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data.py
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data.py
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import torch
import torchvision
from torch import nn
from torchvision.models import resnet18
from torch.utils.data import DataLoader, random_split
from fedlab.utils.dataset.partition import CIFAR10Partitioner
import json
import numpy as np
from tqdm import tqdm
from io import BytesIO
from flwr.common import Parameters
import random
def femnist_data(path_to_data_folder="femnist_data", combine_clients=20, subset=50):
"""
Input: the path to the folder of json files.
Data is downloadable from: https://mega.nz/file/XYhhSRIb#PAVgu1zGUoGUU5EzF2xCOnUmGlp5nNQAF8gPdvo_m2U
It can also be downloaded by cloning the LEAF repository, and running the following command in the femnist folder:
./preprocess.sh -s niid --iu 1.0 --sf 1.0 -k 0 -t sample --smplseed 42 --spltseed 42
Returns: a tuple containing the training dataloaders, and test dataloaders,
with a dataloader for each client
"""
all_client_trainloaders = []
all_client_testloaders = []
if combine_clients <= 1:
for i in tqdm(range(0, 36)): # for each json file
with open(f"{path_to_data_folder}/all_data_{i}.json") as file:
# load the 100 clients in each json file
data = json.load(file)
all_clients = data["users"]
for client in all_clients:
# load the dataset from one client
X_data = data["user_data"][client]["x"]
num_samples = len(X_data)
X_data = np.array(X_data, dtype=np.float32).reshape(num_samples, 1, 28, 28) # reshape into BxCxHxW
y_data = np.array(data["user_data"][client]["y"], dtype=np.int64)
# split into test and train data
X_train, X_test = random_split(X_data, (0.9, 0.1), torch.Generator().manual_seed(42))
y_train, y_test = random_split(y_data, (0.9, 0.1), torch.Generator().manual_seed(42))
# put the dataset into dataloaders
torch.manual_seed(47)
train_loader = DataLoader(dataset=list(zip(X_train, y_train)),
batch_size=32,
shuffle=True,
pin_memory=True)
torch.manual_seed(47)
test_loader = DataLoader(dataset=list(zip(X_test, y_test)),
batch_size=32,
shuffle=True,
pin_memory=True)
# add the dataloader to the overall list
all_client_trainloaders.append(train_loader)
all_client_testloaders.append(test_loader)
else:
all_clients = []
for i in tqdm(range(0, 36)): # for each json file
with open(f"{path_to_data_folder}/all_data_{i}.json") as file:
# load the 100 clients in each json file
data = json.load(file)
for client in data["users"]:
X_data = data["user_data"][client]["x"]
num_samples = len(X_data)
X_data = np.array(X_data, dtype=np.float32).reshape(num_samples, 1, 28, 28)
y_data = np.array(data["user_data"][client]["y"], dtype=np.int64)
all_clients.append((X_data, y_data))
# group the given number of clients together
grouped_clients = zip(*[iter(all_clients)] * combine_clients)
for group in grouped_clients:
# merge the data arrays together
X_data = np.concatenate([client[0] for client in group])
y_data = np.concatenate([client[1] for client in group])
# split into test and train data
X_train, X_test = random_split(X_data, (0.9, 0.1), torch.Generator().manual_seed(42))
y_train, y_test = random_split(y_data, (0.9, 0.1), torch.Generator().manual_seed(42))
# put the dataset into dataloaders
torch.manual_seed(47)
train_loader = DataLoader(dataset=list(zip(X_train, y_train)),
batch_size=32,
shuffle=True,
pin_memory=True)
torch.manual_seed(47)
test_loader = DataLoader(dataset=list(zip(X_test, y_test)),
batch_size=32,
shuffle=True,
pin_memory=True)
# add the dataloader to the overall list
all_client_trainloaders.append(train_loader)
all_client_testloaders.append(test_loader)
# subset the data loaders to the given number
random.seed(47)
subset_trainloaders = random.sample(all_client_trainloaders, subset)
random.seed(47)
subset_testloaders = random.sample(all_client_testloaders, subset)
return subset_trainloaders, subset_testloaders
def cifar_data(num_clients=50, balanced_data=False):
"""
Returns: a tuple containing the training data loaders, and test data loaders,
with a dataloader for each client
"""
# Download and reshape the dataset
train_data = torchvision.datasets.CIFAR10(root="cifar_data", train=True, download=True)
test_data = torchvision.datasets.CIFAR10(root="cifar_data", train=False, download=True)
X_train = (train_data.data / 255).astype(np.float32).transpose(0, 3, 1, 2)
y_train = np.array(train_data.targets, dtype=np.int64)
X_test = (test_data.data / 255).astype(np.float32).transpose(0, 3, 1, 2)
y_test = np.array(test_data.targets, dtype=np.int64)
if balanced_data:
balance=True
partition="iid"
dir_alpha=None
else: # data not balanced
balance=None
partition="dirichlet"
dir_alpha=0.3
# Partition the data into an imbalanced and non-iid form
partitioned_train_data = CIFAR10Partitioner(train_data.targets,
num_clients,
balance=balance,
partition=partition,
dir_alpha=dir_alpha,
seed=42)
partitioned_test_data = CIFAR10Partitioner(test_data.targets,
num_clients,
balance=True,
partition="iid",
seed=42)
all_client_trainloaders = []
all_client_testloaders = []
# Put the data onto a dataloader for each client, following the partitions
for client in range(num_clients):
client_X_train = X_train[partitioned_train_data[client], :, :, :]
client_y_train = y_train[partitioned_train_data[client]]
torch.manual_seed(47)
train_loader = DataLoader(dataset=list(zip(client_X_train, client_y_train)),
batch_size=32,
shuffle=True,
pin_memory=True)
client_X_test = X_test[partitioned_test_data[client], :, :, :]
client_y_test = y_test[partitioned_test_data[client]]
torch.manual_seed(47)
test_loader = DataLoader(dataset=list(zip(client_X_test, client_y_test)),
batch_size=32,
shuffle=True,
pin_memory=True)
all_client_trainloaders.append(train_loader)
all_client_testloaders.append(test_loader)
return all_client_trainloaders, all_client_testloaders