background). Returns: A batch with num_images RGB images of the same size as the ...	decode_labels	python	iyah4888/SIGGRAPH18SSS	deeplab_resnet/utils.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/utils.py	MIT
def prepare_label(input_batch, new_size, num_classes, one_hot=True): """Resize masks and perform one-hot encoding. Args: input_batch: input tensor of shape [batch_size H W 1]. new_size: a tensor with new height and width. num_classes: number of classes to predict (including background). ...	Resize masks and perform one-hot encoding. Args: input_batch: input tensor of shape [batch_size H W 1]. new_size: a tensor with new height and width. num_classes: number of classes to predict (including background). one_hot: whether perform one-hot encoding. Returns: Outputs a te...	prepare_label	python	iyah4888/SIGGRAPH18SSS	deeplab_resnet/utils.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/utils.py	MIT
def inv_preprocess(imgs, num_images, img_mean): """Inverse preprocessing of the batch of images. Add the mean vector and convert from BGR to RGB. Args: imgs: batch of input images. num_images: number of images to apply the inverse transformations on. img_mean: vector of mean col...	Inverse preprocessing of the batch of images. Add the mean vector and convert from BGR to RGB. Args: imgs: batch of input images. num_images: number of images to apply the inverse transformations on. img_mean: vector of mean colour values. Returns: The batch of the size...	inv_preprocess	python	iyah4888/SIGGRAPH18SSS	deeplab_resnet/utils.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/utils.py	MIT
def __init__(self, def_path, phase='test'): ''' def_path: Path to the model definition (.prototxt) data_path: Path to the model data (.caffemodel) phase: Either 'test' or 'train'. Used for filtering phase-specific nodes. ''' self.def_path = def_path self.phase = p...	def_path: Path to the model definition (.prototxt) data_path: Path to the model data (.caffemodel) phase: Either 'test' or 'train'. Used for filtering phase-specific nodes.	__init__	python	iyah4888/SIGGRAPH18SSS	kaffe/graph.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/graph.py	MIT
def load(self): '''Load the layer definitions from the prototxt.''' self.params = get_caffe_resolver().NetParameter() with open(self.def_path, 'rb') as def_file: text_format.Merge(def_file.read(), self.params)	Load the layer definitions from the prototxt.	load	python	iyah4888/SIGGRAPH18SSS	kaffe/graph.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/graph.py	MIT
def filter_layers(self, layers): '''Filter out layers based on the current phase.''' phase_map = {0: 'train', 1: 'test'} filtered_layer_names = set() filtered_layers = [] for layer in layers: phase = self.phase if len(layer.include): phase ...	Filter out layers based on the current phase.	filter_layers	python	iyah4888/SIGGRAPH18SSS	kaffe/graph.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/graph.py	MIT
def make_node(self, layer): '''Create a graph node for the given layer.''' kind = NodeKind.map_raw_kind(layer.type) if kind is None: raise KaffeError('Unknown layer type encountered: %s' % layer.type) # We want to use the layer's top names (the "output" names), rather than th...	Create a graph node for the given layer.	make_node	python	iyah4888/SIGGRAPH18SSS	kaffe/graph.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/graph.py	MIT
def make_input_nodes(self): ''' Create data input nodes. This method is for old-style inputs, where the input specification was not treated as a first-class layer in the prototext. Newer models use the "Input layer" type. ''' nodes = [Node(name, NodeKind.Data) fo...	Create data input nodes. This method is for old-style inputs, where the input specification was not treated as a first-class layer in the prototext. Newer models use the "Input layer" type.	make_input_nodes	python	iyah4888/SIGGRAPH18SSS	kaffe/graph.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/graph.py	MIT
def build(self): ''' Builds the graph from the Caffe layer definitions. ''' # Get the layers layers = self.params.layers or self.params.layer # Filter out phase-excluded layers layers = self.filter_layers(layers) # Get any separately-specified input layers...	Builds the graph from the Caffe layer definitions.	build	python	iyah4888/SIGGRAPH18SSS	kaffe/graph.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/graph.py	MIT
def load(self, data_path, session, ignore_missing=False): '''Load network weights. data_path: The path to the numpy-serialized network weights session: The current TensorFlow session ignore_missing: If true, serialized weights for missing layers are ignored. ''' data_dict...	Load network weights. data_path: The path to the numpy-serialized network weights session: The current TensorFlow session ignore_missing: If true, serialized weights for missing layers are ignored.	load	python	iyah4888/SIGGRAPH18SSS	kaffe/tensorflow/network.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/tensorflow/network.py	MIT
def feed(self, *args): '''Set the input(s) for the next operation by replacing the terminal nodes. The arguments can be either layer names or the actual layers. ''' assert len(args) != 0 self.terminals = [] for fed_layer in args: if isinstance(fed_layer, str):...	Set the input(s) for the next operation by replacing the terminal nodes. The arguments can be either layer names or the actual layers.	feed	python	iyah4888/SIGGRAPH18SSS	kaffe/tensorflow/network.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/tensorflow/network.py	MIT
def get_unique_name(self, prefix): '''Returns an index-suffixed unique name for the given prefix. This is used for auto-generating layer names based on the type-prefix. ''' ident = sum(t.startswith(prefix) for t, _ in list(self.layers.items())) + 1 return '%s_%d' % (prefix, ident...	Returns an index-suffixed unique name for the given prefix. This is used for auto-generating layer names based on the type-prefix.	get_unique_name	python	iyah4888/SIGGRAPH18SSS	kaffe/tensorflow/network.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/tensorflow/network.py	MIT
def get_padding_type(kernel_params, input_shape, output_shape): '''Translates Caffe's numeric padding to one of ('SAME', 'VALID'). Caffe supports arbitrary padding values, while TensorFlow only supports 'SAME' and 'VALID' modes. So, not all Caffe paddings can be translated to TensorFlow. There are some ...	Translates Caffe's numeric padding to one of ('SAME', 'VALID'). Caffe supports arbitrary padding values, while TensorFlow only supports 'SAME' and 'VALID' modes. So, not all Caffe paddings can be translated to TensorFlow. There are some subtleties to how the padding edge-cases are handled. These are des...	get_padding_type	python	iyah4888/SIGGRAPH18SSS	kaffe/tensorflow/transformer.py	https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/tensorflow/transformer.py	MIT
def run( self, query="What is a lagrangian?", limit_broad_results=1_000, limit_deduped_url_results=50, limit_hierarchical_url_results=50, limit_final_pagerank_results=20, url_contains_filter=None, ): """Run a search query using the WebSearchEngine clie...	Run a search query using the WebSearchEngine client	run	python	SciPhi-AI/agent-search	agent_search/app/server.py	https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/app/server.py	Apache-2.0
def to_string_dict(self) -> dict: """Returns a dictionary representation with all values as strings.""" return { "score": str(self.score), "url": self.url, "title": self.title, "dataset": self.dataset, "metadata": self.metadata, "te...	Returns a dictionary representation with all values as strings.	to_string_dict	python	SciPhi-AI/agent-search	agent_search/core/search_types.py	https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/core/search_types.py	Apache-2.0
def select_top_urls( ordered_points: List[AgentSearchResult], max_urls: int = 10, url_contains: Optional[List[str]] = None, ) -> List[str]: """A function to return the top unique URLs from the given poitns results.""" if not url_contains: url_contains = [] top_urls = set([]) for poi...	A function to return the top unique URLs from the given poitns results.	select_top_urls	python	SciPhi-AI/agent-search	agent_search/core/utils.py	https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/core/utils.py	Apache-2.0
def cosine_similarity(v1: np.ndarray, v2: np.ndarray) -> float: """Compute the cosine similarity between two vectors.""" dot_product = np.dot(v1, v2) norm_v1 = np.linalg.norm(v1) norm_v2 = np.linalg.norm(v2) return dot_product / (norm_v1 * norm_v2)	Compute the cosine similarity between two vectors.	cosine_similarity	python	SciPhi-AI/agent-search	agent_search/core/utils.py	https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/core/utils.py	Apache-2.0
def __init__( self, api_base: Optional[str] = None, api_key: Optional[str] = None, timeout: int = 30, ) -> None: """ Initializes the SciPhi client. Args: api_base (Optional[str]): Base URL for the SciPhi API. api_key (Optional[str]): A...	Initializes the SciPhi client. Args: api_base (Optional[str]): Base URL for the SciPhi API. api_key (Optional[str]): API key for authenticating requests. timeout (int): Timeout for API requests in seconds. Raises: ValueError: If `api_key` is not...	__init__	python	SciPhi-AI/agent-search	agent_search/providers/sciphi.py	https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/providers/sciphi.py	Apache-2.0
def _handle_api_response(self, response: httpx.Response) -> Dict: """ Handles the HTTP response from the API. Args: response (httpx.Response): The response from the API request. Returns: Dict: JSON response content. Raises: Exception: If the...	Handles the HTTP response from the API. Args: response (httpx.Response): The response from the API request. Returns: Dict: JSON response content. Raises: Exception: If the response indicates an error.	_handle_api_response	python	SciPhi-AI/agent-search	agent_search/providers/sciphi.py	https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/providers/sciphi.py	Apache-2.0
def _handle_search_response(self, search_results: Dict[str, str]) -> None: """ Handles dictionary search resopnses from the API. Args: search_results (Dict[str, str]): The response from the API request. Returns: Dict: JSON response content. Raises: ...	Handles dictionary search resopnses from the API. Args: search_results (Dict[str, str]): The response from the API request. Returns: Dict: JSON response content. Raises: Exception: If the response indicates an error.	_handle_search_response	python	SciPhi-AI/agent-search	agent_search/providers/sciphi.py	https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/providers/sciphi.py	Apache-2.0
def _retry_api_request( self, method: str, url: str, payload: Dict, max_retries: int = 3 ): """ Common method for retrying API requests with exponential backoff. Args: method (str): The HTTP method to use ('get' or 'post'). url (str): The API endpoint. ...	Common method for retrying API requests with exponential backoff. Args: method (str): The HTTP method to use ('get' or 'post'). url (str): The API endpoint. payload (Dict): The payload for the request. max_retries (int): Maximum number of retry attempts....	_retry_api_request	python	SciPhi-AI/agent-search	agent_search/providers/sciphi.py	https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/providers/sciphi.py	Apache-2.0
def search( self, query: str, search_provider: str, max_retries: int = 3 ) -> List[Dict]: """ Performs a search query using the SciPhi API with retry and backoff logic. Args: query (str): The search query string. search_provider (str): The search provider to ...	Performs a search query using the SciPhi API with retry and backoff logic. Args: query (str): The search query string. search_provider (str): The search provider to use. max_retries (int): Maximum number of retry attempts. Returns: List[Dict]: A lis...	search	python	SciPhi-AI/agent-search	agent_search/providers/sciphi.py	https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/providers/sciphi.py	Apache-2.0
def get_search_rag_response( self, query: str, search_provider: str, llm_model: str = "SciPhi/Sensei-7B-V1", temperature: int = 0.2, top_p: int = 0.95, ): """ Retrieves a search RAG (Retrieval-Augmented Generation) response from the API. Args:...	Retrieves a search RAG (Retrieval-Augmented Generation) response from the API. Args: query (str): The search query string. search_provider (str): The search provider to use. llm_model (str): The language model to use. temperature (int): The temperature s...	get_search_rag_response	python	SciPhi-AI/agent-search	agent_search/providers/sciphi.py	https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/providers/sciphi.py	Apache-2.0
def completion( self, prompt: str, llm_model_name: str = "SciPhi/Sensei-7B-V1", llm_max_tokens_to_sample: int = 1_024, llm_temperature: float = 0.2, llm_top_p: float = 0.90, ) -> SearchRAGResponse: """ Generates a completion for a given prompt using th...	Generates a completion for a given prompt using the SciPhi API. Args: prompt (str): The prompt for generating completion. llm_model_name (str): The language model to use. llm_max_tokens_to_sample (int): Maximum number of tokens for the sample. llm_temper...	completion	python	SciPhi-AI/agent-search	agent_search/providers/sciphi.py	https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/providers/sciphi.py	Apache-2.0
def process_rows(rows, output_queue): """Process the rows into qdrant point objects.""" qdrant_points = [] for row in rows: _, url, __, text_chunks, embeddings_binary, ___, ____ = row embeddings = np.frombuffer( embeddings_binary, dtype=np.float32 ).reshape(-1, EMBEDDING_...	Process the rows into qdrant point objects.	process_rows	python	SciPhi-AI/agent-search	agent_search/scripts/populate_qdrant_from_postgres.py	https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/scripts/populate_qdrant_from_postgres.py	Apache-2.0
def qdrant_writer(config, qdrant_queue, delete_existing): """A writer that listens for output events in a separate thread.""" qclient = QdrantClient( config["qdrant_host"], port=config["qdrant_grpc_port"], prefer_grpc=config["qdrant_prefer_grpc"], ) if delete_existing: qc...	A writer that listens for output events in a separate thread.	qdrant_writer	python	SciPhi-AI/agent-search	agent_search/scripts/populate_qdrant_from_postgres.py	https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/scripts/populate_qdrant_from_postgres.py	Apache-2.0
def process_batches(config, start, end, batch_size, output_queue): """Processes the batches in steps of the given batch_size""" # Connect to the database conn = psycopg2.connect( dbname=config["postgres_db"], user=config["postgres_user"], password=config["postgres_password"], ...	Processes the batches in steps of the given batch_size	process_batches	python	SciPhi-AI/agent-search	agent_search/scripts/populate_qdrant_from_postgres.py	https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/scripts/populate_qdrant_from_postgres.py	Apache-2.0
def run(self, num_processes=16, batch_size=1_024, delete_existing=False): """Runs the population process for the qdrant database""" qdrant_queue = multiprocessing.Queue() qdrant_writer_thread = multiprocessing.Process( target=qdrant_writer, args=( self.con...	Runs the population process for the qdrant database	run	python	SciPhi-AI/agent-search	agent_search/scripts/populate_qdrant_from_postgres.py	https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/scripts/populate_qdrant_from_postgres.py	Apache-2.0
def hierarchical_similarity_reranking( self, query_vector: np.ndarray, urls: List[str], limit: int = 100, ) -> List[AgentSearchResult]: """Hierarchical URL search to find the most similar text chunk for the given query and URLs""" results = self.execute_batch_query(ur...	Hierarchical URL search to find the most similar text chunk for the given query and URLs	hierarchical_similarity_reranking	python	SciPhi-AI/agent-search	agent_search/search/base.py	https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/search/base.py	Apache-2.0
def pagerank_reranking( self, similarity_results: List[AgentSearchResult], limit: int = 100, ) -> List[AgentSearchResult]: """Reranks the results based on the PageRank score of the domain""" if not self.pagerank_rerank_module: raise Exception( "Pag...	Reranks the results based on the PageRank score of the domain	pagerank_reranking	python	SciPhi-AI/agent-search	agent_search/search/base.py	https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/search/base.py	Apache-2.0
def scrub_str(string): """ The purpose of this function is to scrub the weird template mark-up out of strings that Veekun is using for their pokedex. Example: []{move:dragon-tail} will effect the opponents [HP]{mechanic:hp}. Becomes: dragon tail will effect the opponents HP. If ...	The purpose of this function is to scrub the weird template mark-up out of strings that Veekun is using for their pokedex. Example: []{move:dragon-tail} will effect the opponents [HP]{mechanic:hp}. Becomes: dragon tail will effect the opponents HP. If you find this results in weird...	scrub_str	python	PokeAPI/pokeapi	data/v2/build.py	https://github.com/PokeAPI/pokeapi/blob/master/data/v2/build.py	BSD-3-Clause
def __SectionLength(this): """(4 bytes) Gets the length of characters the given section is""" offset = this.__SectionDataOffset return struct.unpack_from("<I", this.__data, offset)[0]	(4 bytes) Gets the length of characters the given section is	__SectionLength	python	PokeAPI/pokeapi	Resources/scripts/data/gen8/read_swsh.py	https://github.com/PokeAPI/pokeapi/blob/master/Resources/scripts/data/gen8/read_swsh.py	BSD-3-Clause
def __LineOffsets(this): """Figures out the offset for each entry based on the data section offset""" result = [None] * this.__LineCount sdo = int(this.__SectionDataOffset) for i in range(0, len(result)): result[i] = TextLine() result[i].offset = struct.unpack_from("<i", this.__data, (i * 8) + sdo + 4)[0]...	Figures out the offset for each entry based on the data section offset	__LineOffsets	python	PokeAPI/pokeapi	Resources/scripts/data/gen8/read_swsh.py	https://github.com/PokeAPI/pokeapi/blob/master/Resources/scripts/data/gen8/read_swsh.py	BSD-3-Clause
def HashFNV1_64(this, word): """Fowler-Noll-Vo hash function; 64-bit""" fnvPrime_64 = 0x100000001b3 offsetBasis_64 = 0xCBF29CE484222645 hash = offsetBasis_64 for c in word: hash = hash ^ ord(c) # Cast hash to at 64-bit value hash = (hash * fnvPrime_64) % 2**64 return hash	Fowler-Noll-Vo hash function; 64-bit	HashFNV1_64	python	PokeAPI/pokeapi	Resources/scripts/data/gen8/read_swsh.py	https://github.com/PokeAPI/pokeapi/blob/master/Resources/scripts/data/gen8/read_swsh.py	BSD-3-Clause
def __LineData(this, data): """Loads the file into a list to later decrypt""" key = copy.copy(this.__KEY_BASE) result = [None] * this.__LineCount lines = this.__LineOffsets for i in range(0, len(lines)): # Make a list twice the size of the current text line size encrypted = lines[i].length * 2 # The...	Loads the file into a list to later decrypt	__LineData	python	PokeAPI/pokeapi	Resources/scripts/data/gen8/read_swsh.py	https://github.com/PokeAPI/pokeapi/blob/master/Resources/scripts/data/gen8/read_swsh.py	BSD-3-Clause
def __CryptLineData(this, data, key): """Decrypts the given line into a list of bytes""" copied = copy.copy(data) result = [None] * len(copied) for i in range(0, len(copied), 2): result[i] = copied[i] ^ (key % 256) result[i + 1] = copied[i + 1] ^ ((key >> 8) % 256) # Bit-shift and OR key, then cast to...	Decrypts the given line into a list of bytes	__CryptLineData	python	PokeAPI/pokeapi	Resources/scripts/data/gen8/read_swsh.py	https://github.com/PokeAPI/pokeapi/blob/master/Resources/scripts/data/gen8/read_swsh.py	BSD-3-Clause

def logerror(logger: logging.Logger = logging.root): """A decorator that wraps the passed in function and logs exceptions. Parameters ---------- logger: logging.Logger The logger to which to log the error. """ def log_wrapper(function): @functools.wraps(function) def wra...

A decorator that wraps the passed in function and logs exceptions. Parameters ---------- logger: logging.Logger The logger to which to log the error.

logerror

python

dmlc/gluon-nlp

src/gluonnlp/utils/misc.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py

Apache-2.0

def grouper(iterable, n, fillvalue=None): """Collect data into fixed-length chunks or blocks""" # grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx args = [iter(iterable)] * n return itertools.zip_longest(*args, fillvalue=fillvalue)

Collect data into fixed-length chunks or blocks

grouper

python

dmlc/gluon-nlp

src/gluonnlp/utils/misc.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py

Apache-2.0

def repeat(iterable, count=None): """Repeat a basic iterator for multiple rounds Parameters ---------- iterable The basic iterable count Repeat the basic iterable for "count" times. If it is None, it will be an infinite iterator. Returns ------- new_iterable A n...

Repeat a basic iterator for multiple rounds Parameters ---------- iterable The basic iterable count Repeat the basic iterable for "count" times. If it is None, it will be an infinite iterator. Returns ------- new_iterable A new iterable in which the basic iterator h...

repeat

python

dmlc/gluon-nlp

src/gluonnlp/utils/misc.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py

Apache-2.0

def load_checksum_stats(path: str) -> dict: """ Parameters ---------- path Path to the stored checksum Returns ------- file_stats """ file_stats = dict() with open(path, 'r', encoding='utf-8') as f: for line in f: name, hex_hash, file_size = line.str...

Parameters ---------- path Path to the stored checksum Returns ------- file_stats

load_checksum_stats

python

dmlc/gluon-nlp

src/gluonnlp/utils/misc.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py

Apache-2.0

def download_file_from_google_drive(file_id, dest_path, overwrite=False, showsize=False): """Downloads a shared file from google drive into a given folder. Optionally unzips it. Parameters ---------- file_id: str the file identifier. You can obtain it fro...

Downloads a shared file from google drive into a given folder. Optionally unzips it. Parameters ---------- file_id: str the file identifier. You can obtain it from the sharable link. dest_path: str the destination where to save the downloaded ...

download_file_from_google_drive

python

dmlc/gluon-nlp

src/gluonnlp/utils/misc.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py

Apache-2.0

def download(url: str, path: Optional[str] = None, overwrite: Optional[bool] = False, sha1_hash: Optional[str] = None, retries: Optional[int] = 5, verify_ssl: Optional[bool] = True, anonymous_credential: Optional[bool] = True) -> str: """...

Download a given URL Parameters ---------- url URL to download path Destination path to store downloaded file. By default stores to the current directory with same name as in url. overwrite Whether to overwrite destination file if already exists. sha1_hash ...

download

python

dmlc/gluon-nlp

src/gluonnlp/utils/misc.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py

Apache-2.0

def check_version(min_version: str, warning_only: bool = False, library: Optional[ModuleType] = None): """Check the version of gluonnlp satisfies the provided minimum version. An exception is thrown if the check does not pass. Parameters ---------- min_version ...

Check the version of gluonnlp satisfies the provided minimum version. An exception is thrown if the check does not pass. Parameters ---------- min_version Minimum version warning_only Printing a warning instead of throwing an exception. library The target library for ver...

check_version

python

dmlc/gluon-nlp

src/gluonnlp/utils/misc.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py

Apache-2.0

def init_comm(backend, gpus): """Init communication backend Parameters ---------- backend The communication backend gpus Returns ------- store The kvstore num_workers The total number of workers rank local_rank is_master_node ctx_l """ ...

Init communication backend Parameters ---------- backend The communication backend gpus Returns ------- store The kvstore num_workers The total number of workers rank local_rank is_master_node ctx_l

init_comm

python

dmlc/gluon-nlp

src/gluonnlp/utils/misc.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py

Apache-2.0

def get_mxnet_visible_ctx(): """Get the visible contexts in MXNet. - If GPU is available it will return all the visible GPUs, which can be controlled via "CUDA_VISIBLE_DEVICES". - If no GPU is available it will return the cpu device. Returns ------- ctx_l The recommende...

Get the visible contexts in MXNet. - If GPU is available it will return all the visible GPUs, which can be controlled via "CUDA_VISIBLE_DEVICES". - If no GPU is available it will return the cpu device. Returns ------- ctx_l The recommended contexts to use for MXNet

get_mxnet_visible_ctx

python

dmlc/gluon-nlp

src/gluonnlp/utils/misc.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/misc.py

Apache-2.0

def __init__(self, params=None): """Maintain a set of shadow variables "v" that is calculated by v[:] = (1 - 1/t) v + 1/t \theta The t is the number of training steps. It is also known as "Polyak-Rupert averaging" applied to SGD and was rediscovered in "Towards Optimal One...

Maintain a set of shadow variables "v" that is calculated by v[:] = (1 - 1/t) v + 1/t heta The t is the number of training steps. It is also known as "Polyak-Rupert averaging" applied to SGD and was rediscovered in "Towards Optimal One Pass Large Scale Learning withAveraged Stoch...

__init__

python

dmlc/gluon-nlp

src/gluonnlp/utils/parameter.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py

Apache-2.0

def apply(self, params): """ Tell the moving average tracker which parameters we are going to track. Parameters ---------- params : ParameterDict The parameters that we are going to track and calculate the moving average. """ assert self._track_params is None...

Tell the moving average tracker which parameters we are going to track. Parameters ---------- params : ParameterDict The parameters that we are going to track and calculate the moving average.

apply

python

dmlc/gluon-nlp

src/gluonnlp/utils/parameter.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py

Apache-2.0

def copy_back(self, params=None): """ Copy the average parameters back to the given parameters Parameters ---------- params : ParameterDict The parameters that we will copy tha average params to. If it is not given, the tracked parameters will be updated ...

Copy the average parameters back to the given parameters Parameters ---------- params : ParameterDict The parameters that we will copy tha average params to. If it is not given, the tracked parameters will be updated

copy_back

python

dmlc/gluon-nlp

src/gluonnlp/utils/parameter.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py

Apache-2.0

def grad_global_norm(parameters: Iterable[Parameter]) -> float: """Calculate the 2-norm of gradients of parameters, and how much they should be scaled down such that their 2-norm does not exceed `max_norm`, if `max_norm` if provided. If gradients exist for more than one context for a parameter, user needs t...

Calculate the 2-norm of gradients of parameters, and how much they should be scaled down such that their 2-norm does not exceed `max_norm`, if `max_norm` if provided. If gradients exist for more than one context for a parameter, user needs to explicitly call ``trainer.allreduce_grads`` so that the gradients...

grad_global_norm

python

dmlc/gluon-nlp

src/gluonnlp/utils/parameter.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py

Apache-2.0

def clip_grad_global_norm(parameters: Iterable[Parameter], max_norm: float, check_isfinite: bool = True) -> Tuple[float, float, bool]: """Rescales gradients of parameters so that the sum of their 2-norm is smaller than `max_norm`. If gradients exist for more t...

Rescales gradients of parameters so that the sum of their 2-norm is smaller than `max_norm`. If gradients exist for more than one context for a parameter, user needs to explicitly call ``trainer.allreduce_grads`` so that the gradients are summed first before calculating the 2-norm. .. note:: T...

clip_grad_global_norm

python

dmlc/gluon-nlp

src/gluonnlp/utils/parameter.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py

Apache-2.0

def move_to_ctx(arr, ctx): """Move a nested structure of array to the given context Parameters ---------- arr The input array ctx The MXNet context Returns ------- new_arr The array that has been moved to context """ if isinstance(arr, tuple): re...

Move a nested structure of array to the given context Parameters ---------- arr The input array ctx The MXNet context Returns ------- new_arr The array that has been moved to context

move_to_ctx

python

dmlc/gluon-nlp

src/gluonnlp/utils/parameter.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py

Apache-2.0

def deduplicate_param_dict(param_dict): """Get a parameter dict that has been deduplicated Parameters ---------- param_dict The parameter dict returned by `model.collect_params()` Returns ------- dedup_param_dict """ dedup_param_dict = dict() param_uuid_set = set() ...

Get a parameter dict that has been deduplicated Parameters ---------- param_dict The parameter dict returned by `model.collect_params()` Returns ------- dedup_param_dict

deduplicate_param_dict

python

dmlc/gluon-nlp

src/gluonnlp/utils/parameter.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py

Apache-2.0

def count_parameters(params) -> Tuple[int, int]: """ Parameters ---------- params The input parameter dict Returns ------- num_params The number of parameters that requires gradient num_fixed_params The number of parameters that does not require gradient """...

Parameters ---------- params The input parameter dict Returns ------- num_params The number of parameters that requires gradient num_fixed_params The number of parameters that does not require gradient

count_parameters

python

dmlc/gluon-nlp

src/gluonnlp/utils/parameter.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/parameter.py

Apache-2.0

def get_trimmed_lengths(lengths: List[int], max_length: int, do_merge: bool = False) -> np.ndarray: """Get the trimmed lengths of multiple text data. It will make sure that the trimmed length is smaller than or equal to the max_length - do_merge is True ...

Get the trimmed lengths of multiple text data. It will make sure that the trimmed length is smaller than or equal to the max_length - do_merge is True Make sure that sum(trimmed_lengths) <= max_length. The strategy is to always try to trim the longer lengths. - do_merge is False Mak...

get_trimmed_lengths

python

dmlc/gluon-nlp

src/gluonnlp/utils/preprocessing.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/preprocessing.py

Apache-2.0

def match_tokens_with_char_spans(token_offsets: np.ndarray, spans: np.ndarray) -> np.ndarray: """Match the span offsets with the character-level offsets. For each span, we perform the following: 1: Cutoff the boundary span[0] = max(span[0], token_offsets[0, 0]) ...

Match the span offsets with the character-level offsets. For each span, we perform the following: 1: Cutoff the boundary span[0] = max(span[0], token_offsets[0, 0]) span[1] = min(span[1], token_offsets[-1, 1]) 2: Find start + end We try to select the smallest number of tokens that c...

match_tokens_with_char_spans

python

dmlc/gluon-nlp

src/gluonnlp/utils/preprocessing.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/preprocessing.py

Apache-2.0

def register(self, *args): """ Register the given object under either the nickname or `obj.__name__`. It can be used as either a decorator or not. See docstring of this class for usage. """ if len(args) == 2: # Register an object with nick name by function call ...

Register the given object under either the nickname or `obj.__name__`. It can be used as either a decorator or not. See docstring of this class for usage.

register

python

dmlc/gluon-nlp

src/gluonnlp/utils/registry.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/registry.py

Apache-2.0

def create(self, name: str, *args, **kwargs) -> object: """Create the class object with the given args and kwargs Parameters ---------- name The name in the registry args kwargs Returns ------- ret The created object ...

Create the class object with the given args and kwargs Parameters ---------- name The name in the registry args kwargs Returns ------- ret The created object

create

python

dmlc/gluon-nlp

src/gluonnlp/utils/registry.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/registry.py

Apache-2.0

def serialize(path, tbl): """Serialize tbl with out-of-band data to path for zero-copy shared memory usage. If the object to be serialized itself, or the objects it uses for data storage (such as numpy arrays) implement the the pickle protocol version 5 pickle.PickleBuffer type in __reduce_ex__, then t...

Serialize tbl with out-of-band data to path for zero-copy shared memory usage. If the object to be serialized itself, or the objects it uses for data storage (such as numpy arrays) implement the the pickle protocol version 5 pickle.PickleBuffer type in __reduce_ex__, then this function can store these ...

serialize

python

dmlc/gluon-nlp

src/gluonnlp/utils/shm.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/shm.py

Apache-2.0

def load(path): """Load serialized object with out-of-band data from path based on zero-copy shared memory. Parameters ---------- path : pathlib.Path Folder used to save serialized data with serialize(). Usually a folder /dev/shm """ num_buffers = len(list(path.iterdir())) - 1 # exclu...

Load serialized object with out-of-band data from path based on zero-copy shared memory. Parameters ---------- path : pathlib.Path Folder used to save serialized data with serialize(). Usually a folder /dev/shm

load

python

dmlc/gluon-nlp

src/gluonnlp/utils/shm.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/shm.py

Apache-2.0

def is_match_states_batch_size(states, states_batch_axis, batch_size) -> bool: """Test whether the generated states have the specified batch size Parameters ---------- states The states structure states_batch_axis The states batch axis structure batch_size The batch size...

Test whether the generated states have the specified batch size Parameters ---------- states The states structure states_batch_axis The states batch axis structure batch_size The batch size Returns ------- ret

is_match_states_batch_size

python

dmlc/gluon-nlp

src/gluonnlp/utils/testing.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/testing.py

Apache-2.0

def verify_nmt_model(model, batch_size: int = 4, src_seq_length: int = 5, tgt_seq_length: int = 10, atol: float = 1E-4, rtol: float = 1E-3): """Verify the correctness of an NMT model. Raise error message if it detects problems. ...

Verify the correctness of an NMT model. Raise error message if it detects problems. Parameters ---------- model The machine translation model batch_size The batch size to test the nmt model src_seq_length Length of the source sequence tgt_seq_length Length of the...

verify_nmt_model

python

dmlc/gluon-nlp

src/gluonnlp/utils/testing.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/testing.py

Apache-2.0

def verify_nmt_inference(train_model, inference_model, batch_size=4, src_seq_length=5, tgt_seq_length=10, atol=1E-4, rtol=1E-3): """Verify the correctness of an NMT inference model. Raise error message if it detects any problems. Parameters ---------- ...

Verify the correctness of an NMT inference model. Raise error message if it detects any problems. Parameters ---------- train_model The training model inference_model The inference model batch_size Batch size src_seq_length Length of the source sequence t...

verify_nmt_inference

python

dmlc/gluon-nlp

src/gluonnlp/utils/testing.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/testing.py

Apache-2.0

def _cast_nested_to_fp16(nested_dat): """Cast the nested input to fp16 Parameters ---------- dat The input nested data structure Returns ------- output The casted output data """ if isinstance(nested_dat, (mx.np.ndarray, np.ndarray)): if nested_dat.dtype == ...

Cast the nested input to fp16 Parameters ---------- dat The input nested data structure Returns ------- output The casted output data

_cast_nested_to_fp16

python

dmlc/gluon-nlp

src/gluonnlp/utils/testing.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/testing.py

Apache-2.0

def verify_backbone_fp16(model_cls, cfg, ctx, inputs, atol=1E-2, rtol=1E-2, check_amp=True): """Test whether the backbone model has the comparable parameter gradient + Parameters ---------- model_cls The modeling class cfg The configuration ctx T...

Test whether the backbone model has the comparable parameter gradient + Parameters ---------- model_cls The modeling class cfg The configuration ctx The context inputs The input tensors of the model. We will atol The absolute tolerance rtol ...

verify_backbone_fp16

python

dmlc/gluon-nlp

src/gluonnlp/utils/testing.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/testing.py

Apache-2.0

def get_ec2_tvm_flags() -> Dict[str, Dict]: r"""Return the recommended flags for TVM compilation in AWS EC2 instances. Including C4, C5, G4, P3. For more details about AWS EC2 instances, refer to https://aws.amazon.com/ec2/instance-types/. Returns ------- info_dict A dictionary that c...

Return the recommended flags for TVM compilation in AWS EC2 instances. Including C4, C5, G4, P3. For more details about AWS EC2 instances, refer to https://aws.amazon.com/ec2/instance-types/. Returns ------- info_dict A dictionary that contains the mapping between instance type and the ...

get_ec2_tvm_flags

python

dmlc/gluon-nlp

src/gluonnlp/utils/tvm_utils.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/tvm_utils.py

Apache-2.0

def update_tvm_convert_map() -> None: """A Monkey Patch to update convert map in tvm/relay/frontend/mxnet.py""" op = (('masked_softmax', _mx_masked_softmax),) _convert_map.update({key: value for key, value in op})

A Monkey Patch to update convert map in tvm/relay/frontend/mxnet.py

update_tvm_convert_map

python

dmlc/gluon-nlp

src/gluonnlp/utils/tvm_utils.py

https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/utils/tvm_utils.py

Apache-2.0

def grouper(iterable, n, fillvalue=None): """Collect data into fixed-length chunks or blocks""" # grouper('ABCDEFG', 3, 'x') --> ABC DEF Gxx args = [iter(iterable)] * n return itertools.zip_longest(*args, fillvalue=fillvalue)

Collect data into fixed-length chunks or blocks

grouper

python

dmlc/gluon-nlp

tests/test_gluon_block.py

https://github.com/dmlc/gluon-nlp/blob/master/tests/test_gluon_block.py

Apache-2.0

def test_test(): """Test that fixing a random seed works.""" py_rnd = random.randint(0, 100) np_rnd = np.random.randint(0, 100) mx_rnd = mx.nd.random_uniform(shape=(1, )).asscalar() random.seed(1) mx.random.seed(1) np.random.seed(1) assert py_rnd == random.randint(0, 100) assert np...

Test that fixing a random seed works.

test_test

python

dmlc/gluon-nlp

tests/test_pytest.py

https://github.com/dmlc/gluon-nlp/blob/master/tests/test_pytest.py

Apache-2.0

def is_image_file(filename): """Checks if a file is an image. Args: filename (string): path to a file Returns: bool: True if the filename ends with a known image extension """ filename_lower = filename.lower() return any(filename_lower.endswith(ext) for ext in IMG_EXTENSIONS)

Checks if a file is an image. Args: filename (string): path to a file Returns: bool: True if the filename ends with a known image extension

is_image_file

python

ajbrock/BigGAN-PyTorch

datasets.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/datasets.py

MIT

def __getitem__(self, index): """ Args: index (int): Index Returns: tuple: (image, target) where target is class_index of the target class. """ if self.load_in_mem: img = self.data[index] target = self.labels[index] else: path, target = self.imgs[index] ...

Args: index (int): Index Returns: tuple: (image, target) where target is class_index of the target class.

__getitem__

python

ajbrock/BigGAN-PyTorch

datasets.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/datasets.py

MIT

def __getitem__(self, index): """ Args: index (int): Index Returns: tuple: (image, target) where target is class_index of the target class. """ # If loaded the entire dataset in RAM, get image from memory if self.load_in_mem: img = self.data[index] target = self.labe...

Args: index (int): Index Returns: tuple: (image, target) where target is class_index of the target class.

__getitem__

python

ajbrock/BigGAN-PyTorch

datasets.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/datasets.py

MIT

def __getitem__(self, index): """ Args: index (int): Index Returns: tuple: (image, target) where target is index of the target class. """ img, target = self.data[index], self.labels[index] # doing this so that it is consistent with all other datasets # to return a PIL Image ...

Args: index (int): Index Returns: tuple: (image, target) where target is index of the target class.

__getitem__

python

ajbrock/BigGAN-PyTorch

datasets.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/datasets.py

MIT

def torch_cov(m, rowvar=False): '''Estimate a covariance matrix given data. Covariance indicates the level to which two variables vary together. If we examine N-dimensional samples, `X = [x_1, x_2, ... x_N]^T`, then the covariance matrix element `C_{ij}` is the covariance of `x_i` and `x_j`. The el...

Estimate a covariance matrix given data. Covariance indicates the level to which two variables vary together. If we examine N-dimensional samples, `X = [x_1, x_2, ... x_N]^T`, then the covariance matrix element `C_{ij}` is the covariance of `x_i` and `x_j`. The element `C_{ii}` is the variance of `x_i`...

torch_cov

python

ajbrock/BigGAN-PyTorch

inception_utils.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/inception_utils.py

MIT

def numpy_calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): """Numpy implementation of the Frechet Distance. Taken from https://github.com/bioinf-jku/TTUR The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is d^2 = ||mu_1 - mu_2||^2 + Tr(C_1...

Numpy implementation of the Frechet Distance. Taken from https://github.com/bioinf-jku/TTUR The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). Stable version by Dougal J. Sutherland. Params: ...

numpy_calculate_frechet_distance

python

ajbrock/BigGAN-PyTorch

inception_utils.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/inception_utils.py

MIT

def torch_calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): """Pytorch implementation of the Frechet Distance. Taken from https://github.com/bioinf-jku/TTUR The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is d^2 = ||mu_1 - mu_2||^2 + Tr(C...

Pytorch implementation of the Frechet Distance. Taken from https://github.com/bioinf-jku/TTUR The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). Stable version by Dougal J. Sutherland. Params...

torch_calculate_frechet_distance

python

ajbrock/BigGAN-PyTorch

inception_utils.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/inception_utils.py

MIT

def __call__(self, img): """ Args: img (PIL Image): Image to be cropped. Returns: PIL Image: Cropped image. """ size = (min(img.size), min(img.size)) # Only step forward along this edge if it's the long edge i = (0 if size[0] == img.size[0] else np.random.randint(l...

Args: img (PIL Image): Image to be cropped. Returns: PIL Image: Cropped image.

__call__

python

ajbrock/BigGAN-PyTorch

utils.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/utils.py

MIT

def log(self, record=None, **kwargs): """ Assumption: no newlines in the input. """ if record is None: record = {} record.update(kwargs) record['_stamp'] = time.time() with open(self.fname, 'a') as f: f.write(json.dumps(record, ensure_ascii=True) + '\n')

Assumption: no newlines in the input.

log

python

ajbrock/BigGAN-PyTorch

utils.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/utils.py

MIT

def progress(items, desc='', total=None, min_delay=0.1, displaytype='s1k'): """ Returns a generator over `items`, printing the number and percentage of items processed and the estimated remaining processing time before yielding the next item. `total` gives the total number of items (required if `items` has no...

Returns a generator over `items`, printing the number and percentage of items processed and the estimated remaining processing time before yielding the next item. `total` gives the total number of items (required if `items` has no length), and `min_delay` gives the minimum time in seconds between subsequent ...

progress

python

ajbrock/BigGAN-PyTorch

utils.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/utils.py

MIT

def step(self, closure=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: loss = closure() for group in self.param_groups: for p in g...

Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss.

step

python

ajbrock/BigGAN-PyTorch

utils.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/utils.py

MIT

def _data_parallel_master(self, intermediates): """Reduce the sum and square-sum, compute the statistics, and broadcast it.""" # Always using same "device order" makes the ReduceAdd operation faster. # Thanks to:: Tete Xiao (http://tetexiao.com/) intermediates = sorted(intermediates, ke...

Reduce the sum and square-sum, compute the statistics, and broadcast it.

_data_parallel_master

python

ajbrock/BigGAN-PyTorch

sync_batchnorm/batchnorm.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/batchnorm.py

MIT

def _compute_mean_std(self, sum_, ssum, size): """Compute the mean and standard-deviation with sum and square-sum. This method also maintains the moving average on the master device.""" assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.' mean = sum...

Compute the mean and standard-deviation with sum and square-sum. This method also maintains the moving average on the master device.

_compute_mean_std

python

ajbrock/BigGAN-PyTorch

sync_batchnorm/batchnorm.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/batchnorm.py

MIT

def __init__(self, master_callback): """ Args: master_callback: a callback to be invoked after having collected messages from slave devices. """ self._master_callback = master_callback self._queue = queue.Queue() self._registry = collections.OrderedDict() ...

Args: master_callback: a callback to be invoked after having collected messages from slave devices.

__init__

python

ajbrock/BigGAN-PyTorch

sync_batchnorm/comm.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/comm.py

MIT

def register_slave(self, identifier): """ Register an slave device. Args: identifier: an identifier, usually is the device id. Returns: a `SlavePipe` object which can be used to communicate with the master device. """ if self._activated: assert ...

Register an slave device. Args: identifier: an identifier, usually is the device id. Returns: a `SlavePipe` object which can be used to communicate with the master device.

register_slave

python

ajbrock/BigGAN-PyTorch

sync_batchnorm/comm.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/comm.py

MIT

def run_master(self, master_msg): """ Main entry for the master device in each forward pass. The messages were first collected from each devices (including the master device), and then an callback will be invoked to compute the message to be sent back to each devices (including t...

Main entry for the master device in each forward pass. The messages were first collected from each devices (including the master device), and then an callback will be invoked to compute the message to be sent back to each devices (including the master device). Args: ...

run_master

python

ajbrock/BigGAN-PyTorch

sync_batchnorm/comm.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/comm.py

MIT

def execute_replication_callbacks(modules): """ Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` Note that, as all modules are isomorphism, we assign eac...

Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` Note that, as all modules are isomorphism, we assign each sub-module with a context (shared among multi...

execute_replication_callbacks

python

ajbrock/BigGAN-PyTorch

sync_batchnorm/replicate.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/replicate.py

MIT

def patch_replication_callback(data_parallel): """ Monkey-patch an existing `DataParallel` object. Add the replication callback. Useful when you have customized `DataParallel` implementation. Examples: > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) > sync_bn = DataParal...

Monkey-patch an existing `DataParallel` object. Add the replication callback. Useful when you have customized `DataParallel` implementation. Examples: > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) > patch_replic...

patch_replication_callback

python

ajbrock/BigGAN-PyTorch

sync_batchnorm/replicate.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/sync_batchnorm/replicate.py

MIT

def dump_tfhub_to_hdf5(module_path, hdf5_path, redownload=False): """Loads TFHub weights and saves them to intermediate HDF5 file. Args: module_path ([Path-like]): Path to TFHub module. hdf5_path ([Path-like]): Path to output HDF5 file. Returns: [h5py.File]: Loaded hdf5 file containing module weight...

Loads TFHub weights and saves them to intermediate HDF5 file. Args: module_path ([Path-like]): Path to TFHub module. hdf5_path ([Path-like]): Path to output HDF5 file. Returns: [h5py.File]: Loaded hdf5 file containing module weights.

dump_tfhub_to_hdf5

python

ajbrock/BigGAN-PyTorch

TFHub/converter.py

https://github.com/ajbrock/BigGAN-PyTorch/blob/master/TFHub/converter.py

MIT

def read_img(t_imgfname, input_size, img_mean): # optional pre-processing arguments """Read one image and its corresponding mask with optional pre-processing. Args: input_queue: tf queue with paths to the image and its mask. input_size: a tuple with (height, width) values. If not given, return images of...

Read one image and its corresponding mask with optional pre-processing. Args: input_queue: tf queue with paths to the image and its mask. input_size: a tuple with (height, width) values. If not given, return images of original size. random_scale: whether to randomly scale the images prior to rand...

read_img

python

iyah4888/SIGGRAPH18SSS

main_hyper.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/main_hyper.py

MIT

def get_arguments(): """Parse all the arguments provided from the CLI. Returns: A list of parsed arguments. """ parser = argparse.ArgumentParser(description="DeepLab-ResNet Network") parser.add_argument("--batch-size", type=int, default=BATCH_SIZE, help="Number of ...

Parse all the arguments provided from the CLI. Returns: A list of parsed arguments.

get_arguments

python

iyah4888/SIGGRAPH18SSS

parse_opt.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/parse_opt.py

MIT

def __init__(self, sess, args): """Initialize the parameters. sess: tensorflow session """ self.sess = sess self.batch_size = args.batch_size self.args = args # parameters used to save a checkpoint self.dataset = "Hypcol" self.options = [] self._attrs = ['batch_size', 'dataset'] self.build_mo...

Initialize the parameters. sess: tensorflow session

__init__

python

iyah4888/SIGGRAPH18SSS

deeplab_resnet/hc_deeplab.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/hc_deeplab.py

MIT

def image_scaling(img, label): """ Randomly scales the images between 0.5 to 1.5 times the original size. Args: img: Training image to scale. label: Segmentation mask to scale. """ scale = tf.random_uniform([1], minval=0.5, maxval=1.5, dtype=tf.float32, seed=None) h_new = tf.to...

Randomly scales the images between 0.5 to 1.5 times the original size. Args: img: Training image to scale. label: Segmentation mask to scale.

image_scaling

python

iyah4888/SIGGRAPH18SSS

deeplab_resnet/image_reader.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py

MIT

def image_mirroring(img, label): """ Randomly mirrors the images. Args: img: Training image to mirror. label: Segmentation mask to mirror. """ distort_left_right_random = tf.random_uniform([1], 0, 1.0, dtype=tf.float32)[0] mirror = tf.less(tf.stack([1.0, distort_left_right_rand...

Randomly mirrors the images. Args: img: Training image to mirror. label: Segmentation mask to mirror.

image_mirroring

python

iyah4888/SIGGRAPH18SSS

deeplab_resnet/image_reader.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py

MIT

def random_crop_and_pad_image_and_labels(image, label, crop_h, crop_w, ignore_label=255): """ Randomly crop and pads the input images. Args: image: Training image to crop/ pad. label: Segmentation mask to crop/ pad. crop_h: Height of cropped segment. crop_w: Width of cropped segment...

Randomly crop and pads the input images. Args: image: Training image to crop/ pad. label: Segmentation mask to crop/ pad. crop_h: Height of cropped segment. crop_w: Width of cropped segment. ignore_label: Label to ignore during the training.

random_crop_and_pad_image_and_labels

python

iyah4888/SIGGRAPH18SSS

deeplab_resnet/image_reader.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py

MIT

def read_labeled_image_list(data_dir, data_list): """Reads txt file containing paths to images and ground truth masks. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/path/to/image /path/to/mask'. Returns: Two l...

Reads txt file containing paths to images and ground truth masks. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/path/to/image /path/to/mask'. Returns: Two lists with all file names for images and masks, respective...

read_labeled_image_list

python

iyah4888/SIGGRAPH18SSS

deeplab_resnet/image_reader.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py

MIT

def read_data_list(data_dir, data_list, ext): """Reads txt file containing paths to images and ground truth masks. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/path/to/image /path/to/mask'. Returns: Two lists...

Reads txt file containing paths to images and ground truth masks. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/path/to/image /path/to/mask'. Returns: Two lists with all file names for images and masks, respective...

read_data_list

python

iyah4888/SIGGRAPH18SSS

deeplab_resnet/image_reader.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py

MIT

def read_images_from_disk(input_queue, input_size, random_scale, random_mirror, ignore_label, img_mean): # optional pre-processing arguments """Read one image and its corresponding mask with optional pre-processing. Args: input_queue: tf queue with paths to the image and its mask. input_size: a...

Read one image and its corresponding mask with optional pre-processing. Args: input_queue: tf queue with paths to the image and its mask. input_size: a tuple with (height, width) values. If not given, return images of original size. random_scale: whether to randomly scale th...

read_images_from_disk

python

iyah4888/SIGGRAPH18SSS

deeplab_resnet/image_reader.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py

MIT

def __init__(self, data_dir, data_list, input_size, random_scale, random_mirror, ignore_label, img_mean, coord): '''Initialise an ImageReader. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/...

Initialise an ImageReader. Args: data_dir: path to the directory with images and masks. data_list: path to the file with lines of the form '/path/to/image /path/to/mask'. input_size: a tuple with (height, width) values, to which all the images will be resized. ra...

__init__

python

iyah4888/SIGGRAPH18SSS

deeplab_resnet/image_reader.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py

MIT

def dequeue(self, num_elements): '''Pack images and labels into a batch. Args: num_elements: the batch size. Returns: Two tensors of size (batch_size, h, w, {3, 1}) for images and masks.''' image_batch, label_batch = tf.train.batch([self.image, sel...

Pack images and labels into a batch. Args: num_elements: the batch size. Returns: Two tensors of size (batch_size, h, w, {3, 1}) for images and masks.

dequeue

python

iyah4888/SIGGRAPH18SSS

deeplab_resnet/image_reader.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py

MIT

def read_an_image_from_disk(t_imgfname, t_labelfname, input_size, random_scale, random_mirror, ignore_label, img_mean): # optional pre-processing arguments """Read one image and its corresponding mask with optional pre-processing. Args: input_queue: tf queue with paths to the image and its mask. ...

Read one image and its corresponding mask with optional pre-processing. Args: input_queue: tf queue with paths to the image and its mask. input_size: a tuple with (height, width) values. If not given, return images of original size. random_scale: whether to randomly scale th...

read_an_image_from_disk

python

iyah4888/SIGGRAPH18SSS

deeplab_resnet/image_reader.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/image_reader.py

MIT

def setup(self, is_training, num_classes): '''Network definition. Args: is_training: whether to update the running mean and variance of the batch normalisation layer. If the batch size is small, it is better to keep the running mean and variance of ...

Network definition. Args: is_training: whether to update the running mean and variance of the batch normalisation layer. If the batch size is small, it is better to keep the running mean and variance of the-pretrained model frozen. num_...

setup

python

iyah4888/SIGGRAPH18SSS

deeplab_resnet/model.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/model.py

MIT

def decode_labels(mask, num_images=1, num_classes=21): """Decode batch of segmentation masks. Args: mask: result of inference after taking argmax. num_images: number of images to decode from the batch. num_classes: number of classes to predict (including background). Returns: ...

Decode batch of segmentation masks. Args: mask: result of inference after taking argmax. num_images: number of images to decode from the batch. num_classes: number of classes to predict (including background). Returns: A batch with num_images RGB images of the same size as the ...

decode_labels

python

iyah4888/SIGGRAPH18SSS

deeplab_resnet/utils.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/utils.py

MIT

def prepare_label(input_batch, new_size, num_classes, one_hot=True): """Resize masks and perform one-hot encoding. Args: input_batch: input tensor of shape [batch_size H W 1]. new_size: a tensor with new height and width. num_classes: number of classes to predict (including background). ...

Resize masks and perform one-hot encoding. Args: input_batch: input tensor of shape [batch_size H W 1]. new_size: a tensor with new height and width. num_classes: number of classes to predict (including background). one_hot: whether perform one-hot encoding. Returns: Outputs a te...

prepare_label

python

iyah4888/SIGGRAPH18SSS

deeplab_resnet/utils.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/utils.py

MIT

def inv_preprocess(imgs, num_images, img_mean): """Inverse preprocessing of the batch of images. Add the mean vector and convert from BGR to RGB. Args: imgs: batch of input images. num_images: number of images to apply the inverse transformations on. img_mean: vector of mean col...

Inverse preprocessing of the batch of images. Add the mean vector and convert from BGR to RGB. Args: imgs: batch of input images. num_images: number of images to apply the inverse transformations on. img_mean: vector of mean colour values. Returns: The batch of the size...

inv_preprocess

python

iyah4888/SIGGRAPH18SSS

deeplab_resnet/utils.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/deeplab_resnet/utils.py

MIT

def __init__(self, def_path, phase='test'): ''' def_path: Path to the model definition (.prototxt) data_path: Path to the model data (.caffemodel) phase: Either 'test' or 'train'. Used for filtering phase-specific nodes. ''' self.def_path = def_path self.phase = p...

def_path: Path to the model definition (.prototxt) data_path: Path to the model data (.caffemodel) phase: Either 'test' or 'train'. Used for filtering phase-specific nodes.

__init__

python

iyah4888/SIGGRAPH18SSS

kaffe/graph.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/graph.py

MIT

def load(self): '''Load the layer definitions from the prototxt.''' self.params = get_caffe_resolver().NetParameter() with open(self.def_path, 'rb') as def_file: text_format.Merge(def_file.read(), self.params)

Load the layer definitions from the prototxt.

load

python

iyah4888/SIGGRAPH18SSS

kaffe/graph.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/graph.py

MIT

def filter_layers(self, layers): '''Filter out layers based on the current phase.''' phase_map = {0: 'train', 1: 'test'} filtered_layer_names = set() filtered_layers = [] for layer in layers: phase = self.phase if len(layer.include): phase ...

Filter out layers based on the current phase.

filter_layers

python

iyah4888/SIGGRAPH18SSS

kaffe/graph.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/graph.py

MIT

def make_node(self, layer): '''Create a graph node for the given layer.''' kind = NodeKind.map_raw_kind(layer.type) if kind is None: raise KaffeError('Unknown layer type encountered: %s' % layer.type) # We want to use the layer's top names (the "output" names), rather than th...

Create a graph node for the given layer.

make_node

python

iyah4888/SIGGRAPH18SSS

kaffe/graph.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/graph.py

MIT

def make_input_nodes(self): ''' Create data input nodes. This method is for old-style inputs, where the input specification was not treated as a first-class layer in the prototext. Newer models use the "Input layer" type. ''' nodes = [Node(name, NodeKind.Data) fo...

Create data input nodes. This method is for old-style inputs, where the input specification was not treated as a first-class layer in the prototext. Newer models use the "Input layer" type.

make_input_nodes

python

iyah4888/SIGGRAPH18SSS

kaffe/graph.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/graph.py

MIT

def build(self): ''' Builds the graph from the Caffe layer definitions. ''' # Get the layers layers = self.params.layers or self.params.layer # Filter out phase-excluded layers layers = self.filter_layers(layers) # Get any separately-specified input layers...

Builds the graph from the Caffe layer definitions.

build

python

iyah4888/SIGGRAPH18SSS

kaffe/graph.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/graph.py

MIT

def load(self, data_path, session, ignore_missing=False): '''Load network weights. data_path: The path to the numpy-serialized network weights session: The current TensorFlow session ignore_missing: If true, serialized weights for missing layers are ignored. ''' data_dict...

Load network weights. data_path: The path to the numpy-serialized network weights session: The current TensorFlow session ignore_missing: If true, serialized weights for missing layers are ignored.

load

python

iyah4888/SIGGRAPH18SSS

kaffe/tensorflow/network.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/tensorflow/network.py

MIT

def feed(self, *args): '''Set the input(s) for the next operation by replacing the terminal nodes. The arguments can be either layer names or the actual layers. ''' assert len(args) != 0 self.terminals = [] for fed_layer in args: if isinstance(fed_layer, str):...

Set the input(s) for the next operation by replacing the terminal nodes. The arguments can be either layer names or the actual layers.

feed

python

iyah4888/SIGGRAPH18SSS

kaffe/tensorflow/network.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/tensorflow/network.py

MIT

def get_unique_name(self, prefix): '''Returns an index-suffixed unique name for the given prefix. This is used for auto-generating layer names based on the type-prefix. ''' ident = sum(t.startswith(prefix) for t, _ in list(self.layers.items())) + 1 return '%s_%d' % (prefix, ident...

Returns an index-suffixed unique name for the given prefix. This is used for auto-generating layer names based on the type-prefix.

get_unique_name

python

iyah4888/SIGGRAPH18SSS

kaffe/tensorflow/network.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/tensorflow/network.py

MIT

def get_padding_type(kernel_params, input_shape, output_shape): '''Translates Caffe's numeric padding to one of ('SAME', 'VALID'). Caffe supports arbitrary padding values, while TensorFlow only supports 'SAME' and 'VALID' modes. So, not all Caffe paddings can be translated to TensorFlow. There are some ...

Translates Caffe's numeric padding to one of ('SAME', 'VALID'). Caffe supports arbitrary padding values, while TensorFlow only supports 'SAME' and 'VALID' modes. So, not all Caffe paddings can be translated to TensorFlow. There are some subtleties to how the padding edge-cases are handled. These are des...

get_padding_type

python

iyah4888/SIGGRAPH18SSS

kaffe/tensorflow/transformer.py

https://github.com/iyah4888/SIGGRAPH18SSS/blob/master/kaffe/tensorflow/transformer.py

MIT

def run( self, query="What is a lagrangian?", limit_broad_results=1_000, limit_deduped_url_results=50, limit_hierarchical_url_results=50, limit_final_pagerank_results=20, url_contains_filter=None, ): """Run a search query using the WebSearchEngine clie...

Run a search query using the WebSearchEngine client

run

python

SciPhi-AI/agent-search

agent_search/app/server.py

https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/app/server.py

Apache-2.0

def to_string_dict(self) -> dict: """Returns a dictionary representation with all values as strings.""" return { "score": str(self.score), "url": self.url, "title": self.title, "dataset": self.dataset, "metadata": self.metadata, "te...

Returns a dictionary representation with all values as strings.

to_string_dict

python

SciPhi-AI/agent-search

agent_search/core/search_types.py

https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/core/search_types.py

Apache-2.0

def select_top_urls( ordered_points: List[AgentSearchResult], max_urls: int = 10, url_contains: Optional[List[str]] = None, ) -> List[str]: """A function to return the top unique URLs from the given poitns results.""" if not url_contains: url_contains = [] top_urls = set([]) for poi...

A function to return the top unique URLs from the given poitns results.

select_top_urls

python

SciPhi-AI/agent-search

agent_search/core/utils.py

https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/core/utils.py

Apache-2.0

def cosine_similarity(v1: np.ndarray, v2: np.ndarray) -> float: """Compute the cosine similarity between two vectors.""" dot_product = np.dot(v1, v2) norm_v1 = np.linalg.norm(v1) norm_v2 = np.linalg.norm(v2) return dot_product / (norm_v1 * norm_v2)

Compute the cosine similarity between two vectors.

cosine_similarity

python

SciPhi-AI/agent-search

agent_search/core/utils.py

https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/core/utils.py

Apache-2.0

def __init__( self, api_base: Optional[str] = None, api_key: Optional[str] = None, timeout: int = 30, ) -> None: """ Initializes the SciPhi client. Args: api_base (Optional[str]): Base URL for the SciPhi API. api_key (Optional[str]): A...

Initializes the SciPhi client. Args: api_base (Optional[str]): Base URL for the SciPhi API. api_key (Optional[str]): API key for authenticating requests. timeout (int): Timeout for API requests in seconds. Raises: ValueError: If `api_key` is not...

__init__

python

SciPhi-AI/agent-search

agent_search/providers/sciphi.py

https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/providers/sciphi.py

Apache-2.0

def _handle_api_response(self, response: httpx.Response) -> Dict: """ Handles the HTTP response from the API. Args: response (httpx.Response): The response from the API request. Returns: Dict: JSON response content. Raises: Exception: If the...

Handles the HTTP response from the API. Args: response (httpx.Response): The response from the API request. Returns: Dict: JSON response content. Raises: Exception: If the response indicates an error.

_handle_api_response

python

SciPhi-AI/agent-search

agent_search/providers/sciphi.py

https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/providers/sciphi.py

Apache-2.0

def _handle_search_response(self, search_results: Dict[str, str]) -> None: """ Handles dictionary search resopnses from the API. Args: search_results (Dict[str, str]): The response from the API request. Returns: Dict: JSON response content. Raises: ...

Handles dictionary search resopnses from the API. Args: search_results (Dict[str, str]): The response from the API request. Returns: Dict: JSON response content. Raises: Exception: If the response indicates an error.

_handle_search_response

python

SciPhi-AI/agent-search

agent_search/providers/sciphi.py

https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/providers/sciphi.py

Apache-2.0

def _retry_api_request( self, method: str, url: str, payload: Dict, max_retries: int = 3 ): """ Common method for retrying API requests with exponential backoff. Args: method (str): The HTTP method to use ('get' or 'post'). url (str): The API endpoint. ...

Common method for retrying API requests with exponential backoff. Args: method (str): The HTTP method to use ('get' or 'post'). url (str): The API endpoint. payload (Dict): The payload for the request. max_retries (int): Maximum number of retry attempts....

_retry_api_request

python

SciPhi-AI/agent-search

agent_search/providers/sciphi.py

https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/providers/sciphi.py

Apache-2.0

def search( self, query: str, search_provider: str, max_retries: int = 3 ) -> List[Dict]: """ Performs a search query using the SciPhi API with retry and backoff logic. Args: query (str): The search query string. search_provider (str): The search provider to ...

Performs a search query using the SciPhi API with retry and backoff logic. Args: query (str): The search query string. search_provider (str): The search provider to use. max_retries (int): Maximum number of retry attempts. Returns: List[Dict]: A lis...

search

python

SciPhi-AI/agent-search

agent_search/providers/sciphi.py

https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/providers/sciphi.py

Apache-2.0

def get_search_rag_response( self, query: str, search_provider: str, llm_model: str = "SciPhi/Sensei-7B-V1", temperature: int = 0.2, top_p: int = 0.95, ): """ Retrieves a search RAG (Retrieval-Augmented Generation) response from the API. Args:...

Retrieves a search RAG (Retrieval-Augmented Generation) response from the API. Args: query (str): The search query string. search_provider (str): The search provider to use. llm_model (str): The language model to use. temperature (int): The temperature s...

get_search_rag_response

python

SciPhi-AI/agent-search

agent_search/providers/sciphi.py

https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/providers/sciphi.py

Apache-2.0

def completion( self, prompt: str, llm_model_name: str = "SciPhi/Sensei-7B-V1", llm_max_tokens_to_sample: int = 1_024, llm_temperature: float = 0.2, llm_top_p: float = 0.90, ) -> SearchRAGResponse: """ Generates a completion for a given prompt using th...

Generates a completion for a given prompt using the SciPhi API. Args: prompt (str): The prompt for generating completion. llm_model_name (str): The language model to use. llm_max_tokens_to_sample (int): Maximum number of tokens for the sample. llm_temper...

completion

python

SciPhi-AI/agent-search

agent_search/providers/sciphi.py

https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/providers/sciphi.py

Apache-2.0

def process_rows(rows, output_queue): """Process the rows into qdrant point objects.""" qdrant_points = [] for row in rows: _, url, __, text_chunks, embeddings_binary, ___, ____ = row embeddings = np.frombuffer( embeddings_binary, dtype=np.float32 ).reshape(-1, EMBEDDING_...

Process the rows into qdrant point objects.

process_rows

python

SciPhi-AI/agent-search

agent_search/scripts/populate_qdrant_from_postgres.py

https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/scripts/populate_qdrant_from_postgres.py

Apache-2.0

def qdrant_writer(config, qdrant_queue, delete_existing): """A writer that listens for output events in a separate thread.""" qclient = QdrantClient( config["qdrant_host"], port=config["qdrant_grpc_port"], prefer_grpc=config["qdrant_prefer_grpc"], ) if delete_existing: qc...

A writer that listens for output events in a separate thread.

qdrant_writer

python

SciPhi-AI/agent-search

agent_search/scripts/populate_qdrant_from_postgres.py

https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/scripts/populate_qdrant_from_postgres.py

Apache-2.0

def process_batches(config, start, end, batch_size, output_queue): """Processes the batches in steps of the given batch_size""" # Connect to the database conn = psycopg2.connect( dbname=config["postgres_db"], user=config["postgres_user"], password=config["postgres_password"], ...

Processes the batches in steps of the given batch_size

process_batches

python

SciPhi-AI/agent-search

agent_search/scripts/populate_qdrant_from_postgres.py

https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/scripts/populate_qdrant_from_postgres.py

Apache-2.0

def run(self, num_processes=16, batch_size=1_024, delete_existing=False): """Runs the population process for the qdrant database""" qdrant_queue = multiprocessing.Queue() qdrant_writer_thread = multiprocessing.Process( target=qdrant_writer, args=( self.con...

Runs the population process for the qdrant database

run

python

SciPhi-AI/agent-search

agent_search/scripts/populate_qdrant_from_postgres.py

https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/scripts/populate_qdrant_from_postgres.py

Apache-2.0

def hierarchical_similarity_reranking( self, query_vector: np.ndarray, urls: List[str], limit: int = 100, ) -> List[AgentSearchResult]: """Hierarchical URL search to find the most similar text chunk for the given query and URLs""" results = self.execute_batch_query(ur...

Hierarchical URL search to find the most similar text chunk for the given query and URLs

hierarchical_similarity_reranking

python

SciPhi-AI/agent-search

agent_search/search/base.py

https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/search/base.py

Apache-2.0

def pagerank_reranking( self, similarity_results: List[AgentSearchResult], limit: int = 100, ) -> List[AgentSearchResult]: """Reranks the results based on the PageRank score of the domain""" if not self.pagerank_rerank_module: raise Exception( "Pag...

Reranks the results based on the PageRank score of the domain

pagerank_reranking

python

SciPhi-AI/agent-search

agent_search/search/base.py

https://github.com/SciPhi-AI/agent-search/blob/master/agent_search/search/base.py

Apache-2.0

def scrub_str(string): """ The purpose of this function is to scrub the weird template mark-up out of strings that Veekun is using for their pokedex. Example: []{move:dragon-tail} will effect the opponents [HP]{mechanic:hp}. Becomes: dragon tail will effect the opponents HP. If ...

The purpose of this function is to scrub the weird template mark-up out of strings that Veekun is using for their pokedex. Example: []{move:dragon-tail} will effect the opponents [HP]{mechanic:hp}. Becomes: dragon tail will effect the opponents HP. If you find this results in weird...

scrub_str

python

PokeAPI/pokeapi

data/v2/build.py

https://github.com/PokeAPI/pokeapi/blob/master/data/v2/build.py

BSD-3-Clause

def __SectionLength(this): """(4 bytes) Gets the length of characters the given section is""" offset = this.__SectionDataOffset return struct.unpack_from("<I", this.__data, offset)[0]

(4 bytes) Gets the length of characters the given section is

__SectionLength

python

PokeAPI/pokeapi

Resources/scripts/data/gen8/read_swsh.py

https://github.com/PokeAPI/pokeapi/blob/master/Resources/scripts/data/gen8/read_swsh.py

BSD-3-Clause

def __LineOffsets(this): """Figures out the offset for each entry based on the data section offset""" result = [None] * this.__LineCount sdo = int(this.__SectionDataOffset) for i in range(0, len(result)): result[i] = TextLine() result[i].offset = struct.unpack_from("<i", this.__data, (i * 8) + sdo + 4)[0]...

Figures out the offset for each entry based on the data section offset

__LineOffsets

python

PokeAPI/pokeapi

Resources/scripts/data/gen8/read_swsh.py

https://github.com/PokeAPI/pokeapi/blob/master/Resources/scripts/data/gen8/read_swsh.py

BSD-3-Clause

def HashFNV1_64(this, word): """Fowler-Noll-Vo hash function; 64-bit""" fnvPrime_64 = 0x100000001b3 offsetBasis_64 = 0xCBF29CE484222645 hash = offsetBasis_64 for c in word: hash = hash ^ ord(c) # Cast hash to at 64-bit value hash = (hash * fnvPrime_64) % 2**64 return hash

Fowler-Noll-Vo hash function; 64-bit

HashFNV1_64

python

PokeAPI/pokeapi

Resources/scripts/data/gen8/read_swsh.py

https://github.com/PokeAPI/pokeapi/blob/master/Resources/scripts/data/gen8/read_swsh.py

BSD-3-Clause

def __LineData(this, data): """Loads the file into a list to later decrypt""" key = copy.copy(this.__KEY_BASE) result = [None] * this.__LineCount lines = this.__LineOffsets for i in range(0, len(lines)): # Make a list twice the size of the current text line size encrypted = lines[i].length * 2 # The...

Loads the file into a list to later decrypt

__LineData

python

PokeAPI/pokeapi

Resources/scripts/data/gen8/read_swsh.py

https://github.com/PokeAPI/pokeapi/blob/master/Resources/scripts/data/gen8/read_swsh.py

BSD-3-Clause

def __CryptLineData(this, data, key): """Decrypts the given line into a list of bytes""" copied = copy.copy(data) result = [None] * len(copied) for i in range(0, len(copied), 2): result[i] = copied[i] ^ (key % 256) result[i + 1] = copied[i + 1] ^ ((key >> 8) % 256) # Bit-shift and OR key, then cast to...

Decrypts the given line into a list of bytes

__CryptLineData

python

PokeAPI/pokeapi

Resources/scripts/data/gen8/read_swsh.py

https://github.com/PokeAPI/pokeapi/blob/master/Resources/scripts/data/gen8/read_swsh.py

BSD-3-Clause