change path

Code/1_data_prepare/1_2_split_dataset.py

@@ -8,10 +8,10 @@ import glob
 import random
 import shutil
 
-dataset_dir = '../../Data/cifar-10-png/raw_test/'
-train_dir = '../../Data/train/'
-valid_dir = '../../Data/valid/'
-test_dir = '../../Data/test/'
+dataset_dir = os.path.join("..", "..", "Data", "cifar-10-png", "raw_test")
+train_dir = os.path.join("..", "..", "Data", "train")
+valid_dir = os.path.join("..", "..", "Data", "valid")
+test_dir = os.path.join("..", "..", "Data", "test")
 
 train_per = 0.8
 valid_per = 0.1
@@ -27,7 +27,7 @@ if __name__ == '__main__':
 
     for root, dirs, files in os.walk(dataset_dir):
         for sDir in dirs:
-            imgs_list = glob.glob(os.path.join(root, sDir)+'/*.png')
+            imgs_list = glob.glob(os.path.join(root, sDir) + '*.png')
             random.seed(666)
             random.shuffle(imgs_list)
             imgs_num = len(imgs_list)
@@ -37,14 +37,14 @@ if __name__ == '__main__':
 
             for i in range(imgs_num):
                 if i < train_point:
-                    out_dir = train_dir + sDir + '/'
+                    out_dir = os.path.join(train_dir, sDir)
                 elif i < valid_point:
-                    out_dir = valid_dir + sDir + '/'
+                    out_dir = os.path.join(valid_dir + sDir)
                 else:
-                    out_dir = test_dir + sDir + '/'
+                    out_dir = os.path.join(test_dir, sDir)
 
                 makedir(out_dir)
-                out_path = out_dir + os.path.split(imgs_list[i])[-1]
+                out_path = os.path.join(out_dir, os.path.split(imgs_list[i])[-1])
                 shutil.copy(imgs_list[i], out_path)
 
             print('Class:{}, train:{}, valid:{}, test:{}'.format(sDir, train_point, valid_point-train_point, imgs_num-valid_point))

Code/1_data_prepare/1_3_generate_txt.py

@@ -4,11 +4,11 @@ import os
     为数据集生成对应的txt文件
 '''
 
-train_txt_path = '../../Data/train.txt'
-train_dir = '../../Data/train/'
+train_txt_path = os.path.join("..", "..", "Data", "train.txt")
+train_dir = os.path.join("..", "..", "Data", "train")
 
-valid_txt_path = '../../Data/valid.txt'
-valid_dir = '../../Data/valid/'
+valid_txt_path = os.path.join("..", "..", "Data", "valid.txt")
+valid_dir = os.path.join("..", "..", "Data", "valid")
 
 
 def gen_txt(txt_path, img_dir):
@@ -30,4 +30,5 @@ def gen_txt(txt_path, img_dir):
 
 if __name__ == '__main__':
     gen_txt(train_txt_path, train_dir)
-    gen_txt(valid_txt_path, valid_dir)
\ No newline at end of file
+    gen_txt(valid_txt_path, valid_dir)
+

Code/2_model/2_finetune.py

@@ -14,8 +14,8 @@ sys.path.append("..")
 from utils.utils import MyDataset, validate, show_confMat
 from datetime import datetime
 
-train_txt_path = '../../Data/train.txt'
-valid_txt_path = '../../Data/valid.txt'
+train_txt_path = os.path.join("..", "..", "Data", "train.txt")
+valid_txt_path = os.path.join("..", "..", "Data", "valid.txt")
 
 classes_name = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
 
@@ -25,7 +25,7 @@ lr_init = 0.001
 max_epoch = 1
 
 # log
-result_dir = '../../Result/'
+result_dir = os.path.join("..", "..", "Result")
 
 now_time = datetime.now()
 time_str = datetime.strftime(now_time, '%m-%d_%H-%M-%S')

Code/4_viewer/1_tensorboardX_demo.py

 # coding: utf-8
+import os
 import torch
 import torchvision.utils as vutils
 import numpy as np
@@ -7,7 +8,7 @@ from torchvision import datasets
 from tensorboardX import SummaryWriter
 
 resnet18 = models.resnet18(False)
-writer = SummaryWriter('../../Result/runs')
+writer = SummaryWriter(os.path.join("..", "..", "Result", "runs"))
 sample_rate = 44100
 freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]
 
@@ -23,10 +24,10 @@ for n_iter in range(100):
     s1 = torch.rand(1)  # value to keep
     s2 = torch.rand(1)
     # data grouping by `slash`
-    writer.add_scalar('data/scalar_systemtime', s1[0], n_iter)
+    writer.add_scalar(os.path.join("data", "scalar_systemtime"), s1[0], n_iter)
     # data grouping by `slash`
-    writer.add_scalar('data/scalar_customtime', s1[0], n_iter, walltime=n_iter)
-    writer.add_scalars('data/scalar_group', {"xsinx": n_iter * np.sin(n_iter),
+    writer.add_scalar(os.path.join("data", "scalar_customtime"), s1[0], n_iter, walltime=n_iter)
+    writer.add_scalars(os.path.join("data", "scalar_group"), {"xsinx": n_iter * np.sin(n_iter),
                                              "xcosx": n_iter * np.cos(n_iter),
                                              "arctanx": np.arctan(n_iter)}, n_iter)
     x = torch.rand(32, 3, 64, 64)  # output from network
@@ -56,15 +57,15 @@ for n_iter in range(100):
                                 precision,
                                 recall, n_iter)
 # export scalar data to JSON for external processing
-writer.export_scalars_to_json("../../Result/all_scalars.json")
+writer.export_scalars_to_json(os.path.join("..", "..", "Result", "all_scalars.json"))
 
-dataset = datasets.MNIST('../../Data/mnist', train=False, download=True)
+dataset = datasets.MNIST(os.path.join("..", "..", "Data", "mnist"), train=False, download=True)
 images = dataset.test_data[:100].float()
 label = dataset.test_labels[:100]
 features = images.view(100, 784)
 writer.add_embedding(features, metadata=label, label_img=images.unsqueeze(1))
 writer.add_embedding(features, global_step=1, tag='noMetadata')
-dataset = datasets.MNIST('../../Data/mnist', train=True, download=True)
+dataset = datasets.MNIST(os.path.join("..", "..", "Data", "mnist"), train=True, download=True)
 images_train = dataset.train_data[:100].float()
 labels_train = dataset.train_labels[:100]
 features_train = images_train.view(100, 784)

Code/4_viewer/2_visual_weights.py

 # coding: utf-8
+import os
 import torch
 import torchvision.utils as vutils
 from tensorboardX import SummaryWriter
@@ -42,10 +43,10 @@ class Net(nn.Module):
 
 
 net = Net()     # 创建一个网络
-pretrained_dict = torch.load('../2_model/net_params.pkl')
+pretrained_dict = torch.load(os.path.join("..", "2_model", "net_params.pkl"))
 net.load_state_dict(pretrained_dict)
 
-writer = SummaryWriter(log_dir='../../Result/visual_weights')
+writer = SummaryWriter(log_dir=os.path.join("..", ".." "Result", "visual_weights"))
 params = net.state_dict()
 for k, v in params.items():
     if 'conv' in k and 'weight' in k:

Code/4_viewer/3_visual_featuremaps.py

 # coding: utf-8
+import os
 import torch
 import torchvision.utils as vutils
 import numpy as np
@@ -12,9 +13,9 @@ from torch.utils.data import DataLoader
 
 
 vis_layer = 'conv1'
-log_dir = '../../Result/visual_featuremaps'
-txt_path = '../../Data/visual.txt'
-pretrained_path = '../../Data/net_params_72p.pkl'
+log_dir = os.path.join("..", ".." "Result", "visual_featuremaps")
+txt_path = os.path.join("..", "..", "Data", "visual.txt")
+pretrained_path = os.path.join("..", "..", "Data", "net_params_72p.pkl")
 
 net = Net()
 pretrained_dict = torch.load(pretrained_path)

Code/4_viewer/4_hist_grad_weight.py

@@ -9,13 +9,14 @@ from torch.autograd import Variable
 import torch.nn as nn
 import torch.optim as optim
 import sys
+import os
 sys.path.append("..")
 from utils.utils import MyDataset, validate, show_confMat, Net
 from tensorboardX import SummaryWriter
 from datetime import datetime
 
-train_txt_path = '../../Data/train.txt'
-valid_txt_path = '../../Data/valid.txt'
+train_txt_path = os.path.join("..", "..", "Data", "train.txt")
+valid_txt_path = os.path.join("..", "..", "Data", "valid.txt")
 
 classes_name = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
 
@@ -25,7 +26,7 @@ lr_init = 0.001
 max_epoch = 1
 
 # log
-log_dir = '../../Result/hist_grad_weight'
+log_dir = os.path.join("..", "..", "Result", "hist_grad_weight")
 
 writer = SummaryWriter(log_dir=log_dir)
 

Code/4_viewer/6_hook_for_grad_cam.py

@@ -130,9 +130,9 @@ def gen_cam(feature_map, grads):
 if __name__ == '__main__':
 
     BASE_DIR = os.path.dirname(os.path.abspath(__file__))
-    path_img = os.path.join(BASE_DIR, "..", "..", "Data/cam_img/", "test_img_8.png")
-    path_net = os.path.join(BASE_DIR, "..", "..", "Data/", "net_params_72p.pkl")
-    output_dir = os.path.join(BASE_DIR, "..", "..", "Result/backward_hook_cam/")
+    path_img = os.path.join(BASE_DIR, "..", "..", "Data", "cam_img", "test_img_8.png")
+    path_net = os.path.join(BASE_DIR, "..", "..", "Data", "net_params_72p.pkl")
+    output_dir = os.path.join(BASE_DIR, "..", "..", "Result", "backward_hook_cam")
 
     classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
     fmap_block = list()

Code/main_training/main.py

@@ -15,8 +15,8 @@ from utils.utils import MyDataset, validate, show_confMat
 from tensorboardX import SummaryWriter
 from datetime import datetime
 
-train_txt_path = '../../Data/train.txt'
-valid_txt_path = '../../Data/valid.txt'
+train_txt_path = os.path.join("..", "..", "Data", "train.txt")
+valid_txt_path = os.path.join("..", "..", "Data", "valid.txt")
 
 classes_name = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
 
@@ -26,7 +26,7 @@ lr_init = 0.001
 max_epoch = 1
 
 # log
-result_dir = '../../Result/'
+result_dir = os.path.join("..", "..", "Result")
 
 now_time = datetime.now()
 time_str = datetime.strftime(now_time, '%m-%d_%H-%M-%S')