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import mmcv |
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import torch |
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from mmdet.models.dense_heads import TOODHead |
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def test_tood_head_loss(): |
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"""Tests paa head loss when truth is empty and non-empty.""" |
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s = 256 |
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img_metas = [{ |
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'img_shape': (s, s, 3), |
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'scale_factor': 1, |
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'pad_shape': (s, s, 3) |
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}] |
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train_cfg = mmcv.Config( |
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dict( |
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initial_epoch=4, |
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initial_assigner=dict(type='ATSSAssigner', topk=9), |
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assigner=dict(type='TaskAlignedAssigner', topk=13), |
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alpha=1, |
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beta=6, |
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allowed_border=-1, |
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pos_weight=-1, |
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debug=False)) |
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test_cfg = mmcv.Config( |
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dict( |
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nms_pre=1000, |
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min_bbox_size=0, |
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score_thr=0.05, |
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nms=dict(type='nms', iou_threshold=0.6), |
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max_per_img=100)) |
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self = TOODHead( |
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num_classes=80, |
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in_channels=1, |
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stacked_convs=6, |
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feat_channels=256, |
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anchor_type='anchor_free', |
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anchor_generator=dict( |
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type='AnchorGenerator', |
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ratios=[1.0], |
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octave_base_scale=8, |
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scales_per_octave=1, |
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strides=[8, 16, 32, 64, 128]), |
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bbox_coder=dict( |
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type='DeltaXYWHBBoxCoder', |
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target_means=[.0, .0, .0, .0], |
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target_stds=[0.1, 0.1, 0.2, 0.2]), |
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initial_loss_cls=dict( |
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type='FocalLoss', |
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use_sigmoid=True, |
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activated=True, |
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gamma=2.0, |
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alpha=0.25, |
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loss_weight=1.0), |
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loss_cls=dict( |
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type='QualityFocalLoss', |
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use_sigmoid=True, |
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activated=True, |
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beta=2.0, |
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loss_weight=1.0), |
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loss_bbox=dict(type='GIoULoss', loss_weight=2.0), |
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train_cfg=train_cfg, |
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test_cfg=test_cfg) |
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self.init_weights() |
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feat = [ |
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torch.rand(1, 1, s // feat_size, s // feat_size) |
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for feat_size in [8, 16, 32, 64, 128] |
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] |
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cls_scores, bbox_preds = self(feat) |
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self.epoch = 0 |
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gt_bboxes = [torch.empty((0, 4))] |
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gt_labels = [torch.LongTensor([])] |
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gt_bboxes_ignore = None |
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empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, |
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img_metas, gt_bboxes_ignore) |
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empty_cls_loss = empty_gt_losses['loss_cls'] |
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empty_box_loss = empty_gt_losses['loss_bbox'] |
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assert sum(empty_cls_loss).item() > 0, 'cls loss should be non-zero' |
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assert sum(empty_box_loss).item() == 0, ( |
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'there should be no box loss when there are no true boxes') |
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gt_bboxes = [ |
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torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), |
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] |
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gt_labels = [torch.LongTensor([2])] |
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one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, |
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img_metas, gt_bboxes_ignore) |
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onegt_cls_loss = one_gt_losses['loss_cls'] |
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onegt_box_loss = one_gt_losses['loss_bbox'] |
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assert sum(onegt_cls_loss).item() > 0, 'cls loss should be non-zero' |
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assert sum(onegt_box_loss).item() > 0, 'box loss should be non-zero' |
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self.epoch = 10 |
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gt_bboxes = [torch.empty((0, 4))] |
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gt_labels = [torch.LongTensor([])] |
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gt_bboxes_ignore = None |
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empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, |
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img_metas, gt_bboxes_ignore) |
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empty_cls_loss = empty_gt_losses['loss_cls'] |
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empty_box_loss = empty_gt_losses['loss_bbox'] |
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assert sum(empty_cls_loss).item() > 0, 'cls loss should be non-zero' |
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assert sum(empty_box_loss).item() == 0, ( |
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'there should be no box loss when there are no true boxes') |
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gt_bboxes = [ |
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torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), |
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] |
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gt_labels = [torch.LongTensor([2])] |
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one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, |
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img_metas, gt_bboxes_ignore) |
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onegt_cls_loss = one_gt_losses['loss_cls'] |
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onegt_box_loss = one_gt_losses['loss_bbox'] |
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assert sum(onegt_cls_loss).item() > 0, 'cls loss should be non-zero' |
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assert sum(onegt_box_loss).item() > 0, 'box loss should be non-zero' |
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