chore: import upstream snapshot with attribution
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wehub-resource-sync
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#
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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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from network_pool import pippin_28_28
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from tensorflow_quantization import custom_qdq_cases
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import pytest
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from tensorflow_quantization import quantize_model
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from tensorflow_quantization.utils import convert_saved_model_to_onnx
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from tensorflow_quantization.utils import CreateAssetsFolders
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import tensorflow as tf
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test_assets = CreateAssetsFolders("test_custom_qdq_cases")
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def test_resnet_residual_qdq_case():
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model = pippin_28_28()
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test_assets.add_folder("pipin_28_28")
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tf.keras.models.save_model(model, test_assets.pipin_28_28.fp32_saved_model)
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convert_saved_model_to_onnx(
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saved_model_dir=test_assets.pipin_28_28.fp32_saved_model,
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onnx_model_path=test_assets.pipin_28_28.fp32_onnx_model,
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)
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resnet_residual_qdq = custom_qdq_cases.ResNetV1QDQCase()
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r = resnet_residual_qdq.case(model, None)
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expected_qdq_insertion = {
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"add": 1,
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"add_1": "any",
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}
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assert (
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len(r.layers) == 2
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), "There should be 2 custom layers, but found {}".format(len(r.layers))
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for l in r.layers:
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if l.name not in expected_qdq_insertion:
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raise Exception(
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"Layer {} is not expected to be treated as custom layer".format(l.name)
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)
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else:
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if l.quantization_index != None:
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if expected_qdq_insertion[l.name] == "any":
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continue
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assert (
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l.quantization_index[0] == expected_qdq_insertion[l.name]
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), "For layer {l_name}, only {expected_qdq} indices should be quantized".format(
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l_name=l.name, expected_qdq=expected_qdq_insertion[l.name]
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)
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def assert_add_bn_expected_layers(
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r, expected_add_layer_behavior, expected_bn_layer_behavior, expected_mp_layer_behavior
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):
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assert len(r.layers) == (
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len(expected_add_layer_behavior) + len(expected_bn_layer_behavior) + len(expected_mp_layer_behavior)
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), "Not all expected layers are captured for ResNet custom QDQ case."
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for l in r.layers:
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assert (
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l.name in expected_add_layer_behavior
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or l.name in expected_bn_layer_behavior
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or l.name in expected_mp_layer_behavior
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), "layer {} is not expected to be captured for ResNet custom QDQ case".format(
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l.name
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)
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if "add" in l.name:
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if expected_add_layer_behavior[l.name] == "any":
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continue
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assert l.quantization_index[0] == expected_add_layer_behavior[l.name], (
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"For layer {l_name}, expected quantization index is {expected_add_behavior} but index {l_quant_id} "
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"is captured in ResNet custom QDQ case.".format(
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l_name=l.name,
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expected_add_behavior=expected_add_layer_behavior[l.name],
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l_quant_idx=l.quantization_index[0],
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)
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)
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def test_resnet50_residual_qdq_case():
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resnet50 = tf.keras.applications.resnet50.ResNet50(weights=None)
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test_assets.add_folder("resnet50_v1")
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tf.keras.models.save_model(resnet50, test_assets.resnet50_v1.fp32_saved_model)
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convert_saved_model_to_onnx(
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saved_model_dir=test_assets.resnet50_v1.fp32_saved_model,
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onnx_model_path=test_assets.resnet50_v1.fp32_onnx_model,
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)
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resnet_custom_qdq_case = custom_qdq_cases.ResNetV1QDQCase()
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r = resnet_custom_qdq_case.case(resnet50, None)
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for r_1 in r.layers:
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print("\"{}\",".format(r_1.name))
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expected_add_layer_behavior = {
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"conv2_block1_add": "any",
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"conv2_block2_add": 0,
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"conv2_block3_add": 0,
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"conv3_block1_add": "any",
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"conv3_block2_add": 0,
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"conv3_block3_add": 0,
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"conv3_block4_add": 0,
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"conv4_block1_add": "any",
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"conv4_block2_add": 0,
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"conv4_block3_add": 0,
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"conv4_block4_add": 0,
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"conv4_block5_add": 0,
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"conv4_block6_add": 0,
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"conv5_block1_add": "any",
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"conv5_block2_add": 0,
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"conv5_block3_add": 0,
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}
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# Empty, no BatchNorm layers should be quantized in ResNet-v1
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expected_bn_layer_behavior = {}
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# MaxPool quantization is actually not needed in ResNet-v1
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expected_mp_layer_behavior = {}
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assert_add_bn_expected_layers(
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r, expected_add_layer_behavior, expected_bn_layer_behavior, expected_mp_layer_behavior
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)
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q_resnet50 = quantize_model(resnet50, custom_qdq_cases=[resnet_custom_qdq_case])
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tf.keras.models.save_model(q_resnet50, test_assets.resnet50_v1.int8_saved_model)
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convert_saved_model_to_onnx(
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saved_model_dir=test_assets.resnet50_v1.int8_saved_model,
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onnx_model_path=test_assets.resnet50_v1.int8_onnx_model,
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)
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def test_resnet50v2_bn_qdq_case():
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resnet50_v2 = tf.keras.applications.resnet_v2.ResNet50V2(weights=None)
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test_assets.add_folder("resnet50_v2")
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tf.keras.models.save_model(resnet50_v2, test_assets.resnet50_v2.fp32_saved_model)
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convert_saved_model_to_onnx(
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saved_model_dir=test_assets.resnet50_v2.fp32_saved_model,
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onnx_model_path=test_assets.resnet50_v2.fp32_onnx_model,
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)
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resnet_custom_qdq_case = custom_qdq_cases.ResNetV2QDQCase()
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r = resnet_custom_qdq_case.case(resnet50_v2, None)
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for r_1 in r.layers:
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print("\"{}\",".format(r_1.name))
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expected_add_layer_behavior = {
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"conv2_block1_out": 0,
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"conv2_block2_out": 0,
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"conv2_block3_out": 0,
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"conv3_block1_out": 0,
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"conv3_block2_out": 0,
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"conv3_block3_out": 0,
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"conv3_block4_out": 0,
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"conv4_block1_out": 0,
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"conv4_block2_out": 0,
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"conv4_block3_out": 0,
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"conv4_block4_out": 0,
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"conv4_block5_out": 0,
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"conv4_block6_out": 0,
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"conv5_block1_out": 0,
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"conv5_block2_out": 0,
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"conv5_block3_out": 0,
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}
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# ResNet-v2 quantizes BatchNorms that are not connected to Conv layers
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expected_bn_layer_behavior = {
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"conv2_block1_preact_bn",
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"conv2_block2_preact_bn",
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"conv2_block3_preact_bn",
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"conv3_block1_preact_bn",
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"conv3_block2_preact_bn",
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"conv3_block3_preact_bn",
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"conv3_block4_preact_bn",
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"conv4_block1_preact_bn",
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"conv4_block2_preact_bn",
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"conv4_block3_preact_bn",
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"conv4_block4_preact_bn",
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"conv4_block5_preact_bn",
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"conv4_block6_preact_bn",
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"conv5_block1_preact_bn",
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"conv5_block2_preact_bn",
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"conv5_block3_preact_bn",
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"post_bn",
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}
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# ResNet-v2 quantizes all MaxPool layers
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expected_mp_layer_behavior = {
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"pool1_pool",
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"max_pooling2d",
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"max_pooling2d_1",
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"max_pooling2d_2",
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}
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assert_add_bn_expected_layers(
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r, expected_add_layer_behavior, expected_bn_layer_behavior, expected_mp_layer_behavior
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)
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q_resnet50_v2 = quantize_model(
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resnet50_v2, custom_qdq_cases=[resnet_custom_qdq_case]
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)
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tf.keras.models.save_model(q_resnet50_v2, test_assets.resnet50_v2.int8_saved_model)
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convert_saved_model_to_onnx(
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saved_model_dir=test_assets.resnet50_v2.int8_saved_model,
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onnx_model_path=test_assets.resnet50_v2.int8_onnx_model,
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)
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