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 ..utils.misc import is_dist_avail_and_initialized 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 AvismSetCriterion(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, sim_use_clip): """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 empty_weight = torch.ones(self.num_classes + 1) empty_weight[-1] = self.eos_coef 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 self.sim_use_clip = sim_use_clip 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'] L, B, cQ, _ = src_logits.shape src_logits = src_logits.reshape(L*B, cQ, self.num_classes+1) idx = self._get_src_permutation_idx(indices) target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets * L, 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) losses = {'loss_avism_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 idx = self._get_src_permutation_idx(indices) src_masks = outputs["pred_masks"] L, B, cQ, T, H, W = src_masks.shape src_masks = src_masks.reshape(L*B, cQ, T, H, W) src_masks = src_masks[idx] # Nt x T x Hp x Wp target_masks = torch.cat([t['masks'][i] for t, (_, i) in zip(targets * L, indices)]).to(src_masks) # Nt x T x Ht x Wt src_masks = src_masks.flatten(0, 1)[:, None] target_masks = target_masks.flatten(0, 1)[:, 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) # Nt*T, randN -> Nt, T*randN point_logits = point_logits.view(len(idx[0]), T * self.num_points) point_labels = point_labels.view(len(idx[0]), T * self.num_points) losses = { "loss_avism_mask": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks), "loss_avism_dice": dice_loss_jit(point_logits, point_labels, num_masks), } del src_masks del target_masks return losses def loss_fg_sim( self, outputs, clip_targets, frame_targets, clip_indices, frame_indices, num_masks, MULTIPLIER=1000 ): total_src_q, total_tgt_ids, total_batch_idx = [], [], [] # Frame src_fq = outputs["pred_fq_embed"] # L, B, T, fQ, C # L = number of frame_decoder layers L, B, T, fQ, C = src_fq.shape src_fq = src_fq.flatten(0, 2) # LBT, fQ, C frame_indices = sum(frame_indices, []) frame_src_idx = self._get_src_permutation_idx(frame_indices) # len = LBT src_fq = src_fq[frame_src_idx] # Nf, C target_frame_ids = torch.cat( [t["ids"][J] for t, (_, J) in zip(frame_targets * L, frame_indices)] ) frame_batch_idx = torch.div(frame_src_idx[0].to(device=src_fq.device), T, rounding_mode="floor") is_frame_valid = target_frame_ids != -1 target_frame_ids += frame_batch_idx * MULTIPLIER total_src_q.append(src_fq[is_frame_valid]) total_tgt_ids.append(target_frame_ids[is_frame_valid]) total_batch_idx.append(frame_batch_idx[is_frame_valid]) # Clip if self.sim_use_clip: src_cq = outputs["pred_cq_embed"] # L, B, cQ, C src_cq = src_cq.flatten(0, 1) # LB , cQ, C clip_src_idx = self._get_src_permutation_idx(clip_indices) # len = LB src_cq = src_cq[clip_src_idx] # Nc, C target_clip_ids = torch.cat( # clip_ids' shape = (N, num_frames) -> (N,) [t["ids"][J] for t, (_, J) in zip(clip_targets * L, clip_indices)] ).amax(dim=1) clip_batch_idx = clip_src_idx[0].to(device=src_fq.device) is_clip_valid = target_clip_ids != -1 target_clip_ids += clip_batch_idx * MULTIPLIER total_src_q.append(src_cq[is_clip_valid]) total_tgt_ids.append(target_clip_ids[is_clip_valid]) total_batch_idx.append(clip_batch_idx[is_clip_valid]) # Clip + Frame total_src_q = torch.cat(total_src_q) # Nc+Nf, C total_tgt_ids = torch.cat(total_tgt_ids) # Nc+Nf total_batch_idx = torch.cat(total_batch_idx) # Nc+Nf sim_pred_logits = torch.matmul(total_src_q, total_src_q.T) # Nc+Nf, Nc+Nf sim_tgt = (total_tgt_ids[:, None] == total_tgt_ids[None]).float() # Nc+Nf, Nc+Nf same_clip = (total_batch_idx[:, None] == total_batch_idx[None]).float() loss = F.binary_cross_entropy_with_logits(sim_pred_logits, sim_tgt, reduction='none') loss = loss * same_clip loss_clip_sim = loss.sum() / (same_clip.sum() + 1e-6) return {"loss_clip_sim": loss_clip_sim} 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, clip_targets, frame_targets, clip_indices, frame_indices, num_masks ): loss_map = { 'avism_labels': self.loss_labels, 'avism_masks': self.loss_masks, 'fg_sim': self.loss_fg_sim, } assert loss in loss_map, f"do you really want to compute {loss} loss?" if loss == 'fg_sim': return loss_map[loss]( outputs, clip_targets, frame_targets, clip_indices, frame_indices, num_masks ) return loss_map[loss](outputs, clip_targets, clip_indices, num_masks) def forward(self, outputs, clip_targets, frame_targets, frame_indices=None): """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 clip_indices = self.matcher(outputs_without_aux, clip_targets) # Compute the average number of target boxes accross all nodes, for normalization purposes num_masks = sum(len(t["labels"]) for t in clip_targets) * len(outputs_without_aux["pred_masks"]) 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, clip_targets, frame_targets, clip_indices, frame_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"]): clip_indices = self.matcher(aux_outputs, clip_targets) for loss in self.losses: if loss == "fg_sim": continue l_dict = self.get_loss( loss, aux_outputs, clip_targets, frame_targets, clip_indices, frame_indices, num_masks ) 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)