from data_loaders.cifar10 import Cifar10
from models.resnet164_basic import resnet164Basic
from learners.gluon import GluonLearner
if __name__ == "__main__":
run_id = construct_run_id(__file__)
configure_root_logger(run_id)
logging.info(__file__)
args = process_args()
mx.random.seed(args.seed)
batch_size = 128
train_data, valid_data = Cifar10(
batch_size=batch_size,
data_shape=(3, 32, 32),
padding=4,
padding_value=0,
normalization_type="channel").return_dataloaders()
lr_schedule = {0: 0.01, 5: 0.1, 95: 0.01, 140: 0.001}
model = resnet164Basic(num_classes=10)
learner = GluonLearner(model,
run_id,
gpu_idxs=args.gpu_idxs,
hybridize=True)
learner.fit(train_data=train_data,
valid_data=valid_data,
epochs=185,
lr_schedule=lr_schedule,
from arg_parsing import process_args
from logger import construct_run_id, configure_root_logger
from data_loaders.cifar10 import Cifar10
from models.resnet164_basic import resnet164Basic
from learners.module import ModuleLearner
if __name__ == "__main__":
run_id = construct_run_id(__file__)
configure_root_logger(run_id)
logging.info(__file__)
args = process_args()
mx.random.seed(args.seed)
_, test_data = Cifar10(batch_size=1, data_shape=(3, 32, 32),
normalization_type="channel").return_dataiters()
# download model symbol and params (if doesn't already exist)
for filename in ["resnet164_basic_module-0000.params", "resnet164_basic_module-symbol.json"]:
folder = os.path.realpath(os.path.join(os.path.dirname(os.path.realpath(__file__)), "../logs/checkpoints/"))
filepath = os.path.join(folder, filename)
if not os.path.exists(filepath):
os.system("aws s3 cp s3://benchmark-ai-models/{} {}".format(filename, folder))
logging.info("Downloading {} to {}".format(filename, folder))
model = resnet164Basic(num_classes=10)
learner = ModuleLearner(model, run_id, gpu_idxs=args.gpu_idxs)
learner.load(prefix="resnet164_basic_module", data_iter=test_data)
learner.predict(test_data=test_data, log_frequency=100)