Samples and creates mask training targets.
tfm.vision.layers.MaskSampler(
mask_target_size: int, num_sampled_masks: int, **kwargs
)
Methods
call
View source
call(
candidate_rois: tf.Tensor,
candidate_gt_boxes: tf.Tensor,
candidate_gt_classes: tf.Tensor,
candidate_gt_indices: tf.Tensor,
gt_masks: tf.Tensor
)
Samples and creates mask targets for training.
Args |
candidate_rois
|
A tf.Tensor of shape of [batch_size, N, 4], where N is
the number of candidate RoIs to be considered for mask sampling. It
includes both positive and negative RoIs. The
num_mask_samples_per_image positive RoIs will be sampled to create
mask training targets.
|
candidate_gt_boxes
|
A tf.Tensor of shape of [batch_size, N, 4], storing
the corresponding groundtruth boxes to the candidate_rois .
|
candidate_gt_classes
|
A tf.Tensor of shape of [batch_size, N], storing
the corresponding groundtruth classes to the candidate_rois . 0 in the
tensor corresponds to the background class, i.e. negative RoIs.
|
candidate_gt_indices
|
A tf.Tensor of shape [batch_size, N], storing the
corresponding groundtruth instance indices to the candidate_gt_boxes ,
i.e. gt_boxes[candidate_gt_indices[:, i]] = candidate_gt_boxes[:, i],
where gt_boxes which is of shape [batch_size, MAX_INSTANCES, 4], M >=
N, is the superset of candidate_gt_boxes.
|
gt_masks
|
A tf.Tensor of [batch_size, MAX_INSTANCES, mask_height,
mask_width] containing all the groundtruth masks which sample masks are
drawn from. after sampling. The output masks are resized w.r.t the
sampled RoIs.
|
Returns |
foreground_rois
|
A tf.Tensor of shape of [batch_size, K, 4] storing the
RoI that corresponds to the sampled foreground masks, where
K = num_mask_samples_per_image.
|
foreground_classes
|
A tf.Tensor of shape of [batch_size, K] storing the
classes corresponding to the sampled foreground masks.
|
cropoped_foreground_masks
|
A tf.Tensor of shape of
[batch_size, K, mask_target_size, mask_target_size] storing the
cropped foreground masks used for training.
|