# coding=utf-8 # Copyright 2022 The IDEA Authors. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ This is the original implementation of SetCriterion which will be deprecated in the next version. We keep it here because our modified Criterion module is still under test. """ from typing import List import torch import torch.nn as nn import torch.nn.functional as F from detrex.layers import box_cxcywh_to_xyxy, generalized_box_iou from detrex.utils import get_world_size, is_dist_avail_and_initialized def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2): """ Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. Args: inputs (torch.Tensor): A float tensor of arbitrary shape. The predictions for each example. targets (torch.Tensor): 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). num_boxes (int): The number of boxes. alpha (float, optional): Weighting factor in range (0, 1) to balance positive vs negative examples. Default: 0.25. gamma (float): Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples. Default: 2. Returns: torch.Tensor: The computed sigmoid focal loss. """ 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_boxes class GroupSetCriterion(nn.Module): """This class computes the loss for Group 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, group_nums: int = 11, losses: List[str] = ["class", "boxes"], alpha: float = 0.25, gamma: float = 2.0, ): """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. losses: list of all the losses to be applied. See get_loss for list of available losses. focal_alpha: alpha in Focal Loss """ super().__init__() self.num_classes = num_classes self.matcher = matcher self.group_nums = group_nums self.weight_dict = weight_dict self.losses = losses self.alpha = alpha self.gamma = gamma def loss_labels(self, outputs, targets, indices, num_boxes): """Classification loss (Binary focal loss) 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 # src_logits: (b, num_queries, num_classes) = (2, 300, 80) # target_classes_one_hot = (2, 300, 80) target_classes_onehot = torch.zeros( [src_logits.shape[0], src_logits.shape[1], src_logits.shape[2] + 1], dtype=src_logits.dtype, layout=src_logits.layout, device=src_logits.device, ) target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1) target_classes_onehot = target_classes_onehot[:, :, :-1] loss_class = ( sigmoid_focal_loss( src_logits, target_classes_onehot, num_boxes=num_boxes, alpha=self.alpha, gamma=self.gamma, ) * src_logits.shape[1] ) losses = {"loss_class": loss_class} return losses def loss_boxes(self, outputs, targets, indices, num_boxes): """Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4] The target boxes are expected in format (center_x, center_y, w, h), normalized by the image size. """ assert "pred_boxes" in outputs idx = self._get_src_permutation_idx(indices) src_boxes = outputs["pred_boxes"][idx] target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0) loss_bbox = F.l1_loss(src_boxes, target_boxes, reduction="none") losses = {} losses["loss_bbox"] = loss_bbox.sum() / num_boxes loss_giou = 1 - torch.diag( generalized_box_iou( box_cxcywh_to_xyxy(src_boxes), box_cxcywh_to_xyxy(target_boxes), ) ) losses["loss_giou"] = loss_giou.sum() / num_boxes 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_boxes, **kwargs): loss_map = { "class": self.loss_labels, "boxes": self.loss_boxes, } assert loss in loss_map, f"do you really want to compute {loss} loss?" return loss_map[loss](outputs, targets, indices, num_boxes, **kwargs) 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 return_indices: used for vis. if True, the layer0-5 indices will be returned as well. """ group_nums = self.group_nums if self.training else 1 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, group_nums=group_nums) # Compute the average number of target boxes accross all nodes, for normalization purposes num_boxes = sum(len(t["labels"]) for t in targets) * group_nums num_boxes = torch.as_tensor( [num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device ) if is_dist_avail_and_initialized(): torch.distributed.all_reduce(num_boxes) num_boxes = torch.clamp(num_boxes / 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_boxes)) # 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, group_nums=group_nums) for loss in self.losses: l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_boxes) l_dict = {k + f"_{i}": v for k, v in l_dict.items()} losses.update(l_dict) return losses