本文整理匯總了Python中object_detection.builders.dataset_builder.make_initializable_iterator方法的典型用法代碼示例。如果您正苦於以下問題：Python dataset_builder.make_initializable_iterator方法的具體用法？Python dataset_builder.make_initializable_iterator怎麽用？Python dataset_builder.make_initializable_iterator使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類object_detection.builders.dataset_builder的用法示例。
在下文中一共展示了dataset_builder.make_initializable_iterator方法的12個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_build_tf_record_input_reader_and_load_instance_masks
# 需要導入模塊: from object_detection.builders import dataset_builder [as 別名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 別名]
def test_build_tf_record_input_reader_and_load_instance_masks(self):
tf_record_path = self.create_tf_record()
input_reader_text_proto = """
shuffle: false
num_readers: 1
load_instance_masks: true
tf_record_input_reader {{
input_path: '{0}'
}}
""".format(tf_record_path)
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
tensor_dict = dataset_builder.make_initializable_iterator(
dataset_builder.build(input_reader_proto, batch_size=1)).get_next()
with tf.train.MonitoredSession() as sess:
output_dict = sess.run(tensor_dict)
self.assertAllEqual(
(1, 1, 4, 5),
output_dict[fields.InputDataFields.groundtruth_instance_masks].shape) 示例2: test_sample_one_of_n_shards
# 需要導入模塊: from object_detection.builders import dataset_builder [as 別名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 別名]
def test_sample_one_of_n_shards(self):
tf_record_path = self.create_tf_record(num_examples=4)
input_reader_text_proto = """
shuffle: false
num_readers: 1
sample_1_of_n_examples: 2
tf_record_input_reader {{
input_path: '{0}'
}}
""".format(tf_record_path)
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
tensor_dict = dataset_builder.make_initializable_iterator(
dataset_builder.build(input_reader_proto, batch_size=1)).get_next()
with tf.train.MonitoredSession() as sess:
output_dict = sess.run(tensor_dict)
self.assertAllEqual(['0'], output_dict[fields.InputDataFields.source_id])
output_dict = sess.run(tensor_dict)
self.assertEquals(['2'], output_dict[fields.InputDataFields.source_id]) 示例3: test_build_tf_record_input_reader_and_load_instance_masks
# 需要導入模塊: from object_detection.builders import dataset_builder [as 別名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 別名]
def test_build_tf_record_input_reader_and_load_instance_masks(self):
tf_record_path = self.create_tf_record()
input_reader_text_proto = """
shuffle: false
num_readers: 1
load_instance_masks: true
tf_record_input_reader {{
input_path: '{0}'
}}
""".format(tf_record_path)
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
tensor_dict = dataset_builder.make_initializable_iterator(
dataset_builder.build(input_reader_proto, batch_size=1)).get_next()
sv = tf.train.Supervisor(logdir=self.get_temp_dir())
with sv.prepare_or_wait_for_session() as sess:
sv.start_queue_runners(sess)
output_dict = sess.run(tensor_dict)
self.assertAllEqual(
(1, 1, 4, 5),
output_dict[fields.InputDataFields.groundtruth_instance_masks].shape) 示例4: test_sample_all_data
# 需要導入模塊: from object_detection.builders import dataset_builder [as 別名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 別名]
def test_sample_all_data(self):
tf_record_path = self.create_tf_record(num_examples=2)
input_reader_text_proto = """
shuffle: false
num_readers: 1
sample_1_of_n_examples: 1
tf_record_input_reader {{
input_path: '{0}'
}}
""".format(tf_record_path)
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
tensor_dict = dataset_builder.make_initializable_iterator(
dataset_builder.build(input_reader_proto, batch_size=1)).get_next()
with tf.train.MonitoredSession() as sess:
output_dict = sess.run(tensor_dict)
self.assertAllEqual(['0'], output_dict[fields.InputDataFields.source_id])
output_dict = sess.run(tensor_dict)
self.assertEquals(['1'], output_dict[fields.InputDataFields.source_id]) 開發者ID:ShivangShekhar，項目名稱:Live-feed-object-device-identification-using-Tensorflow-and-OpenCV，代碼行數:23，代碼來源:dataset_builder_test.py
示例5: test_make_initializable_iterator_with_hashTable
# 需要導入模塊: from object_detection.builders import dataset_builder [as 別名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 別名]
def test_make_initializable_iterator_with_hashTable(self):
def graph_fn():
keys = [1, 0, -1]
dataset = tf.data.Dataset.from_tensor_slices([[1, 2, -1, 5]])
try:
# Dynamically try to load the tf v2 lookup, falling back to contrib
lookup = tf.compat.v2.lookup
hash_table_class = tf.compat.v2.lookup.StaticHashTable
except AttributeError:
lookup = contrib_lookup
hash_table_class = contrib_lookup.HashTable
table = hash_table_class(
initializer=lookup.KeyValueTensorInitializer(
keys=keys, values=list(reversed(keys))),
default_value=100)
dataset = dataset.map(table.lookup)
return dataset_builder.make_initializable_iterator(dataset).get_next()
result = self.execute(graph_fn, [])
self.assertAllEqual(result, [-1, 100, 1, 100]) 示例6: test_build_tf_record_input_reader
# 需要導入模塊: from object_detection.builders import dataset_builder [as 別名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 別名]
def test_build_tf_record_input_reader(self):
tf_record_path = self.create_tf_record()
input_reader_text_proto = """
shuffle: false
num_readers: 1
tf_record_input_reader {{
input_path: '{0}'
}}
""".format(tf_record_path)
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
tensor_dict = dataset_builder.make_initializable_iterator(
dataset_builder.build(input_reader_proto, batch_size=1)).get_next()
with tf.train.MonitoredSession() as sess:
output_dict = sess.run(tensor_dict)
self.assertTrue(
fields.InputDataFields.groundtruth_instance_masks not in output_dict)
self.assertEquals((1, 4, 5, 3),
output_dict[fields.InputDataFields.image].shape)
self.assertAllEqual([[2]],
output_dict[fields.InputDataFields.groundtruth_classes])
self.assertEquals(
(1, 1, 4), output_dict[fields.InputDataFields.groundtruth_boxes].shape)
self.assertAllEqual(
[0.0, 0.0, 1.0, 1.0],
output_dict[fields.InputDataFields.groundtruth_boxes][0][0]) 示例7: test_build_tf_record_input_reader_with_batch_size_two
# 需要導入模塊: from object_detection.builders import dataset_builder [as 別名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 別名]
def test_build_tf_record_input_reader_with_batch_size_two(self):
tf_record_path = self.create_tf_record()
input_reader_text_proto = """
shuffle: false
num_readers: 1
tf_record_input_reader {{
input_path: '{0}'
}}
""".format(tf_record_path)
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
def one_hot_class_encoding_fn(tensor_dict):
tensor_dict[fields.InputDataFields.groundtruth_classes] = tf.one_hot(
tensor_dict[fields.InputDataFields.groundtruth_classes] - 1, depth=3)
return tensor_dict
tensor_dict = dataset_builder.make_initializable_iterator(
dataset_builder.build(
input_reader_proto,
transform_input_data_fn=one_hot_class_encoding_fn,
batch_size=2)).get_next()
with tf.train.MonitoredSession() as sess:
output_dict = sess.run(tensor_dict)
self.assertAllEqual([2, 4, 5, 3],
output_dict[fields.InputDataFields.image].shape)
self.assertAllEqual(
[2, 1, 3],
output_dict[fields.InputDataFields.groundtruth_classes].shape)
self.assertAllEqual(
[2, 1, 4], output_dict[fields.InputDataFields.groundtruth_boxes].shape)
self.assertAllEqual([[[0.0, 0.0, 1.0, 1.0]], [[0.0, 0.0, 1.0, 1.0]]],
output_dict[fields.InputDataFields.groundtruth_boxes]) 示例8: test_build_tf_record_input_reader_with_batch_size_two_and_masks
# 需要導入模塊: from object_detection.builders import dataset_builder [as 別名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 別名]
def test_build_tf_record_input_reader_with_batch_size_two_and_masks(self):
tf_record_path = self.create_tf_record()
input_reader_text_proto = """
shuffle: false
num_readers: 1
load_instance_masks: true
tf_record_input_reader {{
input_path: '{0}'
}}
""".format(tf_record_path)
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
def one_hot_class_encoding_fn(tensor_dict):
tensor_dict[fields.InputDataFields.groundtruth_classes] = tf.one_hot(
tensor_dict[fields.InputDataFields.groundtruth_classes] - 1, depth=3)
return tensor_dict
tensor_dict = dataset_builder.make_initializable_iterator(
dataset_builder.build(
input_reader_proto,
transform_input_data_fn=one_hot_class_encoding_fn,
batch_size=2)).get_next()
with tf.train.MonitoredSession() as sess:
output_dict = sess.run(tensor_dict)
self.assertAllEqual(
[2, 1, 4, 5],
output_dict[fields.InputDataFields.groundtruth_instance_masks].shape) 示例9: test_make_initializable_iterator_with_hashTable
# 需要導入模塊: from object_detection.builders import dataset_builder [as 別名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 別名]
def test_make_initializable_iterator_with_hashTable(self):
keys = [1, 0, -1]
dataset = tf.data.Dataset.from_tensor_slices([[1, 2, -1, 5]])
table = tf.contrib.lookup.HashTable(
initializer=tf.contrib.lookup.KeyValueTensorInitializer(
keys=keys, values=list(reversed(keys))),
default_value=100)
dataset = dataset.map(table.lookup)
data = dataset_builder.make_initializable_iterator(dataset).get_next()
init = tf.tables_initializer()
with self.test_session() as sess:
sess.run(init)
self.assertAllEqual(sess.run(data), [-1, 100, 1, 100]) 示例10: test_build_tf_record_input_reader
# 需要導入模塊: from object_detection.builders import dataset_builder [as 別名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 別名]
def test_build_tf_record_input_reader(self):
tf_record_path = self.create_tf_record()
input_reader_text_proto = """
shuffle: false
num_readers: 1
tf_record_input_reader {{
input_path: '{0}'
}}
""".format(tf_record_path)
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
tensor_dict = dataset_builder.make_initializable_iterator(
dataset_builder.build(input_reader_proto, batch_size=1)).get_next()
sv = tf.train.Supervisor(logdir=self.get_temp_dir())
with sv.prepare_or_wait_for_session() as sess:
sv.start_queue_runners(sess)
output_dict = sess.run(tensor_dict)
self.assertTrue(
fields.InputDataFields.groundtruth_instance_masks not in output_dict)
self.assertEquals((1, 4, 5, 3),
output_dict[fields.InputDataFields.image].shape)
self.assertAllEqual([[2]],
output_dict[fields.InputDataFields.groundtruth_classes])
self.assertEquals(
(1, 1, 4), output_dict[fields.InputDataFields.groundtruth_boxes].shape)
self.assertAllEqual(
[0.0, 0.0, 1.0, 1.0],
output_dict[fields.InputDataFields.groundtruth_boxes][0][0]) 示例11: test_build_tf_record_input_reader_with_batch_size_two
# 需要導入模塊: from object_detection.builders import dataset_builder [as 別名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 別名]
def test_build_tf_record_input_reader_with_batch_size_two(self):
tf_record_path = self.create_tf_record()
input_reader_text_proto = """
shuffle: false
num_readers: 1
tf_record_input_reader {{
input_path: '{0}'
}}
""".format(tf_record_path)
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
def one_hot_class_encoding_fn(tensor_dict):
tensor_dict[fields.InputDataFields.groundtruth_classes] = tf.one_hot(
tensor_dict[fields.InputDataFields.groundtruth_classes] - 1, depth=3)
return tensor_dict
tensor_dict = dataset_builder.make_initializable_iterator(
dataset_builder.build(
input_reader_proto,
transform_input_data_fn=one_hot_class_encoding_fn,
batch_size=2)).get_next()
sv = tf.train.Supervisor(logdir=self.get_temp_dir())
with sv.prepare_or_wait_for_session() as sess:
sv.start_queue_runners(sess)
output_dict = sess.run(tensor_dict)
self.assertAllEqual([2, 4, 5, 3],
output_dict[fields.InputDataFields.image].shape)
self.assertAllEqual([2, 1, 3],
output_dict[fields.InputDataFields.groundtruth_classes].
shape)
self.assertAllEqual([2, 1, 4],
output_dict[fields.InputDataFields.groundtruth_boxes].
shape)
self.assertAllEqual(
[[[0.0, 0.0, 1.0, 1.0]],
[[0.0, 0.0, 1.0, 1.0]]],
output_dict[fields.InputDataFields.groundtruth_boxes]) 示例12: test_build_tf_record_input_reader_with_batch_size_two_and_masks
# 需要導入模塊: from object_detection.builders import dataset_builder [as 別名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 別名]
def test_build_tf_record_input_reader_with_batch_size_two_and_masks(self):
tf_record_path = self.create_tf_record()
input_reader_text_proto = """
shuffle: false
num_readers: 1
load_instance_masks: true
tf_record_input_reader {{
input_path: '{0}'
}}
""".format(tf_record_path)
input_reader_proto = input_reader_pb2.InputReader()
text_format.Merge(input_reader_text_proto, input_reader_proto)
def one_hot_class_encoding_fn(tensor_dict):
tensor_dict[fields.InputDataFields.groundtruth_classes] = tf.one_hot(
tensor_dict[fields.InputDataFields.groundtruth_classes] - 1, depth=3)
return tensor_dict
tensor_dict = dataset_builder.make_initializable_iterator(
dataset_builder.build(
input_reader_proto,
transform_input_data_fn=one_hot_class_encoding_fn,
batch_size=2)).get_next()
sv = tf.train.Supervisor(logdir=self.get_temp_dir())
with sv.prepare_or_wait_for_session() as sess:
sv.start_queue_runners(sess)
output_dict = sess.run(tensor_dict)
self.assertAllEqual(
[2, 1, 4, 5],
output_dict[fields.InputDataFields.groundtruth_instance_masks].shape)