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according to the src\lib\dataset\generic_dataset.py code: hm ->ct_int ret['reg'][k] = ct - ct_int ret['amodel_offset'][k] = amodel_center - ct_int
so, in inference, should be: ct_int: hm ct: ret['reg'][k] + ct_int = ret['reg'][k] + hm amodel_center: ret['amodel_offset'][k] + ct_int = ret['amodel_offset'][k] + hm
in training, reg value is not used when calc amodel_offset. But in inference, when calc bboxes, the code used reg values. So there is a gap??
if 'amodel_offset' in dets and len(dets['amodel_offset'][i]) > j: ct_output = dets['bboxes'][i][j].reshape(2, 2).mean(axis=0) amodel_ct_output = ct_output + dets['amodel_offset'][i][j] ct = transform_preds_with_trans( amodel_ct_output.reshape(1, 2), trans).reshape(2).tolist()
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according to the src\lib\dataset\generic_dataset.py code:
hm ->ct_int
ret['reg'][k] = ct - ct_int
ret['amodel_offset'][k] = amodel_center - ct_int
so, in inference, should be:
ct_int: hm
ct: ret['reg'][k] + ct_int = ret['reg'][k] + hm
amodel_center: ret['amodel_offset'][k] + ct_int = ret['amodel_offset'][k] + hm
in training, reg value is not used when calc amodel_offset.
But in inference, when calc bboxes, the code used reg values.
So there is a gap??
The text was updated successfully, but these errors were encountered: