after first overfit test
This commit is contained in:
102
core/dataset.py
102
core/dataset.py
@@ -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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@@ -44,7 +65,27 @@ class NBVReconstructionDataset(BaseDataset):
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}
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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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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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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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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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