win环境启动线程是靠spwan(),而基于的Dataset基类定义的数据集,getitem()的过程会和脚本原来进程冲突等等。冲突会造成报错,然而你的自定义Dataset里调用了getitem()如opencv,numpy等包的话会发生卡死。 解决办法一: 把num_workers设置成0。 解决方法二、 ifname== 'main’ 不同平台的区别,在 windows平台,python是...
环境:windows10 cuda11.0 dataloader设置: train_loader = DataLoader(dataset=train_dataset, batch_size=512, shuffle=True, num_workers=0 ) test_loader = DataLoader(dataset=test_dataset, batch_size=512, shuffle=False, num_workers=4 ) 报错: RuntimeError: An attempt has been made to start ...
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worker_counts=[0,1,2,4,8]times=[]forworkersinworker_counts:dataloader=DataLoader(dataset,batch_size=32,shuffle=True,num_workers=workers)start_time=time.time()fordataindataloader:# 模拟训练过程passtimes.append(time.time()-start_time)# 打印结果print("Num Workers | Time taken (seconds)")print...
batch_size就是samples_per_gpu,目前我的GPU只能设置成1才能跑。 num_workers就是workers_per_gpu,这个设置涉及到一个报错,必须改成0让它在主线程才行 copy 1 2 samples_per_gpu=1, workers_per_gpu=0, 4.epoch可调 第223行 copy 1 total_epochs =17 ...
num_worker设置为0以上的值会导致内存泄漏。另外代码入口得用以下的if语句包裹。这里稍微解释下为什么要这样,因为Windows不支持fork,多进程只能采用spawn,而如果你不用这个条件包裹,那么代码就会被再次执行一遍,这样如果不加限制,那么就会有无限多的进程了。 if __name__ == '__main__': 另外这两天还有个好消息...
🐛 Bug In windows, DataLoader with num_workers > 0 is extremely slow (pytorch=0.41) To Reproduce Step 1: create two loader, one with num_workers and one without. import torch.utils.data as Data train_loader = Data.DataLoader(dataset=train...
num_workers = 0 # 0 表示不用额外的进程来加速读取数据。 else: num_workers = 4 train_iter = gdata.DataLoader(mnist_train.transform_first(transformer), batch_size, shuffle=True, num_workers=num_workers) batch_size, shuffle=True, num_workers=4) test_iter = gdata.DataLoader(mnist_test.transfo...
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