Comments (3)
happyharrycn
commented on July 26, 2024
3
I think it is possible to also make testing faster with cuDNN, although I have not got a chance to test it on PVANET. The performance issue is that cuDNN had bad implementations for certain convolutions (e.g. 1x1 convolutions with stride=1, which is used a couple of times in PVANET). You can still compile Caffe with cuDNN and put engine: CAFFE under convolution_param in these layers.
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jay2002
commented on July 26, 2024
1
@happyharrycn cool solution!
I have tried it and it really does work
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sanghoon
commented on July 26, 2024
I'm sorry we made a confusion here.
It's totally okay to uncomment USE_CUDNN if it makes your training faster.
It just means that commenting 'USE_CUDNN' worked faster in our computational environments.
I'll update README.
FYI, for our published results,
training of a network took 7~14 days with Titan X or GTX1080.
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Related Issues (20)
- PVANET with Deformable Convolution
- Initialization fails in GPU
- What is the parameter` dets_NMS` and `dets_all` in box voting function??
- caffe loss=0, after relabeling and debugging for a million of times
- docker 运行
- train.yml
- end-2-end训练，还是two step 交替训练呢？
- where is the PVANET model code?
- Scale and BN on training
- How to calculate detection speed?
- generated prototxt and caffemodel ?
- error
- About the detection time HOT 1
- What is the train details on ImageNet2012? HOT 7
- Check failed: error == cudaSuccess (2 vs. 0) out of memory *** Check failure stack trace: *** HOT 2
- How to achieve 30 fps as referenced in paper?
- why the "box_deltas" does't rescale to the raw images space int the test phase
- The data and label should have the same first dimension. HOT 1
- Attributeerror:'model' object has no attribute 'text_format' HOT 1
- keep = np.where((ws >= min_size) & (hs >= min_size))[0];Check failed: error == cudaSuccess (9 vs. 0) invalid configuration argument HOT 2
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