| import torch
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| import numpy as np
|
|
|
| from ..utils import misc
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|
|
|
|
| class TrainMetric(object):
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| def __init__(self, pred_outputs, gt_outputs):
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| self.pred_outputs = pred_outputs
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| self.gt_outputs = gt_outputs
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|
|
| def update(self, *args, **kwargs):
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| raise NotImplementedError
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|
|
| def get_epoch_value(self):
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| raise NotImplementedError
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|
|
| def reset_epoch_stats(self):
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| raise NotImplementedError
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|
|
| def log_states(self, sw, tag_prefix, global_step):
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| pass
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|
|
| @property
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| def name(self):
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| return type(self).__name__
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|
|
|
|
| class AdaptiveIoU(TrainMetric):
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| def __init__(self, init_thresh=0.4, thresh_step=0.025, thresh_beta=0.99, iou_beta=0.9,
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| ignore_label=-1, from_logits=True,
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| pred_output='instances', gt_output='instances'):
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| super().__init__(pred_outputs=(pred_output,), gt_outputs=(gt_output,))
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| self._ignore_label = ignore_label
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| self._from_logits = from_logits
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| self._iou_thresh = init_thresh
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| self._thresh_step = thresh_step
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| self._thresh_beta = thresh_beta
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| self._iou_beta = iou_beta
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| self._ema_iou = 0.0
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| self._epoch_iou_sum = 0.0
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| self._epoch_batch_count = 0
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|
|
| def update(self, pred, gt):
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| gt_mask = gt > 0
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| if self._from_logits:
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| pred = torch.sigmoid(pred)
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|
|
| gt_mask_area = torch.sum(gt_mask, dim=(1, 2)).detach().cpu().numpy()
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| if np.all(gt_mask_area == 0):
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| return
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|
|
| ignore_mask = gt == self._ignore_label
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| max_iou = _compute_iou(pred > self._iou_thresh, gt_mask, ignore_mask).mean()
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| best_thresh = self._iou_thresh
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| for t in [best_thresh - self._thresh_step, best_thresh + self._thresh_step]:
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| temp_iou = _compute_iou(pred > t, gt_mask, ignore_mask).mean()
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| if temp_iou > max_iou:
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| max_iou = temp_iou
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| best_thresh = t
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|
|
| self._iou_thresh = self._thresh_beta * self._iou_thresh + (1 - self._thresh_beta) * best_thresh
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| self._ema_iou = self._iou_beta * self._ema_iou + (1 - self._iou_beta) * max_iou
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| self._epoch_iou_sum += max_iou
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| self._epoch_batch_count += 1
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|
|
| def get_epoch_value(self):
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| if self._epoch_batch_count > 0:
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| return self._epoch_iou_sum / self._epoch_batch_count
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| else:
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| return 0.0
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|
|
| def reset_epoch_stats(self):
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| self._epoch_iou_sum = 0.0
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| self._epoch_batch_count = 0
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|
|
| def log_states(self, sw, tag_prefix, global_step):
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| sw.add_scalar(tag=tag_prefix + '_ema_iou', value=self._ema_iou, global_step=global_step)
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| sw.add_scalar(tag=tag_prefix + '_iou_thresh', value=self._iou_thresh, global_step=global_step)
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|
|
| @property
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| def iou_thresh(self):
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| return self._iou_thresh
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|
|
|
|
| def _compute_iou(pred_mask, gt_mask, ignore_mask=None, keep_ignore=False):
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| if ignore_mask is not None:
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| pred_mask = torch.where(ignore_mask, torch.zeros_like(pred_mask), pred_mask)
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|
|
| reduction_dims = misc.get_dims_with_exclusion(gt_mask.dim(), 0)
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| union = torch.mean((pred_mask | gt_mask).float(), dim=reduction_dims).detach().cpu().numpy()
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| intersection = torch.mean((pred_mask & gt_mask).float(), dim=reduction_dims).detach().cpu().numpy()
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| nonzero = union > 0
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|
|
| iou = intersection[nonzero] / union[nonzero]
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| if not keep_ignore:
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| return iou
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| else:
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| result = np.full_like(intersection, -1)
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| result[nonzero] = iou
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| return result
|
|
|
