local_segment/sam3/train/loss/mask_sampling.py

# Copyright (c) Meta Platforms, Inc. and affiliates. All Rights Reserved

# pyre-unsafe

from typing import Callable

import torch
from torch.nn import functional as F


# Adapted from https://github.com/facebookresearch/detectron2/blob/main/projects/PointRend/point_rend/point_features.py
def point_sample(input, point_coords, **kwargs):
    """
    A wrapper around :function:`torch.nn.functional.grid_sample` to support 3D point_coords tensors.
    Unlike :function:`torch.nn.functional.grid_sample` it assumes `point_coords` to lie inside
    [0, 1] x [0, 1] square.

    Args:
        input (Tensor): A tensor of shape (N, C, H, W) that contains features map on a H x W grid.
        point_coords (Tensor): A tensor of shape (N, P, 2) or (N, Hgrid, Wgrid, 2) that contains
        [0, 1] x [0, 1] normalized point coordinates.

    Returns:
        output (Tensor): A tensor of shape (N, C, P) or (N, C, Hgrid, Wgrid) that contains
            features for points in `point_coords`. The features are obtained via bilinear
            interplation from `input` the same way as :function:`torch.nn.functional.grid_sample`.
    """
    add_dim = False
    if point_coords.dim() == 3:
        add_dim = True
        point_coords = point_coords.unsqueeze(2)
    normalized_point_coords = 2.0 * point_coords - 1.0  # Normalize to [-1,1]
    output = F.grid_sample(input, normalized_point_coords, **kwargs)
    if add_dim:
        output = output.squeeze(3)
    return output


# Adapted from https://github.com/facebookresearch/detectron2/blob/main/projects/PointRend/point_rend/point_features.py
def get_uncertain_point_coords_with_randomness(
    logits: torch.Tensor,
    uncertainty_func: Callable,
    num_points: int,
    oversample_ratio: int,
    importance_sample_ratio: float,
) -> torch.Tensor:
    """
    Sample points in [0, 1] x [0, 1] coordinate space based on their uncertainty. The unceratinties
        are calculated for each point using 'uncertainty_func' function that takes point's logit
        prediction as input.
    See PointRend paper for details.

    Args:
        logits (Tensor): A tensor of shape (N, C, Hmask, Wmask) or (N, 1, Hmask, Wmask) for
            class-specific or class-agnostic prediction.
        uncertainty_func: A function that takes a Tensor of shape (N, C, P) or (N, 1, P) that
            contains logit predictions for P points and returns their uncertainties as a Tensor of
            shape (N, 1, P).
        num_points (int): The number of points P to sample.
        oversample_ratio (int): Oversampling parameter.
        importance_sample_ratio (float): Ratio of points that are sampled via importnace sampling.

    Returns:
        point_coords (Tensor): A tensor of shape (N, P, 2) that contains the coordinates of P
            sampled points.
    """
    assert oversample_ratio >= 1
    assert importance_sample_ratio <= 1 and importance_sample_ratio >= 0
    num_boxes = logits.shape[0]
    num_sampled = int(num_points * oversample_ratio)
    point_coords = torch.rand(num_boxes, num_sampled, 2, device=logits.device)
    point_logits = point_sample(logits, point_coords, align_corners=False)
    # It is crucial to calculate uncertainty based on the sampled prediction value for the points.
    # Calculating uncertainties of the 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 predictions with -1 and 1 logits has 0 logits, and therefore 0 uncertainty value.
    # However, if we calculate uncertainties for the predictions first,
    # both will have -1 uncertainty, and the sampled point will get -1 uncertainty.
    point_uncertainties = uncertainty_func(point_logits)
    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]
    # Flatten the indices
    shift = num_sampled * torch.arange(
        num_boxes, dtype=torch.long, device=logits.device
    )
    idx += shift[:, None]
    point_coords = point_coords.view(-1, 2)[idx.view(-1), :].view(
        num_boxes, num_uncertain_points, 2
    )
    if num_random_points > 0:
        point_coords = torch.cat(
            [
                point_coords,
                torch.rand(num_boxes, num_random_points, 2, device=logits.device),
            ],
            dim=1,
        )
    return point_coords


# Adapted from https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/criterion.py
def calculate_uncertainty(logits: torch.Tensor) -> torch.Tensor:
    """
    Estimates uncerainty as L1 distance between 0.0 and the logit prediction.
    Args:
        logits (Tensor): A tensor of shape (R, 1, ...) for class-agnostic
        predicted masks
    Returns:
        scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with
            the most uncertain locations having the highest uncertainty score.
    """
    assert logits.shape[1] == 1
    return -(torch.abs(logits))