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Image classification on the CIFAR10 dataset

The following files are also available on GitHub -

import cv2
import torchvision


class Cifar10SearchDataset(torchvision.datasets.CIFAR10):
    def __init__(self, root="~/data/cifar10", train=True, download=True, transform=None):
        super().__init__(root=root, train=train, download=download, transform=transform)

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

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

        return image, label


# @package _global_

_version: 2  # An internal value that indicates a version of the config schema. This value is used by
# `autoalbument-search` and `autoalbument-migrate` to upgrade the config to the latest version if necessary.
# Please do not change it manually.

task: classification # Deep learning task. Should either be `classification` or `semantic_segmentation`.

  # Settings for Policy Model that searches augmentation policies.

  task_factor: 0.1
  # 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.

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

  temperature: 0.05
  # 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`.

  num_sub_policies: 100
  # 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_chunks: 8
  # 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.

  operation_count: 4
  # 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.

  # 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.

  _target_: model.Cifar10ClassificationModel
  # A custom classification model is used. This model is defined inside the `` file which is located
  # in the same directory with `search.yaml` and ``.
  #  # As an alternative, you could use a built-in AutoAlbument model using the following config:
  #  #  _target_: autoalbument.faster_autoaugment.models.ClassificationModel
  #  # 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
  #  # Please refer to its documentation to get a list of available
  #  # models -
  #  architecture: resnet18
  #  # Boolean flag that indicates whether the selected model architecture should load pretrained weights or use randomly
  #  # initialized weights.
  #  pretrained: False

    _target_: dataset.Cifar10SearchDataset
    root: ~/data/cifar10
    train: true
    download: true
  # Class for the PyTorch Dataset and arguments to it. AutoAlbument will create an object of this class using
  # the `instantiate` method from Hydra -
  # 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 `` file. This `` 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:
  # - Example of an absolute path:
  # dataset_file: /projects/pytorch/

  input_dtype: uint8
  # 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].

  preprocessing: null
  # 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

    mean: [0.4914, 0.4822, 0.4465]
    std: [0.247, 0.243, 0.261]
  # 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.

    batch_size: 128
    shuffle: true
    num_workers: 8
    pin_memory: true
    drop_last: true
  # Parameters for the PyTorch DataLoader. Please refer to the PyTorch documentation for the description of parameters -

    _target_: torch.optim.Adam
    lr: 1e-3
    betas: [0, 0.999]
  # Optimizer configuration for the main (either Classification or Semantic Segmentation) Model

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

seed: 42 # Random seed. If the value is not null, it will be passed to `seed_everything` -

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

  # Configuration for PyTorch Lightning Trainer. You can read more about Trainer and its arguments at

  max_epochs: 40
  # Number of epochs to search for augmentation parameters.
  # More detailed description -

  benchmark: true
  # If true enables cudnn.benchmark.
  # More detailed description -

  gpus: 1
  # Number of GPUs to train on. Set to `0` or None` to use CPU for training.
  # More detailed description -

"""WideResNet code from"""

import torch
import torch.nn as nn
import torch.nn.functional as F

from autoalbument.faster_autoaugment.models import BaseDiscriminator

class BasicBlock(nn.Module):
    def __init__(self, in_planes, out_planes, stride):
        super(BasicBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_planes)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.equal_in_out = in_planes == out_planes
        self.conv_shortcut = (
            (not self.equal_in_out)
            and nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, padding=0, bias=False)
            or None

    def forward(self, x):
        if not self.equal_in_out:
            x = self.relu1(self.bn1(x))
            out = self.relu1(self.bn1(x))
        out = self.relu2(self.bn2(self.conv1(out if self.equal_in_out else x)))
        out = self.conv2(out)
        return torch.add(x if self.equal_in_out else self.conv_shortcut(x), out)

class NetworkBlock(nn.Module):
    def __init__(self, nb_layers, in_planes, out_planes, block, stride):
        super(NetworkBlock, self).__init__()
        self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride)

    def _make_layer(self, block, in_planes, out_planes, nb_layers, stride):
        layers = []
        for i in range(int(nb_layers)):
            layers.append(block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1))
        return nn.Sequential(*layers)

    def forward(self, x):
        return self.layer(x)

class WideResNet(nn.Module):
    def __init__(self, depth, num_classes, widen_factor=1):
        super(WideResNet, self).__init__()
        n_channels = [16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor]
        assert (depth - 4) % 6 == 0
        n = (depth - 4) / 6
        block = BasicBlock
        # 1st conv before any network block
        self.conv1 = nn.Conv2d(3, n_channels[0], kernel_size=3, stride=1, padding=1, bias=False)
        # 1st block
        self.block1 = NetworkBlock(n, n_channels[0], n_channels[1], block, 1)
        # 2nd block
        self.block2 = NetworkBlock(n, n_channels[1], n_channels[2], block, 2)
        # 3rd block
        self.block3 = NetworkBlock(n, n_channels[2], n_channels[3], block, 2)
        # global average pooling and classifier
        self.bn1 = nn.BatchNorm2d(n_channels[3])
        self.relu = nn.ReLU(inplace=True)
        self.fc = nn.Linear(n_channels[3], num_classes)
        self.n_channels = n_channels[3]

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
            elif isinstance(m, nn.BatchNorm2d):
            elif isinstance(m, nn.Linear):

    def forward_features(self, x):
        x = self.conv1(x)
        x = self.block1(x)
        x = self.block2(x)
        x = self.block3(x)
        x = self.relu(self.bn1(x))
        x = F.avg_pool2d(x, 8, 1, 0)
        x = x.view(-1, self.n_channels)
        return x

    def forward_classifier(self, x):
        return self.fc(x)

    def forward(self, x):
        x = self.forward_features(x)
        x = self.forward_classifier(x)
        return x

def wide_resnet_28x10(num_classes):
    return WideResNet(depth=28, widen_factor=10, num_classes=num_classes)

class Cifar10ClassificationModel(BaseDiscriminator):
    def __init__(self, *args, **kwargs):
        self.base_model = wide_resnet_28x10(num_classes=10)
        num_features = self.base_model.fc.in_features
        self.discriminator = nn.Sequential(
            nn.Linear(num_features, num_features), nn.ReLU(), nn.Linear(num_features, 1)

    def forward(self, input):
        x = self.base_model.forward_features(input)
        return self.base_model.forward_classifier(x), self.discriminator(x).view(-1)