1、注：本次操作使用的MNIST数据集 这里我们把数据集处理主要分为四个步骤：1. 定义函数create_dataset来创建数据集。2. 定义需要进行的数据增强和处理操作，为之后进行map映射做准备。3. 使用map映射函数，将数据操作应用到数据集。4. 进行数据shuffle、batch操作。
2、import min蟠校盯昂dspore.dataset as dsimport mindspore.dataset.transforms.c_transforms as Cimport mindspore.dataset.vision.c_transforms as CVfrom mindspore.dataset.vision import Interfrom mindspore import dtype as mstypedef create_dataset(data_path, batch_size=32, repeat_size=1, num_parallel_workers=1): # 定义数据集 mnist_ds = ds.MnistDataset(data_path) resize_height, resize_width = 32, 32 rescale = 1.0 / 255.0 shift = 0.0 rescale_nml = 1 / 0.3081 shift_nml = -1 * 0.1307 / 0.3081
3、 # 定义所需要操作的map映射 resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR) rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) rescale_op = CV.Rescale(rescale, shift) hwc2chw_op = CV.HWC2CHW() type_cast_op = C.TypeCast(mstype.int32)
4、 # 使用map映射函数，将数据操作应用到数据集 mnist_蟠校盯昂ds = mnist_ds.map(op髫潋啜缅erations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers) mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers)
5、 # 进行shuffle、batch操作 buffer_size = 10000 mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size) mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True) return mnist_ds
