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| # coding=utf-8 | |
| # Copyright 2021 The Deeplab2 Authors. | |
| # | |
| # 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 max_deeplab_loss.py.""" | |
| import tensorflow as tf | |
| from deeplab2 import common | |
| from deeplab2 import trainer_pb2 | |
| from deeplab2.data import dataset | |
| from deeplab2.model.loss import max_deeplab_loss | |
| class MaXDeepLabLossTest(tf.test.TestCase): | |
| def test_max_deeplab_loss(self): | |
| # Build the loss layer. | |
| dataset_info = dataset.COCO_PANOPTIC_INFORMATION | |
| semantic_loss_options = trainer_pb2.LossOptions.SingleLossOptions( | |
| name='softmax_cross_entropy') | |
| pq_style_loss_options = trainer_pb2.LossOptions.SingleLossOptions() | |
| mask_id_cross_entropy_loss_options = ( | |
| trainer_pb2.LossOptions.SingleLossOptions()) | |
| instance_discrimination_loss_options = ( | |
| trainer_pb2.LossOptions.SingleLossOptions()) | |
| loss_options_1 = trainer_pb2.LossOptions( | |
| semantic_loss=semantic_loss_options, | |
| pq_style_loss=pq_style_loss_options, | |
| mask_id_cross_entropy_loss=mask_id_cross_entropy_loss_options, | |
| instance_discrimination_loss=instance_discrimination_loss_options) | |
| loss_layer_1 = max_deeplab_loss.MaXDeepLabLoss( | |
| loss_options_1, | |
| ignore_label=dataset_info.ignore_label, | |
| thing_class_ids=dataset_info.class_has_instances_list) | |
| loss_options_2 = trainer_pb2.LossOptions( | |
| pq_style_loss=pq_style_loss_options) | |
| loss_layer_2 = max_deeplab_loss.MaXDeepLabLoss( | |
| loss_options_2, | |
| ignore_label=dataset_info.ignore_label, | |
| thing_class_ids=dataset_info.class_has_instances_list) | |
| # Build the inputs. | |
| pred_dict = { | |
| common.PRED_PIXEL_SPACE_NORMALIZED_FEATURE_KEY: | |
| tf.random.uniform(shape=[2, 9, 9, 8]), | |
| common.PRED_PIXEL_SPACE_MASK_LOGITS_KEY: | |
| tf.random.uniform(shape=[2, 9, 9, 128]), | |
| common.PRED_TRANSFORMER_CLASS_LOGITS_KEY: | |
| tf.random.uniform(shape=[2, 128, 134]), | |
| } | |
| gt_dict = { | |
| common.GT_SEMANTIC_KEY: tf.ones(shape=[2, 33, 33], dtype=tf.int32), | |
| common.GT_THING_ID_MASK_KEY: tf.ones(shape=[2, 33, 33], | |
| dtype=tf.int32), | |
| common.GT_THING_ID_CLASS_KEY: tf.concat( | |
| # An image with ten people (class_id = 1) and 118 void masks. | |
| [tf.ones(shape=[2, 10], dtype=tf.int32), | |
| -tf.ones(shape=[2, 118], dtype=tf.int32)], axis=-1), | |
| } | |
| loss_dict_1 = loss_layer_1((gt_dict, pred_dict)) | |
| self.assertIn(common.PQ_STYLE_LOSS_CLASS_TERM, loss_dict_1) | |
| self.assertIn(common.PQ_STYLE_LOSS_MASK_DICE_TERM, loss_dict_1) | |
| self.assertIn(common.MASK_ID_CROSS_ENTROPY_LOSS, loss_dict_1) | |
| self.assertIn(common.INSTANCE_DISCRIMINATION_LOSS, loss_dict_1) | |
| self.assertNotIn(common.PQ_STYLE_LOSS, loss_dict_1) | |
| self.assertIn(common.PQ_STYLE_LOSS_CLASS_TERM, loss_layer_1.loss_terms) | |
| self.assertIn(common.PQ_STYLE_LOSS_MASK_DICE_TERM, loss_layer_1.loss_terms) | |
| self.assertIn(common.MASK_ID_CROSS_ENTROPY_LOSS, loss_layer_1.loss_terms) | |
| self.assertIn(common.INSTANCE_DISCRIMINATION_LOSS, loss_layer_1.loss_terms) | |
| self.assertNotIn(common.PQ_STYLE_LOSS, loss_layer_1.loss_terms) | |
| loss_dict_2 = loss_layer_2((gt_dict, pred_dict)) | |
| self.assertIn(common.PQ_STYLE_LOSS_CLASS_TERM, loss_dict_2) | |
| self.assertIn(common.PQ_STYLE_LOSS_MASK_DICE_TERM, loss_dict_2) | |
| self.assertNotIn(common.MASK_ID_CROSS_ENTROPY_LOSS, loss_dict_2) | |
| self.assertNotIn(common.INSTANCE_DISCRIMINATION_LOSS, loss_dict_2) | |
| self.assertNotIn(common.PQ_STYLE_LOSS, loss_dict_2) | |
| self.assertIn(common.PQ_STYLE_LOSS_CLASS_TERM, loss_layer_2.loss_terms) | |
| self.assertIn(common.PQ_STYLE_LOSS_MASK_DICE_TERM, loss_layer_2.loss_terms) | |
| self.assertNotIn(common.MASK_ID_CROSS_ENTROPY_LOSS, loss_layer_2.loss_terms) | |
| self.assertNotIn(common.INSTANCE_DISCRIMINATION_LOSS, | |
| loss_layer_2.loss_terms) | |
| self.assertNotIn(common.PQ_STYLE_LOSS, loss_layer_2.loss_terms) | |
| if __name__ == '__main__': | |
| tf.test.main() | |