Python chainer.cuda.check_cuda_available() Examples

The following are 2 code examples of chainer.cuda.check_cuda_available(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module chainer.cuda , or try the search function .
Example #1
Source File: convolution_rbm.py    From SeRanet with MIT License 6 votes vote down vote up 
def __init__(self, in_channels, out_channels, ksize, stride=1, real=0, wscale=1.0):
        super(ConvolutionRBM, self).__init__(
            conv=L.Convolution2D(in_channels, out_channels, ksize, stride=stride, wscale=wscale),
        )

#        if gpu >= 0:
#            cuda.check_cuda_available()
#            xp = cuda.cupy # if gpu >= 0 else np
        self.conv.add_param("a", in_channels)  # dtype=xp.float32
        self.conv.a.data.fill(0.)
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.ksize = ksize
        self.real = real

        self.rbm_train = False  # default value is false
Example #2
Source File: chainer_alex.py    From mlimages with MIT License 5 votes vote down vote up 
def train(epoch=10, batch_size=32, gpu=False):
    if gpu:
        cuda.check_cuda_available()
    xp = cuda.cupy if gpu else np

    td = TrainingData(LABEL_FILE, img_root=IMAGES_ROOT, image_property=IMAGE_PROP)

    # make mean image
    if not os.path.isfile(MEAN_IMAGE_FILE):
        print("make mean image...")
        td.make_mean_image(MEAN_IMAGE_FILE)
    else:
        td.mean_image_file = MEAN_IMAGE_FILE

    # train model
    label_def = LabelingMachine.read_label_def(LABEL_DEF_FILE)
    model = alex.Alex(len(label_def))
    optimizer = optimizers.MomentumSGD(lr=0.01, momentum=0.9)
    optimizer.setup(model)
    epoch = epoch
    batch_size = batch_size

    print("Now our model is {0} classification task.".format(len(label_def)))
    print("begin training the model. epoch:{0} batch size:{1}.".format(epoch, batch_size))

    if gpu:
        model.to_gpu()

    for i in range(epoch):
        print("epoch {0}/{1}: (learning rate={2})".format(i + 1, epoch, optimizer.lr))
        td.shuffle(overwrite=True)

        for x_batch, y_batch in td.generate_batches(batch_size):
            x = chainer.Variable(xp.asarray(x_batch))
            t = chainer.Variable(xp.asarray(y_batch))

            optimizer.update(model, x, t)
            print("loss: {0}, accuracy: {1}".format(float(model.loss.data), float(model.accuracy.data)))

        serializers.save_npz(MODEL_FILE, model)
        optimizer.lr *= 0.97