Source code for

# Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# software distributed under the License is distributed on an "AS IS" BASIS,
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Functionality related to detecting and getting information for
support and sparsification in the TensorFlow 1.x framework.

import logging
from typing import Any

from sparseml.base import Framework, get_version
from sparseml.framework import FrameworkInferenceProviderInfo, FrameworkInfo
from sparseml.sparsification import SparsificationInfo
from sparseml.tensorflow_v1.base import check_tensorflow_install, tf_compat
from sparseml.tensorflow_v1.sparsification import sparsification_info

__all__ = ["is_supported", "detect_framework", "framework_info"]

_LOGGER = logging.getLogger(__name__)

[docs]def is_supported(item: Any) -> bool: """ :param item: The item to detect the support for :type item: Any :return: True if the item is supported by tensorflow, False otherwise :rtype: bool """ framework = detect_framework(item) return framework == Framework.tensorflow_v1
[docs]def detect_framework(item: Any) -> Framework: """ Detect the supported ML framework for a given item specifically for the tensorflow package. Supported input types are the following: - A Framework enum - A string of any case representing the name of the framework (deepsparse, onnx, keras, tensorflow, tensorflow_v1) - A supported file type within the framework such as model files: (onnx, pth, h5, pb) - An object from a supported ML framework such as a model instance If the framework cannot be determined, will return Framework.unknown :param item: The item to detect the ML framework for :type item: Any :return: The detected framework from the given item :rtype: Framework """ framework = Framework.unknown if isinstance(item, Framework): _LOGGER.debug("framework detected from Framework instance") framework = item elif isinstance(item, str) and item.lower().strip() in Framework.__members__: _LOGGER.debug("framework detected from Framework string instance") framework = Framework[item.lower().strip()] elif isinstance(item, str) and ( "tensorflow" in item.lower().strip() or "tf" in item.lower().strip() ): _LOGGER.debug("framework detected from tensorflow text") # string, check if it's a string saying onnx first framework = Framework.tensorflow_v1 elif isinstance(item, str) and ".pb" in item.lower().strip(): _LOGGER.debug("framework detected from .pb") # string, check if it's a file url or path that ends with onnx extension framework = Framework.tensorflow_v1 elif check_tensorflow_install(raise_on_error=False): if isinstance(item, tf_compat.Graph) or isinstance(item, tf_compat.Session): _LOGGER.debug("framework detected from tensorflow instance") # tensorflow native support framework = Framework.tensorflow_v1 return framework
[docs]def framework_info() -> FrameworkInfo: """ Detect the information for the tensorflow framework such as package versions, availability for core actions such as training and inference, sparsification support, and inference provider support. :return: The framework info for tensorflow :rtype: FrameworkInfo """ cpu_provider = FrameworkInferenceProviderInfo( name="cpu", description="Base CPU provider within TensorFlow", device="cpu", supported_sparsification=SparsificationInfo(), # TODO: fill in when available available=check_tensorflow_install(raise_on_error=False), properties={}, warnings=[], ) gpu_provider = FrameworkInferenceProviderInfo( name="cuda", description="Base GPU CUDA provider within TensorFlow", device="gpu", supported_sparsification=SparsificationInfo(), # TODO: fill in when available available=( check_tensorflow_install(raise_on_error=False) and get_version("tensorflow_gpu", raise_on_error=False) is not None and tf_compat.test.is_gpu_available() ), properties={}, warnings=[], ) return FrameworkInfo( framework=Framework.tensorflow_v1, package_versions={ "tensorflow": ( get_version(package_name="tensorflow", raise_on_error=False) or get_version(package_name="tensorflow_gpu", raise_on_error=False) ), "onnx": get_version(package_name="onnx", raise_on_error=False), "tf2onnx": get_version(package_name="tf2onnx", raise_on_error=False), "sparsezoo": get_version( package_name="sparsezoo", raise_on_error=False, alternate_package_names=["sparsezoo-nightly"], ), "sparseml": get_version( package_name="sparseml", raise_on_error=False, alternate_package_names=["sparseml-nightly"], ), }, sparsification=sparsification_info(), inference_providers=[cpu_provider, gpu_provider], properties={}, training_available=True, sparsification_available=True, exporting_onnx_available=True, inference_available=True, )