def main():
args = get_args()
if args.opts:
cfg.merge_from_list(args.opts)
cfg.freeze()
# create model
print("=> creating model '{}'".format(cfg.MODEL.ARCH))
model = get_model(model_name=cfg.MODEL.ARCH, pretrained=None)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
# load checkpoint
resume_path = args.resume
if Path(resume_path).is_file():
print("=> loading checkpoint '{}'".format(resume_path))
checkpoint = torch.load(resume_path, map_location="cpu")
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}'".format(resume_path))
else:
raise ValueError("=> no checkpoint found at '{}'".format(resume_path))
if device == "cuda":
cudnn.benchmark = True
test_dataset = FaceDataset(args.data_dir,
"test",
img_size=cfg.MODEL.IMG_SIZE,
augment=False)
test_loader = DataLoader(test_dataset,
batch_size=cfg.TEST.BATCH_SIZE,
shuffle=False,
num_workers=cfg.TRAIN.WORKERS,
drop_last=False)
####################################################### KE LIANG #######################################################
print("=> start testing")
_, _, test_mae = train(test_loader, model, None, None, 0, device)
print(f"test mae: {test_mae:.3f}")
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
# general
parser.add_argument('--cfg',
help='experiment configure file name',
required=False,
type=str)
parser.add_argument('--batchsize',
help='batchsize',
type=int,
required=False)
parser.add_argument('--epochs', help='episodes', type=int, required=False)
parser.add_argument('opts',
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER)
args = parser.parse_args()
if args.cfg is not None:
cfg.merge_from_file(args.cfg)
if args.batchsize is not None:
cfg.EPOCHS = params['epochs']
if args.epochs is not None:
cfg.BATCHSIZE = params['batchsize']
cfg.merge_from_list(args.opts)
pred_per_level
}]
logger.info('batch_time: {time:.4f} batch_memory: {memory:.2f}'.format(
time=(time.time() - end) / iteration,
memory=torch.cuda.max_memory_allocated() / 1024.0**3))
return pred_dict
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="inference")
parser.add_argument("opts", default=None, nargs=argparse.REMAINDER)
args = parser.parse_args()
config.merge_from_list(args.opts)
config.freeze()
save_dir = os.path.join(config.save_dir)
mkdir(save_dir)
logger = setup_logger("inference", save_dir, 0)
logger.info("Running with config:\n{}".format(config))
device = torch.device(config.device)
num_types = len(config.boundaries) + 2
generator = Generator(BertConfig(type_vocab_size=num_types))
generator = generator.to(device)
g_checkpointer = Checkpointer(model=generator, logger=logger)
g_checkpointer.load(config.model_path, True)
def main():
args = get_args()
if args.opts:
cfg.merge_from_list(args.opts)
cfg.freeze()
start_epoch = 0
checkpoint_dir = Path(args.checkpoint)
checkpoint_dir.mkdir(parents=True, exist_ok=True)
# create model
print("=> creating model '{}'".format(cfg.MODEL.ARCH))
model = get_model(model_name=cfg.MODEL.ARCH)
if cfg.TRAIN.OPT == "sgd":
optimizer = torch.optim.SGD(model.parameters(),
lr=cfg.TRAIN.LR,
momentum=cfg.TRAIN.MOMENTUM,
weight_decay=cfg.TRAIN.WEIGHT_DECAY)
else:
optimizer = torch.optim.Adam(model.parameters(), lr=cfg.TRAIN.LR)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
# optionally resume from a checkpoint
resume_path = args.resume
if resume_path:
if Path(resume_path).is_file():
print("=> loading checkpoint '{}'".format(resume_path))
checkpoint = torch.load(resume_path, map_location="cpu")
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
resume_path, checkpoint['epoch']))
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
else:
print("=> no checkpoint found at '{}'".format(resume_path))
if args.multi_gpu:
model = nn.DataParallel(model)
if device == "cuda":
cudnn.benchmark = True
############################################### KE LIANG ###############################################################
criterion = nn.CrossEntropyLoss().to(device)
train_dataset = FaceDataset(args.data_dir,
"train",
img_size=cfg.MODEL.IMG_SIZE,
augment=True,
age_stddev=cfg.TRAIN.AGE_STDDEV)
train_loader = DataLoader(train_dataset,
batch_size=cfg.TRAIN.BATCH_SIZE,
shuffle=True,
num_workers=cfg.TRAIN.WORKERS,
drop_last=True)
#for i, (input, target) in enumerate(train_loader):
# print("target: {}".format(target))
scheduler = StepLR(optimizer,
step_size=cfg.TRAIN.LR_DECAY_STEP,
gamma=cfg.TRAIN.LR_DECAY_RATE,
last_epoch=start_epoch - 1)
best_train_mae = 10000.0
train_writer = None
if args.tensorboard is not None:
opts_prefix = "_".join(args.opts)
train_writer = SummaryWriter(log_dir=args.tensorboard + "/" +
opts_prefix + "_train")
for epoch in range(start_epoch, cfg.TRAIN.EPOCHS):
# train
train_loss, train_acc, train_mae = train(train_loader, model,
criterion, optimizer, epoch,
device)
if args.tensorboard is not None:
train_writer.add_scalar("loss", train_loss, epoch)
train_writer.add_scalar("acc", train_acc, epoch)
train_writer.add_scalar("mae", train_mae, epoch)
# checkpoint
if train_mae < best_train_mae:
print(
f"=> [epoch {epoch:03d}] best val mae was improved from {best_train_mae:.3f} to {train_mae:.3f}"
)
model_state_dict = model.module.state_dict(
) if args.multi_gpu else model.state_dict()
torch.save(
{
'epoch': epoch + 1,
'arch': cfg.MODEL.ARCH,
'state_dict': model_state_dict,
'optimizer_state_dict': optimizer.state_dict()
},
str(
checkpoint_dir.joinpath(
"epoch{:03d}_{:.5f}_{:.4f}.pth".format(
epoch, train_loss, train_mae))))
best_train_mae = train_mae
else:
print(
f"=> [epoch {epoch:03d}] best val mae was not improved from {best_train_mae:.3f} ({train_mae:.3f})"
)
# adjust learning rate
scheduler.step()
print("=> training finished")
print(f"additional opts: {args.opts}")
print(f"best train mae: {best_train_mae:.3f}")
def main():
args = get_args()
if args.opts:
cfg.merge_from_list(args.opts)
cfg.freeze()
if args.output_dir is not None:
if args.img_dir is None:
raise ValueError("=> --img_dir argument is required if --output_dir is used")
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
# create model
print("=> creating model '{}'".format(cfg.MODEL.ARCH))
model = get_model(model_name=cfg.MODEL.ARCH, pretrained=None)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
# load checkpoint
resume_path = args.resume
if resume_path is None:
resume_path = Path(__file__).resolve().parent.joinpath("misc", "epoch044_0.02343_3.9984.pth")
if not resume_path.is_file():
print(f"=> model path is not set; start downloading trained model to {resume_path}")
url = "https://github.com/yu4u/age-estimation-pytorch/releases/download/v1.0/epoch044_0.02343_3.9984.pth"
urllib.request.urlretrieve(url, str(resume_path))
print("=> download finished")
if Path(resume_path).is_file():
print("=> loading checkpoint '{}'".format(resume_path))
checkpoint = torch.load(resume_path, map_location="cpu")
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}'".format(resume_path))
else:
raise ValueError("=> no checkpoint found at '{}'".format(resume_path))
if device == "cuda":
cudnn.benchmark = True
model.eval()
margin = args.margin
img_dir = args.img_dir
detector = dlib.get_frontal_face_detector()
img_size = cfg.MODEL.IMG_SIZE
image_generator = yield_images_from_dir(img_dir) if img_dir else yield_images()
with torch.no_grad():
for img, name in image_generator:
input_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_h, img_w, _ = np.shape(input_img)
# detect faces using dlib detector
detected = detector(input_img, 1)
faces = np.empty((len(detected), img_size, img_size, 3))
if len(detected) > 0:
for i, d in enumerate(detected):
x1, y1, x2, y2, w, h = d.left(), d.top(), d.right() + 1, d.bottom() + 1, d.width(), d.height()
xw1 = max(int(x1 - margin * w), 0)
yw1 = max(int(y1 - margin * h), 0)
xw2 = min(int(x2 + margin * w), img_w - 1)
yw2 = min(int(y2 + margin * h), img_h - 1)
cv2.rectangle(img, (x1, y1), (x2, y2), (255, 255, 255), 2)
cv2.rectangle(img, (xw1, yw1), (xw2, yw2), (255, 0, 0), 2)
faces[i] = cv2.resize(img[yw1:yw2 + 1, xw1:xw2 + 1], (img_size, img_size))
# predict ages
inputs = torch.from_numpy(np.transpose(faces.astype(np.float32), (0, 3, 1, 2))).to(device)
outputs = F.softmax(model(inputs), dim=-1).cpu().numpy()
ages = np.arange(0, 101)
predicted_ages = (outputs * ages).sum(axis=-1)
# draw results
for i, d in enumerate(detected):
label = "{}".format(int(predicted_ages[i]))
draw_label(img, (d.left(), d.top()), label)
if args.output_dir is not None:
output_path = output_dir.joinpath(name)
cv2.imwrite(str(output_path), img)
else:
cv2.imshow("result", img)
key = cv2.waitKey(-1) if img_dir else cv2.waitKey(30)
if key == 27: # ESC
break
