official/vision/dataloaders/tf_example_decoder_test.py
# Copyright 2024 The TensorFlow Authors. All Rights Reserved.
#
# 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 tf_example_decoder.py."""
# Import libraries
from absl.testing import parameterized
import numpy as np
import tensorflow as tf, tf_keras
from official.vision.dataloaders import tf_example_decoder
from official.vision.dataloaders import tfexample_utils
class TfExampleDecoderTest(tf.test.TestCase, parameterized.TestCase):
@parameterized.parameters(
(100, 100, 0, True, True),
(100, 100, 1, True, True),
(100, 100, 2, True, True),
(100, 100, 0, False, True),
(100, 100, 1, False, True),
(100, 100, 2, False, True),
(100, 100, 0, True, False),
(100, 100, 1, True, False),
(100, 100, 2, True, False),
(100, 100, 0, False, False),
(100, 100, 1, False, False),
(100, 100, 2, False, False),
)
def test_result_shape(self, image_height, image_width, num_instances,
regenerate_source_id, fill_image_size):
decoder = tf_example_decoder.TfExampleDecoder(
include_mask=True, regenerate_source_id=regenerate_source_id)
serialized_example = tfexample_utils.create_detection_test_example(
image_height=image_height,
image_width=image_width,
image_channel=3,
num_instances=num_instances,
fill_image_size=fill_image_size,
).SerializeToString()
decoded_tensors = decoder.decode(
tf.convert_to_tensor(value=serialized_example))
results = tf.nest.map_structure(lambda x: x.numpy(), decoded_tensors)
self.assertAllEqual(
(image_height, image_width, 3), results['image'].shape)
if not regenerate_source_id:
self.assertEqual(tfexample_utils.DUMP_SOURCE_ID, results['source_id'])
self.assertEqual(image_height, results['height'])
self.assertEqual(image_width, results['width'])
self.assertAllEqual(
(num_instances,), results['groundtruth_classes'].shape)
self.assertAllEqual(
(num_instances,), results['groundtruth_is_crowd'].shape)
self.assertAllEqual(
(num_instances,), results['groundtruth_area'].shape)
self.assertAllEqual(
(num_instances, 4), results['groundtruth_boxes'].shape)
self.assertAllEqual(
(num_instances, image_height, image_width),
results['groundtruth_instance_masks'].shape)
self.assertAllEqual(
(num_instances,), results['groundtruth_instance_masks_png'].shape)
def test_result_content(self):
decoder = tf_example_decoder.TfExampleDecoder(
include_mask=True, attribute_names=['attr1', 'attr2']
)
image_content = [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[0, 0, 0], [255, 255, 255], [255, 255, 255], [0, 0, 0]],
[[0, 0, 0], [255, 255, 255], [255, 255, 255], [0, 0, 0]],
[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]]
image = tfexample_utils.encode_image(np.uint8(image_content), fmt='PNG')
image_height = 4
image_width = 4
num_instances = 2
xmins = [0, 0.25]
xmaxs = [0.5, 1.0]
ymins = [0, 0]
ymaxs = [0.5, 1.0]
labels = [3, 1]
attr1 = np.array([[0], [2]])
attr2 = np.array([[1], [3]])
areas = [
0.25 * image_height * image_width, 0.75 * image_height * image_width
]
is_crowds = [1, 0]
mask_content = [[[255, 255, 0, 0],
[255, 255, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]],
[[0, 255, 255, 255],
[0, 255, 255, 255],
[0, 255, 255, 255],
[0, 255, 255, 255]]]
masks = [
tfexample_utils.encode_image(np.uint8(m), fmt='PNG')
for m in list(mask_content)
]
serialized_example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded': tf.train.Feature(
bytes_list=tf.train.BytesList(value=[image])
),
'image/source_id': tf.train.Feature(
bytes_list=tf.train.BytesList(
value=[tfexample_utils.DUMP_SOURCE_ID]
)
),
'image/height': tf.train.Feature(
int64_list=tf.train.Int64List(value=[image_height])
),
'image/width': tf.train.Feature(
int64_list=tf.train.Int64List(value=[image_width])
),
'image/object/bbox/xmin': tf.train.Feature(
float_list=tf.train.FloatList(value=xmins)
),
'image/object/bbox/xmax': tf.train.Feature(
float_list=tf.train.FloatList(value=xmaxs)
),
'image/object/bbox/ymin': tf.train.Feature(
float_list=tf.train.FloatList(value=ymins)
),
'image/object/bbox/ymax': tf.train.Feature(
float_list=tf.train.FloatList(value=ymaxs)
),
'image/object/class/label': tf.train.Feature(
int64_list=tf.train.Int64List(value=labels)
),
'image/object/is_crowd': tf.train.Feature(
int64_list=tf.train.Int64List(value=is_crowds)
),
'image/object/area': tf.train.Feature(
float_list=tf.train.FloatList(value=areas)
),
'image/object/mask': tf.train.Feature(
bytes_list=tf.train.BytesList(value=masks)
),
'image/object/attribute/attr1': tf.train.Feature(
int64_list=tf.train.Int64List(value=attr1.flatten())
),
'image/object/attribute/attr2': tf.train.Feature(
int64_list=tf.train.Int64List(value=attr2.flatten())
),
}
)
).SerializeToString()
decoded_tensors = decoder.decode(
tf.convert_to_tensor(value=serialized_example))
results = tf.nest.map_structure(lambda x: x.numpy(), decoded_tensors)
self.assertAllEqual(
(image_height, image_width, 3), results['image'].shape)
self.assertAllEqual(image_content, results['image'])
self.assertEqual(tfexample_utils.DUMP_SOURCE_ID, results['source_id'])
self.assertEqual(image_height, results['height'])
self.assertEqual(image_width, results['width'])
self.assertAllEqual(
(num_instances,), results['groundtruth_classes'].shape)
self.assertAllEqual(
(num_instances,), results['groundtruth_is_crowd'].shape)
self.assertAllEqual(
(num_instances,), results['groundtruth_area'].shape)
self.assertAllEqual(
(num_instances, 4), results['groundtruth_boxes'].shape)
self.assertAllEqual(
(num_instances, image_height, image_width),
results['groundtruth_instance_masks'].shape)
self.assertAllEqual(
(num_instances,), results['groundtruth_instance_masks_png'].shape)
self.assertAllEqual(
[3, 1], results['groundtruth_classes'])
np.testing.assert_array_equal(
attr1, results['groundtruth_attributes']['attr1']
)
np.testing.assert_array_equal(
attr2, results['groundtruth_attributes']['attr2']
)
self.assertAllEqual([True, False], results['groundtruth_is_crowd'])
self.assertNDArrayNear(
[0.25 * image_height * image_width, 0.75 * image_height * image_width],
results['groundtruth_area'], 1e-4)
self.assertNDArrayNear(
[[0, 0, 0.5, 0.5], [0, 0.25, 1.0, 1.0]],
results['groundtruth_boxes'], 1e-4)
self.assertNDArrayNear(
mask_content, results['groundtruth_instance_masks'], 1e-4)
self.assertAllEqual(
masks, results['groundtruth_instance_masks_png'])
def test_handling_missing_fields(self):
decoder = tf_example_decoder.TfExampleDecoder(include_mask=True)
image_content = [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]],
[[0, 0, 0], [255, 255, 255], [255, 255, 255], [0, 0, 0]],
[[0, 0, 0], [255, 255, 255], [255, 255, 255], [0, 0, 0]],
[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]]]
image = tfexample_utils.encode_image(np.uint8(image_content), fmt='PNG')
image_height = 4
image_width = 4
num_instances = 2
xmins = [0, 0.25]
xmaxs = [0.5, 1.0]
ymins = [0, 0]
ymaxs = [0.5, 1.0]
labels = [3, 1]
mask_content = [[[255, 255, 0, 0],
[255, 255, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]],
[[0, 255, 255, 255],
[0, 255, 255, 255],
[0, 255, 255, 255],
[0, 255, 255, 255]]]
masks = [
tfexample_utils.encode_image(np.uint8(m), fmt='PNG')
for m in list(mask_content)
]
serialized_example = tf.train.Example(
features=tf.train.Features(
feature={
'image/encoded': (tf.train.Feature(
bytes_list=tf.train.BytesList(value=[image]))),
'image/source_id': (tf.train.Feature(
bytes_list=tf.train.BytesList(
value=[tfexample_utils.DUMP_SOURCE_ID]))),
'image/height': (tf.train.Feature(
int64_list=tf.train.Int64List(value=[image_height]))),
'image/width': (tf.train.Feature(
int64_list=tf.train.Int64List(value=[image_width]))),
'image/object/bbox/xmin': (tf.train.Feature(
float_list=tf.train.FloatList(value=xmins))),
'image/object/bbox/xmax': (tf.train.Feature(
float_list=tf.train.FloatList(value=xmaxs))),
'image/object/bbox/ymin': (tf.train.Feature(
float_list=tf.train.FloatList(value=ymins))),
'image/object/bbox/ymax': (tf.train.Feature(
float_list=tf.train.FloatList(value=ymaxs))),
'image/object/class/label': (tf.train.Feature(
int64_list=tf.train.Int64List(value=labels))),
'image/object/mask': (tf.train.Feature(
bytes_list=tf.train.BytesList(value=masks))),
})).SerializeToString()
decoded_tensors = decoder.decode(
tf.convert_to_tensor(serialized_example))
results = tf.nest.map_structure(lambda x: x.numpy(), decoded_tensors)
self.assertAllEqual(
(image_height, image_width, 3), results['image'].shape)
self.assertAllEqual(image_content, results['image'])
self.assertEqual(tfexample_utils.DUMP_SOURCE_ID, results['source_id'])
self.assertEqual(image_height, results['height'])
self.assertEqual(image_width, results['width'])
self.assertAllEqual(
(num_instances,), results['groundtruth_classes'].shape)
self.assertAllEqual(
(num_instances,), results['groundtruth_is_crowd'].shape)
self.assertAllEqual(
(num_instances,), results['groundtruth_area'].shape)
self.assertAllEqual(
(num_instances, 4), results['groundtruth_boxes'].shape)
self.assertAllEqual(
(num_instances, image_height, image_width),
results['groundtruth_instance_masks'].shape)
self.assertAllEqual(
(num_instances,), results['groundtruth_instance_masks_png'].shape)
self.assertAllEqual(
[3, 1], results['groundtruth_classes'])
self.assertAllEqual(
[False, False], results['groundtruth_is_crowd'])
self.assertNDArrayNear(
[0.25 * image_height * image_width, 0.75 * image_height * image_width],
results['groundtruth_area'], 1e-4)
self.assertNDArrayNear(
[[0, 0, 0.5, 0.5], [0, 0.25, 1.0, 1.0]],
results['groundtruth_boxes'], 1e-4)
self.assertNDArrayNear(
mask_content, results['groundtruth_instance_masks'], 1e-4)
self.assertAllEqual(
masks, results['groundtruth_instance_masks_png'])
if __name__ == '__main__':
tf.test.main()