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deeplab2 / model /post_processor /post_processor_builder_test.py

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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 post_processor_builder.py."""

	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.post_processor import post_processor_builder


	class EvaluatorTest(tf.test.TestCase):

	def test_evaluates_panoptic_deeplab_model(self):
	experiment_options_textproto = """
	experiment_name: "evaluation_test"
	eval_dataset_options {
	dataset: "cityscapes_panoptic"
	file_pattern: "EMPTY"
	batch_size: 1
	crop_size: 1025
	crop_size: 2049
	# Skip resizing.
	min_resize_value: 0
	max_resize_value: 0
	}
	evaluator_options {
	continuous_eval_timeout: 43200
	stuff_area_limit: 2048
	center_score_threshold: 0.1
	nms_kernel: 13
	save_predictions: true
	save_raw_predictions: false
	}
	"""
	config = text_format.Parse(experiment_options_textproto,
	config_pb2.ExperimentOptions())
	config.model_options.panoptic_deeplab.instance.enable = True
	post_processor = post_processor_builder.get_post_processor(
	config, dataset.CITYSCAPES_PANOPTIC_INFORMATION)

	result_dict = {
	common.PRED_SEMANTIC_PROBS_KEY:
	tf.zeros([1, 1025, 2049, 19], dtype=tf.float32),
	common.PRED_CENTER_HEATMAP_KEY:
	tf.zeros([1, 1025, 2049, 1], dtype=tf.float32),
	common.PRED_OFFSET_MAP_KEY:
	tf.zeros([1, 1025, 2049, 2], dtype=tf.float32)
	}
	processed_dict = post_processor(result_dict)
	expected_keys = {
	common.PRED_PANOPTIC_KEY,
	common.PRED_SEMANTIC_KEY,
	common.PRED_INSTANCE_KEY,
	common.PRED_INSTANCE_CENTER_KEY,
	common.PRED_INSTANCE_SCORES_KEY
	}
	self.assertCountEqual(processed_dict.keys(), expected_keys)


	if __name__ == '__main__':
	tf.test.main()