chore: import upstream snapshot with attribution

2026-07-13 13:36:55 +08:00
commit c8a779b1bb
1887 changed files with 3245738 additions and 0 deletions
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+#
+# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import os
+import sys
+import tensorflow_quantization.utils as utils
+import tensorflow as tf
+from tensorflow_quantization import quantize_model
+from tensorflow_quantization.utils import (
+    CreateAssetsFolders,
+    convert_saved_model_to_onnx,
+)
+from network_pool import sam_32_32
+import pytest
+
+test_assets = CreateAssetsFolders("test_utils")
+
+
+def test_keras_traveller():
+    kmt = utils.KerasModelTraveller()
+    model = sam_32_32()
+    layer_names = kmt.get_layer_names(keras_model=model)
+    expected_layer_names = [
+        "input_1",
+        "conv2d",
+        "re_lu",
+        "conv2d_1",
+        "re_lu_1",
+        "conv2d_2",
+        "re_lu_2",
+        "conv2d_3",
+        "add",
+        "re_lu_3",
+        "conv2d_4",
+        "re_lu_4",
+        "conv2d_5",
+        "add_1",
+        "re_lu_5",
+        "conv2d_6",
+        "re_lu_6",
+        "conv2d_7",
+        "conv2d_8",
+        "add_2",
+        "re_lu_7",
+        "max_pooling2d",
+        "flatten",
+        "dense",
+        "re_lu_8",
+        "dense_1",
+    ]
+    assert layer_names == expected_layer_names, "Keras model traveller failed."
+    tf.keras.backend.clear_session()
+
+
+def test_convert_to_onnx():
+    test_assets.add_folder("test_convert_to_onnx")
+
+    model = sam_32_32()
+    q_model = quantize_model(model)
+    # Create experiment specific directory
+    tf.keras.models.save_model(
+        q_model, test_assets.test_convert_to_onnx.int8_saved_model
+    )
+    convert_saved_model_to_onnx(
+        saved_model_dir=test_assets.test_convert_to_onnx.int8_saved_model,
+        onnx_model_path=test_assets.test_convert_to_onnx.int8_onnx_model,
+    )
+    tf.keras.backend.clear_session()
+
+
+def test_find_my_predecessors():
+    resnet50 = tf.keras.applications.resnet.ResNet50(weights=None)
+    r = utils.find_my_predecessors(resnet50, "conv2_block1_add")
+    assert r[0]["class"] == "BatchNormalization"
+    assert r[0]["name"] == "conv2_block1_0_bn"
+    assert r[1]["class"] == "BatchNormalization"
+    assert r[1]["name"] == "conv2_block1_3_bn"
+
+
+def test_find_my_successors():
+    resnet50 = tf.keras.applications.resnet.ResNet50(weights=None)
+    r = utils.find_my_successors(resnet50, "pool1_pool")
+    assert r[0]["class"] == "Conv2D"
+    assert r[0]["name"] == "conv2_block1_1_conv"
+    assert r[1]["class"] == "Conv2D"
+    assert r[1]["name"] == "conv2_block1_0_conv"