更新算法模版

projects/codes/common/utils.py

@@ -5,7 +5,7 @@ Author: John
 Email: johnjim0816@gmail.com
 Date: 2021-03-12 16:02:24
 LastEditor: John
-LastEditTime: 2022-08-24 10:31:30
+LastEditTime: 2022-10-26 07:38:17
 Discription: 
 Environment: 
 '''
@@ -14,8 +14,13 @@ import numpy as np
 from pathlib import Path
 import matplotlib.pyplot as plt
 import seaborn as sns
-import json
+import yaml
 import pandas as pd
+from functools import wraps
+from time import time
+import logging
+from pathlib import Path
+
 
 from matplotlib.font_manager import FontProperties  # 导入字体模块
 
@@ -61,17 +66,17 @@ def smooth(data, weight=0.9):
         last = smoothed_val                                
     return smoothed
 
-def plot_rewards(rewards,cfg,path=None,tag='train'):
+def plot_rewards(rewards,title="learning curve",fpath=None,save_fig=True,show_fig=False):
     sns.set()
     plt.figure()  # 创建一个图形实例，方便同时多画几个图
-    plt.title(f"{tag}ing curve on {cfg['device']} of {cfg['algo_name']} for {cfg['env_name']}")
+    plt.title(f"{title}")
     plt.xlabel('epsiodes')
     plt.plot(rewards, label='rewards')
     plt.plot(smooth(rewards), label='smoothed')
     plt.legend()
-    if cfg['save_fig']:
-        plt.savefig(f"{path}/{tag}ing_curve.png")
-    if cfg['show_fig']:
+    if save_fig:
+        plt.savefig(f"{fpath}/learning_curve.png")
+    if show_fig:
         plt.show()
 
 def plot_losses(losses, algo="DQN", save=True, path='./'):
@@ -85,48 +90,86 @@ def plot_losses(losses, algo="DQN", save=True, path='./'):
         plt.savefig(path+"losses_curve")
     plt.show()
 
-def save_results(res_dic, tag='train', path = None):
-    ''' 保存奖励
+def save_results(res_dic,fpath = None):
+    ''' save results
     '''
-    Path(path).mkdir(parents=True, exist_ok=True)
+    Path(fpath).mkdir(parents=True, exist_ok=True)
     df = pd.DataFrame(res_dic)
-    df.to_csv(f"{path}/{tag}ing_results.csv",index=None)
-    print('Results saved！')
-
-
-def make_dir(*paths):
-    ''' 创建文件夹
+    df.to_csv(f"{fpath}/res.csv",index=None)
+def merge_class_attrs(ob1, ob2):
+    ob1.__dict__.update(ob2.__dict__)
+    return ob1
+def get_logger(fpath):
+    Path(fpath).mkdir(parents=True, exist_ok=True)
+    logger = logging.getLogger(name='r')  # set root logger if not set name
+    logger.setLevel(logging.DEBUG)
+    formatter = logging.Formatter(
+        '%(asctime)s - %(name)s - %(levelname)s: - %(message)s',
+        datefmt='%Y-%m-%d %H:%M:%S')
+    # output to file by using FileHandler
+    fh = logging.FileHandler(fpath+"log.txt")
+    fh.setLevel(logging.DEBUG)
+    fh.setFormatter(formatter)
+    # output to screen by using StreamHandler
+    ch = logging.StreamHandler()
+    ch.setLevel(logging.DEBUG)
+    ch.setFormatter(formatter)
+    # add Handler
+    logger.addHandler(ch)
+    logger.addHandler(fh)
+    return logger
+def save_cfgs(cfgs, fpath):
+    ''' save config
     '''
-    for path in paths:
-        Path(path).mkdir(parents=True, exist_ok=True)
+    Path(fpath).mkdir(parents=True, exist_ok=True)
+ 
+    with open(f"{fpath}/config.yaml", 'w') as f:
+        for cfg_type in cfgs:
+            yaml.dump({cfg_type: cfgs[cfg_type].__dict__}, f, default_flow_style=False)
+def load_cfgs(cfgs, fpath):
+    with open(fpath) as f:
+        load_cfg = yaml.load(f,Loader=yaml.FullLoader)
+        for cfg_type in cfgs:
+            for k, v in load_cfg[cfg_type].items():
+                setattr(cfgs[cfg_type], k, v)
+# def del_empty_dir(*paths):
+#     ''' 删除目录下所有空文件夹
+#     '''
+#     for path in paths:
+#         dirs = os.listdir(path)
+#         for dir in dirs:
+#             if not os.listdir(os.path.join(path, dir)):
+#                 os.removedirs(os.path.join(path, dir))
 
-
-def del_empty_dir(*paths):
-    ''' 删除目录下所有空文件夹
-    '''
-    for path in paths:
-        dirs = os.listdir(path)
-        for dir in dirs:
-            if not os.listdir(os.path.join(path, dir)):
-                os.removedirs(os.path.join(path, dir))
-
-class NpEncoder(json.JSONEncoder):
-    def default(self, obj):
-        if isinstance(obj, np.integer):
-            return int(obj)
-        if isinstance(obj, np.floating):
-            return float(obj)
-        if isinstance(obj, np.ndarray):
-            return obj.tolist()
-        return json.JSONEncoder.default(self, obj)
+# class NpEncoder(json.JSONEncoder):
+#     def default(self, obj):
+#         if isinstance(obj, np.integer):
+#             return int(obj)
+#         if isinstance(obj, np.floating):
+#             return float(obj)
+#         if isinstance(obj, np.ndarray):
+#             return obj.tolist()
+#         return json.JSONEncoder.default(self, obj)
         
-def save_args(args,path=None):
-    # save parameters  
-    Path(path).mkdir(parents=True, exist_ok=True) 
-    with open(f"{path}/params.json", 'w') as fp:
-        json.dump(args, fp,cls=NpEncoder)   
-    print("Parameters saved!")
+# def save_args(args,path=None):
+#     # save parameters  
+#     Path(path).mkdir(parents=True, exist_ok=True) 
+#     with open(f"{path}/params.json", 'w') as fp:
+#         json.dump(args, fp,cls=NpEncoder)   
+#     print("Parameters saved!")
 
+
+def timing(func):
+    ''' a decorator to print the running time of a function
+    ''' 
+    @wraps(func)
+    def wrap(*args, **kw):
+        ts = time()
+        result = func(*args, **kw)
+        te = time()
+        print(f"func: {func.__name__}, took: {te-ts:2.4f} seconds")
+        return result
+    return wrap
 def all_seed(env,seed = 1):
     ''' omnipotent seed for RL, attention the position of seed function, you'd better put it just following the env create function
     Args:
@@ -136,7 +179,7 @@ def all_seed(env,seed = 1):
     import torch
     import numpy as np
     import random
-    print(f"seed = {seed}")
+    # print(f"seed = {seed}")
     env.seed(seed) # env config
     np.random.seed(seed)
     random.seed(seed)