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inputs_test.py
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inputs_test.py
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# Copyright 2017 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 object_detection.tflearn.inputs."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import os
from absl.testing import parameterized
import numpy as np
import tensorflow as tf
from object_detection import inputs
from object_detection.core import preprocessor
from object_detection.core import standard_fields as fields
from object_detection.utils import config_util
from object_detection.utils import test_case
FLAGS = tf.flags.FLAGS
def _get_configs_for_model(model_name):
"""Returns configurations for model."""
fname = os.path.join(tf.resource_loader.get_data_files_path(),
'samples/configs/' + model_name + '.config')
label_map_path = os.path.join(tf.resource_loader.get_data_files_path(),
'data/pet_label_map.pbtxt')
data_path = os.path.join(tf.resource_loader.get_data_files_path(),
'test_data/pets_examples.record')
configs = config_util.get_configs_from_pipeline_file(fname)
override_dict = {
'train_input_path': data_path,
'eval_input_path': data_path,
'label_map_path': label_map_path
}
return config_util.merge_external_params_with_configs(
configs, kwargs_dict=override_dict)
def _make_initializable_iterator(dataset):
"""Creates an iterator, and initializes tables.
Args:
dataset: A `tf.data.Dataset` object.
Returns:
A `tf.data.Iterator`.
"""
iterator = dataset.make_initializable_iterator()
tf.add_to_collection(tf.GraphKeys.TABLE_INITIALIZERS, iterator.initializer)
return iterator
class InputsTest(test_case.TestCase, parameterized.TestCase):
def test_faster_rcnn_resnet50_train_input(self):
"""Tests the training input function for FasterRcnnResnet50."""
configs = _get_configs_for_model('faster_rcnn_resnet50_pets')
model_config = configs['model']
model_config.faster_rcnn.num_classes = 37
train_input_fn = inputs.create_train_input_fn(
configs['train_config'], configs['train_input_config'], model_config)
features, labels = _make_initializable_iterator(train_input_fn()).get_next()
self.assertAllEqual([1, None, None, 3],
features[fields.InputDataFields.image].shape.as_list())
self.assertEqual(tf.float32, features[fields.InputDataFields.image].dtype)
self.assertAllEqual([1],
features[inputs.HASH_KEY].shape.as_list())
self.assertEqual(tf.int32, features[inputs.HASH_KEY].dtype)
self.assertAllEqual(
[1, 100, 4],
labels[fields.InputDataFields.groundtruth_boxes].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_boxes].dtype)
self.assertAllEqual(
[1, 100, model_config.faster_rcnn.num_classes],
labels[fields.InputDataFields.groundtruth_classes].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_classes].dtype)
self.assertAllEqual(
[1, 100],
labels[fields.InputDataFields.groundtruth_weights].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_weights].dtype)
self.assertAllEqual(
[1, 100, model_config.faster_rcnn.num_classes],
labels[fields.InputDataFields.groundtruth_confidences].shape.as_list())
self.assertEqual(
tf.float32,
labels[fields.InputDataFields.groundtruth_confidences].dtype)
def test_faster_rcnn_resnet50_train_input_with_additional_channels(self):
"""Tests the training input function for FasterRcnnResnet50."""
configs = _get_configs_for_model('faster_rcnn_resnet50_pets')
model_config = configs['model']
configs['train_input_config'].num_additional_channels = 2
configs['train_config'].retain_original_images = True
model_config.faster_rcnn.num_classes = 37
train_input_fn = inputs.create_train_input_fn(
configs['train_config'], configs['train_input_config'], model_config)
features, labels = _make_initializable_iterator(train_input_fn()).get_next()
self.assertAllEqual([1, None, None, 5],
features[fields.InputDataFields.image].shape.as_list())
self.assertAllEqual(
[1, None, None, 3],
features[fields.InputDataFields.original_image].shape.as_list())
self.assertEqual(tf.float32, features[fields.InputDataFields.image].dtype)
self.assertAllEqual([1],
features[inputs.HASH_KEY].shape.as_list())
self.assertEqual(tf.int32, features[inputs.HASH_KEY].dtype)
self.assertAllEqual(
[1, 100, 4],
labels[fields.InputDataFields.groundtruth_boxes].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_boxes].dtype)
self.assertAllEqual(
[1, 100, model_config.faster_rcnn.num_classes],
labels[fields.InputDataFields.groundtruth_classes].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_classes].dtype)
self.assertAllEqual(
[1, 100],
labels[fields.InputDataFields.groundtruth_weights].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_weights].dtype)
self.assertAllEqual(
[1, 100, model_config.faster_rcnn.num_classes],
labels[fields.InputDataFields.groundtruth_confidences].shape.as_list())
self.assertEqual(
tf.float32,
labels[fields.InputDataFields.groundtruth_confidences].dtype)
@parameterized.parameters(
{'eval_batch_size': 1},
{'eval_batch_size': 8}
)
def test_faster_rcnn_resnet50_eval_input(self, eval_batch_size=1):
"""Tests the eval input function for FasterRcnnResnet50."""
configs = _get_configs_for_model('faster_rcnn_resnet50_pets')
model_config = configs['model']
model_config.faster_rcnn.num_classes = 37
eval_config = configs['eval_config']
eval_config.batch_size = eval_batch_size
eval_input_fn = inputs.create_eval_input_fn(
eval_config, configs['eval_input_configs'][0], model_config)
features, labels = _make_initializable_iterator(eval_input_fn()).get_next()
self.assertAllEqual([eval_batch_size, None, None, 3],
features[fields.InputDataFields.image].shape.as_list())
self.assertEqual(tf.float32, features[fields.InputDataFields.image].dtype)
self.assertAllEqual(
[eval_batch_size, None, None, 3],
features[fields.InputDataFields.original_image].shape.as_list())
self.assertEqual(tf.uint8,
features[fields.InputDataFields.original_image].dtype)
self.assertAllEqual([eval_batch_size],
features[inputs.HASH_KEY].shape.as_list())
self.assertEqual(tf.int32, features[inputs.HASH_KEY].dtype)
self.assertAllEqual(
[eval_batch_size, 100, 4],
labels[fields.InputDataFields.groundtruth_boxes].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_boxes].dtype)
self.assertAllEqual(
[eval_batch_size, 100, model_config.faster_rcnn.num_classes],
labels[fields.InputDataFields.groundtruth_classes].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_classes].dtype)
self.assertAllEqual(
[eval_batch_size, 100],
labels[fields.InputDataFields.groundtruth_weights].shape.as_list())
self.assertEqual(
tf.float32,
labels[fields.InputDataFields.groundtruth_weights].dtype)
self.assertAllEqual(
[eval_batch_size, 100],
labels[fields.InputDataFields.groundtruth_area].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_area].dtype)
self.assertAllEqual(
[eval_batch_size, 100],
labels[fields.InputDataFields.groundtruth_is_crowd].shape.as_list())
self.assertEqual(
tf.bool, labels[fields.InputDataFields.groundtruth_is_crowd].dtype)
self.assertAllEqual(
[eval_batch_size, 100],
labels[fields.InputDataFields.groundtruth_difficult].shape.as_list())
self.assertEqual(
tf.int32, labels[fields.InputDataFields.groundtruth_difficult].dtype)
def test_ssd_inceptionV2_train_input(self):
"""Tests the training input function for SSDInceptionV2."""
configs = _get_configs_for_model('ssd_inception_v2_pets')
model_config = configs['model']
model_config.ssd.num_classes = 37
batch_size = configs['train_config'].batch_size
train_input_fn = inputs.create_train_input_fn(
configs['train_config'], configs['train_input_config'], model_config)
features, labels = _make_initializable_iterator(train_input_fn()).get_next()
self.assertAllEqual([batch_size, 300, 300, 3],
features[fields.InputDataFields.image].shape.as_list())
self.assertEqual(tf.float32, features[fields.InputDataFields.image].dtype)
self.assertAllEqual([batch_size],
features[inputs.HASH_KEY].shape.as_list())
self.assertEqual(tf.int32, features[inputs.HASH_KEY].dtype)
self.assertAllEqual(
[batch_size],
labels[fields.InputDataFields.num_groundtruth_boxes].shape.as_list())
self.assertEqual(tf.int32,
labels[fields.InputDataFields.num_groundtruth_boxes].dtype)
self.assertAllEqual(
[batch_size, 100, 4],
labels[fields.InputDataFields.groundtruth_boxes].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_boxes].dtype)
self.assertAllEqual(
[batch_size, 100, model_config.ssd.num_classes],
labels[fields.InputDataFields.groundtruth_classes].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_classes].dtype)
self.assertAllEqual(
[batch_size, 100],
labels[
fields.InputDataFields.groundtruth_weights].shape.as_list())
self.assertEqual(
tf.float32,
labels[fields.InputDataFields.groundtruth_weights].dtype)
@parameterized.parameters(
{'eval_batch_size': 1},
{'eval_batch_size': 8}
)
def test_ssd_inceptionV2_eval_input(self, eval_batch_size=1):
"""Tests the eval input function for SSDInceptionV2."""
configs = _get_configs_for_model('ssd_inception_v2_pets')
model_config = configs['model']
model_config.ssd.num_classes = 37
eval_config = configs['eval_config']
eval_config.batch_size = eval_batch_size
eval_input_fn = inputs.create_eval_input_fn(
eval_config, configs['eval_input_configs'][0], model_config)
features, labels = _make_initializable_iterator(eval_input_fn()).get_next()
self.assertAllEqual([eval_batch_size, 300, 300, 3],
features[fields.InputDataFields.image].shape.as_list())
self.assertEqual(tf.float32, features[fields.InputDataFields.image].dtype)
self.assertAllEqual(
[eval_batch_size, 300, 300, 3],
features[fields.InputDataFields.original_image].shape.as_list())
self.assertEqual(tf.uint8,
features[fields.InputDataFields.original_image].dtype)
self.assertAllEqual([eval_batch_size],
features[inputs.HASH_KEY].shape.as_list())
self.assertEqual(tf.int32, features[inputs.HASH_KEY].dtype)
self.assertAllEqual(
[eval_batch_size, 100, 4],
labels[fields.InputDataFields.groundtruth_boxes].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_boxes].dtype)
self.assertAllEqual(
[eval_batch_size, 100, model_config.ssd.num_classes],
labels[fields.InputDataFields.groundtruth_classes].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_classes].dtype)
self.assertAllEqual(
[eval_batch_size, 100],
labels[
fields.InputDataFields.groundtruth_weights].shape.as_list())
self.assertEqual(
tf.float32,
labels[fields.InputDataFields.groundtruth_weights].dtype)
self.assertAllEqual(
[eval_batch_size, 100],
labels[fields.InputDataFields.groundtruth_area].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_area].dtype)
self.assertAllEqual(
[eval_batch_size, 100],
labels[fields.InputDataFields.groundtruth_is_crowd].shape.as_list())
self.assertEqual(
tf.bool, labels[fields.InputDataFields.groundtruth_is_crowd].dtype)
self.assertAllEqual(
[eval_batch_size, 100],
labels[fields.InputDataFields.groundtruth_difficult].shape.as_list())
self.assertEqual(
tf.int32, labels[fields.InputDataFields.groundtruth_difficult].dtype)
def test_ssd_inceptionV2_eval_input_with_additional_channels(
self, eval_batch_size=1):
"""Tests the eval input function for SSDInceptionV2 with additional channels.
Args:
eval_batch_size: Batch size for eval set.
"""
configs = _get_configs_for_model('ssd_inception_v2_pets')
model_config = configs['model']
model_config.ssd.num_classes = 37
configs['eval_input_configs'][0].num_additional_channels = 1
eval_config = configs['eval_config']
eval_config.batch_size = eval_batch_size
eval_config.retain_original_image_additional_channels = True
eval_input_fn = inputs.create_eval_input_fn(
eval_config, configs['eval_input_configs'][0], model_config)
features, labels = _make_initializable_iterator(eval_input_fn()).get_next()
self.assertAllEqual([eval_batch_size, 300, 300, 4],
features[fields.InputDataFields.image].shape.as_list())
self.assertEqual(tf.float32, features[fields.InputDataFields.image].dtype)
self.assertAllEqual(
[eval_batch_size, 300, 300, 3],
features[fields.InputDataFields.original_image].shape.as_list())
self.assertEqual(tf.uint8,
features[fields.InputDataFields.original_image].dtype)
self.assertAllEqual([eval_batch_size, 300, 300, 1], features[
fields.InputDataFields.image_additional_channels].shape.as_list())
self.assertEqual(
tf.uint8,
features[fields.InputDataFields.image_additional_channels].dtype)
self.assertAllEqual([eval_batch_size],
features[inputs.HASH_KEY].shape.as_list())
self.assertEqual(tf.int32, features[inputs.HASH_KEY].dtype)
self.assertAllEqual(
[eval_batch_size, 100, 4],
labels[fields.InputDataFields.groundtruth_boxes].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_boxes].dtype)
self.assertAllEqual(
[eval_batch_size, 100, model_config.ssd.num_classes],
labels[fields.InputDataFields.groundtruth_classes].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_classes].dtype)
self.assertAllEqual(
[eval_batch_size, 100],
labels[fields.InputDataFields.groundtruth_weights].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_weights].dtype)
self.assertAllEqual(
[eval_batch_size, 100],
labels[fields.InputDataFields.groundtruth_area].shape.as_list())
self.assertEqual(tf.float32,
labels[fields.InputDataFields.groundtruth_area].dtype)
self.assertAllEqual(
[eval_batch_size, 100],
labels[fields.InputDataFields.groundtruth_is_crowd].shape.as_list())
self.assertEqual(tf.bool,
labels[fields.InputDataFields.groundtruth_is_crowd].dtype)
self.assertAllEqual(
[eval_batch_size, 100],
labels[fields.InputDataFields.groundtruth_difficult].shape.as_list())
self.assertEqual(tf.int32,
labels[fields.InputDataFields.groundtruth_difficult].dtype)
def test_predict_input(self):
"""Tests the predict input function."""
configs = _get_configs_for_model('ssd_inception_v2_pets')
predict_input_fn = inputs.create_predict_input_fn(
model_config=configs['model'],
predict_input_config=configs['eval_input_configs'][0])
serving_input_receiver = predict_input_fn()
image = serving_input_receiver.features[fields.InputDataFields.image]
receiver_tensors = serving_input_receiver.receiver_tensors[
inputs.SERVING_FED_EXAMPLE_KEY]
self.assertEqual([1, 300, 300, 3], image.shape.as_list())
self.assertEqual(tf.float32, image.dtype)
self.assertEqual(tf.string, receiver_tensors.dtype)
def test_predict_input_with_additional_channels(self):
"""Tests the predict input function with additional channels."""
configs = _get_configs_for_model('ssd_inception_v2_pets')
configs['eval_input_configs'][0].num_additional_channels = 2
predict_input_fn = inputs.create_predict_input_fn(
model_config=configs['model'],
predict_input_config=configs['eval_input_configs'][0])
serving_input_receiver = predict_input_fn()
image = serving_input_receiver.features[fields.InputDataFields.image]
receiver_tensors = serving_input_receiver.receiver_tensors[
inputs.SERVING_FED_EXAMPLE_KEY]
# RGB + 2 additional channels = 5 channels.
self.assertEqual([1, 300, 300, 5], image.shape.as_list())
self.assertEqual(tf.float32, image.dtype)
self.assertEqual(tf.string, receiver_tensors.dtype)
def test_error_with_bad_train_config(self):
"""Tests that a TypeError is raised with improper train config."""
configs = _get_configs_for_model('ssd_inception_v2_pets')
configs['model'].ssd.num_classes = 37
train_input_fn = inputs.create_train_input_fn(
train_config=configs['eval_config'], # Expecting `TrainConfig`.
train_input_config=configs['train_input_config'],
model_config=configs['model'])
with self.assertRaises(TypeError):
train_input_fn()
def test_error_with_bad_train_input_config(self):
"""Tests that a TypeError is raised with improper train input config."""
configs = _get_configs_for_model('ssd_inception_v2_pets')
configs['model'].ssd.num_classes = 37
train_input_fn = inputs.create_train_input_fn(
train_config=configs['train_config'],
train_input_config=configs['model'], # Expecting `InputReader`.
model_config=configs['model'])
with self.assertRaises(TypeError):
train_input_fn()
def test_error_with_bad_train_model_config(self):
"""Tests that a TypeError is raised with improper train model config."""
configs = _get_configs_for_model('ssd_inception_v2_pets')
configs['model'].ssd.num_classes = 37
train_input_fn = inputs.create_train_input_fn(
train_config=configs['train_config'],
train_input_config=configs['train_input_config'],
model_config=configs['train_config']) # Expecting `DetectionModel`.
with self.assertRaises(TypeError):
train_input_fn()
def test_error_with_bad_eval_config(self):
"""Tests that a TypeError is raised with improper eval config."""
configs = _get_configs_for_model('ssd_inception_v2_pets')
configs['model'].ssd.num_classes = 37
eval_input_fn = inputs.create_eval_input_fn(
eval_config=configs['train_config'], # Expecting `EvalConfig`.
eval_input_config=configs['eval_input_configs'][0],
model_config=configs['model'])
with self.assertRaises(TypeError):
eval_input_fn()
def test_error_with_bad_eval_input_config(self):
"""Tests that a TypeError is raised with improper eval input config."""
configs = _get_configs_for_model('ssd_inception_v2_pets')
configs['model'].ssd.num_classes = 37
eval_input_fn = inputs.create_eval_input_fn(
eval_config=configs['eval_config'],
eval_input_config=configs['model'], # Expecting `InputReader`.
model_config=configs['model'])
with self.assertRaises(TypeError):
eval_input_fn()
def test_error_with_bad_eval_model_config(self):
"""Tests that a TypeError is raised with improper eval model config."""
configs = _get_configs_for_model('ssd_inception_v2_pets')
configs['model'].ssd.num_classes = 37
eval_input_fn = inputs.create_eval_input_fn(
eval_config=configs['eval_config'],
eval_input_config=configs['eval_input_configs'][0],
model_config=configs['eval_config']) # Expecting `DetectionModel`.
with self.assertRaises(TypeError):
eval_input_fn()
def test_output_equal_in_replace_empty_string_with_random_number(self):
string_placeholder = tf.placeholder(tf.string, shape=[])
replaced_string = inputs._replace_empty_string_with_random_number(
string_placeholder)
test_string = b'hello world'
feed_dict = {string_placeholder: test_string}
with self.test_session() as sess:
out_string = sess.run(replaced_string, feed_dict=feed_dict)
self.assertEqual(test_string, out_string)
def test_output_is_integer_in_replace_empty_string_with_random_number(self):
string_placeholder = tf.placeholder(tf.string, shape=[])
replaced_string = inputs._replace_empty_string_with_random_number(
string_placeholder)
empty_string = ''
feed_dict = {string_placeholder: empty_string}
tf.set_random_seed(0)
with self.test_session() as sess:
out_string = sess.run(replaced_string, feed_dict=feed_dict)
# Test whether out_string is a string which represents an integer.
int(out_string) # throws an error if out_string is not castable to int.
self.assertEqual(out_string, b'2798129067578209328')
def test_force_no_resize(self):
"""Tests the functionality of force_no_reisze option."""
configs = _get_configs_for_model('ssd_inception_v2_pets')
configs['eval_config'].force_no_resize = True
eval_input_fn = inputs.create_eval_input_fn(
eval_config=configs['eval_config'],
eval_input_config=configs['eval_input_configs'][0],
model_config=configs['model']
)
train_input_fn = inputs.create_train_input_fn(
train_config=configs['train_config'],
train_input_config=configs['train_input_config'],
model_config=configs['model']
)
features_train, _ = _make_initializable_iterator(
train_input_fn()).get_next()
features_eval, _ = _make_initializable_iterator(
eval_input_fn()).get_next()
images_train, images_eval = features_train['image'], features_eval['image']
self.assertEqual([1, None, None, 3], images_eval.shape.as_list())
self.assertEqual([24, 300, 300, 3], images_train.shape.as_list())
class DataAugmentationFnTest(test_case.TestCase):
def test_apply_image_and_box_augmentation(self):
data_augmentation_options = [
(preprocessor.resize_image, {
'new_height': 20,
'new_width': 20,
'method': tf.image.ResizeMethod.NEAREST_NEIGHBOR
}),
(preprocessor.scale_boxes_to_pixel_coordinates, {}),
]
data_augmentation_fn = functools.partial(
inputs.augment_input_data,
data_augmentation_options=data_augmentation_options)
tensor_dict = {
fields.InputDataFields.image:
tf.constant(np.random.rand(10, 10, 3).astype(np.float32)),
fields.InputDataFields.groundtruth_boxes:
tf.constant(np.array([[.5, .5, 1., 1.]], np.float32))
}
augmented_tensor_dict = data_augmentation_fn(tensor_dict=tensor_dict)
with self.test_session() as sess:
augmented_tensor_dict_out = sess.run(augmented_tensor_dict)
self.assertAllEqual(
augmented_tensor_dict_out[fields.InputDataFields.image].shape,
[20, 20, 3]
)
self.assertAllClose(
augmented_tensor_dict_out[fields.InputDataFields.groundtruth_boxes],
[[10, 10, 20, 20]]
)
def test_apply_image_and_box_augmentation_with_scores(self):
data_augmentation_options = [
(preprocessor.resize_image, {
'new_height': 20,
'new_width': 20,
'method': tf.image.ResizeMethod.NEAREST_NEIGHBOR
}),
(preprocessor.scale_boxes_to_pixel_coordinates, {}),
]
data_augmentation_fn = functools.partial(
inputs.augment_input_data,
data_augmentation_options=data_augmentation_options)
tensor_dict = {
fields.InputDataFields.image:
tf.constant(np.random.rand(10, 10, 3).astype(np.float32)),
fields.InputDataFields.groundtruth_boxes:
tf.constant(np.array([[.5, .5, 1., 1.]], np.float32)),
fields.InputDataFields.groundtruth_classes:
tf.constant(np.array([1.0], np.float32)),
fields.InputDataFields.groundtruth_weights:
tf.constant(np.array([0.8], np.float32)),
}
augmented_tensor_dict = data_augmentation_fn(tensor_dict=tensor_dict)
with self.test_session() as sess:
augmented_tensor_dict_out = sess.run(augmented_tensor_dict)
self.assertAllEqual(
augmented_tensor_dict_out[fields.InputDataFields.image].shape,
[20, 20, 3]
)
self.assertAllClose(
augmented_tensor_dict_out[fields.InputDataFields.groundtruth_boxes],
[[10, 10, 20, 20]]
)
self.assertAllClose(
augmented_tensor_dict_out[fields.InputDataFields.groundtruth_classes],
[1.0]
)
self.assertAllClose(
augmented_tensor_dict_out[
fields.InputDataFields.groundtruth_weights],
[0.8]
)
def test_include_masks_in_data_augmentation(self):
data_augmentation_options = [
(preprocessor.resize_image, {
'new_height': 20,
'new_width': 20,
'method': tf.image.ResizeMethod.NEAREST_NEIGHBOR
})
]
data_augmentation_fn = functools.partial(
inputs.augment_input_data,
data_augmentation_options=data_augmentation_options)
tensor_dict = {
fields.InputDataFields.image:
tf.constant(np.random.rand(10, 10, 3).astype(np.float32)),
fields.InputDataFields.groundtruth_instance_masks:
tf.constant(np.zeros([2, 10, 10], np.uint8))
}
augmented_tensor_dict = data_augmentation_fn(tensor_dict=tensor_dict)
with self.test_session() as sess:
augmented_tensor_dict_out = sess.run(augmented_tensor_dict)
self.assertAllEqual(
augmented_tensor_dict_out[fields.InputDataFields.image].shape,
[20, 20, 3])
self.assertAllEqual(augmented_tensor_dict_out[
fields.InputDataFields.groundtruth_instance_masks].shape, [2, 20, 20])
def test_include_keypoints_in_data_augmentation(self):
data_augmentation_options = [
(preprocessor.resize_image, {
'new_height': 20,
'new_width': 20,
'method': tf.image.ResizeMethod.NEAREST_NEIGHBOR
}),
(preprocessor.scale_boxes_to_pixel_coordinates, {}),
]
data_augmentation_fn = functools.partial(
inputs.augment_input_data,
data_augmentation_options=data_augmentation_options)
tensor_dict = {
fields.InputDataFields.image:
tf.constant(np.random.rand(10, 10, 3).astype(np.float32)),
fields.InputDataFields.groundtruth_boxes:
tf.constant(np.array([[.5, .5, 1., 1.]], np.float32)),
fields.InputDataFields.groundtruth_keypoints:
tf.constant(np.array([[[0.5, 1.0], [0.5, 0.5]]], np.float32))
}
augmented_tensor_dict = data_augmentation_fn(tensor_dict=tensor_dict)
with self.test_session() as sess:
augmented_tensor_dict_out = sess.run(augmented_tensor_dict)
self.assertAllEqual(
augmented_tensor_dict_out[fields.InputDataFields.image].shape,
[20, 20, 3]
)
self.assertAllClose(
augmented_tensor_dict_out[fields.InputDataFields.groundtruth_boxes],
[[10, 10, 20, 20]]
)
self.assertAllClose(
augmented_tensor_dict_out[fields.InputDataFields.groundtruth_keypoints],
[[[10, 20], [10, 10]]]
)
def _fake_model_preprocessor_fn(image):
return (image, tf.expand_dims(tf.shape(image)[1:], axis=0))
def _fake_image_resizer_fn(image, mask):
return (image, mask, tf.shape(image))
def _fake_resize50_preprocess_fn(image):
image = image[0]
image, shape = preprocessor.resize_to_range(
image, min_dimension=50, max_dimension=50, pad_to_max_dimension=True)
return tf.expand_dims(image, 0), tf.expand_dims(shape, axis=0)
class DataTransformationFnTest(test_case.TestCase):
def test_combine_additional_channels_if_present(self):
image = np.random.rand(4, 4, 3).astype(np.float32)
additional_channels = np.random.rand(4, 4, 2).astype(np.float32)
tensor_dict = {
fields.InputDataFields.image:
tf.constant(image),
fields.InputDataFields.image_additional_channels:
tf.constant(additional_channels),
fields.InputDataFields.groundtruth_classes:
tf.constant(np.array([1, 1], np.int32))
}
input_transformation_fn = functools.partial(
inputs.transform_input_data,
model_preprocess_fn=_fake_model_preprocessor_fn,
image_resizer_fn=_fake_image_resizer_fn,
num_classes=1)
with self.test_session() as sess:
transformed_inputs = sess.run(
input_transformation_fn(tensor_dict=tensor_dict))
self.assertAllEqual(transformed_inputs[fields.InputDataFields.image].dtype,
tf.float32)
self.assertAllEqual(transformed_inputs[fields.InputDataFields.image].shape,
[4, 4, 5])
self.assertAllClose(transformed_inputs[fields.InputDataFields.image],
np.concatenate((image, additional_channels), axis=2))
def test_use_multiclass_scores_when_present(self):
image = np.random.rand(4, 4, 3).astype(np.float32)
tensor_dict = {
fields.InputDataFields.image:
tf.constant(image),
fields.InputDataFields.groundtruth_boxes:
tf.constant(np.array([[.5, .5, 1, 1], [.5, .5, 1, 1]], np.float32)),
fields.InputDataFields.multiclass_scores:
tf.constant(np.array([0.2, 0.3, 0.5, 0.1, 0.6, 0.3], np.float32)),
fields.InputDataFields.groundtruth_classes:
tf.constant(np.array([1, 2], np.int32))
}
input_transformation_fn = functools.partial(
inputs.transform_input_data,
model_preprocess_fn=_fake_model_preprocessor_fn,
image_resizer_fn=_fake_image_resizer_fn,
num_classes=3, use_multiclass_scores=True)
with self.test_session() as sess:
transformed_inputs = sess.run(
input_transformation_fn(tensor_dict=tensor_dict))
self.assertAllClose(
np.array([[0.2, 0.3, 0.5], [0.1, 0.6, 0.3]], np.float32),
transformed_inputs[fields.InputDataFields.groundtruth_classes])
def test_use_multiclass_scores_when_not_present(self):
image = np.random.rand(4, 4, 3).astype(np.float32)
tensor_dict = {
fields.InputDataFields.image:
tf.constant(image),
fields.InputDataFields.groundtruth_boxes:
tf.constant(np.array([[.5, .5, 1, 1], [.5, .5, 1, 1]], np.float32)),
fields.InputDataFields.multiclass_scores:
tf.placeholder(tf.float32),
fields.InputDataFields.groundtruth_classes:
tf.constant(np.array([1, 2], np.int32))
}
input_transformation_fn = functools.partial(
inputs.transform_input_data,
model_preprocess_fn=_fake_model_preprocessor_fn,
image_resizer_fn=_fake_image_resizer_fn,
num_classes=3, use_multiclass_scores=True)
with self.test_session() as sess:
transformed_inputs = sess.run(
input_transformation_fn(tensor_dict=tensor_dict),
feed_dict={
tensor_dict[fields.InputDataFields.multiclass_scores]:
np.array([], dtype=np.float32)
})
self.assertAllClose(
np.array([[0, 1, 0], [0, 0, 1]], np.float32),
transformed_inputs[fields.InputDataFields.groundtruth_classes])
def test_returns_correct_class_label_encodings(self):
tensor_dict = {
fields.InputDataFields.image:
tf.constant(np.random.rand(4, 4, 3).astype(np.float32)),
fields.InputDataFields.groundtruth_boxes:
tf.constant(np.array([[0, 0, 1, 1], [.5, .5, 1, 1]], np.float32)),
fields.InputDataFields.groundtruth_classes:
tf.constant(np.array([3, 1], np.int32))
}
num_classes = 3
input_transformation_fn = functools.partial(
inputs.transform_input_data,
model_preprocess_fn=_fake_model_preprocessor_fn,
image_resizer_fn=_fake_image_resizer_fn,
num_classes=num_classes)
with self.test_session() as sess:
transformed_inputs = sess.run(
input_transformation_fn(tensor_dict=tensor_dict))
self.assertAllClose(
transformed_inputs[fields.InputDataFields.groundtruth_classes],
[[0, 0, 1], [1, 0, 0]])
self.assertAllClose(
transformed_inputs[fields.InputDataFields.groundtruth_confidences],
[[0, 0, 1], [1, 0, 0]])
def test_returns_correct_labels_with_unrecognized_class(self):
tensor_dict = {
fields.InputDataFields.image:
tf.constant(np.random.rand(4, 4, 3).astype(np.float32)),
fields.InputDataFields.groundtruth_boxes:
tf.constant(
np.array([[0, 0, 1, 1], [.2, .2, 4, 4], [.5, .5, 1, 1]],
np.float32)),
fields.InputDataFields.groundtruth_area:
tf.constant(np.array([.5, .4, .3])),
fields.InputDataFields.groundtruth_classes:
tf.constant(np.array([3, -1, 1], np.int32)),
fields.InputDataFields.groundtruth_keypoints:
tf.constant(
np.array([[[.1, .1]], [[.2, .2]], [[.5, .5]]],
np.float32)),
fields.InputDataFields.groundtruth_keypoint_visibilities:
tf.constant([True, False, True]),
fields.InputDataFields.groundtruth_instance_masks:
tf.constant(np.random.rand(3, 4, 4).astype(np.float32)),
fields.InputDataFields.groundtruth_is_crowd:
tf.constant([False, True, False]),
fields.InputDataFields.groundtruth_difficult:
tf.constant(np.array([0, 0, 1], np.int32))
}
num_classes = 3
input_transformation_fn = functools.partial(
inputs.transform_input_data,
model_preprocess_fn=_fake_model_preprocessor_fn,
image_resizer_fn=_fake_image_resizer_fn,
num_classes=num_classes)
with self.test_session() as sess:
transformed_inputs = sess.run(
input_transformation_fn(tensor_dict=tensor_dict))
self.assertAllClose(
transformed_inputs[fields.InputDataFields.groundtruth_classes],
[[0, 0, 1], [1, 0, 0]])
self.assertAllEqual(
transformed_inputs[fields.InputDataFields.num_groundtruth_boxes], 2)
self.assertAllClose(
transformed_inputs[fields.InputDataFields.groundtruth_area], [.5, .3])
self.assertAllEqual(
transformed_inputs[fields.InputDataFields.groundtruth_confidences],
[[0, 0, 1], [1, 0, 0]])
self.assertAllClose(
transformed_inputs[fields.InputDataFields.groundtruth_boxes],
[[0, 0, 1, 1], [.5, .5, 1, 1]])
self.assertAllClose(
transformed_inputs[fields.InputDataFields.groundtruth_keypoints],
[[[.1, .1]], [[.5, .5]]])
self.assertAllEqual(
transformed_inputs[
fields.InputDataFields.groundtruth_keypoint_visibilities],
[True, True])
self.assertAllEqual(
transformed_inputs[
fields.InputDataFields.groundtruth_instance_masks].shape, [2, 4, 4])
self.assertAllEqual(
transformed_inputs[fields.InputDataFields.groundtruth_is_crowd],
[False, False])
self.assertAllEqual(
transformed_inputs[fields.InputDataFields.groundtruth_difficult],
[0, 1])
def test_returns_correct_merged_boxes(self):
tensor_dict = {
fields.InputDataFields.image:
tf.constant(np.random.rand(4, 4, 3).astype(np.float32)),
fields.InputDataFields.groundtruth_boxes:
tf.constant(np.array([[.5, .5, 1, 1], [.5, .5, 1, 1]], np.float32)),
fields.InputDataFields.groundtruth_classes:
tf.constant(np.array([3, 1], np.int32))
}
num_classes = 3
input_transformation_fn = functools.partial(
inputs.transform_input_data,
model_preprocess_fn=_fake_model_preprocessor_fn,
image_resizer_fn=_fake_image_resizer_fn,
num_classes=num_classes,
merge_multiple_boxes=True)
with self.test_session() as sess:
transformed_inputs = sess.run(
input_transformation_fn(tensor_dict=tensor_dict))
self.assertAllClose(
transformed_inputs[fields.InputDataFields.groundtruth_boxes],
[[.5, .5, 1., 1.]])
self.assertAllClose(
transformed_inputs[fields.InputDataFields.groundtruth_classes],
[[1, 0, 1]])
self.assertAllClose(
transformed_inputs[fields.InputDataFields.groundtruth_confidences],
[[1, 0, 1]])
self.assertAllClose(
transformed_inputs[fields.InputDataFields.num_groundtruth_boxes],
1)
def test_returns_correct_groundtruth_confidences_when_input_present(self):
tensor_dict = {
fields.InputDataFields.image:
tf.constant(np.random.rand(4, 4, 3).astype(np.float32)),
fields.InputDataFields.groundtruth_boxes:
tf.constant(np.array([[0, 0, 1, 1], [.5, .5, 1, 1]], np.float32)),
fields.InputDataFields.groundtruth_classes:
tf.constant(np.array([3, 1], np.int32)),
fields.InputDataFields.groundtruth_confidences:
tf.constant(np.array([1.0, -1.0], np.float32))
}
num_classes = 3
input_transformation_fn = functools.partial(
inputs.transform_input_data,
model_preprocess_fn=_fake_model_preprocessor_fn,
image_resizer_fn=_fake_image_resizer_fn,
num_classes=num_classes)
with self.test_session() as sess:
transformed_inputs = sess.run(
input_transformation_fn(tensor_dict=tensor_dict))
self.assertAllClose(
transformed_inputs[fields.InputDataFields.groundtruth_classes],
[[0, 0, 1], [1, 0, 0]])
self.assertAllClose(
transformed_inputs[fields.InputDataFields.groundtruth_confidences],
[[0, 0, 1], [-1, 0, 0]])
def test_returns_resized_masks(self):
tensor_dict = {
fields.InputDataFields.image:
tf.constant(np.random.rand(4, 4, 3).astype(np.float32)),
fields.InputDataFields.groundtruth_instance_masks:
tf.constant(np.random.rand(2, 4, 4).astype(np.float32)),
fields.InputDataFields.groundtruth_classes:
tf.constant(np.array([3, 1], np.int32)),
fields.InputDataFields.original_image_spatial_shape:
tf.constant(np.array([4, 4], np.int32))
}
def fake_image_resizer_fn(image, masks=None):
resized_image = tf.image.resize_images(image, [8, 8])
results = [resized_image]
if masks is not None:
resized_masks = tf.transpose(
tf.image.resize_images(tf.transpose(masks, [1, 2, 0]), [8, 8]),
[2, 0, 1])
results.append(resized_masks)
results.append(tf.shape(resized_image))
return results
num_classes = 3
input_transformation_fn = functools.partial(
inputs.transform_input_data,
model_preprocess_fn=_fake_model_preprocessor_fn,
image_resizer_fn=fake_image_resizer_fn,
num_classes=num_classes,
retain_original_image=True)
with self.test_session() as sess:
transformed_inputs = sess.run(
input_transformation_fn(tensor_dict=tensor_dict))
self.assertAllEqual(transformed_inputs[
fields.InputDataFields.original_image].dtype, tf.uint8)
self.assertAllEqual(transformed_inputs[
fields.InputDataFields.original_image_spatial_shape], [4, 4])
self.assertAllEqual(transformed_inputs[
fields.InputDataFields.original_image].shape, [8, 8, 3])
self.assertAllEqual(transformed_inputs[
fields.InputDataFields.groundtruth_instance_masks].shape, [2, 8, 8])
def test_applies_model_preprocess_fn_to_image_tensor(self):
np_image = np.random.randint(256, size=(4, 4, 3))
tensor_dict = {
fields.InputDataFields.image:
tf.constant(np_image),
fields.InputDataFields.groundtruth_classes:
tf.constant(np.array([3, 1], np.int32))
}
def fake_model_preprocessor_fn(image):
return (image / 255., tf.expand_dims(tf.shape(image)[1:], axis=0))
num_classes = 3
input_transformation_fn = functools.partial(
inputs.transform_input_data,
model_preprocess_fn=fake_model_preprocessor_fn,
image_resizer_fn=_fake_image_resizer_fn,
num_classes=num_classes)
with self.test_session() as sess:
transformed_inputs = sess.run(
input_transformation_fn(tensor_dict=tensor_dict))
self.assertAllClose(transformed_inputs[fields.InputDataFields.image],
np_image / 255.)
self.assertAllClose(transformed_inputs[fields.InputDataFields.
true_image_shape],
[4, 4, 3])
def test_applies_data_augmentation_fn_to_tensor_dict(self):
np_image = np.random.randint(256, size=(4, 4, 3))
tensor_dict = {
fields.InputDataFields.image:
tf.constant(np_image),
fields.InputDataFields.groundtruth_classes:
tf.constant(np.array([3, 1], np.int32))
}