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Hi,
I recently migrated from BVLC/caffe to nvcaffe, and there is a significant speedup during inference.
The options for layer-specific mixed-precision support is awesome.
I would also like to use nvcaffe for training and I have a few questions:
enum Packing
in caffe.proto which can be set to NCHW
(default) or NHWC
. If we set Packing to NHWC
in the data layer, will the packing info be automatically transmitted to all the layers in the network ? Intuitively, it feels as though NHWC
should be more efficient for both convolution and batch-norm layers, particularly for CUDNN.Finally, if there is some specific documentation for nvcaffe, that would also be awesome.
Where is the mean_file in https://github.com/borisgin/nvcaffe-0.16/blob/caffe-0.16/models/bvlc_alexnet/train_val_fp16.prototxt
?
In your paper, I find
(1) we get the local learning rate for each learnable parameter by α = l×||w||2/(||∇w||2+β||∇w||2);
But in your code,
rate = gw_ratio * w_norm / (wgrad_norm + weight_decay * w_norm);
The code and equation doesn't match. Is it a type error in your paper?
α
equals l×||w||2/(||∇w||2+β||w||2);
I think.
LD -o .build_release/lib/libcaffe-nv.so.0.16.4
/usr/bin/ld: /usr/lib/gcc/x86_64-linux-gnu/5/../../../x86_64-linux-gnu/libturbojpeg.a(libturbojpeg_la-turbojpeg.o): relocation R_X86_64_32 against `.data' can not be used when making a shared object; recompile with -fPIC
/usr/lib/gcc/x86_64-linux-gnu/5/../../../x86_64-linux-gnu/libturbojpeg.a: error adding symbols: Bad value
collect2: error: ld returned 1 exit status
Makefile:605: recipe for target '.build_release/lib/libcaffe-nv.so.0.16.4' failed
make: *** [.build_release/lib/libcaffe-nv.so.0.16.4] Error 1
Our workstation with...
Ubuntu:16.04
Cuda: 8.0
CuDNN: 7.05
Hi, did i mistake something? I'm seeking for answer about this. Thank you.
hello, I have read your paper, is the batch_size in your code the same as nvcaffe, which is means the global batch_size?Thanks.
Conda 2.7.9 + Digits 6.0.0 + NVcaffe 0.16.4 + 4 X P40
All goes well with last line in caffe_oupt.log
#####################################################################
I1117 11:02:59.804045 15357 caffe.cpp:226] Starting Optimization
I1117 11:02:59.804064 15357 solver.cpp:386] Solving
I1117 11:02:59.804067 15357 solver.cpp:387] Learning Rate Policy: exp
I1117 11:02:59.822211 15357 net.cpp:1358] [3] Reserving 23918336 bytes of shared learnable space
I1117 11:02:59.823530 15357 solver.cpp:457] Iteration 0, Testing net (#0)
I1117 11:02:59.823545 15357 net.cpp:1004] Ignoring source layer train_data
I1117 11:02:59.823549 15357 net.cpp:1004] Ignoring source layer train_label
I1117 11:02:59.823552 15357 net.cpp:1004] Ignoring source layer train_transform
I1117 11:02:59.824621 15370 device_alternate.hpp:116] NVML initialized on thread 139987149186816
I1117 11:02:59.953352 15370 common.cpp:585] NVML succeeded to set CPU affinity on device 3
#####################################################################
But Caffe & Digit Freeze At
#####################################################################
Train Caffe Model Running
0%
#####################################################################
For several hours.
I shift to nvcaffe 0.15.13, its the same.
While ALL CLASSIFICATION JOBS GOES WELL .
I followed this demo link provided by nv:
https://github.com/dusty-nv/jetson-inference#locating-object-coordinates-using-detectnet
with our own dataset preprocessed by digits.
Can anyone help me out?
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