使用较为复杂的拟合网络; 将训练结果输出至文件;

data_module.py

@@ -31,7 +31,10 @@ class DataModule(pl.LightningDataModule):
         if not os.path.exists(self.dataset_path + '/dataset_list.txt'):
             x = torch.randn(self.config['dataset_len'], self.config['dim_in'])
             noise = torch.randn(self.config['dataset_len'])
-            y = torch.cos(1.5 * x[:, 0]) * (x[:, 1] ** 2.0) + noise
+            y = torch.cos(1.5 * x[:, 0]) * (x[:, 1] ** 2.0) + torch.cos(torch.sin(x[:, 2] ** 3)) + torch.arctan(
+                x[:, 4]) + noise
+            assert (x[torch.isnan(x)].shape[0] == 0)
+            assert (y[torch.isnan(y)].shape[0] == 0)
             with open(self.dataset_path + '/dataset_list.txt', 'w', encoding='utf-8') as f:
                 for line in range(self.config['dataset_len']):
                     f.write(' '.join([str(temp) for temp in x[line].tolist()]) + ' ' + str(y[line].item()) + '\n')

main.py

@@ -54,11 +54,11 @@ def main(stage,
     # TODO 获得最优的batch size
     num_workers = cpu_count()
     # 获得非通用参数
-    config = {'dim_in': 2,
+    config = {'dim_in': 5,
               'dim': 10,
               'res_coef': 0.5,
               'dropout_p': 0.1,
-              'n_layers': 2,
+              'n_layers': 3,
               'dataset_len': 100000}
     for kth_fold in range(kth_fold_start, k_fold):
         load_checkpoint_path = get_ckpt_path(version_nth, kth_fold)

save_checkpoint.py

@@ -66,6 +66,31 @@ class SaveCheckpoint(ModelCheckpoint):
 
         if self.check_monitor_top_k(trainer, current):
             self._update_best_and_save(current, trainer, monitor_candidates)
+            best_model_value = max([float(item) for item in list(self.best_k_models.values())])
+            # 保存版本信息(准确率等)到txt中
+            if not os.path.exists('./logs/default/version_info.txt'):
+                with open('./logs/default/version_info.txt', 'w', encoding='utf-8') as f:
+                    f.write(self.dirpath.split('\\')[1] + ' ' + str(best_model_value) + '\n')
+            else:
+                with open('./logs/default/version_info.txt', 'r', encoding='utf-8') as f:
+                    info_list = f.readlines()
+                info_list = [item.strip('\n').split(' ') for item in info_list]
+                # 对list进行转置, 现在行为版本号和其数据, 列为不同的版本
+                info_list = list(map(list, zip(*info_list)))
+                if self.dirpath.split('\\')[1] in info_list[0]:
+                    for cou in range(len(info_list[0])):
+                        if self.dirpath.split('\\')[1] == info_list[0][cou]:
+                            info_list[1][cou] = str(best_model_value)
+                else:
+                    info_list[0].append(self.dirpath.split('\\')[1])
+                    info_list[1].append(str(best_model_value))
+                # 对list进行转置
+                info_list = list(map(list, zip(*info_list)))
+                with open('./logs/default/version_info.txt', 'w', encoding='utf-8') as f:
+                    for line in info_list:
+                        line = " ".join(line)
+                        f.write(line + '\n')
+            # 每次更新ckpt文件后, 将其存放到另一个位置
             if self.path_final_save is not None:
                 zip_dir('./logs', './logs.zip')
                 if os.path.exists(self.path_final_save + '/logs.zip'):

utils.py

@@ -2,10 +2,8 @@
 import glob
 import os
 import random
-import string
 import zipfile
 import cv2
-import numpy
 import torch