import os import json from utils.render import RenderUtil from utils.pose import PoseUtil from utils.pts import PtsUtil from utils.reconstruction import ReconstructionUtil from beans.predict_result import PredictResult import torch from tqdm import tqdm import numpy as np import pickle from PytorchBoot.config import ConfigManager import PytorchBoot.namespace as namespace import PytorchBoot.stereotype as stereotype from PytorchBoot.factory import ComponentFactory from PytorchBoot.dataset import BaseDataset from PytorchBoot.runners.runner import Runner from PytorchBoot.utils import Log from PytorchBoot.status import status_manager from utils.data_load import DataLoadUtil @stereotype.runner("global_and_local_points_inferencer") class GlobalAndLocalPointsInferencer(Runner): def __init__(self, config_path): super().__init__(config_path) self.script_path = ConfigManager.get(namespace.Stereotype.RUNNER, "blender_script_path") self.output_dir = ConfigManager.get(namespace.Stereotype.RUNNER, "output_dir") self.voxel_size = ConfigManager.get(namespace.Stereotype.RUNNER, "voxel_size") self.min_new_area = ConfigManager.get(namespace.Stereotype.RUNNER, "min_new_area") CM = 0.01 self.min_new_pts_num = self.min_new_area * (CM / self.voxel_size) **2 self.overlap_limit = ConfigManager.get(namespace.Stereotype.RUNNER, "overlap_limit") self.enable_cluster = ConfigManager.get(namespace.Stereotype.RUNNER, "enable_cluster") ''' Pipeline ''' self.pipeline_name = self.config[namespace.Stereotype.PIPELINE] self.pipeline:torch.nn.Module = ComponentFactory.create(namespace.Stereotype.PIPELINE, self.pipeline_name) self.pipeline = self.pipeline.to(self.device) ''' Experiment ''' self.load_experiment("nbv_evaluator") self.stat_result_path = os.path.join(self.output_dir, "stat.json") if os.path.exists(self.stat_result_path): with open(self.stat_result_path, "r") as f: self.stat_result = json.load(f) else: self.stat_result = {} ''' Test ''' self.test_config = ConfigManager.get(namespace.Stereotype.RUNNER, namespace.Mode.TEST) self.test_dataset_name_list = self.test_config["dataset_list"] self.test_set_list = [] self.test_writer_list = [] seen_name = set() for test_dataset_name in self.test_dataset_name_list: if test_dataset_name not in seen_name: seen_name.add(test_dataset_name) else: raise ValueError("Duplicate test dataset name: {}".format(test_dataset_name)) test_set: BaseDataset = ComponentFactory.create(namespace.Stereotype.DATASET, test_dataset_name) self.test_set_list.append(test_set) self.print_info() def run(self): Log.info("Loading from epoch {}.".format(self.current_epoch)) self.inference() Log.success("Inference finished.") def inference(self): self.pipeline.eval() with torch.no_grad(): test_set: BaseDataset for dataset_idx, test_set in enumerate(self.test_set_list): status_manager.set_progress("inference", "inferencer", f"dataset", dataset_idx, len(self.test_set_list)) test_set_name = test_set.get_name() total=int(len(test_set)) for i in tqdm(range(total), desc=f"Processing {test_set_name}", ncols=100): try: #import ipdb; ipdb.set_trace() data = test_set.__getitem__(i) scene_name = data["scene_name"] inference_result_path = os.path.join(self.output_dir, test_set_name, f"{scene_name}.pkl") if os.path.exists(inference_result_path): Log.info(f"Inference result already exists for scene: {scene_name}") continue status_manager.set_progress("inference", "inferencer", f"Batch[{test_set_name}]", i+1, total) output = self.predict_sequence(data) self.save_inference_result(test_set_name, data["scene_name"], output) except Exception as e: print(e) Log.error(f"Error, {e}") continue status_manager.set_progress("inference", "inferencer", f"dataset", len(self.test_set_list), len(self.test_set_list)) def predict_sequence(self, data, cr_increase_threshold=0, overlap_area_threshold=25, scan_points_threshold=10, max_iter=50, max_retry = 10, max_success=3): scene_name = data["scene_name"] Log.info(f"Processing scene: {scene_name}") status_manager.set_status("inference", "inferencer", "scene", scene_name) ''' data for rendering ''' scene_path = data["scene_path"] O_to_L_pose = data["O_to_L_pose"] voxel_threshold = self.voxel_size filter_degree = 75 down_sampled_model_pts = data["gt_pts"] first_frame_to_world_9d = data["first_scanned_n_to_world_pose_9d"][0] first_frame_to_world = np.eye(4) first_frame_to_world[:3,:3] = PoseUtil.rotation_6d_to_matrix_numpy(first_frame_to_world_9d[:6]) first_frame_to_world[:3,3] = first_frame_to_world_9d[6:] ''' data for inference ''' input_data = {} input_data["combined_scanned_pts"] = torch.tensor(data["first_scanned_pts"][0], dtype=torch.float32).to(self.device).unsqueeze(0) input_data["scanned_pts"] = [torch.tensor(data["first_scanned_pts"][0], dtype=torch.float32).to(self.device).unsqueeze(0)] input_data["scanned_pts_mask"] = [torch.zeros(input_data["combined_scanned_pts"].shape[1], dtype=torch.bool).to(self.device).unsqueeze(0)] input_data["scanned_n_to_world_pose_9d"] = [torch.tensor(data["first_scanned_n_to_world_pose_9d"], dtype=torch.float32).to(self.device)] input_data["mode"] = namespace.Mode.TEST input_pts_N = input_data["combined_scanned_pts"].shape[1] root = os.path.dirname(scene_path) display_table_info = DataLoadUtil.get_display_table_info(root, scene_name) radius = display_table_info["radius"] scan_points = np.asarray(ReconstructionUtil.generate_scan_points(display_table_top=0,display_table_radius=radius)) # first_frame_target_pts, first_frame_target_normals, first_frame_scan_points_indices = RenderUtil.render_pts(first_frame_to_world, scene_path, self.script_path, scan_points, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=O_to_L_pose) scanned_view_pts = [first_frame_target_pts] history_indices = [first_frame_scan_points_indices] last_pred_cr, added_pts_num = self.compute_coverage_rate(scanned_view_pts, None, down_sampled_model_pts, threshold=voxel_threshold) retry_duplication_pose = [] retry_no_pts_pose = [] retry_overlap_pose = [] retry = 0 pred_cr_seq = [last_pred_cr] success = 0 last_pts_num = PtsUtil.voxel_downsample_point_cloud(data["first_scanned_pts"][0], voxel_threshold).shape[0] #import time #import ipdb; ipdb.set_trace() while len(pred_cr_seq) < max_iter and retry < max_retry and success < max_success: #import ipdb; ipdb.set_trace() Log.green(f"iter: {len(pred_cr_seq)}, retry: {retry}/{max_retry}, success: {success}/{max_success}") combined_scanned_pts = np.vstack(scanned_view_pts) voxel_downsampled_combined_scanned_pts_np, inverse = self.voxel_downsample_with_mapping(combined_scanned_pts, voxel_threshold) #import ipdb; ipdb.set_trace() output = self.pipeline(input_data) pred_pose_9d = output["pred_pose_9d"] if not self.enable_cluster: pred_pose_9d_candidates = [pred_pose_9d[0]] else: predict_result = PredictResult(pred_pose_9d.cpu().numpy(), input_pts=input_data["combined_scanned_pts"][0].cpu().numpy(), cluster_params=dict(eps=0.25, min_samples=3)) pred_pose_9d_candidates = predict_result.candidate_9d_poses pred_pose = torch.eye(4, device=pred_pose_9d.device) for pred_pose_9d in pred_pose_9d_candidates: #import ipdb; ipdb.set_trace() pred_pose_9d = torch.tensor(pred_pose_9d, dtype=torch.float32).to(self.device).unsqueeze(0) pred_pose[:3,:3] = PoseUtil.rotation_6d_to_matrix_tensor_batch(pred_pose_9d[:,:6])[0] pred_pose[:3,3] = pred_pose_9d[0,6:] try: new_target_pts, new_target_normals, new_scan_points_indices = RenderUtil.render_pts(pred_pose, scene_path, self.script_path, scan_points, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=O_to_L_pose) #import ipdb; ipdb.set_trace() if not ReconstructionUtil.check_scan_points_overlap(history_indices, new_scan_points_indices, scan_points_threshold): curr_overlap_area_threshold = overlap_area_threshold else: curr_overlap_area_threshold = overlap_area_threshold * 0.5 downsampled_new_target_pts = PtsUtil.voxel_downsample_point_cloud(new_target_pts, voxel_threshold) if self.overlap_limit: overlap, _ = ReconstructionUtil.check_overlap(downsampled_new_target_pts, voxel_downsampled_combined_scanned_pts_np, overlap_area_threshold = curr_overlap_area_threshold, voxel_size=voxel_threshold, require_new_added_pts_num = True) if not overlap: Log.yellow("no overlap!") retry += 1 retry_overlap_pose.append(pred_pose.cpu().numpy().tolist()) continue history_indices.append(new_scan_points_indices) except Exception as e: Log.error(f"Error in scene {scene_path}, {e}") print("current pose: ", pred_pose) print("curr_pred_cr: ", last_pred_cr) retry_no_pts_pose.append(pred_pose.cpu().numpy().tolist()) retry += 1 continue if new_target_pts.shape[0] == 0: Log.red("no pts in new target") retry_no_pts_pose.append(pred_pose.cpu().numpy().tolist()) retry += 1 continue pred_cr, _ = self.compute_coverage_rate(scanned_view_pts, new_target_pts, down_sampled_model_pts, threshold=voxel_threshold) Log.yellow(f"{pred_cr}, {last_pred_cr}, max: , {data['seq_max_coverage_rate']}") if pred_cr >= data["seq_max_coverage_rate"] - 1e-3: print("max coverage rate reached!: ", pred_cr) pred_cr_seq.append(pred_cr) scanned_view_pts.append(new_target_pts) input_data["scanned_n_to_world_pose_9d"] = [torch.cat([input_data["scanned_n_to_world_pose_9d"][0], pred_pose_9d], dim=0)] combined_scanned_pts = np.vstack(scanned_view_pts) voxel_downsampled_combined_scanned_pts_np = PtsUtil.voxel_downsample_point_cloud(combined_scanned_pts, voxel_threshold) random_downsampled_combined_scanned_pts_np = PtsUtil.random_downsample_point_cloud(voxel_downsampled_combined_scanned_pts_np, input_pts_N) input_data["combined_scanned_pts"] = torch.tensor(random_downsampled_combined_scanned_pts_np, dtype=torch.float32).unsqueeze(0).to(self.device) input_data["scanned_pts"] = [torch.cat([input_data["scanned_pts"][0], torch.tensor(random_downsampled_combined_scanned_pts_np, dtype=torch.float32).unsqueeze(0).to(self.device)], dim=0)] last_pred_cr = pred_cr pts_num = voxel_downsampled_combined_scanned_pts_np.shape[0] Log.info(f"delta pts num:,{pts_num - last_pts_num },{pts_num}, {last_pts_num}") if pts_num - last_pts_num < self.min_new_pts_num and pred_cr <= data["seq_max_coverage_rate"] - 1e-2: retry += 1 retry_duplication_pose.append(pred_pose.cpu().numpy().tolist()) Log.red(f"delta pts num < {self.min_new_pts_num}:, {pts_num}, {last_pts_num}") elif pts_num - last_pts_num < self.min_new_pts_num and pred_cr > data["seq_max_coverage_rate"] - 1e-2: success += 1 Log.success(f"delta pts num < {self.min_new_pts_num}:, {pts_num}, {last_pts_num}") last_pts_num = pts_num input_data["scanned_n_to_world_pose_9d"] = input_data["scanned_n_to_world_pose_9d"][0].cpu().numpy().tolist() result = { "pred_pose_9d_seq": input_data["scanned_n_to_world_pose_9d"], "combined_scanned_pts": input_data["combined_scanned_pts"], "target_pts_seq": scanned_view_pts, "coverage_rate_seq": pred_cr_seq, "max_coverage_rate": data["seq_max_coverage_rate"], "pred_max_coverage_rate": max(pred_cr_seq), "scene_name": scene_name, "retry_no_pts_pose": retry_no_pts_pose, "retry_duplication_pose": retry_duplication_pose, "retry_overlap_pose": retry_overlap_pose, "best_seq_len": data["best_seq_len"], } self.stat_result[scene_name] = { "coverage_rate_seq": pred_cr_seq, "pred_max_coverage_rate": max(pred_cr_seq), "pred_seq_len": len(pred_cr_seq), } print('success rate: ', max(pred_cr_seq)) return result def voxel_downsample_with_mapping(self, point_cloud, voxel_size=0.003): voxel_indices = np.floor(point_cloud / voxel_size).astype(np.int32) unique_voxels, inverse, counts = np.unique(voxel_indices, axis=0, return_inverse=True, return_counts=True) idx_sort = np.argsort(inverse) idx_unique = idx_sort[np.cumsum(counts)-counts] downsampled_points = point_cloud[idx_unique] return downsampled_points, inverse def compute_coverage_rate(self, scanned_view_pts, new_pts, model_pts, threshold=0.005): if new_pts is not None: new_scanned_view_pts = scanned_view_pts + [new_pts] else: new_scanned_view_pts = scanned_view_pts combined_point_cloud = np.vstack(new_scanned_view_pts) down_sampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_point_cloud,threshold) return ReconstructionUtil.compute_coverage_rate(model_pts, down_sampled_combined_point_cloud, threshold) def voxel_downsample_with_mapping(self, point_cloud, voxel_size=0.003): voxel_indices = np.floor(point_cloud / voxel_size).astype(np.int32) unique_voxels, inverse, counts = np.unique(voxel_indices, axis=0, return_inverse=True, return_counts=True) idx_sort = np.argsort(inverse) idx_unique = idx_sort[np.cumsum(counts)-counts] downsampled_points = point_cloud[idx_unique] return downsampled_points, inverse def save_inference_result(self, dataset_name, scene_name, output): dataset_dir = os.path.join(self.output_dir, dataset_name) if not os.path.exists(dataset_dir): os.makedirs(dataset_dir) output_path = os.path.join(dataset_dir, f"{scene_name}.pkl") pickle.dump(output, open(output_path, "wb")) with open(self.stat_result_path, "w") as f: json.dump(self.stat_result, f) def get_checkpoint_path(self, is_last=False): return os.path.join(self.experiment_path, namespace.Direcotry.CHECKPOINT_DIR_NAME, "Epoch_{}.pth".format( self.current_epoch if self.current_epoch != -1 and not is_last else "last")) def load_checkpoint(self, is_last=False): self.load(self.get_checkpoint_path(is_last)) Log.success(f"Loaded checkpoint from {self.get_checkpoint_path(is_last)}") if is_last: checkpoint_root = os.path.join(self.experiment_path, namespace.Direcotry.CHECKPOINT_DIR_NAME) meta_path = os.path.join(checkpoint_root, "meta.json") if not os.path.exists(meta_path): raise FileNotFoundError( "No checkpoint meta.json file in the experiment {}".format(self.experiments_config["name"])) file_path = os.path.join(checkpoint_root, "meta.json") with open(file_path, "r") as f: meta = json.load(f) self.current_epoch = meta["last_epoch"] self.current_iter = meta["last_iter"] def load_experiment(self, backup_name=None): super().load_experiment(backup_name) self.current_epoch = self.experiments_config["epoch"] self.load_checkpoint(is_last=(self.current_epoch == -1)) def create_experiment(self, backup_name=None): super().create_experiment(backup_name) def load(self, path): state_dict = torch.load(path) self.pipeline.load_state_dict(state_dict) def print_info(self): def print_dataset(dataset: BaseDataset): config = dataset.get_config() name = dataset.get_name() Log.blue(f"Dataset: {name}") for k,v in config.items(): Log.blue(f"\t{k}: {v}") super().print_info() table_size = 70 Log.blue(f"{'+' + '-' * (table_size // 2)} Pipeline {'-' * (table_size // 2)}" + '+') Log.blue(self.pipeline) Log.blue(f"{'+' + '-' * (table_size // 2)} Datasets {'-' * (table_size // 2)}" + '+') for i, test_set in enumerate(self.test_set_list): Log.blue(f"test dataset {i}: ") print_dataset(test_set) Log.blue(f"{'+' + '-' * (table_size // 2)}----------{'-' * (table_size // 2)}" + '+')