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JiangWeixian avatar JiangWeixian commented on July 29, 2024 2

感谢指导,现在以及出结果了。还有我在说一下还需要修改的地方,还要把setup.py里面所有关于cuda注释掉,以及其中ext_modules里面gpu_nms删除。然后你再python demo.py

然而你还要改东西,因为代码里面很计算的地方用到torch.cuda()计算。而且传递给函数参数中都默认is_cuda=True,而且还有地方使用gpu_nms,因为比较多,所以就不贴了。所以大家就看着命令行哪里报错,就在对应的文件里面该注释的注释,该改参数的该参数,基本上没有问题了。

感谢作者分享,hhhhhh

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longcw avatar longcw commented on July 29, 2024

可以,但是只支持inference,不能训练,没有实现roi pooling cpu版本的backward.
去掉demo 里面的 .cuda() 并注释mask.sh中的:

echo "Compiling roi pooling kernels by nvcc..."
nvcc -c -o roi_pooling.cu.o roi_pooling_kernel.cu \
	 -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC -arch=sm_52

faster_rcnn_pytorch/faster_rcnn/roi_pooling/build.py 中的:

extra_objects = ['src/cuda/roi_pooling.cu.o']
extra_objects = [os.path.join(this_file, fname) for fname in extra_objects]

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andrewssobral avatar andrewssobral commented on July 29, 2024

Hi @longcw
How can I compile and run training and test with CPU_ONLY mode?
Thanks!

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sajjad-askari avatar sajjad-askari commented on July 29, 2024

Hi JiangWeixian.
please explain full of details of actions that achieve reply.
please list this actions.

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chenlx92 avatar chenlx92 commented on July 29, 2024

@JiangWeixian 能否push你修改后的cpu-only的代码?

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