Python object_detection.core.prefetcher.prefetch() Examples
The following are 30
code examples of object_detection.core.prefetcher.prefetch().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
object_detection.core.prefetcher
, or try the search function
.
.
Example #1
| Source File: batcher.py From object_detection_kitti with Apache License 2.0 | 5 votes |
|
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
Example #2
| Source File: prefetcher_test.py From object_detection_with_tensorflow with MIT License | 5 votes |
|
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)
Example #3
| Source File: batcher.py From Elphas with Apache License 2.0 | 5 votes |
|
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
Example #4
| Source File: batcher.py From AniSeg with Apache License 2.0 | 5 votes |
|
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
Example #5
| Source File: prefetcher_test.py From MAX-Object-Detector with Apache License 2.0 | 5 votes |
|
def test_prefetch_tensors_with_fully_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, image_size,
image_size, 3],
dtype=tf.float32,
name='images')
label = tf.random_uniform([batch_size, 1], 0, 10,
dtype=tf.int32, name='labels')
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, image_size, image_size, 3])
self.assertAllEqual(tensor_dict['label'].get_shape().as_list(),
[batch_size, 1])
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)
Example #6
| Source File: prefetcher_test.py From MAX-Object-Detector with Apache License 2.0 | 5 votes |
|
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)
Example #7
| Source File: batcher.py From MAX-Object-Detector with Apache License 2.0 | 5 votes |
|
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
Example #8
| Source File: prefetcher_test.py From Accident-Detection-on-Indian-Roads with GNU Affero General Public License v3.0 | 5 votes |
|
def test_prefetch_tensors_with_fully_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, image_size,
image_size, 3],
dtype=tf.float32,
name='images')
label = tf.random_uniform([batch_size, 1], 0, 10,
dtype=tf.int32, name='labels')
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, image_size, image_size, 3])
self.assertAllEqual(tensor_dict['label'].get_shape().as_list(),
[batch_size, 1])
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)
Example #9
| Source File: prefetcher_test.py From Accident-Detection-on-Indian-Roads with GNU Affero General Public License v3.0 | 5 votes |
|
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)
Example #10
| Source File: batcher.py From Accident-Detection-on-Indian-Roads with GNU Affero General Public License v3.0 | 5 votes |
|
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
Example #11
| Source File: prefetcher_test.py From g-tensorflow-models with Apache License 2.0 | 5 votes |
|
def test_prefetch_tensors_with_fully_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, image_size,
image_size, 3],
dtype=tf.float32,
name='images')
label = tf.random_uniform([batch_size, 1], 0, 10,
dtype=tf.int32, name='labels')
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, image_size, image_size, 3])
self.assertAllEqual(tensor_dict['label'].get_shape().as_list(),
[batch_size, 1])
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)
Example #12
| Source File: prefetcher_test.py From AniSeg with Apache License 2.0 | 5 votes |
|
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)
Example #13
| Source File: batcher.py From MBMD with MIT License | 5 votes |
|
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
Example #14
| Source File: prefetcher_test.py From MBMD with MIT License | 5 votes |
|
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)
Example #15
| Source File: prefetcher_test.py From MBMD with MIT License | 5 votes |
|
def test_prefetch_tensors_with_fully_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, image_size,
image_size, 3],
dtype=tf.float32,
name='images')
label = tf.random_uniform([batch_size, 1], 0, 10,
dtype=tf.int32, name='labels')
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, image_size, image_size, 3])
self.assertAllEqual(tensor_dict['label'].get_shape().as_list(),
[batch_size, 1])
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)
Example #16
| Source File: prefetcher_test.py From Elphas with Apache License 2.0 | 5 votes |
|
def test_prefetch_tensors_with_fully_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, image_size,
image_size, 3],
dtype=tf.float32,
name='images')
label = tf.random_uniform([batch_size, 1], 0, 10,
dtype=tf.int32, name='labels')
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, image_size, image_size, 3])
self.assertAllEqual(tensor_dict['label'].get_shape().as_list(),
[batch_size, 1])
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)
Example #17
| Source File: prefetcher_test.py From object_detection_kitti with Apache License 2.0 | 5 votes |
|
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)
Example #18
| Source File: prefetcher_test.py From object_detection_kitti with Apache License 2.0 | 5 votes |
|
def test_prefetch_tensors_with_fully_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, image_size,
image_size, 3],
dtype=tf.float32,
name='images')
label = tf.random_uniform([batch_size, 1], 0, 10,
dtype=tf.int32, name='labels')
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, image_size, image_size, 3])
self.assertAllEqual(tensor_dict['label'].get_shape().as_list(),
[batch_size, 1])
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)
Example #19
| Source File: batcher.py From hands-detection with MIT License | 5 votes |
|
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
Example #20
| Source File: prefetcher_test.py From hands-detection with MIT License | 5 votes |
|
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)
Example #21
| Source File: prefetcher_test.py From hands-detection with MIT License | 5 votes |
|
def test_prefetch_tensors_with_fully_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, image_size,
image_size, 3],
dtype=tf.float32,
name='images')
label = tf.random_uniform([batch_size, 1], 0, 10,
dtype=tf.int32, name='labels')
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, image_size, image_size, 3])
self.assertAllEqual(tensor_dict['label'].get_shape().as_list(),
[batch_size, 1])
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)
Example #22
| Source File: batcher.py From moveo_ros with MIT License | 5 votes |
|
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.iteritems()})
# Remember runtime shapes to unpad tensors after batching.
runtime_shapes = collections.OrderedDict(
{(key, 'runtime_shapes'): tf.shape(tensor)
for key, tensor in tensor_dict.iteritems()})
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
Example #23
| Source File: prefetcher_test.py From moveo_ros with MIT License | 5 votes |
|
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)
Example #24
| Source File: prefetcher_test.py From moveo_ros with MIT License | 5 votes |
|
def test_prefetch_tensors_with_fully_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, image_size,
image_size, 3],
dtype=tf.float32,
name='images')
label = tf.random_uniform([batch_size, 1], 0, 10,
dtype=tf.int32, name='labels')
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, image_size, image_size, 3])
self.assertAllEqual(tensor_dict['label'].get_shape().as_list(),
[batch_size, 1])
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)
Example #25
| Source File: batcher.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 5 votes |
|
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
Example #26
| Source File: prefetcher_test.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 5 votes |
|
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)
Example #27
| Source File: prefetcher_test.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 5 votes |
|
def test_prefetch_tensors_with_fully_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, image_size,
image_size, 3],
dtype=tf.float32,
name='images')
label = tf.random_uniform([batch_size, 1], 0, 10,
dtype=tf.int32, name='labels')
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, image_size, image_size, 3])
self.assertAllEqual(tensor_dict['label'].get_shape().as_list(),
[batch_size, 1])
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)
Example #28
| Source File: prefetcher_test.py From DOTA_models with Apache License 2.0 | 5 votes |
|
def test_prefetch_tensors_with_fully_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, image_size,
image_size, 3],
dtype=tf.float32,
name='images')
label = tf.random_uniform([batch_size, 1], 0, 10,
dtype=tf.int32, name='labels')
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, image_size, image_size, 3])
self.assertAllEqual(tensor_dict['label'].get_shape().as_list(),
[batch_size, 1])
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)
Example #29
| Source File: prefetcher_test.py From ros_tensorflow with Apache License 2.0 | 5 votes |
|
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)
Example #30
| Source File: prefetcher_test.py From ros_tensorflow with Apache License 2.0 | 5 votes |
|
def test_prefetch_tensors_with_fully_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, image_size,
image_size, 3],
dtype=tf.float32,
name='images')
label = tf.random_uniform([batch_size, 1], 0, 10,
dtype=tf.int32, name='labels')
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, image_size, image_size, 3])
self.assertAllEqual(tensor_dict['label'].get_shape().as_list(),
[batch_size, 1])
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)
