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Commit 545cd6ce authored by Hongkun Yu's avatar Hongkun Yu Committed by A. Unique TensorFlower
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official/vision/beta/dataloaders/maskrcnn_input_test.py deleted 100644 → 0
View file @ 5e5cdaea
# Copyright 2020 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 maskrcnn_input."""
# Import libraries
from absl.testing import parameterized
import tensorflow as tf
from official.core import input_reader
from official.modeling.hyperparams import config_definitions as cfg
from official.vision.beta.dataloaders import maskrcnn_input
from official.vision.beta.dataloaders import tf_example_decoder
class InputReaderTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(
([1024, 1024], True, True, True),
([1024, 1024], True, False, True),
([1024, 1024], False, True, True),
([1024, 1024], False, False, True),
([1024, 1024], True, True, False),
([1024, 1024], True, False, False),
([1024, 1024], False, True, False),
([1024, 1024], False, False, False),
)
def testMaskRCNNInputReader(self,
output_size,
skip_crowd_during_training,
include_mask,
is_training):
min_level = 3
max_level = 7
num_scales = 3
aspect_ratios = [1.0, 2.0, 0.5]
max_num_instances = 100
batch_size = 2
mask_crop_size = 112
anchor_size = 4.0
params = cfg.DataConfig(
input_path='/placer/prod/home/snaggletooth/test/data/coco/val*',
global_batch_size=batch_size,
is_training=is_training)
parser = maskrcnn_input.Parser(
output_size=output_size,
min_level=min_level,
max_level=max_level,
num_scales=num_scales,
aspect_ratios=aspect_ratios,
anchor_size=anchor_size,
rpn_match_threshold=0.7,
rpn_unmatched_threshold=0.3,
rpn_batch_size_per_im=256,
rpn_fg_fraction=0.5,
aug_rand_hflip=True,
aug_scale_min=0.8,
aug_scale_max=1.2,
skip_crowd_during_training=skip_crowd_during_training,
max_num_instances=max_num_instances,
include_mask=include_mask,
mask_crop_size=mask_crop_size,
dtype='bfloat16')
decoder = tf_example_decoder.TfExampleDecoder(include_mask=include_mask)
reader = input_reader.InputReader(
params,
dataset_fn=tf.data.TFRecordDataset,
decoder_fn=decoder.decode,
parser_fn=parser.parse_fn(params.is_training))
dataset = reader.read()
iterator = iter(dataset)
images, labels = next(iterator)
np_images = images.numpy()
np_labels = tf.nest.map_structure(lambda x: x.numpy(), labels)
if is_training:
self.assertAllEqual(np_images.shape,
[batch_size, output_size[0], output_size[1], 3])
self.assertAllEqual(np_labels['image_info'].shape, [batch_size, 4, 2])
self.assertAllEqual(np_labels['gt_boxes'].shape,
[batch_size, max_num_instances, 4])
self.assertAllEqual(np_labels['gt_classes'].shape,
[batch_size, max_num_instances])
if include_mask:
self.assertAllEqual(np_labels['gt_masks'].shape,
[batch_size, max_num_instances,
mask_crop_size, mask_crop_size])
for level in range(min_level, max_level + 1):
stride = 2 ** level
output_size_l = [output_size[0] / stride, output_size[1] / stride]
anchors_per_location = num_scales * len(aspect_ratios)
self.assertAllEqual(
np_labels['rpn_score_targets'][level].shape,
[batch_size, output_size_l[0], output_size_l[1],
anchors_per_location])
self.assertAllEqual(
np_labels['rpn_box_targets'][level].shape,
[batch_size, output_size_l[0], output_size_l[1],
4 * anchors_per_location])
self.assertAllEqual(
np_labels['anchor_boxes'][level].shape,
[batch_size, output_size_l[0], output_size_l[1],
4 * anchors_per_location])
else:
self.assertAllEqual(np_images.shape,
[batch_size, output_size[0], output_size[1], 3])
self.assertAllEqual(np_labels['image_info'].shape, [batch_size, 4, 2])
if __name__ == '__main__':
tf.test.main()
official/vision/beta/dataloaders/retinanet_input_test.py deleted 100644 → 0
View file @ 5e5cdaea
# Copyright 2020 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 retinanet_parser.py."""
# Import libraries
from absl.testing import parameterized
import tensorflow as tf
from official.core import input_reader
from official.modeling.hyperparams import config_definitions as cfg
from official.vision.beta.dataloaders import retinanet_input
from official.vision.beta.dataloaders import tf_example_decoder
class RetinaNetInputTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(
([512, 640], True, True, True),
([640, 640], False, False, False),
)
def testRetinanetInputReader(self,
output_size,
skip_crowd_during_training,
use_autoaugment,
is_training):
batch_size = 2
min_level = 3
max_level = 7
num_scales = 3
aspect_ratios = [0.5, 1.0, 2.0]
anchor_size = 3
max_num_instances = 100
params = cfg.DataConfig(
input_path='/placer/prod/home/snaggletooth/test/data/coco/val*',
global_batch_size=batch_size,
is_training=is_training)
decoder = tf_example_decoder.TfExampleDecoder()
parser = retinanet_input.Parser(
output_size=output_size,
min_level=min_level,
max_level=max_level,
num_scales=num_scales,
aspect_ratios=aspect_ratios,
anchor_size=anchor_size,
skip_crowd_during_training=skip_crowd_during_training,
use_autoaugment=use_autoaugment,
max_num_instances=max_num_instances,
dtype='bfloat16')
reader = input_reader.InputReader(
params,
dataset_fn=tf.data.TFRecordDataset,
decoder_fn=decoder.decode,
parser_fn=parser.parse_fn(params.is_training))
dataset = reader.read()
iterator = iter(dataset)
image, labels = next(iterator)
np_image = image.numpy()
np_labels = tf.nest.map_structure(lambda x: x.numpy(), labels)
# Checks image shape.
self.assertEqual(list(np_image.shape),
[batch_size, output_size[0], output_size[1], 3])
# Checks keys in labels.
if is_training:
self.assertCountEqual(
np_labels.keys(),
['cls_targets', 'box_targets', 'anchor_boxes', 'cls_weights',
'box_weights', 'image_info'])
else:
self.assertCountEqual(
np_labels.keys(),
['cls_targets', 'box_targets', 'anchor_boxes', 'cls_weights',
'box_weights', 'groundtruths', 'image_info'])
# Checks shapes of `image_info` and `anchor_boxes`.
self.assertEqual(np_labels['image_info'].shape, (batch_size, 4, 2))
n_anchors = 0
for level in range(min_level, max_level + 1):
stride = 2 ** level
output_size_l = [output_size[0] / stride, output_size[1] / stride]
anchors_per_location = num_scales * len(aspect_ratios)
self.assertEqual(
list(np_labels['anchor_boxes'][level].shape),
[batch_size, output_size_l[0], output_size_l[1],
4 * anchors_per_location])
n_anchors += output_size_l[0] * output_size_l[1] * anchors_per_location
# Checks shapes of training objectives.
self.assertEqual(np_labels['cls_weights'].shape, (batch_size, n_anchors))
for level in range(min_level, max_level + 1):
stride = 2 ** level
output_size_l = [output_size[0] / stride, output_size[1] / stride]
anchors_per_location = num_scales * len(aspect_ratios)
self.assertEqual(
list(np_labels['cls_targets'][level].shape),
[batch_size, output_size_l[0], output_size_l[1],
anchors_per_location])
self.assertEqual(
list(np_labels['box_targets'][level].shape),
[batch_size, output_size_l[0], output_size_l[1],
4 * anchors_per_location])
# Checks shape of groundtruths for eval.
if not is_training:
self.assertEqual(np_labels['groundtruths']['source_id'].shape,
(batch_size,))
self.assertEqual(np_labels['groundtruths']['classes'].shape,
(batch_size, max_num_instances))
self.assertEqual(np_labels['groundtruths']['boxes'].shape,
(batch_size, max_num_instances, 4))
self.assertEqual(np_labels['groundtruths']['areas'].shape,
(batch_size, max_num_instances))
self.assertEqual(np_labels['groundtruths']['is_crowds'].shape,
(batch_size, max_num_instances))
if __name__ == '__main__':
tf.test.main()
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