edit coding style according PEP8

.gitignore

@@ -0,0 +1 @@
+.idea/

manager.py

@@ -16,6 +16,7 @@
 import tensorflow as tf
 #from tensorflow.python.client import device_lib
 
+
 def check_gpus():
     '''
     GPU available check
@@ -33,8 +34,9 @@ def check_gpus():
         return False
     return True
 
+
 if check_gpus():
-    def parse(line,qargs):
+    def parse(line, qargs):
         '''
         line:
             a line of text
@@ -45,11 +47,11 @@ def parse(line,qargs):
         Pasing a line of csv format text returned by nvidia-smi
         解析一行nvidia-smi返回的csv格式文本
         '''
-        numberic_args = ['memory.free', 'memory.total', 'power.draw', 'power.limit']#可计数的参数
-        power_manage_enable=lambda v:(not 'Not Support' in v)#lambda表达式，显卡是否滋瓷power management（笔记本可能不滋瓷）
-        to_numberic=lambda v:float(v.upper().strip().replace('MIB','').replace('W',''))#带单位字符串去掉单位
-        process = lambda k,v:((int(to_numberic(v)) if power_manage_enable(v) else 1) if k in numberic_args else v.strip())
-        return {k:process(k,v) for k,v in zip(qargs,line.strip().split(','))}
+        numberic_args = ['memory.free', 'memory.total', 'power.draw', 'power.limit']  # 可计数的参数
+        power_manage_enable = lambda v: ('Not Support' not in v)  # lambda表达式，显卡是否支持power management（笔记本可能不支持）
+        to_numberic = lambda v: float(v.upper().strip().replace('MIB', '').replace('W', ''))  # 带单位字符串去掉单位
+        process = lambda k, v: ((int(to_numberic(v)) if power_manage_enable(v) else 1) if k in numberic_args else v.strip())
+        return {k: process(k, v) for k, v in zip(qargs, line.strip().split(','))}
 
     def query_gpu(qargs=[]):
         '''
@@ -60,16 +62,16 @@ def query_gpu(qargs=[]):
         Querying GPUs infos
         查询GPU信息
         '''
-        qargs =['index','gpu_name', 'memory.free', 'memory.total', 'power.draw', 'power.limit']+ qargs
+        qargs =['index', 'gpu_name', 'memory.free', 'memory.total', 'power.draw', 'power.limit'] + qargs
         cmd = 'nvidia-smi --query-gpu={} --format=csv,noheader'.format(','.join(qargs))
         results = os.popen(cmd).readlines()
-        return [parse(line,qargs) for line in results]
+        return [parse(line, qargs) for line in results]
 
     def by_power(d):
         '''
         helper function fo sorting gpus by power
         '''
-        power_infos=(d['power.draw'],d['power.limit'])
+        power_infos = (d['power.draw'], d['power.limit'])
         if any(v==1 for v in power_infos):
             print('Power management unable for GPU {}'.format(d['index']))
             return 1
@@ -86,34 +88,33 @@ class GPUManager():
         最空闲的设备。在一个GPUManager对象内会记录每个GPU是否已被指定，
         优先选择未指定的GPU。
         '''
-        def __init__(self,qargs=[]):
-            '''
-            '''
-            self.qargs=qargs
-            self.gpus=query_gpu(qargs)
+
+        def __init__(self, qargs=[]):
+            self.qargs = qargs
+            self.gpus = query_gpu(qargs)
             for gpu in self.gpus:
-                gpu['specified']=False
-            self.gpu_num=len(self.gpus)
+                gpu['specified'] = False
+            self.gpu_num = len(self.gpus)
 
-        def _sort_by_memory(self,gpus,by_size=False):
+        def _sort_by_memory(self, gpus, by_size=False):
             if by_size:
                 print('Sorted by free memory size')
-                return sorted(gpus,key=lambda d:d['memory.free'],reverse=True)
+                return sorted(gpus, key=lambda d: d['memory.free'], reverse=True)
             else:
                 print('Sorted by free memory rate')
-                return sorted(gpus,key=lambda d:float(d['memory.free'])/ d['memory.total'],reverse=True)
+                return sorted(gpus, key=lambda d: float(d['memory.free'])/ d['memory.total'], reverse=True)
 
-        def _sort_by_power(self,gpus):
-            return sorted(gpus,key=by_power)
+        def _sort_by_power(self, gpus):
+            return sorted(gpus, key=by_power)
 
-        def _sort_by_custom(self,gpus,key,reverse=False,qargs=[]):
-            if isinstance(key,str) and (key in qargs):
-                return sorted(gpus,key=lambda d:d[key],reverse=reverse)
-            if isinstance(key,type(lambda a:a)):
-                return sorted(gpus,key=key,reverse=reverse)
-            raise ValueError("The argument 'key' must be a function or a key in query args,please read the documention of nvidia-smi")
+        def _sort_by_custom(self, gpus, key, reverse=False, qargs=[]):
+            if isinstance(key, str) and (key in qargs):
+                return sorted(gpus, key=lambda d: d[key], reverse=reverse)
+            if isinstance(key, type(lambda a: a)):
+                return sorted(gpus, key=key, reverse=reverse)
+            raise ValueError("The argument 'key' must be a function or a key in query args, please read the documention of nvidia-smi")
 
-        def auto_choice(self,mode=0):
+        def auto_choice(self, mode=0):
             '''
             mode:
                 0:(default)sorted by free memory size
@@ -123,25 +124,25 @@ def auto_choice(self,mode=0):
             ones 
             自动选择最空闲GPU
             '''
-            for old_infos,new_infos in zip(self.gpus,query_gpu(self.qargs)):
+            for old_infos, new_infos in zip(self.gpus, query_gpu(self.qargs)):
                 old_infos.update(new_infos)
-            unspecified_gpus=[gpu for gpu in self.gpus if not gpu['specified']] or self.gpus
+            unspecified_gpus = [gpu for gpu in self.gpus if not gpu['specified']] or self.gpus
 
-            if mode==0:
+            if mode == 0:
                 print('Choosing the GPU device has largest free memory...')
-                chosen_gpu=self._sort_by_memory(unspecified_gpus,True)[0]
-            elif mode==1:
+                chosen_gpu = self._sort_by_memory(unspecified_gpus, True)[0]
+            elif mode == 1:
                 print('Choosing the GPU device has highest free memory rate...')
-                chosen_gpu=self._sort_by_power(unspecified_gpus)[0]
-            elif mode==2:
+                chosen_gpu = self._sort_by_power(unspecified_gpus)[0]
+            elif mode == 2:
                 print('Choosing the GPU device by power...')
-                chosen_gpu=self._sort_by_power(unspecified_gpus)[0]
+                chosen_gpu = self._sort_by_power(unspecified_gpus)[0]
             else:
                 print('Given an unaviliable mode,will be chosen by memory')
-                chosen_gpu=self._sort_by_memory(unspecified_gpus)[0]
-            chosen_gpu['specified']=True
-            index=chosen_gpu['index']
-            print('Using GPU {i}:\n{info}'.format(i=index,info='\n'.join([str(k)+':'+str(v) for k,v in chosen_gpu.items()])))
+                chosen_gpu = self._sort_by_memory(unspecified_gpus)[0]
+            chosen_gpu['specified'] = True
+            index = chosen_gpu['index']
+            print('Using GPU {i}:\n{info}'.format(i=index, info='\n'.join([str(k)+':'+str(v) for k, v in chosen_gpu.items()])))
             return tf.device('/gpu:{}'.format(index))
 else:
     raise ImportError('GPU available check failed')