Python context.extension_context函数代码示例

def main():
    batch_size, m, h, w = 4, 3, 32, 32
    extension_module = "cpu"
    device_id = 0
    ctx = extension_context(extension_module, device_id=device_id)

    x_l_data = np.random.randn(batch_size, m, h, w)
    y_l_data = (np.random.rand(batch_size, 1) * 10).astype(np.int32)
    x_l = nn.Variable(x_l_data.shape)
    y_l = nn.Variable(y_l_data.shape)
    x_l.d = x_l_data
    y_l.d = y_l_data

    # CNN
    print("# CNN")
    pred = cnn_model_003(ctx, x_l)
    s = 0
    for n, v in nn.get_parameters().iteritems():
        n_params = np.prod(v.shape)
        print(n, n_params)
        s += n_params
    print("n_params={}".format(s))
    nn.clear_parameters()
    
    # Resnet
    print("# Resnet")
    inmaps = 256
    pred = resnet_model(ctx, x_l, inmaps=inmaps)
    s = 0
    for n, v in nn.get_parameters().iteritems():
        n_params = np.prod(v.shape)
        print(n, n_params)
        s += n_params
    print("n_params={}".format(s))
    nn.clear_parameters()

def test_forward_backward():
    batch_size, m, h, w = 4, 3, 32, 32
    extension_module = "cpu"
    device_id = 0
    ctx = extension_context(extension_module, device_id=device_id)

    x_l_data = np.random.randn(batch_size, m, h, w)
    y_l_data = (np.random.rand(batch_size, 1) * 10).astype(np.int32)
    x_l = nn.Variable(x_l_data.shape)
    y_l = nn.Variable(y_l_data.shape)
    x_l.d = x_l_data
    y_l.d = y_l_data
    pred = cnn_model_003(ctx, x_l)
    with nn.context_scope(ctx):
        loss = F.mean(F.softmax_cross_entropy(pred, y_l))

    loss.forward()
    loss.backward()

def train(args):
    """
    Main script.
    """

    # Get context.
    from nnabla.contrib.context import extension_context
    extension_module = args.context
    if args.context is None:
        extension_module = 'cpu'
    logger.info("Running in %s" % extension_module)
    ctx = extension_context(extension_module, device_id=args.device_id)
    nn.set_default_context(ctx)

    # Create CNN network for both training and testing.
    # TRAIN

    # Fake path
    z = nn.Variable([args.batch_size, 100, 1, 1])
    fake = generator(z)
    fake.persistent = True  # Not to clear at backward
    pred_fake = discriminator(fake)
    loss_gen = F.mean(F.sigmoid_cross_entropy(
        pred_fake, F.constant(1, pred_fake.shape)))
    fake_dis = fake.unlinked()
    pred_fake_dis = discriminator(fake_dis)
    loss_dis = F.mean(F.sigmoid_cross_entropy(
        pred_fake_dis, F.constant(0, pred_fake_dis.shape)))

    # Real path
    x = nn.Variable([args.batch_size, 1, 28, 28])
    pred_real = discriminator(x)
    loss_dis += F.mean(F.sigmoid_cross_entropy(pred_real,
                                               F.constant(1, pred_real.shape)))

    # Create Solver.
    solver_gen = S.Adam(args.learning_rate, beta1=0.5)
    solver_dis = S.Adam(args.learning_rate, beta1=0.5)
    with nn.parameter_scope("gen"):
        solver_gen.set_parameters(nn.get_parameters())
    with nn.parameter_scope("dis"):
        solver_dis.set_parameters(nn.get_parameters())

    # Create monitor.
    import nnabla.monitor as M
    monitor = M.Monitor(args.monitor_path)
    monitor_loss_gen = M.MonitorSeries("Generator loss", monitor, interval=10)
    monitor_loss_dis = M.MonitorSeries(
        "Discriminator loss", monitor, interval=10)
    monitor_time = M.MonitorTimeElapsed("Time", monitor, interval=100)
    monitor_fake = M.MonitorImageTile(
        "Fake images", monitor, normalize_method=lambda x: x + 1 / 2.)

    data = data_iterator_mnist(args.batch_size, True)
    # Training loop.
    for i in range(args.max_iter):
        if i % args.model_save_interval == 0:
            with nn.parameter_scope("gen"):
                nn.save_parameters(os.path.join(
                    args.model_save_path, "generator_param_%06d.h5" % i))
            with nn.parameter_scope("dis"):
                nn.save_parameters(os.path.join(
                    args.model_save_path, "discriminator_param_%06d.h5" % i))

        # Training forward
        image, _ = data.next()
        x.d = image / 255. - 0.5  # [0, 255] to [-1, 1]
        z.d = np.random.randn(*z.shape)

        # Generator update.
        solver_gen.zero_grad()
        loss_gen.forward(clear_no_need_grad=True)
        loss_gen.backward(clear_buffer=True)
        solver_gen.weight_decay(args.weight_decay)
        solver_gen.update()
        monitor_fake.add(i, fake)
        monitor_loss_gen.add(i, loss_gen.d.copy())

        # Discriminator update.
        solver_dis.zero_grad()
        loss_dis.forward(clear_no_need_grad=True)
        loss_dis.backward(clear_buffer=True)
        solver_dis.weight_decay(args.weight_decay)
        solver_dis.update()
        monitor_loss_dis.add(i, loss_dis.d.copy())
        monitor_time.add(i)

    nnp = os.path.join(
        args.model_save_path, 'dcgan_%06d.nnp' % args.max_iter)
    runtime_contents = {
        'networks': [
            {'name': 'Generator',
             'batch_size': args.batch_size,
             'outputs': {'G': fake},
             'names': {'z': z}},
            {'name': 'Discriminator',
             'batch_size': args.batch_size,
             'outputs': {'D': pred_real},
             'names': {'x': x}}],
        'executors': [
#.........这里部分代码省略.........

def train(args):
    """
    Main script.
    """

    # Get context.
    from nnabla.contrib.context import extension_context
    extension_module = args.context
    if args.context is None:
        extension_module = 'cpu'
    logger.info("Running in %s" % extension_module)
    ctx = extension_context(extension_module, device_id=args.device_id)
    nn.set_default_context(ctx)

    # Create CNN network for both training and testing.
    margin = 1.0  # Margin for contrastive loss.

    # TRAIN
    # Create input variables.
    image0 = nn.Variable([args.batch_size, 1, 28, 28])
    image1 = nn.Variable([args.batch_size, 1, 28, 28])
    label = nn.Variable([args.batch_size])
    # Create predition graph.
    pred = mnist_lenet_siamese(image0, image1, test=False)
    # Create loss function.
    loss = F.mean(contrastive_loss(pred, label, margin))

    # TEST
    # Create input variables.
    vimage0 = nn.Variable([args.batch_size, 1, 28, 28])
    vimage1 = nn.Variable([args.batch_size, 1, 28, 28])
    vlabel = nn.Variable([args.batch_size])
    # Create predition graph.
    vpred = mnist_lenet_siamese(vimage0, vimage1, test=True)
    vloss = F.mean(contrastive_loss(vpred, vlabel, margin))

    # Create Solver.
    solver = S.Adam(args.learning_rate)
    solver.set_parameters(nn.get_parameters())

    # Create monitor.
    import nnabla.monitor as M
    monitor = M.Monitor(args.monitor_path)
    monitor_loss = M.MonitorSeries("Training loss", monitor, interval=10)
    monitor_time = M.MonitorTimeElapsed("Training time", monitor, interval=100)
    monitor_vloss = M.MonitorSeries("Test loss", monitor, interval=10)

    # Initialize DataIterator for MNIST.
    rng = np.random.RandomState(313)
    data = siamese_data_iterator(args.batch_size, True, rng)
    vdata = siamese_data_iterator(args.batch_size, False, rng)
    # Training loop.
    for i in range(args.max_iter):
        if i % args.val_interval == 0:
            # Validation
            ve = 0.0
            for j in range(args.val_iter):
                vimage0.d, vimage1.d, vlabel.d = vdata.next()
                vloss.forward(clear_buffer=True)
                ve += vloss.d
            monitor_vloss.add(i, ve / args.val_iter)
        if i % args.model_save_interval == 0:
            nn.save_parameters(os.path.join(
                args.model_save_path, 'params_%06d.h5' % i))
        image0.d, image1.d, label.d = data.next()
        solver.zero_grad()
        # Training forward, backward and update
        loss.forward(clear_no_need_grad=True)
        loss.backward(clear_buffer=True)
        solver.weight_decay(args.weight_decay)
        solver.update()
        monitor_loss.add(i, loss.d.copy())
        monitor_time.add(i)

    parameter_file = os.path.join(
        args.model_save_path, 'params_%06d.h5' % args.max_iter)
    nn.save_parameters(parameter_file)

    nnp_file = os.path.join(
        args.model_save_path, 'siamese_%06d.nnp' % (args.max_iter))
    runtime_contents = {
        'networks': [
            {'name': 'Validation',
             'batch_size': args.batch_size,
             'outputs': {'y': vpred},
             'names': {'x0': vimage0, 'x1': vimage1}}],
        'executors': [
            {'name': 'Runtime',
             'network': 'Validation',
             'data': ['x0', 'x1'],
             'output': ['y']}]}
    save.save(nnp_file, runtime_contents)

    from cpp_forward_check import check_cpp_forward
    check_cpp_forward(args.model_save_path, [vimage0.d, vimage1.d], [
                      vimage0, vimage1], vpred, nnp_file)

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.........

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 = args.n_label
    n_train_data = 73257
    n_cls = 10
    learning_rate = 1. * 1e-3
    n_epoch = args.epoch
    act = F.relu
    iter_epoch = n_train_data / batch_size
    n_iter = int(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/svhn/train.mat")
    separator = Separator(n_l_train_data)
    separator.separate_then_save(fpath)

    l_train_path = os.path.join(home, "datasets/svhn/l_train.mat")
    u_train_path = os.path.join(home, "datasets/svhn/u_train.mat")
    test_path = os.path.join(home, "datasets/svhn/test.mat")

    # data reader
    data_reader = SVHNDataReader(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.........

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 = 50
    act = F.relu
    iter_epoch = n_train_data / batch_size
    n_iter = n_epoch * iter_epoch
    extension_module = args.context
    n_images = args.n_images 
    fname, _ = os.path.splitext(__file__)
    dpath = "./{}_images_{}".format(fname, int(time.time()))

    # Model
    batch_size, m, h, w = batch_size, 3, 32, 32
    ctx = extension_context(extension_module, device_id=device_id)
    x_u = nn.Variable((batch_size, m, h, w))
    pred = cnn_ae_model_001(ctx, x_u)
    loss_recon = recon_loss(ctx, pred, x_u)

    ## 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_ae_model_001(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_u_data, _, _ = data_reader.get_u_train_batch()
        x_u.d = x_u_data

        # Train
        loss_recon.forward(clear_no_need_grad=True)
        solver.zero_grad()
        loss_recon.backward(clear_buffer=True)
        solver.update()
        
        # Evaluate
        if (i+1) % iter_epoch == 0:
            # Get data and forward
            x_data, y_data = data_reader.get_test_batch()
            pred_eval.forward(clear_buffer=True)
            images = pred_eval.d

            # Save n images
            if not os.path.exists(dpath):
                os.makedirs(dpath)
            save_images(dpath, epoch, images[:n_images])
            fpath = os.path.join(dpath, "epoch_{:05d}.h5".format(epoch))
            nn.save_parameters(fpath)

            st = time.time()
            epoch +=1

def test_data_parallel_communicator():
    try:
        import nnabla_ext
        import nnabla_ext.cuda
        from nnabla.contrib.context import extension_context

    except:
        pytest.skip("DataParallelCommunicator are only supported in CUDA now.")

    n_devices = nnabla_ext.cuda.init.get_device_count()
    if n_devices < 2:
        pytest.skip("Number of cuda devices is less than 2.")

    # Contexts and Computation Graph
    extension_module = "cuda"
    ctxs = []
    for d in range(n_devices):
        ctx = extension_context(extension_module,
                                device_id="{}".format(d))
        ctxs.append(ctx)
        with nn.context_scope(ctx):
            x_data = np.random.rand(4, 5)
            x = nn.Variable(x_data.shape)
            with nn.parameter_scope("gpu{}".format(d)):
                with nn.parameter_scope("affine1"):
                    z = PF.affine(x, 6)
                with nn.parameter_scope("affine2"):
                    y = PF.affine(z, 5)

    # Init w.g
    grads = []
    for d in range(n_devices):
        with nn.parameter_scope("gpu{}".format(d)):
            params = nn.get_parameters()
            grad = []
            for i, elm in enumerate(params.items()):
                k, v = elm
                grad_ = np.random.randn(*v.shape)
                v.g = grad_
                v.grad.cast(np.float32, ctxs[d])
                grad.append(grad_)
            grads.append(grad)

    # Reference
    ref_grads = []
    with nn.parameter_scope("gpu{}".format(d)):
        params = nn.get_parameters()
        for i in range(len(params)):
            ave_grad = 0
            for d in range(n_devices):
                ave_grad += grads[d][i]
            ave_grad /= n_devices
            ref_grads.append(ave_grad)

    # Communicator
    try:
        comm = C.DataParalellCommunicator(ctxs[0])
    except:
        pytest.skip(
            "DataParalellCommunicator is not supported in cpu or not linux platform.")

    for d in range(n_devices):
        with nn.parameter_scope("gpu{}".format(d)):
            comm.add_context_and_parameters(
                (ctxs[d], nn.get_parameters()))
    comm.init()
    comm.allreduce(division=True)

    # Check
    atol = 1e-6
    for d in range(n_devices):
        with nn.parameter_scope("gpu{}".format(d)):
            params = nn.get_parameters()
            for i, elm in enumerate(params.items()):
                k, v = elm
                assert np.allclose(ref_grads[i], v.g, atol=atol)

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.........

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.........

def train():
    """
    Naive Multi-Device Training

    NOTE: the communicator exposes low-level interfaces

    * Parse command line arguments.
    * Instantiate a communicator and set parameter variables.
    * Specify contexts for computation.
    * Initialize DataIterator.
    * Construct a computation graph for training and one for validation.
    * Initialize solver and set parameter variables to that.
    * Create monitor instances for saving and displaying training stats.
    * Training loop
      * Computate error rate for validation data (periodically)
      * Get a next minibatch.
      * Execute forwardprop
      * Set parameter gradients zero
      * Execute backprop.
      * Inplace allreduce (THIS IS THE MAIN difference from a single device training)
      * Solver updates parameters by using gradients computed by backprop.
      * Compute training error
    """
    # Parse args
    args = get_args()
    n_train_samples = 50000
    bs_valid = args.batch_size

    # Communicator and Context
    extension_module = "cuda.cudnn"
    ctx = extension_context(extension_module)
    comm = C.MultiProcessDataParalellCommunicator(ctx)
    comm.init()
    n_devices = comm.size
    mpi_rank = comm.rank
    device_id = mpi_rank
    ctx = extension_context(extension_module, device_id=device_id)

    # Create training graphs
    test = False
    image_train = nn.Variable((args.batch_size, 3, 32, 32))
    label_train = nn.Variable((args.batch_size, 1))
    pred_train = cifar100_resnet23_prediction(
        image_train, ctx, test)
    loss_train = cifar100_resnet32_loss(pred_train, label_train)
    input_image_train = {"image": image_train, "label": label_train}

    # add parameters to communicator
    comm.add_context_and_parameters((ctx, nn.get_parameters()))

    # Create validation graph
    test = True
    image_valid = nn.Variable((bs_valid, 3, 32, 32))
    pred_valid = cifar100_resnet23_prediction(
        image_valid, ctx, test)
    input_image_valid = {"image": image_valid}

    # Solvers
    solver = S.Adam()
    solver.set_parameters(nn.get_parameters())
    base_lr = args.learning_rate
    warmup_iter = int(1. * n_train_samples /
                      args.batch_size / n_devices) * args.warmup_epoch
    warmup_slope = 1. * n_devices / warmup_iter

    # Create monitor
    from nnabla.monitor import Monitor, MonitorSeries, MonitorTimeElapsed
    monitor = Monitor(args.monitor_path)
    monitor_loss = MonitorSeries("Training loss", monitor, interval=10)
    monitor_err = MonitorSeries("Training error", monitor, interval=10)
    monitor_time = MonitorTimeElapsed("Training time", monitor, interval=100)
    monitor_verr = MonitorSeries("Test error", monitor, interval=10)
    with data_iterator_cifar100(args.batch_size, True) as tdata, \
            data_iterator_cifar100(bs_valid, False) as vdata:
        # Training-loop
        for i in range(int(args.max_iter / n_devices)):
            # Validation
            if mpi_rank == 0:
                if i % int(n_train_samples / args.batch_size / n_devices) == 0:
                    ve = 0.
                    for j in range(args.val_iter):
                        image, label = vdata.next()
                        input_image_valid["image"].d = image
                        pred_valid.forward()
                        ve += categorical_error(pred_valid.d, label)
                    ve /= args.val_iter
                    monitor_verr.add(i * n_devices, ve)
                if i % int(args.model_save_interval / n_devices) == 0:
                    nn.save_parameters(os.path.join(
                        args.model_save_path, 'params_%06d.h5' % i))

            # Forward/Zerograd/Backward
            image, label = tdata.next()
            input_image_train["image"].d = image
            input_image_train["label"].d = label
            loss_train.forward()
            solver.zero_grad()
            loss_train.backward()

            # In-place Allreduce
#.........这里部分代码省略.........

def train():
    """
    Main script.
    """

    args = get_args()

    # Get context.
    from nnabla.contrib.context import extension_context
    extension_module = args.context
    if args.context is None:
        extension_module = 'cpu'
    logger.info("Running in %s" % extension_module)
    ctx = extension_context(extension_module, device_id=args.device_id)
    nn.set_default_context(ctx)

    # Dataset
    # We use Tiny ImageNet from Stanford CS231N class.
    # https://tiny-imagenet.herokuapp.com/
    # Tiny ImageNet consists of 200 categories, each category has 500 images
    # in training set. The image size is 64x64. To adapt ResNet into 64x64
    # image inputs, the input image size of ResNet is set as 56x56, and
    # the stride in the first conv and the first max pooling are removed.
    data = data_iterator_tiny_imagenet(args.batch_size, 'train')
    vdata = data_iterator_tiny_imagenet(args.batch_size, 'val')

    num_classes = 200
    tiny = True  # TODO: Switch ILSVRC2012 dataset and TinyImageNet.
    t_model = get_model(
        args, num_classes, test=False, tiny=tiny)
    t_model.pred.persistent = True  # Not clearing buffer of pred in backward
    v_model = get_model(
        args, num_classes, test=True, tiny=tiny)
    v_model.pred.persistent = True  # Not clearing buffer of pred in forward

    # Create Solver.
    solver = S.Momentum(args.learning_rate, 0.9)
    solver.set_parameters(nn.get_parameters())

    # Create monitor.
    import nnabla.monitor as M
    monitor = M.Monitor(args.monitor_path)
    monitor_loss = M.MonitorSeries("Training loss", monitor, interval=10)
    monitor_err = M.MonitorSeries("Training error", monitor, interval=10)
    monitor_vloss = M.MonitorSeries("Validation loss", monitor, interval=10)
    monitor_verr = M.MonitorSeries("Validation error", monitor, interval=10)
    monitor_time = M.MonitorTimeElapsed("Training time", monitor, interval=10)

    # Training loop.
    for i in range(args.max_iter):
        # Save parameters
        if i % args.model_save_interval == 0:
            nn.save_parameters(os.path.join(
                args.model_save_path, 'param_%06d.h5' % i))

        # Validation
        if i % args.val_interval == 0:

            # Clear all intermediate memory to save memory.
            # t_model.loss.clear_recursive()

            l = 0.0
            e = 0.0
            for j in range(args.val_iter):
                images, labels = vdata.next()
                v_model.image.d = images
                v_model.label.d = labels
                v_model.image.data.cast(np.uint8, ctx)
                v_model.label.data.cast(np.int32, ctx)
                v_model.loss.forward(clear_buffer=True)
                l += v_model.loss.d
                e += categorical_error(v_model.pred.d, v_model.label.d)
            monitor_vloss.add(i, l / args.val_iter)
            monitor_verr.add(i, e / args.val_iter)

            # Clear all intermediate memory to save memory.
            # v_model.loss.clear_recursive()

        # Training
        l = 0.0
        e = 0.0
        solver.zero_grad()

        # Gradient accumulation loop
        for j in range(args.accum_grad):
            images, labels = data.next()
            t_model.image.d = images
            t_model.label.d = labels
            t_model.image.data.cast(np.uint8, ctx)
            t_model.label.data.cast(np.int32, ctx)
            t_model.loss.forward(clear_no_need_grad=True)
            t_model.loss.backward(clear_buffer=True)  # Accumulating gradients
            l += t_model.loss.d
            e += categorical_error(t_model.pred.d, t_model.label.d)
        solver.weight_decay(args.weight_decay)
        solver.update()
        monitor_loss.add(i, l / args.accum_grad)
        monitor_err.add(i, e / args.accum_grad)
        monitor_time.add(i)

#.........这里部分代码省略.........

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.........

def train():
    """
    Naive Multi-Device Training

    NOTE: the communicator exposes low-level interfaces

    * Parse command line arguments.
    * Specify contexts for computation.
    * Initialize DataIterator.
    * Construct computation graphs for training and one for validation.
    * Initialize solvers and set parameter variables to those.
    * Instantiate a communicator and set parameter variables.
    * Create monitor instances for saving and displaying training stats.
    * Training loop
      * Computate error rate for validation data (periodically)
      * Get a next minibatch.
      * Execute forwardprops
      * Set parameter gradients zero
      * Execute backprop.
      * Inplace allreduce (THIS IS THE MAIN difference from a single device training)
      * Solver updates parameters by using gradients computed by backprop.
      * Compute training error
    """
    # Parse args
    args = get_args()
    n_train_samples = 50000
    bs_valid = args.batch_size

    # Create contexts
    extension_module = args.context
    if extension_module != "cuda" and \
            extension_module != "cuda.cudnn":
        raise Exception("Use `cuda` or `cuda.cudnn` extension_module.")
    n_devices = args.n_devices
    ctxs = []
    for i in range(n_devices):
        ctx = extension_context(extension_module, device_id=i)
        ctxs.append(ctx)
    ctx = ctxs[-1]

    # Create training graphs
    input_image_train = []
    preds_train = []
    losses_train = []
    test = False
    for i in range(n_devices):
        image = nn.Variable((args.batch_size, 3, 32, 32))
        label = nn.Variable((args.batch_size, 1))
        device_scope_name = "device{}".format(i)

        pred = cifar100_resnet23_prediction(
            image, ctxs[i], device_scope_name, test)
        loss = cifar100_resnet32_loss(pred, label)

        input_image_train.append({"image": image, "label": label})
        preds_train.append(pred)
        losses_train.append(loss)

    # Create validation graph
    test = True
    device_scope_name = "device{}".format(0)
    image_valid = nn.Variable((bs_valid, 3, 32, 32))
    pred_valid = cifar100_resnet23_prediction(
        image_valid, ctxs[i], device_scope_name, test)
    input_image_valid = {"image": image_valid}

    # Solvers
    solvers = []
    for i in range(n_devices):
        with nn.context_scope(ctxs[i]):
            solver = S.Adam()
            device_scope_name = "device{}".format(i)
            with nn.parameter_scope(device_scope_name):
                params = nn.get_parameters()
                solver.set_parameters(params)
            solvers.append(solver)

    # Communicator
    comm = C.DataParalellCommunicator(ctx)
    for i in range(n_devices):
        device_scope_name = "device{}".format(i)
        with nn.parameter_scope(device_scope_name):
            ctx = ctxs[i]
            params = nn.get_parameters()
            comm.add_context_and_parameters((ctx, params))
    comm.init()

    # Create threadpools with one thread
    pools = []
    for _ in range(n_devices):
        pool = ThreadPool(processes=1)
        pools.append(pool)

    # Once forward/backward to safely secure memory
    for device_id in range(n_devices):
        data, label = \
            (np.random.randn(*input_image_train[device_id]["image"].shape),
             (np.random.rand(*input_image_train[device_id]["label"].shape) * 10).astype(np.int32))

        ret = pools[device_id].apply_async(forward_backward,
#.........这里部分代码省略.........

def main():

    # Get arguments
    args = get_args()
    data_file = "https://raw.githubusercontent.com/tomsercu/lstm/master/data/ptb.train.txt"
    model_file = args.work_dir + "model.h5"

    # Load Dataset
    itow, wtoi, dataset = load_ptbset(data_file)

    # Computation environment settings
    from nnabla.contrib.context import extension_context
    extension_module = args.context
    if args.context is None:
        extension_module = 'cpu'
    logger.info("Running in %s" % extension_module)
    ctx = extension_context(extension_module, device_id=args.device_id)
    nn.set_default_context(ctx)

    # Create data provider
    n_word = len(wtoi)
    n_dim = args.embed_dim
    batchsize = args.batchsize
    half_window = args.half_window_length
    n_negative = args.n_negative_sample

    di = DataIteratorForEmbeddingLearning(
        batchsize=batchsize,
        half_window=half_window,
        n_negative=n_negative,
        dataset=dataset)

    # Create model
    # - Real batch size including context samples and negative samples
    size = batchsize * (1 + n_negative) * (2 * (half_window - 1))

    # Model for learning
    # - input variables
    xl = nn.Variable((size,))  # variable for word
    yl = nn.Variable((size,))  # variable for context

    # Embed layers for word embedding function
    # - f_embed : word index x to get y, the n_dim vector
    # --  for each sample in a minibatch
    hx = PF.embed(xl, n_word, n_dim, name="e1")  # feature vector for word
    hy = PF.embed(yl, n_word, n_dim, name="e1")  # feature vector for context
    hl = F.sum(hx * hy, axis=1)

    # -- Approximated likelihood of context prediction
    # pos: word context, neg negative samples
    tl = nn.Variable([size, ], need_grad=False)
    loss = F.sigmoid_cross_entropy(hl, tl)
    loss = F.mean(loss)

    # Model for test of searching similar words
    xr = nn.Variable((1,), need_grad=False)
    hr = PF.embed(xr, n_word, n_dim, name="e1")  # feature vector for test

    # Create solver
    solver = S.Adam(args.learning_rate)
    solver.set_parameters(nn.get_parameters())

    # Create monitor.
    monitor = M.Monitor(args.work_dir)
    monitor_loss = M.MonitorSeries(
        "Training loss", monitor, interval=args.monitor_interval)
    monitor_time = M.MonitorTimeElapsed(
        "Training time", monitor, interval=args.monitor_interval)

    # Do training
    max_epoch = args.max_epoch
    for epoch in range(max_epoch):

        # iteration per epoch
        for i in range(di.n_batch):

            # get minibatch
            xi, yi, ti = di.next()

            # learn
            solver.zero_grad()
            xl.d, yl.d, tl.d = xi, yi, ti
            loss.forward(clear_no_need_grad=True)
            loss.backward(clear_buffer=True)
            solver.update()

            # monitor
            itr = epoch * di.n_batch + i
            monitor_loss.add(itr, loss.d)
            monitor_time.add(itr)

    # Save model
    nn.save_parameters(model_file)

    # Evaluate by similarity
    max_check_words = args.max_check_words
    for i in range(max_check_words):

        # prediction
        xr.d = i
#.........这里部分代码省略.........