examples/autograd/mnist_cnn.py

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import numpy as np
import argparse
import os

from singa import device
from singa import tensor
from singa import autograd
from singa import opt


def load_data(path):
    f = np.load(path)
    x_train, y_train = f['x_train'], f['y_train']
    x_test, y_test = f['x_test'], f['y_test']
    f.close()
    return (x_train, y_train), (x_test, y_test)


def to_categorical(y, num_classes):
    '''
    Converts a class vector (integers) to binary class matrix.

    Args
        y: class vector to be converted into a matrix
            (integers from 0 to num_classes).
        num_classes: total number of classes.

    Return
        A binary matrix representation of the input.
    '''
    y = np.array(y, dtype='int')
    n = y.shape[0]
    categorical = np.zeros((n, num_classes))
    categorical[np.arange(n), y] = 1
    categorical = categorical.astype(np.float32)
    return categorical


def preprocess(data):
    data = data.astype(np.float32)
    data /= 255
    data = np.expand_dims(data, axis=1)
    return data


def accuracy(pred, target):
    y = np.argmax(pred, axis=1)
    t = np.argmax(target, axis=1)
    a = y == t
    return np.array(a, 'int').sum() / float(len(t))


if __name__ == '__main__':

    parser = argparse.ArgumentParser(description='Train CNN over MNIST')
    parser.add_argument('file_path', type=str, help='the dataset path')
    parser.add_argument('--use_cpu', action='store_true')
    args = parser.parse_args()

    assert os.path.exists(args.file_path), \
        'Pls download the MNIST dataset from https://s3.amazonaws.com/img-datasets/mnist.npz'

    if args.use_cpu:
        print('Using CPU')
        dev = device.get_default_device()
    else:
        print('Using GPU')
        dev = device.create_cuda_gpu()

    train, test = load_data(args.file_path)

    batch_number = 600
    num_classes = 10
    epochs = 1

    sgd = opt.SGD(lr=0.01)

    x_train = preprocess(train[0])
    y_train = to_categorical(train[1], num_classes)

    x_test = preprocess(test[0])
    y_test = to_categorical(test[1], num_classes)
    print('the shape of training data is', x_train.shape)
    print('the shape of training label is', y_train.shape)
    print('the shape of testing data is', x_test.shape)
    print('the shape of testing label is', y_test.shape)

    # operations initialization
    conv1 = autograd.Conv2d(1, 32, 3, padding=1, bias=False)
    bn1 = autograd.BatchNorm2d(32)
    conv21 = autograd.Conv2d(32, 16, 3, padding=1)
    conv22 = autograd.Conv2d(32, 16, 3, padding=1)
    bn2 = autograd.BatchNorm2d(32)
    linear = autograd.Linear(32 * 28 * 28, 10)
    pooling1 = autograd.MaxPool2d(3, 1, padding=1)
    pooling2 = autograd.AvgPool2d(3, 1, padding=1)

    def forward(x, t):
        y = conv1(x)
        y = autograd.relu(y)
        y = bn1(y)
        y = pooling1(y)
        y1 = conv21(y)
        y2 = conv22(y)
        y = autograd.cat((y1, y2), 1)
        y = bn2(y)
        y = autograd.relu(y)
        y = bn2(y)
        y = pooling2(y)
        y = autograd.flatten(y)
        y = linear(y)
        loss = autograd.softmax_cross_entropy(y, t)
        return loss, y

    autograd.training = True
    for epoch in range(epochs):
        for i in range(batch_number):
            inputs = tensor.Tensor(device=dev, data=x_train[
                                   i * 100:(1 + i) * 100], stores_grad=False)
            targets = tensor.Tensor(device=dev, data=y_train[
                                    i * 100:(1 + i) * 100], requires_grad=False, stores_grad=False)

            loss, y = forward(inputs, targets)

            accuracy_rate = accuracy(tensor.to_numpy(y),
                                     tensor.to_numpy(targets))
            if (i % 5 == 0):
                print('accuracy is:', accuracy_rate, 'loss is:',
                      tensor.to_numpy(loss)[0])

            for p, gp in autograd.backward(loss):
                sgd.update(p, gp)

            sgd.step()