import argparse
import sys
import time
from models import *
from utils.datasets import *
from utils.utils import *
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='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=False, help='resume training flag')
parser.add_argument('-batch_report', default=False, help='report TP, FP, FN, P and R per batch (slower)')
parser.add_argument('-optimizer', default='SGD', help='optimizer')
parser.add_argument('-freeze_darknet53', default=False, help='freeze darknet53.conv.74 layers for first epoch')
parser.add_argument('-var', type=float, default=0, help='optional test variable')
opt = parser.parse_args()
print(opt)
# Import test.py to get mAP after each epoch
sys.argv[1:] = [] # delete any train.py command-line arguments before they reach test.py
import test # must follow sys.argv[1:] = []
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('weights', 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)
lr0 = 0.001
if opt.resume:
checkpoint = torch.load('weights/latest.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 (train only YOLO layers)
# for i, (name, p) in enumerate(model.named_parameters()):
# if p.shape[0] != 650: # not YOLO layer
# p.requires_grad = False
# Set optimizer
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=lr0, momentum=.9)
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:
start_epoch = 0
best_loss = float('inf')
# Initialize model with darknet53 weights (optional)
if not os.path.isfile('weights/darknet53.conv.74'):
os.system('wget https://pjreddie.com/media/files/darknet53.conv.74 -P weights')
load_weights(model, 'weights/darknet53.conv.74')
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.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=lr0, momentum=.9)
# Set scheduler
# scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[54, 61], gamma=0.1)
model_info(model)
t0, t1 = time.time(), time.time()
mean_recall, mean_precision = 0, 0
print('%11s' * 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
# Update scheduler (automatic)
# scheduler.step()
# Update scheduler (manual) at 0, 54, 61 epochs to 1e-3, 1e-4, 1e-5
if epoch > 50:
lr = lr0 / 10
else:
lr = lr0
for g in optimizer.param_groups:
g['lr'] = lr
# Freeze darknet53.conv.74 layers for first epoch
if opt.freeze_darknet53:
if epoch == 0:
for i, (name, p) in enumerate(model.named_parameters()):
if int(name.split('.')[1]) < 75: # if layer < 75
p.requires_grad = False
elif epoch == 1:
for i, (name, p) in enumerate(model.named_parameters()):
if int(name.split('.')[1]) < 75: # if layer < 75
p.requires_grad = True
ui = -1
rloss = defaultdict(float) # running loss
metrics = torch.zeros(3, num_classes)
optimizer.zero_grad()
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 = lr0 * (i / 1000) ** 4
for g in optimizer.param_groups:
g['lr'] = lr
# Compute loss, compute gradient, update parameters
loss = model(imgs.to(device), targets, batch_report=opt.batch_report, var=opt.var)
loss.backward()
# accumulated_batches = 1 # accumulate gradient for 4 batches before stepping optimizer
# if ((i+1) % accumulated_batches == 0) or (i == len(dataloader) - 1):
optimizer.step()
optimizer.zero_grad()
# Running epoch-means of tracked metrics
ui += 1
for key, val in model.losses.items():
rloss[key] = (rloss[key] * ui + val) / (ui + 1)
if opt.batch_report:
TP, FP, FN = metrics
metrics += model.losses['metrics']
# Precision
precision = TP / (TP + FP)
k = (TP + FP) > 0
if k.sum() > 0:
mean_precision = precision[k].mean()
# Recall
recall = TP / (TP + FN)
k = (TP + FN) > 0
if k.sum() > 0:
mean_recall = recall[k].mean()
s = ('%11s%11s' + '%11.3g' * 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)
# 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, 'weights/latest.pt')
# Save best checkpoint
if best_loss == loss_per_target:
os.system('cp weights/latest.pt weights/best.pt')
# Save backup weights every 5 epochs
if (epoch > 0) & (epoch % 5 == 0):
os.system('cp weights/latest.pt weights/backup' + str(epoch) + '.pt')
# Calculate mAP
test.opt.weights_path = 'weights/latest.pt'
mAP, R, P = test.main(test.opt)
# Write epoch results
with open('results.txt', 'a') as file:
file.write(s + '%11.3g' * 3 % (mAP, P, R) + '\n')
# 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()