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# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmdet.models.dense_heads import YOLOXHead
def test_yolox_head_loss():
"""Tests yolox head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3)
}]
train_cfg = mmcv.Config(
dict(
assigner=dict(
type='SimOTAAssigner',
center_radius=2.5,
candidate_topk=10,
iou_weight=3.0,
cls_weight=1.0)))
self = YOLOXHead(
num_classes=4, in_channels=1, use_depthwise=False, train_cfg=train_cfg)
assert not self.use_l1
assert isinstance(self.multi_level_cls_convs[0][0], ConvModule)
feat = [
torch.rand(1, 1, s // feat_size, s // feat_size)
for feat_size in [4, 8, 16]
]
cls_scores, bbox_preds, objectnesses = self.forward(feat)
# Test that empty ground truth encourages the network to predict background
gt_bboxes = [torch.empty((0, 4))]
gt_labels = [torch.LongTensor([])]
empty_gt_losses = self.loss(cls_scores, bbox_preds, objectnesses,
gt_bboxes, gt_labels, img_metas)
# When there is no truth, the cls loss should be nonzero but there should
# be no box loss.
empty_cls_loss = empty_gt_losses['loss_cls'].sum()
empty_box_loss = empty_gt_losses['loss_bbox'].sum()
empty_obj_loss = empty_gt_losses['loss_obj'].sum()
assert empty_cls_loss.item() == 0, (
'there should be no cls loss when there are no true boxes')
assert empty_box_loss.item() == 0, (
'there should be no box loss when there are no true boxes')
assert empty_obj_loss.item() > 0, 'objectness loss should be non-zero'
# When truth is non-empty then both cls and box loss should be nonzero for
# random inputs
self = YOLOXHead(
num_classes=4, in_channels=1, use_depthwise=True, train_cfg=train_cfg)
assert isinstance(self.multi_level_cls_convs[0][0],
DepthwiseSeparableConvModule)
self.use_l1 = True
gt_bboxes = [
torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
]
gt_labels = [torch.LongTensor([2])]
one_gt_losses = self.loss(cls_scores, bbox_preds, objectnesses, gt_bboxes,
gt_labels, img_metas)
onegt_cls_loss = one_gt_losses['loss_cls'].sum()
onegt_box_loss = one_gt_losses['loss_bbox'].sum()
onegt_obj_loss = one_gt_losses['loss_obj'].sum()
onegt_l1_loss = one_gt_losses['loss_l1'].sum()
assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
assert onegt_obj_loss.item() > 0, 'obj loss should be non-zero'
assert onegt_l1_loss.item() > 0, 'l1 loss should be non-zero'
# Test groud truth out of bound
gt_bboxes = [torch.Tensor([[s * 4, s * 4, s * 4 + 10, s * 4 + 10]])]
gt_labels = [torch.LongTensor([2])]
empty_gt_losses = self.loss(cls_scores, bbox_preds, objectnesses,
gt_bboxes, gt_labels, img_metas)
# When gt_bboxes out of bound, the assign results should be empty,
# so the cls and bbox loss should be zero.
empty_cls_loss = empty_gt_losses['loss_cls'].sum()
empty_box_loss = empty_gt_losses['loss_bbox'].sum()
empty_obj_loss = empty_gt_losses['loss_obj'].sum()
assert empty_cls_loss.item() == 0, (
'there should be no cls loss when gt_bboxes out of bound')
assert empty_box_loss.item() == 0, (
'there should be no box loss when gt_bboxes out of bound')
assert empty_obj_loss.item() > 0, 'objectness loss should be non-zero'
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