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| # Copyright 2023 The TensorFlow Authors. 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. | |
| """Tests for instance_heads.py.""" | |
| # Import libraries | |
| from absl.testing import parameterized | |
| import numpy as np | |
| import tensorflow as tf, tf_keras | |
| from official.vision.modeling.heads import instance_heads | |
| class DetectionHeadTest(parameterized.TestCase, tf.test.TestCase): | |
| def test_forward(self, num_convs, num_fcs, use_separable_conv, use_sync_bn): | |
| detection_head = instance_heads.DetectionHead( | |
| num_classes=3, | |
| num_convs=num_convs, | |
| num_filters=16, | |
| use_separable_conv=use_separable_conv, | |
| num_fcs=num_fcs, | |
| fc_dims=4, | |
| activation='relu', | |
| use_sync_bn=use_sync_bn, | |
| norm_momentum=0.99, | |
| norm_epsilon=0.001, | |
| kernel_regularizer=None, | |
| bias_regularizer=None, | |
| ) | |
| roi_features = np.random.rand(2, 10, 128, 128, 16) | |
| scores, boxes = detection_head(roi_features) | |
| self.assertAllEqual(scores.numpy().shape, [2, 10, 3]) | |
| self.assertAllEqual(boxes.numpy().shape, [2, 10, 12]) | |
| def test_serialize_deserialize(self): | |
| detection_head = instance_heads.DetectionHead( | |
| num_classes=91, | |
| num_convs=0, | |
| num_filters=256, | |
| use_separable_conv=False, | |
| num_fcs=2, | |
| fc_dims=1024, | |
| activation='relu', | |
| use_sync_bn=False, | |
| norm_momentum=0.99, | |
| norm_epsilon=0.001, | |
| kernel_regularizer=None, | |
| bias_regularizer=None, | |
| ) | |
| config = detection_head.get_config() | |
| new_detection_head = instance_heads.DetectionHead.from_config(config) | |
| self.assertAllEqual( | |
| detection_head.get_config(), new_detection_head.get_config()) | |
| class MaskHeadTest(parameterized.TestCase, tf.test.TestCase): | |
| def test_forward(self, upsample_factor, num_convs, use_sync_bn): | |
| mask_head = instance_heads.MaskHead( | |
| num_classes=3, | |
| upsample_factor=upsample_factor, | |
| num_convs=num_convs, | |
| num_filters=16, | |
| use_separable_conv=False, | |
| activation='relu', | |
| use_sync_bn=use_sync_bn, | |
| norm_momentum=0.99, | |
| norm_epsilon=0.001, | |
| kernel_regularizer=None, | |
| bias_regularizer=None, | |
| ) | |
| roi_features = np.random.rand(2, 10, 14, 14, 16) | |
| roi_classes = np.zeros((2, 10)) | |
| masks = mask_head([roi_features, roi_classes]) | |
| self.assertAllEqual( | |
| masks.numpy().shape, | |
| [2, 10, 14 * upsample_factor, 14 * upsample_factor]) | |
| def test_serialize_deserialize(self): | |
| mask_head = instance_heads.MaskHead( | |
| num_classes=3, | |
| upsample_factor=2, | |
| num_convs=1, | |
| num_filters=256, | |
| use_separable_conv=False, | |
| activation='relu', | |
| use_sync_bn=False, | |
| norm_momentum=0.99, | |
| norm_epsilon=0.001, | |
| kernel_regularizer=None, | |
| bias_regularizer=None, | |
| ) | |
| config = mask_head.get_config() | |
| new_mask_head = instance_heads.MaskHead.from_config(config) | |
| self.assertAllEqual( | |
| mask_head.get_config(), new_mask_head.get_config()) | |
| def test_forward_class_agnostic(self): | |
| mask_head = instance_heads.MaskHead( | |
| num_classes=3, | |
| class_agnostic=True | |
| ) | |
| roi_features = np.random.rand(2, 10, 14, 14, 16) | |
| roi_classes = np.zeros((2, 10)) | |
| masks = mask_head([roi_features, roi_classes]) | |
| self.assertAllEqual(masks.numpy().shape, [2, 10, 28, 28]) | |
| if __name__ == '__main__': | |
| tf.test.main() | |