def test_get_spatial_image_size_from_aspect_preserving_resizer_dynamic(
self):
image_resizer_config = image_resizer_pb2.ImageResizer()
image_resizer_config.keep_aspect_ratio_resizer.min_dimension = 100
image_resizer_config.keep_aspect_ratio_resizer.max_dimension = 600
image_shape = config_util.get_spatial_image_size(image_resizer_config)
self.assertAllEqual(image_shape, [-1, -1])
def testGetSpatialImageSizeFromAspectPreservingResizerConfig(self):
image_resizer_config = image_resizer_pb2.ImageResizer()
image_resizer_config.keep_aspect_ratio_resizer.min_dimension = 100
image_resizer_config.keep_aspect_ratio_resizer.max_dimension = 600
image_resizer_config.keep_aspect_ratio_resizer.pad_to_max_dimension = True
image_shape = config_util.get_spatial_image_size(image_resizer_config)
self.assertAllEqual(image_shape, [600, 600])
def _resized_image_given_text_proto(self, image, text_proto):
image_resizer_config = image_resizer_pb2.ImageResizer()
text_format.Merge(text_proto, image_resizer_config)
image_resizer_fn = image_resizer_builder.build(image_resizer_config)
image_placeholder = tf.placeholder(tf.uint8, [1, None, None, 3])
resized_image, _ = image_resizer_fn(image_placeholder)
with self.test_session() as sess:
return sess.run(resized_image, feed_dict={image_placeholder: image})
def _shape_of_resized_random_image_given_text_proto(
self, input_shape, text_proto):
image_resizer_config = image_resizer_pb2.ImageResizer()
text_format.Merge(text_proto, image_resizer_config)
image_resizer_fn = image_resizer_builder.build(image_resizer_config)
images = tf.to_float(tf.random_uniform(
input_shape, minval=0, maxval=255, dtype=tf.int32))
resized_images = image_resizer_fn(images)
with self.test_session() as sess:
return sess.run(resized_images).shape
def _resized_image_given_text_proto(self, image, text_proto):
image_resizer_config = image_resizer_pb2.ImageResizer()
text_format.Merge(text_proto, image_resizer_config)
image_resizer_fn = image_resizer_builder.build(image_resizer_config)
def graph_fn(image):
resized_image, _ = image_resizer_fn(image)
return resized_image
return self.execute_cpu(graph_fn, [image])
def build_input():
image_tensor = image_ph = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3], name='image_ph')
image_resizer_text_proto = """
keep_aspect_ratio_resizer {
min_dimension: 800
max_dimension: 1365
}
"""
image_resizer_config = image_resizer_pb2.ImageResizer()
text_format.Merge(image_resizer_text_proto, image_resizer_config)
image_resizer_fn = image_resizer_builder.build(image_resizer_config)
resized_image_tensor, _ = image_resizer_fn(image_tensor)
resized_image_tensor = tf.cast(resized_image_tensor, dtype=tf.uint8)
resized_image_tensor = tf.expand_dims(resized_image_tensor, 0)
return image_ph, resized_image_tensor
def _shape_of_resized_random_image_given_text_proto(
self, input_shape, text_proto):
image_resizer_config = image_resizer_pb2.ImageResizer()
text_format.Merge(text_proto, image_resizer_config)
image_resizer_fn = image_resizer_builder.build(image_resizer_config)
def graph_fn():
images = tf.cast(tf.random_uniform(input_shape,
minval=0,
maxval=255,
dtype=tf.int32),
dtype=tf.float32)
resized_images, _ = image_resizer_fn(images)
return resized_images
return self.execute_cpu(graph_fn, []).shape
def build_input(tfrecord_paths):
"""Builds the graph's input.
Args:
tfrecord_paths: List of paths to the input TFRecords
Returns:
serialized_example_tensor: The next serialized example. String scalar Tensor
image_tensor: The decoded image of the example. Uint8 tensor,
shape=[1, None, None,3]
"""
filename_queue = tf.train.string_input_producer(
tfrecord_paths, shuffle=False, num_epochs=1)
tf_record_reader = tf.TFRecordReader()
_, serialized_example_tensor = tf_record_reader.read(filename_queue)
# *** MODIFIED
prefetch_queue = prefetcher.prefetch({'serialized_example_tensor': serialized_example_tensor}, 100)
dequeue = prefetch_queue.dequeue()
serialized_example_tensor = dequeue['serialized_example_tensor']
# *** MODIFIED ENDS
features = tf.parse_single_example(
serialized_example_tensor,
features={
standard_fields.TfExampleFields.image_encoded:
tf.FixedLenFeature([], tf.string),
})
encoded_image = features[standard_fields.TfExampleFields.image_encoded]
image_tensor = tf.image.decode_image(encoded_image, channels=3)
image_tensor.set_shape([None, None, 3])
# image_tensor = tf.expand_dims(image_tensor, 0)
# # *** MODIFIED
# batch = tf.train.batch(
# [serialized_example_tensor, image_tensor],
# batch_size=24,
# enqueue_many=False,
# num_threads=6,
# capacity=5 * 24)
# return batch[0], batch[1]
image_resizer_text_proto = """
keep_aspect_ratio_resizer {
min_dimension: 800
max_dimension: 1365
}
"""
image_resizer_config = image_resizer_pb2.ImageResizer()
text_format.Merge(image_resizer_text_proto, image_resizer_config)
image_resizer_fn = image_resizer_builder.build(image_resizer_config)
resized_image_tensor, _ = image_resizer_fn(image_tensor)
# resized_image_tensor = tf.image.convert_image_dtype(resized_image_tensor, dtype=tf.uint8)
resized_image_tensor = tf.cast(resized_image_tensor, dtype=tf.uint8)
resized_image_tensor = tf.expand_dims(resized_image_tensor, 0)
# # *** MODIFIED ENDS
return serialized_example_tensor, resized_image_tensor#image_tensor
def test_get_spatial_image_size_from_fixed_shape_resizer_config(self):
image_resizer_config = image_resizer_pb2.ImageResizer()
image_resizer_config.fixed_shape_resizer.height = 100
image_resizer_config.fixed_shape_resizer.width = 200
image_shape = config_util.get_spatial_image_size(image_resizer_config)
self.assertAllEqual(image_shape, [100, 200])
def eval_input(eval_config,
eval_input_config,
model_config,
model=None,
params=None):
"""Returns `features` and `labels` tensor dictionaries for evaluation.
Args:
eval_config: An eval_pb2.EvalConfig.
eval_input_config: An input_reader_pb2.InputReader.
model_config: A model_pb2.DetectionModel.
model: A pre-constructed Detection Model.
If None, one will be created from the config.
params: Parameter dictionary passed from the estimator.
Returns:
A tf.data.Dataset that holds (features, labels) tuple.
features: Dictionary of feature tensors.
features[fields.InputDataFields.image] is a [1, H, W, C] float32 tensor
with preprocessed images.
features[HASH_KEY] is a [1] int32 tensor representing unique
identifiers for the images.
features[fields.InputDataFields.true_image_shape] is a [1, 3]
int32 tensor representing the true image shapes, as preprocessed
images could be padded.
features[fields.InputDataFields.original_image] is a [1, H', W', C]
float32 tensor with the original image.
labels: Dictionary of groundtruth tensors.
labels[fields.InputDataFields.groundtruth_boxes] is a [1, num_boxes, 4]
float32 tensor containing the corners of the groundtruth boxes.
labels[fields.InputDataFields.groundtruth_classes] is a
[num_boxes, num_classes] float32 one-hot tensor of classes.
labels[fields.InputDataFields.groundtruth_area] is a [1, num_boxes]
float32 tensor containing object areas.
labels[fields.InputDataFields.groundtruth_is_crowd] is a [1, num_boxes]
bool tensor indicating if the boxes enclose a crowd.
labels[fields.InputDataFields.groundtruth_difficult] is a [1, num_boxes]
int32 tensor indicating if the boxes represent difficult instances.
-- Optional --
labels[fields.InputDataFields.groundtruth_instance_masks] is a
[1, num_boxes, H, W] float32 tensor containing only binary values,
which represent instance masks for objects.
Raises:
TypeError: if the `eval_config`, `eval_input_config` or `model_config`
are not of the correct type.
"""
params = params or {}
if not isinstance(eval_config, eval_pb2.EvalConfig):
raise TypeError('For eval mode, the `eval_config` must be a '
'train_pb2.EvalConfig.')
if not isinstance(eval_input_config, input_reader_pb2.InputReader):
raise TypeError('The `eval_input_config` must be a '
'input_reader_pb2.InputReader.')
if not isinstance(model_config, model_pb2.DetectionModel):
raise TypeError('The `model_config` must be a '
'model_pb2.DetectionModel.')
if eval_config.force_no_resize:
arch = model_config.WhichOneof('model')
arch_config = getattr(model_config, arch)
image_resizer_proto = image_resizer_pb2.ImageResizer()
image_resizer_proto.identity_resizer.CopyFrom(
image_resizer_pb2.IdentityResizer())
arch_config.image_resizer.CopyFrom(image_resizer_proto)
if model is None:
model_preprocess_fn = INPUT_BUILDER_UTIL_MAP['model_build'](
model_config, is_training=False).preprocess
else:
model_preprocess_fn = model.preprocess
def transform_and_pad_input_data_fn(tensor_dict):
"""Combines transform and pad operation."""
num_classes = config_util.get_number_of_classes(model_config)
image_resizer_config = config_util.get_image_resizer_config(
model_config)
image_resizer_fn = image_resizer_builder.build(image_resizer_config)
transform_data_fn = functools.partial(
transform_input_data,
model_preprocess_fn=model_preprocess_fn,
image_resizer_fn=image_resizer_fn,
num_classes=num_classes,
data_augmentation_fn=None,
retain_original_image=eval_config.retain_original_images,
retain_original_image_additional_channels=eval_config.
retain_original_image_additional_channels)
tensor_dict = pad_input_data_to_static_shapes(
tensor_dict=transform_data_fn(tensor_dict),
max_num_boxes=eval_input_config.max_number_of_boxes,
num_classes=config_util.get_number_of_classes(model_config),
spatial_image_shape=config_util.get_spatial_image_size(
image_resizer_config))
return (_get_features_dict(tensor_dict), _get_labels_dict(tensor_dict))
dataset = INPUT_BUILDER_UTIL_MAP['dataset_build'](
eval_input_config,
batch_size=params['batch_size'] if params else eval_config.batch_size,
transform_input_data_fn=transform_and_pad_input_data_fn)
return dataset
def testGetSpatialImageSizeFromConditionalShapeResizer(self):
image_resizer_config = image_resizer_pb2.ImageResizer()
image_resizer_config.conditional_shape_resizer.size_threshold = 100
image_shape = config_util.get_spatial_image_size(image_resizer_config)
self.assertAllEqual(image_shape, [-1, -1])
