Auto Augmentation
Ascend
GPU
CPU
Data Preparation
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.
For a complete example, see Application of Auto Augmentation.
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. Whensync_update
is called, the specifiedcallback
function is executed.sync_update(condition_name, num_batch=None, data=None)
This API releases the block corresponding to
condition_name
and triggers the specifiedcallback
function fordata
.
The following demonstrates the use of automatic data augmentation based on callback parameters.
Customize the
Augment
class wherepreprocess
is a custom data augmentation function andupdate
is a callback function for updating the data augmentation policy.import mindspore.dataset.vision.py_transforms as transforms 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 inpreprocess
based on the updated policy in themap
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)]