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import numpy as np |
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import os |
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import sys |
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import random |
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import torch |
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import torchvision |
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import torchvision.transforms as transforms |
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from utils.dataset_utils import check, separate_data, split_data, save_file |
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random.seed(1) |
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np.random.seed(1) |
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num_clients = 20 |
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dir_path = "GTSRB/" |
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def generate_dataset(dir_path, num_clients, niid, balance, partition): |
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if not os.path.exists(dir_path): |
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os.makedirs(dir_path) |
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config_path = dir_path + "config.json" |
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train_path = dir_path + "train/" |
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test_path = dir_path + "test/" |
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if check(config_path, train_path, test_path, num_clients, niid, balance, partition): |
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return |
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dataset_image = [] |
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dataset_label = [] |
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transform = transforms.Compose( |
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[transforms.Resize((32, 32)), |
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transforms.ToTensor(), |
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transforms.Normalize((0.5), (0.5))] |
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) |
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def load_data(split="train"): |
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trainset = torchvision.datasets.GTSRB( |
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root=dir_path+"rawdata", split=split, download=True, transform=transform) |
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trainloader = torch.utils.data.DataLoader( |
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trainset, batch_size=len(trainset), shuffle=False) |
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for _, train_data in enumerate(trainloader, 0): |
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trainset.data, trainset.targets = train_data |
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dataset_image.extend(trainset.data.cpu().detach().numpy()) |
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dataset_label.extend(trainset.targets.cpu().detach().numpy()) |
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load_data("train") |
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load_data("test") |
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dataset_image = np.array(dataset_image) |
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dataset_label = np.array(dataset_label) |
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num_classes = len(set(dataset_label)) |
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print(f'Number of classes: {num_classes}') |
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X, y, statistic = separate_data((dataset_image, dataset_label), num_clients, num_classes, |
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niid, balance, partition, class_per_client=4) |
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train_data, test_data = split_data(X, y) |
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save_file(config_path, train_path, test_path, train_data, test_data, num_clients, num_classes, |
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statistic, niid, balance, partition) |
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if __name__ == "__main__": |
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niid = True if sys.argv[1] == "noniid" else False |
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balance = True if sys.argv[2] == "balance" else False |
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partition = sys.argv[3] if sys.argv[3] != "-" else None |
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generate_dataset(dir_path, num_clients, niid, balance, partition) |
