rocks = batch['rocks'].cuda() trees = batch['trees'].cuda() gt_masks = torch.cat((skier, flags, rocks, trees), dim=1) pred_masks, latent = model(rgb) all_images = model.make_visuals(rgb, gt_masks, pred_masks) pass if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--dataset_dir', type=str, default='data/20210413_182405') parser.add_argument('--batch_size', type=int, default=4) parser.add_argument('--num_workers', type=int, default=4) parser.add_argument('--max_epochs', type=int, default=2) parser.add_argument( '--model_dir', type=str, default= '/home/aaron/workspace/vlr/vlr-project/checkpoints/20210423_184757') args = parser.parse_args() ckpts = sorted(list(Path(args.model_dir).glob('*.ckpt'))) model = AutoEncoder.load_from_checkpoint(str(ckpts[-1])) #evaluate_on_dataset(model)
encoder_ids = [ '20210505_165831', '20210505_165834', '20210505_165837', '20210505_165841', '20210505_165845', '20210505_165848', '20210505_165852', '20210505_165856', ] ID = 1 #encoder_path = f"/home/aaronhua/vlr/dqn-pong/autoencoder/checkpoints/{encoder_ids[ID]}/epoch=19.ckpt" encoder_path = f"/home/aaron/workspace/vlr/dqn-pong/autoencoder/checkpoints/{encoder_ids[ID]}/epoch=19.ckpt" auto_encoder = AutoEncoder.load_from_checkpoint(encoder_path).to(device) encoder = auto_encoder.encoder decoder = auto_encoder.decoder encoder.eval() Transition = namedtuple('Transition', ('state', 'action', 'next_state', 'reward')) # initialize replay memory memory = ReplayMemory(MEMORY_SIZE) # create networks policy_net = DQNBase(n_actions=4).to(device) target_net = DQNBase(n_actions=4).to(device) target_net.load_state_dict(policy_net.state_dict())