torchsig.models.spectrogram_models.detr.criterion¶

Criterion and matching modules from Detectron2, Mask2Former, and DETR codebases

Functions

`batch_dice_loss`	Compute the DICE loss, similar to generalized IOU for masks :param inputs: A float tensor of arbitrary shape. The predictions for each example. :param targets: A float tensor with the same shape as inputs. Stores the binary classification label for each element in inputs (0 for the negative class and 1 for the positive class).
`batch_sigmoid_ce_loss`
`calculate_uncertainty`	We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the
`dice_loss`	Compute the DICE loss, similar to generalized IOU for masks :param inputs: A float tensor of arbitrary shape. The predictions for each example. :param targets: A float tensor with the same shape as inputs. Stores the binary classification label for each element in inputs (0 for the negative class and 1 for the positive class).
`get_uncertain_point_coords_with_randomness`	Sample points in [0, 1] x [0, 1] coordinate space based on their uncertainty. The unceratinties
`get_world_size`
`is_dist_avail_and_initialized`
`nested_tensor_from_tensor_list`
`point_sample`	A wrapper around :function:`torch.nn.functional.grid_sample` to support 3D point_coords tensors. Unlike :function:`torch.nn.functional.grid_sample` it assumes point_coords to lie inside [0, 1] x [0, 1] square. :param input: A tensor of shape (N, C, H, W) that contains features map on a H x W grid. :type input: Tensor :param point_coords: A tensor of shape (N, P, 2) or (N, Hgrid, Wgrid, 2) that contains :type point_coords: Tensor :param [0: :param 1] x [0: :param 1] normalized point coordinates.:.
`sigmoid_ce_loss`

Classes

`HungarianMatcher`	This class computes an assignment between the targets and the predictions of the network
`NestedTensor`
`SetCriterion`	This class computes the loss for DETR. The process happens in two steps: 1) we compute hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair of matched ground-truth / prediction (supervise class and box).