| """Create a Cifar data iterator. |
| |
| This example shows how to create a iterator reading from recordio, |
| introducing image augmentations and using a backend thread to hide IO cost. |
| All you need to do is to set some parameters. |
| """ |
| import mxnet as mx |
| |
| dataiter = mx.io.ImageRecordIter( |
| # Dataset Parameter |
| # Impulsary |
| # indicating the data file, please check the data is already there |
| path_imgrec="data/cifar/train.rec", |
| # Dataset/Augment Parameter |
| # Impulsary |
| # indicating the image size after preprocessing |
| data_shape=(3,28,28), |
| # Batch Parameter |
| # Impulsary |
| # tells how many images in a batch |
| batch_size=100, |
| # Augmentation Parameter |
| # Optional |
| # when offers mean_img, each image will subtract the mean value at each pixel |
| mean_img="data/cifar/cifar10_mean.bin", |
| # Augmentation Parameter |
| # Optional |
| # randomly crop a patch of the data_shape from the original image |
| rand_crop=True, |
| # Augmentation Parameter |
| # Optional |
| # randomly mirror the image horizontally |
| rand_mirror=True, |
| # Augmentation Parameter |
| # Optional |
| # randomly shuffle the data |
| shuffle=False, |
| # Backend Parameter |
| # Optional |
| # Preprocessing thread number |
| preprocess_threads=4, |
| # Backend Parameter |
| # Optional |
| # Prefetch buffer size |
| prefetch_buffer=4, |
| # Backend Parameter, |
| # Optional |
| # Whether round batch, |
| round_batch=True) |
| |
| batchidx = 0 |
| for dbatch in dataiter: |
| data = dbatch.data[0] |
| label = dbatch.label[0] |
| pad = dbatch.pad |
| index = dbatch.index |
| print("Batch", batchidx) |
| print(label.asnumpy().flatten()) |
| batchidx += 1 |