-
Notifications
You must be signed in to change notification settings - Fork 21
/
visualize_demo.py
281 lines (254 loc) · 10.8 KB
/
visualize_demo.py
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
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
from pyvirtualdisplay import Display
display = Display(visible=False, size=(2560, 1440))
display.start()
from mayavi import mlab
import mayavi
mlab.options.offscreen = True
print("Set mlab.options.offscreen={}".format(mlab.options.offscreen))
import time, argparse, os.path as osp, os
import torch, numpy as np
import mmcv
from mmengine import Config
from mmengine.runner import set_random_seed
from mmengine.logging import MMLogger
from mmengine.registry import MODELS
import warnings
warnings.filterwarnings("ignore")
def pass_print(*args, **kwargs):
pass
def get_grid_coords(dims, resolution):
"""
:param dims: the dimensions of the grid [x, y, z] (i.e. [256, 256, 32])
:return coords_grid: is the center coords of voxels in the grid
"""
g_xx = np.arange(0, dims[0]) # [0, 1, ..., 256]
# g_xx = g_xx[::-1]
g_yy = np.arange(0, dims[1]) # [0, 1, ..., 256]
# g_yy = g_yy[::-1]
g_zz = np.arange(0, dims[2]) # [0, 1, ..., 32]
# Obtaining the grid with coords...
xx, yy, zz = np.meshgrid(g_xx, g_yy, g_zz)
coords_grid = np.array([xx.flatten(), yy.flatten(), zz.flatten()]).T
coords_grid = coords_grid.astype(np.float32)
resolution = np.array(resolution, dtype=np.float32).reshape([1, 3])
coords_grid = (coords_grid * resolution) + resolution / 2
return coords_grid
def draw(
voxels, # semantic occupancy predictions
pred_pts, # lidarseg predictions
vox_origin,
voxel_size=0.2, # voxel size in the real world
grid=None, # voxel coordinates of point cloud
pt_label=None, # label of point cloud
save_dir=None,
cam_positions=None,
focal_positions=None,
timestamp=None,
mode=0,
sem=False,
):
w, h, z = voxels.shape
# Compute the voxels coordinates
grid_coords = get_grid_coords(
[voxels.shape[0], voxels.shape[1], voxels.shape[2]], voxel_size
) + np.array(vox_origin, dtype=np.float32).reshape([1, 3])
if mode == 0:
grid_coords = np.vstack([grid_coords.T, voxels.reshape(-1)]).T
elif mode == 1:
indexes = grid[:, 0] * h * z + grid[:, 1] * z + grid[:, 2]
indexes, pt_index = np.unique(indexes, return_index=True)
pred_pts = pred_pts[pt_index]
grid_coords = grid_coords[indexes]
grid_coords = np.vstack([grid_coords.T, pred_pts.reshape(-1)]).T
elif mode == 2:
indexes = grid[:, 0] * h * z + grid[:, 1] * z + grid[:, 2]
indexes, pt_index = np.unique(indexes, return_index=True)
gt_label = pt_label[pt_index]
grid_coords = grid_coords[indexes]
grid_coords = np.vstack([grid_coords.T, gt_label.reshape(-1)]).T
else:
raise NotImplementedError
# Get the voxels inside FOV
fov_grid_coords = grid_coords
# Remove empty and unknown voxels
fov_voxels = fov_grid_coords[
(fov_grid_coords[:, 3] > 0) & (fov_grid_coords[:, 3] < 17)
]
print(len(fov_voxels))
figure = mlab.figure(size=(2560, 1440), bgcolor=(1, 1, 1))
voxel_size = sum(voxel_size) / 3
plt_plot_fov = mlab.points3d(
# fov_voxels[:, 1],
# fov_voxels[:, 0],
fov_voxels[:, 0],
fov_voxels[:, 1],
fov_voxels[:, 2],
fov_voxels[:, 3],
scale_factor=1.0 * voxel_size,
mode="cube",
opacity=1.0,
vmin=1,
vmax=16, # 16
)
colors = np.array(
[
[255, 120, 50, 255], # barrier orange
[255, 192, 203, 255], # bicycle pink
[255, 255, 0, 255], # bus yellow
[ 0, 150, 245, 255], # car blue
[ 0, 255, 255, 255], # construction_vehicle cyan
[255, 127, 0, 255], # motorcycle dark orange
[255, 0, 0, 255], # pedestrian red
[255, 240, 150, 255], # traffic_cone light yellow
[135, 60, 0, 255], # trailer brown
[160, 32, 240, 255], # truck purple
[255, 0, 255, 255], # driveable_surface dark pink
# [175, 0, 75, 255], # other_flat dark red
[139, 137, 137, 255],
[ 75, 0, 75, 255], # sidewalk dard purple
[150, 240, 80, 255], # terrain light green
[230, 230, 250, 255], # manmade white
[ 0, 175, 0, 255], # vegetation green
# [ 0, 255, 127, 255], # ego car dark cyan
# [255, 99, 71, 255], # ego car
# [ 0, 191, 255, 255] # ego car
]
).astype(np.uint8)
plt_plot_fov.glyph.scale_mode = "scale_by_vector"
plt_plot_fov.module_manager.scalar_lut_manager.lut.table = colors
mlab.savefig(os.path.join(save_dir, f'vis_{timestamp}.png'))
mlab.close()
def main(args):
# global settings
set_random_seed(args.seed)
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
# load config
cfg = Config.fromfile(args.py_config)
cfg.work_dir = args.work_dir
os.makedirs(args.work_dir, exist_ok=True)
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(args.work_dir, f'{cfg.get("data_type", "gts")}_visualize_autoreg_{timestamp}.log')
logger = MMLogger('genocc', log_file=log_file)
MMLogger._instance_dict['genocc'] = logger
logger.info(f'Config:\n{cfg.pretty_text}')
# build model
import model
my_model = MODELS.build(cfg.model)
my_model.init_weights()
n_parameters = sum(p.numel() for p in my_model.parameters() if p.requires_grad)
logger.info(f'Number of params: {n_parameters}')
my_model = my_model.cuda()
raw_model = my_model
logger.info('done ddp model')
from dataset import get_dataloader
train_dataset_loader, val_dataset_loader = get_dataloader(
cfg.train_dataset_config,
cfg.val_dataset_config,
cfg.train_wrapper_config,
cfg.val_wrapper_config,
cfg.train_loader,
cfg.val_loader,
dist=False)
cfg.resume_from = ''
if osp.exists(osp.join(args.work_dir, 'latest.pth')):
cfg.resume_from = osp.join(args.work_dir, 'latest.pth')
if args.resume_from:
cfg.resume_from = args.resume_from
logger.info('resume from: ' + cfg.resume_from)
logger.info('work dir: ' + args.work_dir)
epoch = 'last'
if cfg.resume_from and osp.exists(cfg.resume_from):
map_location = 'cpu'
ckpt = torch.load(cfg.resume_from, map_location=map_location)
print(raw_model.load_state_dict(ckpt['state_dict'], strict=False))
epoch = ckpt['epoch']
print(f'successfully resumed from epoch {epoch}')
elif cfg.load_from:
ckpt = torch.load(cfg.load_from, map_location='cpu')
if 'state_dict' in ckpt:
state_dict = ckpt['state_dict']
else:
state_dict = ckpt
print(raw_model.load_state_dict(state_dict, strict=False))
# eval
my_model.eval()
os.environ['eval'] = 'true'
recon_dir = os.path.join(args.work_dir, args.dir_name+f'{cfg.get("data_type", "gts")}_autoreg', str(epoch))
os.makedirs(recon_dir, exist_ok=True)
dataset = cfg.val_dataset_config['type']
recon_dir = os.path.join(recon_dir, dataset)
start_frame = 48
with torch.no_grad():
for i_iter_val, (input_occs, target_occs, metas) in enumerate(val_dataset_loader):
if i_iter_val not in args.scene_idx:
continue
if i_iter_val > max(args.scene_idx):
break
'''
if i_iter_val < start_frame:
continue'''
input_occs = input_occs.cuda()
#result = my_model(x=input_occs, metas=metas)
result = my_model.forward_autoreg_with_pose(
x=input_occs, metas=metas,
start_frame=cfg.get('start_frame', 0),
mid_frame=cfg.get('mid_frame', 5),
end_frame=cfg.get('end_frame', 11))
logits = result['logits']
n_frames = logits.shape[1]
dst_dir = os.path.join(recon_dir, str(i_iter_val))
input_dir = os.path.join(recon_dir, f'{i_iter_val}_input')
input_occs = result['input_occs']
os.makedirs(dst_dir, exist_ok=True)
os.makedirs(input_dir, exist_ok=True)
assert n_frames < input_occs.shape[1]
for frame in range(n_frames+1):
input_occ = input_occs[:, frame, ...].squeeze().cpu().numpy()
draw(input_occ,
None, # predict_pts,
[-40, -40, -1],
[0.4] * 3,
None, # grid.squeeze(0).cpu().numpy(),
None,# pt_label.squeeze(-1),
input_dir,#recon_dir,
None, # img_metas[0]['cam_positions'],
None, # img_metas[0]['focal_positions'],
timestamp=str(i_iter_val) + '_' + str(frame),
mode=0,
sem=False)
if frame == n_frames:
continue
logit = logits[:, frame, ...]
pred = logit.argmax(dim=-1).squeeze().cpu().numpy() # 1, 1, 200, 200, 16
draw(pred,
None, # predict_pts,
[-40, -40, -1],
[0.4] * 3,
None, # grid.squeeze(0).cpu().numpy(),
None,# pt_label.squeeze(-1),
dst_dir,#recon_dir,
None, # img_metas[0]['cam_positions'],
None, # img_metas[0]['focal_positions'],
timestamp=str(i_iter_val) + '_' + str(frame),
mode=0,
sem=False)
logger.info('[EVAL] Iter %5d / %5d'%(i_iter_val, len(val_dataset_loader)))
logger.info(f'gt_poses_{result["gt_poses_"]}')
logger.info(f'poses_{result["poses_"]}')
if __name__ == '__main__':
# Eval settings
parser = argparse.ArgumentParser(description='')
parser.add_argument('--py-config', default='config/tpv_lidarseg.py')
parser.add_argument('--work-dir', type=str, default='./out/tpv_lidarseg')
parser.add_argument('--resume-from', type=str, default='')
parser.add_argument('--dir-name', type=str, default='vis')
parser.add_argument('--seed', type=int, default=42)
parser.add_argument('--num-trials', type=int, default=10)
parser.add_argument('--frame-idx', nargs='+', type=int, default=[0, 10])
parser.add_argument('--scene-idx', nargs='+', type=int, default=[6,7,16,18,19,87,89,96,101])
args = parser.parse_args()
ngpus = 1
args.gpus = ngpus
print(args)
main(args)