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main.py
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main.py
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#!/usr/bin/env python
import argparse
import atexit
import random
import matplotlib
matplotlib.use('Agg')
import basic_utils
from data_loader import *
from model import Model
DATA_DIR = 'data/miniimagenet'
def main(mode, seed):
model = Model(mode)
model_func = model.model_builder(seed)
if mode == Model.TRAIN_MODE:
# if Config.model.dataset == "miniimagenet":
# train_and_valid_loaders = read_dataset(DATA_DIR) # train_set, val_set, test_set
# else:
train_and_valid_loaders = get_loader("train")
model_func(train_and_valid_loaders)
elif mode == Model.PREDICT_MODE:
model_func
else:
raise ValueError(f"unknown mode: {Model.mode}")
if __name__ == '__main__':
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--config', type=str, default='config', help='config file name in config dir')
parser.add_argument('--mode', type=str, default='train', help='Mode (train/test/train_and_evaluate)')
parser.add_argument('--seed', type=int, default=0)
args = parser.parse_args()
# Print Config setting
Config(args.config)
print("Config: ", Config)
if Config.get("description", None):
print("Config Description")
for key, value in Config.description.items():
print(f" - {key}: {value}")
seed = args.seed
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
# After terminated Notification to Slack
atexit.register(basic_utils.send_message_to_slack, config_name=args.config)
main(args.mode, seed)