436 lines
22 KiB
Python
436 lines
22 KiB
Python
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.datasets import *
|
||
from utils.utils import *
|
||
|
||
mixed_precision = True
|
||
try: # Mixed precision training https://github.com/NVIDIA/apex
|
||
from apex import amp
|
||
except:
|
||
mixed_precision = False # not installed
|
||
|
||
# 320 --epochs 1
|
||
# 0.109 0.297 0.150 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
|
||
# 0.250 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
|
||
# 0.269 0.225 0.149 0.218 6.71 1.13 5.25 0.246 22.4 3.64 17.8 1.31 0.256 0.00146 -4 0.936 0.00042 0.123 0.18 1.81 0.0987 0.0788 0.441 g
|
||
# 0.179 0.274 0.165 0.187 7.95 1.22 7.62 0.224 17 5.71 17.7 3.28 0.295 0.00136 -4 0.875 0.000319 0.131 0.208 2.14 0.14 0.0773 0.228 h
|
||
# 0.296 0.228 0.152 0.220 5.18 1.43 4.27 0.265 11.7 4.81 11.5 1.56 0.281 0.0013 -4 0.944 0.000427 0.0599 0.142 1.03 0.0552 0.0555 0.434 i
|
||
|
||
# 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
|
||
|
||
# Hyperparameters (j-series, 50.5 mAP yolov3-320) evolved by @ktian08 https://github.com/ultralytics/yolov3/issues/310
|
||
hyp = {'giou': 1.582, # giou loss gain
|
||
'xy': 4.688, # xy loss gain
|
||
'wh': 0.1857, # wh loss gain
|
||
'cls': 27.76, # cls loss gain (CE=~1.0, uCE=~20, uBCE=~200,~30)
|
||
'cls_pw': 1.446, # cls BCELoss positive_weight
|
||
'obj': 21.35, # obj loss gain
|
||
'obj_pw': 3.941, # obj BCELoss positive_weight
|
||
'iou_t': 0.2635, # iou training threshold
|
||
'lr0': 0.002324, # initial learning rate
|
||
'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
|
||
'momentum': 0.97, # SGD momentum
|
||
'weight_decay': 0.0004569, # optimizer weight decay
|
||
'hsv_s': 0.5703, # image HSV-Saturation augmentation (fraction)
|
||
'hsv_v': 0.3174, # image HSV-Value augmentation (fraction)
|
||
'degrees': 1.113, # image rotation (+/- deg)
|
||
'translate': 0.06797, # image translation (+/- fraction)
|
||
'scale': 0.1059, # image scale (+/- gain)
|
||
'shear': 0.5768} # image shear (+/- deg)
|
||
|
||
# # Hyperparameters (i-series)
|
||
# hyp = {'giou': 1.43, # giou loss gain
|
||
# 'xy': 4.688, # xy loss gain
|
||
# 'wh': 0.1857, # wh loss gain
|
||
# 'cls': 11.7, # cls loss gain
|
||
# 'cls_pw': 4.81, # cls BCELoss positive_weight
|
||
# 'obj': 11.5, # obj loss gain
|
||
# 'obj_pw': 1.56, # obj BCELoss positive_weight
|
||
# 'iou_t': 0.281, # iou training threshold
|
||
# 'lr0': 0.0013, # initial learning rate
|
||
# 'lrf': -4., # final LambdaLR learning rate = lr0 * (10 ** lrf)
|
||
# 'momentum': 0.944, # SGD momentum
|
||
# 'weight_decay': 0.000427, # optimizer weight decay
|
||
# 'hsv_s': 0.0599, # image HSV-Saturation augmentation (fraction)
|
||
# 'hsv_v': 0.142, # image HSV-Value augmentation (fraction)
|
||
# 'degrees': 1.03, # image rotation (+/- deg)
|
||
# 'translate': 0.0552, # image translation (+/- fraction)
|
||
# 'scale': 0.0555, # image scale (+/- gain)
|
||
# 'shear': 0.434} # image shear (+/- deg)
|
||
|
||
|
||
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) + 1
|
||
img_sz_max = round(img_size / 32 * 1.5) - 1
|
||
img_size = img_sz_max * 32 # initiate with maximum multi_scale size
|
||
print('Using multi-scale %g - %g' % (img_sz_min * 32, img_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.get('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, 0.9]], 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)
|
||
model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level
|
||
|
||
# Dataset
|
||
dataset = LoadImagesAndLabels(train_path,
|
||
img_size,
|
||
batch_size,
|
||
augment=True,
|
||
hyp=hyp, # augmentation hyperparameters
|
||
rect=opt.rect, # rectangular training
|
||
image_weights=opt.img_weights,
|
||
cache_images=opt.cache_images)
|
||
|
||
# Dataloader
|
||
dataloader = torch.utils.data.DataLoader(dataset,
|
||
batch_size=batch_size,
|
||
num_workers=min(os.cpu_count(), batch_size),
|
||
shuffle=not opt.rect, # Shuffle=True unless rectangular training is used
|
||
pin_memory=True,
|
||
collate_fn=dataset.collate_fn)
|
||
|
||
# Start training
|
||
model.nc = nc # attach number of classes to model
|
||
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, 0, 0) # P, R, mAP, F1, test_loss
|
||
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
|
||
if epoch > 0:
|
||
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)
|
||
if dataset.image_weights:
|
||
w = model.class_weights.cpu().numpy() * (1 - maps) ** 2 # 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) # rand weighted idx
|
||
|
||
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
|
||
ni = (i + nb * epoch) # number integrated batches (since train start)
|
||
if multi_scale:
|
||
if ni / 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 (stretched to 32-multiple)
|
||
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
|
||
|
||
# Plot images with bounding boxes
|
||
if epoch == 0 and i == 0:
|
||
fname = 'train_batch%g.jpg' % i
|
||
plot_images(imgs=imgs, targets=targets, paths=paths, fname=fname)
|
||
if tb_writer:
|
||
tb_writer.add_image(fname, cv2.imread(fname)[:, :, ::-1], dataformats='HWC')
|
||
|
||
# Hyperparameter burn-in
|
||
# n_burn = nb - 1 # min(nb // 5 + 1, 1000) # number of burn-in batches
|
||
# if ni <= n_burn:
|
||
# for m in model.named_modules():
|
||
# if m[0].endswith('BatchNorm2d'):
|
||
# m[1].momentum = 1 - i / n_burn * 0.99 # BatchNorm2d momentum falls from 1 - 0.01
|
||
# g = (i / n_burn) ** 4 # gain rises from 0 - 1
|
||
# 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)
|
||
|
||
# Calculate mAP (always test final epoch, skip first 5 if opt.nosave)
|
||
final_epoch = epoch + 1 == epochs
|
||
if not (opt.notest or (opt.nosave and epoch < 10)) or final_epoch:
|
||
with torch.no_grad():
|
||
results, maps = test.test(cfg,
|
||
data,
|
||
batch_size=batch_size,
|
||
img_size=opt.img_size,
|
||
model=model,
|
||
conf_thres=0.001 if final_epoch else 0.1, # 0.1 for speed
|
||
save_json=final_epoch and 'coco.data' in data)
|
||
|
||
# Write epoch results
|
||
with open('results.txt', 'a') as file:
|
||
file.write(s + '%11.3g' * 7 % results + '\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
|
||
|
||
# Write Tensorboard results
|
||
if tb_writer:
|
||
x = list(mloss[:5]) + list(results[:7])
|
||
titles = ['GIoU/XY', 'Width/Height', 'Objectness', 'Classification', 'Train loss', 'Precision', 'Recall',
|
||
'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification']
|
||
for xi, title in zip(x, titles):
|
||
tb_writer.add_scalar(title, xi, epoch)
|
||
|
||
# Update best map
|
||
fitness = results[2] # mAP
|
||
if fitness > best_fitness:
|
||
best_fitness = fitness
|
||
|
||
# Save training results
|
||
save = (not opt.nosave) or ((not opt.evolve) and final_epoch)
|
||
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=273, help='number of epochs')
|
||
parser.add_argument('--batch-size', type=int, default=32, help='batch size')
|
||
parser.add_argument('--accumulate', type=int, default=2, 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.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('--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('--img-weights', action='store_true', help='select training images by weight')
|
||
parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
|
||
opt = parser.parse_args()
|
||
print(opt)
|
||
|
||
tb_writer = None
|
||
if not opt.evolve: # Train normally
|
||
try:
|
||
# Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/
|
||
from torch.utils.tensorboard import SummaryWriter
|
||
|
||
tb_writer = SummaryWriter()
|
||
except:
|
||
pass
|
||
|
||
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(100): # 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 + 7]
|
||
|
||
# Mutate
|
||
init_seeds(seed=int(time.time()))
|
||
s = [.15, .15, .15, .15, .15, .15, .15, .15, .15, .00, .02, .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, 0.98), (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)
|