Spaces:
Paused
Paused
File size: 9,177 Bytes
aeba71c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 |
import os
import torch
import numpy as np
from scipy.spatial.transform import Rotation as R
from torch.utils.data import Dataset, DataLoader, Subset
class TrumansDataset(Dataset):
def __init__(self, folder, device, mesh_grid, batch_size=1, seq_len=32, step=1, nb_voxels=32, train=True, load_scene=True, load_action=True, no_objects=False, **kwargs):
self.device = device
self.train = train
self.load_scene = load_scene
self.load_action = load_action
# self.body_pose = np.load(os.path.join(folder, 'human_pose.npy'))
# self.transl = np.load(os.path.join(folder, 'human_transl.npy'))
# self.global_orient = np.load(os.path.join(folder, 'human_orient.npy'))
# self.motion_ind = np.load(os.path.join(folder, 'idx_start.npy'))
# self.joints = np.load(os.path.join(folder, 'human_joints.npy'))
# self.file_blend = np.load(os.path.join(folder, 'file_blend.npy'))
self.seq_len=seq_len
self.step = step
self.batch_size = batch_size
# if self.load_action:
# self.action_label = np.load(os.path.join(folder, 'action_label.npy')).astype(np.float32)
if self.load_scene:
self.mesh_grid = mesh_grid
self.nb_voxels = nb_voxels
self.no_objects = no_objects
self.nb_voxels = nb_voxels
self.scene_occ = []
self.scene_dict = {}
self.scene_folder = os.path.join(folder, 'Scene')
# self.scene_flag = np.load(os.path.join(folder, 'scene_flag.npy'))
if not no_objects:
# self.object_flag = np.load(os.path.join(folder, 'object_flag.npy'))
# self.object_mat = np.load(os.path.join(folder, 'object_mat.npy'))
self.object_occ = {}
self.object_folder = os.path.join(folder, 'Object')
for file in sorted(os.listdir(self.object_folder)):
print(f"Loading object occupied coordinates {file}")
obj_name = file.replace('.npy', '')
self.object_occ[obj_name] = torch.from_numpy(np.load(os.path.join(self.object_folder, file))).to(device)
for sid, file in enumerate(sorted(os.listdir(self.scene_folder))):
# if scene_name != '' and scene_name not in file:
# continue
print(f"{sid} Loading Scene Mesh {file}")
scene_occ = np.load(os.path.join(self.scene_folder, file))
scene_occ = torch.from_numpy(scene_occ).to(device=device, dtype=bool)
self.scene_occ.append(scene_occ)
self.scene_dict[file] = sid
self.scene_occ = torch.stack(self.scene_occ)
self.scene_grid_np = np.array([-3, 0, -4, 3, 2, 4, 300, 100, 400])
self.scene_grid_torch = torch.tensor([-3, 0, -4, 3, 2, 4, 300, 100, 400]).to(device)
self.batch_id = torch.linspace(0, batch_size - 1, batch_size).tile((nb_voxels ** 3, 1)).T\
.reshape(-1, 1).to(device=device, dtype=torch.long)
self.batch_id_obj = torch.linspace(0, batch_size - 1, batch_size).tile((9000, 1)).T \
.reshape(-1, 1).to(device=device, dtype=torch.long)
# TODO CHANGE STEP
norm = np.load(os.path.join(folder, 'norm.npy'), allow_pickle=True).item()[f'{seq_len, 3}']
self.min = norm[0].astype(np.float32)
self.max = norm[1].astype(np.float32)
self.min_torch = torch.tensor(self.min).to(device)
self.max_torch = torch.tensor(self.max).to(device)
def add_object_points(self, points, occ):
points = points.reshape(-1, 3)
voxel_size = torch.div(self.scene_grid_torch[3: 6] - self.scene_grid_torch[:3], self.scene_grid_torch[6:])
voxel = torch.div((points - self.scene_grid_torch[:3]), voxel_size)
voxel = voxel.to(dtype=torch.long)
# voxel = rearrange(voxel, 'b p c -> (b p) c')
lb = torch.all(voxel >= 0, dim=-1)
ub = torch.all(voxel < self.scene_grid_torch[6:] - 0, dim=-1)
in_bound = torch.logical_and(lb, ub)
# voxel = torch.cat([self.batch_id_obj, voxel], dim=-1)
voxel = voxel[in_bound]
occ[0, voxel[:, 0], voxel[:, 1], voxel[:, 2]] = True
def get_occ_for_points(self, points, obj_locs, scene_flag):
#TODO
# points_new = points.reshape(-1, 3)
# center_xz = points_new[:, [0, 2]].mean(axis=0)
# if torch.norm(center_xz) > 0.:
# occ_for_points = torch.load('occ_for_points_at_clear_space.pt').to(points.device)
# return occ_for_points
if isinstance(scene_flag, str):
for k, v in self.scene_dict.items():
if scene_flag in k:
scene_flag = [v]
break
batch_size = points.shape[0]
seq_len = points.shape[1]
points = points.reshape(-1, 3)
voxel_size = torch.div(self.scene_grid_torch[3: 6] - self.scene_grid_torch[:3], self.scene_grid_torch[6:])
voxel = torch.div((points - self.scene_grid_torch[:3]), voxel_size)
voxel = voxel.to(dtype=torch.long)
# voxel = rearrange(voxel, 'b p c -> (b p) c')
lb = torch.all(voxel >= 0, dim=-1)
ub = torch.all(voxel < self.scene_grid_torch[6:] - 0, dim=-1)
in_bound = torch.logical_and(lb, ub)
voxel[torch.logical_not(in_bound)] = 0
voxel = torch.cat([self.batch_id, voxel], dim=1)
occ = self.scene_occ[scene_flag]
#TODO
# occ[:] = False
# occ[:, :, 0, :] = True
# import cv2
# img = occ[0, :, 10, :].detach().cpu().numpy()
# im = np.zeros((300, 400))
# im[img] = 255
# cv2.imwrite('gray.jpg', im.T)
if obj_locs:
for obj_name, obj_loc in obj_locs.items():
obj_points = self.object_occ[obj_name].clone()
obj_points[:, 0] += obj_loc['x']
obj_points[:, 2] += obj_loc['z']
# import pdb
# pdb.set_trace()
self.add_object_points(obj_points, occ)
occ_for_points = occ[voxel[:, 0], voxel[:, 1], voxel[:, 2], voxel[:, 3]]
occ_for_points[torch.logical_not(in_bound)] = True
occ_for_points = occ_for_points.reshape(batch_size, seq_len, -1)
# torch.save(occ_for_points, 'occ_for_points_at_clear_space.pt')
# occ_for_points = torch.ones(batch_size, seq_len, 22).to('cuda')
return occ_for_points
def create_meshgrid(self, batch_size=1):
bbox = self.mesh_grid
size = (self.nb_voxels, self.nb_voxels, self.nb_voxels)
x = torch.linspace(bbox[0], bbox[1], size[0])
y = torch.linspace(bbox[2], bbox[3], size[1])
z = torch.linspace(bbox[4], bbox[5], size[2])
xx, yy, zz = torch.meshgrid(x, y, z, indexing='ij')
grid = torch.stack([xx, yy, zz], dim=-1).reshape(-1, 3)
grid = grid.repeat(batch_size, 1, 1)
# aug_z = 0.75 + torch.rand(batch_size, 1) * 0.35
# grid[:, :, 2] = grid[:, :, 2] * aug_z
return grid
@staticmethod
def combine_mesh(vert_list, face_list):
assert len(vert_list) == len(face_list)
verts = None
faces = None
for v, f in zip(vert_list, face_list):
if verts is None:
verts = v
faces = f
else:
f = f + verts.shape[0]
verts = torch.cat([verts, v])
faces = torch.cat([faces, f])
return verts, faces
@staticmethod
def transform_mesh(vert_list, trans_mats):
assert len(vert_list) == len(trans_mats)
vert_list_new = []
for v, m in zip(vert_list, trans_mats):
v = v @ m[:3, :3].T + m[:3, 3]
vert_list_new.append(v)
vert_list_new = torch.stack(vert_list_new)
return vert_list_new
def __len__(self):
return len(self.motion_ind)
def normalize(self, data):
shape_orig = data.shape
data = data.reshape((-1, 3))
# data = (data - self.mean) / self.std
data = -1. + 2. * (data - self.min) / (self.max - self.min)
data = data.reshape(shape_orig)
return data
def normalize_torch(self, data):
shape_orig = data.shape
data = data.reshape((-1, 3))
# data = (data - self.mean) / self.std
data = -1. + 2. * (data - self.min_torch) / (self.max_torch - self.min_torch)
data = data.reshape(shape_orig)
return data
def denormalize(self, data):
shape_orig = data.shape
data = data.reshape((-1, 3))
# data = data * self.std + self.mean
data = (data + 1.) * (self.max - self.min) / 2. + self.min
data = data.reshape(shape_orig)
return data
def denormalize_torch(self, data):
shape_orig = data.shape
data = data.reshape((-1, 3))
# data = data * self.std + self.mean
import pdb
data = (data + 1.) * (self.max_torch - self.min_torch) / 2. + self.min_torch
data = data.reshape(shape_orig)
return data |