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# Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_SAMPLERS
from .base_sampler import BaseSampler
@BBOX_SAMPLERS.register_module()
class RandomSampler(BaseSampler):
"""Random sampler.
Args:
num (int): Number of samples
pos_fraction (float): Fraction of positive samples
neg_pos_ub (int, optional): Upper bound number of negative and
positive samples. Defaults to -1.
add_gt_as_proposals (bool, optional): Whether to add ground truth
boxes as proposals. Defaults to True.
"""
def __init__(self,
num,
pos_fraction,
neg_pos_ub=-1,
add_gt_as_proposals=True,
**kwargs):
from mmdet.core.bbox import demodata
super(RandomSampler, self).__init__(num, pos_fraction, neg_pos_ub,
add_gt_as_proposals)
self.rng = demodata.ensure_rng(kwargs.get('rng', None))
def random_choice(self, gallery, num):
"""Random select some elements from the gallery.
If `gallery` is a Tensor, the returned indices will be a Tensor;
If `gallery` is a ndarray or list, the returned indices will be a
ndarray.
Args:
gallery (Tensor | ndarray | list): indices pool.
num (int): expected sample num.
Returns:
Tensor or ndarray: sampled indices.
"""
assert len(gallery) >= num
is_tensor = isinstance(gallery, torch.Tensor)
if not is_tensor:
if torch.cuda.is_available():
device = torch.cuda.current_device()
else:
device = 'cpu'
gallery = torch.tensor(gallery, dtype=torch.long, device=device)
# This is a temporary fix. We can revert the following code
# when PyTorch fixes the abnormal return of torch.randperm.
# See: https://github.com/open-mmlab/mmdetection/pull/5014
perm = torch.randperm(gallery.numel())[:num].to(device=gallery.device)
rand_inds = gallery[perm]
if not is_tensor:
rand_inds = rand_inds.cpu().numpy()
return rand_inds
def _sample_pos(self, assign_result, num_expected, **kwargs):
"""Randomly sample some positive samples."""
pos_inds = torch.nonzero(assign_result.gt_inds > 0, as_tuple=False)
if pos_inds.numel() != 0:
pos_inds = pos_inds.squeeze(1)
if pos_inds.numel() <= num_expected:
return pos_inds
else:
return self.random_choice(pos_inds, num_expected)
def _sample_neg(self, assign_result, num_expected, **kwargs):
"""Randomly sample some negative samples."""
neg_inds = torch.nonzero(assign_result.gt_inds == 0, as_tuple=False)
if neg_inds.numel() != 0:
neg_inds = neg_inds.squeeze(1)
if len(neg_inds) <= num_expected:
return neg_inds
else:
return self.random_choice(neg_inds, num_expected)
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