Python Separator.separate_then_save方法代码示例

# 需要导入模块: from st2.cifar10.datasets import Separator [as 别名]
# 或者: from st2.cifar10.datasets.Separator import separate_then_save [as 别名]
def main(args):
    # Settings
    device_id = args.device_id
    batch_size = 100
    batch_size_eval = 100
    n_l_train_data = 4000
    n_train_data = 50000
    n_cls = 10
    learning_rate = 1. * 1e-3
    n_epoch = 300
    act = F.relu
    iter_epoch = n_train_data / batch_size
    n_iter = n_epoch * iter_epoch
    extension_module = args.context

    # Model
    ## supervised 
    batch_size, m, h, w = batch_size, 3, 32, 32
    ctx = extension_context(extension_module, device_id=device_id)
    x_l = nn.Variable((batch_size, m, h, w))
    y_l = nn.Variable((batch_size, 1))
    pred = cnn_model_003(ctx, x_l)
    loss_ce = ce_loss(ctx, pred, y_l)
    loss_er = er_loss(ctx, pred)
    loss_supervised = loss_ce + loss_er

    ## stochastic regularization
    x_u0 = nn.Variable((batch_size, m, h, w), need_grad=False)
    x_u1 = nn.Variable((batch_size, m, h, w), need_grad=False)
    pred_x_u0 = cnn_model_003(ctx, x_u0)
    pred_x_u1 = cnn_model_003(ctx, x_u1)
    loss_sr = sr_loss(ctx, pred_x_u0, pred_x_u1)
    loss_er0 = er_loss(ctx, pred_x_u0)
    loss_er1 = er_loss(ctx, pred_x_u1)
    loss_unsupervised = loss_sr + loss_er0 + loss_er1

    ## autoencoder
    path = args.model_path
    nn.load_parameters(path)
    x_u0_rc = cnn_ae_model_000(ctx, x_u0, act=F.relu, test=True)
    x_u1_rc = cnn_ae_model_000(ctx, x_u1, act=F.relu, test=True)
    x_u0_rc.need_grad = False
    x_u1_rc.need_grad = False
    pred_x_u0_rc = cnn_model_003(ctx, x_u0_rc, test=False)
    pred_x_u1_rc = cnn_model_003(ctx, x_u1_rc, test=False)
    loss_sr_rc = sr_loss(ctx, pred_x_u0_rc, pred_x_u1_rc)
    loss_er0_rc = er_loss(ctx, pred_x_u0_rc)
    loss_er1_rc = er_loss(ctx, pred_x_u1_rc)
    loss_unsupervised_rc = loss_sr_rc + loss_er0_rc + loss_er1_rc
    loss_unsupervised += loss_unsupervised_rc

    ## evaluate
    batch_size_eval, m, h, w = batch_size, 3, 32, 32
    x_eval = nn.Variable((batch_size_eval, m, h, w))
    pred_eval = cnn_model_003(ctx, x_eval, test=True)
    
    # Solver
    with nn.context_scope(ctx):
        solver = S.Adam(alpha=learning_rate)
        solver.set_parameters(nn.get_parameters())

    # Dataset
    ## separate dataset
    home = os.environ.get("HOME")
    fpath = os.path.join(home, "datasets/cifar10/cifar-10.npz")
    separator = Separator(n_l_train_data)
    separator.separate_then_save(fpath)

    l_train_path = os.path.join(home, "datasets/cifar10/l_cifar-10.npz")
    u_train_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")
    test_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")

    # data reader
    data_reader = Cifar10DataReader(l_train_path, u_train_path, test_path,
                                  batch_size=batch_size,
                                  n_cls=n_cls,
                                  da=True,
                                  shape=True)

    # Training loop
    print("# Training loop")
    epoch = 1
    st = time.time()
    acc_prev = 0.
    for i in range(n_iter):
        # Get data and set it to the varaibles
        x_l0_data, x_l1_data, y_l_data = data_reader.get_l_train_batch()
        x_u0_data, x_u1_data, y_u_data = data_reader.get_u_train_batch()
        
        x_l.d, _ , y_l.d= x_l0_data, x_l1_data, y_l_data
        x_u0.d, x_u1.d= x_u0_data, x_u1_data

        # Train
        loss_supervised.forward(clear_no_need_grad=True)
        solver.zero_grad()
        loss_supervised.backward(clear_buffer=True)
        solver.update()
        loss_unsupervised.forward(clear_no_need_grad=True)
        solver.zero_grad()
        loss_unsupervised.backward(clear_buffer=True)
#.........这里部分代码省略.........

# 需要导入模块: from st2.cifar10.datasets import Separator [as 别名]
# 或者: from st2.cifar10.datasets.Separator import separate_then_save [as 别名]
def main(args):
    # Settings
    device_id = args.device_id
    batch_size = args.batch_size
    batch_size_eval = args.batch_size_eval
    n_l_train_data = 4000
    n_train_data = 50000
    n_cls = 10
    learning_rate = 1. * 1e-3
    n_epoch = 300
    act = F.relu
    iter_epoch = n_train_data / batch_size
    n_iter = n_epoch * iter_epoch
    extension_module = args.context

    # Model
    ## supervised 
    batch_size, m, h, w = batch_size, 3, 32, 32
    ctx = extension_context(extension_module, device_id=device_id)
    x_l = nn.Variable((batch_size, m, h, w))
    y_l = nn.Variable((batch_size, 1))
    pred, log_var = cnn_model_003(ctx, x_l)
    one = F.constant(1., log_var.shape)
    loss_ce = ce_loss_with_uncertainty(ctx, pred, y_l, log_var)
    reg_sigma = sigma_regularization(ctx, log_var, one)
    loss_supervised = loss_ce + reg_sigma

    ## stochastic regularization
    x_u0 = nn.Variable((batch_size, m, h, w))
    x_u1 = nn.Variable((batch_size, m, h, w))
    pred_x_u0, log_var0 = cnn_model_003(ctx, x_u0)
    pred_x_u1, log_var1 = cnn_model_003(ctx, x_u1)
    loss_sr = sr_loss_with_uncertainty(ctx, 
                                       pred_x_u0, pred_x_u1, log_var0, log_var1)
    loss_er0 = er_loss(ctx, pred_x_u0)
    loss_er1 = er_loss(ctx, pred_x_u1)
    reg_sigma0 = sigma_regularization(ctx, log_var0, one)
    reg_sigma1 = sigma_regularization(ctx, log_var1, one)
    loss_unsupervised = loss_sr + loss_er0 + loss_er1 \
                        + reg_sigma0 + reg_sigma1

    ## evaluate
    batch_size_eval, m, h, w = batch_size, 3, 32, 32
    x_eval = nn.Variable((batch_size_eval, m, h, w))
    pred_eval, _ = cnn_model_003(ctx, x_eval, test=True)
    
    # Solver
    with nn.context_scope(ctx):
        solver_l= S.Adam(alpha=learning_rate)
        solver_l.set_parameters(nn.get_parameters())
        solver_u= S.Adam(alpha=learning_rate)
        solver_u.set_parameters(nn.get_parameters())

    # Dataset
    ## separate dataset
    home = os.environ.get("HOME")
    fpath = os.path.join(home, "datasets/cifar10/cifar-10.npz")
    separator = Separator(n_l_train_data)
    separator.separate_then_save(fpath)

    l_train_path = os.path.join(home, "datasets/cifar10/l_cifar-10.npz")
    u_train_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")
    test_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")

    # data reader
    data_reader = Cifar10DataReader(l_train_path, u_train_path, test_path,
                                  batch_size=batch_size,
                                  n_cls=n_cls,
                                  da=True,
                                  shape=True)

    # Training loop
    print("# Training loop")
    epoch = 1
    st = time.time()
    acc_prev = 0.
    for i in range(n_iter):
        # Get data and set it to the varaibles
        x_l0_data, x_l1_data, y_l_data = data_reader.get_l_train_batch()
        x_u0_data, x_u1_data, y_u_data = data_reader.get_u_train_batch()
        
        x_l.d, _ , y_l.d= x_l0_data, x_l1_data, y_l_data
        x_u0.d, x_u1.d= x_u0_data, x_u1_data

        # Train
        ## for supervised loss
        loss_supervised.forward(clear_no_need_grad=True)
        solver_l.zero_grad()
        loss_supervised.backward(clear_buffer=True)
        solver_l.update()
        ## for unsupervised loss
        loss_unsupervised.forward(clear_no_need_grad=True)
        solver_u.zero_grad()
        loss_unsupervised.backward(clear_buffer=True)
        solver_u.update()
        
        # Evaluate
        if (i+1) % iter_epoch == 0:
            # Get data and set it to the varaibles
            x_data, y_data = data_reader.get_test_batch()
#.........这里部分代码省略.........

# 需要导入模块: from st2.cifar10.datasets import Separator [as 别名]
# 或者: from st2.cifar10.datasets.Separator import separate_then_save [as 别名]
def main(args):
    # Settings
    device_id = args.device_id
    batch_sizes = [16, 32, 64]
    batch_size_eval = 64
    c, h, w = 3, 32, 32
    n_l_train_data = 4000
    n_train_data = 50000
    n_cls = 10
    learning_rate = 1. * 1e-3
    n_epoch = 300
    act = F.relu
    iter_epoch = n_train_data / int(np.mean(batch_sizes))  # approximate epoch
    n_iter = n_epoch * iter_epoch
    extension_module = args.context

    # Model (Batch-Stochastic)
    ctx = extension_context(extension_module, device_id=device_id)
    ## supervised
    x_list, y_list, preds, losses_ce = batch_stochastic_supervised_network(
        ctx, batch_sizes, c, h, w)
    
    ## stochastic regularization
    x0_list, x1_list, _, losses_sr = batch_stochastic_unsupervised_network(
        ctx, batch_sizes, c, h, w)

    ## evaluate
    batch_size_eval, m, h, w = batch_size_eval, c, h, w
    x_eval = nn.Variable((batch_size_eval, m, h, w))
    pred_eval = cnn_model_003(ctx, x_eval, test=True)
    
    # Solver
    with nn.context_scope(ctx):
        solver = S.Adam(alpha=learning_rate)
        solver.set_parameters(nn.get_parameters())

    # Dataset
    ## separate dataset
    home = os.environ.get("HOME")
    fpath = os.path.join(home, "datasets/cifar10/cifar-10.npz")
    separator = Separator(n_l_train_data)
    separator.separate_then_save(fpath)

    l_train_path = os.path.join(home, "datasets/cifar10/l_cifar-10.npz")
    u_train_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")
    test_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")

    # data reader
    data_reader = Cifar10DataReader(l_train_path, u_train_path, test_path,
                                  batch_size=batch_sizes[0],
                                  n_cls=n_cls,
                                  da=True,
                                  shape=True)

    # Training loop
    print("# Training loop")
    epoch = 1
    st = time.time()
    acc_prev = 0.
    iter_ = 0
    for i in range(n_iter):
        idx = np.random.choice(np.arange(0, len(batch_sizes)))
        idx_u = np.random.choice(np.arange(0, len(batch_sizes)))
        # Get data
        bs = batch_sizes[idx]
        bs_u = batch_sizes[idx_u]
        x_l0_data, x_l1_data, y_l_data = data_reader.get_l_train_batch(bs)
        x_u0_data, x_u1_data, y_u_data = data_reader.get_u_train_batch(bs_u)

        #  Set it to the varaibles
        x_l = x_list[idx]
        y_l = y_list[idx]
        x_u0 = x0_list[idx_u]
        x_u1 = x1_list[idx_u]
        x_l.d, _ , y_l.d= x_l0_data, x_l1_data, y_l_data
        x_u0.d, x_u1.d= x_u0_data, x_u1_data

        # Train
        loss_ce = losses_ce[idx]
        loss_sr = losses_sr[idx_u]
        loss_ce.forward(clear_no_need_grad=True)
        loss_sr.forward(clear_no_need_grad=True)
        solver.zero_grad()
        loss_ce.backward(clear_buffer=True)
        loss_sr.backward(clear_buffer=True)
        solver.update()
        
        # Evaluate
        if (i+1) % iter_epoch == 0:  # approximate epoch
            # Get data and set it to the varaibles
            x_data, y_data = data_reader.get_test_batch()

            # Evaluation loop
            ve = 0.
            iter_val = 0
            for k in range(0, len(x_data), batch_size_eval):
                x_eval.d = get_test_data(x_data, k, batch_size_eval)
                label = get_test_data(y_data, k, batch_size_eval)
                pred_eval.forward(clear_buffer=True)
                ve += categorical_error(pred_eval.d, label)
#.........这里部分代码省略.........

# 需要导入模块: from st2.cifar10.datasets import Separator [as 别名]
# 或者: from st2.cifar10.datasets.Separator import separate_then_save [as 别名]
def main(args):
    # Settings
    device_id = args.device_id
    batch_size = args.batch_size
    batch_size_eval = args.batch_size_eval
    n_l_train_data = 4000
    n_train_data = 50000
    n_cls = 10
    learning_rate = 1. * 1e-3
    n_epoch = 300
    act = F.relu
    iter_epoch = int(n_train_data / batch_size)
    n_iter = n_epoch * iter_epoch
    extension_module = args.context

    # Model
    views = [global_view, spatial_view, feature_view]
    ## supervised 
    batch_size, m, h, w = batch_size, 3, 32, 32
    ctx = extension_context(extension_module, device_id=device_id)
    x_l = nn.Variable((batch_size, m, h, w))
    y_l = nn.Variable((batch_size, 1))
    feature = cnn_model_003(ctx, x_l)
    loss_supervised = []
    for view in views:
        pred = view(ctx, feature)
        loss_ce = ce_loss(ctx, pred, y_l)
        loss_er = er_loss(ctx, pred)
        loss_supervised += [loss_ce, loss_er]
    loss_supervised = reduce(lambda x, y: x+y, loss_supervised)

    ## cross view loss
    x_u0 = nn.Variable((batch_size, m, h, w))
    x_u1 = nn.Variable((batch_size, m, h, w))
    feature_x_u0 = cnn_model_003(ctx, x_u0)
    feature_x_u1 = cnn_model_003(ctx, x_u1)
    pred_x_u0 = []
    pred_x_u1 = []
    loss_er = []
    loss_unsupervised = []    
    for view in views:
        pred = view(ctx, feature_x_u0)
        pred_x_u0 += [pred]
        loss_er +=[er_loss(ctx, pred)]
        pred = view(ctx, feature_x_u1)
        pred_x_u1 += [pred]
        loss_er += [er_loss(ctx, pred)]
    for pred_a, pred_b in itertools.product(pred_x_u0, pred_x_u1): # multi-view
        if pred_a == pred_b:
            continue
        loss_unsupervised += [sr_loss(ctx, pred_a, pred_b)]
    loss_unsupervised = reduce(lambda x, y: x+y, loss_unsupervised) \
                        + reduce(lambda x, y: x+y, loss_er)

    ## evaluate
    batch_size_eval, m, h, w = batch_size, 3, 32, 32
    x_eval = nn.Variable((batch_size_eval, m, h, w))
    feature_eval = cnn_model_003(ctx, x_eval, test=True)
    pred_eval = []
    for view in views:
        pred_eval += [view(ctx, feature_eval)]
        
    # Solver
    with nn.context_scope(ctx):
        solver = S.Adam(alpha=learning_rate)
        solver.set_parameters(nn.get_parameters())

    # Dataset
    ## separate dataset
    home = os.environ.get("HOME")
    fpath = os.path.join(home, "datasets/cifar10/cifar-10.npz")
    separator = Separator(n_l_train_data)
    separator.separate_then_save(fpath)

    l_train_path = os.path.join(home, "datasets/cifar10/l_cifar-10.npz")
    u_train_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")
    test_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")

    # data reader
    data_reader = Cifar10DataReader(l_train_path, u_train_path, test_path,
                                  batch_size=batch_size,
                                  n_cls=n_cls,
                                  da=True,
                                  shape=True)

    # Training loop
    print("# Training loop")
    epoch = 1
    st = time.time()
    acc_prev = 0.
    ve_best = 1.
    save_path_prev = ""
    for i in range(n_iter):
        # Get data and set it to the varaibles
        x_l0_data, x_l1_data, y_l_data = data_reader.get_l_train_batch()
        x_u0_data, x_u1_data, y_u_data = data_reader.get_u_train_batch()
        
        x_l.d, _ , y_l.d= x_l0_data, x_l1_data, y_l_data
        x_u0.d, x_u1.d= x_u0_data, x_u1_data

#.........这里部分代码省略.........

# 需要导入模块: from st2.cifar10.datasets import Separator [as 别名]
# 或者: from st2.cifar10.datasets.Separator import separate_then_save [as 别名]
def main(args):
    # Settings
    device_id = args.device_id
    batch_size = args.batch_size
    batch_size_eval = args.batch_size_eval
    n_l_train_data = 4000
    n_train_data = 50000
    n_cls = 10
    learning_rate = 1. * 1e-3
    n_epoch = 300
    act = F.swish
    iter_epoch = int(n_train_data / batch_size)
    n_iter = n_epoch * iter_epoch
    extension_module = args.context

    # Model
    ## supervised 
    batch_size, m, h, w = batch_size, 3, 32, 32
    ctx = extension_context(extension_module, device_id=device_id)
    x_l = nn.Variable((batch_size, m, h, w))
    y_l = nn.Variable((batch_size, 1))
    pred = cnn_model_003(ctx, x_l)
    loss_ce = ce_loss(ctx, pred, y_l)
    loss_er = er_loss(ctx, pred)
    loss_supervised = loss_ce + loss_er

    ## stochastic regularization
    x_u0 = nn.Variable((batch_size, m, h, w))
    x_u1 = nn.Variable((batch_size, m, h, w))
    pred_x_u0 = cnn_model_003(ctx, x_u0)
    pred_x_u1 = cnn_model_003(ctx, x_u1)
    loss_sr = sr_loss(ctx, pred_x_u0, pred_x_u1)
    loss_er0 = er_loss(ctx, pred_x_u0)
    loss_er1 = er_loss(ctx, pred_x_u1)
    loss_unsupervised = loss_sr + loss_er0 + loss_er1

    ## evaluate
    batch_size_eval, m, h, w = batch_size, 3, 32, 32
    x_eval = nn.Variable((batch_size_eval, m, h, w))
    pred_eval = cnn_model_003(ctx, x_eval, test=True)
    
    # Solver
    with nn.context_scope(ctx):
        solver = S.Adam(alpha=learning_rate)
        solver.set_parameters(nn.get_parameters())

    # Dataset
    ## separate dataset
    home = os.environ.get("HOME")
    fpath = os.path.join(home, "datasets/cifar10/cifar-10.npz")
    separator = Separator(n_l_train_data)
    separator.separate_then_save(fpath)

    l_train_path = os.path.join(home, "datasets/cifar10/l_cifar-10.npz")
    u_train_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")
    test_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")

    # data reader
    data_reader = Cifar10DataReader(l_train_path, u_train_path, test_path,
                                  batch_size=batch_size,
                                  n_cls=n_cls,
                                  da=False,
                                  shape=True)

    # Training loop
    print("# Training loop")
    epoch = 1
    st = time.time()
    acc_prev = 0.
    ve_best = 1.
    save_path_prev = ""
    for i in range(n_iter):
        # Get data and set it to the varaibles
        x_l0_data, x_l1_data, y_l_data = data_reader.get_l_train_batch()
        x_u0_data, x_u1_data, y_u_data = data_reader.get_u_train_batch()
        
        x_l.d, _ , y_l.d= x_l0_data, x_l1_data, y_l_data
        x_u0.d, x_u1.d= x_u0_data, x_u1_data

        # Train
        loss_supervised.forward(clear_no_need_grad=True)
        loss_unsupervised.forward(clear_no_need_grad=True)
        solver.zero_grad()
        loss_supervised.backward(clear_buffer=True)
        loss_unsupervised.backward(clear_buffer=True)
        solver.update()
        
        # Evaluate
        if int((i+1) % iter_epoch) == 0:
            # Get data and set it to the varaibles
            x_data, y_data = data_reader.get_test_batch()

            # Evaluation loop
            ve = 0.
            iter_val = 0
            for k in range(0, len(x_data), batch_size_eval):
                x_eval.d = get_test_data(x_data, k, batch_size_eval)
                label = get_test_data(y_data, k, batch_size_eval)
                pred_eval.forward(clear_buffer=True)
                ve += categorical_error(pred_eval.d, label)
#.........这里部分代码省略.........

# 需要导入模块: from st2.cifar10.datasets import Separator [as 别名]
# 或者: from st2.cifar10.datasets.Separator import separate_then_save [as 别名]
def main(args):
    # Settings
    device_id = args.device_id
    batch_size = args.batch_size
    batch_size_eval = args.batch_size_eval
    n_l_train_data = 4000
    n_train_data = 50000
    n_cls = 10
    learning_rate = 1. * 1e-3
    n_epoch = 300
    act = F.relu
    iter_epoch = n_train_data / batch_size
    n_iter = n_epoch * iter_epoch
    extension_module = args.context

    # Model
    ## supervised cnn
    batch_size, m, h, w = batch_size, 3, 32, 32
    ctx = extension_context(extension_module, device_id=device_id)
    x_l = nn.Variable((batch_size, m, h, w))
    x_l.persistent = True
    y_l = nn.Variable((batch_size, 1))
    y_l.persistent = True
    pred = cnn_model_003(ctx, "cnn", x_l)
    loss_ce = ce_loss(ctx, pred, y_l)
    loss_er = er_loss(ctx, pred)
    loss_supervised = loss_ce + loss_er

    ## supervised resnet
    pred_res = cifar10_resnet23_prediction(ctx, "resnet", x_l)
    loss_res_ce = ce_loss(ctx, pred_res, y_l)
    loss_res_supervised = loss_res_ce
    
    ## stochastic regularization for cnn
    x_u0 = nn.Variable((batch_size, m, h, w))
    x_u0.persistent = True
    x_u1 = nn.Variable((batch_size, m, h, w))
    pred_x_u0 = cnn_model_003(ctx, "cnn", x_u0)
    pred_x_u0.persistent = True
    pred_x_u1 = cnn_model_003(ctx, "cnn", x_u1)
    loss_sr = sr_loss(ctx, pred_x_u0, pred_x_u1)
    loss_er0 = er_loss(ctx, pred_x_u0)
    loss_er1 = er_loss(ctx, pred_x_u1)
    loss_unsupervised = loss_sr + loss_er0 + loss_er1

    ## knowledge transfer for resnet
    pred_res_x_u0 = cifar10_resnet23_prediction(ctx, "resnet", x_u0)
    loss_res_unsupervised = kl_divergence(ctx, pred_res_x_u0, pred_x_u0)

    ## evaluate
    batch_size_eval, m, h, w = batch_size, 3, 32, 32
    x_eval = nn.Variable((batch_size_eval, m, h, w))
    x_eval.persistent = True  # reused
    pred_eval = cnn_model_003(ctx, "cnn", x_eval, test=True)
    pred_res_eval = cifar10_resnet23_prediction(ctx, "resnet", x_eval, test=True)
    
    # Solver
    with nn.context_scope(ctx):
        with nn.parameter_scope("cnn"):
            solver = S.Adam(alpha=learning_rate)
            solver.set_parameters(nn.get_parameters())
        with nn.parameter_scope("resnet"):
            solver_res = S.Adam(alpha=learning_rate)
            solver_res.set_parameters(nn.get_parameters())

    # Dataset
    ## separate dataset
    home = os.environ.get("HOME")
    fpath = os.path.join(home, "datasets/cifar10/cifar-10.npz")
    separator = Separator(n_l_train_data)
    separator.separate_then_save(fpath)

    l_train_path = os.path.join(home, "datasets/cifar10/l_cifar-10.npz")
    u_train_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")
    test_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")

    # data reader
    data_reader = Cifar10DataReader(l_train_path, u_train_path, test_path,
                                  batch_size=batch_size,
                                  n_cls=n_cls,
                                  da=True,
                                  shape=True)

    # Training loop
    print("# Training loop")
    epoch = 1
    st = time.time()
    acc_prev = 0.
    for i in range(n_iter):
        # Get data and set it to the varaibles
        x_l0_data, x_l1_data, y_l_data = data_reader.get_l_train_batch()
        x_u0_data, x_u1_data, y_u_data = data_reader.get_u_train_batch()
        
        x_l.d, _ , y_l.d= x_l0_data, x_l1_data, y_l_data
        x_u0.d, x_u1.d= x_u0_data, x_u1_data

        # Train for cnn
        loss_supervised.forward(clear_no_need_grad=True)
        loss_unsupervised.forward(clear_no_need_grad=True)
        solver.zero_grad()
#.........这里部分代码省略.........

# 需要导入模块: from st2.cifar10.datasets import Separator [as 别名]
# 或者: from st2.cifar10.datasets.Separator import separate_then_save [as 别名]
def main(args):
    # Settings
    device_id = args.device_id
    batch_size = args.batch_size
    batch_size_eval = args.batch_size_eval
    n_l_train_data = 4000
    n_train_data = 50000
    n_cls = 10
    learning_rate = 1. * 1e-3
    n_epoch = 300
    act = F.relu
    iter_epoch = int(n_train_data / batch_size)
    n_iter = n_epoch * iter_epoch
    extension_module = args.context
    alpha = args.alpha

    # Supervised Model 
    ## ERM
    batch_size, m, h, w = batch_size, 3, 32, 32
    ctx = extension_context(extension_module, device_id=device_id)
    x_l_0 = nn.Variable((batch_size, m, h, w))
    y_l_0 = nn.Variable((batch_size, 1))
    pred = cnn_model_003(ctx, x_l_0)
    loss_ce = ce_loss(ctx, pred, y_l_0)
    loss_er = er_loss(ctx, pred)
    loss_supervised = loss_ce + loss_er
    ## VRM (mixup)
    x_l_1 = nn.Variable((batch_size, m, h, w))
    y_l_1 = nn.Variable((batch_size, 1))
    coef = nn.Variable()
    coef_b = F.broadcast(coef.reshape([1]*x_l_0.ndim, unlink=True), x_l_0.shape)
    x_l_m = coef_b * x_l_0 + (1 - coef_b) * x_l_1
    coef_b = F.broadcast(coef.reshape([1]*pred.ndim, unlink=True), pred.shape)
    y_l_m = coef_b * F.one_hot(y_l_0, (n_cls, )) \
            + (1-coef_b) * F.one_hot(y_l_1, (n_cls, ))
    x_l_m.need_grad, y_l_m.need_grad = False, False
    pred_m = cnn_model_003(ctx, x_l_m)
    loss_er_m = er_loss(ctx, pred_m)  #todo: need?
    loss_ce_m = ce_loss_soft(ctx, pred, y_l_m)
    loss_supervised_m = loss_ce_m #+ loss_er_m
    
    # Semi-Supervised Model
    ## ERM
    x_u0 = nn.Variable((batch_size, m, h, w))
    x_u1 = nn.Variable((batch_size, m, h, w))
    pred_x_u0 = cnn_model_003(ctx, x_u0)
    pred_x_u1 = cnn_model_003(ctx, x_u1)
    pred_x_u0.persistent, pred_x_u1.persistent = True, True
    loss_sr = sr_loss(ctx, pred_x_u0, pred_x_u1)
    loss_er0 = er_loss(ctx, pred_x_u0)
    loss_er1 = er_loss(ctx, pred_x_u1)
    loss_unsupervised = loss_sr + loss_er0 + loss_er1
    ## VRM (mixup)
    x_u2 = nn.Variable((batch_size, m, h, w))  # not to overwrite x_u1.d
    coef_u = nn.Variable()
    coef_u_b = F.broadcast(coef_u.reshape([1]*x_u0.ndim, unlink=True), x_u0.shape)
    x_u_m = coef_u_b * x_u0 + (1-coef_u_b) * x_u2
    pred_x_u0_ = nn.Variable(pred_x_u0.shape)  # unlink forward pass but reuse result
    pred_x_u1_ = nn.Variable(pred_x_u1.shape)
    pred_x_u0_.data = pred_x_u0.data
    pred_x_u1_.data = pred_x_u1.data
    coef_u_b = F.broadcast(coef_u.reshape([1]*pred_x_u0.ndim, unlink=True), pred_x_u0.shape)
    y_u_m = coef_u_b * pred_x_u0_ + (1-coef_u_b) * pred_x_u1_
    x_u_m.need_grad, y_u_m.need_grad = False, False
    pred_x_u_m = cnn_model_003(ctx, x_u_m)
    loss_er_u_m = er_loss(ctx, pred_x_u_m)  #todo: need?
    loss_ce_u_m = ce_loss_soft(ctx, pred_x_u_m, y_u_m)
    loss_unsupervised_m = loss_ce_u_m #+ loss_er_u_m
    
    # Evaluatation Model
    batch_size_eval, m, h, w = batch_size, 3, 32, 32
    x_eval = nn.Variable((batch_size_eval, m, h, w))
    pred_eval = cnn_model_003(ctx, x_eval, test=True)
    
    # Solver
    with nn.context_scope(ctx):
        solver = S.Adam(alpha=learning_rate)
        solver.set_parameters(nn.get_parameters())

    # Dataset
    ## separate dataset
    home = os.environ.get("HOME")
    fpath = os.path.join(home, "datasets/cifar10/cifar-10.npz")
    separator = Separator(n_l_train_data)
    separator.separate_then_save(fpath)

    l_train_path = os.path.join(home, "datasets/cifar10/l_cifar-10.npz")
    u_train_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")
    test_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")

    # data reader
    data_reader = Cifar10DataReader(l_train_path, u_train_path, test_path,
                                  batch_size=batch_size,
                                  n_cls=n_cls,
                                  da=True,
                                  shape=True)

    # Training loop
    print("# Training loop")
    epoch = 1
#.........这里部分代码省略.........