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# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.ops import point_sample
from torch import Tensor
def get_uncertainty(mask_preds: Tensor, labels: Tensor) -> Tensor:
"""Estimate uncertainty based on pred logits.
We estimate uncertainty as L1 distance between 0.0 and the logits
prediction in 'mask_preds' for the foreground class in `classes`.
Args:
mask_preds (Tensor): mask predication logits, shape (num_rois,
num_classes, mask_height, mask_width).
labels (Tensor): Either predicted or ground truth label for
each predicted mask, of length num_rois.
Returns:
scores (Tensor): Uncertainty scores with the most uncertain
locations having the highest uncertainty score,
shape (num_rois, 1, mask_height, mask_width)
"""
if mask_preds.shape[1] == 1:
gt_class_logits = mask_preds.clone()
else:
inds = torch.arange(mask_preds.shape[0], device=mask_preds.device)
gt_class_logits = mask_preds[inds, labels].unsqueeze(1)
return -torch.abs(gt_class_logits)
def get_uncertain_point_coords_with_randomness(
mask_preds: Tensor, labels: Tensor, num_points: int,
oversample_ratio: float, importance_sample_ratio: float) -> Tensor:
"""Get ``num_points`` most uncertain points with random points during
train.
Sample points in [0, 1] x [0, 1] coordinate space based on their
uncertainty. The uncertainties are calculated for each point using
'get_uncertainty()' function that takes point's logit prediction as
input.
Args:
mask_preds (Tensor): A tensor of shape (num_rois, num_classes,
mask_height, mask_width) for class-specific or class-agnostic
prediction.
labels (Tensor): The ground truth class for each instance.
num_points (int): The number of points to sample.
oversample_ratio (float): Oversampling parameter.
importance_sample_ratio (float): Ratio of points that are sampled
via importnace sampling.
Returns:
point_coords (Tensor): A tensor of shape (num_rois, num_points, 2)
that contains the coordinates sampled points.
"""
assert oversample_ratio >= 1
assert 0 <= importance_sample_ratio <= 1
batch_size = mask_preds.shape[0]
num_sampled = int(num_points * oversample_ratio)
point_coords = torch.rand(
batch_size, num_sampled, 2, device=mask_preds.device)
point_logits = point_sample(mask_preds, point_coords)
# It is crucial to calculate uncertainty based on the sampled
# prediction value for the points. Calculating uncertainties of the
# coarse predictions first and sampling them for points leads to
# incorrect results. To illustrate this: assume uncertainty func(
# logits)=-abs(logits), a sampled point between two coarse
# predictions with -1 and 1 logits has 0 logits, and therefore 0
# uncertainty value. However, if we calculate uncertainties for the
# coarse predictions first, both will have -1 uncertainty,
# and sampled point will get -1 uncertainty.
point_uncertainties = get_uncertainty(point_logits, labels)
num_uncertain_points = int(importance_sample_ratio * num_points)
num_random_points = num_points - num_uncertain_points
idx = torch.topk(
point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1]
shift = num_sampled * torch.arange(
batch_size, dtype=torch.long, device=mask_preds.device)
idx += shift[:, None]
point_coords = point_coords.view(-1, 2)[idx.view(-1), :].view(
batch_size, num_uncertain_points, 2)
if num_random_points > 0:
rand_roi_coords = torch.rand(
batch_size, num_random_points, 2, device=mask_preds.device)
point_coords = torch.cat((point_coords, rand_roi_coords), dim=1)
return point_coords
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