import warnings
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.data as torchdata
from scipy.io import loadmat
from ..models import nn as mnn
from ..models.utils import top_n_accuracy
from ..utils._download_data import url_is_reachable
from ..utils.const import CACHED_DATA_DIR, MNIST_LABEL_MAP
from ._register import register_fed_dataset
from .fed_dataset import FedVisionDataset
__all__ = [
"FedProxMNIST",
]
FEDPROX_MNIST_DATA_DIR = CACHED_DATA_DIR / "fedprox_mnist"
FEDPROX_MNIST_DATA_DIR.mkdir(parents=True, exist_ok=True)
[docs]@register_fed_dataset()
class FedProxMNIST(FedVisionDataset):
"""Federeated MNIST proposed in FedProx.
This dataset is proposed and used in [1]_ [2]_,
where the data is partitioned in a non-IID manner.
Parameters
----------
datadir : Union[pathlib.Path, str], optional
Directory to store data.
If ``None``, use default directory.
transform : Union[str, Callable], default "none"
Transform to apply to data. Conventions:
``"none"`` means no transform, using TensorDataset.
seed : int, default 0
Random seed for data partitioning.
**extra_config : dict, optional
Extra configurations.
References
----------
.. [1] https://github.com/litian96/FedProx/tree/master/data/mnist
.. [2] https://github.com/litian96/FedProx/blob/master/data/mnist/generate_niid.py
"""
__name__ = "FedProxMNIST"
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
"""
self.datadir = Path(datadir or FEDPROX_MNIST_DATA_DIR).expanduser().resolve()
if hasattr(self, "num_clients"):
self.DEFAULT_TRAIN_CLIENTS_NUM = self.num_clients
self.DEFAULT_TEST_CLIENTS_NUM = self.num_clients
else:
self.DEFAULT_TRAIN_CLIENTS_NUM = 1000
self.DEFAULT_TEST_CLIENTS_NUM = 1000
self.DEFAULT_BATCH_SIZE = 20
self.DEFAULT_TRAIN_FILE = "fedprox-mnist.mat"
self.DEFAULT_TEST_FILE = "fedprox-mnist.mat"
self._EXAMPLE = ""
self._IMGAE = "data"
self._LABEL = "label"
if self.transform != "none":
warnings.warn(
"The images are not raw pixels, but processed. " "The transform argument will be ignored.",
RuntimeWarning,
)
self.transform = "none"
self.criterion = torch.nn.CrossEntropyLoss()
self.download_if_needed()
self.__raw_data = loadmat(self.datadir / self.DEFAULT_TRAIN_FILE)
self._client_data = generate_niid(self.__raw_data, num_clients=self.DEFAULT_TRAIN_CLIENTS_NUM, seed=self.seed)
self._client_ids_train = list(range(self.DEFAULT_TRAIN_CLIENTS_NUM))
self._client_ids_test = list(range(self.DEFAULT_TEST_CLIENTS_NUM))
self._n_class = len(
np.unique(np.concatenate([item["train_y"].tolist() + item["test_y"].tolist() for item in self._client_data]))
)
[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:
# get ids of all clients
train_ids = self._client_ids_train
test_ids = self._client_ids_test
else:
# get ids of single client
train_ids = [self._client_ids_train[client_idx]]
test_ids = [self._client_ids_test[client_idx]]
# load data
train_x = np.vstack([self._client_data[client_id]["train_x"] for client_id in train_ids])
train_y = np.concatenate([self._client_data[client_id]["train_y"] for client_id in train_ids])
test_x = np.vstack([self._client_data[client_id]["test_x"] for client_id in test_ids])
test_y = np.concatenate([self._client_data[client_id]["test_y"] for client_id in test_ids])
# dataloader
train_ds = torchdata.TensorDataset(
torch.from_numpy(train_x.astype(np.float32)).unsqueeze(1),
torch.from_numpy(train_y.astype(np.int64)),
)
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(
torch.from_numpy(test_x.astype(np.float32)).unsqueeze(1),
torch.from_numpy(test_y.astype(np.int64)),
)
test_dl = torchdata.DataLoader(
dataset=test_ds,
batch_size=test_bs or self.DEFAULT_BATCH_SIZE,
shuffle=True,
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."""
# https://drive.google.com/file/d/1tCEcJgRJ8NdRo11UJZR6WSKMNdmox4GC/view?usp=sharing
# "http://218.245.5.12/NLP/federated/fedprox-mnist.zip"
if url_is_reachable("https://www.dropbox.com/"):
return "https://www.dropbox.com/s/ndri55jt0w9juk1/fedprox-mnist.zip?dl=1"
else:
return "https://deep-psp.tech/Data/FL/fedprox-mnist.zip"
@property
def candidate_models(self) -> Dict[str, torch.nn.Module]:
"""A set of candidate models."""
return {
"cnn_mnist": mnn.CNNMnist(num_classes=self.n_class),
"cnn_femmist_tiny": mnn.CNNFEMnist_Tiny(num_classes=self.n_class),
"cnn_femmist": mnn.CNNFEMnist(num_classes=self.n_class),
# "resnet10": mnn.ResNet10(num_classes=self.n_class),
"mlp": mnn.MLP(dim_in=28 * 28, dim_out=self.n_class, ndim=2),
}
@property
def doi(self) -> List[str]:
"""DOIs related to the dataset."""
return [
"10.1109/5.726791", # MNIST
"10.48550/ARXIV.1812.01097", # LEAF
"10.48550/ARXIV.1812.06127", # FedProx
]
@property
def raw_data(self) -> Dict[str, np.ndarray]:
"""Raw data."""
return self.__raw_data
@property
def label_map(self) -> dict:
"""Label map for the dataset."""
return MNIST_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.DEFAULT_TRAIN_CLIENTS_NUM:
raise ValueError(f"client_idx should be less than {self.DEFAULT_TRAIN_CLIENTS_NUM}")
tot_images = self._client_data[client_idx]["train_x"].shape[0] + self._client_data[client_idx]["test_x"].shape[0]
if image_idx >= tot_images:
raise ValueError(f"image_idx should be less than {tot_images}")
if image_idx < self._client_data[client_idx]["train_x"].shape[0]:
img = self._client_data[client_idx]["train_x"][image_idx]
label = self._client_data[client_idx]["train_y"][image_idx]
else:
img = self._client_data[client_idx]["test_x"][image_idx - self._client_data[client_idx]["train_x"].shape[0]]
label = self._client_data[client_idx]["test_y"][image_idx - self._client_data[client_idx]["train_x"].shape[0]]
img = img + img.min()
# to 0-255
img = (img * 255 / img.max()).astype(np.uint8)
plt.imshow(img, cmap="gray")
plt.title(f"client {client_idx}, label {label} ({self.label_map[int(label)]})")
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.DEFAULT_TRAIN_CLIENTS_NUM)
tot_images = (
self._client_data[client_idx]["train_x"].shape[0] + self._client_data[client_idx]["test_x"].shape[0]
)
image_idx = rng.integers(tot_images)
if (client_idx, image_idx) not in selected:
selected.append((client_idx, image_idx))
break
if image_idx < self._client_data[client_idx]["train_x"].shape[0]:
img = self._client_data[client_idx]["train_x"][image_idx]
label = self._client_data[client_idx]["train_y"][image_idx]
else:
img = self._client_data[client_idx]["test_x"][image_idx - self._client_data[client_idx]["train_x"].shape[0]]
img = img + img.min()
# to 0-255
img = (img * 255 / img.max()).astype(np.uint8)
axes[i, j].imshow(img, cmap="gray")
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()
def generate_niid(
mnist_data: Dict[str, np.ndarray],
num_clients: int = 1000,
lower_bound: int = 10,
class_per_client: int = 2,
seed: int = 42,
train_ratio: float = 0.9,
) -> List[Dict[str, np.ndarray]]:
"""
modified from
`FedProx <https://github.com/litian96/FedProx/blob/master/data/mnist/generate_niid.py>`_.
Parameters
----------
mnist_data : Dict[str, np.ndarray]
Raw MNIST data.
num_clients : int, default 1000
Number of clients.
lower_bound : int, default 10
Lower bound of number of samples per client.
class_per_client : int, default 2
Number of classes per client.
seed : int, default 42
Random seed for data partitioning.
train_ratio : float, default 0.9
Ratio of training data.
Returns
-------
List[Dict[str, np.ndarray]]
Partitioned data.
"""
NUM_CLASSES = 10
IMG_SHAPE = (28, 28)
mnist_data["data"] = (mnist_data["data"] / 255.0).astype(np.float32)
eps = 1e-5
options = dict(axis=0, keepdims=True)
mean = mnist_data["data"].mean(**options)
std = mnist_data["data"].std(**options)
mnist_data["data"] = (mnist_data["data"] - mean) / (std + eps)
mnist_data["data"] = mnist_data["data"].T.reshape((-1, *IMG_SHAPE))
mnist_data["label"] = mnist_data["label"].flatten()
class_inds = {i: np.where(mnist_data["label"] == i)[0] for i in range(NUM_CLASSES)}
class_nums = [lower_bound // class_per_client for _ in range(class_per_client - 1)]
class_nums.append(lower_bound - sum(class_nums))
clients_data = [
{
k: np.empty((0, *IMG_SHAPE), dtype=np.float32) if k.startswith("train") else np.array([], dtype=np.int64)
for k in [
"train_x",
"train_y",
"test_x",
"test_y",
]
}
for _ in range(num_clients)
]
# idx = np.zeros(NUM_CLASSES, dtype=np.int64)
idx = {i: 0 for i in range(NUM_CLASSES)}
for c in range(num_clients):
for j, n in enumerate(class_nums):
label = (c + j) % NUM_CLASSES
inds = class_inds[label][idx[label] : idx[label] + n]
clients_data[c]["train_x"] = np.append(clients_data[c]["train_x"], mnist_data["data"][inds, ...], axis=0)
clients_data[c]["train_y"] = np.append(clients_data[c]["train_y"], np.full_like(inds, label, dtype=np.int64))
idx[label] += n
# print(f"idx = {idx}")
# print(f"class_inds = {[(l, len(class_inds[l])) for l in range(NUM_CLASSES)]}")
rng = np.random.default_rng(seed)
probs = rng.lognormal(0, 2.0, (NUM_CLASSES, num_clients // NUM_CLASSES, class_per_client))
probs = (
np.array([[[len(class_inds[i]) - idx[i]]] for i in range(NUM_CLASSES)]) * probs / probs.sum(axis=(1, 2), keepdims=True)
)
for c in range(num_clients):
for j, n in enumerate(class_nums):
label = (c + j) % NUM_CLASSES
num_samples = round(probs[label, c // NUM_CLASSES, j])
if idx[label] + num_samples < len(class_inds[label]):
inds = class_inds[label][idx[label] : idx[label] + num_samples]
clients_data[c]["train_x"] = np.append(clients_data[c]["train_x"], mnist_data["data"][inds, ...], axis=0)
clients_data[c]["train_y"] = np.append(
clients_data[c]["train_y"],
np.full_like(inds, label, dtype=np.int64),
)
idx[label] += num_samples
num_samples = clients_data[c]["train_x"].shape[0]
inds = rng.choice(num_samples, num_samples, replace=False)
train_len = int(train_ratio * num_samples)
clients_data[c]["test_x"] = clients_data[c]["train_x"][inds[train_len:], ...]
clients_data[c]["test_y"] = clients_data[c]["train_y"][inds[train_len:]]
clients_data[c]["train_x"] = clients_data[c]["train_x"][inds[:train_len], ...]
clients_data[c]["train_y"] = clients_data[c]["train_y"][inds[:train_len]]
# print(f"idx = {idx}")
return clients_data