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Python dataset_builder.make_initializable_iterator方法代码示例

本文整理汇总了Python中object_detection.builders.dataset_builder.make_initializable_iterator方法的典型用法代码示例。如果您正苦于以下问题:Python dataset_builder.make_initializable_iterator方法的具体用法?Python dataset_builder.make_initializable_iterator怎么用?Python dataset_builder.make_initializable_iterator使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在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个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: test_build_tf_record_input_reader_and_load_instance_masks

# 需要导入模块: from object_detection.builders import dataset_builder [as 别名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 别名]
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) 
开发者ID:ahmetozlu,项目名称:vehicle_counting_tensorflow,代码行数:23,代码来源:dataset_builder_test.py

示例2: test_sample_one_of_n_shards

# 需要导入模块: from object_detection.builders import dataset_builder [as 别名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 别名]
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]) 
开发者ID:ahmetozlu,项目名称:vehicle_counting_tensorflow,代码行数:23,代码来源:dataset_builder_test.py

示例3: test_build_tf_record_input_reader_and_load_instance_masks

# 需要导入模块: from object_detection.builders import dataset_builder [as 别名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 别名]
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) 
开发者ID:BMW-InnovationLab,项目名称:BMW-TensorFlow-Training-GUI,代码行数:25,代码来源:dataset_builder_test.py

示例4: test_sample_all_data

# 需要导入模块: from object_detection.builders import dataset_builder [as 别名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 别名]
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]) 
开发者ID:ShivangShekhar,项目名称:Live-feed-object-device-identification-using-Tensorflow-and-OpenCV,代码行数:23,代码来源:dataset_builder_test.py

示例5: test_make_initializable_iterator_with_hashTable

# 需要导入模块: from object_detection.builders import dataset_builder [as 别名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 别名]
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]) 
开发者ID:tensorflow,项目名称:models,代码行数:23,代码来源:dataset_builder_test.py

示例6: test_build_tf_record_input_reader

# 需要导入模块: from object_detection.builders import dataset_builder [as 别名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 别名]
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]) 
开发者ID:ahmetozlu,项目名称:vehicle_counting_tensorflow,代码行数:31,代码来源:dataset_builder_test.py

示例7: test_build_tf_record_input_reader_with_batch_size_two

# 需要导入模块: from object_detection.builders import dataset_builder [as 别名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 别名]
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]) 
开发者ID:ahmetozlu,项目名称:vehicle_counting_tensorflow,代码行数:38,代码来源:dataset_builder_test.py

示例8: test_build_tf_record_input_reader_with_batch_size_two_and_masks

# 需要导入模块: from object_detection.builders import dataset_builder [as 别名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 别名]
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) 
开发者ID:ahmetozlu,项目名称:vehicle_counting_tensorflow,代码行数:33,代码来源:dataset_builder_test.py

示例9: test_make_initializable_iterator_with_hashTable

# 需要导入模块: from object_detection.builders import dataset_builder [as 别名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 别名]
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]) 
开发者ID:ahmetozlu,项目名称:vehicle_counting_tensorflow,代码行数:16,代码来源:dataset_builder_test.py

示例10: test_build_tf_record_input_reader

# 需要导入模块: from object_detection.builders import dataset_builder [as 别名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 别名]
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]) 
开发者ID:BMW-InnovationLab,项目名称:BMW-TensorFlow-Training-GUI,代码行数:33,代码来源:dataset_builder_test.py

示例11: test_build_tf_record_input_reader_with_batch_size_two

# 需要导入模块: from object_detection.builders import dataset_builder [as 别名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 别名]
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]) 
开发者ID:BMW-InnovationLab,项目名称:BMW-TensorFlow-Training-GUI,代码行数:43,代码来源:dataset_builder_test.py

示例12: test_build_tf_record_input_reader_with_batch_size_two_and_masks

# 需要导入模块: from object_detection.builders import dataset_builder [as 别名]
# 或者: from object_detection.builders.dataset_builder import make_initializable_iterator [as 别名]
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) 
开发者ID:BMW-InnovationLab,项目名称:BMW-TensorFlow-Training-GUI,代码行数:35,代码来源:dataset_builder_test.py


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