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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 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()
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