這幾天學習人臉識別的時候,雖然運行的沒有問題,但我卻意識到了一個問題
在圖片進行傳輸的時候,GPU的利用率爲0
也就是說,圖片的傳輸速度和GPU的處理速度不能很好銜接
於是,我打算利用多線程開發一個buffer緩存
實現的思路如下
定義一個Buffer類,再其構造函數中創建一個buffer空間(這裏最好使用list類型)
我們還需要的定義線程鎖LOCK(數據傳輸和提取的時候會用到)
因爲需要兩種方法(讀數據和取數據),所以我們需要定義兩個鎖
實現的代碼如下:
#-*-coding:utf-8-*-
import threading
class Buffer:
def __init__(self,size):
self.size = size
self.buffer = []
self.lock = threading.Lock()
self.has_data = threading.Condition(self.lock) # small sock depand on big sock
self.has_pos = threading.Condition(self.lock)
def get_size(self):
return self.size
def get(self):
with self.has_data:
while len(self.buffer) == 0:
print("I can't go out has_data")
self.has_data.wait()
print("I can go out has_data")
result = self.buffer[0]
del self.buffer[0]
self.has_pos.notify_all()
return result
def put(self, data):
with self.has_pos:
#print(self.count)
while len(self.buffer)>=self.size:
print("I can't go out has_pos")
self.has_pos.wait()
print("I can go out has_pos")
# If the length of data bigger than buffer's will wait
self.buffer.append(data)
# some thread is wait data ,so data need release
self.has_data.notify_all()
if __name__ == "__main__":
buffer = Buffer(3)
def get():
for _ in range(10000):
print(buffer.get())
def put():
a = [[1,2,3,4,5,6,7,8,9],[1,2,3,4,5,6,7,8,9],[1,2,3,4,5,6,7,8,9]]
for _ in range(10000):
buffer.put(a)
th1 = threading.Thread(target=put)
th2 = threading.Thread(target=get)
th1.start()
th2.start()
th1.join()
th2.join()