Python EncoderA.load_state_dict Examples

Example #1

File: main.py Project: augustdemi/jcvae

    random.shuffle(total_random_idx)

    imgs = []
    labels = []
    for idx in total_random_idx:
        img, label = data.dataset.__getitem__(idx)
        imgs.append(img)
        labels.append(torch.tensor(label))
    imgs = torch.stack(imgs, dim=0)
    labels = torch.stack(labels, dim=0)
    return imgs, labels


if args.ckpt_epochs > 0:
    if CUDA:
        encA.load_state_dict(torch.load('%s/%s-encA_epoch%s.rar' % (args.ckpt_path, MODEL_NAME, args.ckpt_epochs)))
        decA.load_state_dict(torch.load('%s/%s-decA_epoch%s.rar' % (args.ckpt_path, MODEL_NAME, args.ckpt_epochs)))
        encB.load_state_dict(torch.load('%s/%s-encB_epoch%s.rar' % (args.ckpt_path, MODEL_NAME, args.ckpt_epochs)))
        decB.load_state_dict(torch.load('%s/%s-decB_epoch%s.rar' % (args.ckpt_path, MODEL_NAME, args.ckpt_epochs)))
    else:
        encA.load_state_dict(torch.load('%s/%s-encA_epoch%s.rar' % (args.ckpt_path, MODEL_NAME, args.ckpt_epochs),
                                        map_location=torch.device('cpu')))
        decA.load_state_dict(torch.load('%s/%s-decA_epoch%s.rar' % (args.ckpt_path, MODEL_NAME, args.ckpt_epochs),
                                        map_location=torch.device('cpu')))
        encB.load_state_dict(torch.load('%s/%s-encB_epoch%s.rar' % (args.ckpt_path, MODEL_NAME, args.ckpt_epochs),
                                        map_location=torch.device('cpu')))
        decB.load_state_dict(torch.load('%s/%s-decB_epoch%s.rar' % (args.ckpt_path, MODEL_NAME, args.ckpt_epochs),
                                        map_location=torch.device('cpu')))

mask = {}
fixed_imgs = None

Example #2

File: pretrain_std.py Project: augustdemi/jcvae

    for i in range(10):
        std.append(torch.std(torch.cat(pred_val[i], dim=0), dim=0))

    return torch.stack(std)


def save_ckpt(e):
    if not os.path.isdir(args.ckpt_path):
        os.mkdir(args.ckpt_path)
    torch.save(encA.state_dict(),
               '%s/%s-encA_epoch%s.rar' % (args.ckpt_path, MODEL_NAME, e))


pretrain_model = '../weights/mnist_mvae_pretrain/mnist_mvae_pretrain-run_id1-shared10-bs100-lr0.001-encA_epoch140.rar'
if CUDA:
    encA.load_state_dict(torch.load(pretrain_model))
else:
    encA.load_state_dict(
        torch.load(pretrain_model, map_location=torch.device('cpu')))

mask = {}
for e in range(args.ckpt_epochs, args.epochs):
    gt_std = get_std(train_data, encA)

    train_start = time.time()
    train(train_data, encA, e, optimizer, gt_std)
    train_end = time.time()

    test_start = time.time()
    test_accuracy, test_loss = test(test_data, encA)
    test_end = time.time()