preprocess aircraft dataset to get the statistics. which can be used in swap-nas

calculate_datasets_statistics.py

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+# import torch
+# import torchvision
+# import torchvision.transforms as transforms
+
+# # 加载CIFAR-10数据集
+# transform = transforms.Compose([transforms.ToTensor()])
+# trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
+# trainloader = torch.utils.data.DataLoader(trainset, batch_size=10000, shuffle=False, num_workers=2)
+
+# # 将所有数据加载到内存中
+# data = next(iter(trainloader))
+# images, _ = data
+
+# # 计算每个通道的均值和标准差
+# mean = images.mean([0, 2, 3])
+# std = images.std([0, 2, 3])
+
+# print(f'Mean: {mean}')
+# print(f'Std: {std}')
+
+import torch
+from torchvision import datasets, transforms
+from torch.utils.data import DataLoader
+import argparse
+
+parser = argparse.ArgumentParser(description='Calculate mean and std of dataset')
+parser.add_argument('--dataset', type=str, default='cifar10', help='dataset name')
+parser.add_argument('--data_path', type=str, default='./datasets/cifar-10-batches-py', help='path to dataset image folder')
+
+args = parser.parse_args()
+
+# 设置数据集路径
+dataset_path = args.data_path
+dataset_name = args.dataset
+
+# 设置数据集的transform（这里只使用了ToTensor）
+transform = transforms.Compose([
+    transforms.ToTensor()
+])
+
+# 使用ImageFolder加载数据集
+dataset = datasets.ImageFolder(root=dataset_path, transform=transform)
+dataloader = DataLoader(dataset, batch_size=64, shuffle=False, num_workers=4)
+
+# 初始化变量来累积均值和标准差
+mean = torch.zeros(3)
+std = torch.zeros(3)
+nb_samples = 0
+
+for data in dataloader:
+    batch_samples = data[0].size(0)
+    data = data[0].view(batch_samples, data[0].size(1), -1)
+    mean += data.mean(2).sum(0)
+    std += data.std(2).sum(0)
+    nb_samples += batch_samples
+
+mean /= nb_samples
+std /= nb_samples
+
+print(f'Mean: {mean}')
+print(f'Std: {std}')

preprocess_aircraft.py

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+import os
+import shutil
+
+# 数据集路径
+dataset_path = '/mnt/Study/DataSet/DataSet/fgvc-aircraft-2013b/fgvc-aircraft-2013b/data/images'
+output_path = '/mnt/Study/DataSet/DataSet/fgvc-aircraft-2013b/fgvc-aircraft-2013b/data/sorted_images'
+
+# 类别文件，例如 'images_variant_trainval.txt'
+labels_file = '/mnt/Study/DataSet/DataSet/fgvc-aircraft-2013b/fgvc-aircraft-2013b/data/images_variant_test.txt'
+
+# 创建输出文件夹
+if not os.path.exists(output_path):
+    os.makedirs(output_path)
+
+# 读取类别文件
+with open(labels_file, 'r') as f:
+    lines = f.readlines()
+
+count = 0
+
+for line in lines:
+    count += 1
+    print(f'Processing image {count}/{len(lines)}', end='\r')
+    parts = line.strip().split(' ')
+    image_name = parts[0] + '.jpg'
+    category = '_'.join(parts[1:]).replace('/', '_')
+
+    # 创建类别文件夹
+    category_path = os.path.join(output_path, category)
+    if not os.path.exists(category_path):
+        os.makedirs(category_path)
+
+    # 移动图像到对应类别文件夹
+    src = os.path.join(dataset_path, image_name)
+    dst = os.path.join(category_path, image_name)
+    if os.path.exists(src):
+        shutil.move(src, dst)
+    else:
+        print(f'Image {image_name} not found!')
+
+print("Images have been sorted into folders by category.")