parser.add_argument("--batch_size", type=int, default=32)
parser.add_argument("--n_imgs", type=int, default=16)
parser.add_argument("--seed", default=1, type=int, help="Random seed")
args = parser.parse_args()
if __name__ == "__main__":
torch.manual_seed(args.seed)
np.random.seed(args.seed)
agent = Agent(args.n_imgs)
agent.init_from_save(filename=f'{args.output_dir}/{args.model_name}.pkl')
agent.prep_eval()
env = Env(args.n_imgs)
env.batch_size = args.batch_size
env.load_labels(data_path='data/train.txt')
env.load_video(video_path='data/train.mp4')
env.shuffle_data()
env.prep_eval()
criterion = nn.MSELoss()
test_records = EvalRecords()
for i_ep in range(1000):
state, labels = env.get_data()
torch_state = Variable(torch.from_numpy(state))
torch_labels = Variable(torch.from_numpy(labels))
# forward
outputs = agent.predict(torch_state)
