from tinyms.data import MnistDataset
from tinyms.vision import mnist_transform

mnist_ds = MnistDataset(data_path, shuffle=True)
mnist_ds = mnist_transform.apply_ds(mnist_ds)

import cv2
import numpy as np
from tinyms.vision import mnist_transform

img_data = np.asarray(cv2.imread(img_path, cv2.IMREAD_GRAYSCALE), dtype=np.float32)
img_data = mnist_transform(img_data)

from tinyms.vision import mnist_transform

# By default the strategy is `TOP1_CLASS`
mnist_transform.postprocess(input)
# Specify the strategy manually
mnist_transform.postprocess(input, strategy='TOP5_CLASS')

from tinyms.data import Cifar10Dataset
from tinyms.vision import cifar10_transform

cifar_ds = Cifar10Dataset(data_path, shuffle=True)
cifar_ds = cifar10_transform.apply_ds(cifar_ds)

import numpy as np
from PIL import Image
from tinyms.vision import cifar10_transform

img_data = np.asarray(Image.open(img_path), dtype=np.float32)
img_data = cifar10_transform(img_data)

from tinyms.vision import cifar_transform

# By default the strategy is `TOP1_CLASS`
cifar10_transform.postprocess(input)
# Specify the strategy manually
cifar10_transform.postprocess(input, strategy='TOP5_CLASS')