def train(args):
# CONFIGS = yaml.load(open(args.config)) # deprecated, please set the configs in parse_args()
# Set device
if torch.cuda.is_available():
os.environ["CUDA_VISIBLE_DEVICES"] = args.device.strip()
device = torch.device("cuda")
else:
device = torch.device("cpu") # Not suggested
# Set save folder & logging config
subfolder = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime(time.time()))
if not args.save_folder or (not os.path.isdir(args.save_folder)):
print(
"Warning: Not invalid value of 'save_folder', set as default value: './save_folder'.."
)
save_folder = "./save_folder"
else:
save_folder = args.save_folder
if not os.path.exists(save_folder):
os.mkdir(save_folder)
save_folder = os.path.join(save_folder, subfolder)
os.mkdir(save_folder)
#TODO:logging
# Load Dataset
trainloader = get_loader(args.train_gtfile,
batch_size=args.batch_size,
num_thread=args.num_workers)
valloader = get_loader(args.val_gtfile,
batch_size=args.batch_size,
num_thread=args.num_workers)
# Init Net
model = Net(numAngle=args.num_angle,
numRho=args.num_rho,
backbone=args.backbone)
if args.resume:
model.load_state_dict(torch.load(args.resume))
model = torch.nn.DataParallel(model).to(device)
# Optimizer
optimizer = optim.Adam(model.parameters())
# Loss
criterion = torch.nn.CrossEntropyLoss()
losses = AverageMeter()
# Start Training
model.train()
iter = 0 # iter id start from 1
for epoch in range(args.max_epoch):
for batch in trainloader:
start = time.time()
iter += 1
img_tensor, gt_tensor = batch
optimizer.zero_grad()
# Forwarding
preds = model(img_tensor)
# Calculate Loss
loss = criterion(preds, gt_tensor)
loss.backward()
optimizer.step()
losses.update(loss.item(), args.batch_size)
if iter % args.show_interval == 0:
logging.info(
f"Training [{epoch}/{args.max_epoch}][{iter}] Loss:{losses.avg} Time:{time.time()-start:.1f}s"
)
if iter % args.val_interval == 0:
pass
def main():
logger.info(args)
assert os.path.isdir(CONFIGS["DATA"]["DIR"])
if CONFIGS['TRAIN']['SEED'] is not None:
random.seed(CONFIGS['TRAIN']['SEED'])
torch.manual_seed(CONFIGS['TRAIN']['SEED'])
cudnn.deterministic = True
model = Net(numAngle=CONFIGS["MODEL"]["NUMANGLE"],
numRho=CONFIGS["MODEL"]["NUMRHO"],
backbone=CONFIGS["MODEL"]["BACKBONE"])
if CONFIGS["TRAIN"]["DATA_PARALLEL"]:
logger.info("Model Data Parallel")
model = nn.DataParallel(model).cuda()
else:
model = model.cuda(device=CONFIGS["TRAIN"]["GPU_ID"])
# optimizer
optimizer = torch.optim.Adam(
model.parameters(),
lr=CONFIGS["OPTIMIZER"]["LR"],
weight_decay=CONFIGS["OPTIMIZER"]["WEIGHT_DECAY"])
# learning rate scheduler
scheduler = lr_scheduler.MultiStepLR(
optimizer,
milestones=CONFIGS["OPTIMIZER"]["STEPS"],
gamma=CONFIGS["OPTIMIZER"]["GAMMA"])
best_acc1 = 0
if args.resume:
if isfile(args.resume):
logger.info("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_acc1 = checkpoint['best_acc1']
model.load_state_dict(checkpoint['state_dict'])
# optimizer.load_state_dict(checkpoint['optimizer'])
logger.info("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
logger.info("=> no checkpoint found at '{}'".format(args.resume))
# dataloader
train_loader = get_loader(CONFIGS["DATA"]["DIR"],
CONFIGS["DATA"]["LABEL_FILE"],
batch_size=CONFIGS["DATA"]["BATCH_SIZE"],
num_thread=CONFIGS["DATA"]["WORKERS"],
split='train')
val_loader = get_loader(CONFIGS["DATA"]["VAL_DIR"],
CONFIGS["DATA"]["VAL_LABEL_FILE"],
batch_size=1,
num_thread=CONFIGS["DATA"]["WORKERS"],
split='val')
logger.info("Data loading done.")
# Tensorboard summary
writer = SummaryWriter(log_dir=os.path.join(CONFIGS["MISC"]["TMP"]))
start_epoch = 0
best_acc = best_acc1
is_best = False
start_time = time.time()
if CONFIGS["TRAIN"]["RESUME"] is not None:
raise (NotImplementedError)
if CONFIGS["TRAIN"]["TEST"]:
validate(val_loader, model, 0, writer, args)
return
logger.info("Start training.")
for epoch in range(start_epoch, CONFIGS["TRAIN"]["EPOCHS"]):
train(train_loader, model, optimizer, epoch, writer, args)
acc = validate(val_loader, model, epoch, writer, args)
#return
scheduler.step()
if best_acc < acc:
is_best = True
best_acc = acc
else:
is_best = False
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_acc1': best_acc,
'optimizer': optimizer.state_dict()
},
is_best,
path=CONFIGS["MISC"]["TMP"])
t = time.time() - start_time
elapsed = DayHourMinute(t)
t /= (epoch + 1) - start_epoch # seconds per epoch
t = (CONFIGS["TRAIN"]["EPOCHS"] - epoch - 1) * t
remaining = DayHourMinute(t)
logger.info(
"Epoch {0}/{1} finishied, auxiliaries saved to {2} .\t"
"Elapsed {elapsed.days:d} days {elapsed.hours:d} hours {elapsed.minutes:d} minutes.\t"
"Remaining {remaining.days:d} days {remaining.hours:d} hours {remaining.minutes:d} minutes."
.format(epoch,
CONFIGS["TRAIN"]["EPOCHS"],
CONFIGS["MISC"]["TMP"],
elapsed=elapsed,
remaining=remaining))
logger.info("Optimization done, ALL results saved to %s." %
CONFIGS["MISC"]["TMP"])
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=batch_size,
num_workers=num_workers,
shuffle=False)
device = 'cuda'
if torch.cuda.device_count() > 1 and device == 'cuda':
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = Net(num_classes=n_classes)
# pretrained model in my pc. now i will train on all images for 2 epochs
# model.load_state_dict(torch.load('./epoch_5_val_loss_7.03_auc_0.844.pth'))
model = nn.DataParallel(model).to(device)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
criterion = torch.nn.CrossEntropyLoss()
train_loss, val_loss = [], []
for epoch in range(epochs):
print('Epoch {}/{}'.format(epoch, epochs - 1))
print('-' * 10)
model.train()
running_loss = 0
tk0 = tqdm(train_loader, total=int(len(train_loader)))
for im, labels in tk0:
inputs = im["image"].to(device, dtype=torch.float)
labels = labels.to(device, dtype=torch.long)
optimizer.zero_grad()
outputs = model(inputs)
shuffle=False,
num_workers=2)
# dataiter = iter(loader)
# images, labels = dataiter.next()
# print (images)
# images=tensor_to_img(images)
# print (labels)
# print (images)
net = Net(14 * batch_size)
lstm = LSTMLayer(7 * 7 * (16 + 5 * 2), 64, 14 * 14 * (num_class + 5 * 2),
2, batch_size)
lossfunction = Loss(batch_size)
optimizer = optim.Adam([{
'params': net.parameters()
}, {
'params': lstm.parameters(),
'lr': 0.0001
}],
lr=0,
weight_decay=0)
if load_checkpoint:
net.load_state_dict(torch.load(SAVE_PATH))
net.cuda()
optimizer = optim.Adam(net.parameters(), lr=0.0001)
for epoch in range(2000):
for i, data in enumerate(loader, 0):
# get the inputs
