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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()
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