def __init__(self, tensor_dict, batch_size, batch_queue_capacity,
num_batch_queue_threads, prefetch_queue_capacity):
"""Constructs a batch queue holding tensor_dict.
Args:
tensor_dict: dictionary of tensors to batch.
batch_size: batch size.
batch_queue_capacity: max capacity of the queue from which the tensors are
batched.
num_batch_queue_threads: number of threads to use for batching.
prefetch_queue_capacity: max capacity of the queue used to prefetch
assembled batches.
"""
# Remember static shapes to set shapes of batched tensors.
static_shapes = collections.OrderedDict(
{key: tensor.get_shape() for key, tensor in tensor_dict.items()})
# Remember runtime shapes to unpad tensors after batching.
runtime_shapes = collections.OrderedDict(
{(key + rt_shape_str): tf.shape(tensor)
for key, tensor in tensor_dict.items()})
all_tensors = tensor_dict
all_tensors.update(runtime_shapes)
batched_tensors = tf.train.batch(
all_tensors,
capacity=batch_queue_capacity,
batch_size=batch_size,
dynamic_pad=True,
num_threads=num_batch_queue_threads)
self._queue = prefetcher.prefetch(batched_tensors,
prefetch_queue_capacity)
self._static_shapes = static_shapes
self._batch_size = batch_size
def test_prefetch_tensors_with_partially_defined_shapes(self):
with self.test_session() as sess:
batch_size = 10
image_size = 32
num_batches = 5
examples = tf.Variable(tf.constant(0, dtype=tf.int64))
counter = examples.count_up_to(num_batches)
image = tf.random_normal([
batch_size,
tf.Variable(image_size),
tf.Variable(image_size), 3
],
dtype=tf.float32,
name='image')
image.set_shape([batch_size, None, None, 3])
label = tf.random_uniform([batch_size, tf.Variable(1)],
0,
10,
dtype=tf.int32,
name='label')
label.set_shape([batch_size, None])
prefetch_queue = prefetcher.prefetch(tensor_dict={
'counter': counter,
'image': image,
'label': label
},
capacity=100)
tensor_dict = prefetch_queue.dequeue()
self.assertAllEqual(tensor_dict['image'].get_shape().as_list(),
[batch_size, None, None, 3])
self.assertAllEqual(tensor_dict['label'].get_shape().as_list(),
[batch_size, None])
tf.initialize_all_variables().run()
with slim.queues.QueueRunners(sess):
for _ in range(num_batches):
results = sess.run(tensor_dict)
self.assertEquals(results['image'].shape,
(batch_size, image_size, image_size, 3))
self.assertEquals(results['label'].shape, (batch_size, 1))
with self.assertRaises(tf.errors.OutOfRangeError):
sess.run(tensor_dict)
def _extract_predictions_and_losses(model,
create_input_dict_fn,
ignore_groundtruth=False):
"""Constructs tensorflow detection graph and returns output tensors.
Args:
model: model to perform predictions with.
create_input_dict_fn: function to create input tensor dictionaries.
ignore_groundtruth: whether groundtruth should be ignored.
Returns:
prediction_groundtruth_dict: A dictionary with postprocessed tensors (keyed
by standard_fields.DetectionResultsFields) and optional groundtruth
tensors (keyed by standard_fields.InputDataFields).
losses_dict: A dictionary containing detection losses. This is empty when
ignore_groundtruth is true.
"""
input_dict = create_input_dict_fn()
prefetch_queue = prefetcher.prefetch(input_dict, capacity=500)
input_dict = prefetch_queue.dequeue()
original_image = tf.expand_dims(input_dict[fields.InputDataFields.image], 0)
preprocessed_image, true_image_shapes = model.preprocess(
tf.to_float(original_image))
prediction_dict = model.predict(preprocessed_image, true_image_shapes)
detections = model.postprocess(prediction_dict, true_image_shapes)
groundtruth = None
losses_dict = {}
if not ignore_groundtruth:
groundtruth = {
fields.InputDataFields.groundtruth_boxes:
input_dict[fields.InputDataFields.groundtruth_boxes],
fields.InputDataFields.groundtruth_classes:
input_dict[fields.InputDataFields.groundtruth_classes],
fields.InputDataFields.groundtruth_area:
input_dict[fields.InputDataFields.groundtruth_area],
fields.InputDataFields.groundtruth_is_crowd:
input_dict[fields.InputDataFields.groundtruth_is_crowd],
fields.InputDataFields.groundtruth_difficult:
input_dict[fields.InputDataFields.groundtruth_difficult]
}
if fields.InputDataFields.groundtruth_group_of in input_dict:
groundtruth[fields.InputDataFields.groundtruth_group_of] = (
input_dict[fields.InputDataFields.groundtruth_group_of])
groundtruth_masks_list = None
if fields.DetectionResultFields.detection_masks in detections:
groundtruth[fields.InputDataFields.groundtruth_instance_masks] = (
input_dict[fields.InputDataFields.groundtruth_instance_masks])
groundtruth_masks_list = [
input_dict[fields.InputDataFields.groundtruth_instance_masks]]
groundtruth_keypoints_list = None
if fields.DetectionResultFields.detection_keypoints in detections:
groundtruth[fields.InputDataFields.groundtruth_keypoints] = (
input_dict[fields.InputDataFields.groundtruth_keypoints])
groundtruth_keypoints_list = [
input_dict[fields.InputDataFields.groundtruth_keypoints]]
label_id_offset = 1
model.provide_groundtruth(
[input_dict[fields.InputDataFields.groundtruth_boxes]],
[tf.one_hot(input_dict[fields.InputDataFields.groundtruth_classes]
- label_id_offset, depth=model.num_classes)],
groundtruth_masks_list, groundtruth_keypoints_list)
losses_dict.update(model.loss(prediction_dict, true_image_shapes))
result_dict = eval_util.result_dict_for_single_example(
original_image,
input_dict[fields.InputDataFields.source_id],
detections,
groundtruth,
class_agnostic=(
fields.DetectionResultFields.detection_classes not in detections),
scale_to_absolute=True)
return result_dict, losses_dict