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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 panoptic_deeplab."""
import tensorflow as tf
from deeplab2 import common
from deeplab2 import config_pb2
from deeplab2.model.decoder import panoptic_deeplab
from deeplab2.utils import test_utils
def _create_panoptic_deeplab_example_proto(num_classes=19):
semantic_decoder = config_pb2.DecoderOptions(
feature_key='res5', atrous_rates=[6, 12, 18])
semantic_head = config_pb2.HeadOptions(
output_channels=num_classes, head_channels=256)
instance_decoder = config_pb2.DecoderOptions(
feature_key='res5', decoder_channels=128, atrous_rates=[6, 12, 18])
center_head = config_pb2.HeadOptions(
output_channels=1, head_channels=32)
regression_head = config_pb2.HeadOptions(
output_channels=2, head_channels=32)
instance_branch = config_pb2.InstanceOptions(
instance_decoder_override=instance_decoder,
center_head=center_head,
regression_head=regression_head)
panoptic_deeplab_options = config_pb2.ModelOptions.PanopticDeeplabOptions(
semantic_head=semantic_head, instance=instance_branch)
# Add features from lowest to highest.
panoptic_deeplab_options.low_level.add(
feature_key='res3', channels_project=64)
panoptic_deeplab_options.low_level.add(
feature_key='res2', channels_project=32)
return config_pb2.ModelOptions(
decoder=semantic_decoder, panoptic_deeplab=panoptic_deeplab_options)
def _create_expected_shape(input_shape, output_channels):
output_shape = input_shape.copy()
output_shape[3] = output_channels
return output_shape
class PanopticDeeplabTest(tf.test.TestCase):
def test_panoptic_deeplab_single_decoder_init_errors(self):
with self.assertRaises(ValueError):
_ = panoptic_deeplab.PanopticDeepLabSingleDecoder(
high_level_feature_name='test',
low_level_feature_names=['only_one_name'], # Error: Only one name.
low_level_channels_project=[64, 32],
aspp_output_channels=256,
decoder_output_channels=256,
atrous_rates=[6, 12, 18],
name='test_decoder')
with self.assertRaises(ValueError):
_ = panoptic_deeplab.PanopticDeepLabSingleDecoder(
high_level_feature_name='test',
low_level_feature_names=['one', 'two'],
low_level_channels_project=[64], # Error: Only one projection size.
aspp_output_channels=256,
decoder_output_channels=256,
atrous_rates=[6, 12, 18],
name='test_decoder')
def test_panoptic_deeplab_single_decoder_call_errors(self):
decoder = panoptic_deeplab.PanopticDeepLabSingleDecoder(
high_level_feature_name='high',
low_level_feature_names=['low_one', 'low_two'],
low_level_channels_project=[64, 32],
aspp_output_channels=256,
decoder_output_channels=256,
atrous_rates=[6, 12, 18],
name='test_decoder')
with self.assertRaises(KeyError):
input_dict = {'not_high': tf.random.uniform(shape=(2, 32, 32, 512)),
'low_one': tf.random.uniform(shape=(2, 128, 128, 128)),
'low_two': tf.random.uniform(shape=(2, 256, 256, 64))}
_ = decoder(input_dict)
with self.assertRaises(KeyError):
input_dict = {'high': tf.random.uniform(shape=(2, 32, 32, 512)),
'not_low_one': tf.random.uniform(shape=(2, 128, 128, 128)),
'low_two': tf.random.uniform(shape=(2, 256, 256, 64))}
_ = decoder(input_dict)
with self.assertRaises(KeyError):
input_dict = {'high': tf.random.uniform(shape=(2, 32, 32, 512)),
'low_one': tf.random.uniform(shape=(2, 128, 128, 128)),
'not_low_two': tf.random.uniform(shape=(2, 256, 256, 64))}
_ = decoder(input_dict)
def test_panoptic_deeplab_single_decoder_reset_pooling(self):
decoder = panoptic_deeplab.PanopticDeepLabSingleDecoder(
high_level_feature_name='high',
low_level_feature_names=['low_one', 'low_two'],
low_level_channels_project=[64, 32],
aspp_output_channels=256,
decoder_output_channels=256,
atrous_rates=[6, 12, 18],
name='test_decoder')
pool_size = (None, None)
decoder.reset_pooling_layer()
self.assertTupleEqual(decoder._aspp._aspp_pool._pool_size,
pool_size)
def test_panoptic_deeplab_single_decoder_set_pooling(self):
decoder = panoptic_deeplab.PanopticDeepLabSingleDecoder(
high_level_feature_name='high',
low_level_feature_names=['low_one', 'low_two'],
low_level_channels_project=[64, 32],
aspp_output_channels=256,
decoder_output_channels=256,
atrous_rates=[6, 12, 18],
name='test_decoder')
pool_size = (10, 10)
decoder.set_pool_size(pool_size)
self.assertTupleEqual(decoder._aspp._aspp_pool._pool_size,
pool_size)
def test_panoptic_deeplab_single_decoder_output_shape(self):
decoder_channels = 256
decoder = panoptic_deeplab.PanopticDeepLabSingleDecoder(
high_level_feature_name='high',
low_level_feature_names=['low_one', 'low_two'],
low_level_channels_project=[64, 32],
aspp_output_channels=256,
decoder_output_channels=decoder_channels,
atrous_rates=[6, 12, 18],
name='test_decoder')
input_shapes_list = [[[2, 128, 128, 128], [2, 256, 256, 64],
[2, 32, 32, 512]],
[[2, 129, 129, 128], [2, 257, 257, 64],
[2, 33, 33, 512]]]
for shapes in input_shapes_list:
input_dict = {'low_one': tf.random.uniform(shape=shapes[0]),
'low_two': tf.random.uniform(shape=shapes[1]),
'high': tf.random.uniform(shape=shapes[2])}
expected_shape = _create_expected_shape(shapes[1], decoder_channels)
resulting_tensor = decoder(input_dict)
self.assertListEqual(resulting_tensor.shape.as_list(), expected_shape)
def test_panoptic_deeplab_single_head_output_shape(self):
output_channels = 19
head = panoptic_deeplab.PanopticDeepLabSingleHead(
intermediate_channels=256,
output_channels=output_channels,
pred_key='pred',
name='test_head')
input_shapes_list = [[2, 256, 256, 48], [2, 257, 257, 48]]
for shape in input_shapes_list:
input_tensor = tf.random.uniform(shape=shape)
expected_shape = _create_expected_shape(shape, output_channels)
resulting_tensor = head(input_tensor)
self.assertListEqual(resulting_tensor['pred'].shape.as_list(),
expected_shape)
def test_panoptic_deeplab_decoder_output_shape(self):
num_classes = 31
model_options = _create_panoptic_deeplab_example_proto(
num_classes=num_classes)
decoder = panoptic_deeplab.PanopticDeepLab(
panoptic_deeplab_options=model_options.panoptic_deeplab,
decoder_options=model_options.decoder)
input_shapes_list = [[[2, 256, 256, 64], [2, 128, 128, 128],
[2, 32, 32, 512]],
[[2, 257, 257, 64], [2, 129, 129, 128],
[2, 33, 33, 512]]]
for shapes in input_shapes_list:
input_dict = {'res2': tf.random.uniform(shape=shapes[0]),
'res3': tf.random.uniform(shape=shapes[1]),
'res5': tf.random.uniform(shape=shapes[2])}
expected_semantic_shape = _create_expected_shape(shapes[0], num_classes)
expected_instance_center_shape = _create_expected_shape(shapes[0], 1)
expected_instance_regression_shape = _create_expected_shape(shapes[0], 2)
resulting_dict = decoder(input_dict)
self.assertListEqual(
resulting_dict[common.PRED_SEMANTIC_LOGITS_KEY].shape.as_list(),
expected_semantic_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)
@test_utils.test_all_strategies
def test_panoptic_deeplab_sync_bn(self, strategy):
num_classes = 31
model_options = _create_panoptic_deeplab_example_proto(
num_classes=num_classes)
input_dict = {'res2': tf.random.uniform(shape=[2, 257, 257, 64]),
'res3': tf.random.uniform(shape=[2, 129, 129, 128]),
'res5': tf.random.uniform(shape=[2, 33, 33, 512])}
with strategy.scope():
for bn_layer in test_utils.NORMALIZATION_LAYERS:
decoder = panoptic_deeplab.PanopticDeepLab(
panoptic_deeplab_options=model_options.panoptic_deeplab,
decoder_options=model_options.decoder,
bn_layer=bn_layer)
_ = decoder(input_dict)
def test_panoptic_deeplab_single_decoder_logging_feature_order(self):
with self.assertLogs(level='WARN'):
_ = panoptic_deeplab.PanopticDeepLabSingleDecoder(
high_level_feature_name='high',
low_level_feature_names=['low_two', 'low_one'],
low_level_channels_project=[32, 64], # Potentially wrong order.
aspp_output_channels=256,
decoder_output_channels=256,
atrous_rates=[6, 12, 18],
name='test_decoder')
def test_panoptic_deeplab_decoder_ckpt_tems(self):
num_classes = 31
model_options = _create_panoptic_deeplab_example_proto(
num_classes=num_classes)
decoder = panoptic_deeplab.PanopticDeepLab(
panoptic_deeplab_options=model_options.panoptic_deeplab,
decoder_options=model_options.decoder)
ckpt_dict = decoder.checkpoint_items
self.assertIn(common.CKPT_SEMANTIC_DECODER, ckpt_dict)
self.assertIn(common.CKPT_SEMANTIC_HEAD_WITHOUT_LAST_LAYER, ckpt_dict)
self.assertIn(common.CKPT_SEMANTIC_LAST_LAYER, ckpt_dict)
self.assertIn(common.CKPT_INSTANCE_DECODER, ckpt_dict)
self.assertIn(common.CKPT_INSTANCE_REGRESSION_HEAD_WITHOUT_LAST_LAYER,
ckpt_dict)
self.assertIn(common.CKPT_INSTANCE_REGRESSION_HEAD_LAST_LAYER, ckpt_dict)
self.assertIn(common.CKPT_INSTANCE_CENTER_HEAD_WITHOUT_LAST_LAYER,
ckpt_dict)
self.assertIn(common.CKPT_INSTANCE_CENTER_HEAD_LAST_LAYER, ckpt_dict)
if __name__ == '__main__':
tf.test.main()
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