forked from liucheng/DeepBurning-MixQ
385 lines
18 KiB
Python
385 lines
18 KiB
Python
import argparse
|
|
|
|
import torch.distributed as dist
|
|
import torch.optim as optim
|
|
import torch.optim.lr_scheduler as lr_scheduler
|
|
|
|
import sys
|
|
sys.path.append('..')
|
|
import localconfig
|
|
import test
|
|
from datasets import *
|
|
from yolo_utils import *
|
|
|
|
from mymodel import *
|
|
import mymodel
|
|
|
|
wdir = 'weights' + os.sep # weights dir
|
|
|
|
# Hyperparameters (results68: 59.9 mAP@0.5 yolov3-spp-416) https://github.com/ultralytics/yolov3/issues/310
|
|
|
|
hyp = {'giou': 3.54, # giou loss gain
|
|
'cls': 37.4, # cls loss gain
|
|
'cls_pw': 1.0, # cls BCELoss positive_weight
|
|
'obj': 64.3, # obj loss gain (*=img_size/320 if img_size != 320)
|
|
'obj_pw': 1.0, # obj BCELoss positive_weight
|
|
'iou_t': 0.225, # iou training threshold
|
|
'lr0': 0.01, # initial learning rate (SGD=5E-3, Adam=5E-4)
|
|
'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
|
|
'momentum': 0.937, # SGD momentum
|
|
'weight_decay': 0.000484, # optimizer weight decay
|
|
'fl_gamma': 0.5, # focal loss gamma
|
|
'hsv_h': 0.0138, # image HSV-Hue augmentation (fraction)
|
|
'hsv_s': 0.678, # image HSV-Saturation augmentation (fraction)
|
|
'hsv_v': 0.36, # image HSV-Value augmentation (fraction)
|
|
'degrees': 1.98, # image rotation (+/- deg)
|
|
'translate': 0.05, # image translation (+/- fraction)
|
|
'scale': 0.05, # image scale (+/- gain)
|
|
'shear': 0.641} # image shear (+/- deg)
|
|
|
|
# Overwrite hyp with hyp*.txt (optional)
|
|
f = glob.glob('hyp*.txt')
|
|
if f:
|
|
print('Using %s' % f[0])
|
|
for k, v in zip(hyp.keys(), np.loadtxt(f[0])):
|
|
hyp[k] = v
|
|
|
|
def train():
|
|
img_size, img_size_test = opt.img_size if len(opt.img_size) == 2 else opt.img_size * 2 # train, test sizes
|
|
epochs = opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs
|
|
batch_size = opt.batch_size
|
|
accumulate = opt.accumulate # effective bs = batch_size * accumulate = 16 * 4 = 64
|
|
weights = opt.weights # initial training weights
|
|
|
|
# Initialize
|
|
init_seeds()
|
|
|
|
# Configure run
|
|
train_path = localconfig.train_path
|
|
test_path = localconfig.test_path
|
|
nc = 1
|
|
|
|
results_file = 'results/%s.txt'%opt.name
|
|
|
|
# Initialize model
|
|
if opt.model != '':
|
|
model = getattr(mymodel, opt.model)(not opt.no_share).to(device)
|
|
else:
|
|
if opt.bypass:
|
|
model = UltraNetBypass_MixQ(not opt.no_share).to(device)
|
|
else:
|
|
model = UltraNet_MixQ(not opt.no_share).to(device)
|
|
|
|
# Optimizer
|
|
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
|
|
for k, v in dict(model.named_parameters()).items():
|
|
if '.bias' in k:
|
|
pg2 += [v] # biases
|
|
elif 'Conv2d.weight' in k:
|
|
pg1 += [v] # apply weight_decay
|
|
elif 'alpha' not in k:
|
|
pg0 += [v] # all else
|
|
|
|
if opt.adam:
|
|
# hyp['lr0'] *= 0.1 # reduce lr (i.e. SGD=5E-3, Adam=5E-4)
|
|
optimizer = optim.Adam(pg0, lr=hyp['lr0'])
|
|
# optimizer = AdaBound(pg0, lr=hyp['lr0'], final_lr=0.1)
|
|
else:
|
|
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
|
|
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
|
|
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
|
|
optimizer.param_groups[2]['lr'] *= 2.0 # bias lr
|
|
|
|
# arch_optimizer
|
|
alpha_params = []
|
|
for name, param in model.named_parameters():
|
|
if 'alpha' in name:
|
|
alpha_params += [param]
|
|
arch_optimizer = torch.optim.SGD(alpha_params, opt.lra, momentum=hyp['momentum'],
|
|
weight_decay=hyp['weight_decay'])
|
|
|
|
del pg0, pg1, pg2
|
|
|
|
start_epoch = 0
|
|
test_best_iou = 0.0
|
|
|
|
# load weights
|
|
if weights.endswith('.pt'): # pytorch format
|
|
# possible weights are '*.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
|
|
chkpt = torch.load(weights, map_location=device)
|
|
|
|
# load model
|
|
try:
|
|
chkpt['model'] = {k: v for k, v in chkpt['model'].items() if model.state_dict()[k].numel() == v.numel()}
|
|
model.load_state_dict(chkpt['model'], strict=False)
|
|
except KeyError as e:
|
|
s = "%s is not compatible with %s. Specify --weights '' or specify a --cfg compatible with %s. " % (opt.weights, opt.cfg, opt.weights)
|
|
raise KeyError(s) from e
|
|
|
|
if opt.resume:
|
|
# load optimizer
|
|
if chkpt['optimizer'] is not None:
|
|
optimizer.load_state_dict(chkpt['optimizer'])
|
|
best_fitness = chkpt['best_fitness']
|
|
|
|
# load results
|
|
if chkpt.get('training_results') is not None:
|
|
with open(results_file, 'w') as file:
|
|
file.write(chkpt['training_results']) # write results.txt
|
|
|
|
start_epoch = chkpt['epoch'] + 1
|
|
|
|
del chkpt
|
|
|
|
# Scheduler https://github.com/ultralytics/yolov3/issues/238
|
|
lf = lambda x: (1 + math.cos(x * math.pi / epochs)) / 2 * 0.999 + 0.001 # cosine https://arxiv.org/pdf/1812.01187.pdf
|
|
lf2 = lambda x: (1 + math.cos(x * math.pi / epochs)) / 2 * 0.9 + 0.1 # cosine https://arxiv.org/pdf/1812.01187.pdf
|
|
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
|
|
scheduler.last_epoch = start_epoch
|
|
|
|
arch_scheduler = lr_scheduler.LambdaLR(arch_optimizer, lr_lambda=lf2)
|
|
arch_scheduler.last_epoch = start_epoch
|
|
|
|
# Initialize distributed training
|
|
if device.type != 'cpu' and torch.cuda.device_count() > 1 and torch.distributed.is_available():
|
|
dist.init_process_group(backend='nccl', # 'distributed backend'
|
|
init_method='tcp://127.0.0.1:5000', # distributed training init method
|
|
world_size=1, # number of nodes for distributed training
|
|
rank=0) # distributed training node rank
|
|
model = torch.nn.parallel.DistributedDataParallel(model, find_unused_parameters=True)
|
|
model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level
|
|
|
|
# Dataloader
|
|
#batch_size = min(batch_size, len(dataset))
|
|
nw = min([os.cpu_count()//4, batch_size//4 if batch_size > 1 else 0, 8]) # number of workers
|
|
|
|
# Testloader
|
|
testset = LoadImagesAndLabels(test_path, img_size_test, batch_size,
|
|
hyp=hyp,
|
|
rect=False,
|
|
cache_images=opt.cache_images,
|
|
single_cls=opt.single_cls)
|
|
testloader = torch.utils.data.DataLoader(testset,
|
|
batch_size=batch_size,
|
|
num_workers=0,
|
|
pin_memory=True,
|
|
collate_fn=testset.collate_fn)
|
|
|
|
# Dataset
|
|
dataset = LoadImagesAndLabels(train_path, img_size, batch_size,
|
|
augment=True,
|
|
hyp=hyp, # augmentation hyperparameters
|
|
rect=opt.rect, # rectangular training
|
|
cache_images=opt.cache_images,
|
|
single_cls=opt.single_cls)
|
|
|
|
dataloader = torch.utils.data.DataLoader(dataset,
|
|
batch_size=batch_size,
|
|
num_workers=nw,
|
|
shuffle=not opt.rect, # Shuffle=True unless rectangular training is used
|
|
pin_memory=True,
|
|
collate_fn=dataset.collate_fn)
|
|
|
|
# Start training
|
|
nb = len(dataloader)
|
|
prebias = start_epoch == 0
|
|
model.nc = nc # attach number of classes to model
|
|
model.arc = opt.arc # attach yolo architecture
|
|
model.hyp = hyp # attach hyperparameters to model
|
|
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
|
|
maps = np.zeros(nc) # mAP per class
|
|
# torch.autograd.set_detect_anomaly(True)
|
|
results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
|
|
t0 = time.time()
|
|
torch_utils.model_info(model, report='summary') # 'full' or 'summary'
|
|
print('Using %g dataloader workers' % nw)
|
|
print('Starting training for %g epochs...' % epochs)
|
|
|
|
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
|
|
model.train()
|
|
model.gr = 1 - (1 + math.cos(min(epoch * 2, epochs) * math.pi / epochs)) / 2 # GIoU <-> 1.0 loss ratio
|
|
|
|
# Prebias
|
|
if prebias:
|
|
ne = max(round(30 / nb), 3) # number of prebias epochs
|
|
ps = np.interp(epoch, [0, ne], [0.1, hyp['lr0'] * 2]), \
|
|
np.interp(epoch, [0, ne], [0.9, hyp['momentum']]) # prebias settings (lr=0.1, momentum=0.9)
|
|
if epoch == ne:
|
|
# print_model_biases(model)
|
|
prebias = False
|
|
|
|
# Bias optimizer settings
|
|
optimizer.param_groups[2]['lr'] = ps[0]
|
|
if optimizer.param_groups[2].get('momentum') is not None: # for SGD but not Adam
|
|
optimizer.param_groups[2]['momentum'] = ps[1]
|
|
|
|
curr_lr = optimizer.param_groups[0]['lr']
|
|
curr_lra = arch_optimizer.param_groups[0]['lr']
|
|
print(f'lr:{curr_lr}, lra:{curr_lra}')
|
|
|
|
mloss = torch.zeros(4).to(device) # mean losses
|
|
print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'iouloss', 'objloss', 'triou', 'mloss', 'targets', 'img_size'))
|
|
pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
|
|
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
|
|
ni = i + nb * epoch # number integrated batches (since train start)
|
|
imgs = imgs.to(device).float() / 256.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
|
|
targets = targets.to(device)
|
|
|
|
# Run model
|
|
pred = model(imgs)
|
|
|
|
# Compute loss
|
|
loss, loss_items = compute_loss(pred, targets, model)
|
|
if not torch.isfinite(loss):
|
|
print('WARNING: non-finite loss, ending training ', loss_items)
|
|
return results
|
|
|
|
# Scale loss by nominal batch_size of 64
|
|
loss *= batch_size / 64
|
|
|
|
# complexity penalty
|
|
if opt.complexity_decay != 0:
|
|
loss_complexity = opt.complexity_decay * model.complexity_loss()
|
|
loss += loss_complexity * 4.0
|
|
|
|
if opt.complexity_decay_trivial != 0:
|
|
loss_complexity_trivial = opt.complexity_decay_trivial * model.complexity_loss_trivial()
|
|
loss += loss_complexity_trivial * 4.0
|
|
|
|
if opt.bram_decay != 0:
|
|
if hasattr(model, 'module'):
|
|
loss_bram = opt.bram_decay * model.bram_loss()
|
|
loss += loss_bram * 4.0
|
|
|
|
loss.backward()
|
|
|
|
# Optimize accumulated gradient
|
|
if ni % accumulate == 0:
|
|
optimizer.step()
|
|
arch_optimizer.step()
|
|
optimizer.zero_grad()
|
|
arch_optimizer.zero_grad()
|
|
|
|
# Print batch results
|
|
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
|
|
mem = '%.3gG' % (torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0) # (GB)
|
|
s = ('%10s' * 2 + '%10.3g' * 6) % ('%g/%g' % (epoch, epochs - 1), mem, *mloss, len(targets), img_size)
|
|
pbar.set_description(s)
|
|
|
|
# end batch ------------------------------------------------------------------------------------------------
|
|
|
|
print('========= architecture =========')
|
|
if hasattr(model, 'module'):
|
|
best_arch, bitops, bita, bitw, mixbitops, mixbita, mixbitw, dsps, mixdsps, mixbram_weight, mixbram_cache = model.module.fetch_best_arch()
|
|
else:
|
|
best_arch, bitops, bita, bitw, mixbitops, mixbita, mixbitw, dsps, mixdsps, mixbram_weight, mixbram_cache = model.fetch_best_arch()
|
|
print('best model with bitops: {:.3f}M, bita: {:.3f}K, bitw: {:.3f}M, dsps: {:.3f}M'.format(
|
|
bitops, bita, bitw, dsps))
|
|
print('expected model with bitops: {:.3f}M, bita: {:.3f}K, bitw: {:.3f}M, dsps: {:.3f}M, bram_wa:({:.3f},{:.3f})K'.format(
|
|
mixbitops, mixbita, mixbitw, mixdsps, mixbram_weight, mixbram_cache))
|
|
|
|
bestw_str = "".join([str(x+2) for x in best_arch["best_weight"]])
|
|
besta_str = "".join([str(x+2) for x in best_arch["best_activ"]])
|
|
print(f'best_weight: {best_arch["best_weight"]}')
|
|
print(f'best_activ: {best_arch["best_activ"]}')
|
|
|
|
# Update scheduler
|
|
scheduler.step()
|
|
arch_scheduler.step()
|
|
|
|
train_iou = mloss[2]
|
|
|
|
# Process epoch results
|
|
final_epoch = epoch + 1 == epochs
|
|
if not opt.notest or final_epoch: # Calculate mAP
|
|
results = test.test(batch_size=batch_size,
|
|
img_size=img_size_test,
|
|
model=model,
|
|
dataloader=testloader)
|
|
|
|
# Write epoch results
|
|
with open(results_file, 'a') as f:
|
|
f.write(s + '%10.3g' * len(results) % results + '\n') # test_losses=(iou, loss_sum, lobj, lcls)
|
|
|
|
# Update best mAP
|
|
results = torch.tensor(results, device = 'cpu')
|
|
|
|
test_iou = results[0]
|
|
if test_iou > test_best_iou:
|
|
test_best_iou = test_iou
|
|
|
|
# Save training results
|
|
save = (not opt.nosave) or (final_epoch)
|
|
if save:
|
|
with open(results_file, 'r') as f:
|
|
# Create checkpoint
|
|
chkpt = {'epoch': epoch,
|
|
'training_results': f.read(),
|
|
'model': model.module.state_dict() if type(
|
|
model) is nn.parallel.DistributedDataParallel else model.state_dict(),
|
|
'optimizer': None if final_epoch else optimizer.state_dict(),
|
|
'extra': {'time': time.ctime(), 'name': opt.name, 'bestw': bestw_str, 'besta': besta_str}}
|
|
|
|
# Save last checkpoint
|
|
torch.save(chkpt, wdir + '%s_last.pt'%opt.name)
|
|
|
|
if test_iou == test_best_iou:
|
|
torch.save(chkpt, wdir + '%s_best.pt'%opt.name)
|
|
|
|
# Delete checkpoint
|
|
del chkpt
|
|
|
|
# end epoch ----------------------------------------------------------------------------------------------------
|
|
|
|
# end training
|
|
n = opt.name
|
|
|
|
print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
|
|
dist.destroy_process_group() if torch.cuda.device_count() > 1 else None
|
|
torch.cuda.empty_cache()
|
|
|
|
with open('results.csv', 'a') as f:
|
|
print("mixed,%s,%d/%d, , , , ,%.1f,%.1f, ,%s,%s,%d,%d,%.3f,%.3f"%
|
|
(opt.name,epochs-1,epochs,train_iou*100,(test_iou+test_best_iou)*50,
|
|
bestw_str,besta_str,
|
|
int(round(bitops)), int(round(mixbitops)), dsps, mixdsps), file=f)
|
|
|
|
return results
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('--bypass', action='store_true', help='use bypass model')
|
|
parser.add_argument('--epochs', type=int, default=35) # 500200 batches at bs 16, 117263 COCO images = 273 epochs
|
|
parser.add_argument('--batch-size', type=int, default=64) # effective bs = batch_size * accumulate = 16 * 4 = 64
|
|
parser.add_argument('--accumulate', type=int, default=1, help='batches to accumulate before optimizing')
|
|
parser.add_argument('--cfg', type=str, default='cfg/yolov3-tiny-1cls_1.cfg', help='*.cfg path')
|
|
parser.add_argument('--data', type=str, default='data/coco2017.data', help='*.data path')
|
|
parser.add_argument('--img-size', nargs='+', type=int, default=[320], help='train and test image-sizes')
|
|
parser.add_argument('--rect', action='store_true', help='rectangular training')
|
|
parser.add_argument('--resume', action='store_true', help='resume training from last.pt')
|
|
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
|
|
parser.add_argument('--notest', action='store_true', help='only test final epoch')
|
|
parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
|
|
parser.add_argument('--weights', type=str, default='', help='initial weights path')
|
|
parser.add_argument('--arc', type=str, default='default', help='yolo architecture') # default, uCE, uBCE
|
|
parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
|
|
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)')
|
|
parser.add_argument('--adam', action='store_true', help='use adam optimizer')
|
|
parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
|
|
parser.add_argument('--var', type=float, help='debug variable')
|
|
parser.add_argument('--complexity-decay', '--cd', default=0, type=float, metavar='W', help='complexity decay (default: 0)')
|
|
parser.add_argument('--complexity-decay-trivial', '--cdt', default=0, type=float, metavar='W', help='complexity decay (default: 0)')
|
|
parser.add_argument('--bram-decay', '--bd', default=0, type=float, metavar='W', help='complexity decay (default: 0)')
|
|
parser.add_argument('--lra', '--learning-rate-alpha', default=0.01, type=float, metavar='LR', help='initial alpha learning rate')
|
|
parser.add_argument('--no-share', action='store_true', help='no share weight quantization')
|
|
parser.add_argument('--model', type=str, default='', help='use specific model')
|
|
|
|
opt = parser.parse_args()
|
|
last = wdir + 'last_%s.pt'%opt.name
|
|
opt.weights = last if opt.resume else opt.weights
|
|
print(opt)
|
|
device = torch_utils.select_device(opt.device, batch_size=opt.batch_size)
|
|
|
|
train() # train normally
|