Auto Augmentation

Overview

MindSpore not only allows you to customize data augmentation, but also provides an auto augmentation method to automatically perform data augmentation on images based on specific policies.

Auto augmentation can be implemented based on probability or callback parameters.

Probability Based Auto Augmentation

MindSpore provides a series of probability-based auto augmentation APIs. You can randomly select and combine various data augmentation operations to make data augmentation more flexible.

For details about APIs, see MindSpore API.

RandomApply

The API receives a data augmentation operation list transforms and executes the data augmentation operations in the list in sequence at a certain probability or executes none of them. The default probability is 0.5.

In the following code example, the RandomCrop and RandomColorAdjust operations are executed in sequence with a probability of 0.5 or none of them are executed.

import mindspore.dataset.vision.c_transforms as c_vision
from mindspore.dataset.transforms.c_transforms import RandomApply

rand_apply_list = RandomApply([c_vision.RandomCrop(512), c_vision.RandomColorAdjust()])

RandomChoice

The API receives a data augmentation operation list transforms and randomly selects a data augmentation operation to perform.

In the following code example, an operation is selected from CenterCrop and RandomCrop for execution with equal probability.

import mindspore.dataset.vision.c_transforms as c_vision
from mindspore.dataset.transforms.c_transforms import RandomChoice

rand_choice = RandomChoice([c_vision.CenterCrop(512), c_vision.RandomCrop(512)])

RandomSelectSubpolicy

The API receives a preset policy list, including a series of sub-policy combinations. Each sub-policy consists of several data augmentation operations executed in sequence and their execution probabilities.

First, a sub-policy is randomly selected for each image with equal probability, and then operations are performed according to the probability sequence in the sub-policy.

In the following code example, two sub-policies are preset. Sub-policy 1 contains the RandomRotation, RandomVerticalFlip, and RandomColorAdjust operations, whose probabilities are 0.5, 1.0, and 0.8, respectively. Sub-policy 2 contains the RandomRotation and RandomColorAdjust operations, with the probabilities of 1.0 and 0.2, respectively.

import mindspore.dataset.vision.c_transforms as c_vision
from mindspore.dataset.vision.c_transforms import RandomSelectSubpolicy

policy_list = [
      [(c_vision.RandomRotation((45, 45)), 0.5), (c_vision.RandomVerticalFlip(), 1.0), (c_vision.RandomColorAdjust(), 0.8)],
      [(c_vision.RandomRotation((90, 90)), 1.0), (c_vision.RandomColorAdjust(), 0.2)]
      ]
policy = RandomSelectSubpolicy(policy_list)

Callback Parameter based Auto Augmentation

The sync_wait API of MindSpore supports dynamic adjustment of the data augmentation policy by batch or epoch granularity during training. You can set blocking conditions to trigger specific data augmentation operations.

sync_wait blocks the entire data processing pipeline until sync_update triggers the customized callback function. The two APIs must be used together. Their descriptions are as follows:

sync_wait(condition_name, num_batch=1, callback=None)

This API adds a blocking condition condition_name to a dataset. When sync_update is called, the specified callback function is executed.
sync_update(condition_name, num_batch=None, data=None)

This API releases the block corresponding to condition_name and triggers the specified callback function for data.

The following demonstrates the use of automatic data augmentation based on callback parameters.

Customize the Augment class where preprocess is a custom data augmentation function and update is a callback function for updating the data augmentation policy.

import mindspore.dataset as ds
import numpy as np

class Augment:
    def __init__(self):
        self.ep_num = 0
        self.step_num = 0

    def preprocess(self, input_):
        return np.array((input_ + self.step_num ** self.ep_num - 1), )

    def update(self, data):
        self.ep_num = data['ep_num']
        self.step_num = data['step_num']

The data processing pipeline calls back the custom data augmentation policy update function update, and then performs the data augmentation operation defined in preprocess based on the updated policy in the map operation.

arr = list(range(1, 4))
dataset = ds.NumpySlicesDataset(arr, shuffle=False)
aug = Augment()
dataset = dataset.sync_wait(condition_name="policy", callback=aug.update)
dataset = dataset.map(operations=[aug.preprocess])

Call sync_update in each step to update the data augmentation policy.

epochs = 5
itr = dataset.create_tuple_iterator(num_epochs=epochs)
step_num = 0
for ep_num in range(epochs):
    for data in itr:
        print("epcoh: {}, step:{}, data :{}".format(ep_num, step_num, data))
        step_num += 1
        dataset.sync_update(condition_name="policy", data={'ep_num': ep_num, 'step_num': step_num})

The output is as follows:

epcoh: 0, step:0, data :[Tensor(shape=[], dtype=Int64, value= 1)]
epcoh: 0, step:1, data :[Tensor(shape=[], dtype=Int64, value= 2)]
epcoh: 0, step:2, data :[Tensor(shape=[], dtype=Int64, value= 3)]
epcoh: 1, step:3, data :[Tensor(shape=[], dtype=Int64, value= 1)]
epcoh: 1, step:4, data :[Tensor(shape=[], dtype=Int64, value= 5)]
epcoh: 1, step:5, data :[Tensor(shape=[], dtype=Int64, value= 7)]
epcoh: 2, step:6, data :[Tensor(shape=[], dtype=Int64, value= 6)]
epcoh: 2, step:7, data :[Tensor(shape=[], dtype=Int64, value= 50)]
epcoh: 2, step:8, data :[Tensor(shape=[], dtype=Int64, value= 66)]
epcoh: 3, step:9, data :[Tensor(shape=[], dtype=Int64, value= 81)]
epcoh: 3, step:10, data :[Tensor(shape=[], dtype=Int64, value= 1001)]
epcoh: 3, step:11, data :[Tensor(shape=[], dtype=Int64, value= 1333)]
epcoh: 4, step:12, data :[Tensor(shape=[], dtype=Int64, value= 1728)]
epcoh: 4, step:13, data :[Tensor(shape=[], dtype=Int64, value= 28562)]
epcoh: 4, step:14, data :[Tensor(shape=[], dtype=Int64, value= 38418)]