def main():
parser = argparse.ArgumentParser()
parser.add_argument('raw_path')
parser.add_argument('model_path')
args = parser.parse_args()
raw_path = args.raw_path
model_path = args.model_path
mask_path = raw_to_mask_path(raw_path)
raw = imread(raw_path)
mask = imread(mask_path)
y_min, x_min, y_max, x_max = mask_to_roi(mask)
im = raw[y_min:y_max, x_min:x_max]
model = VGG_mini_ABN()
serializers.load_hdf5(model_path, model)
im = resize(im, (128, 128), preserve_range=True)
x_data = np.array([im_to_blob(im)], dtype=np.float32)
x = Variable(x_data, volatile=True)
model.train = False
y = model(x)
y_data = y.data
print(OBJECT_CLASSES[np.argmax(y_data[0])])
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'supervised_or_not', type=str, choices=['supervised', 'unsupervised'],
help='do supervised or unsupervised training')
parser.add_argument('--epoch', type=int, default=50,
help='number of recursion (default: 50)')
parser.add_argument('--no-logging', action='store_true',
help='logging to tmp dir')
parser.add_argument('--save-interval', type=int, default=None,
help='save interval of x and x_hat')
parser.add_argument('-m', '--model', required=True, help='name of model')
args = parser.parse_args()
n_epoch = args.epoch
save_interval = args.save_interval
is_supervised = True if args.supervised_or_not == 'supervised' else False
on_gpu = True
is_pipeline = False
batch_size = 10
save_encoded = False
crop_roi = False
optimizers = [O.Adam()]
if is_supervised:
if args.model == 'VGG_mini_ABN':
from apc_od.models import VGG_mini_ABN
model = VGG_mini_ABN()
if on_gpu:
model.to_gpu()
optimizers[0].setup(model)
crop_roi = True
elif args.model == 'CAEOnesRoiVGG':
from apc_od.pipeline import CAEOnesRoiVGG
is_pipeline = True
batch_size = 10
initial_roi = np.array([0, 0, 356, 534])
logging.info('initial_roi: {}'.format(initial_roi))
initial_roi = roi_preprocess(initial_roi)
# setup model
model = CAEOnesRoiVGG(initial_roi=initial_roi, learning_rate=0.2, learning_n_sample=300)
if on_gpu:
model.to_gpu()
optimizers = [O.Adam(), O.Adam()]
optimizers[0].setup(model.cae_ones1)
optimizers[1].setup(model.vgg2)
# load trained models
serializers.load_hdf5(os.path.join(here, 'cae_ones_model.h5'),
model.cae_ones1)
serializers.load_hdf5(os.path.join(here, 'vgg_model.h5'),
model.vgg2)
# load optimizers state
serializers.load_hdf5(os.path.join(here, 'cae_ones_optimizer.h5'),
optimizers[0])
serializers.load_hdf5(os.path.join(here, 'vgg_optimizer.h5'),
optimizers[1])
else:
sys.stderr.write('Unsupported model: {}\n'.format(args.model))
sys.exit(1)
else:
# unsupervised
if args.model == 'CAE':
from apc_od.models import CAE
save_encoded = True
model = CAE()
if on_gpu:
model.to_gpu()
optimizers[0].setup(model)
elif args.model == 'CAEOnes':
from apc_od.models import CAEOnes
model = CAEOnes()
if on_gpu:
model.to_gpu()
optimizers[0].setup(model)
elif args.model == 'CAEPool':
from apc_od.models import CAEPool
save_encoded = True
model = CAEPool()
if on_gpu:
model.to_gpu()
optimizers[0].setup(model)
elif args.model == 'StackedCAE':
from apc_od.models import StackedCAE
save_encoded = True
model = StackedCAE()
if on_gpu:
model.to_gpu()
optimizers[0].setup(model)
else:
sys.stderr.write('Unsupported model: {}\n'.format(args.model))
sys.exit(1)
timestamp = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
# setup for logging
if args.no_logging:
import tempfile
log_dir = tempfile.mkdtemp()
else:
log_dir = osp.join(here, '../logs/{}_{}'.format(timestamp, args.model))
log_dir = osp.realpath(osp.abspath(log_dir))
os.mkdir(log_dir)
log_file = osp.join(log_dir, 'log.txt')
logging.basicConfig(
format='%(asctime)s [%(levelname)s] %(message)s',
filename=log_file,
level=logging.DEBUG,
)
logging.info('args: {};'.format(args))
msg = 'logging in {};'.format(log_dir)
logging.info(msg)
print(msg)
trainer = Trainer(
optimizers=optimizers,
model=model,
model_name=args.model,
is_supervised=is_supervised,
crop_roi=crop_roi,
batch_size=batch_size,
log_dir=log_dir,
log_file=log_file,
on_gpu=on_gpu,
)
trainer.main_loop(
n_epoch=n_epoch,
save_interval=save_interval,
save_encoded=save_encoded,
)
