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# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import VFNetHead
def test_vfnet_head_loss():
"""Tests vfnet 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='ATSSAssigner', topk=9),
allowed_border=-1,
pos_weight=-1,
debug=False))
# since Focal Loss is not supported on CPU
self = VFNetHead(
num_classes=4,
in_channels=1,
train_cfg=train_cfg,
loss_cls=dict(type='VarifocalLoss', use_sigmoid=True, loss_weight=1.0))
if torch.cuda.is_available():
self.cuda()
feat = [
torch.rand(1, 1, s // feat_size, s // feat_size).cuda()
for feat_size in [4, 8, 16, 32, 64]
]
cls_scores, bbox_preds, bbox_preds_refine = self.forward(feat)
# Test that empty ground truth encourages the network to predict
# background
gt_bboxes = [torch.empty((0, 4)).cuda()]
gt_labels = [torch.LongTensor([]).cuda()]
gt_bboxes_ignore = None
empty_gt_losses = self.loss(cls_scores, bbox_preds, bbox_preds_refine,
gt_bboxes, gt_labels, img_metas,
gt_bboxes_ignore)
# 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']
empty_box_loss = empty_gt_losses['loss_bbox']
assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
assert empty_box_loss.item() == 0, (
'there should be no box loss when there are no true boxes')
# When truth is non-empty then both cls and box loss should be nonzero
# for random inputs
gt_bboxes = [
torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]).cuda(),
]
gt_labels = [torch.LongTensor([2]).cuda()]
one_gt_losses = self.loss(cls_scores, bbox_preds, bbox_preds_refine,
gt_bboxes, gt_labels, img_metas,
gt_bboxes_ignore)
onegt_cls_loss = one_gt_losses['loss_cls']
onegt_box_loss = one_gt_losses['loss_bbox']
assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
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