260 lines
12 KiB
Python
260 lines
12 KiB
Python
import os
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import json
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from utils.render import RenderUtil
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from utils.pose import PoseUtil
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from utils.pts import PtsUtil
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from utils.reconstruction import ReconstructionUtil
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import torch
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from tqdm import tqdm
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import numpy as np
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import pickle
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from PytorchBoot.config import ConfigManager
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import PytorchBoot.namespace as namespace
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import PytorchBoot.stereotype as stereotype
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from PytorchBoot.factory import ComponentFactory
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from PytorchBoot.dataset import BaseDataset
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from PytorchBoot.runners.runner import Runner
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from PytorchBoot.utils import Log
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from PytorchBoot.status import status_manager
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@stereotype.runner("inferencer")
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class Inferencer(Runner):
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def __init__(self, config_path):
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super().__init__(config_path)
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self.script_path = ConfigManager.get(namespace.Stereotype.RUNNER, "blender_script_path")
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self.output_dir = ConfigManager.get(namespace.Stereotype.RUNNER, "output_dir")
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self.voxel_size = ConfigManager.get(namespace.Stereotype.RUNNER, "voxel_size")
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''' Pipeline '''
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self.pipeline_name = self.config[namespace.Stereotype.PIPELINE]
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self.pipeline:torch.nn.Module = ComponentFactory.create(namespace.Stereotype.PIPELINE, self.pipeline_name)
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self.pipeline = self.pipeline.to(self.device)
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''' Experiment '''
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self.load_experiment("nbv_evaluator")
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self.stat_result = {}
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''' Test '''
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self.test_config = ConfigManager.get(namespace.Stereotype.RUNNER, namespace.Mode.TEST)
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self.test_dataset_name_list = self.test_config["dataset_list"]
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self.test_set_list = []
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self.test_writer_list = []
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seen_name = set()
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for test_dataset_name in self.test_dataset_name_list:
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if test_dataset_name not in seen_name:
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seen_name.add(test_dataset_name)
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else:
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raise ValueError("Duplicate test dataset name: {}".format(test_dataset_name))
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test_set: BaseDataset = ComponentFactory.create(namespace.Stereotype.DATASET, test_dataset_name)
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self.test_set_list.append(test_set)
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self.print_info()
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def run(self):
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Log.info("Loading from epoch {}.".format(self.current_epoch))
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self.inference()
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Log.success("Inference finished.")
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def inference(self):
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self.pipeline.eval()
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with torch.no_grad():
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test_set: BaseDataset
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for dataset_idx, test_set in enumerate(self.test_set_list):
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status_manager.set_progress("inference", "inferencer", f"dataset", dataset_idx, len(self.test_set_list))
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test_set_name = test_set.get_name()
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total=int(len(test_set))
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for i in range(total):
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data = test_set.__getitem__(i)
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status_manager.set_progress("inference", "inferencer", f"Batch[{test_set_name}]", i+1, total)
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output = self.predict_sequence(data)
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self.save_inference_result(test_set_name, data["scene_name"], output)
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status_manager.set_progress("inference", "inferencer", f"dataset", len(self.test_set_list), len(self.test_set_list))
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def predict_sequence(self, data, cr_increase_threshold=0, max_iter=50, max_retry=5):
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scene_name = data["scene_name"]
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Log.info(f"Processing scene: {scene_name}")
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status_manager.set_status("inference", "inferencer", "scene", scene_name)
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''' data for rendering '''
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scene_path = data["scene_path"]
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O_to_L_pose = data["O_to_L_pose"]
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voxel_threshold = self.voxel_size
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filter_degree = 75
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down_sampled_model_pts = data["gt_pts"]
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first_frame_to_world_9d = data["first_scanned_n_to_world_pose_9d"][0]
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first_frame_to_world = np.eye(4)
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first_frame_to_world[:3,:3] = PoseUtil.rotation_6d_to_matrix_numpy(first_frame_to_world_9d[:6])
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first_frame_to_world[:3,3] = first_frame_to_world_9d[6:]
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''' data for inference '''
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input_data = {}
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input_data["combined_scanned_pts"] = torch.tensor(data["first_scanned_pts"][0], dtype=torch.float32).to(self.device).unsqueeze(0)
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input_data["scanned_n_to_world_pose_9d"] = [torch.tensor(data["first_scanned_n_to_world_pose_9d"], dtype=torch.float32).to(self.device)]
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input_data["mode"] = namespace.Mode.TEST
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input_pts_N = input_data["combined_scanned_pts"].shape[1]
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first_frame_target_pts, first_frame_target_normals = RenderUtil.render_pts(first_frame_to_world, scene_path, self.script_path, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=O_to_L_pose)
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scanned_view_pts = [first_frame_target_pts]
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last_pred_cr, added_pts_num = self.compute_coverage_rate(scanned_view_pts, None, down_sampled_model_pts, threshold=voxel_threshold)
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retry_duplication_pose = []
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retry_no_pts_pose = []
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retry = 0
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pred_cr_seq = [last_pred_cr]
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success = 0
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while len(pred_cr_seq) < max_iter and retry < max_retry:
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output = self.pipeline(input_data)
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pred_pose_9d = output["pred_pose_9d"]
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pred_pose = torch.eye(4, device=pred_pose_9d.device)
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pred_pose[:3,:3] = PoseUtil.rotation_6d_to_matrix_tensor_batch(pred_pose_9d[:,:6])[0]
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pred_pose[:3,3] = pred_pose_9d[0,6:]
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try:
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new_target_pts, new_target_normals = RenderUtil.render_pts(pred_pose, scene_path, self.script_path, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=O_to_L_pose)
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except Exception as e:
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Log.warning(f"Error in scene {scene_path}, {e}")
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print("current pose: ", pred_pose)
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print("curr_pred_cr: ", last_pred_cr)
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retry_no_pts_pose.append(pred_pose.cpu().numpy().tolist())
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retry += 1
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continue
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if new_target_pts.shape[0] == 0:
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print("no pts in new target")
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retry_no_pts_pose.append(pred_pose.cpu().numpy().tolist())
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retry += 1
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continue
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pred_cr, new_added_pts_num = self.compute_coverage_rate(scanned_view_pts, new_target_pts, down_sampled_model_pts, threshold=voxel_threshold)
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print(pred_cr, last_pred_cr, " max: ", data["seq_max_coverage_rate"])
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if pred_cr >= data["seq_max_coverage_rate"] - 1e-3:
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print("max coverage rate reached!: ", pred_cr)
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success += 1
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elif new_added_pts_num < 10:
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print("min added pts num reached!: ", new_added_pts_num)
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if pred_cr <= last_pred_cr + cr_increase_threshold:
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retry += 1
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retry_duplication_pose.append(pred_pose.cpu().numpy().tolist())
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continue
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retry = 0
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pred_cr_seq.append(pred_cr)
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scanned_view_pts.append(new_target_pts)
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down_sampled_new_pts_world = PtsUtil.random_downsample_point_cloud(new_target_pts, input_pts_N)
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new_pts = down_sampled_new_pts_world
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input_data["scanned_n_to_world_pose_9d"] = [torch.cat([input_data["scanned_n_to_world_pose_9d"][0], pred_pose_9d], dim=0)]
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combined_scanned_pts = np.concatenate([input_data["combined_scanned_pts"][0].cpu().numpy(), new_pts], axis=0)
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voxel_downsampled_combined_scanned_pts_np = PtsUtil.voxel_downsample_point_cloud(combined_scanned_pts, 0.002)
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random_downsampled_combined_scanned_pts_np = PtsUtil.random_downsample_point_cloud(voxel_downsampled_combined_scanned_pts_np, input_pts_N)
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input_data["combined_scanned_pts"] = torch.tensor(random_downsampled_combined_scanned_pts_np, dtype=torch.float32).unsqueeze(0).to(self.device)
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if success > 3:
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break
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last_pred_cr = pred_cr
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input_data["scanned_n_to_world_pose_9d"] = input_data["scanned_n_to_world_pose_9d"][0].cpu().numpy().tolist()
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result = {
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"pred_pose_9d_seq": input_data["scanned_n_to_world_pose_9d"],
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"combined_scanned_pts": input_data["combined_scanned_pts"],
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"target_pts_seq": scanned_view_pts,
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"coverage_rate_seq": pred_cr_seq,
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"max_coverage_rate": data["seq_max_coverage_rate"],
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"pred_max_coverage_rate": max(pred_cr_seq),
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"scene_name": scene_name,
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"retry_no_pts_pose": retry_no_pts_pose,
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"retry_duplication_pose": retry_duplication_pose,
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"best_seq_len": data["best_seq_len"],
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}
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self.stat_result[scene_name] = {
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"coverage_rate_seq": pred_cr_seq,
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"pred_max_coverage_rate": max(pred_cr_seq),
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"pred_seq_len": len(pred_cr_seq),
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}
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print('success rate: ', max(pred_cr_seq))
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return result
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def compute_coverage_rate(self, scanned_view_pts, new_pts, model_pts, threshold=0.005):
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if new_pts is not None:
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new_scanned_view_pts = scanned_view_pts + [new_pts]
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else:
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new_scanned_view_pts = scanned_view_pts
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combined_point_cloud = np.vstack(new_scanned_view_pts)
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down_sampled_combined_point_cloud = PtsUtil.voxel_downsample_point_cloud(combined_point_cloud,threshold)
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return ReconstructionUtil.compute_coverage_rate(model_pts, down_sampled_combined_point_cloud, threshold)
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def save_inference_result(self, dataset_name, scene_name, output):
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dataset_dir = os.path.join(self.output_dir, dataset_name)
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if not os.path.exists(dataset_dir):
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os.makedirs(dataset_dir)
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output_path = os.path.join(dataset_dir, f"{scene_name}.pkl")
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pickle.dump(output, open(output_path, "wb"))
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with open(os.path.join(dataset_dir, "stat.json"), "w") as f:
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json.dump(self.stat_result, f)
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def get_checkpoint_path(self, is_last=False):
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return os.path.join(self.experiment_path, namespace.Direcotry.CHECKPOINT_DIR_NAME,
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"Epoch_{}.pth".format(
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self.current_epoch if self.current_epoch != -1 and not is_last else "last"))
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def load_checkpoint(self, is_last=False):
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self.load(self.get_checkpoint_path(is_last))
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Log.success(f"Loaded checkpoint from {self.get_checkpoint_path(is_last)}")
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if is_last:
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checkpoint_root = os.path.join(self.experiment_path, namespace.Direcotry.CHECKPOINT_DIR_NAME)
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meta_path = os.path.join(checkpoint_root, "meta.json")
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if not os.path.exists(meta_path):
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raise FileNotFoundError(
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"No checkpoint meta.json file in the experiment {}".format(self.experiments_config["name"]))
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file_path = os.path.join(checkpoint_root, "meta.json")
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with open(file_path, "r") as f:
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meta = json.load(f)
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self.current_epoch = meta["last_epoch"]
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self.current_iter = meta["last_iter"]
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def load_experiment(self, backup_name=None):
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super().load_experiment(backup_name)
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self.current_epoch = self.experiments_config["epoch"]
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self.load_checkpoint(is_last=(self.current_epoch == -1))
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def create_experiment(self, backup_name=None):
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super().create_experiment(backup_name)
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def load(self, path):
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state_dict = torch.load(path)
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self.pipeline.load_state_dict(state_dict)
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def print_info(self):
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def print_dataset(dataset: BaseDataset):
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config = dataset.get_config()
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name = dataset.get_name()
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Log.blue(f"Dataset: {name}")
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for k,v in config.items():
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Log.blue(f"\t{k}: {v}")
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super().print_info()
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table_size = 70
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Log.blue(f"{'+' + '-' * (table_size // 2)} Pipeline {'-' * (table_size // 2)}" + '+')
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Log.blue(self.pipeline)
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Log.blue(f"{'+' + '-' * (table_size // 2)} Datasets {'-' * (table_size // 2)}" + '+')
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for i, test_set in enumerate(self.test_set_list):
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Log.blue(f"test dataset {i}: ")
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print_dataset(test_set)
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Log.blue(f"{'+' + '-' * (table_size // 2)}----------{'-' * (table_size // 2)}" + '+')
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