# Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. from collections import defaultdict from typing import Dict, List, Sequence import cv2 import numpy as np import torch import torch.nn.functional as F from prettytable import PrettyTable from sapiens.engine.evaluators import BaseEvaluator from sapiens.registry import MODELS @MODELS.register_module() class PointmapEvaluator(BaseEvaluator): def __init__( self, distance_thresholds: List[float] = [0.05, 0.10, 0.20], angle_thresholds: List[float] = [5.0, 11.25, 22.5, 30.0], ): super().__init__() self.distance_thresholds = distance_thresholds self.angle_thresholds = angle_thresholds def _compute_surface_normals( self, point_map: torch.Tensor, valid_mask: torch.Tensor ) -> torch.Tensor: """ Compute surface normals from a point map. Args: point_map (torch.Tensor): Point map of shape (H, W, 3). valid_mask (torch.Tensor): Boolean mask of valid points of shape (H, W). Returns: torch.Tensor: Surface normals for valid points of shape (N, 3). """ points_np = point_map.cpu().numpy().astype(np.float32) grad_x = cv2.Sobel(points_np, cv2.CV_32F, 1, 0, ksize=5) grad_y = cv2.Sobel(points_np, cv2.CV_32F, 0, 1, ksize=5) normals = np.cross(grad_x, grad_y) norms = np.linalg.norm(normals, axis=2, keepdims=True) normals = normals / (norms + 1e-6) normals_tensor = torch.from_numpy(normals).to(point_map.device) valid_normals = normals_tensor[valid_mask] return valid_normals @torch.no_grad() def process(self, predictions: tuple, data_samples: dict, accelerator=None): """ Process a single batch of predictions and ground truth data. Args: predictions (tuple): A tuple containing the predicted pointmap and scale. data_samples (List[Dict]): A list of dictionaries, each containing ground truth data. """ assert accelerator is not None, "evaluation process expects an accelerator" pred_pointmaps, _ = predictions ## gt pointmaps are canonicalized gt_masks = data_samples["mask"] # B x 1 x H x W gt_pointmaps = data_samples["gt_pointmap"] # B x 3 x H x W if pred_pointmaps.shape[2:] != gt_pointmaps.shape[2:]: pred_pointmaps = F.interpolate( input=pred_pointmaps, size=gt_pointmaps.shape[2:], mode="bilinear", align_corners=False, antialias=False, ) B = gt_pointmaps.shape[0] D = len(self.distance_thresholds) A = len(self.angle_thresholds) per_sample_vecs = [] # (B_local, K) for i in range(B): pred_pm = pred_pointmaps[i].permute(1, 2, 0) # (H, W, 3) gt_pm = gt_pointmaps[i].permute(1, 2, 0) # (H, W, 3) valid = gt_masks[i][0] > 0 # (H, W) bool # Keep shapes consistent even if there are no valid points if valid.any(): gt_pts = gt_pm[valid] # (N, 3) pred_pts = pred_pm[valid] # (N, 3) diff = pred_pts - gt_pts # (N, 3) # distances & axis errors distances = torch.norm(diff, dim=-1) # (N,) axis_err = torch.abs(diff) # (N, 3) # normals (computed from *full* maps, then masked) gt_normals = self._compute_surface_normals(gt_pm, valid) # (N, 3) pred_normals = self._compute_surface_normals(pred_pm, valid) # (N, 3) dot = (gt_normals * pred_normals).sum(dim=1).clamp(-1.0, 1.0) angles = torch.acos(dot) * (180.0 / np.pi) # (N,) num_points = float(distances.shape[0]) # base sums l2_sum = distances.sum() x_abs_sum = axis_err[:, 0].sum() y_abs_sum = axis_err[:, 1].sum() z_abs_sum = axis_err[:, 2].sum() squared_dist_sum = (distances**2).sum() angle_sum = angles.sum() squared_angle_sum = (angles**2).sum() # threshold counts dist_counts = [(distances < t).sum() for t in self.distance_thresholds] ang_counts = [(angles < t).sum() for t in self.angle_thresholds] else: # all zeros when no valid points l2_sum = x_abs_sum = y_abs_sum = z_abs_sum = 0.0 squared_dist_sum = angle_sum = squared_angle_sum = 0.0 num_points = 0.0 dist_counts = [0.0] * D ang_counts = [0.0] * A # assemble fixed-length vector on the same device vec_list = [ l2_sum, x_abs_sum, y_abs_sum, z_abs_sum, squared_dist_sum, angle_sum, squared_angle_sum, num_points, *dist_counts, *ang_counts, ] vec = torch.tensor( [float(v) for v in vec_list], device=pred_pointmaps.device, dtype=torch.float64, ) # stable accumulation per_sample_vecs.append(vec) # (B_local, K) pack = torch.stack(per_sample_vecs, dim=0) # Global per-step totals via Accelerate (dedups final step automatically) gpack = accelerator.gather_for_metrics(pack) # (B_global_this_step, K) step_totals = gpack.sum(dim=0) # (K,) if accelerator.is_main_process: self.results.append(step_totals) # store one vector per step on rank-0 return def evaluate(self, logger=None, accelerator=None) -> Dict[str, float]: """ Compute and log the final metrics after processing all batches. Returns: Dict[str, float]: A dictionary of the final computed metrics. """ assert accelerator is not None, "evaluation aggregation expects an accelerator" if not accelerator.is_main_process: self.reset() return {} if not self.results: if logger is not None: logger.info("No results to evaluate.") return {} totals_vec = torch.stack(self.results, dim=0).sum(dim=0) # (K,) D = len(self.distance_thresholds) A = len(self.angle_thresholds) # unpack idx = 0 l2_sum = totals_vec[idx] idx += 1 x_abs_sum = totals_vec[idx] idx += 1 y_abs_sum = totals_vec[idx] idx += 1 z_abs_sum = totals_vec[idx] idx += 1 squared_dist_sum = totals_vec[idx] idx += 1 angle_sum = totals_vec[idx] idx += 1 squared_angle_sum = totals_vec[idx] idx += 1 num_points = totals_vec[idx] idx += 1 dist_counts = totals_vec[idx : idx + D] idx += D ang_counts = totals_vec[idx : idx + A] idx += A total_points = float(num_points.item()) if total_points <= 0: if logger is not None: logger.info("No valid points found to evaluate.") self.reset() return {} # metrics metrics: Dict[str, float] = {} metrics["l2_mean"] = (l2_sum / total_points).item() metrics["x_mae"] = (x_abs_sum / total_points).item() metrics["y_mae"] = (y_abs_sum / total_points).item() metrics["z_mae"] = (z_abs_sum / total_points).item() metrics["rmse"] = torch.sqrt(squared_dist_sum / total_points).item() metrics["normal_mae"] = (angle_sum / total_points).item() metrics["normal_rmse"] = torch.sqrt(squared_angle_sum / total_points).item() for i, t in enumerate(self.distance_thresholds): out_key = f"within_{int(t * 100):02d}_cm" metrics[out_key] = (dist_counts[i] / total_points).item() for j, t in enumerate(self.angle_thresholds): suf = str(t).replace(".", "_") out_key = f"within_{suf}_deg" metrics[out_key] = (ang_counts[j] / total_points).item() # pretty print table = PrettyTable() table.field_names = list(metrics.keys()) table.add_row([f"{float(val):.5f}" for val in metrics.values()]) if logger is not None: logger.info("\n" + table.get_string()) self.reset() return metrics