import argparse
import time
import torch.distributed as dist
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
import test # import test.py to get mAP after each epoch
from models import *
from utils.adabound import *
from utils.datasets import *
from utils.utils import *
mixed_precision = True
try: # Mixed precision training https://github.com/NVIDIA/apex
from apex import amp
except: # not installed: install help: https://github.com/NVIDIA/apex/issues/259
mixed_precision = False
# 320 --epochs 1
# 0.109 0.297 0.15 0.126 7.04 1.666 4.062 0.1845 42.6 3.34 12.61 8.338 0.2705 0.001 -4 0.9 0.0005 a 320 giou + best_anchor False
# 0.223 0.218 0.138 0.189 9.28 1.153 4.376 0.08263 24.28 3.05 20.93 2.842 0.2759 0.001357 -5.036 0.9158 0.0005722 b mAP/F1 - 50/50 weighting
# 0.231 0.215 0.135 0.191 9.51 1.432 3.007 0.06082 24.87 3.477 24.13 2.802 0.3436 0.001127 -5.036 0.9232 0.0005874 c
# 0.246 0.194 0.128 0.192 8.12 1.101 3.954 0.0817 22.83 3.967 19.83 1.779 0.3352 0.000895 -5.036 0.9238 0.0007973 d
# 0.187 0.237 0.144 0.186 14.6 1.607 4.202 0.09439 39.27 3.726 31.26 2.634 0.273 0.001542 -5.036 0.8364 0.0008393 e
# 0.25 0.217 0.136 0.195 3.3 1.2 2 0.604 15.7 3.67 20 1.36 0.194 0.00128 -4 0.95 0.000201 0.8 0.388 1.2 0.119 0.0589 0.401 f
# 320 --epochs 2
# 0.242 0.296 0.196 0.231 5.67 0.8541 4.286 0.1539 21.61 1.957 22.9 2.894 0.3689 0.001844 -4 0.913 0.000467 # ha 0.417 mAP @ epoch 100
# 0.298 0.244 0.167 0.247 4.99 0.8896 4.067 0.1694 21.41 2.033 25.61 1.783 0.4115 0.00128 -4 0.950 0.000377 # hb
# 0.268 0.268 0.178 0.240 4.36 1.104 5.596 0.2087 14.47 2.599 16.27 2.406 0.4114 0.001585 -4 0.950 0.000524 # hc
# 0.161 0.327 0.190 0.193 7.82 1.153 4.062 0.1845 24.28 3.05 20.93 2.842 0.2759 0.001357 -4 0.916 0.000572 # hd 0.438 mAP @ epoch 100
# Training hyperparameters f
hyp = {'giou': 1.2, # giou loss gain
'xy': 4.062, # xy loss gain
'wh': 0.1845, # wh loss gain
'cls': 15.7, # cls loss gain
'cls_pw': 3.67, # cls BCELoss positive_weight
'obj': 20.0, # obj loss gain
'obj_pw': 1.36, # obj BCELoss positive_weight
'iou_t': 0.194, # iou training threshold
'lr0': 0.00128, # initial learning rate
'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
'momentum': 0.95, # SGD momentum
'weight_decay': 0.000201, # optimizer weight decay
'hsv_s': 0.8, # image HSV-Saturation augmentation (fraction)
'hsv_v': 0.388, # image HSV-Value augmentation (fraction)
'degrees': 1.2, # image rotation (+/- deg)
'translate': 0.119, # image translation (+/- fraction)
'scale': 0.0589, # image scale (+/- gain)
'shear': 0.401} # image shear (+/- deg)
# # Training hyperparameters e
# hyp = {'giou': 1.607, # giou loss gain
# 'xy': 4.062, # xy loss gain
# 'wh': 0.1845, # wh loss gain
# 'cls': 39.27, # cls loss gain
# 'cls_pw': 3.726, # cls BCELoss positive_weight
# 'obj': 31.26, # obj loss gain
# 'obj_pw': 2.634, # obj BCELoss positive_weight
# 'iou_t': 0.273, # iou target-anchor training threshold
# 'lr0': 0.001542, # initial learning rate
# 'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
# 'momentum': 0.8364, # SGD momentum
# 'weight_decay': 0.0008393} # optimizer weight decay
def train(cfg,
data,
img_size=416,
epochs=100, # 500200 batches at bs 16, 117263 images = 273 epochs
batch_size=16,
accumulate=4): # effective bs = batch_size * accumulate = 16 * 4 = 64
# Initialize
init_seeds()
weights = 'weights' + os.sep
last = weights + 'last.pt'
best = weights + 'best.pt'
device = torch_utils.select_device(apex=mixed_precision)
multi_scale = opt.multi_scale
if multi_scale:
img_sz_min = round(img_size / 32 / 1.5)
img_sz_max = round(img_size / 32 * 1.5)
img_size = img_sz_max * 32 # initiate with maximum multi_scale size
# Configure run
data_dict = parse_data_cfg(data)
train_path = data_dict['train']
nc = int(data_dict['classes']) # number of classes
# Initialize model
model = Darknet(cfg).to(device)
# Optimizer
optimizer = optim.SGD(model.parameters(), lr=hyp['lr0'], momentum=hyp['momentum'], weight_decay=hyp['weight_decay'],
nesterov=True)
# optimizer = AdaBound(model.parameters(), lr=hyp['lr0'], final_lr=0.1)
cutoff = -1 # backbone reaches to cutoff layer
start_epoch = 0
best_fitness = 0.
if opt.resume or opt.transfer: # Load previously saved model
if opt.transfer: # Transfer learning
nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255)
chkpt = torch.load(weights + 'yolov3-spp.pt', map_location=device)
model.load_state_dict({k: v for k, v in chkpt['model'].items() if v.numel() > 1 and v.shape[0] != 255},
strict=False)
for p in model.parameters():
p.requires_grad = True if p.shape[0] == nf else False
else: # resume from last.pt
if opt.bucket:
os.system('gsutil cp gs://%s/last.pt %s' % (opt.bucket, last)) # download from bucket
chkpt = torch.load(last, map_location=device) # load checkpoint
model.load_state_dict(chkpt['model'])
if chkpt['optimizer'] is not None:
optimizer.load_state_dict(chkpt['optimizer'])
best_fitness = chkpt['best_fitness']
if chkpt['training_results'] is not None:
with open('results.txt', 'w') as file:
file.write(chkpt['training_results']) # write results.txt
start_epoch = chkpt['epoch'] + 1
del chkpt
else: # Initialize model with backbone (optional)
if '-tiny.cfg' in cfg:
cutoff = load_darknet_weights(model, weights + 'yolov3-tiny.conv.15')
else:
cutoff = load_darknet_weights(model, weights + 'darknet53.conv.74')
# Remove old results
for f in glob.glob('*_batch*.jpg') + glob.glob('results.txt'):
os.remove(f)
# Scheduler https://github.com/ultralytics/yolov3/issues/238
# lf = lambda x: 1 - x / epochs # linear ramp to zero
# lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp
# lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp
# scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[round(opt.epochs * x) for x in [0.8]], gamma=0.1)
scheduler.last_epoch = start_epoch - 1
# # Plot lr schedule
# y = []
# for _ in range(epochs):
# scheduler.step()
# y.append(optimizer.param_groups[0]['lr'])
# plt.plot(y, label='LambdaLR')
# plt.xlabel('epoch')
# plt.ylabel('LR')
# plt.tight_layout()
# plt.savefig('LR.png', dpi=300)
# Mixed precision training https://github.com/NVIDIA/apex
if mixed_precision:
model, optimizer = amp.initialize(model, optimizer, opt_level='O1', verbosity=0)
# Initialize distributed training
if torch.cuda.device_count() > 1:
dist.init_process_group(backend='nccl', # 'distributed backend'
init_method='tcp://127.0.0.1:9999', # 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)
# Dataset
dataset = LoadImagesAndLabels(train_path,
img_size,
batch_size,
augment=True,
hyp=hyp, # augmentation hyperparameters
rect=opt.rect) # rectangular training
# Dataloader
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
num_workers=opt.num_workers,
shuffle=not opt.rect, # Shuffle=True unless rectangular training is used
pin_memory=True,
collate_fn=dataset.collate_fn)
# Start training
model.hyp = hyp # attach hyperparameters to model
# model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
model_info(model, report='summary') # 'full' or 'summary'
nb = len(dataloader)
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0) # P, R, mAP, F1, test_loss
# n_burnin = min(round(nb / 5 + 1), 1000) # burn-in batches
t0 = time.time()
for epoch in range(start_epoch, epochs):
model.train()
print(('\n' + '%10s' * 9) %
('Epoch', 'gpu_mem', 'GIoU/xy', 'wh', 'obj', 'cls', 'total', 'targets', 'img_size'))
# Update scheduler
scheduler.step()
# Freeze backbone at epoch 0, unfreeze at epoch 1 (optional)
freeze_backbone = False
if freeze_backbone and epoch < 2:
for name, p in model.named_parameters():
if int(name.split('.')[1]) < cutoff: # if layer < 75
p.requires_grad = False if epoch == 0 else True
# # Update image weights (optional)
# w = model.class_weights.cpu().numpy() * (1 - maps) # class weights
# image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w)
# dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # random weighted index
mloss = torch.zeros(5).to(device) # mean losses
pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
for i, (imgs, targets, paths, _) in pbar:
imgs = imgs.to(device)
targets = targets.to(device)
# Multi-Scale training TODO: short-side to 32-multiple https://github.com/ultralytics/yolov3/issues/358
if multi_scale:
if (i + nb * epoch) / accumulate % 10 == 0: # adjust (67% - 150%) every 10 batches
img_size = random.randrange(img_sz_min, img_sz_max + 1) * 32
sf = img_size / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / 32.) * 32 for x in imgs.shape[2:]] # new shape
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
# Plot images with bounding boxes
if epoch == 0 and i == 0:
plot_images(imgs=imgs, targets=targets, paths=paths, fname='train_batch%g.jpg' % i)
# SGD burn-in
# if epoch == 0 and i <= n_burnin:
# g = (i / n_burnin) ** 4 # gain
# for x in optimizer.param_groups:
# x['lr'] = hyp['lr0'] * g
# x['weight_decay'] = hyp['weight_decay'] * g
# Run model
pred = model(imgs)
# Compute loss
loss, loss_items = compute_loss(pred, targets, model, giou_loss=not opt.xywh)
if torch.isnan(loss):
print('WARNING: nan loss detected, ending training')
return results
# Compute gradient
if mixed_precision:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Accumulate gradient for x batches before optimizing
if (i + 1) % accumulate == 0 or (i + 1) == nb:
optimizer.step()
optimizer.zero_grad()
# Print batch results
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0 # (GB)
s = ('%10s' * 2 + '%10.3g' * 7) % (
'%g/%g' % (epoch, epochs - 1), '%.3gG' % mem, *mloss, len(targets), img_size)
pbar.set_description(s) # print(s)
# Calculate mAP (always test final epoch, skip first 5 if opt.nosave)
if not (opt.notest or (opt.nosave and epoch < 10)) or epoch == epochs - 1:
with torch.no_grad():
results, maps = test.test(cfg, data, batch_size=batch_size, img_size=opt.img_size, model=model,
conf_thres=0.1)
# Write epoch results
with open('results.txt', 'a') as file:
file.write(s + '%11.3g' * 5 % results + '\n') # P, R, mAP, F1, test_loss
# Update best map
fitness = results[2]
if fitness > best_fitness:
best_fitness = fitness
# Save training results
save = (not opt.nosave) or ((not opt.evolve) and (epoch == epochs - 1))
if save:
with open('results.txt', 'r') as file:
# Create checkpoint
chkpt = {'epoch': epoch,
'best_fitness': best_fitness,
'training_results': file.read(),
'model': model.module.state_dict() if type(
model) is nn.parallel.DistributedDataParallel else model.state_dict(),
'optimizer': optimizer.state_dict()}
# Save last checkpoint
torch.save(chkpt, last)
if opt.bucket:
os.system('gsutil cp %s gs://%s' % (last, opt.bucket)) # upload to bucket
# Save best checkpoint
if best_fitness == fitness:
torch.save(chkpt, best)
# Save backup every 10 epochs (optional)
if epoch > 0 and epoch % 10 == 0:
torch.save(chkpt, weights + 'backup%g.pt' % epoch)
# Delete checkpoint
del chkpt
# Report time
print('%g epochs completed in %.3f hours.' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
dist.destroy_process_group() if torch.cuda.device_count() > 1 else None
torch.cuda.empty_cache()
return results
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=100, help='number of epochs')
parser.add_argument('--batch-size', type=int, default=16, help='batch size')
parser.add_argument('--accumulate', type=int, default=4, help='number of batches to accumulate before optimizing')
parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
parser.add_argument('--data', type=str, default='data/coco_64img.data', help='coco.data file path')
parser.add_argument('--multi-scale', action='store_true', help='train at (1/1.5)x - 1.5x sizes')
parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
parser.add_argument('--rect', action='store_true', help='rectangular training')
parser.add_argument('--resume', action='store_true', help='resume training flag')
parser.add_argument('--transfer', action='store_true', help='transfer learning flag')
parser.add_argument('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers')
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('--xywh', action='store_true', help='use xywh loss instead of GIoU loss')
parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
parser.add_argument('--var', default=0, type=int, help='debug variable')
opt = parser.parse_args()
print(opt)
if not opt.evolve: # Train normally
results = train(opt.cfg,
opt.data,
img_size=opt.img_size,
epochs=opt.epochs,
batch_size=opt.batch_size,
accumulate=opt.accumulate)
else: # Evolve hyperparameters (optional)
opt.notest = True # only test final epoch
opt.nosave = True # only save final checkpoint
if opt.bucket:
os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists
for _ in range(1): # generations to evolve
if os.path.exists('evolve.txt'): # if evolve.txt exists: select best hyps and mutate
# Get best hyperparameters
x = np.loadtxt('evolve.txt', ndmin=2)
x = x[fitness(x).argmax()] # select best fitness hyps
for i, k in enumerate(hyp.keys()):
hyp[k] = x[i + 5]
# Mutate
init_seeds(seed=int(time.time()))
s = [.15, .15, .15, .15, .15, .15, .15, .15, .15, .00, .05, .20, .20, .20, .20, .20, .20, .20] # sigmas
for i, k in enumerate(hyp.keys()):
x = (np.random.randn(1) * s[i] + 1) ** 2.0 # plt.hist(x.ravel(), 300)
hyp[k] *= float(x) # vary by sigmas
# Clip to limits
keys = ['lr0', 'iou_t', 'momentum', 'weight_decay', 'hsv_s', 'hsv_v', 'translate', 'scale']
limits = [(1e-4, 1e-2), (0.00, 0.70), (0.60, 0.97), (0, 0.001), (0, .9), (0, .9), (0, .9), (0, .9)]
for k, v in zip(keys, limits):
hyp[k] = np.clip(hyp[k], v[0], v[1])
# Train mutation
results = train(opt.cfg,
opt.data,
img_size=opt.img_size,
epochs=opt.epochs,
batch_size=opt.batch_size,
accumulate=opt.accumulate)
# Write mutation results
print_mutation(hyp, results, opt.bucket)
# Plot results
plot_evolution_results(hyp)