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dataset.py
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dataset.py
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import os
import glob
import math
import json
import torch
import numpy as np
from torch.utils.data import Dataset
class MultipleModel(Dataset):
def __init__(self, flag, seed, pointcloud_folder, on_surface_points):
super().__init__()
self.flag = flag
self.seed = seed
self.pointcloud_folder = pointcloud_folder
self.on_surface_points = on_surface_points
self.all_filelist = self.get_all_filelist()
self.robot_state_dict = self.load_robot_state()
def get_all_filelist(self):
filelist = []
with open(os.path.join('../assets', 'datainfo', f'multiple_models_data_split_dict_{self.seed}.json'), 'r') as file:
seq_dict = json.load(file)
id_lst = seq_dict[self.flag]
for idx in id_lst:
filepath = os.path.join(self.pointcloud_folder, f'mesh_{idx}.xyzn')
filelist.append(filepath)
return filelist
def __len__(self):
return len(self.all_filelist)
def __getitem__(self, idx):
# =====> sdf
coords, normals = self.load_pcd(idx)
off_surface_samples = self.on_surface_points # **2
total_samples = self.on_surface_points + off_surface_samples
# random coords
point_cloud_size = coords.shape[0]
rand_idcs = np.random.choice(point_cloud_size, size=self.on_surface_points)
on_surface_coords = coords[rand_idcs, :]
on_surface_normals = normals[rand_idcs, :]
off_surface_coords = np.random.uniform(-1, 1, size=(off_surface_samples, 3))
off_surface_normals = np.ones((off_surface_samples, 3)) * -1
sdf = np.zeros((total_samples, 1)) # on-surface = 0
sdf[self.on_surface_points:, :] = -1 # off-surface = -1
final_coords = np.concatenate((on_surface_coords, off_surface_coords), axis=0)
final_normals = np.concatenate((on_surface_normals, off_surface_normals), axis=0)
# =====> robot state
index = self.all_filelist[idx].split('/')[-1].split('.')[0].split('_')[1]
robot_state = self.robot_state_dict[index]
sel_robot_state = np.array([robot_state[0][0], robot_state[1][0], robot_state[2][0], robot_state[3][0]])
sel_robot_state = sel_robot_state / np.pi
sel_robot_state = sel_robot_state.reshape(1, -1)
final_robot_states = np.tile(sel_robot_state, (total_samples, 1))
return {'coords': torch.from_numpy(final_coords).float(), 'states': torch.from_numpy(final_robot_states).float()},{'sdf': torch.from_numpy(sdf).float(), 'normals': torch.from_numpy(final_normals).float()}
def load_pcd(self, idx):
point_cloud = np.genfromtxt(self.all_filelist[idx])
coords = point_cloud[:, :3]
normals = point_cloud[:, 3:]
# reshape point cloud such that it lies in bounding box of (-1, 1) (distorts geometry, but makes for high sample efficiency)
coords[:, 0] = coords[:, 0] / 0.45 # (-1, 1)
coords[:, 1] = coords[:, 1] / 0.45 # (-1, 1)
coords[:, 2] = coords[:, 2] - 0.13 # zero centering (-0.13, 0.51)
coords[:, 2] = (coords[: ,2] + 0.13) / (0.51 + 0.13) # (0, 1)
coords[:, 2] = coords[:, 2] - 0.5 # (-0.5, 0.5)
coords[:, 2] = coords[:, 2] / 0.5 # (-1, 1)
return coords, normals
def load_robot_state(self):
robot_state_filepath = os.path.join(self.pointcloud_folder, 'robot_state.json')
with open(robot_state_filepath, 'r') as file:
robot_state_dict = json.load(file)
return robot_state_dict
class MultipleModelLink(Dataset):
def __init__(self, flag, seed, pointcloud_folder):
super().__init__()
self.flag = flag
self.seed = seed
self.pointcloud_folder = pointcloud_folder
self.all_filelist = self.get_all_filelist()
self.robot_state_dict = self.load_robot_state()
def get_all_filelist(self):
filelist = []
if self.flag == 'val':
for idx in range(10000, 11000):
filepath = os.path.join(self.pointcloud_folder, f'mesh_{idx}.xyzn')
filelist.append(filepath)
else:
with open(os.path.join('../assets', 'datainfo', f'multiple_models_data_split_dict_{self.seed}.json'), 'r') as file:
seq_dict = json.load(file)
id_lst = seq_dict[self.flag]
for idx in id_lst:
filepath = os.path.join(self.pointcloud_folder, f'mesh_{idx}.xyzn')
filelist.append(filepath)
return filelist
def __len__(self):
return len(self.all_filelist)
def __getitem__(self, idx):
# =====> robot state
index = self.all_filelist[idx].split('/')[-1].split('.')[0].split('_')[1]
robot_state = self.robot_state_dict[index]
sel_robot_state = np.array([robot_state[0][0], robot_state[1][0], robot_state[2][0], robot_state[3][0]])
tar_robot_state = np.array([robot_state[5][0], robot_state[5][1], robot_state[5][2]])
sel_robot_state = sel_robot_state / np.pi
return {'states': torch.from_numpy(sel_robot_state).float()},{'target_states': torch.from_numpy(tar_robot_state).float()}
def load_robot_state(self):
if self.flag == 'val':
robot_state_filepath = os.path.join(self.pointcloud_folder, 'robot_state_kinematic_val.json')
else:
robot_state_filepath = os.path.join(self.pointcloud_folder, 'robot_state.json')
with open(robot_state_filepath, 'r') as file:
robot_state_dict = json.load(file)
return robot_state_dict