after first overfit test
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@ -14,8 +14,8 @@ runner:
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voxel_threshold: 0.01
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overlap_threshold: 0.5
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filter_degree: 75
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to_specified_dir: True # if True, output_dir is used, otherwise, root_dir is used
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save_points: False
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to_specified_dir: False # if True, output_dir is used, otherwise, root_dir is used
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save_points: True
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save_best_combined_points: True
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save_mesh: True
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overwrite: False
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@ -1,18 +1,18 @@
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runner:
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general:
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seed: 0
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seed: 1
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device: cuda
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cuda_visible_devices: "0,1,2,3,4,5,6,7"
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parallel: False
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experiment:
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name: test_overfit
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name: new_test_overfit_2
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root_dir: "experiments"
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use_checkpoint: False
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epoch: -1 # -1 stands for last epoch
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max_epochs: 5000
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save_checkpoint_interval: 1
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save_checkpoint_interval: 3
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test_first: False
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train:
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@ -32,22 +32,29 @@ runner:
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dataset:
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OmniObject3d_train:
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root_dir: "../data/sample_for_training/scenes"
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model_dir: "../data/scaled_object_meshes"
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source: nbv_reconstruction_dataset
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split_file: "../data/sample_for_training/OmniObject3d_train.txt"
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ratio: 1.0
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batch_size: 1
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type: train
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cache: True
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ratio: 1
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batch_size: 128
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num_workers: 12
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pts_num: 4096
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OmniObject3d_test:
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root_dir: "../data/sample_for_training/scenes"
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model_dir: "../data/scaled_object_meshes"
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source: nbv_reconstruction_dataset
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split_file: "../data/sample_for_training/OmniObject3d_train.txt"
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type: test
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cache: True
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filter_degree: 75
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eval_list:
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- pose_diff
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ratio: 0.1
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batch_size: 1
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num_workers: 1
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num_workers: 12
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pts_num: 4096
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pipeline:
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@ -93,3 +100,5 @@ loss_function:
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evaluation_method:
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pose_diff:
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coverage_rate_increase:
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renderer_path: "../blender/data_renderer.py"
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@ -1,11 +1,19 @@
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import numpy as np
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from PytorchBoot.dataset import BaseDataset
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import PytorchBoot.namespace as namespace
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import PytorchBoot.stereotype as stereotype
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from PytorchBoot.config import ConfigManager
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from PytorchBoot.utils.log_util import Log
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import torch
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import os
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import sys
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sys.path.append(r"/media/hofee/data/project/python/nbv_reconstruction/nbv_reconstruction")
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from utils.data_load import DataLoadUtil
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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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@stereotype.dataset("nbv_reconstruction_dataset")
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class NBVReconstructionDataset(BaseDataset):
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@ -16,7 +24,20 @@ class NBVReconstructionDataset(BaseDataset):
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self.split_file_path = config["split_file"]
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self.scene_name_list = self.load_scene_name_list()
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self.datalist = self.get_datalist()
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self.pts_num = config["pts_num"]
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self.type = config["type"]
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self.cache = config["cache"]
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if self.type == namespace.Mode.TEST:
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self.model_dir = config["model_dir"]
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self.filter_degree = config["filter_degree"]
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if self.type == namespace.Mode.TRAIN:
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self.datalist = self.datalist*100
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if self.cache:
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expr_root = ConfigManager.get("runner", "experiment", "root_dir")
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expr_name = ConfigManager.get("runner", "experiment", "name")
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self.cache_dir = os.path.join(expr_root, expr_name, "cache")
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def load_scene_name_list(self):
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scene_name_list = []
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@ -45,6 +66,26 @@ class NBVReconstructionDataset(BaseDataset):
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)
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return datalist
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def load_from_cache(self, scene_name, first_frame_idx, curr_frame_idx):
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cache_name = f"{scene_name}_{first_frame_idx}_{curr_frame_idx}.txt"
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cache_path = os.path.join(self.cache_dir, cache_name)
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if os.path.exists(cache_path):
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data = np.loadtxt(cache_path)
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return data
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else:
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return None
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def save_to_cache(self, scene_name, first_frame_idx, curr_frame_idx, data):
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cache_name = f"{scene_name}_{first_frame_idx}_{curr_frame_idx}.txt"
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cache_path = os.path.join(self.cache_dir, cache_name)
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try:
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np.savetxt(cache_path, data)
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except Exception as e:
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Log.error(f"Save cache failed: {e}")
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# ----- Debug Trace ----- #
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import ipdb; ipdb.set_trace()
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# ------------------------ #
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def __getitem__(self, index):
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data_item_info = self.datalist[index]
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scanned_views = data_item_info["scanned_views"]
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@ -64,14 +105,21 @@ class NBVReconstructionDataset(BaseDataset):
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nR_to_world_pose = cam_info["cam_to_world_R"]
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n_to_1_pose = np.dot(np.linalg.inv(first_frame_to_world), n_to_world_pose)
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nR_to_1_pose = np.dot(np.linalg.inv(first_frame_to_world), nR_to_world_pose)
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depth_L, depth_R = DataLoadUtil.load_depth(view_path, cam_info['near_plane'], cam_info['far_plane'], binocular=True)
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point_cloud_L = DataLoadUtil.get_point_cloud(depth_L, cam_info['cam_intrinsic'], n_to_1_pose)['points_world']
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point_cloud_R = DataLoadUtil.get_point_cloud(depth_R, cam_info['cam_intrinsic'], nR_to_1_pose)['points_world']
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cached_data = self.load_from_cache(scene_name, first_frame_idx, frame_idx)
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if cached_data is None:
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depth_L, depth_R = DataLoadUtil.load_depth(view_path, cam_info['near_plane'], cam_info['far_plane'], binocular=True)
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point_cloud_L = DataLoadUtil.get_point_cloud(depth_L, cam_info['cam_intrinsic'], n_to_1_pose)['points_world']
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point_cloud_R = DataLoadUtil.get_point_cloud(depth_R, cam_info['cam_intrinsic'], nR_to_1_pose)['points_world']
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point_cloud_L = PtsUtil.random_downsample_point_cloud(point_cloud_L, 65536)
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point_cloud_R = PtsUtil.random_downsample_point_cloud(point_cloud_R, 65536)
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overlap_points = DataLoadUtil.get_overlapping_points(point_cloud_L, point_cloud_R)
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downsampled_target_point_cloud = PtsUtil.random_downsample_point_cloud(overlap_points, self.pts_num)
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self.save_to_cache(scene_name, first_frame_idx, frame_idx, downsampled_target_point_cloud)
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else:
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downsampled_target_point_cloud = cached_data
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point_cloud_L = PtsUtil.random_downsample_point_cloud(point_cloud_L, 65536)
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point_cloud_R = PtsUtil.random_downsample_point_cloud(point_cloud_R, 65536)
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overlap_points = DataLoadUtil.get_overlapping_points(point_cloud_L, point_cloud_R)
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downsampled_target_point_cloud = PtsUtil.random_downsample_point_cloud(overlap_points, self.pts_num)
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scanned_views_pts.append(downsampled_target_point_cloud)
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scanned_coverages_rate.append(coverage_rate)
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n_to_1_6d = PoseUtil.matrix_to_rotation_6d_numpy(np.asarray(n_to_1_pose[:3,:3]))
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@ -97,7 +145,28 @@ class NBVReconstructionDataset(BaseDataset):
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"max_coverage_rate": max_coverage_rate,
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"scene_name": scene_name
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}
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# if self.type == namespace.Mode.TEST:
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# diag = DataLoadUtil.get_bbox_diag(self.model_dir, scene_name)
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# voxel_threshold = diag*0.02
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# model_points_normals = DataLoadUtil.load_points_normals(self.root_dir, scene_name)
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# pts_list = []
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# for view in scanned_views:
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# frame_idx = view[0]
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# view_path = DataLoadUtil.get_path(self.root_dir, scene_name, frame_idx)
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# point_cloud = DataLoadUtil.get_target_point_cloud_world_from_path(view_path, binocular=True)
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# cam_params = DataLoadUtil.load_cam_info(view_path, binocular=True)
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# sampled_point_cloud = ReconstructionUtil.filter_points(point_cloud, model_points_normals, cam_pose=cam_params["cam_to_world"], voxel_size=voxel_threshold, theta=self.filter_degree)
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# pts_list.append(sampled_point_cloud)
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# nL_to_world_pose = cam_params["cam_to_world"]
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# nO_to_world_pose = cam_params["cam_to_world_O"]
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# nO_to_nL_pose = np.dot(np.linalg.inv(nL_to_world_pose), nO_to_world_pose)
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# data_item["scanned_target_pts_list"] = pts_list
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# data_item["model_points_normals"] = model_points_normals
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# data_item["voxel_threshold"] = voxel_threshold
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# data_item["filter_degree"] = self.filter_degree
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# data_item["scene_path"] = os.path.join(self.root_dir, scene_name)
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# data_item["first_frame_to_world"] = np.asarray(first_frame_to_world, dtype=np.float32)
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# data_item["nO_to_nL_pose"] = np.asarray(nO_to_nL_pose, dtype=np.float32)
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return data_item
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def __len__(self):
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@ -109,8 +178,10 @@ class NBVReconstructionDataset(BaseDataset):
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collate_data["scanned_pts"] = [torch.tensor(item['scanned_pts']) for item in batch]
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collate_data["scanned_n_to_1_pose_9d"] = [torch.tensor(item['scanned_n_to_1_pose_9d']) for item in batch]
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collate_data["best_to_1_pose_9d"] = torch.stack([torch.tensor(item['best_to_1_pose_9d']) for item in batch])
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if "first_frame_to_world" in batch[0]:
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collate_data["first_frame_to_world"] = torch.stack([torch.tensor(item["first_frame_to_world"]) for item in batch])
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for key in batch[0].keys():
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if key not in ["scanned_pts", "scanned_n_to_1_pose_9d", "best_to_1_pose_9d"]:
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if key not in ["scanned_pts", "scanned_n_to_1_pose_9d", "best_to_1_pose_9d", "first_frame_to_world"]:
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collate_data[key] = [item[key] for item in batch]
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return collate_data
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return collate_fn
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@ -123,10 +194,13 @@ if __name__ == "__main__":
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config = {
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"root_dir": "/media/hofee/data/project/python/nbv_reconstruction/sample_for_training/scenes",
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"split_file": "/media/hofee/data/project/python/nbv_reconstruction/sample_for_training/OmniObject3d_train.txt",
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"model_dir": "/media/hofee/data/data/scaled_object_meshes",
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"ratio": 0.5,
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"batch_size": 2,
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"filter_degree": 75,
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"num_workers": 0,
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"pts_num": 32684
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"pts_num": 32684,
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"type": namespace.Mode.TEST,
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}
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ds = NBVReconstructionDataset(config)
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print(len(ds))
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@ -135,7 +209,9 @@ if __name__ == "__main__":
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for idx, data in enumerate(dl):
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data = ds.process_batch(data, "cuda:0")
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print(data)
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break
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# ------ Debug Start ------
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import ipdb;ipdb.set_trace()
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# ------ Debug End ------
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#
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# for idx, data in enumerate(dl):
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# cnt=0
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@ -1,10 +1,43 @@
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import torch
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import os
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import json
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import numpy as np
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import subprocess
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import tempfile
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from utils.data_load import DataLoadUtil
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from utils.reconstruction import ReconstructionUtil
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from utils.pose import PoseUtil
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from utils.pts import PtsUtil
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import PytorchBoot.stereotype as stereotype
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import PytorchBoot.namespace as namespace
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from PytorchBoot.utils.log_util import Log
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def get_view_data(cam_pose, scene_name):
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pass
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def render_pts(cam_pose, scene_path,script_path, model_points_normals, voxel_threshold=0.005, filter_degree=75, nO_to_nL_pose=None):
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nO_to_world_pose = cam_pose.cpu().numpy() @ nO_to_nL_pose
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nO_to_world_pose = DataLoadUtil.cam_pose_transformation(nO_to_world_pose)
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with tempfile.TemporaryDirectory() as temp_dir:
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params = {
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"cam_pose": nO_to_world_pose.tolist(),
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"scene_path": scene_path
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}
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params_data_path = os.path.join(temp_dir, "params.json")
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with open(params_data_path, 'w') as f:
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json.dump(params, f)
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result = subprocess.run([
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'blender', '-b', '-P', script_path, '--', temp_dir
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], capture_output=True, text=True)
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if result.returncode != 0:
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print("Blender script failed:")
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print(result.stderr)
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return None
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path = os.path.join(temp_dir, "tmp")
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point_cloud = DataLoadUtil.get_target_point_cloud_world_from_path(path, binocular=True)
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cam_params = DataLoadUtil.load_cam_info(path, binocular=True)
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sampled_point_cloud = ReconstructionUtil.filter_points(point_cloud, model_points_normals, cam_pose=cam_params["cam_to_world"], voxel_size=voxel_threshold, theta=filter_degree)
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return sampled_point_cloud
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@stereotype.evaluation_method("pose_diff")
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class PoseDiff:
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@ -36,11 +69,11 @@ class PoseDiff:
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@stereotype.evaluation_method("coverage_rate_increase",comment="unfinished")
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@stereotype.evaluation_method("coverage_rate_increase")
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class ConverageRateIncrease:
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def __init__(self, config):
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self.config = config
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self.renderer_path = config["renderer_path"]
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def evaluate(self, output_list, data_list):
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results = {namespace.TensorBoard.SCALAR: {}}
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@ -48,31 +81,57 @@ class ConverageRateIncrease:
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pred_coverate_increase_list = []
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cr_diff_list = []
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for output, data in zip(output_list, data_list):
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scanned_cr = data['scanned_coverages_rate']
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scanned_cr = data['scanned_coverage_rate']
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gt_cr = data["best_coverage_rate"]
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scene_name_list = data['scene_name']
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scanned_view_pts_list = data['scanned_pts']
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scene_path_list = data['scene_path']
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model_points_normals_list = data['model_points_normals']
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scanned_view_pts_list = data['scanned_target_pts_list']
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pred_pose_9ds = output['pred_pose_9d']
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pred_rot_mats = PoseUtil.rotation_6d_to_matrix_tensor_batch(pred_pose_9ds[:, :6])
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pred_pose_mats = torch.cat([pred_rot_mats, pred_pose_9ds[:, 6:]], dim=-1)
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nO_to_nL_pose_batch = data["nO_to_nL_pose"]
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voxel_threshold_list = data["voxel_threshold"]
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filter_degree_list = data["filter_degree"]
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first_frame_to_world = data["first_frame_to_world"]
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pred_n_to_1_pose_mats = torch.eye(4, device=pred_pose_9ds.device).unsqueeze(0).repeat(pred_pose_9ds.shape[0], 1, 1)
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pred_n_to_1_pose_mats[:,:3,:3] = PoseUtil.rotation_6d_to_matrix_tensor_batch(pred_pose_9ds[:, :6])
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pred_n_to_1_pose_mats[:,:3,3] = pred_pose_9ds[:, 6:]
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pred_n_to_world_pose_mats = torch.matmul(first_frame_to_world, pred_n_to_1_pose_mats)
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for idx in range(len(scanned_cr)):
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gt_coverate_increase_list.append(gt_cr-scanned_cr[idx])
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scene_name = scene_name_list[idx]
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pred_pose = pred_pose_mats[idx]
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model_points_normals = model_points_normals_list[idx]
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scanned_view_pts = scanned_view_pts_list[idx]
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view_data = get_view_data(pred_pose, scene_name)
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pred_cr = self.compute_coverage_rate(pred_pose, scanned_view_pts, view_data)
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pred_coverate_increase_list.append(pred_cr-scanned_cr[idx])
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cr_diff_list.append(gt_cr-pred_cr)
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voxel_threshold = voxel_threshold_list[idx]
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model_pts = model_points_normals[:,:3]
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down_sampled_model_pts = PtsUtil.voxel_downsample_point_cloud(model_pts, voxel_threshold)
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old_scanned_cr = self.compute_coverage_rate(scanned_view_pts, None, down_sampled_model_pts, threshold=voxel_threshold)
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gt_coverate_increase_list.append(gt_cr[idx]-old_scanned_cr)
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scene_path = scene_path_list[idx]
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pred_pose = pred_n_to_world_pose_mats[idx]
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filter_degree = filter_degree_list[idx]
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nO_to_nL_pose = nO_to_nL_pose_batch[idx]
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try:
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new_pts = render_pts(pred_pose, scene_path, self.renderer_path, model_points_normals, voxel_threshold=voxel_threshold, filter_degree=filter_degree, nO_to_nL_pose=nO_to_nL_pose)
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pred_cr = self.compute_coverage_rate(scanned_view_pts, new_pts, down_sampled_model_pts, threshold=voxel_threshold)
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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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pred_cr = old_scanned_cr
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pred_coverate_increase_list.append(pred_cr-old_scanned_cr)
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cr_diff_list.append(gt_cr[idx]-pred_cr)
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results[namespace.TensorBoard.SCALAR]["gt_cr_increase"] = float(sum(gt_coverate_increase_list) / len(gt_coverate_increase_list))
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results[namespace.TensorBoard.SCALAR]["pred_cr_increase"] = float(sum(pred_coverate_increase_list) / len(pred_coverate_increase_list))
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results[namespace.TensorBoard.SCALAR]["cr_diff"] = float(sum(cr_diff_list) / len(cr_diff_list))
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return results
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def compute_coverage_rate(self, pred_pose, scanned_view_pts, view_data):
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pass
|
||||
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)
|
||||
|
||||
|
||||
|
||||
|
||||
@ -5,7 +5,7 @@ import PytorchBoot.stereotype as stereotype
|
||||
from PytorchBoot.factory.component_factory import ComponentFactory
|
||||
from PytorchBoot.utils import Log
|
||||
|
||||
@stereotype.pipeline("nbv_reconstruction_pipeline")
|
||||
@stereotype.pipeline("nbv_reconstruction_pipeline", comment="should be tested")
|
||||
class NBVReconstructionPipeline(nn.Module):
|
||||
def __init__(self, config):
|
||||
super(NBVReconstructionPipeline, self).__init__()
|
||||
@ -72,10 +72,14 @@ class NBVReconstructionPipeline(nn.Module):
|
||||
pose_feat_seq_list = []
|
||||
|
||||
for scanned_pts,scanned_n_to_1_pose_9d in zip(scanned_pts_batch,scanned_n_to_1_pose_9d_batch):
|
||||
|
||||
scanned_pts = scanned_pts.to(best_to_1_pose_9d_batch.device)
|
||||
scanned_n_to_1_pose_9d = scanned_n_to_1_pose_9d.to(best_to_1_pose_9d_batch.device)
|
||||
pts_feat_seq_list.append(self.pts_encoder.encode_points(scanned_pts))
|
||||
pose_feat_seq_list.append(self.pose_encoder.encode_pose(scanned_n_to_1_pose_9d))
|
||||
|
||||
seq_feat = self.seq_encoder.encode_sequence(pts_feat_seq_list, pose_feat_seq_list)
|
||||
if torch.isnan(seq_feat).any():
|
||||
Log.error("nan in seq_feat", True)
|
||||
return seq_feat
|
||||
|
||||
|
||||
@ -177,6 +177,9 @@ class DataLoadUtil:
|
||||
cam_to_world_R = np.asarray(label_data["extrinsic_R"])
|
||||
cam_to_world_R = DataLoadUtil.cam_pose_transformation(cam_to_world_R)
|
||||
cam_info["cam_to_world_R"] = cam_to_world_R
|
||||
cam_to_world_O = np.asarray(label_data["extrinsic_cam_object"])
|
||||
cam_to_world_O = DataLoadUtil.cam_pose_transformation(cam_to_world_O)
|
||||
cam_info["cam_to_world_O"] = cam_to_world_O
|
||||
return cam_info
|
||||
|
||||
@staticmethod
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user