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| # 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. | |
| import torch | |
| from detrex.modeling import SetCriterion | |
| from detrex.utils import get_world_size, is_dist_avail_and_initialized | |
| class DNCriterion(SetCriterion): | |
| """This class computes the loss for DN-DETR.""" | |
| def forward(self, outputs, targets): | |
| losses = super(DNCriterion, self).forward(outputs, targets) | |
| num_boxes = sum(len(t["labels"]) for t in targets) | |
| 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() | |
| aux_num = 0 | |
| if "aux_outputs" in outputs: | |
| aux_num = len(outputs["aux_outputs"]) | |
| dn_losses = self.calculate_dn_loss(outputs, targets, aux_num, num_boxes) | |
| losses.update(dn_losses) | |
| return losses | |
| def calculate_dn_loss(self, outputs, targets, aux_num, num_boxes): | |
| """ | |
| Calculate dn loss in criterion | |
| """ | |
| losses = {} | |
| if outputs and "denoising_output" in outputs: | |
| denoising_output, denoising_groups, single_padding = ( | |
| outputs["denoising_output"], | |
| outputs["denoising_groups"], | |
| outputs["max_gt_num_per_image"], | |
| ) | |
| dn_idx = [] | |
| for i in range(len(targets)): | |
| if len(targets[i]["labels"]) > 0: | |
| t = torch.arange(0, len(targets[i]["labels"])).long().cuda() | |
| t = t.unsqueeze(0).repeat(denoising_groups, 1) | |
| tgt_idx = t.flatten() | |
| output_idx = ( | |
| torch.tensor(range(denoising_groups)).cuda() * single_padding | |
| ).long().cuda().unsqueeze(1) + t | |
| output_idx = output_idx.flatten() | |
| else: | |
| output_idx = tgt_idx = torch.tensor([]).long().cuda() | |
| dn_idx.append((output_idx, tgt_idx)) | |
| l_dict = {} | |
| for loss in self.losses: | |
| kwargs = {} | |
| if "labels" in loss: | |
| kwargs = {"log": False} | |
| l_dict.update( | |
| self.get_loss( | |
| loss, | |
| denoising_output, | |
| targets, | |
| dn_idx, | |
| num_boxes * denoising_groups, | |
| **kwargs, | |
| ) | |
| ) | |
| l_dict = {k + "_dn": v for k, v in l_dict.items()} | |
| losses.update(l_dict) | |
| else: | |
| losses["loss_bbox_dn"] = torch.as_tensor(0.0).to("cuda") | |
| losses["loss_giou_dn"] = torch.as_tensor(0.0).to("cuda") | |
| losses["loss_class_dn"] = torch.as_tensor(0.0).to("cuda") | |
| for i in range(aux_num): | |
| # dn aux loss | |
| l_dict = {} | |
| if outputs and "denoising_output" in outputs: | |
| denoising_output_aux = denoising_output["aux_outputs"][i] | |
| for loss in self.losses: | |
| kwargs = {} | |
| if "labels" in loss: | |
| kwargs = {"log": False} | |
| l_dict.update( | |
| self.get_loss( | |
| loss, | |
| denoising_output_aux, | |
| targets, | |
| dn_idx, | |
| num_boxes * denoising_groups, | |
| **kwargs, | |
| ) | |
| ) | |
| l_dict = {k + f"_dn_{i}": v for k, v in l_dict.items()} | |
| else: | |
| l_dict["loss_bbox_dn"] = torch.as_tensor(0.0).to("cuda") | |
| l_dict["loss_giou_dn"] = torch.as_tensor(0.0).to("cuda") | |
| l_dict["loss_class_dn"] = torch.as_tensor(0.0).to("cuda") | |
| l_dict = {k + f"_{i}": v for k, v in l_dict.items()} | |
| losses.update(l_dict) | |
| return losses | |