7b89b8fc3f
Differential Revision: D90237984 fbshipit-source-id: 526fd760f303bf31be4f743bdcd77760496de0de
116 lines
5.0 KiB
Python
116 lines
5.0 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved
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# pyre-unsafe
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from typing import Callable
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import torch
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from torch.nn import functional as F
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# Adapted from https://github.com/facebookresearch/detectron2/blob/main/projects/PointRend/point_rend/point_features.py
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def point_sample(input, point_coords, **kwargs):
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"""
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A wrapper around :function:`torch.nn.functional.grid_sample` to support 3D point_coords tensors.
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Unlike :function:`torch.nn.functional.grid_sample` it assumes `point_coords` to lie inside
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[0, 1] x [0, 1] square.
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Args:
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input (Tensor): A tensor of shape (N, C, H, W) that contains features map on a H x W grid.
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point_coords (Tensor): A tensor of shape (N, P, 2) or (N, Hgrid, Wgrid, 2) that contains
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[0, 1] x [0, 1] normalized point coordinates.
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Returns:
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output (Tensor): A tensor of shape (N, C, P) or (N, C, Hgrid, Wgrid) that contains
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features for points in `point_coords`. The features are obtained via bilinear
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interplation from `input` the same way as :function:`torch.nn.functional.grid_sample`.
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"""
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add_dim = False
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if point_coords.dim() == 3:
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add_dim = True
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point_coords = point_coords.unsqueeze(2)
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normalized_point_coords = 2.0 * point_coords - 1.0 # Normalize to [-1,1]
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output = F.grid_sample(input, normalized_point_coords, **kwargs)
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if add_dim:
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output = output.squeeze(3)
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return output
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# Adapted from https://github.com/facebookresearch/detectron2/blob/main/projects/PointRend/point_rend/point_features.py
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def get_uncertain_point_coords_with_randomness(
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logits: torch.Tensor,
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uncertainty_func: Callable,
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num_points: int,
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oversample_ratio: int,
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importance_sample_ratio: float,
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) -> torch.Tensor:
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"""
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Sample points in [0, 1] x [0, 1] coordinate space based on their uncertainty. The unceratinties
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are calculated for each point using 'uncertainty_func' function that takes point's logit
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prediction as input.
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See PointRend paper for details.
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Args:
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logits (Tensor): A tensor of shape (N, C, Hmask, Wmask) or (N, 1, Hmask, Wmask) for
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class-specific or class-agnostic prediction.
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uncertainty_func: A function that takes a Tensor of shape (N, C, P) or (N, 1, P) that
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contains logit predictions for P points and returns their uncertainties as a Tensor of
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shape (N, 1, P).
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num_points (int): The number of points P to sample.
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oversample_ratio (int): Oversampling parameter.
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importance_sample_ratio (float): Ratio of points that are sampled via importnace sampling.
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Returns:
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point_coords (Tensor): A tensor of shape (N, P, 2) that contains the coordinates of P
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sampled points.
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"""
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assert oversample_ratio >= 1
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assert importance_sample_ratio <= 1 and importance_sample_ratio >= 0
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num_boxes = logits.shape[0]
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num_sampled = int(num_points * oversample_ratio)
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point_coords = torch.rand(num_boxes, num_sampled, 2, device=logits.device)
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point_logits = point_sample(logits, point_coords, align_corners=False)
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# It is crucial to calculate uncertainty based on the sampled prediction value for the points.
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# Calculating uncertainties of the predictions first and sampling them for points leads
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# to incorrect results.
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# To illustrate this: assume uncertainty_func(logits)=-abs(logits), a sampled point between
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# two predictions with -1 and 1 logits has 0 logits, and therefore 0 uncertainty value.
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# However, if we calculate uncertainties for the predictions first,
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# both will have -1 uncertainty, and the sampled point will get -1 uncertainty.
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point_uncertainties = uncertainty_func(point_logits)
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num_uncertain_points = int(importance_sample_ratio * num_points)
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num_random_points = num_points - num_uncertain_points
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idx = torch.topk(point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1]
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# Flatten the indices
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shift = num_sampled * torch.arange(
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num_boxes, dtype=torch.long, device=logits.device
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)
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idx += shift[:, None]
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point_coords = point_coords.view(-1, 2)[idx.view(-1), :].view(
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num_boxes, num_uncertain_points, 2
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)
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if num_random_points > 0:
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point_coords = torch.cat(
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[
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point_coords,
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torch.rand(num_boxes, num_random_points, 2, device=logits.device),
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],
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dim=1,
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)
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return point_coords
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# Adapted from https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/criterion.py
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def calculate_uncertainty(logits: torch.Tensor) -> torch.Tensor:
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"""
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Estimates uncerainty as L1 distance between 0.0 and the logit prediction.
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Args:
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logits (Tensor): A tensor of shape (R, 1, ...) for class-agnostic
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predicted masks
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Returns:
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scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with
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the most uncertain locations having the highest uncertainty score.
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"""
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assert logits.shape[1] == 1
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return -(torch.abs(logits))
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