wehub-resource-sync
159 lines
5.4 KiB
Python
159 lines
5.4 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import sys
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import tensorflow as tf
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from tensorflow_quantization import QuantizationSpec
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from tensorflow_quantization.custom_qdq_cases import ResNetV1QDQCase
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from network_pool import frodo_32_32
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from onnx_graph_qdq_validator import validate_quantized_model
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from tensorflow_quantization.utils import CreateAssetsFolders
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import pytest
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test_assets = CreateAssetsFolders("test_quantize_qdq_insertion")
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# ############################################
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# ######### Full Quantize Test ###############
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# ############################################
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def test_quantize_full():
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this_function_name = sys._getframe().f_code.co_name
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nn_model_original = frodo_32_32()
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q_model, vr = validate_quantized_model(
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test_assets, nn_model_original, test_name=this_function_name
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)
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assert vr, "ONNX QDQ Validation for full network quantization failed!"
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# Necessary to clear model layer names from the memory
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tf.keras.backend.clear_session()
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def test_quantize_full_residual():
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this_function_name = sys._getframe().f_code.co_name
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nn_model_original = frodo_32_32()
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q_model, vr = validate_quantized_model(
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test_assets, nn_model_original, custom_qdq_cases=[ResNetV1QDQCase()], test_name=this_function_name
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)
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assert vr, "ONNX QDQ Validation for quantizing full network with special residual failed!"
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tf.keras.backend.clear_session()
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# ############################################
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# ######### Full Special Quantize Test #######
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# ############################################
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def test_quantize_full_special_layer():
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this_function_name = sys._getframe().f_code.co_name
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nn_model_original = frodo_32_32()
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# Create a Quantization Spec (dictionary telling how `add` layer should be treated differently).
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qspec = QuantizationSpec()
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qspec.add(name="add", quantization_index=[0])
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q_model, vr = validate_quantized_model(
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test_assets, nn_model_original, qspec=qspec, test_name=this_function_name
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)
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assert vr, "QDQ Validation for full network but one special layer quantization failed!"
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tf.keras.backend.clear_session()
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# ##########################################
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# ######### Partial Quantize Test ##########
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# ##########################################
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def test_quantize_partial():
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this_function_name = sys._getframe().f_code.co_name
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nn_model_original = frodo_32_32()
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# Create a qspec dictionary to quantize only two layers named 'conv2d_2' and 'dense'
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qspec = QuantizationSpec()
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qspec.add(name="conv2d_2")
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qspec.add(name="dense")
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q_model, vr = validate_quantized_model(
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test_assets, nn_model_original, quantization_mode="partial", qspec=qspec, test_name=this_function_name
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)
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assert vr, "ONNX QDQ Validation for partial network quantization failed!"
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tf.keras.backend.clear_session()
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# ####################################################
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# ######### Subset layers Test - Full quantize #######
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# ####################################################
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def test_quantize_specific_class_maxpool():
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this_function_name = sys._getframe().f_code.co_name
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nn_model_original = frodo_32_32()
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# Create a list with keras layer classes to quantize
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qspec = QuantizationSpec()
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qspec.add(name="MaxPooling2D", is_keras_class=True)
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q_model, vr = validate_quantized_model(
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test_assets, nn_model_original, qspec=qspec, test_name=this_function_name
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)
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assert vr, "ONNX QDQ Validation for specific class `Dense` quantization failed!"
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tf.keras.backend.clear_session()
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def test_quantize_specific_class_add():
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this_function_name = sys._getframe().f_code.co_name
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nn_model_original = frodo_32_32()
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# Create a list with keras layer classes to quantize
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qspec = QuantizationSpec()
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qspec.add(name="Add", is_keras_class=True)
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q_model, vr = validate_quantized_model(
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test_assets, nn_model_original, qspec=qspec, test_name=this_function_name
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)
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assert vr, "ONNX QDQ Validation for quantizing specific class `Add` failed!"
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tf.keras.backend.clear_session()
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# ####################################################
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# ####### Subset layers Test - Partial quantize ######
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# ####################################################
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def test_quantize_specific_class_conv2d_partial():
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this_function_name = sys._getframe().f_code.co_name
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nn_model_original = frodo_32_32()
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# Create a list with keras layer classes to quantize
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qspec = QuantizationSpec()
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qspec.add(name="Conv2D", is_keras_class=True)
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q_model, vr = validate_quantized_model(
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test_assets, nn_model_original, quantization_mode="partial", qspec=qspec, test_name=this_function_name
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)
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assert vr, "ONNX QDQ Validation for quantizing specific class `Conv2D` and `conv2d_1` layer failed!"
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tf.keras.backend.clear_session()
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