-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathtrain_pfc_launch.py
522 lines (484 loc) · 28.2 KB
/
train_pfc_launch.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
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
import os, time, argparse, os.path as osp, numpy as np
import torch
import torch.distributed as dist
# os.environ['CUDA_VISIBLE_DEVICES'] = '2'
import random
from torch.optim.lr_scheduler import MultiStepLR, CosineAnnealingLR, CosineAnnealingWarmRestarts
from torch.nn.parallel import DistributedDataParallel as DDP
from mmengine import Config
from mmengine.optim.scheduler.lr_scheduler import CosineAnnealingParamScheduler
from mmengine.logging.logger import MMLogger
from mmengine.utils import symlink
from timm.scheduler import CosineLRScheduler # 0.4.12
# from torch.optim.lr_scheduler import
from mmengine.optim import CosineAnnealingParamScheduler
from utils.AppLogger import AppLogger
import torch.optim as optim
# from network.PFC import PFC
from network.PFC import PFC
from network.P3Former import P3Former
from nuscenes import NuScenes
from dataloader.dataset import collate_fn_OV, Nuscenes_pt, spherical_dataset, OV_Nuscenes_pt,collate_dataset_info, SemKITTI_pt,ov_spherical_dataset,close_spherical_dataset,Close_Nuscenes_pt
import warnings
warnings.filterwarnings("ignore")
from mmengine import ProgressBar
import yaml
from tqdm import tqdm
from utils.load_save_util import revise_ckpt,revise_ckpt_2,SemKITTI2train,transform_map,inverse_transform, SemKITTI2train_single, get_model
from utils.eval_pq import PanopticEval,OV_PanopticEval
from utils.metric_util import per_class_iu, fast_hist_crop
from utils.metric_util import cal_PQ_dagger
from mmengine import Config
import pickle
import datetime
import shutil
warnings.filterwarnings("ignore")
def pass_print(*args, **kwargs):
pass
def main():
torch.set_num_threads(6)
# Training settings
parser = argparse.ArgumentParser(description='')
# parser.add_argument('--launcher', choices=['none', 'pytorch'], default='pytorch')
parser.add_argument('-c', '--configs', default='configs/open_pa_po_nuscenes_pfc.py')
parser.add_argument('-w', '--work_dir', default='work_dir/nusc_pfc_test/')
parser.add_argument("--local-rank", default=-1, type=int)
parser.add_argument('-r', "--resume", type=str, default='')
args = parser.parse_args()
print(args)
if "WORLD_SIZE" in os.environ.keys() and int(os.environ["WORLD_SIZE"]) > 1:
args.local_rank = int(os.environ['LOCAL_RANK'])
if args.local_rank == 0:
print("|| MASTER_ADDR:",os.environ["MASTER_ADDR"],
"|| MASTER_PORT:",os.environ["MASTER_PORT"],
"|| LOCAL_RANK:",os.environ["LOCAL_RANK"],
"|| RANK:",os.environ["RANK"],
"|| WORLD_SIZE:",os.environ["WORLD_SIZE"])
# global settings
torch.backends.cudnn.benchmark = True # 是否自动加速，自动选择合适算法，false选择固定算法
torch.backends.cudnn.deterministic = True # 为了消除该算法本身的不确定性
# load config
cfg =Config.fromfile(args.configs)
cfg.work_dir = args.work_dir
# 分布式初始化
if args.local_rank != -1:
seed = 1
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend="nccl", timeout=datetime.timedelta(seconds=5400))
torch.cuda.set_device(args.local_rank)
if args.local_rank != 0:
import builtins
builtins.print = pass_print
# configure logger
if args.local_rank < 1:
os.makedirs(args.work_dir, exist_ok=True)
cfg.dump(osp.join(args.work_dir, osp.basename(args.configs)))
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = osp.join(args.work_dir, f'{timestamp}.log')
logger = MMLogger(name='train_log', log_file=log_file, log_level='INFO')
logger.info(f'Config:\n{cfg.pretty_text}')
datasetname = cfg['dataset']['name']
version = cfg['dataset']['version']
data_path = cfg['dataset']['path']
num_worker = cfg['dataset']['num_worker']
train_batch_size = cfg['model']['train_batch_size']
val_batch_size = cfg['model']['val_batch_size']
model_load_path = cfg['model']['model_load_path']
model_save_path = cfg['model']['model_save_path']
lr = cfg['model']['learning_rate']
lr_step = cfg['model']['LR_MILESTONES']
lr_gamma = cfg['model']['LR_GAMMA']
grid_size = cfg['dataset']['grid_size']
pix_fusion = cfg['model']['pix_fusion']
min_points = cfg['dataset']['min_points']
cumulative_iters = 1
# 初始化类别名称和数量，不包括noise类。
unique_label, unique_label_str = collate_dataset_info(cfg)
# 加noise类
nclasses = len(unique_label) + 1
my_model = PFC(cfg, nclasses)
my_model.init_weights()
if args.local_rank != -1:
my_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(my_model)
my_model = my_model.cuda()
if args.local_rank != -1:
find_unused_parameters = cfg.get('find_unused_parameters', True)
my_model = torch.nn.parallel.DistributedDataParallel(my_model, device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=find_unused_parameters)
print('done ddp model')
# NuScenes: MultiStepLR; SemanticKitti: CosineAnnealingLR, CosineAnnealingWarmRestarts
optimizer = optim.Adam(my_model.parameters(), lr=lr,weight_decay=0.01)
scheduler_steplr = MultiStepLR(optimizer, milestones=lr_step, gamma=lr_gamma,)
cfg.resume = ''
if osp.exists(osp.join(osp.abspath(args.work_dir), 'latest.pth')):
cfg.resume = osp.join(osp.abspath(args.work_dir), 'latest.pth')
if args.resume!='':
cfg.resume = args.resume
print('resume from: ', cfg.resume)
print('work dir: ', args.work_dir)
if cfg.resume and osp.exists(cfg.resume):
map_location = 'cpu'
ckpt = torch.load(cfg.resume, map_location=map_location)
print(my_model.load_state_dict(revise_ckpt(ckpt['state_dict']), strict=False))
optimizer.load_state_dict(ckpt['optimizer'])
scheduler_steplr.load_state_dict(ckpt['scheduler'])
epoch = ckpt['epoch']
if 'best_miou' in ckpt:
best_miou = ckpt['best_miou']
if 'best_pq' in ckpt:
best_pq = ckpt['best_pq']
global_iter = ckpt['global_iter']
print(f'successfully resumed from epoch {epoch}')
elif cfg.model.model_load_path:
ckpt = torch.load(cfg.model.model_load_path, map_location='cpu')
if 'state_dict' in ckpt:
state_dict = ckpt['state_dict']
else:
state_dict = ckpt
state_dict = revise_ckpt(state_dict)
try:
print(my_model.load_state_dict(state_dict, strict=False))
except:
state_dict = revise_ckpt_2(state_dict)
print(my_model.load_state_dict(state_dict, strict=False))
n_parameters = sum(p.numel() for p in my_model.parameters() if p.requires_grad)
logger.info(f'Number of params: {n_parameters}')
logger.info(f'Model:\n{my_model}')
# scheduler_steplr = CosineAnnealingLR(optimizer, T_max=5, eta_min=1e-8, verbose=True)
# scheduler_steplr = CosineAnnealingWarmRestarts(optimizer, T_0=5, eta_min=1e-8, verbose=True)
if datasetname == 'SemanticKitti':
train_pt_dataset = SemKITTI_pt(os.path.join(data_path, 'dataset', 'sequences'), cfg, split='train', return_ref=True)
val_pt_dataset = SemKITTI_pt(os.path.join(data_path, 'dataset', 'sequences'), cfg, split='val', return_ref=True)
elif datasetname == 'nuscenes':
nusc = NuScenes(version=version, dataroot=data_path, verbose=True)
assert version == "v1.0-trainval" or version == "v1.0-mini"
train_pt_dataset = OV_Nuscenes_pt(data_path, split='train', cfgs=cfg, nusc=nusc, version=version)
val_pt_dataset = OV_Nuscenes_pt(data_path, split='val', cfgs=cfg, nusc=nusc, version=version)
else:
raise NotImplementedError
train_dataset = ov_spherical_dataset(train_pt_dataset, cfg, ignore_label=0)
val_dataset = ov_spherical_dataset(val_pt_dataset, cfg, ignore_label=0, use_aug=False)
collate_fn = collate_fn_OV
if args.local_rank != -1:
sampler = torch.utils.data.distributed.DistributedSampler(train_dataset,shuffle=True, drop_last=True)
val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False,drop_last=False)
else:
sampler = None
val_sampler = None
train_dataset_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=train_batch_size,
collate_fn=collate_fn,
pin_memory=True,
sampler=sampler,
num_workers=num_worker)
val_dataset_loader = torch.utils.data.DataLoader(dataset=val_dataset,
batch_size=val_batch_size,
collate_fn=collate_fn,
pin_memory=True,
sampler=val_sampler,
num_workers=num_worker)
if datasetname == 'nuscenes':
with open("nuscenes.yaml", 'r') as stream:
nuscenesyaml = yaml.safe_load(stream)
learning_map = nuscenesyaml['learning_map']
# resume and load
start_train = True
epoch = 0
best_miou, best_pq = 0.0, 0.0
global_iter = 0
print_freq = cfg.model.print_freq
evaluator = OV_PanopticEval(nclasses, None, [0], min_points=min_points,offset=2**32)
loss_fn_dict ={
'sem_loss':[],
'class_loss':[],
'mask_loss':[],
'dice_loss':[],
'dice_pos_loss':[]
}
avg_loss = 0.0
to_cuda_list = ['voxel2point_map','point2voxel_map','pol_voxel_ind','grid_mask',
'voxel_instance_labels','point_mask','clip_features','text_features']
while epoch < cfg['model']['max_epoch']:
if start_train:
if args.local_rank < 1:
print(f"Epoch {epoch} => Start Training...")
my_model.train()
if hasattr(train_dataset_loader.sampler, 'set_epoch'):
train_dataset_loader.sampler.set_epoch(epoch)
# for cumulative_iters > 1
if cumulative_iters > 1:
total_iters = len(train_dataset_loader)
divisible_iters = total_iters // cumulative_iters * cumulative_iters
remainder_iters = total_iters - divisible_iters
logger.info(f'cumulative_iters: {cumulative_iters}, total_iters: {total_iters}, \
divisible_iters: {divisible_iters}, remainder_iters: {remainder_iters}')
loss_list = []
# time.sleep(1)
data_time_s = time.time()
time_s = time.time()
bar = tqdm(total=len(train_dataset_loader))
get_model(my_model).label_map = transform_map(np.hstack([0,val_pt_dataset.base_thing_list,val_pt_dataset.base_stuff_list,val_pt_dataset.novel_thing_list,val_pt_dataset.novel_stuff_list]))
get_model(my_model).label_inverse_map = inverse_transform(get_model(my_model).label_map)
get_model(my_model).thing_class = np.sort(np.vectorize(get_model(my_model).label_inverse_map.__getitem__)(train_pt_dataset.thing_list))
get_model(my_model).stuff_class = np.sort(np.vectorize(get_model(my_model).label_inverse_map.__getitem__)(train_pt_dataset.stuff_list))
get_model(my_model).total_class = np.sort(np.vectorize(get_model(my_model).label_inverse_map.__getitem__)(np.hstack([0,train_pt_dataset.base_thing_list+train_pt_dataset.base_stuff_list])))
get_model(my_model).categroy_overlapping_mask = torch.from_numpy(np.hstack((np.full(1,True,dtype=bool),np.full(len(train_pt_dataset.base_thing_list+train_pt_dataset.base_stuff_list), True, dtype=bool),np.full(len(val_pt_dataset.novel_thing_list+val_pt_dataset.novel_stuff_list),False,dtype=bool))))
# if distributed:
# torch.distributed.barrier()
for i_iter, data in enumerate(train_dataset_loader):
for k in to_cuda_list:
if isinstance(data[k],list):
for i in range(len(data[k])):
data[k][i] = torch.from_numpy(data[k][i]).cuda()
else:
data[k] = torch.from_numpy(data[k]).cuda()
data['voxel_semantic_labels'] = [torch.from_numpy(np.vectorize(get_model(my_model).label_inverse_map.__getitem__)(i)).type(torch.LongTensor).cuda() for i in data['voxel_semantic_labels']]
data_time_e = time.time()
# with torch.cuda.amp.autocast():
loss_dict = my_model(data)
loss = torch.sum(torch.stack(list(loss_dict.values())),dim=0)
sem_loss = np.nanmean([loss_dict[k].detach().cpu().numpy() for k in loss_dict.keys() if k=='loss_ce'or k=='loss_lovasz'])
cls_loss = np.nanmean([loss_dict[k].detach().cpu().numpy() for k in loss_dict.keys() if k.startswith('loss_cls')])
mask_loss = np.nanmean([loss_dict[k].detach().cpu().numpy() for k in loss_dict.keys() if k.startswith('loss_mask')])
dice_loss = np.nanmean([loss_dict[k].detach().cpu().numpy() for k in loss_dict.keys() if k.startswith('loss_dice') and not k.startswith('loss_dice_pos')])
dice_pos_loss = np.nanmean([loss_dict[k].detach().cpu().numpy() for k in loss_dict.keys() if k.startswith('loss_dice_pos')])
loss_cpu = torch.tensor(loss, device="cpu").item()
avg_loss = i_iter / (i_iter + 1) * avg_loss + 1 / (i_iter + 1) * loss_cpu
if cumulative_iters > 1:
loss_factor = cumulative_iters if i_iter < divisible_iters else remainder_iters
loss_list.append(loss.item())
loss = loss / loss_factor
loss.backward()
# scaler.scale(loss).backward()
if (i_iter+1) % cumulative_iters == 0 or i_iter + 1 == len(train_dataset_loader):
# scaler.unscale_(optimizer)
# grad_norm = torch.nn.utils.clip_grad_norm_(my_model.parameters(), cfg.grad_max_norm)
optimizer.step()
# scaler.step(optimizer)
# scaler.update()
optimizer.zero_grad()
else:
loss.backward()
# scaler.scale(loss).backward()
# scaler.unscale_(optimizer)
# grad_norm = torch.nn.utils.clip_grad_norm_(my_model.parameters(), cfg.grad_max_norm)
optimizer.step()
# scaler.step(optimizer)
# scaler.update()
optimizer.zero_grad()
loss_list.append(loss.item())
# scheduler.step()
time_e = time.time()
global_iter += 1
if i_iter % print_freq == 0 and args.local_rank == 0:
lr = optimizer.param_groups[0]['lr']
logger.info('\n[TRAIN] Epoch %d Iter %5d/%d: Loss: %.3f (%.3f), lr: %.7f, time: %.3f (%.3f)'%(
epoch+1, i_iter, len(train_dataset_loader),
loss_list[-1], np.mean(loss_list), lr,
time_e - time_s, data_time_e - data_time_s
))
loss_list = []
loss_fn_dict['sem_loss'].append(sem_loss)
loss_fn_dict['class_loss'].append(cls_loss)
loss_fn_dict['dice_loss'].append(dice_loss)
loss_fn_dict['mask_loss'].append(mask_loss)
loss_fn_dict['dice_pos_loss'].append(dice_pos_loss)
data_time_s = time.time()
time_s = time.time()
bar.set_postfix({"sem_loss": sem_loss,
"class_loss": cls_loss,
"mask_loss": mask_loss,
"dice_loss": dice_loss,
"dice_pos_loss": dice_pos_loss,
"avg_loss": avg_loss})
bar.update(1)
bar.close()
scheduler_steplr.step()
epoch += 1
if args.local_rank != -1:
torch.distributed.barrier()
# save checkpoint
if args.local_rank < 1:
dict_to_save = {
'state_dict': my_model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler_steplr.state_dict(),
'epoch': epoch + 1,
'global_iter': global_iter,
'best_miou':best_miou,
'best_pq':best_pq,
}
save_file_name = os.path.join(os.path.abspath(args.work_dir), 'latest.pth')
torch.save(dict_to_save, save_file_name)
# dst_file = osp.join(args.work_dir, 'latest.pth')
# symlink(save_file_name, dst_file)
sem_l, class_l, mask_l,dice_l, dice_pos_l = sem_loss,cls_loss,mask_loss,dice_loss,dice_pos_loss
# eval
my_model.eval()
evaluator.reset()
sem_hist_list = []
get_model(my_model).label_map = transform_map(np.hstack([0,val_pt_dataset.base_thing_list,val_pt_dataset.base_stuff_list,val_pt_dataset.novel_thing_list,val_pt_dataset.novel_stuff_list]))
get_model(my_model).label_inverse_map = inverse_transform(get_model(my_model).label_map)
get_model(my_model).thing_class = np.sort(np.vectorize(get_model(my_model).label_inverse_map.__getitem__)(val_pt_dataset.thing_list))
get_model(my_model).stuff_class = np.sort(np.vectorize(get_model(my_model).label_inverse_map.__getitem__)(val_pt_dataset.stuff_list))
get_model(my_model).total_class = np.sort(np.vectorize(get_model(my_model).label_inverse_map.__getitem__)(np.hstack([0,val_pt_dataset.base_thing_list+val_pt_dataset.base_stuff_list])))
get_model(my_model).categroy_overlapping_mask = torch.from_numpy(np.hstack((np.full(1,True,dtype=bool),np.full(len(val_pt_dataset.base_thing_list+val_pt_dataset.base_stuff_list), True, dtype=bool),np.full(len(val_pt_dataset.novel_thing_list+val_pt_dataset.novel_stuff_list),False,dtype=bool))))
with torch.no_grad():
logger.info("epoch: %d lr: %.5f\n" % (epoch, optimizer.param_groups[0]['lr']))
if args.local_rank < 1:
print(f"Epoch {epoch} => Start Evaluation...")
if args.local_rank > 0:
save_dict = {
'item1': [],
'item2': [],
'item3': [],
'item4': [],
'item5': [],
}
if args.local_rank != -1:
torch.distributed.barrier()
val_bar = tqdm(total=len(val_dataset_loader))
for i_iter_val, data in enumerate(val_dataset_loader):
for k in to_cuda_list:
if isinstance(data[k],list):
for i in range(len(data[k])):
data[k][i] = torch.from_numpy(data[k][i]).cuda()
else:
data[k] = torch.from_numpy(data[k]).cuda()
data['voxel_semantic_labels'] = [torch.from_numpy(np.vectorize(get_model(my_model).label_inverse_map.__getitem__)(i)).type(torch.LongTensor).cuda() for i in data['voxel_semantic_labels']]
predict_labels_sem, pts_instance_preds = my_model(data)
predict_labels_sem = [np.vectorize(get_model(my_model).label_map.__getitem__)(sem) for sem in predict_labels_sem]
val_grid = data['pol_voxel_ind']
val_pt_labels = data['pt_sem_label']
val_pt_inst = data['pt_ins_label']
for count, i_val_grid in enumerate(val_grid):
panoptic = pts_instance_preds[count]
if args.local_rank<1:
if datasetname == 'SemanticKitti':
sem_gt = np.squeeze(val_pt_labels[count])
inst_gt = np.squeeze(val_pt_inst[count])
elif datasetname == 'nuscenes':
sem_gt = np.squeeze(val_pt_labels[count])
inst_gt = np.squeeze(val_pt_inst[count])
else:
raise NotImplementedError
evaluator.addBatch(predict_labels_sem[count], panoptic,sem_gt, inst_gt)
sem_hist_list.append(fast_hist_crop(predict_labels_sem[count],val_pt_labels[count],unique_label))
else:
save_dict['item1'].append(predict_labels_sem[count])
save_dict['item2'].append(panoptic)
save_dict['item3'].append(val_pt_labels[count])
save_dict['item4'].append(val_pt_inst[count])
save_dict['item5'].append(fast_hist_crop(
predict_labels_sem[count],
val_pt_labels[count],
unique_label))
val_bar.set_postfix({"semantic": np.unique(predict_labels_sem[count]),
"instance_id": np.unique(panoptic)})
val_bar.update(1)
val_bar.close()
if args.local_rank!=-1:
torch.distributed.barrier()
if args.local_rank > 0:
os.makedirs(osp.join(osp.abspath(args.work_dir),'tmpdir'), exist_ok=True)
pickle.dump(save_dict,
open(os.path.join(osp.abspath(args.work_dir),'tmpdir', 'result_part_{}.pkl'.format(args.local_rank)), 'wb'))
torch.distributed.barrier()
if args.local_rank < 1:
if args.local_rank == 0:
world_size = torch.distributed.get_world_size()
for i in range(world_size - 1):
part_file = os.path.join(osp.abspath(args.work_dir),'tmpdir', 'result_part_{}.pkl'.format(i + 1))
cur_dict = pickle.load(open(part_file, 'rb'))
for j in range(len(cur_dict['item1'])):
# 用实例标签的语义
if datasetname == 'SemanticKitti':
sem_gt = np.squeeze(cur_dict['item3'][j])
inst_gt = np.squeeze(cur_dict['item4'][j])
elif datasetname == 'nuscenes':
sem_gt = np.squeeze(cur_dict['item3'][j])
inst_gt = np.squeeze(cur_dict['item4'][j])
else:
raise NotImplementedError
evaluator.addBatch(cur_dict['item1'][j], cur_dict['item2'][j], sem_gt,
inst_gt)
sem_hist_list.append(cur_dict['item5'][j])
if os.path.isdir(osp.join(osp.abspath(args.work_dir),'tmpdir')):
shutil.rmtree(osp.join(osp.abspath(args.work_dir),'tmpdir'))
PQ, SQ, RQ, class_all_PQ, class_all_SQ, class_all_RQ = evaluator.getPQ()
miou, ious = evaluator.getSemIoU()
logger.info('Validation per class PQ, SQ, RQ and IoU: ')
for class_name, class_pq, class_sq, class_rq, class_iou in zip(unique_label_str, class_all_PQ[1:],
class_all_SQ[1:], class_all_RQ[1:],
ious[1:]):
logger.info('%20s : %6.8f%% %6.8f%% %6.8f%% %6.8f%%' % (class_name, class_pq * 100, class_sq * 100, class_rq * 100, class_iou * 100))
thing_upper_idx_dict = {"nuscenes": 10, "SemanticKitti":8}
upper_idx = thing_upper_idx_dict[datasetname]
PQ_dagger = cal_PQ_dagger(class_all_PQ, class_all_SQ, upper_idx + 1)
PQ_th = np.nanmean(class_all_PQ[1: upper_idx + 1]) # exclude 0
SQ_th = np.nanmean(class_all_SQ[1: upper_idx + 1])
RQ_th = np.nanmean(class_all_RQ[1: upper_idx + 1])
PQ_st = np.nanmean(class_all_PQ[upper_idx+1:])
SQ_st = np.nanmean(class_all_SQ[upper_idx+1:])
RQ_st = np.nanmean(class_all_RQ[upper_idx+1:])
PQ_N_th = np.nanmean(class_all_PQ[val_pt_dataset.novel_thing_list])
PQ_N_st = np.nanmean(class_all_PQ[val_pt_dataset.novel_stuff_list])
RQ_N_th = np.nanmean(class_all_RQ[val_pt_dataset.novel_thing_list])
RQ_N_st = np.nanmean(class_all_RQ[val_pt_dataset.novel_stuff_list])
SQ_N_th = np.nanmean(class_all_SQ[val_pt_dataset.novel_thing_list])
SQ_N_st = np.nanmean(class_all_SQ[val_pt_dataset.novel_stuff_list])
logger_msg1 = 'PQ %.8f PQ_dagger %.8f SQ %.8f RQ %.8f | PQ_th %.8f SQ_th %.8f RQ_th %.8f | PQ_st %.8f SQ_st %.8f RQ_st %.8f | PQ_N_th %.8f PQ_N_st %.8f RQ_N_th %.8f RQ_N_st %.8f SQ_N_th %.8f SQ_N_st %.8f | mIoU %.8f' %(
PQ * 100, PQ_dagger * 100, SQ * 100, RQ * 100,
PQ_th * 100, SQ_th * 100, RQ_th * 100,
PQ_st * 100, SQ_st * 100, RQ_st * 100,
PQ_N_th * 100,PQ_N_st*100, RQ_N_th*100, RQ_N_st*100, SQ_N_th*100, SQ_N_st*100,
miou * 100)
logger.info(logger_msg1)
if PQ>best_pq:
best_pq = PQ
dict_to_save = {
'state_dict': my_model.state_dict(),
'optimizer': optimizer.state_dict(),
'scheduler': scheduler_steplr.state_dict(),
'epoch': epoch + 1,
'global_iter': global_iter,
'best_miou':best_miou,
'best_pq':best_pq,
}
save_file_name = os.path.join(os.path.abspath(args.work_dir), f'best_pq_{PQ}.pth')
torch.save(dict_to_save, save_file_name)
best_miou = max(best_miou,miou)
logger.info('Current val miou is %.8f while the best val miou is %.8f' %
(miou*100, best_miou*100))
logger.info('Current val PQ is %.8f while the best val PQ is %.8f' %
(PQ*100, best_pq*100))
iou = per_class_iu(sum(sem_hist_list))
logger.info('Validation per class iou: ')
for class_name, class_iou in zip(unique_label_str, iou):
logger.info('%s : %.8f%%' % (class_name, class_iou * 100))
val_miou = np.nanmean(iou) * 100
logger.info('Current val miou is %.1f' %
val_miou)
logger.info('*' * 40)
# print('*' * 40)
sem_l, class_l, dice_l, dice_pos_l,mask_l = np.nanmean(loss_fn_dict['sem_loss']),np.nanmean(loss_fn_dict['class_loss']),\
np.nanmean(loss_fn_dict['dice_loss']), np.nanmean(loss_fn_dict['dice_pos_loss']),np.nanmean(loss_fn_dict['mask_loss'])
logger.info(
'epoch %d iter %5d, avg_loss: %.4f, semantic loss: %.4f, class loss: %.4f, mask_loss: %.4f ,dice loss: %.4f, dice position loss: %.4f\n' %
(epoch, i_iter, avg_loss, sem_l, class_l, mask_l, dice_l, dice_pos_l))
if args.local_rank!=-1:
torch.distributed.barrier()
if args.local_rank!=-1:
torch.distributed.destroy_process_group()
if __name__ == '__main__':
main()
# python train_ov_pfc_spawn.py --launcher pytorch -c configs/open_pa_po_nuscenes_mini.yaml -w work_dir/nusc_pfc/mini
# python train_openseg_pfc.py --launcher pytorch -c configs/open_pa_po_nuscenes.yaml -w work_dir/nusc_pfc/
# python train_openseg_pfc.py --launcher pytorch -c configs/open_nusc_pfc_origin.py -w work_dir/nusc_pfc_origin
# python -m torch.distributed.launch --nproc_per_node=2 --use_env train_pfc_launch.py -c configs/open_nusc_pfc_origin.py -w work_dir/nusc_pfc_launch