Comments (5)
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
from faster_rcnn_pytorch.
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
和 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]
from faster_rcnn_pytorch.
Hi @longcw
How can I compile and run training and test with CPU_ONLY mode?
Thanks!
from faster_rcnn_pytorch.
Hi JiangWeixian.
please explain full of details of actions that achieve reply.
please list this actions.
from faster_rcnn_pytorch.
@JiangWeixian 能否push你修改后的cpu-only的代码?
from faster_rcnn_pytorch.
Related Issues (20)
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from faster_rcnn_pytorch.