-
Notifications
You must be signed in to change notification settings - Fork 0
/
main.py
543 lines (460 loc) · 25.3 KB
/
main.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
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
# --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu
# --------------------------------------------------------
import os
import time
import argparse
import datetime
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.nn.functional as F
import thop
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.utils import accuracy, AverageMeter
from config import get_config
from models import build_model
from data import build_loader
from lr_scheduler import build_scheduler
from optimizer import build_optimizer
from logger import create_logger
from utils import load_checkpoint, save_checkpoint, get_grad_norm, auto_resume_helper, reduce_tensor, load_pretrained
from sklearn.metrics import roc_auc_score
from tqdm import tqdm
#https://pytorch.org/docs/stable/notes/amp_examples.html#amp-examples
#https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html
from torch.cuda.amp import autocast, GradScaler
def parse_option():
parser = argparse.ArgumentParser('MaxVit and Swin Transformer training and evaluation script', add_help=False)
parser.add_argument('--cfg', type=str, required=True, metavar="FILE", help='path to config file', )
parser.add_argument(
"--opts",
help="Modify config options by adding 'KEY VALUE' pairs. ",
default=None,
nargs='+',
)
# easy config modification
parser.add_argument('--batch-size', type=int, help="batch size for single GPU")
parser.add_argument('--data-path', type=str, help='path to dataset')
parser.add_argument('--zip', action='store_true', help='use zipped dataset instead of folder dataset')
parser.add_argument('--cache-mode', type=str, default='part', choices=['no', 'full', 'part'],
help='no: no cache, '
'full: cache all data, '
'part: sharding the dataset into nonoverlapping pieces and only cache one piece')
parser.add_argument('--pretrained',
help='pretrained weight from checkpoint, could be imagenet22k pretrained weight')
parser.add_argument('--resume', help='resume from checkpoint')
parser.add_argument('--accumulation-steps', type=int, help="gradient accumulation steps")
parser.add_argument('--use-checkpoint', action='store_true',
help="whether to use gradient checkpointing to save memory")
parser.add_argument('--amp-opt-level', action='store_true',
help='mixed precision opt level, if False, no amp is used')
parser.add_argument('--output', default='output', type=str, metavar='PATH',
help='root of output folder, the full path is <output>/<model_name>/<tag> (default: output)')
parser.add_argument('--tag', help='tag of experiment')
parser.add_argument('--eval', action='store_true', help='Perform evaluation only')
parser.add_argument('--throughput', action='store_true', help='Test throughput only')
# distributed training
# parser.add_argument("--local_rank", type=int, required=True, help='local rank for DistributedDataParallel')
#nih
parser.add_argument("--trainset", type=str, required=True, help='path to train dataset')
parser.add_argument("--validset", type=str, required=True, help='path to validation dataset')
parser.add_argument("--testset", type=str, required=True, help='path to test dataset')
# parser.add_argument("--class_num", required=True, type=int,
# help="Class number for binary classification, 0-13 for nih")
parser.add_argument("--train_csv_path", type=str, required=True, help='path to train csv file')
parser.add_argument("--valid_csv_path", type=str, required=True, help='path to validation csv file')
parser.add_argument("--test_csv_path", type=str, required=True, help='path to test csv file')
parser.add_argument("--num_mlp_heads", type=int, default=3, choices=[0, 1, 2, 3],
help='number of mlp layers at end of network')
args, unparsed = parser.parse_known_args()
config = get_config(args)
return args, config
def main(config):
dataset_train, dataset_val, dataset_test, data_loader_train, data_loader_val, data_loader_test, mixup_fn = build_loader(config)
logger.info(f"Creating model:{config.MODEL.TYPE}/{config.MODEL.NAME}")
model = build_model(config)
model.cuda()
logger.info(str(model))
optimizer = build_optimizer(config, model)
scaler = GradScaler(enabled=config.AMP_OPT_LEVEL)
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[config.LOCAL_RANK], broadcast_buffers=False)
model_without_ddp = model.module
n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
logger.info(f"number of params: {n_parameters}")
# if hasattr(model_without_ddp, 'flops'):
# flops = model_without_ddp.flops()
# logger.info(f"number of GFLOPs: {flops / 1e9}")
flops(model_without_ddp, logger=logger, config=config)
lr_scheduler = build_scheduler(config, optimizer, len(data_loader_train))
if config.AUG.MIXUP > 0.:
# smoothing is handled with mixup label transform
criterion = SoftTargetCrossEntropy()
elif config.MODEL.LABEL_SMOOTHING > 0.:
criterion = LabelSmoothingCrossEntropy(smoothing=config.MODEL.LABEL_SMOOTHING)
else:
criterion = torch.nn.CrossEntropyLoss()
max_auc = 0.0
min_loss = np.inf
if config.TRAIN.AUTO_RESUME:
resume_file = auto_resume_helper(config.OUTPUT)
if resume_file:
if config.MODEL.RESUME:
logger.warning(f"auto-resume changing resume file from {config.MODEL.RESUME} to {resume_file}")
config.defrost()
config.MODEL.RESUME = resume_file
config.freeze()
logger.info(f'auto resuming from {resume_file}')
else:
logger.info(f'no checkpoint found in {config.OUTPUT}, ignoring auto resume')
if config.MODEL.RESUME:
max_auc, min_loss = load_checkpoint(config, model_without_ddp, optimizer, lr_scheduler, logger, scaler)
acc1, acc5, auc, loss = validate(config, data_loader_val, model, is_validation=True)
logger.info(f"Mean Accuracy of the network on the {len(dataset_val)} validation images: {acc1:.2f}%")
logger.info(f"Mean AUC of the network on the {len(dataset_val)} validation images: {auc:.2f}%")
logger.info(f"Mean Loss of the network on the {len(dataset_val)} validation images: {loss:.5f}")
acc1, acc5, auc, loss = validate(config, data_loader_test, model, is_validation=False)
logger.info(f"Mean Accuracy of the network on the {len(dataset_test)} test images: {acc1:.2f}%")
logger.info(f"Mean AUC of the network on the {len(dataset_test)} test images: {auc:.2f}%")
logger.info(f"Mean Loss of the network on the {len(dataset_test)} test images: {loss:.5f}")
if config.EVAL_MODE:
return
if config.MODEL.PRETRAINED and (not config.MODEL.RESUME):
load_pretrained(config, model_without_ddp, logger)
# acc1, acc5, auc, loss = validate(config, data_loader_val, model, is_validation=True)
# logger.info(f"AUC of the network on the {len(dataset_val)} val images: {auc:.1f}%")
if config.THROUGHPUT_MODE:
# throughput(data_loader_val, model, logger)
throughput(data_loader_test, model, logger)
return
logger.info("Start training")
if config.TRAIN.EARLYSTOPPING.MONITOR is not None:
logger.info(f"Early Stopping is monitoring: {config.TRAIN.EARLYSTOPPING.MONITOR}")
best_epoch = config.TRAIN.START_EPOCH
no_improvement_epochs = 0
max_no_improvement_epochs = config.TRAIN.EARLYSTOPPING.PATIENCE
if config.TRAIN.EARLYSTOPPING.MONITOR == "auc":
monitor = max_auc
else:
monitor = min_loss
start_time = time.time()
for epoch in range(config.TRAIN.START_EPOCH, config.TRAIN.EPOCHS):
data_loader_train.sampler.set_epoch(epoch)
train_one_epoch(config, model, criterion, data_loader_train, optimizer, epoch, mixup_fn, lr_scheduler, scaler)
acc1, acc5, auc, loss = validate(config, data_loader_val, model, is_validation=True)
logger.info(f"Mean Accuracy of the network on the {len(dataset_val)} validation images: {acc1:.2f}%")
logger.info(f"Mean AUC of the network on the {len(dataset_val)} validation images: {auc:.2f}%")
logger.info(f"Mean Loss of the network on the {len(dataset_val)} validation images: {loss:.5f}")
if config.TRAIN.EARLYSTOPPING.MONITOR is not None:
has_improved, value = early_stopping(config.TRAIN.EARLYSTOPPING.MONITOR, auc, loss, monitor)
if has_improved:
monitor = value
best_epoch = epoch
no_improvement_epochs = 0
if dist.get_rank() == 0:
save_checkpoint(config, epoch, model_without_ddp, max_auc, min_loss, optimizer, lr_scheduler, logger, scaler)
else:
if config.TRAIN.WARMUP_EPOCHS<epoch:
no_improvement_epochs += 1
if no_improvement_epochs >= max_no_improvement_epochs:
logger.info(f"No improvement in validation {config.TRAIN.EARLYSTOPPING.MONITOR} \n"
f"for {max_no_improvement_epochs} epochs. Stopping training.")
break
else:
if dist.get_rank() == 0 and (epoch % config.SAVE_FREQ == 0 or epoch == (config.TRAIN.EPOCHS - 1)):
save_checkpoint(config, epoch, model_without_ddp, max_auc, min_loss, optimizer, lr_scheduler, logger, scaler)
acc1, acc5, auc, loss = validate(config, data_loader_test, model, is_validation=False)
logger.info(f"Mean Accuracy of the network on the {len(dataset_test)} test images: {acc1:.2f}%")
logger.info(f"Mean AUC of the network on the {len(dataset_test)} test images: {auc:.2f}%")
logger.info(f"Mean Loss of the network on the {len(dataset_test)} test images: {loss:.5f}")
max_auc = max(max_auc, auc)
min_loss = min(min_loss, loss)
logger.info(f'Test Max mean AUC: {max_auc:.2f}%')
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
logger.info('Training time {}'.format(total_time_str))
def train_one_epoch(config, model, criterion, data_loader, optimizer, epoch, mixup_fn, lr_scheduler, scaler):
model.train()
optimizer.zero_grad()
num_steps = len(data_loader)
batch_time = AverageMeter()
loss_meter = AverageMeter()
norm_meter = AverageMeter()
start = time.time()
end = time.time()
grad_norm = 0.0
for idx, (samples, targets) in enumerate(data_loader):
samples = samples.cuda(non_blocking=True)
for i in range(len(targets)):
targets[i] = targets[i].cuda(non_blocking=True)
if mixup_fn is not None:
samples, targets = mixup_fn(samples, targets) #todo iterate on targets
if config.TRAIN.ACCUMULATION_STEPS > 1:
with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=config.AMP_OPT_LEVEL):
outputs = model(samples)
loss = criterion(outputs[0], targets[0])
for i in range(1, len(targets)):
loss += criterion(outputs[i], targets[i])
loss = loss / config.TRAIN.ACCUMULATION_STEPS
# Accumulates scaled gradients.
scaler.scale(loss).backward()
if (idx + 1) % config.TRAIN.ACCUMULATION_STEPS == 0:
scaler.unscale_(optimizer)
if config.TRAIN.CLIP_GRAD:
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), config.TRAIN.CLIP_GRAD)
else:
grad_norm = get_grad_norm(model.parameters())
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad()
if lr_scheduler is not None:
lr_scheduler.step_update(epoch * num_steps + idx)
else:
with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=config.AMP_OPT_LEVEL):
outputs = model(samples)
loss = criterion(outputs[0], targets[0])
for i in range(1, len(targets)):
loss += criterion(outputs[i], targets[i])
# Accumulates scaled gradients.
scaler.scale(loss).backward()
if config.TRAIN.CLIP_GRAD:
scaler.unscale_(optimizer)
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), config.TRAIN.CLIP_GRAD)
else:
grad_norm = get_grad_norm(model.parameters())
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad()
if lr_scheduler is not None:
lr_scheduler.step_update(epoch * num_steps + idx)
torch.cuda.synchronize()
loss_meter.update(loss.item(), targets[0].size(0))
norm_meter.update(grad_norm)
batch_time.update(time.time() - end)
end = time.time()
if idx % config.PRINT_FREQ == 0:
lr = optimizer.param_groups[0]['lr']
memory_used = torch.cuda.max_memory_allocated() / (1024.0 * 1024.0)
etas = batch_time.avg * (num_steps - idx)
logger.info(
f'Train: [{epoch}/{config.TRAIN.EPOCHS}][{idx}/{num_steps}]\t'
f'eta {datetime.timedelta(seconds=int(etas))} lr {lr:.6f}\t'
f'time {batch_time.val:.4f} ({batch_time.avg:.4f})\t'
f'loss {loss_meter.val:.4f} ({loss_meter.avg:.4f})\t'
f'grad_norm {norm_meter.val:.4f} ({norm_meter.avg:.4f})\t'
f'mem {memory_used:.0f}MB')
lr = optimizer.param_groups[0]['lr']
memory_used = torch.cuda.max_memory_allocated() / (1024.0 * 1024.0)
logger.info(
f'Train: [{epoch}/{config.TRAIN.EPOCHS}]\t'
f'lr {lr:.6f}\t'
f'time {batch_time.val:.4f} ({batch_time.avg:.4f})\t'
f'loss {loss_meter.val:.4f} ({loss_meter.avg:.4f})\t'
f'grad_norm {norm_meter.val:.4f} ({norm_meter.avg:.4f})\t'
f'mem {memory_used:.0f}MB')
epoch_time = time.time() - start
logger.info(f"EPOCH {epoch} training takes {datetime.timedelta(seconds=int(epoch_time))}")
@torch.no_grad()
def validate(config, data_loader, model, is_validation):
valid_or_test = "Validation" if is_validation else "Test"
criterion = torch.nn.CrossEntropyLoss()
model.eval()
batch_time = [AverageMeter() for _ in range(14)]
loss_meter = [AverageMeter() for _ in range(14)]
acc1_meter = [AverageMeter() for _ in range(14)]
acc5_meter = [AverageMeter() for _ in range(14)]
acc1s = []
acc5s = []
losses = []
aucs = []
end = time.time()
all_preds = [[] for _ in range(14)]
all_label = [[] for _ in range(14)]
for idx, (images, target) in enumerate(data_loader):
images = images.cuda(non_blocking=True)
for i in range(len(target)):
target[i] = target[i].cuda(non_blocking=True)
# compute output
output = model(images)
for i in range(len(target)):
# measure accuracy and record loss
loss = criterion(output[i], target[i])
# acc1, acc5 = accuracy(output, target, topk=(1, 5)) #https://huggingface.co/spaces/Roll20/pet_score/blob/3653888366407445408f2bfa8c68d6cdbdd4cba6/lib/timm/utils/metrics.py
acc1 = accuracy(output[i], target[i], topk=(1,))
acc1 = torch.Tensor(acc1).to(device='cuda')
acc1 = reduce_tensor(acc1)
# acc5 = reduce_tensor(acc5)
loss = reduce_tensor(loss)
loss_meter[i].update(loss.item(), target[i].size(0))
acc1_meter[i].update(acc1.item(), target[i].size(0))
# acc5_meter.update(acc5.item(), target.size(0))
# auc
preds = F.softmax(output[i], dim=1)
if len(all_preds[i]) == 0:
all_preds[i].append(preds.detach().cpu().numpy())
all_label[i].append(target[i].detach().cpu().numpy())
else:
all_preds[i][0] = np.append(
all_preds[i][0], preds.detach().cpu().numpy(), axis=0
)
all_label[i][0] = np.append(
all_label[i][0], target[i].detach().cpu().numpy(), axis=0
)
# measure elapsed time
batch_time[i].update(time.time() - end)
end = time.time()
if idx % config.PRINT_FREQ == 0:
memory_used = torch.cuda.max_memory_allocated() / (1024.0 * 1024.0)
logger.info(
f'{valid_or_test}: [{idx}/{len(data_loader)}]\t'
f'Time {batch_time[i].val:.3f} ({batch_time[i].avg:.3f})\t'
f'Loss {loss_meter[i].val:.4f} ({loss_meter[i].avg:.4f})\t'
f'Acc@1 {acc1_meter[i].val:.3f} ({acc1_meter[i].avg:.3f})\t'
# f'Acc@5 {acc5_meter[i].val:.3f} ({acc5_meter[i].avg:.3f})\t'
f'Mem {memory_used:.0f}MB\t'
f'Class {i}')
for i in range(14):
# auc
all_preds[i], all_label[i] = all_preds[i][0], all_label[i][0]
auc = roc_auc_score(all_label[i], all_preds[i][:, 1], multi_class='ovr')
# logger.info("Valid AUC: %2.5f" % auc)
logger.info(f' * Acc@1 {acc1_meter[i].avg:.3f}\t'
f'Acc@5 {acc5_meter[i].avg:.3f}\t'
f'{valid_or_test} AUC {auc:.5f}\t'
f'Class {i}')
acc1s.append(acc1_meter[i].avg)
acc5s.append(acc5_meter[i].avg)
losses.append(loss_meter[i].avg)
aucs.append(auc)
from statistics import mean
logger.info(f'{valid_or_test} MEAN AUC: {mean(aucs):.5f}')
return mean(acc1s), mean(acc5s), mean(aucs), mean(losses)
@torch.no_grad()
def throughput(data_loader, model, logger):
model.eval()
for idx, (images, _) in enumerate(data_loader):
images = images.cuda(non_blocking=True)
batch_size = images.shape[0]
for i in range(50):
model(images)
torch.cuda.synchronize()
logger.info(f"throughput averaged with 30 times")
tic1 = time.time()
for i in range(30):
model(images)
torch.cuda.synchronize()
tic2 = time.time()
logger.info(f"batch_size {batch_size} throughput {30 * batch_size / (tic2 - tic1)}")
return
@torch.no_grad()
def flops(model, logger, config):
model.eval()
#TODO: change number of channels if channel data is different to 3
input_tensor = torch.randn(1, 3, config.DATA.IMG_SIZE, config.DATA.IMG_SIZE).cuda()
flops, params = thop.profile(model, inputs=(input_tensor,))
logger.info(f"Number of GFLOPs getting with thop: {flops / 1e9}")
logger.info(f"Number of params getting with thop: {params}")
return
def early_stopping(monitor, monitor_auc, monitor_loss, monitor_previus_value):
if monitor == 'auc':
if monitor_auc > monitor_previus_value:
return True, monitor_auc
if monitor == 'loss':
if monitor_loss < monitor_previus_value:
return True, monitor_loss
return False, None
if __name__ == '__main__':
_, config = parse_option()
# if config.AMP_OPT_LEVEL:
# assert amp is not None, "amp not installed!"
if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
rank = int(os.environ["RANK"])
world_size = int(os.environ['WORLD_SIZE'])
print(f"RANK and WORLD_SIZE in environ: {rank}/{world_size}")
else:
rank = -1
world_size = -1
torch.cuda.set_device(config.LOCAL_RANK)
torch.distributed.init_process_group(backend='nccl', init_method='env://', world_size=world_size, rank=rank)
torch.distributed.barrier()
seed = config.SEED + dist.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
cudnn.benchmark = True
# linear scale the learning rate according to total batch size, may not be optimal
linear_scaled_lr = config.TRAIN.BASE_LR * config.DATA.BATCH_SIZE * dist.get_world_size() / 512.0
linear_scaled_warmup_lr = config.TRAIN.WARMUP_LR * config.DATA.BATCH_SIZE * dist.get_world_size() / 512.0
linear_scaled_min_lr = config.TRAIN.MIN_LR * config.DATA.BATCH_SIZE * dist.get_world_size() / 512.0
# gradient accumulation also need to scale the learning rate
if config.TRAIN.ACCUMULATION_STEPS > 1:
linear_scaled_lr = linear_scaled_lr * config.TRAIN.ACCUMULATION_STEPS
linear_scaled_warmup_lr = linear_scaled_warmup_lr * config.TRAIN.ACCUMULATION_STEPS
linear_scaled_min_lr = linear_scaled_min_lr * config.TRAIN.ACCUMULATION_STEPS
config.defrost()
config.TRAIN.BASE_LR = linear_scaled_lr
config.TRAIN.WARMUP_LR = linear_scaled_warmup_lr
config.TRAIN.MIN_LR = linear_scaled_min_lr
config.freeze()
os.makedirs(config.OUTPUT, exist_ok=True)
logger = create_logger(output_dir=config.OUTPUT, dist_rank=dist.get_rank(), name=f"{config.MODEL.NAME}")
if dist.get_rank() == 0:
path = os.path.join(config.OUTPUT, "config.json")
with open(path, "w") as f:
f.write(config.dump())
logger.info(f"Full config saved to {path}")
# print config
logger.info(config.dump())
main(config)
#https://pytorch.org/docs/stable/elastic/run.html
# nohup torchrun --nproc_per_node 1 --master_port 12345 main.py \
# --cfg configs/MAXVIT/maxvit_small_tf_224.in1k.yaml \
# --trainset ../data/images/ --validset ../data/images/ --testset ../data/images/ \
# --train_csv_path configs/NIH/train.csv --valid_csv_path configs/NIH/validation.csv --test_csv_path configs/NIH/test.csv \
# --batch-size 32 --output output/ --tag paper --num_mlp_heads 3 --amp-opt-level > log.txt & disown
#COMMAND FOR TRAINING MAXVIT, SWIN, VIT
# nohup torchrun --nproc_per_node 1 --master_port 12345 main.py \
# --cfg configs/MAXVIT/maxvit_large_tf_384.in21k.yaml \
# --trainset ../data/images/ --validset ../data/images/ --testset ../data/images/ \
# --train_csv_path configs/NIH/train.csv --valid_csv_path configs/NIH/validation.csv --test_csv_path configs/NIH/test.csv \
# --batch-size 8 --output output/ --tag paper3 --num_mlp_heads 3 --accumulation-steps 4 --amp-opt-level > log.txt & disown
# nohup torchrun --nproc_per_node 1 --master_port 12345 main.py \
# --cfg configs/SWIN_TIMM/swin_tiny_patch4_window7_224.ms_in1k.yaml \
# --trainset ../data/images/ --validset ../data/images/ --testset ../data/images/ \
# --train_csv_path configs/NIH/train.csv --valid_csv_path configs/NIH/validation.csv --test_csv_path configs/NIH/test.csv \
# --batch-size 32 --output output/ --tag paper --num_mlp_heads 3 --accumulation-steps 8 --amp-opt-level > log.txt & disown
# nohup torchrun --nproc_per_node 1 --master_port 12345 main.py \
# --cfg configs/VIT_TIMM/vit_tiny_patch16_224.augreg_in21k.yaml \
# --trainset ../data/images/ --validset ../data/images/ --testset ../data/images/ \
# --train_csv_path configs/NIH/train.csv --valid_csv_path configs/NIH/validation.csv --test_csv_path configs/NIH/test.csv \
# --batch-size 32 --output output/ --tag paper --num_mlp_heads 3 --amp-opt-level > log.txt & disown
#COMMAND FOR THROUGHTPUT
#At firts: pip install htop
#deactivate config.TRAIN.AUTO_RESUME and run command without amp-opt-level:
# nohup torchrun --nproc_per_node 1 --master_port 12345 main.py \
# --cfg configs/SWIN_TIMM/swin_base_patch4_window7_224.ms_in21k.yaml \
# --trainset ../data/images/ --validset ../data/images/ --testset ../data/images/ \
# --train_csv_path configs/NIH/train.csv --valid_csv_path configs/NIH/validation.csv --test_csv_path configs/NIH/test.csv \
# --batch-size 32 --output output/ --tag paper --num_mlp_heads 3 --throughput > log.txt & disown
#CoMMAND FOR RESCALE
#FOR SWIN
# nohup torchrun --nproc_per_node 1 --master_port 12345 main.py \
# --cfg configs/SWIN/swin_tiny_patch4_window12_384.yaml --pretrained output/swin_tiny_patch4_window12_384/ckpt_epoch_21.pth \
# --trainset ../data/images/ --validset ../data/images/ --testset ../data/images/ \
# --train_csv_path configs/NIH/train.csv --valid_csv_path configs/NIH/validation.csv --test_csv_path configs/NIH/test.csv \
# --batch-size 32 --output output/ --tag paper --num_mlp_heads 3 --accumulation-steps 8 --amp-opt-level > log.txt & disown
#FOR VIT
# nohup torchrun --nproc_per_node 1 --master_port 12345 main.py \
# --cfg configs/VIT/vit_small_patch16_512.yaml --pretrained output/vit_small_patch16_512/ckpt_epoch_12.pth \
# --trainset ../data/images/ --validset ../data/images/ --testset ../data/images/ \
# --train_csv_path configs/NIH/train.csv --valid_csv_path configs/NIH/validation.csv --test_csv_path configs/NIH/test.csv \
# --batch-size 16 --output output/ --tag paper --num_mlp_heads 3 --accumulation-steps 2 --amp-opt-level > log.txt & disown
# nohup torchrun --nproc_per_node 1 --master_port 12345 main.py \
# --cfg configs/VIT/vit_small_patch16_512.yaml --resume output/vit_small_patch16_512/ckpt_epoch_12.pth \
# --trainset ../data/images/ --validset ../data/images/ --testset ../data/images/ \
# --train_csv_path configs/NIH/train.csv --valid_csv_path configs/NIH/validation.csv --test_csv_path configs/NIH/test.csv \
# --batch-size 32 --output output/ --tag paper --num_mlp_heads 3 > log.txt & disown
#PID:1452597