192 lines
7.1 KiB
Python
192 lines
7.1 KiB
Python
import argparse
|
|
import time
|
|
|
|
from models import *
|
|
from utils.datasets import *
|
|
from utils.utils import *
|
|
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument('-epochs', type=int, default=160, help='number of epochs')
|
|
parser.add_argument('-batch_size', type=int, default=12, help='size of each image batch')
|
|
parser.add_argument('-data_config_path', type=str, default='cfg/coco.data', help='data config file path')
|
|
parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
|
|
parser.add_argument('-img_size', type=int, default=32 * 13, help='size of each image dimension')
|
|
parser.add_argument('-resume', default=True, help='resume training flag')
|
|
opt = parser.parse_args()
|
|
print(opt)
|
|
|
|
cuda = torch.cuda.is_available()
|
|
device = torch.device('cuda:0' if cuda else 'cpu')
|
|
|
|
random.seed(0)
|
|
np.random.seed(0)
|
|
torch.manual_seed(0)
|
|
if cuda:
|
|
torch.cuda.manual_seed(0)
|
|
torch.cuda.manual_seed_all(0)
|
|
torch.backends.cudnn.benchmark = True
|
|
|
|
|
|
def main(opt):
|
|
os.makedirs('checkpoints', exist_ok=True)
|
|
|
|
# Configure run
|
|
data_config = parse_data_config(opt.data_config_path)
|
|
num_classes = int(data_config['classes'])
|
|
if platform == 'darwin': # MacOS (local)
|
|
train_path = data_config['train']
|
|
else: # linux (cloud, i.e. gcp)
|
|
train_path = '../coco/trainvalno5k.part'
|
|
|
|
# Initialize model
|
|
model = Darknet(opt.cfg, opt.img_size)
|
|
|
|
# Get dataloader
|
|
dataloader = load_images_and_labels(train_path, batch_size=opt.batch_size, img_size=opt.img_size, augment=True)
|
|
|
|
# Reload saved optimizer state
|
|
start_epoch = 0
|
|
best_loss = float('inf')
|
|
if opt.resume:
|
|
checkpoint = torch.load('checkpoints/yolov3.pt', map_location='cpu')
|
|
|
|
model.load_state_dict(checkpoint['model'])
|
|
if torch.cuda.device_count() > 1:
|
|
print('Using ', torch.cuda.device_count(), ' GPUs')
|
|
model = nn.DataParallel(model)
|
|
model.to(device).train()
|
|
|
|
# # Transfer learning
|
|
# for i, (name, p) in enumerate(model.named_parameters()):
|
|
# #name = name.replace('module_list.', '')
|
|
# #print('%4g %70s %9s %12g %20s %12g %12g' % (
|
|
# # i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
|
|
# if p.shape[0] != 650: # not YOLO layer
|
|
# p.requires_grad = False
|
|
|
|
# Set optimizer
|
|
# optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()))
|
|
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=1e-3,
|
|
momentum=.9, weight_decay=5e-4, nesterov=True)
|
|
|
|
start_epoch = checkpoint['epoch'] + 1
|
|
if checkpoint['optimizer'] is not None:
|
|
optimizer.load_state_dict(checkpoint['optimizer'])
|
|
best_loss = checkpoint['best_loss']
|
|
|
|
del checkpoint # current, saved
|
|
else:
|
|
if torch.cuda.device_count() > 1:
|
|
print('Using ', torch.cuda.device_count(), ' GPUs')
|
|
model = nn.DataParallel(model)
|
|
model.to(device).train()
|
|
|
|
# Set optimizer
|
|
# optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=5e-4)
|
|
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3, momentum=.9, weight_decay=5e-4, nesterov=True)
|
|
|
|
# Set scheduler
|
|
# scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.99082, last_epoch=start_epoch - 1)
|
|
|
|
modelinfo(model)
|
|
t0, t1 = time.time(), time.time()
|
|
print('%10s' * 16 % (
|
|
'Epoch', 'Batch', 'x', 'y', 'w', 'h', 'conf', 'cls', 'total', 'P', 'R', 'nTargets', 'TP', 'FP', 'FN', 'time'))
|
|
for epoch in range(opt.epochs):
|
|
epoch += start_epoch
|
|
|
|
# Multi-Scale YOLO Training
|
|
# img_size = random.choice(range(10, 20)) * 32 # 320 - 608 pixels
|
|
# dataloader = load_images_and_labels(train_path, batch_size=opt.batch_size, img_size=img_size, augment=True)
|
|
# print('Running this epoch with image size %g' % img_size)
|
|
|
|
# Update scheduler (automatic)
|
|
# scheduler.step()
|
|
|
|
# Update scheduler (manual)
|
|
# for g in optimizer.param_groups:
|
|
# g['lr'] = 1e-3 * (g ** epoch) # 1/10th every [30, 50, 100, 250] epochs using g = [.926, .955, .977, .992]
|
|
|
|
ui = -1
|
|
rloss = defaultdict(float) # running loss
|
|
metrics = torch.zeros(4, num_classes)
|
|
for i, (imgs, targets) in enumerate(dataloader):
|
|
if sum([len(x) for x in targets]) < 1: # if no targets continue
|
|
continue
|
|
|
|
# SGD burn-in
|
|
if (epoch == 0) & (i <= 1000):
|
|
lr = 1e-3 * (i / 1000) ** 4
|
|
for g in optimizer.param_groups:
|
|
g['lr'] = lr
|
|
|
|
# Compute loss, compute gradient, update parameters
|
|
loss = model(imgs.to(device), targets, requestPrecision=True)
|
|
optimizer.zero_grad()
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
# Compute running epoch-means of tracked metrics
|
|
ui += 1
|
|
metrics += model.losses['metrics']
|
|
for key, val in model.losses.items():
|
|
rloss[key] = (rloss[key] * ui + val) / (ui + 1)
|
|
|
|
# Precision
|
|
precision = metrics[0] / (metrics[0] + metrics[1] + 1e-16)
|
|
k = (metrics[0] + metrics[1]) > 0
|
|
if k.sum() > 0:
|
|
mean_precision = precision[k].mean()
|
|
else:
|
|
mean_precision = 0
|
|
|
|
# Recall
|
|
recall = metrics[0] / (metrics[0] + metrics[2] + 1e-16)
|
|
k = (metrics[0] + metrics[2]) > 0
|
|
if k.sum() > 0:
|
|
mean_recall = recall[k].mean()
|
|
else:
|
|
mean_recall = 0
|
|
|
|
s = ('%10s%10s' + '%10.3g' * 14) % (
|
|
'%g/%g' % (epoch, opt.epochs - 1), '%g/%g' % (i, len(dataloader) - 1), rloss['x'],
|
|
rloss['y'], rloss['w'], rloss['h'], rloss['conf'], rloss['cls'],
|
|
rloss['loss'], mean_precision, mean_recall, model.losses['nT'], model.losses['TP'],
|
|
model.losses['FP'], model.losses['FN'], time.time() - t1)
|
|
t1 = time.time()
|
|
print(s)
|
|
|
|
# Write epoch results
|
|
with open('results.txt', 'a') as file:
|
|
file.write(s + '\n')
|
|
|
|
# Update best loss
|
|
loss_per_target = rloss['loss'] / rloss['nT']
|
|
if loss_per_target < best_loss:
|
|
best_loss = loss_per_target
|
|
|
|
# Save latest checkpoint
|
|
checkpoint = {'epoch': epoch,
|
|
'best_loss': best_loss,
|
|
'model': model.state_dict(),
|
|
'optimizer': optimizer.state_dict()}
|
|
torch.save(checkpoint, 'checkpoints/latest.pt')
|
|
|
|
# Save best checkpoint
|
|
if best_loss == loss_per_target:
|
|
os.system('cp checkpoints/latest.pt checkpoints/best.pt')
|
|
|
|
# Save backup checkpoint
|
|
if (epoch > 0) & (epoch % 5 == 0):
|
|
os.system('cp checkpoints/latest.pt checkpoints/backup' + str(epoch) + '.pt')
|
|
|
|
# Save final model
|
|
dt = time.time() - t0
|
|
print('Finished %g epochs in %.2fs (%.2fs/epoch)' % (epoch, dt, dt / (epoch + 1)))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
torch.cuda.empty_cache()
|
|
main(opt)
|
|
torch.cuda.empty_cache()
|