57 lines
2.5 KiB
Python
57 lines
2.5 KiB
Python
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# type: ignore
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from __future__ import annotations
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from collections.abc import Iterable
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from importlib import metadata as importlib_metadata
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from openvino.tools.ovc.error import Error
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from openvino_telemetry.backend import backend_ga4
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import importlib as importlib
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import logging as log
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import numpy
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import numpy as np
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import numpy.ma.core
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import openvino_telemetry as tm
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import os as os
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import sys as sys
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__all__ = ['Error', 'Iterable', 'backend_ga4', 'bool_cast', 'check_values_equal', 'dynamic_dimension', 'get_ir_version', 'get_mo_root_dir', 'import_openvino_tokenizers', 'importlib', 'importlib_metadata', 'log', 'mo_array', 'np', 'np_map_cast', 'os', 'refer_to_faq_msg', 'sys', 'tm', 'validate_batch_in_shape']
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def bool_cast(x):
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...
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def check_values_equal(val1, val2):
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...
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def get_ir_version():
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"""
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Default IR version.
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:return: the IR version
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"""
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def get_mo_root_dir():
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"""
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Return the absolute path to the Model Conversion API root directory (where mo folder is located)
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:return: path to the MO root directory
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"""
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def import_openvino_tokenizers():
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...
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def mo_array(value: typing.Union[collections.abc.Iterable[typing.Union[float, int]], float, int], dtype = None) -> numpy.ndarray:
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"""
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This function acts in a same way as np.array except for the case when dtype is not provided
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and np.array return fp64 array this function returns fp32 array
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"""
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def refer_to_faq_msg(question_num: int):
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...
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def validate_batch_in_shape(shape, layer_name: str):
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"""
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Raises Error #39 if shape is not valid for setting batch size
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Parameters
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----------
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shape: current shape of layer under validation
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layer_name: name of layer under validation
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"""
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dynamic_dimension: numpy.ma.core.MaskedConstant # value = masked
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np_map_cast: dict # value = {bool: <function <lambda> at memory_address>, numpy.int8: <function <lambda> at memory_address>, numpy.int16: <function <lambda> at memory_address>, numpy.int32: <function <lambda> at memory_address>, numpy.int64: <function <lambda> at memory_address>, numpy.uint8: <function <lambda> at memory_address>, numpy.uint16: <function <lambda> at memory_address>, numpy.uint32: <function <lambda> at memory_address>, numpy.uint64: <function <lambda> at memory_address>, numpy.float16: <function <lambda> at memory_address>, numpy.float32: <function <lambda> at memory_address>, numpy.float64: <function <lambda> at memory_address>, str: <function <lambda> at memory_address>}
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