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import mmcv |
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import torch |
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from mmdet.models.dense_heads import DDODHead |
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def test_ddod_head_loss(): |
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"""Tests ddod 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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assigner=dict(type='ATSSAssigner', topk=9, alpha=0.8), |
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reg_assigner=dict(type='ATSSAssigner', topk=9, alpha=0.5), |
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allowed_border=-1, |
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pos_weight=-1, |
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debug=False)) |
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self = DDODHead( |
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num_classes=4, |
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in_channels=1, |
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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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train_cfg=train_cfg, |
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norm_cfg=dict(type='GN', num_groups=32, requires_grad=True), |
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loss_iou=dict( |
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type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)) |
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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 [4, 8, 16, 32, 64] |
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] |
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cls_scores, bbox_preds, iou_preds = self.forward(feat) |
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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, iou_preds, gt_bboxes, |
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gt_labels, img_metas, gt_bboxes_ignore) |
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empty_cls_loss = sum(empty_gt_losses['loss_cls']) |
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empty_box_loss = sum(empty_gt_losses['loss_bbox']) |
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empty_iou_loss = sum(empty_gt_losses['loss_iou']) |
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assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' |
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assert 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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assert empty_iou_loss.item() == 0, ( |
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'there should be no iou 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, iou_preds, gt_bboxes, |
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gt_labels, img_metas, gt_bboxes_ignore) |
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onegt_cls_loss = sum(one_gt_losses['loss_cls']) |
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onegt_box_loss = sum(one_gt_losses['loss_bbox']) |
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onegt_iou_loss = sum(one_gt_losses['loss_iou']) |
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assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' |
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assert onegt_box_loss.item() > 0, 'box loss should be non-zero' |
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assert onegt_iou_loss.item() > 0, 'iou loss should be non-zero' |
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