Python resnet_model.DEFAULT_VERSION属性代码示例

# 需要导入模块: from official.resnet import resnet_model [as 别名]
# 或者: from official.resnet.resnet_model import DEFAULT_VERSION [as 别名]
def __init__(self, resnet_size_choices=None):
    super(ResnetArgParser, self).__init__(parents=[
        parsers.BaseParser(),
        parsers.PerformanceParser(),
        parsers.ImageModelParser(),
        parsers.BenchmarkParser(),
    ])

    self.add_argument(
        '--version', '-v', type=int, choices=[1, 2],
        default=resnet_model.DEFAULT_VERSION,
        help='Version of ResNet. (1 or 2) See README.md for details.'
    )

    self.add_argument(
        '--resnet_size', '-rs', type=int, default=50,
        choices=resnet_size_choices,
        help='[default: %(default)s] The size of the ResNet model to use.',
        metavar='<RS>' if resnet_size_choices is None else None
    ) 

# 需要导入模块: from official.resnet import resnet_model [as 别名]
# 或者: from official.resnet.resnet_model import DEFAULT_VERSION [as 别名]
def __init__(self, resnet_size_choices=None):
    super(ResnetArgParser, self).__init__(parents=[
        parsers.BaseParser(multi_gpu=False),
        parsers.PerformanceParser(num_parallel_calls=False),
        parsers.ImageModelParser(),
        parsers.ExportParser(),
        parsers.BenchmarkParser(),
    ])

    self.add_argument(
        '--version', '-v', type=int, choices=[1, 2],
        default=resnet_model.DEFAULT_VERSION,
        help='Version of ResNet. (1 or 2) See README.md for details.'
    )

    self.add_argument(
        '--resnet_size', '-rs', type=int, default=50,
        choices=resnet_size_choices,
        help='[default: %(default)s] The size of the ResNet model to use.',
        metavar='<RS>' if resnet_size_choices is None else None
    ) 

# 需要导入模块: from official.resnet import resnet_model [as 别名]
# 或者: from official.resnet.resnet_model import DEFAULT_VERSION [as 别名]
def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
               version=resnet_model.DEFAULT_VERSION,
               dtype=resnet_model.DEFAULT_DTYPE):
    """These are the parameters that work for CIFAR-10 data.

    Args:
      resnet_size: The number of convolutional layers needed in the model.
      data_format: Either 'channels_first' or 'channels_last', specifying which
        data format to use when setting up the model.
      num_classes: The number of output classes needed from the model. This
        enables users to extend the same model to their own datasets.
      version: Integer representing which version of the ResNet network to use.
        See README for details. Valid values: [1, 2]
      dtype: The TensorFlow dtype to use for calculations.

    Raises:
      ValueError: if invalid resnet_size is chosen
    """
    if resnet_size % 6 != 2:
      raise ValueError('resnet_size must be 6n + 2:', resnet_size)

    num_blocks = (resnet_size - 2) // 6

    super(Cifar10Model, self).__init__(
        resnet_size=resnet_size,
        bottleneck=False,
        num_classes=num_classes,
        num_filters=16,
        kernel_size=3,
        conv_stride=1,
        first_pool_size=None,
        first_pool_stride=None,
        block_sizes=[num_blocks] * 3,
        block_strides=[1, 2, 2],
        final_size=64,
        version=version,
        data_format=data_format,
        dtype=dtype
    ) 

# 需要导入模块: from official.resnet import resnet_model [as 别名]
# 或者: from official.resnet.resnet_model import DEFAULT_VERSION [as 别名]
def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
               version=resnet_model.DEFAULT_VERSION,
               dtype=resnet_model.DEFAULT_DTYPE):
    """These are the parameters that work for Imagenet data.

    Args:
      resnet_size: The number of convolutional layers needed in the model.
      data_format: Either 'channels_first' or 'channels_last', specifying which
        data format to use when setting up the model.
      num_classes: The number of output classes needed from the model. This
        enables users to extend the same model to their own datasets.
      version: Integer representing which version of the ResNet network to use.
        See README for details. Valid values: [1, 2]
      dtype: The TensorFlow dtype to use for calculations.
    """

    # For bigger models, we want to use "bottleneck" layers
    if resnet_size < 50:
      bottleneck = False
      final_size = 512
    else:
      bottleneck = True
      final_size = 2048

    super(ImagenetModel, self).__init__(
        resnet_size=resnet_size,
        bottleneck=bottleneck,
        num_classes=num_classes,
        num_filters=64,
        kernel_size=7,
        conv_stride=2,
        first_pool_size=3,
        first_pool_stride=2,
        block_sizes=_get_block_sizes(resnet_size),
        block_strides=[1, 2, 2, 2],
        final_size=final_size,
        version=version,
        data_format=data_format,
        dtype=dtype
    ) 

# 需要导入模块: from official.resnet import resnet_model [as 别名]
# 或者: from official.resnet.resnet_model import DEFAULT_VERSION [as 别名]
def __init__(self, resnet_size, data_format=None, num_classes=NUM_CLASSES,
               resnet_version=resnet_model.DEFAULT_VERSION,
               dtype=resnet_model.DEFAULT_DTYPE):
    """These are the parameters that work for CIFAR-10 data.

    Args:
      resnet_size: The number of convolutional layers needed in the model.
      data_format: Either 'channels_first' or 'channels_last', specifying which
        data format to use when setting up the model.
      num_classes: The number of output classes needed from the model. This
        enables users to extend the same model to their own datasets.
      resnet_version: Integer representing which version of the ResNet network
      to use. See README for details. Valid values: [1, 2]
      dtype: The TensorFlow dtype to use for calculations.

    Raises:
      ValueError: if invalid resnet_size is chosen
    """
    if resnet_size % 6 != 2:
      raise ValueError('resnet_size must be 6n + 2:', resnet_size)

    num_blocks = (resnet_size - 2) // 6

    super(Cifar10Model, self).__init__(
        resnet_size=resnet_size,
        bottleneck=False,
        num_classes=num_classes,
        num_filters=16,
        kernel_size=3,
        conv_stride=1,
        first_pool_size=None,
        first_pool_stride=None,
        block_sizes=[num_blocks] * 3,
        block_strides=[1, 2, 2],
        resnet_version=resnet_version,
        data_format=data_format,
        dtype=dtype
    ) 

# 需要导入模块: from official.resnet import resnet_model [as 别名]
# 或者: from official.resnet.resnet_model import DEFAULT_VERSION [as 别名]
def __init__(self, resnet_size, data_format=None, num_classes=NUM_CLASSES,
               resnet_version=resnet_model.DEFAULT_VERSION,
               dtype=resnet_model.DEFAULT_DTYPE):
    """These are the parameters that work for Imagenet data.

    Args:
      resnet_size: The number of convolutional layers needed in the model.
      data_format: Either 'channels_first' or 'channels_last', specifying which
        data format to use when setting up the model.
      num_classes: The number of output classes needed from the model. This
        enables users to extend the same model to their own datasets.
      resnet_version: Integer representing which version of the ResNet network
        to use. See README for details. Valid values: [1, 2]
      dtype: The TensorFlow dtype to use for calculations.
    """

    # For bigger models, we want to use "bottleneck" layers
    if resnet_size < 50:
      bottleneck = False
    else:
      bottleneck = True

    super(ImagenetModel, self).__init__(
        resnet_size=resnet_size,
        bottleneck=bottleneck,
        num_classes=num_classes,
        num_filters=64,
        kernel_size=7,
        conv_stride=2,
        first_pool_size=3,
        first_pool_stride=2,
        block_sizes=_get_block_sizes(resnet_size),
        block_strides=[1, 2, 2, 2],
        resnet_version=resnet_version,
        data_format=data_format,
        dtype=dtype
    ) 

# 需要导入模块: from official.resnet import resnet_model [as 别名]
# 或者: from official.resnet.resnet_model import DEFAULT_VERSION [as 别名]
def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
               resnet_version=resnet_model.DEFAULT_VERSION,
               dtype=resnet_model.DEFAULT_DTYPE):
    """These are the parameters that work for CIFAR-10 data.

    Args:
      resnet_size: The number of convolutional layers needed in the model.
      data_format: Either 'channels_first' or 'channels_last', specifying which
        data format to use when setting up the model.
      num_classes: The number of output classes needed from the model. This
        enables users to extend the same model to their own datasets.
      resnet_version: Integer representing which version of the ResNet network
      to use. See README for details. Valid values: [1, 2]
      dtype: The TensorFlow dtype to use for calculations.

    Raises:
      ValueError: if invalid resnet_size is chosen
    """
    if resnet_size % 6 != 2:
      raise ValueError('resnet_size must be 6n + 2:', resnet_size)

    num_blocks = (resnet_size - 2) // 6

    super(Cifar10Model, self).__init__(
        resnet_size=resnet_size,
        bottleneck=False,
        num_classes=num_classes,
        num_filters=16,
        kernel_size=3,
        conv_stride=1,
        first_pool_size=None,
        first_pool_stride=None,
        block_sizes=[num_blocks] * 3,
        block_strides=[1, 2, 2],
        resnet_version=resnet_version,
        data_format=data_format,
        dtype=dtype
    ) 

# 需要导入模块: from official.resnet import resnet_model [as 别名]
# 或者: from official.resnet.resnet_model import DEFAULT_VERSION [as 别名]
def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
               resnet_version=resnet_model.DEFAULT_VERSION,
               dtype=resnet_model.DEFAULT_DTYPE):
    """These are the parameters that work for Imagenet data.

    Args:
      resnet_size: The number of convolutional layers needed in the model.
      data_format: Either 'channels_first' or 'channels_last', specifying which
        data format to use when setting up the model.
      num_classes: The number of output classes needed from the model. This
        enables users to extend the same model to their own datasets.
      resnet_version: Integer representing which version of the ResNet network
        to use. See README for details. Valid values: [1, 2]
      dtype: The TensorFlow dtype to use for calculations.
    """

    # For bigger models, we want to use "bottleneck" layers
    if resnet_size < 50:
      bottleneck = False
    else:
      bottleneck = True

    super(ImagenetModel, self).__init__(
        resnet_size=resnet_size,
        bottleneck=bottleneck,
        num_classes=num_classes,
        num_filters=64,
        kernel_size=7,
        conv_stride=2,
        first_pool_size=3,
        first_pool_stride=2,
        block_sizes=_get_block_sizes(resnet_size),
        block_strides=[1, 2, 2, 2],
        resnet_version=resnet_version,
        data_format=data_format,
        dtype=dtype
    ) 

# 需要导入模块: from official.resnet import resnet_model [as 别名]
# 或者: from official.resnet.resnet_model import DEFAULT_VERSION [as 别名]
def __init__(self, resnet_size, data_format=None, num_classes=_NUM_CLASSES,
               version=resnet_model.DEFAULT_VERSION,
               dtype=resnet_model.DEFAULT_DTYPE):
    """These are the parameters that work for Imagenet data.

    Args:
      resnet_size: The number of convolutional layers needed in the model.
      data_format: Either 'channels_first' or 'channels_last', specifying which
        data format to use when setting up the model.
      num_classes: The number of output classes needed from the model. This
        enables users to extend the same model to their own datasets.
      version: Integer representing which version of the ResNet network to use.
        See README for details. Valid values: [1, 2]
      dtype: The TensorFlow dtype to use for calculations.
    """

    # For bigger models, we want to use "bottleneck" layers
    if resnet_size < 50:
      bottleneck = False
      final_size = 512
    else:
      bottleneck = True
      final_size = 2048

    super(ImagenetModel, self).__init__(
        resnet_size=resnet_size,
        bottleneck=bottleneck,
        num_classes=num_classes,
        num_filters=64,
        kernel_size=7,
        conv_stride=2,
        first_pool_size=3,
        first_pool_stride=2,
        second_pool_size=7,
        second_pool_stride=1,
        block_sizes=_get_block_sizes(resnet_size),
        block_strides=[1, 2, 2, 2],
        final_size=final_size,
        version=version,
        data_format=data_format,
        dtype=dtype
    )