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import numpy as np
import torch
from isegm.utils import misc
class TrainMetric(object):
def __init__(self, pred_outputs, gt_outputs):
self.pred_outputs = pred_outputs
self.gt_outputs = gt_outputs
def update(self, *args, **kwargs):
raise NotImplementedError
def get_epoch_value(self):
raise NotImplementedError
def reset_epoch_stats(self):
raise NotImplementedError
def log_states(self, sw, tag_prefix, global_step):
pass
@property
def name(self):
return type(self).__name__
class AdaptiveIoU(TrainMetric):
def __init__(
self,
init_thresh=0.4,
thresh_step=0.025,
thresh_beta=0.99,
iou_beta=0.9,
ignore_label=-1,
from_logits=True,
pred_output="instances",
gt_output="instances",
):
super().__init__(pred_outputs=(pred_output,), gt_outputs=(gt_output,))
self._ignore_label = ignore_label
self._from_logits = from_logits
self._iou_thresh = init_thresh
self._thresh_step = thresh_step
self._thresh_beta = thresh_beta
self._iou_beta = iou_beta
self._ema_iou = 0.0
self._epoch_iou_sum = 0.0
self._epoch_batch_count = 0
def update(self, pred, gt):
gt_mask = gt > 0.5
if self._from_logits:
pred = torch.sigmoid(pred)
gt_mask_area = torch.sum(gt_mask, dim=(1, 2)).detach().cpu().numpy()
if np.all(gt_mask_area == 0):
return
ignore_mask = gt == self._ignore_label
max_iou = _compute_iou(pred > self._iou_thresh, gt_mask, ignore_mask).mean()
best_thresh = self._iou_thresh
for t in [best_thresh - self._thresh_step, best_thresh + self._thresh_step]:
temp_iou = _compute_iou(pred > t, gt_mask, ignore_mask).mean()
if temp_iou > max_iou:
max_iou = temp_iou
best_thresh = t
self._iou_thresh = (
self._thresh_beta * self._iou_thresh + (1 - self._thresh_beta) * best_thresh
)
self._ema_iou = self._iou_beta * self._ema_iou + (1 - self._iou_beta) * max_iou
self._epoch_iou_sum += max_iou
self._epoch_batch_count += 1
def get_epoch_value(self):
if self._epoch_batch_count > 0:
return self._epoch_iou_sum / self._epoch_batch_count
else:
return 0.0
def reset_epoch_stats(self):
self._epoch_iou_sum = 0.0
self._epoch_batch_count = 0
def log_states(self, sw, tag_prefix, global_step):
sw.add_scalar(
tag=tag_prefix + "_ema_iou", value=self._ema_iou, global_step=global_step
)
sw.add_scalar(
tag=tag_prefix + "_iou_thresh",
value=self._iou_thresh,
global_step=global_step,
)
@property
def iou_thresh(self):
return self._iou_thresh
def _compute_iou(pred_mask, gt_mask, ignore_mask=None, keep_ignore=False):
if ignore_mask is not None:
pred_mask = torch.where(ignore_mask, torch.zeros_like(pred_mask), pred_mask)
reduction_dims = misc.get_dims_with_exclusion(gt_mask.dim(), 0)
union = (
torch.mean((pred_mask | gt_mask).float(), dim=reduction_dims)
.detach()
.cpu()
.numpy()
)
intersection = (
torch.mean((pred_mask & gt_mask).float(), dim=reduction_dims)
.detach()
.cpu()
.numpy()
)
nonzero = union > 0
iou = intersection[nonzero] / union[nonzero]
if not keep_ignore:
return iou
else:
result = np.full_like(intersection, -1)
result[nonzero] = iou
return result
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