RE-OWOD/projects/PointRend/point_rend/roi_heads.py

# -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import numpy as np
import torch

from detectron2.layers import ShapeSpec, cat, interpolate
from detectron2.modeling import ROI_HEADS_REGISTRY, StandardROIHeads
from detectron2.modeling.roi_heads.mask_head import (
    build_mask_head,
    mask_rcnn_inference,
    mask_rcnn_loss,
)
from detectron2.modeling.roi_heads.roi_heads import select_foreground_proposals

from .point_features import (
    generate_regular_grid_point_coords,
    get_uncertain_point_coords_on_grid,
    get_uncertain_point_coords_with_randomness,
    point_sample,
    point_sample_fine_grained_features,
)
from .point_head import build_point_head, roi_mask_point_loss


def calculate_uncertainty(logits, classes):
    """
    We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the
        foreground class in `classes`.

    Args:
        logits (Tensor): A tensor of shape (R, C, ...) or (R, 1, ...) for class-specific or
            class-agnostic, where R is the total number of predicted masks in all images and C is
            the number of foreground classes. The values are logits.
        classes (list): A list of length R that contains either predicted of ground truth class
            for eash predicted mask.

    Returns:
        scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with
            the most uncertain locations having the highest uncertainty score.
    """
    if logits.shape[1] == 1:
        gt_class_logits = logits.clone()
    else:
        gt_class_logits = logits[
            torch.arange(logits.shape[0], device=logits.device), classes
        ].unsqueeze(1)
    return -(torch.abs(gt_class_logits))


@ROI_HEADS_REGISTRY.register()
class PointRendROIHeads(StandardROIHeads):
    """
    The RoI heads class for PointRend instance segmentation models.

    In this class we redefine the mask head of `StandardROIHeads` leaving all other heads intact.
    To avoid namespace conflict with other heads we use names starting from `mask_` for all
    variables that correspond to the mask head in the class's namespace.
    """

    def __init__(self, cfg, input_shape):
        # TODO use explicit args style
        super().__init__(cfg, input_shape)
        self._init_mask_head(cfg, input_shape)

    def _init_mask_head(self, cfg, input_shape):
        # fmt: off
        self.mask_on                 = cfg.MODEL.MASK_ON
        if not self.mask_on:
            return
        self.mask_coarse_in_features = cfg.MODEL.ROI_MASK_HEAD.IN_FEATURES
        self.mask_coarse_side_size   = cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION
        self._feature_scales         = {k: 1.0 / v.stride for k, v in input_shape.items()}
        # fmt: on

        in_channels = np.sum([input_shape[f].channels for f in self.mask_coarse_in_features])
        self.mask_coarse_head = build_mask_head(
            cfg,
            ShapeSpec(
                channels=in_channels,
                width=self.mask_coarse_side_size,
                height=self.mask_coarse_side_size,
            ),
        )
        self._init_point_head(cfg, input_shape)

    def _init_point_head(self, cfg, input_shape):
        # fmt: off
        self.mask_point_on                      = cfg.MODEL.ROI_MASK_HEAD.POINT_HEAD_ON
        if not self.mask_point_on:
            return
        assert cfg.MODEL.ROI_HEADS.NUM_CLASSES == cfg.MODEL.POINT_HEAD.NUM_CLASSES
        self.mask_point_in_features             = cfg.MODEL.POINT_HEAD.IN_FEATURES
        self.mask_point_train_num_points        = cfg.MODEL.POINT_HEAD.TRAIN_NUM_POINTS
        self.mask_point_oversample_ratio        = cfg.MODEL.POINT_HEAD.OVERSAMPLE_RATIO
        self.mask_point_importance_sample_ratio = cfg.MODEL.POINT_HEAD.IMPORTANCE_SAMPLE_RATIO
        # next two parameters are use in the adaptive subdivions inference procedure
        self.mask_point_subdivision_steps       = cfg.MODEL.POINT_HEAD.SUBDIVISION_STEPS
        self.mask_point_subdivision_num_points  = cfg.MODEL.POINT_HEAD.SUBDIVISION_NUM_POINTS
        # fmt: on

        in_channels = np.sum([input_shape[f].channels for f in self.mask_point_in_features])
        self.mask_point_head = build_point_head(
            cfg, ShapeSpec(channels=in_channels, width=1, height=1)
        )

    def _forward_mask(self, features, instances):
        """
        Forward logic of the mask prediction branch.

        Args:
            features (dict[str, Tensor]): #level input features for mask prediction
            instances (list[Instances]): the per-image instances to train/predict masks.
                In training, they can be the proposals.
                In inference, they can be the predicted boxes.

        Returns:
            In training, a dict of losses.
            In inference, update `instances` with new fields "pred_masks" and return it.
        """
        if not self.mask_on:
            return {} if self.training else instances

        if self.training:
            proposals, _ = select_foreground_proposals(instances, self.num_classes)
            proposal_boxes = [x.proposal_boxes for x in proposals]
            mask_coarse_logits = self._forward_mask_coarse(features, proposal_boxes)

            losses = {"loss_mask": mask_rcnn_loss(mask_coarse_logits, proposals)}
            losses.update(self._forward_mask_point(features, mask_coarse_logits, proposals))
            return losses
        else:
            pred_boxes = [x.pred_boxes for x in instances]
            mask_coarse_logits = self._forward_mask_coarse(features, pred_boxes)

            mask_logits = self._forward_mask_point(features, mask_coarse_logits, instances)
            mask_rcnn_inference(mask_logits, instances)
            return instances

    def _forward_mask_coarse(self, features, boxes):
        """
        Forward logic of the coarse mask head.
        """
        point_coords = generate_regular_grid_point_coords(
            np.sum(len(x) for x in boxes), self.mask_coarse_side_size, boxes[0].device
        )
        mask_coarse_features_list = [features[k] for k in self.mask_coarse_in_features]
        features_scales = [self._feature_scales[k] for k in self.mask_coarse_in_features]
        # For regular grids of points, this function is equivalent to `len(features_list)' calls
        # of `ROIAlign` (with `SAMPLING_RATIO=2`), and concat the results.
        mask_features, _ = point_sample_fine_grained_features(
            mask_coarse_features_list, features_scales, boxes, point_coords
        )
        return self.mask_coarse_head(mask_features)

    def _forward_mask_point(self, features, mask_coarse_logits, instances):
        """
        Forward logic of the mask point head.
        """
        if not self.mask_point_on:
            return {} if self.training else mask_coarse_logits

        mask_features_list = [features[k] for k in self.mask_point_in_features]
        features_scales = [self._feature_scales[k] for k in self.mask_point_in_features]

        if self.training:
            proposal_boxes = [x.proposal_boxes for x in instances]
            gt_classes = cat([x.gt_classes for x in instances])
            with torch.no_grad():
                point_coords = get_uncertain_point_coords_with_randomness(
                    mask_coarse_logits,
                    lambda logits: calculate_uncertainty(logits, gt_classes),
                    self.mask_point_train_num_points,
                    self.mask_point_oversample_ratio,
                    self.mask_point_importance_sample_ratio,
                )

            fine_grained_features, point_coords_wrt_image = point_sample_fine_grained_features(
                mask_features_list, features_scales, proposal_boxes, point_coords
            )
            coarse_features = point_sample(mask_coarse_logits, point_coords, align_corners=False)
            point_logits = self.mask_point_head(fine_grained_features, coarse_features)
            return {
                "loss_mask_point": roi_mask_point_loss(
                    point_logits, instances, point_coords_wrt_image
                )
            }
        else:
            pred_boxes = [x.pred_boxes for x in instances]
            pred_classes = cat([x.pred_classes for x in instances])
            # The subdivision code will fail with the empty list of boxes
            if len(pred_classes) == 0:
                return mask_coarse_logits

            mask_logits = mask_coarse_logits.clone()
            for subdivions_step in range(self.mask_point_subdivision_steps):
                mask_logits = interpolate(
                    mask_logits, scale_factor=2, mode="bilinear", align_corners=False
                )
                # If `mask_point_subdivision_num_points` is larger or equal to the
                # resolution of the next step, then we can skip this step
                H, W = mask_logits.shape[-2:]
                if (
                    self.mask_point_subdivision_num_points >= 4 * H * W
                    and subdivions_step < self.mask_point_subdivision_steps - 1
                ):
                    continue
                uncertainty_map = calculate_uncertainty(mask_logits, pred_classes)
                point_indices, point_coords = get_uncertain_point_coords_on_grid(
                    uncertainty_map, self.mask_point_subdivision_num_points
                )
                fine_grained_features, _ = point_sample_fine_grained_features(
                    mask_features_list, features_scales, pred_boxes, point_coords
                )
                coarse_features = point_sample(
                    mask_coarse_logits, point_coords, align_corners=False
                )
                point_logits = self.mask_point_head(fine_grained_features, coarse_features)

                # put mask point predictions to the right places on the upsampled grid.
                R, C, H, W = mask_logits.shape
                point_indices = point_indices.unsqueeze(1).expand(-1, C, -1)
                mask_logits = (
                    mask_logits.reshape(R, C, H * W)
                    .scatter_(2, point_indices, point_logits)
                    .view(R, C, H, W)
                )
            return mask_logits