# Image classification on the CIFAR10 dataset¶

The following files are also available on GitHub - https://github.com/albumentations-team/autoalbument/tree/master/examples/cifar10

## dataset.py¶

import torchvision

class SearchDataset(torchvision.datasets.CIFAR10):
def __init__(self, transform=None):
super().__init__(root="~/data/cifar10", train=True, download=True, transform=transform)

def __getitem__(self, index):
image, label = self.data[index], self.targets[index]

if self.transform is not None:
transformed = self.transform(image=image)
image = transformed["image"]

return image, label


## search.yaml¶

# @package _global_

task: classification

# Settings for Policy Model that searches augmentation policies.
policy_model:

# Multiplier for classification loss of a model. Faster AutoAugment uses classification loss to prevent augmentations
# from transforming images of a particular class to another class. The authors of Faster AutoAugment use 0.1 as
# default value.
task_factor: 0.1

# Multiplier for the gradient penalty for WGAN-GP training. 10 is the default value that was proposed in
# Improved Training of Wasserstein GANs.
gp_factor: 10

# Temperature for Relaxed Bernoulli distribution. The probability of applying a certain augmentation is sampled from
# Relaxed Bernoulli distribution (because Bernoulli distribution is not differentiable). With lower values of
# temperature Relaxed Bernoulli distribution behaves like Bernoulli distribution. In the paper, the authors
# of Faster AutoAugment used 0.05 as a default value for temperature.
temperature: 0.05

# Number of augmentation sub-policies. When an image passes through an augmentation pipeline, Faster AutoAugment
# randomly chooses one sub-policy and uses augmentations from that sub-policy to transform an input image. A larger
# number of sub-policies leads to a more diverse set of augmentations and better performance of a model trained on
# augmented images. However, an increase in the number of sub-policies leads to the exponential growth of a search
# space of augmentations, so you need more training data for Policy Model to find good augmentation policies.
num_sub_policies: 150

# Number of chunks in a batch. Faster AutoAugment splits each batch of images into num_chunks chunks. Then it
# applies the same sub-policy with the same parameters to each image in a chunk. This parameter controls the tradeoff
# between the speed of augmentation search and diversity of augmentations. Larger num_chunks values will lead to
# faster searching but less diverse set of augmentations. Note that this parameter is used only in the searching
# phase. When you train a model with found sub-policies, Albumentations will apply a distinct set of transformations
# to each image separately.
num_chunks: 8

# Number of consecutive augmentations in each sub-policy. Faster AutoAugment will sequentially apply operation_count
# augmentations from a sub-policy to an image. Larger values of operation_count lead to better performance of
# a model trained on augmented images. Simultaneously, larger values of operation_count affect the speed of search
# and increase the searching time.
operation_count: 4

# Settings for Classification Model that is used for two purposes:
# 1. As a model that performs classification of input images.
# 2. As a Discriminator for Policy Model.
classification_model:

# Number of classes in the dataset. The dataset implementation should return an integer in the range
# [0, num_classes - 1] as a class label of an image.
num_classes: 10

# The architecture of Classification Model. AutoAlbument uses models from
# https://github.com/rwightman/pytorch-image-models/. Please refer to its documentation to get a list of available
# models - https://rwightman.github.io/pytorch-image-models/#list-models-with-pretrained-weights.
architecture: resnet18

# Boolean flag that indicates whether the selected model architecture should load pretrained weights or use randomly
# initialized weights.
pretrained: False

data:
# Class for the PyTorch Dataset and arguments to it. AutoAlbument will create an object of this class using
# the instantiate method from Hydra - https://hydra.cc/docs/next/patterns/instantiate_objects/overview/.
#
# Note that the target class value in the _target_ argument should be located inside PYTHONPATH so Hydra could
# find it. The directory with the config file is automatically added to PYTHONPATH, so the default value
# dataset.SearchDataset points to the class SearchDataset from the dataset.py file. This dataset.py file is
# located along with the search.yaml file in the same directory provided by --config-dir.
#
# As an alternative, you could provide a path to a Python file with the dataset using the dataset_file parameter
# instead of the dataset parameter. The Python file should contain the implementation of a PyTorch dataset for
# augmentation search. The dataset class should have named SearchDataset. The value in dataset_file could either
# be a relative or an absolute path ; in the case of a relative path, the path should be relative to this config
# file's location.
#
# - Example of a relative path:
# dataset_file: dataset.py
#
# - Example of an absolute path:
# dataset_file: /projects/pytorch/dataset.py
#
dataset:
_target_: dataset.SearchDataset

# The data type of input images. Two values are supported:
# - uint8. In that case, all input images should be NumPy arrays with the np.uint8 data type and values in the range
#   [0, 255].
# - float32. In that case, all input images should be NumPy arrays with the np.float32 data type and values in the
#   range [0.0, 1.0].
input_dtype: uint8

# A list of preprocessing augmentations that will be applied to each image before applying augmentations from
# a policy. A preprocessing augmentation should be defined as key: value, where key is the name of augmentation
# from Albumentations, and value is a dictionary with augmentation parameters. The found policy will also apply
# those preprocessing augmentations before applying the main augmentations.
#
# Here is an example of an augmentation pipeline that first pads an image to the size 512x512 pixels, then resizes
# the resulting image to the size 256x256 pixels and finally crops a random patch with the size 224x224 pixels.
#
#  preprocessing:
#    - PadIfNeeded:
#        min_height: 512
#        min_width: 512
#    - Resize:
#        height: 256
#        width: 256
#    - RandomCrop:
#        height: 224
#        width: 224
#
preprocessing: null

# Normalization values for images. For each image, the search pipeline will subtract mean and divide by std.
# Normalization is applied after transforms defined in preprocessing. Note that regardless of input_dtype,
# the normalization function will always receive a float32 input with values in the range [0.0, 1.0], so you should
# define mean and std values accordingly. ImageNet normalization is used by default.
normalization:
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]

# Parameters for the PyTorch DataLoader. Please refer to the PyTorch documentation for the description of parameters -
# https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader.
dataloader:
_target_: torch.utils.data.DataLoader
batch_size: 128
shuffle: True
num_workers: 4
pin_memory: True
drop_last: True

optim:
# Number of epochs to search parameters of augmentations.
epochs:  20

# Optimizer configuration for Classification Model
main:
_target_: torch.optim.Adam
lr: 1e-3
betas: [0, 0.999]

# Optimizer configuration for Policy Model
policy:
_target_: torch.optim.Adam
lr: 1e-3
betas: [0, 0.999]

# Device that will keep PyTorch Tensors and which will be used for training. Please refer to the PyTorch documentation
# for more information -  https://pytorch.org/docs/stable/tensor_attributes.html#torch.torch.device.
device: cuda

# Value for torch.backends.cudnn.benchmark
# https://pytorch.org/docs/stable/notes/randomness.html#cuda-convolution-benchmarking
cudnn_benchmark: True

# If set to True AutoAlbument will save a checkpoint that contains states of models and optimizers at the end of each
# epoch. Checkpoints will be saved to the directory <working directory>/checkpoints.
save_checkpoints: False

# Path to a PyTorch checkpoint that contains saved states of models and optimizers. The value should be an absolute path
# to a file. If set, AutoAlbument will resume the searching process with data from the checkpoint.
checkpoint_path: null

# Path to a directory in which AutoAlbument will save TensorBoard logs. Set the value to null if you want to disable
# this feature.
tensorboard_logs_dir: null

hydra:
run:
# Path to the directory that will contain all outputs produced by the search algorithm. ${config_dir:} contains # path to the directory with the search.yaml config file. Please refer to the Hydra documentation for more # information - https://hydra.cc/docs/configure_hydra/workdir. dir:${config_dir:}/outputs/${now:%Y-%m-%d}/${now:%H-%M-%S}