for class_label in np.unique(y):
idx = y == class_label
acc = (labels[idx] == y[idx]).astype(np.float).mean() * 100
print('accuracy for class', class_label, 'is', acc)
acc = (labels == y).mean() * 100
new_preds = np.zeros((len(preds),))
temp = preds[labels != 0, 1:]
new_preds[labels != 0] = temp.sum(1)
new_preds[labels == 0] = 1 - preds[labels == 0, 0]
y = np.array(y)
y[y != 0] = 1
auc_score = alaska_weighted_auc(y, new_preds)
print(
f'Val Loss: {epoch_loss:.3}, Weighted AUC:{auc_score:.3}, Acc: {acc:.3}')
torch.save(model.state_dict(),
f"epoch_{epoch}_val_loss_{epoch_loss:.3}_auc_{auc_score:.3}_rgb.pth")
test_ids = os.listdir(os.path.join(PATH, 'Test'))
for i in range(len(test_ids)):
test_ids[i] = os.path.join(os.path.join(PATH, 'Test'), test_ids[i])
test_dataset = Alaska2Dataset(test_ids, None, augmentations=AUGMENTATIONS_TEST, test=True, color_mode=color_mode)
batch_size = 16
num_workers = 0
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=batch_size,
num_workers=num_workers,
shuffle=False,
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"])