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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.utils import get_world_size, is_dist_avail_and_initialized
from .two_stage_criterion import TwoStageCriterion
class DINOCriterion(TwoStageCriterion):
"""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 forward(self, outputs, targets, dn_metas=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
"""
losses = super(DINOCriterion, self).forward(outputs, targets)
# import pdb;pdb.set_trace()
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()
# Compute all the requested losses
aux_num = 0
if "aux_outputs" in outputs:
aux_num = len(outputs["aux_outputs"])
dn_losses = self.compute_dn_loss(dn_metas, targets, aux_num, num_boxes)
losses.update(dn_losses)
return losses
def compute_dn_loss(self, dn_metas, targets, aux_num, num_boxes):
"""
compute dn loss in criterion
Args:
dn_metas: a dict for dn information
training: training or inference flag
aux_num: aux loss number
focal_alpha: for focal loss
"""
losses = {}
if dn_metas and "output_known_lbs_bboxes" in dn_metas:
output_known_lbs_bboxes, dn_num, single_padding = (
dn_metas["output_known_lbs_bboxes"],
dn_metas["dn_num"],
dn_metas["single_padding"],
)
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(dn_num, 1)
tgt_idx = t.flatten()
output_idx = (
torch.tensor(range(dn_num)) * 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, output_known_lbs_bboxes, targets, dn_idx, num_boxes * dn_num, **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 dn_metas and "output_known_lbs_bboxes" in dn_metas:
output_known_lbs_bboxes_aux = output_known_lbs_bboxes["aux_outputs"][i]
for loss in self.losses:
kwargs = {}
if "labels" in loss:
kwargs = {"log": False}
l_dict.update(
self.get_loss(
loss,
output_known_lbs_bboxes_aux,
targets,
dn_idx,
num_boxes * dn_num,
**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
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