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tfm.vision.heads.SegmentationHead

Creates a segmentation head.

tfm.vision.heads.SegmentationHead(
    num_classes: int,
    level: Union[int, str],
    num_convs: int = 2,
    num_filters: int = 256,
    use_depthwise_convolution: bool = False,
    prediction_kernel_size: int = 1,
    upsample_factor: int = 1,
    feature_fusion: Optional[str] = None,
    decoder_min_level: Optional[int] = None,
    decoder_max_level: Optional[int] = None,
    low_level: int = 2,
    low_level_num_filters: int = 48,
    num_decoder_filters: int = 256,
    activation: str = 'relu',
    logit_activation: Optional[str] = None,
    use_sync_bn: bool = False,
    norm_momentum: float = 0.99,
    norm_epsilon: float = 0.001,
    kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
    bias_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
    **kwargs
)

Args
`num_classes`	An `int` number of mask classification categories. The number of classes does not include background class.
`level`	An `int` or `str`, level to use to build segmentation head.
`num_convs`	An `int` number of stacked convolution before the last prediction layer.
`num_filters`	An `int` number to specify the number of filters used. Default is 256.
`use_depthwise_convolution`	A bool to specify if use depthwise separable convolutions.
`prediction_kernel_size`	An `int` number to specify the kernel size of the prediction layer.
`upsample_factor`	An `int` number to specify the upsampling factor to generate finer mask. Default 1 means no upsampling is applied.
`feature_fusion`	One of the constants in nn_layers.FeatureFusion, namely `deeplabv3plus`, `pyramid_fusion`, `panoptic_fpn_fusion`, `deeplabv3plus_sum_to_merge`, or None. If `deeplabv3plus`, features from decoder_features[level] will be fused with low level feature maps from backbone. If `pyramid_fusion`, multiscale features will be resized and fused at the target level.
`decoder_min_level`	An `int` of minimum level from decoder to use in feature fusion. It is only used when feature_fusion is set to `panoptic_fpn_fusion`.
`decoder_max_level`	An `int` of maximum level from decoder to use in feature fusion. It is only used when feature_fusion is set to `panoptic_fpn_fusion`.
`low_level`	An `int` of backbone level to be used for feature fusion. It is used when feature_fusion is set to `deeplabv3plus` or `deeplabv3plus_sum_to_merge`.
`low_level_num_filters`	An `int` of reduced number of filters for the low level features before fusing it with higher level features. It is only used when feature_fusion is set to `deeplabv3plus` or `deeplabv3plus_sum_to_merge`.
`num_decoder_filters`	An `int` of number of filters in the decoder outputs. It is only used when feature_fusion is set to `panoptic_fpn_fusion`.
`activation`	A `str` that indicates which activation is used, e.g. 'relu', 'swish', etc.
`logit_activation`	Activation applied to the final classifier layer logits, e.g. 'sigmoid', 'softmax'. Can be useful in cases when the task does not use only cross entropy loss.
`use_sync_bn`	A `bool` that indicates whether to use synchronized batch normalization across different replicas.
`norm_momentum`	A `float` of normalization momentum for the moving average.
`norm_epsilon`	A `float` added to variance to avoid dividing by zero.
`kernel_regularizer`	A `tf.keras.regularizers.Regularizer` object for Conv2D. Default is None.
`bias_regularizer`	A `tf.keras.regularizers.Regularizer` object for Conv2D.
`**kwargs`	Additional keyword arguments to be passed.

Methods

`call`

View source

call(
    inputs: Tuple[Union[tf.Tensor, Mapping[str, tf.Tensor]], Union[tf.Tensor, Mapping[
        str, tf.Tensor]]]
)

Forward pass of the segmentation head.

It supports both a tuple of 2 tensors or 2 dictionaries. The first is backbone endpoints, and the second is decoder endpoints. When inputs are tensors, they are from a single level of feature maps. When inputs are dictionaries, they contain multiple levels of feature maps, where the key is the index of feature map.

Args

Args
`inputs`	A tuple of 2 feature map tensors of shape [batch, height_l, width_l, channels] or 2 dictionaries of tensors: key: A `str` of the level of the multilevel features. values: A `tf.Tensor` of the feature map tensors, whose shape is [batch, height_l, width_l, channels]. The first is backbone endpoints, and the second is decoder endpoints.

inputs

A tuple of 2 feature map tensors of shape [batch, height_l, width_l, channels] or 2 dictionaries of tensors:

key: A str of the level of the multilevel features.
values: A tf.Tensor of the feature map tensors, whose shape is [batch, height_l, width_l, channels]. The first is backbone endpoints, and the second is decoder endpoints.

Returns
segmentation prediction mask: A `tf.Tensor` of the segmentation mask scores predicted from input features.