tfm.vision.decoders.ASPP

Creates an Atrous Spatial Pyramid Pooling (ASPP) layer.

tfm.vision.decoders.ASPP(
    level: int,
    dilation_rates: List[int],
    num_filters: int = 256,
    pool_kernel_size: Optional[int] = None,
    use_sync_bn: bool = False,
    norm_momentum: float = 0.99,
    norm_epsilon: float = 0.001,
    activation: str = 'relu',
    dropout_rate: float = 0.0,
    kernel_initializer: str = 'VarianceScaling',
    kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
    interpolation: str = 'bilinear',
    use_depthwise_convolution: bool = False,
    spp_layer_version: str = 'v1',
    output_tensor: bool = False,
    **kwargs
)

Args
level: An `int` level to apply ASPP. dilation_rates: A `list` of dilation rates. num_filters: An `int` number of output filters in ASPP. pool_kernel_size: A `list` of [height, width] of pooling kernel size or None. Pooling size is with respect to original image size, it will be scaled down by 2**level. If None, global average pooling is used. use_sync_bn: A `bool`. If True, use synchronized batch normalization. norm_momentum: A `float` of normalization momentum for the moving average. norm_epsilon: A `float` added to variance to avoid dividing by zero. activation: A `str` activation to be used in ASPP. dropout_rate: A `float` rate for dropout regularization. kernel_initializer: A `str` name of kernel_initializer for convolutional layers. kernel_regularizer: A `tf.keras.regularizers.Regularizer` object for Conv2D. Default is None. interpolation: A `str` of interpolation method. It should be one of `bilinear`, `nearest`, `bicubic`, `area`, `lanczos3`, `lanczos5`, `gaussian`, or `mitchellcubic`. use_depthwise_convolution: If True depthwise separable convolutions will be added to the Atrous spatial pyramid pooling.
`spp_layer_version`	A `str` of spatial pyramid pooling layer version.
`output_tensor`	Whether to output a single tensor or a dictionary of tensor. Default is false. **kwargs: Additional keyword arguments to be passed.

Methods

`call`

View source

call(
    inputs: TensorMapUnion
) -> TensorMapUnion

Calls the Atrous Spatial Pyramid Pooling (ASPP) layer on an input.

The output of ASPP will be a dict of {level, tf.Tensor} even if only one level is present, if output_tensor is false. Hence, this will be compatible with the rest of the segmentation model interfaces. If output_tensor is true, a single tensot is output.

Args

Args
`inputs`	A `tf.Tensor` of shape [batch, height_l, width_l, filter_size] or a `dict` of `tf.Tensor` where key: A `str` of the level of the multilevel feature maps. values: A `tf.Tensor` of shape [batch, height_l, width_l, filter_size].

inputs

A tf.Tensor of shape [batch, height_l, width_l, filter_size] or a dict of tf.Tensor where

key: A str of the level of the multilevel feature maps.
values: A tf.Tensor of shape [batch, height_l, width_l, filter_size].

Returns

Returns
A `tf.Tensor` of shape [batch, height_l, width_l, filter_size] or a `dict` of `tf.Tensor` where key: A `str` of the level of the multilevel feature maps. values: A `tf.Tensor` of output of ASPP module.

A tf.Tensor of shape [batch, height_l, width_l, filter_size] or a dict of tf.Tensor where

key: A str of the level of the multilevel feature maps.
values: A tf.Tensor of output of ASPP module.