import pickle
from pathlib import Path
from typing import Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.data as torchdata
import torchvision.transforms as transforms
from ..models import nn as mnn
from ..models.utils import top_n_accuracy
from ..utils.const import CACHED_DATA_DIR, CIFAR10_LABEL_MAP, CIFAR10_MEAN, CIFAR10_STD
from ._ops import CategoricalLabelToTensor, FixedDegreeRotation, ImageArrayToTensor, ImageTensorScale, distribute_images
from ._register import register_fed_dataset
from .fed_dataset import FedVisionDataset, VisionDataset
__all__ = [
"FedRotatedCIFAR10",
]
[docs]@register_fed_dataset()
class FedRotatedCIFAR10(FedVisionDataset):
"""CIFAR10 dataset with rotation augmentation.
The rotations are fixed and are multiples of 360 / num_rotations
[:footcite:ct:`Ghosh_2022_cfl`].
The original CIFAR10 dataset
`<https://pytorch.org/vision/stable/_modules/torchvision/datasets/cifar.html#CIFAR10>`_
contains 50k training images and 10k test images.
Images are 32x32 RGB images in 10 classes.
Parameters
----------
datadir : str or pathlib.Path, optional
Path to store the dataset. If not specified, the default path is used.
num_rotations : int, default 2
Number of rotations to apply to the images in the dataset.
num_clients : int, default 200
Number of clients to simulate.
transform : str or callable, default "none"
Transform (augmentation) to apply to the dataset.
If "none", no augmentation is applied,
only the normalization transform is applied.
seed : int, default 0
Random seed for reproducibility.
.. footbibliography::
"""
__name__ = "FedRotatedCIFAR10"
def __init__(
self,
datadir: Optional[Union[Path, str]] = None,
num_rotations: int = 2,
num_clients: int = 200,
transform: Optional[Union[str, Callable]] = "none",
seed: int = 0,
) -> None:
self.num_rotations = num_rotations
self.num_clients = num_clients
assert self.num_clients % self.num_rotations == 0
super().__init__(datadir=datadir, transform=transform, seed=seed)
def _preload(self, datadir: Optional[Union[str, Path]] = None) -> None:
"""Preload the dataset.
Parameters
----------
datadir : Union[pathlib.Path, str], optional
Directory to store data.
If ``None``, use default directory.
Returns
-------
None
"""
default_datadir = CACHED_DATA_DIR / "fed-rotated-cifar10"
self.datadir = Path(datadir or default_datadir).expanduser().resolve()
self.datadir.mkdir(parents=True, exist_ok=True)
# download data
self.download_if_needed()
self.DEFAULT_BATCH_SIZE = 20
self.DEFAULT_TRAIN_CLIENTS_NUM = self.num_clients
self.DEFAULT_TEST_CLIENTS_NUM = self.num_clients
self.DEFAULT_TRAIN_FILE = [f"cifar-10-batches-py/data_batch_{i}" for i in range(1, 6)]
self.DEFAULT_TEST_FILE = ["cifar-10-batches-py/test_batch"]
self._IMGAE = "image"
self._LABEL = "label"
# set criterion
self.criterion = torch.nn.CrossEntropyLoss()
# set transforms for creating dataset
if self.transform is None:
# set dynamic transform for train set
self.transform = transforms.Compose(
[
transforms.AutoAugment(
policy=transforms.AutoAugmentPolicy.CIFAR10,
),
ImageTensorScale(),
transforms.Normalize(CIFAR10_MEAN, CIFAR10_STD),
]
)
self.target_transform = transforms.Compose([CategoricalLabelToTensor()])
# load data
self._train_data_dict = {
self._IMGAE: np.empty((0, 3, 32, 32), dtype=np.uint8),
self._LABEL: np.empty((0,), dtype=np.int64),
}
self._test_data_dict = {
self._IMGAE: np.empty((0, 3, 32, 32), dtype=np.uint8),
self._LABEL: np.empty((0,), dtype=np.int64),
}
for file in self.DEFAULT_TRAIN_FILE:
data = pickle.loads((self.datadir / file).read_bytes(), encoding="bytes")
self._train_data_dict[self._IMGAE] = np.concatenate(
[
self._train_data_dict[self._IMGAE],
data[b"data"].reshape(-1, 3, 32, 32).astype(np.uint8),
]
)
self._train_data_dict[self._LABEL] = np.concatenate(
[
self._train_data_dict[self._LABEL],
np.array(data[b"labels"]).astype(np.int64),
]
)
data = pickle.loads(
(self.datadir / self.DEFAULT_TEST_FILE[0]).read_bytes(),
encoding="bytes",
)
self._test_data_dict[self._IMGAE] = data[b"data"].reshape(-1, 3, 32, 32).astype(np.uint8)
self._test_data_dict[self._LABEL] = np.array(data[b"labels"]).astype(np.int64)
original_num_images = {
"train": len(self._train_data_dict[self._LABEL]),
"test": len(self._test_data_dict[self._LABEL]),
}
# set n_class
self._n_class = len(
np.unique(
np.concatenate(
[
self._train_data_dict[self._LABEL],
self._test_data_dict[self._LABEL],
]
)
)
)
# distribute data to clients
self.indices = {}
self.indices["train"] = distribute_images(
original_num_images["train"],
self.num_clients // self.num_rotations,
random=True,
)
self.indices["test"] = distribute_images(
original_num_images["test"],
self.num_clients // self.num_rotations,
random=False,
)
# perform rotation, and distribute data to clients
print("Performing rotation...")
angles = np.arange(0, 360, 360 / self.num_rotations)[1:]
raw_images = {
"train": torch.from_numpy(self._train_data_dict[self._IMGAE].copy()),
"test": torch.from_numpy(self._test_data_dict[self._IMGAE].copy()),
}
raw_labels = {
"train": self._train_data_dict[self._LABEL].copy(),
"test": self._test_data_dict[self._LABEL].copy(),
}
for idx, angle in enumerate(angles):
transform = FixedDegreeRotation(angle)
self._train_data_dict[self._IMGAE] = np.concatenate(
[
self._train_data_dict[self._IMGAE],
transform(raw_images["train"]).numpy(),
]
)
self._train_data_dict[self._LABEL] = np.concatenate(
[
self._train_data_dict[self._LABEL],
raw_labels["train"].copy(),
]
)
self._test_data_dict[self._IMGAE] = np.concatenate(
[
self._test_data_dict[self._IMGAE],
transform(raw_images["test"]).numpy(),
]
)
self._test_data_dict[self._LABEL] = np.concatenate(
[
self._test_data_dict[self._LABEL],
raw_labels["test"].copy(),
]
)
self.indices["train"].extend(
distribute_images(
np.arange(original_num_images["train"]) + (idx + 1) * original_num_images["train"],
self.num_clients // self.num_rotations,
random=True,
)
)
self.indices["test"].extend(
distribute_images(
np.arange(original_num_images["test"]) + (idx + 1) * original_num_images["test"],
self.num_clients // self.num_rotations,
random=False,
)
)
del raw_images, raw_labels
[docs] def get_dataloader(
self,
train_bs: Optional[int] = None,
test_bs: Optional[int] = None,
client_idx: Optional[int] = None,
) -> Tuple[torchdata.DataLoader, torchdata.DataLoader]:
"""Get local dataloader at client `client_idx` or get the global dataloader.
Parameters
----------
train_bs : int, optional
Batch size for training dataloader.
If ``None``, use default batch size.
test_bs : int, optional
Batch size for testing dataloader.
If ``None``, use default batch size.
client_idx : int, optional
Index of the client to get dataloader.
If ``None``, get the dataloader containing all data.
Usually used for centralized training.
Returns
-------
train_dl : :class:`torch.utils.data.DataLoader`
Training dataloader.
test_dl : :class:`torch.utils.data.DataLoader`
Testing dataloader.
"""
if client_idx is None:
train_slice = slice(None)
test_slice = slice(None)
else:
train_slice = self.indices["train"][client_idx]
test_slice = self.indices["test"][client_idx]
# static transform
static_transform = transforms.Compose(
[
ImageArrayToTensor(),
transforms.Normalize(CIFAR10_MEAN, CIFAR10_STD),
]
)
if self.transform == "none":
# apply only static transform
train_ds = torchdata.TensorDataset(
static_transform(self._train_data_dict[self._IMGAE][train_slice].copy()),
self.target_transform(self._train_data_dict[self._LABEL][train_slice].copy()),
)
else:
# use non-trivial dynamic transform
train_ds = VisionDataset(
images=torch.from_numpy(self._train_data_dict[self._IMGAE][train_slice].copy()).to(torch.uint8),
targets=self.target_transform(self._train_data_dict[self._LABEL][train_slice].copy()),
transform=self.transform,
)
train_dl = torchdata.DataLoader(
dataset=train_ds,
batch_size=train_bs or self.DEFAULT_BATCH_SIZE,
shuffle=True,
drop_last=False,
)
test_ds = torchdata.TensorDataset(
static_transform(self._test_data_dict[self._IMGAE][test_slice].copy()),
self.target_transform(self._test_data_dict[self._LABEL][test_slice].copy()),
)
test_dl = torchdata.DataLoader(
dataset=test_ds,
batch_size=test_bs or self.DEFAULT_BATCH_SIZE,
shuffle=False,
drop_last=False,
)
return train_dl, test_dl
[docs] def evaluate(self, probs: torch.Tensor, truths: torch.Tensor) -> Dict[str, float]:
"""Evaluation using predictions and ground truth.
Parameters
----------
probs : torch.Tensor
Predicted probabilities.
truths : torch.Tensor
Ground truth labels.
Returns
-------
Dict[str, float]
Evaluation results.
"""
return {
"acc": top_n_accuracy(probs, truths, 1),
"top3_acc": top_n_accuracy(probs, truths, 3),
"top5_acc": top_n_accuracy(probs, truths, 5),
"loss": self.criterion(probs, truths).item(),
"num_samples": probs.shape[0],
}
@property
def url(self) -> str:
"""URL for downloading the dataset."""
return "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
@property
def candidate_models(self) -> Dict[str, torch.nn.Module]:
"""A set of candidate models."""
return {
"cnn_cifar": mnn.CNNCifar(num_classes=self.n_class),
"cnn_cifar_small": mnn.CNNCifar_Small(num_classes=self.n_class),
"cnn_cifar_tiny": mnn.CNNCifar_Tiny(num_classes=self.n_class),
"resnet10": mnn.ResNet10(num_classes=self.n_class),
}
@property
def doi(self) -> List[str]:
"""DOI(s) related to the dataset."""
return [
"10.1109/5.726791", # MNIST
"10.1109/tit.2022.3192506", # IFCA
]
@property
def label_map(self) -> dict:
"""Label map for the dataset."""
return CIFAR10_LABEL_MAP
[docs] def view_image(self, client_idx: int, image_idx: int) -> None:
"""View a single image.
Parameters
----------
client_idx : int
Index of the client on which the image is located.
image_idx : int
Index of the image in the client.
Returns
-------
None
"""
import matplotlib.pyplot as plt
if client_idx >= self.num_clients:
raise ValueError(f"client_idx must be less than {self.num_clients}, got {client_idx}")
total_num_images = len(self.indices["train"][client_idx]) + len(self.indices["test"][client_idx])
if image_idx >= total_num_images:
raise ValueError(f"image_idx must be less than {total_num_images}, got {image_idx}")
if image_idx < len(self.indices["train"][client_idx]):
image = self._train_data_dict[self._IMGAE][self.indices["train"][client_idx][image_idx]]
label = self._train_data_dict[self._LABEL][self.indices["train"][client_idx][image_idx]]
image_idx = self.indices["train"][client_idx][image_idx]
angle = image_idx // (len(self._train_data_dict[self._IMGAE]) // self.num_rotations) * (360 // self.num_rotations)
else:
image_idx -= len(self.indices["train"][client_idx])
image = self._test_data_dict[self._IMGAE][self.indices["test"][client_idx][image_idx]]
label = self._test_data_dict[self._LABEL][self.indices["test"][client_idx][image_idx]]
image_idx = self.indices["test"][client_idx][image_idx]
angle = image_idx // (len(self._test_data_dict[self._IMGAE]) // self.num_rotations) * (360 // self.num_rotations)
# image: channel first to channel last
image = image.transpose(1, 2, 0)
plt.imshow(image)
plt.title(f"image_idx: {image_idx}, label: {label} ({self.label_map[int(label)]}), " f"angle: {angle}")
plt.show()
[docs] def random_grid_view(self, nrow: int, ncol: int, save_path: Optional[Union[str, Path]] = None) -> None:
"""Select randomly `nrow` x `ncol` images from the dataset
and plot them in a grid.
Parameters
----------
nrow : int
Number of rows in the grid.
ncol : int
Number of columns in the grid.
save_path : Union[str, Path], optional
Path to save the figure. If ``None``, do not save the figure.
Returns
-------
None
"""
import matplotlib.pyplot as plt
rng = np.random.default_rng()
fig, axes = plt.subplots(nrow, ncol, figsize=(ncol * 1, nrow * 1))
selected = []
for i in range(nrow):
for j in range(ncol):
while True:
client_idx = rng.integers(self.num_clients)
image_idx = rng.integers(len(self.indices["train"][client_idx]))
if (client_idx, image_idx) not in selected:
selected.append((client_idx, image_idx))
break
image = self._train_data_dict[self._IMGAE][self.indices["train"][client_idx][image_idx]]
axes[i, j].imshow(image.transpose(1, 2, 0))
axes[i, j].axis("off")
if save_path is not None:
fig.savefig(save_path, bbox_inches="tight", dpi=600)
plt.tight_layout()
plt.show()