# 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 deeplab.""" import os import numpy as np import tensorflow as tf from google.protobuf import text_format from deeplab2 import common from deeplab2 import config_pb2 from deeplab2.data import dataset from deeplab2.model import deeplab from deeplab2.model import utils # resources dependency _CONFIG_PATH = 'deeplab2/configs/example' def _read_proto_file(filename, proto): filename = filename # OSS: removed internal filename loading. with tf.io.gfile.GFile(filename, 'r') as proto_file: return text_format.ParseLines(proto_file, proto) def _create_model_from_test_proto(file_name, dataset_name='cityscapes_panoptic'): proto_filename = os.path.join(_CONFIG_PATH, file_name) config = _read_proto_file(proto_filename, config_pb2.ExperimentOptions()) return deeplab.DeepLab(config, dataset.MAP_NAME_TO_DATASET_INFO[dataset_name] ), config class DeeplabTest(tf.test.TestCase): def test_deeplab_with_deeplabv3(self): model, experiment_options = _create_model_from_test_proto( 'example_cityscapes_deeplabv3.textproto') train_crop_size = tuple( experiment_options.train_dataset_options.crop_size) input_tensor = tf.random.uniform( shape=(2, train_crop_size[0], train_crop_size[1], 3)) expected_semantic_shape = [ 2, train_crop_size[0], train_crop_size[1], experiment_options.model_options.deeplab_v3.num_classes] resulting_dict = model(input_tensor) self.assertListEqual( resulting_dict[common.PRED_SEMANTIC_LOGITS_KEY].shape.as_list(), expected_semantic_shape) num_params = np.sum( [np.prod(v.get_shape().as_list()) for v in model.trainable_weights]) self.assertEqual(num_params, 39638355) def test_deeplab_with_deeplabv3plus(self): model, experiment_options = _create_model_from_test_proto( 'example_cityscapes_deeplabv3plus.textproto') train_crop_size = tuple( experiment_options.train_dataset_options.crop_size) input_tensor = tf.random.uniform( shape=(2, train_crop_size[0], train_crop_size[1], 3)) expected_semantic_shape = [ 2, train_crop_size[0], train_crop_size[1], experiment_options.model_options.deeplab_v3_plus.num_classes] resulting_dict = model(input_tensor) self.assertListEqual( resulting_dict[common.PRED_SEMANTIC_LOGITS_KEY].shape.as_list(), expected_semantic_shape) num_params = np.sum( [np.prod(v.get_shape().as_list()) for v in model.trainable_weights]) self.assertEqual(num_params, 39210947) def test_deeplab_with_deeplabv3_mv3l(self): model, experiment_options = _create_model_from_test_proto( 'example_cityscapes_deeplabv3_mv3l.textproto') train_crop_size = tuple( experiment_options.train_dataset_options.crop_size) input_tensor = tf.random.uniform( shape=(2, train_crop_size[0], train_crop_size[1], 3)) expected_semantic_shape = [ 2, train_crop_size[0], train_crop_size[1], experiment_options.model_options.deeplab_v3.num_classes] resulting_dict = model(input_tensor) self.assertListEqual( resulting_dict[common.PRED_SEMANTIC_LOGITS_KEY].shape.as_list(), expected_semantic_shape) num_params = np.sum( [np.prod(v.get_shape().as_list()) for v in model.trainable_weights]) self.assertEqual(num_params, 11024963) def test_deeplab_with_panoptic_deeplab(self): model, experiment_options = _create_model_from_test_proto( 'example_cityscapes_panoptic_deeplab.textproto') train_crop_size = tuple( experiment_options.train_dataset_options.crop_size) input_tensor = tf.random.uniform( shape=(2, train_crop_size[0], train_crop_size[1], 3)) expected_semantic_shape = [ 2, train_crop_size[0], train_crop_size[1], experiment_options.model_options.panoptic_deeplab.semantic_head. output_channels] expected_instance_center_shape = [ 2, train_crop_size[0], train_crop_size[1]] expected_instance_regression_shape = [ 2, train_crop_size[0], train_crop_size[1], 2] resulting_dict = model(input_tensor) self.assertListEqual( resulting_dict[common.PRED_SEMANTIC_LOGITS_KEY].shape.as_list(), expected_semantic_shape) self.assertListEqual( resulting_dict[common.PRED_INSTANCE_SCORES_KEY].shape.as_list(), expected_instance_center_shape) self.assertListEqual( resulting_dict[common.PRED_CENTER_HEATMAP_KEY].shape.as_list(), expected_instance_center_shape) self.assertListEqual( resulting_dict[common.PRED_OFFSET_MAP_KEY].shape.as_list(), expected_instance_regression_shape) num_params = np.sum( [np.prod(v.get_shape().as_list()) for v in model.trainable_weights]) self.assertEqual(num_params, 54973702) def test_deeplab_with_panoptic_deeplab_mv3l(self): model, experiment_options = _create_model_from_test_proto( 'example_cityscapes_panoptic_deeplab_mv3l.textproto') train_crop_size = tuple( experiment_options.train_dataset_options.crop_size) input_tensor = tf.random.uniform( shape=(2, train_crop_size[0], train_crop_size[1], 3)) expected_semantic_shape = [ 2, train_crop_size[0], train_crop_size[1], experiment_options.model_options.panoptic_deeplab.semantic_head. output_channels] expected_instance_center_shape = [ 2, train_crop_size[0], train_crop_size[1]] expected_instance_regression_shape = [ 2, train_crop_size[0], train_crop_size[1], 2] resulting_dict = model(input_tensor) self.assertListEqual( resulting_dict[common.PRED_SEMANTIC_LOGITS_KEY].shape.as_list(), expected_semantic_shape) self.assertListEqual( resulting_dict[common.PRED_INSTANCE_SCORES_KEY].shape.as_list(), expected_instance_center_shape) self.assertListEqual( resulting_dict[common.PRED_CENTER_HEATMAP_KEY].shape.as_list(), expected_instance_center_shape) self.assertListEqual( resulting_dict[common.PRED_OFFSET_MAP_KEY].shape.as_list(), expected_instance_regression_shape) num_params = np.sum( [np.prod(v.get_shape().as_list()) for v in model.trainable_weights]) self.assertEqual(num_params, 18226550) def test_deeplab_with_max_deeplab(self): model, experiment_options = _create_model_from_test_proto( 'example_coco_max_deeplab.textproto', dataset_name='coco_panoptic') train_crop_size = tuple( experiment_options.train_dataset_options.crop_size) input_tensor = tf.random.uniform( shape=(2, train_crop_size[0], train_crop_size[1], 3)) stride_4_size = utils.scale_mutable_sequence(train_crop_size, 0.25) expected_semantic_shape = [ 2, stride_4_size[0], stride_4_size[1], experiment_options.model_options. max_deeplab.auxiliary_semantic_head.output_channels] expected_transformer_class_logits_shape = [ 2, 128, experiment_options.model_options. max_deeplab.auxiliary_semantic_head.output_channels] expected_pixel_space_normalized_feature_shape = [ 2, stride_4_size[0], stride_4_size[1], experiment_options.model_options. max_deeplab.pixel_space_head.output_channels] expected_pixel_space_mask_logits_shape = [ 2, stride_4_size[0], stride_4_size[1], 128] resulting_dict = model(input_tensor, training=True) self.assertListEqual( resulting_dict[common.PRED_SEMANTIC_LOGITS_KEY].shape.as_list(), expected_semantic_shape) self.assertListEqual( resulting_dict[ common.PRED_TRANSFORMER_CLASS_LOGITS_KEY].shape.as_list(), expected_transformer_class_logits_shape) self.assertListEqual( resulting_dict[ common.PRED_PIXEL_SPACE_NORMALIZED_FEATURE_KEY].shape.as_list(), expected_pixel_space_normalized_feature_shape) self.assertListEqual( resulting_dict[common.PRED_PIXEL_SPACE_MASK_LOGITS_KEY].shape.as_list(), expected_pixel_space_mask_logits_shape) num_params = 0 for v in model.trainable_weights: params = np.prod(v.get_shape().as_list()) # Exclude the auxiliary semantic head. if 'auxiliary_semantic' not in v.name: num_params += params self.assertEqual(num_params, 61900200) # 61.9M in the paper. def test_deeplab_errors(self): proto_filename = os.path.join( _CONFIG_PATH, 'example_cityscapes_panoptic_deeplab.textproto') experiment_options = _read_proto_file(proto_filename, config_pb2.ExperimentOptions()) with self.subTest('ResNet error.'): with self.assertRaises(ValueError): experiment_options.model_options.backbone.name = 'not_a_resnet_backbone' _ = deeplab.DeepLab(experiment_options, dataset.CITYSCAPES_PANOPTIC_INFORMATION) with self.subTest('Encoder family error.'): with self.assertRaises(ValueError): experiment_options.model_options.backbone.name = 'not_a_backbone' _ = deeplab.DeepLab(experiment_options, dataset.CITYSCAPES_PANOPTIC_INFORMATION) def test_deeplab_set_pooling(self): model, _ = _create_model_from_test_proto( 'example_cityscapes_panoptic_deeplab.textproto') pool_size = (10, 10) model.set_pool_size(pool_size) self.assertTupleEqual( model._decoder._semantic_decoder._aspp._aspp_pool._pool_size, pool_size) self.assertTupleEqual( model._decoder._instance_decoder._aspp._aspp_pool._pool_size, pool_size) def test_deeplab_reset_pooling(self): model, _ = _create_model_from_test_proto( 'example_cityscapes_panoptic_deeplab.textproto') model.reset_pooling_layer() pool_size = (None, None) self.assertTupleEqual( model._decoder._semantic_decoder._aspp._aspp_pool._pool_size, pool_size) self.assertTupleEqual( model._decoder._instance_decoder._aspp._aspp_pool._pool_size, pool_size) if __name__ == '__main__': tf.test.main()