Image Segmentation
Transformers
PyTorch
upernet
Inference Endpoints
test2 / mmseg /core /seg /sampler /ohem_pixel_sampler.py
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import torch
import torch.nn.functional as F
from ..builder import PIXEL_SAMPLERS
from .base_pixel_sampler import BasePixelSampler
@PIXEL_SAMPLERS.register_module()
class OHEMPixelSampler(BasePixelSampler):
"""Online Hard Example Mining Sampler for segmentation.
Args:
context (nn.Module): The context of sampler, subclass of
:obj:`BaseDecodeHead`.
thresh (float, optional): The threshold for hard example selection.
Below which, are prediction with low confidence. If not
specified, the hard examples will be pixels of top ``min_kept``
loss. Default: None.
min_kept (int, optional): The minimum number of predictions to keep.
Default: 100000.
"""
def __init__(self, context, thresh=None, min_kept=100000):
super(OHEMPixelSampler, self).__init__()
self.context = context
assert min_kept > 1
self.thresh = thresh
self.min_kept = min_kept
def sample(self, seg_logit, seg_label):
"""Sample pixels that have high loss or with low prediction confidence.
Args:
seg_logit (torch.Tensor): segmentation logits, shape (N, C, H, W)
seg_label (torch.Tensor): segmentation label, shape (N, 1, H, W)
Returns:
torch.Tensor: segmentation weight, shape (N, H, W)
"""
with torch.no_grad():
assert seg_logit.shape[2:] == seg_label.shape[2:]
assert seg_label.shape[1] == 1
seg_label = seg_label.squeeze(1).long()
batch_kept = self.min_kept * seg_label.size(0)
valid_mask = seg_label != self.context.ignore_index
seg_weight = seg_logit.new_zeros(size=seg_label.size())
valid_seg_weight = seg_weight[valid_mask]
if self.thresh is not None:
seg_prob = F.softmax(seg_logit, dim=1)
tmp_seg_label = seg_label.clone().unsqueeze(1)
tmp_seg_label[tmp_seg_label == self.context.ignore_index] = 0
seg_prob = seg_prob.gather(1, tmp_seg_label).squeeze(1)
sort_prob, sort_indices = seg_prob[valid_mask].sort()
if sort_prob.numel() > 0:
min_threshold = sort_prob[min(batch_kept,
sort_prob.numel() - 1)]
else:
min_threshold = 0.0
threshold = max(min_threshold, self.thresh)
valid_seg_weight[seg_prob[valid_mask] < threshold] = 1.
else:
losses = self.context.loss_decode(
seg_logit,
seg_label,
weight=None,
ignore_index=self.context.ignore_index,
reduction_override='none')
# faster than topk according to https://github.com/pytorch/pytorch/issues/22812 # noqa
_, sort_indices = losses[valid_mask].sort(descending=True)
valid_seg_weight[sort_indices[:batch_kept]] = 1.
seg_weight[valid_mask] = valid_seg_weight
return seg_weight