# Copyright (c) Facebook, Inc. and its affiliates. # Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/detr.py # Modified for Mask3D """ MaskFormer criterion. """ import torch import torch.nn.functional as F from torch import nn from detectron2.utils.comm import get_world_size from detectron2.projects.point_rend.point_features import ( get_uncertain_point_coords_with_randomness, point_sample, ) from models.misc import ( is_dist_avail_and_initialized, nested_tensor_from_tensor_list, ) def dice_loss( inputs: torch.Tensor, targets: torch.Tensor, num_masks: float, ): """ Compute the DICE loss, similar to generalized IOU for masks Args: inputs: A float tensor of arbitrary shape. The predictions for each example. 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). """ inputs = inputs.sigmoid() inputs = inputs.flatten(1) numerator = 2 * (inputs * targets).sum(-1) denominator = inputs.sum(-1) + targets.sum(-1) loss = 1 - (numerator + 1) / (denominator + 1) return loss.sum() / num_masks dice_loss_jit = torch.jit.script(dice_loss) # type: torch.jit.ScriptModule def sigmoid_ce_loss( inputs: torch.Tensor, targets: torch.Tensor, num_masks: float, ): """ Args: inputs: A float tensor of arbitrary shape. The predictions for each example. 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). Returns: Loss tensor """ loss = F.binary_cross_entropy_with_logits( inputs, targets, reduction="none" ) return loss.mean(1).sum() / num_masks sigmoid_ce_loss_jit = torch.jit.script( sigmoid_ce_loss ) # type: torch.jit.ScriptModule def calculate_uncertainty(logits): """ We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the foreground class in `classes`. Args: logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or class-agnostic, where R is the total number of predicted masks in all images and C is the number of foreground classes. The values are logits. Returns: scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with the most uncertain locations having the highest uncertainty score. """ assert logits.shape[1] == 1 gt_class_logits = logits.clone() return -(torch.abs(gt_class_logits)) class SetCriterion(nn.Module): """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) """ def __init__( self, num_classes, matcher, weight_dict, eos_coef, losses, num_points, oversample_ratio, importance_sample_ratio, class_weights, ): """Create the criterion. Parameters: num_classes: number of object categories, omitting the special no-object category matcher: module able to compute a matching between targets and proposals weight_dict: dict containing as key the names of the losses and as values their relative weight. eos_coef: relative classification weight applied to the no-object category losses: list of all the losses to be applied. See get_loss for list of available losses. """ super().__init__() self.num_classes = num_classes - 1 self.class_weights = class_weights self.matcher = matcher self.weight_dict = weight_dict self.eos_coef = eos_coef self.losses = losses empty_weight = torch.ones(self.num_classes + 1) empty_weight[-1] = self.eos_coef if self.class_weights != -1: assert ( len(self.class_weights) == self.num_classes ), "CLASS WEIGHTS DO NOT MATCH" empty_weight[:-1] = torch.tensor(self.class_weights) self.register_buffer("empty_weight", empty_weight) # pointwise mask loss parameters self.num_points = num_points self.oversample_ratio = oversample_ratio self.importance_sample_ratio = importance_sample_ratio def loss_labels(self, outputs, targets, indices, num_masks, mask_type): """Classification loss (NLL) targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes] """ assert "pred_logits" in outputs src_logits = outputs["pred_logits"].float() idx = self._get_src_permutation_idx(indices) target_classes_o = torch.cat( [t["labels"][J] for t, (_, J) in zip(targets, indices)] ) target_classes = torch.full( src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device, ) target_classes[idx] = target_classes_o loss_ce = F.cross_entropy( src_logits.transpose(1, 2), target_classes, self.empty_weight, ignore_index=253, ) losses = {"loss_ce": loss_ce} return losses def loss_masks(self, outputs, targets, indices, num_masks, mask_type): """Compute the losses related to the masks: the focal loss and the dice loss. targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w] """ assert "pred_masks" in outputs loss_masks = [] loss_dices = [] for batch_id, (map_id, target_id) in enumerate(indices): map = outputs["pred_masks"][batch_id][:, map_id].T target_mask = targets[batch_id][mask_type][target_id] if self.num_points != -1: point_idx = torch.randperm( target_mask.shape[1], device=target_mask.device )[: int(self.num_points * target_mask.shape[1])] else: # sample all points point_idx = torch.arange( target_mask.shape[1], device=target_mask.device ) num_masks = target_mask.shape[0] map = map[:, point_idx] target_mask = target_mask[:, point_idx].float() loss_masks.append(sigmoid_ce_loss_jit(map, target_mask, num_masks)) loss_dices.append(dice_loss_jit(map, target_mask, num_masks)) # del target_mask return { "loss_mask": torch.sum(torch.stack(loss_masks)), "loss_dice": torch.sum(torch.stack(loss_dices)), } src_idx = self._get_src_permutation_idx(indices) tgt_idx = self._get_tgt_permutation_idx(indices) src_masks = outputs["pred_masks"] src_masks = src_masks[src_idx] masks = [t[mask_type] for t in targets] # TODO use valid to mask invalid areas due to padding in loss target_masks, valid = nested_tensor_from_tensor_list(masks).decompose() target_masks = target_masks.to(src_masks) target_masks = target_masks[tgt_idx] # No need to upsample predictions as we are using normalized coordinates :) # N x 1 x H x W src_masks = src_masks[:, None] target_masks = target_masks[:, None] with torch.no_grad(): # sample point_coords point_coords = get_uncertain_point_coords_with_randomness( src_masks, lambda logits: calculate_uncertainty(logits), self.num_points, self.oversample_ratio, self.importance_sample_ratio, ) # get gt labels point_labels = point_sample( target_masks, point_coords, align_corners=False, ).squeeze(1) point_logits = point_sample( src_masks, point_coords, align_corners=False, ).squeeze(1) losses = { "loss_mask": sigmoid_ce_loss_jit( point_logits, point_labels, num_masks, mask_type ), "loss_dice": dice_loss_jit( point_logits, point_labels, num_masks, mask_type ), } del src_masks del target_masks return losses def _get_src_permutation_idx(self, indices): # permute predictions following indices batch_idx = torch.cat( [torch.full_like(src, i) for i, (src, _) in enumerate(indices)] ) src_idx = torch.cat([src for (src, _) in indices]) return batch_idx, src_idx def _get_tgt_permutation_idx(self, indices): # permute targets following indices batch_idx = torch.cat( [torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)] ) tgt_idx = torch.cat([tgt for (_, tgt) in indices]) return batch_idx, tgt_idx def get_loss(self, loss, outputs, targets, indices, num_masks, mask_type): loss_map = {"labels": self.loss_labels, "masks": self.loss_masks} assert loss in loss_map, f"do you really want to compute {loss} loss?" return loss_map[loss](outputs, targets, indices, num_masks, mask_type) def forward(self, outputs, targets, mask_type): """This performs the loss computation. Parameters: outputs: dict of tensors, see the output specification of the model for the format targets: list of dicts, such that len(targets) == batch_size. The expected keys in each dict depends on the losses applied, see each loss' doc """ outputs_without_aux = { k: v for k, v in outputs.items() if k != "aux_outputs" } # Retrieve the matching between the outputs of the last layer and the targets indices = self.matcher(outputs_without_aux, targets, mask_type) # Compute the average number of target boxes accross all nodes, for normalization purposes num_masks = sum(len(t["labels"]) for t in targets) num_masks = torch.as_tensor( [num_masks], dtype=torch.float, device=next(iter(outputs.values())).device, ) if is_dist_avail_and_initialized(): torch.distributed.all_reduce(num_masks) num_masks = torch.clamp(num_masks / get_world_size(), min=1).item() # Compute all the requested losses losses = {} for loss in self.losses: losses.update( self.get_loss( loss, outputs, targets, indices, num_masks, mask_type ) ) # In case of auxiliary losses, we repeat this process with the output of each intermediate layer. if "aux_outputs" in outputs: for i, aux_outputs in enumerate(outputs["aux_outputs"]): indices = self.matcher(aux_outputs, targets, mask_type) for loss in self.losses: l_dict = self.get_loss( loss, aux_outputs, targets, indices, num_masks, mask_type, ) l_dict = {k + f"_{i}": v for k, v in l_dict.items()} losses.update(l_dict) return losses def __repr__(self): head = "Criterion " + self.__class__.__name__ body = [ "matcher: {}".format(self.matcher.__repr__(_repr_indent=8)), "losses: {}".format(self.losses), "weight_dict: {}".format(self.weight_dict), "num_classes: {}".format(self.num_classes), "eos_coef: {}".format(self.eos_coef), "num_points: {}".format(self.num_points), "oversample_ratio: {}".format(self.oversample_ratio), "importance_sample_ratio: {}".format(self.importance_sample_ratio), ] _repr_indent = 4 lines = [head] + [" " * _repr_indent + line for line in body] return "\n".join(lines)