Python feature_pb2.FloatList方法代碼示例

本文整理匯總了Python中tensorflow.core.example.feature_pb2.FloatList方法的典型用法代碼示例。如果您正苦於以下問題：Python feature_pb2.FloatList方法的具體用法？Python feature_pb2.FloatList怎麽用？Python feature_pb2.FloatList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tensorflow.core.example.feature_pb2的用法示例。

在下文中一共展示了feature_pb2.FloatList方法的5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: create_tf_record

# 需要導入模塊: from tensorflow.core.example import feature_pb2 [as 別名]
# 或者: from tensorflow.core.example.feature_pb2 import FloatList [as 別名]
def create_tf_record(self):
    path = os.path.join(self.get_temp_dir(), 'tfrecord')
    writer = tf.python_io.TFRecordWriter(path)

    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    with self.test_session():
      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
    example = example_pb2.Example(features=feature_pb2.Features(feature={
        'image/encoded': feature_pb2.Feature(
            bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
        'image/format': feature_pb2.Feature(
            bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])),
        'image/object/bbox/xmin': feature_pb2.Feature(
            float_list=feature_pb2.FloatList(value=[0.0])),
        'image/object/bbox/xmax': feature_pb2.Feature(
            float_list=feature_pb2.FloatList(value=[1.0])),
        'image/object/bbox/ymin': feature_pb2.Feature(
            float_list=feature_pb2.FloatList(value=[0.0])),
        'image/object/bbox/ymax': feature_pb2.Feature(
            float_list=feature_pb2.FloatList(value=[1.0])),
        'image/object/class/label': feature_pb2.Feature(
            int64_list=feature_pb2.Int64List(value=[2])),
    }))
    writer.write(example.SerializeToString())
    writer.close()

    return path

開發者ID:ringringyi，項目名稱:DOTA_models，代碼行數:29，代碼來源:input_reader_builder_test.py

示例2: _EncodedFloatFeature

# 需要導入模塊: from tensorflow.core.example import feature_pb2 [as 別名]
# 或者: from tensorflow.core.example.feature_pb2 import FloatList [as 別名]
def _EncodedFloatFeature(self, ndarray):
    return feature_pb2.Feature(float_list=feature_pb2.FloatList(
        value=ndarray.flatten().tolist()))

開發者ID:abhisuri97，項目名稱:auto-alt-text-lambda-api，代碼行數:5，代碼來源:tfexample_decoder_test.py

示例3: create_tf_record

# 需要導入模塊: from tensorflow.core.example import feature_pb2 [as 別名]
# 或者: from tensorflow.core.example.feature_pb2 import FloatList [as 別名]
def create_tf_record(self):
    path = os.path.join(self.get_temp_dir(), 'tfrecord')
    writer = tf.python_io.TFRecordWriter(path)

    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    flat_mask = (4 * 5) * [1.0]
    with self.test_session():
      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
    example = example_pb2.Example(features=feature_pb2.Features(feature={
        'image/encoded': feature_pb2.Feature(
            bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
        'image/format': feature_pb2.Feature(
            bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])),
        'image/height': feature_pb2.Feature(
            int64_list=feature_pb2.Int64List(value=[4])),
        'image/width': feature_pb2.Feature(
            int64_list=feature_pb2.Int64List(value=[5])),
        'image/object/bbox/xmin': feature_pb2.Feature(
            float_list=feature_pb2.FloatList(value=[0.0])),
        'image/object/bbox/xmax': feature_pb2.Feature(
            float_list=feature_pb2.FloatList(value=[1.0])),
        'image/object/bbox/ymin': feature_pb2.Feature(
            float_list=feature_pb2.FloatList(value=[0.0])),
        'image/object/bbox/ymax': feature_pb2.Feature(
            float_list=feature_pb2.FloatList(value=[1.0])),
        'image/object/class/label': feature_pb2.Feature(
            int64_list=feature_pb2.Int64List(value=[2])),
        'image/object/mask': feature_pb2.Feature(
            float_list=feature_pb2.FloatList(value=flat_mask)),
    }))
    writer.write(example.SerializeToString())
    writer.close()

    return path

開發者ID:cagbal，項目名稱:ros_people_object_detection_tensorflow，代碼行數:36，代碼來源:input_reader_builder_test.py

示例4: create_tf_record

# 需要導入模塊: from tensorflow.core.example import feature_pb2 [as 別名]
# 或者: from tensorflow.core.example.feature_pb2 import FloatList [as 別名]
def create_tf_record(self):
    path = os.path.join(self.get_temp_dir(), 'tfrecord')
    writer = tf.python_io.TFRecordWriter(path)

    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    flat_mask = (4 * 5) * [1.0]
    with self.test_session():
      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
    example = example_pb2.Example(
        features=feature_pb2.Features(
            feature={
                'image/encoded':
                    feature_pb2.Feature(
                        bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
                'image/format':
                    feature_pb2.Feature(
                        bytes_list=feature_pb2.BytesList(
                            value=['jpeg'.encode('utf-8')])),
                'image/height':
                    feature_pb2.Feature(
                        int64_list=feature_pb2.Int64List(value=[4])),
                'image/width':
                    feature_pb2.Feature(
                        int64_list=feature_pb2.Int64List(value=[5])),
                'image/object/bbox/xmin':
                    feature_pb2.Feature(
                        float_list=feature_pb2.FloatList(value=[0.0])),
                'image/object/bbox/xmax':
                    feature_pb2.Feature(
                        float_list=feature_pb2.FloatList(value=[1.0])),
                'image/object/bbox/ymin':
                    feature_pb2.Feature(
                        float_list=feature_pb2.FloatList(value=[0.0])),
                'image/object/bbox/ymax':
                    feature_pb2.Feature(
                        float_list=feature_pb2.FloatList(value=[1.0])),
                'image/object/class/label':
                    feature_pb2.Feature(
                        int64_list=feature_pb2.Int64List(value=[2])),
                'image/object/mask':
                    feature_pb2.Feature(
                        float_list=feature_pb2.FloatList(value=flat_mask)),
            }))
    writer.write(example.SerializeToString())
    writer.close()

    return path

開發者ID:cagbal，項目名稱:ros_people_object_detection_tensorflow，代碼行數:49，代碼來源:dataset_builder_test.py

示例5: create_tf_record

# 需要導入模塊: from tensorflow.core.example import feature_pb2 [as 別名]
# 或者: from tensorflow.core.example.feature_pb2 import FloatList [as 別名]
def create_tf_record(self, has_additional_channels=False):
    path = os.path.join(self.get_temp_dir(), 'tfrecord')
    writer = tf.python_io.TFRecordWriter(path)

    image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
    additional_channels_tensor = np.random.randint(
        255, size=(4, 5, 1)).astype(np.uint8)
    flat_mask = (4 * 5) * [1.0]
    with self.test_session():
      encoded_jpeg = tf.image.encode_jpeg(tf.constant(image_tensor)).eval()
      encoded_additional_channels_jpeg = tf.image.encode_jpeg(
          tf.constant(additional_channels_tensor)).eval()
    features = {
        'image/encoded':
            feature_pb2.Feature(
                bytes_list=feature_pb2.BytesList(value=[encoded_jpeg])),
        'image/format':
            feature_pb2.Feature(
                bytes_list=feature_pb2.BytesList(value=['jpeg'.encode('utf-8')])
            ),
        'image/height':
            feature_pb2.Feature(int64_list=feature_pb2.Int64List(value=[4])),
        'image/width':
            feature_pb2.Feature(int64_list=feature_pb2.Int64List(value=[5])),
        'image/object/bbox/xmin':
            feature_pb2.Feature(float_list=feature_pb2.FloatList(value=[0.0])),
        'image/object/bbox/xmax':
            feature_pb2.Feature(float_list=feature_pb2.FloatList(value=[1.0])),
        'image/object/bbox/ymin':
            feature_pb2.Feature(float_list=feature_pb2.FloatList(value=[0.0])),
        'image/object/bbox/ymax':
            feature_pb2.Feature(float_list=feature_pb2.FloatList(value=[1.0])),
        'image/object/class/label':
            feature_pb2.Feature(int64_list=feature_pb2.Int64List(value=[2])),
        'image/object/mask':
            feature_pb2.Feature(
                float_list=feature_pb2.FloatList(value=flat_mask)),
    }
    if has_additional_channels:
      features['image/additional_channels/encoded'] = feature_pb2.Feature(
          bytes_list=feature_pb2.BytesList(
              value=[encoded_additional_channels_jpeg] * 2))
    example = example_pb2.Example(
        features=feature_pb2.Features(feature=features))
    writer.write(example.SerializeToString())
    writer.close()

    return path

開發者ID:ambakick，項目名稱:Person-Detection-and-Tracking，代碼行數:50，代碼來源:dataset_builder_test.py

注：本文中的tensorflow.core.example.feature_pb2.FloatList方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。