update over 60k plots

analyze.py

@@ -4,16 +4,26 @@ from scipy import stats
 import pandas as pd
 import argparse
 
-def plot(l,filename):
+def plot(l, thousands, filename):
     lenth = len(l)
     threshold = [0, 10000, 20000, 30000, 40000, 50000, 60000, 70000]
     labels = ['0-10k', '10k-20k,', '20k-30k', '30k-40k', '40k-50k', '50k-60k', '60k-70k']
-    l = [i/15625 for i in l]
+    l = [i/lenth for i in l]
     l = l[:7]
+    thousands = thousands[60:]
+    thousands_labels = [str(i) + 'k' for i in range(60, 70)]
+    plt.figure(figsize=(8, 6))
+    plt.subplots_adjust(top=0.85)
+    plt.title('Distribution of Swap Scores over 60k')
+    plt.bar(thousands_labels, thousands)
+    for i, v in enumerate(thousands):
+        plt.text(i, v + 0.01, str(v), ha='center', va='bottom')
+    plt.savefig(filename + '_60k.png') 
+
     datasets = filename.split('_')[-1].split('.')[0]
     plt.figure(figsize=(8, 6))
     plt.subplots_adjust(top=0.85)
-    plt.ylim(0,0.3)
+    # plt.ylim(0,0.3)
     plt.title('Distribution of Swap Scores in ' + datasets)
     plt.bar(labels, l)
     for i, v in enumerate(l):
@@ -29,6 +39,7 @@ def analyse(filename):
         reader = csv.reader(file)
         header = next(reader)
         data = [row for row in reader]
+        thousands = [0 for i in range(70)]
         
         for row in data:
             score = row[0]
@@ -37,6 +48,7 @@ def analyse(filename):
             ind = float(score) // 10000
             ind = int(ind)
             l[ind] += 1
+            thousands[int(float(score) // 1000)] += 1
             acc = row[1]
             index = row[2]
             datas = list(zip(score, acc, index))
@@ -45,7 +57,7 @@ def analyse(filename):
     results = pd.DataFrame(datas, columns=['swap_score', 'valid_acc', 'index'])
     print(results['swap_score'].max())
     print(best_value)
-    plot(l, filename + '.png')
+    plot(l, thousands, filename + '.png')
     return stats.spearmanr(results.swap_score, results.valid_acc)[0]
 
 if __name__ == '__main__':

calculate_datasets_statistics.py

@@ -4,7 +4,7 @@
 
 # # 加载CIFAR-10数据集
 # transform = transforms.Compose([transforms.ToTensor()])
-# trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
+# trainset = torchvision.datasets.CIFAR10(root='./datasets', 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
@@ -18,6 +18,10 @@
 # print(f'Mean: {mean}')
 # print(f'Std: {std}')
 
+# results:
+# Mean: tensor([0.4935, 0.4834, 0.4472])
+# Std: tensor([0.2476, 0.2446, 0.2626])  
+
 import torch
 from torchvision import datasets, transforms
 from torch.utils.data import DataLoader
@@ -35,6 +39,7 @@ dataset_name = args.dataset
 
 # 设置数据集的transform（这里只使用了ToTensor）
 transform = transforms.Compose([
+    transforms.Resize((224, 224)),
     transforms.ToTensor()
 ])
 
@@ -47,7 +52,10 @@ mean = torch.zeros(3)
 std = torch.zeros(3)
 nb_samples = 0
 
+count = 0
 for data in dataloader:
+    count += 1
+    print(f'Processing batch {count}/{len(dataloader)}', end='\r')
     batch_samples = data[0].size(0)
     data = data[0].view(batch_samples, data[0].size(1), -1)
     mean += data.mean(2).sum(0)

correlation.py

@@ -40,9 +40,9 @@ parser.add_argument('--device', default="cuda", type=str, nargs='?', help='setup
 parser.add_argument('--repeats', default=32, type=int, nargs='?', help='times of calculating the training-free metric')
 parser.add_argument('--input_samples', default=16, type=int, nargs='?', help='input batch size for training-free metric')
 parser.add_argument('--datasets', default='cifar10', type=str, help='input datasets')
+parser.add_argument('--start_index', default=0, type=int, help='start index of the networks to evaluate')
 
 args = parser.parse_args()
-
 if __name__ == "__main__":
     
     device = torch.device(args.device)
@@ -58,18 +58,21 @@ if __name__ == "__main__":
 
     # nasbench_len = 15625
     nasbench_len = 15625
+    filename = f'output/swap_results_{args.datasets}.csv'
+    if args.datasets == 'aircraft':
+        api_datasets = 'cifar10'
     
     # for index, i in arch_info.iterrows():
-    for ind in range(nasbench_len):
+    for ind in range(args.start_index,nasbench_len):
         # print(f'Evaluating network: {index}')
         print(f'Evaluating network: {ind}')
-
-        config = api.get_net_config(ind, args.datasets)
+        config = api.get_net_config(ind, api_datasets)
         network = get_cell_based_tiny_net(config)
         # nas_results = api.query_by_index(i, 'cifar10')
         # acc = nas_results[111].get_eval('ori-test')
-        nas_results = api.get_more_info(ind, args.datasets, None, hp=200, is_random=False)
-        acc = nas_results['test-accuracy']
+        # nas_results = api.get_more_info(ind, api_datasets, None, hp=200, is_random=False)
+        # acc = nas_results['test-accuracy']
+        acc = 99
 
         # print(type(network))
         start_time = time.time()
@@ -98,6 +101,8 @@ if __name__ == "__main__":
         print(f'Elapsed time: {end_time - start_time:.2f} seconds')
 
         results.append([np.mean(swap_score), acc, ind])
+        with open(filename, 'a') as f:
+            f.write(f'{np.mean(swap_score)},{acc},{ind}\n')
 
     results = pd.DataFrame(results, columns=['swap_score', 'valid_acc', 'index'])
     results.to_csv('output/swap_results.csv', float_format='%.4f', index=False)

preprocess_aircraft.py

@@ -3,21 +3,28 @@ 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'
+test_output_path = '/mnt/Study/DataSet/DataSet/fgvc-aircraft-2013b/fgvc-aircraft-2013b/data/test_sorted_images'
+train_output_path = '/mnt/Study/DataSet/DataSet/fgvc-aircraft-2013b/fgvc-aircraft-2013b/data/train_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'
+# 有两个文件，一个是训练集和验证集，一个是测试集
+test_labels_file = '/mnt/Study/DataSet/DataSet/fgvc-aircraft-2013b/fgvc-aircraft-2013b/data/images_variant_test.txt'
+train_labels_file = '/mnt/Study/DataSet/DataSet/fgvc-aircraft-2013b/fgvc-aircraft-2013b/data/images_variant_train.txt'
 
 # 创建输出文件夹
-if not os.path.exists(output_path):
-    os.makedirs(output_path)
+if not os.path.exists(test_output_path):
+    os.makedirs(test_output_path)
+if not os.path.exists(train_output_path):
+    os.makedirs(train_output_path)
 
 # 读取类别文件
-with open(labels_file, 'r') as f:
-    lines = f.readlines()
+with open(test_labels_file, 'r') as f:
+    test_lines = f.readlines()
+with open(train_labels_file, 'r') as f:
+    train_lines = f.readlines()
 
+def sort_images(lines, output_path):
     count = 0
-
     for line in lines:
         count += 1
         print(f'Processing image {count}/{len(lines)}', end='\r')
@@ -38,4 +45,9 @@ for line in lines:
         else:
             print(f'Image {image_name} not found!')
 
+print("Sorting test images into folders by category...")
+sort_images(test_lines, test_output_path)
+print("Sorting train images into folders by category...")
+sort_images(train_lines, train_output_path)
+
 print("Images have been sorted into folders by category.")