return testing scl

tests/keras_tests/custom_layers_tests/test_sony_ssd_postprocess_layer.py

@@ -1,62 +1,62 @@
-# # Copyright 2023 Sony Semiconductor Israel, 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.
-# # 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 unittest
-#
-# import numpy as np
-# import tensorflow as tf
-#
-# import model_compression_toolkit as mct
-# from sony_custom_layers.keras.object_detection.ssd_post_process import SSDPostProcess
-#
-# keras = tf.keras
-# layers = keras.layers
-#
-#
-# def get_rep_dataset(n_iters, in_shape):
-#     def rep_dataset():
-#         for _ in range(n_iters):
-#             yield [np.random.randn(*in_shape)]
-#
-#     return rep_dataset
-#
-#
-# class TestSonySsdPostProcessLayer(unittest.TestCase):
-#
-#     def test_custom_layer(self):
-#         inputs = layers.Input(shape=(8, 8, 3))
-#         x = layers.Conv2D(32, 4)(inputs)
-#         x = layers.BatchNormalization()(x)
-#         x = layers.Conv2D(32, 4)(x)
-#         x = layers.ReLU()(x)
-#         x = layers.Reshape((32, 4))(x)
-#         ssd_pp = SSDPostProcess(tf.constant(np.random.random(size=list(x.shape[1:])), dtype=tf.float32), [1, 1, 1, 1],
-#                                 [8, 8], 'sigmoid', score_threshold=0.001,
-#                                 iou_threshold=0.5, max_detections=10)
-#         outputs = ssd_pp((x, x))
-#         model = keras.Model(inputs=inputs, outputs=outputs)
-#
-#         core_config = mct.core.CoreConfig(
-#             mixed_precision_config=mct.core.MixedPrecisionQuantizationConfigV2(
-#                 use_hessian_based_scores=False))
-#         q_model, _ = mct.ptq.keras_post_training_quantization_experimental(model,
-#                                                                            get_rep_dataset(2, (1, 8, 8, 3)),
-#                                                                            core_config=core_config,
-#                                                                            target_kpi=mct.KPI(weights_memory=6000))
-#
-#         # verify the custom layer is in the quantized model
-#         self.assertTrue(isinstance(q_model.layers[-1], SSDPostProcess), 'Custom layer should be in the quantized model')
-#         # verify mixed-precision
-#         self.assertTrue(any([q_model.layers[2].weights_quantizers['kernel'].num_bits < 8,
-#                              q_model.layers[4].weights_quantizers['kernel'].num_bits < 8]))
+# Copyright 2023 Sony Semiconductor Israel, 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.
+# 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 unittest
+
+import numpy as np
+import tensorflow as tf
+
+import model_compression_toolkit as mct
+from sony_custom_layers.keras.object_detection.ssd_post_process import SSDPostProcess
+
+keras = tf.keras
+layers = keras.layers
+
+
+def get_rep_dataset(n_iters, in_shape):
+    def rep_dataset():
+        for _ in range(n_iters):
+            yield [np.random.randn(*in_shape)]
+
+    return rep_dataset
+
+
+class TestSonySsdPostProcessLayer(unittest.TestCase):
+
+    def test_custom_layer(self):
+        inputs = layers.Input(shape=(8, 8, 3))
+        x = layers.Conv2D(32, 4)(inputs)
+        x = layers.BatchNormalization()(x)
+        x = layers.Conv2D(32, 4)(x)
+        x = layers.ReLU()(x)
+        x = layers.Reshape((32, 4))(x)
+        ssd_pp = SSDPostProcess(tf.constant(np.random.random(size=list(x.shape[1:])), dtype=tf.float32), [1, 1, 1, 1],
+                                [8, 8], 'sigmoid', score_threshold=0.001,
+                                iou_threshold=0.5, max_detections=10)
+        outputs = ssd_pp((x, x))
+        model = keras.Model(inputs=inputs, outputs=outputs)
+
+        core_config = mct.core.CoreConfig(
+            mixed_precision_config=mct.core.MixedPrecisionQuantizationConfigV2(
+                use_hessian_based_scores=False))
+        q_model, _ = mct.ptq.keras_post_training_quantization_experimental(model,
+                                                                           get_rep_dataset(2, (1, 8, 8, 3)),
+                                                                           core_config=core_config,
+                                                                           target_kpi=mct.KPI(weights_memory=6000))
+
+        # verify the custom layer is in the quantized model
+        self.assertTrue(isinstance(q_model.layers[-1], SSDPostProcess), 'Custom layer should be in the quantized model')
+        # verify mixed-precision
+        self.assertTrue(any([q_model.layers[2].weights_quantizers['kernel'].num_bits < 8,
+                             q_model.layers[4].weights_quantizers['kernel'].num_bits < 8]))