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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/detr.py | |
| # Copyright (c) Meta Platforms, Inc. All Rights Reserved | |
| """ | |
| MaskFormer criterion. | |
| """ | |
| import torch | |
| import torch.nn.functional as F | |
| from torch import nn | |
| from detectron2.utils.comm import get_world_size | |
| from ..utils.misc import is_dist_avail_and_initialized, nested_tensor_from_tensor_list | |
| def dice_loss(inputs, targets, num_masks): | |
| """ | |
| 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 | |
| def sigmoid_focal_loss( | |
| inputs, targets, num_masks, alpha: float = 0.25, gamma: float = 2 | |
| ): | |
| """ | |
| Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. | |
| 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). | |
| alpha: (optional) Weighting factor in range (0,1) to balance | |
| positive vs negative examples. Default = -1 (no weighting). | |
| gamma: Exponent of the modulating factor (1 - p_t) to | |
| balance easy vs hard examples. | |
| Returns: | |
| Loss tensor | |
| """ | |
| prob = inputs.sigmoid() | |
| ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none") | |
| p_t = prob * targets + (1 - prob) * (1 - targets) | |
| loss = ce_loss * ((1 - p_t) ** gamma) | |
| if alpha >= 0: | |
| alpha_t = alpha * targets + (1 - alpha) * (1 - targets) | |
| loss = alpha_t * loss | |
| return loss.mean(1).sum() / num_masks | |
| 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): | |
| """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 | |
| self.matcher = matcher | |
| self.weight_dict = weight_dict | |
| self.eos_coef = eos_coef | |
| self.losses = losses | |
| if eos_coef > 0: | |
| empty_weight = torch.ones(self.num_classes + 1) | |
| empty_weight[-1] = self.eos_coef | |
| self.register_buffer("empty_weight", empty_weight) | |
| self.use_ignore_idx = False | |
| else: | |
| self.use_ignore_idx = True | |
| self.cur_target = [] | |
| def loss_labels(self, outputs, targets, indices, num_masks): | |
| """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"] | |
| 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 | |
| if self.use_ignore_idx: | |
| loss_ce = F.cross_entropy( | |
| src_logits.transpose(1, 2), | |
| target_classes, | |
| ignore_index=self.num_classes, | |
| ) | |
| else: | |
| if "empty_weight" in outputs: | |
| empty_weight = torch.cat( | |
| [outputs["empty_weight"], self.empty_weight[-1:]] | |
| ).detach() | |
| else: | |
| empty_weight = self.empty_weight | |
| loss_ce = F.cross_entropy( | |
| src_logits.transpose(1, 2), target_classes, empty_weight | |
| ) | |
| losses = {"loss_ce": loss_ce} | |
| return losses | |
| def loss_masks(self, outputs, targets, indices, num_masks): | |
| """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 | |
| 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["masks"] 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] | |
| # upsample predictions to the target size | |
| src_masks = F.interpolate( | |
| src_masks[:, None], | |
| size=target_masks.shape[-2:], | |
| mode="bilinear", | |
| align_corners=False, | |
| ) | |
| src_masks = src_masks[:, 0].flatten(1) | |
| target_masks = target_masks.flatten(1) | |
| target_masks = target_masks.view(src_masks.shape) | |
| losses = { | |
| "loss_mask": sigmoid_focal_loss(src_masks, target_masks, num_masks), | |
| "loss_dice": dice_loss(src_masks, target_masks, num_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): | |
| 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) | |
| def forward(self, outputs, targets): | |
| """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) | |
| # 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)) | |
| # 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) | |
| for loss in self.losses: | |
| l_dict = self.get_loss( | |
| loss, aux_outputs, targets, indices, num_masks | |
| ) | |
| l_dict = {k + f"_{i}": v for k, v in l_dict.items()} | |
| losses.update(l_dict) | |
| return losses | |
| def clean_buffer(self): | |
| self.cur_target = [] | |