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import mmcv |
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import torch |
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from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule |
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from mmdet.models.dense_heads import YOLOXHead |
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def test_yolox_head_loss(): |
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"""Tests yolox 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( |
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type='SimOTAAssigner', |
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center_radius=2.5, |
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candidate_topk=10, |
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iou_weight=3.0, |
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cls_weight=1.0))) |
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self = YOLOXHead( |
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num_classes=4, in_channels=1, use_depthwise=False, train_cfg=train_cfg) |
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assert not self.use_l1 |
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assert isinstance(self.multi_level_cls_convs[0][0], ConvModule) |
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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] |
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] |
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cls_scores, bbox_preds, objectnesses = 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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empty_gt_losses = self.loss(cls_scores, bbox_preds, objectnesses, |
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gt_bboxes, gt_labels, img_metas) |
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empty_cls_loss = empty_gt_losses['loss_cls'].sum() |
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empty_box_loss = empty_gt_losses['loss_bbox'].sum() |
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empty_obj_loss = empty_gt_losses['loss_obj'].sum() |
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assert empty_cls_loss.item() == 0, ( |
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'there should be no cls loss when there are no true boxes') |
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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_obj_loss.item() > 0, 'objectness loss should be non-zero' |
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self = YOLOXHead( |
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num_classes=4, in_channels=1, use_depthwise=True, train_cfg=train_cfg) |
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assert isinstance(self.multi_level_cls_convs[0][0], |
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DepthwiseSeparableConvModule) |
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self.use_l1 = True |
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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, objectnesses, gt_bboxes, |
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gt_labels, img_metas) |
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onegt_cls_loss = one_gt_losses['loss_cls'].sum() |
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onegt_box_loss = one_gt_losses['loss_bbox'].sum() |
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onegt_obj_loss = one_gt_losses['loss_obj'].sum() |
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onegt_l1_loss = one_gt_losses['loss_l1'].sum() |
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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_obj_loss.item() > 0, 'obj loss should be non-zero' |
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assert onegt_l1_loss.item() > 0, 'l1 loss should be non-zero' |
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gt_bboxes = [torch.Tensor([[s * 4, s * 4, s * 4 + 10, s * 4 + 10]])] |
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gt_labels = [torch.LongTensor([2])] |
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empty_gt_losses = self.loss(cls_scores, bbox_preds, objectnesses, |
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gt_bboxes, gt_labels, img_metas) |
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empty_cls_loss = empty_gt_losses['loss_cls'].sum() |
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empty_box_loss = empty_gt_losses['loss_bbox'].sum() |
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empty_obj_loss = empty_gt_losses['loss_obj'].sum() |
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assert empty_cls_loss.item() == 0, ( |
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'there should be no cls loss when gt_bboxes out of bound') |
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assert empty_box_loss.item() == 0, ( |
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'there should be no box loss when gt_bboxes out of bound') |
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assert empty_obj_loss.item() > 0, 'objectness loss should be non-zero' |
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