Comments (10)
dutran
commented on August 24, 2024
@whjxnyzh Thanks for the post. I have fixed the backward comparability with old Blob calls. Please try it again.
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whjxnyzh123
commented on August 24, 2024
@dutran I try again. but this time......
src/caffe/util/math_functions.cu(140): error: calling a __host__ function("std::signbit<float> ") from a __global__ function("caffe::sgnbit_kernel<float> ") is not allowed
src/caffe/util/math_functions.cu(140): error: calling a __host__ function("std::signbit<double> ") from a __global__ function("caffe::sgnbit_kernel<double> ") is not allowed
2 errors detected in the compilation of "/tmp/tmpxft_00009fb3_00000000-12_math_functions.compute_35.cpp1.ii".
make: *** [build/src/caffe/util/math_functions.cuo] Error 2
make: *** Waiting for unfinished jobs....
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dutran
commented on August 24, 2024
It works fine on mine. Which CUDA are you using? Mine is 5.5. Looks like this is problem comes from CUDA.
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whjxnyzh123
commented on August 24, 2024
NVIDIA-SMI 340.29 Driver Version: 340.29 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla K20m Off | 0000:08:00.0 Off | 0 |
| N/A 28C P0 53W / 225W | 11MiB / 4799MiB | 98% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Compute processes: GPU Memory |
| GPU PID Process name Usage |
|=============================================================================|
| No running compute processes found |
+-----------------------------------------------------------------------------+
In caffe installation document.
library version 7.0 and the latest driver version are recommended, but 6.* is fine too
5.5, and 5.0 are compatible but considered legacy
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dutran
commented on August 24, 2024
I googled it. It seems your gcc and nvcc are not compatible. Anyway, try the below hack: http://stackoverflow.com/questions/28985551/caffe-installation-in-ubuntu-14-04 (minor change in caffe/include/caffe/util/math_functions.hpp)
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whjxnyzh123
commented on August 24, 2024
@dutran Thank you very much. I will try it tomorrow. ps: your paper is great.
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whjxnyzh123
commented on August 24, 2024
@dutran In caffe/include/caffe/util/math_functions.hpp I changed
using std::signbit;
DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, y[i] = signbit(x[i]));
to
// using std::signbit;
DEFINE_CAFFE_CPU_UNARY_FUNC(sgnbit, y[i] = std::signbit(x[i]));
so I make test successfully.
but when make runtest......
[kli@node2 C3D]$make runtest
build/test/test_all.testbin 0 --gtest_shuffle
Cuda number of devices: 1
Setting to use device 0
Current device id: 0
Note: Randomizing tests' orders with a seed of 64493 .
[==========] Running 401 tests from 74 test cases.
[----------] Global test environment set-up.
[----------] 2 tests from HingeLossLayerTest/0, where TypeParam = float
[ RUN ] HingeLossLayerTest/0.TestGradientGPU
[ OK ] HingeLossLayerTest/0.TestGradientGPU (288 ms)
[ RUN ] HingeLossLayerTest/0.TestGradientCPU
[ OK ] HingeLossLayerTest/0.TestGradientCPU (17 ms)
[----------] 2 tests from HingeLossLayerTest/0 (305 ms total)
[----------] 5 tests from ConcatLayerTest/0, where TypeParam = float
[ RUN ] ConcatLayerTest/0.TestGPUGradient
[ OK ] ConcatLayerTest/0.TestGPUGradient (222 ms)
[ RUN ] ConcatLayerTest/0.TestSetupNum
[ OK ] ConcatLayerTest/0.TestSetupNum (0 ms)
[ RUN ] ConcatLayerTest/0.TestCPUGradient
[ OK ] ConcatLayerTest/0.TestCPUGradient (165 ms)
[ RUN ] ConcatLayerTest/0.TestCPUNum
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 1.50359e-42
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 0
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 6.9887e-35
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 7.14662e-44
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 7.05658e-35
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 0
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 0
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 0
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 0
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 0
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 6.98088e-35
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 0
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 1.93379e-43
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 0
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 6.98098e-35
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 0
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 1.93379e-43
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 0.000695128
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 1.70212e+25
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 7.15612e+22
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 1.08966e+27
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 4.96403e+28
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 5.07783e+31
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 7.14662e-44
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 7.31572e+28
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 2.60742e-09
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 1.10415e+21
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:96: Failure
Value of: this->blob_bottom_vec_0[0]->data_at(n, c, h, w)
Actual: 2.9643e+29
Expected: this->blob_top_->data_at(n, c, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 2.25609e-43
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 8.08549e-43
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 2.99648e+32
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 3.70227e-35
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 2.82311e+23
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 0
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 4.61141e+24
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 5.02391e-35
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 0.169386
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 0
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 1.10415e+21
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: -nan
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 7.58639e+31
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 1
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 1.7109e+19
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 2.99648e+32
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 1.63856e+19
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 2.82311e+23
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 7.94238e+17
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 4.61141e+24
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 2.63146e+20
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 0.169386
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 2.5754e+20
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 1.10415e+21
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 3.38443e-12
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 7.58639e+31
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 1.46018e-19
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 1.7109e+19
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 3.2487e+33
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 1.63856e+19
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 1.35559e-19
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 7.98741e+17
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 3.09798e+32
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 2.63146e+20
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 2.28424e+20
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 2.5754e+20
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 4.86111e+30
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 3.38443e-12
src/caffe/test/test_concat_layer.cpp:104: Failure
Value of: this->blob_bottom_vec_0[1]->data_at(n, c, h, w)
Actual: 2.46772e+20
Expected: this->blob_top_->data_at(n, c+3, h, w)
Which is: 1.46018e-19
[ FAILED ] ConcatLayerTest/0.TestCPUNum, where TypeParam = float (1 ms)
[ RUN ] ConcatLayerTest/0.TestSetupChannels
[ OK ] ConcatLayerTest/0.TestSetupChannels (0 ms)
[----------] 5 tests from ConcatLayerTest/0 (388 ms total)
[----------] 9 tests from ConvolutionLayerTest/0, where TypeParam = float
[ RUN ] ConvolutionLayerTest/0.TestGPUSimpleConvolution
[ OK ] ConvolutionLayerTest/0.TestGPUSimpleConvolution (1 ms)
[ RUN ] ConvolutionLayerTest/0.TestCPUGradientGroup
[ OK ] ConvolutionLayerTest/0.TestCPUGradientGroup (742 ms)
[ RUN ] ConvolutionLayerTest/0.TestSetup
[ OK ] ConvolutionLayerTest/0.TestSetup (0 ms)
[ RUN ] ConvolutionLayerTest/0.TestCPUSimpleConvolution
[ OK ] ConvolutionLayerTest/0.TestCPUSimpleConvolution (5 ms)
[ RUN ] ConvolutionLayerTest/0.TestGPUSimpleConvolutionGroup
*** glibc detected *** build/test/test_all.testbin: malloc(): memory corruption (fast): 0x0000000006bb9660 ***
======= Backtrace: =========
/lib64/libc.so.6[0x33ed475e66]
/lib64/libc.so.6[0x33ed479c3f]
/lib64/libc.so.6(__libc_malloc+0x71)[0x33ed47a6b1]
/usr/lib64/libstdc++.so.6(_Znwm+0x1d)[0x33f445e15d]
build/test/test_all.testbin[0x4dfd66]
build/test/test_all.testbin[0x54476d]
build/test/test_all.testbin[0x536551]
build/test/test_all.testbin[0x536637]
build/test/test_all.testbin[0x536777]
build/test/test_all.testbin(_ZN7testing8internal12UnitTestImpl11RunAllTestsEv+0x2cf)[0x53b67f]
build/test/test_all.testbin[0x54431d]
build/test/test_all.testbin[0x535b7a]
build/test/test_all.testbin[0x444831]
/lib64/libc.so.6(__libc_start_main+0xfd)[0x33ed41ed5d]
build/test/test_all.testbin[0x444599]
======= Memory map: ========
00400000-007ed000 r-xp 00000000 00:16 589966976 /public/home/kli/C3D/build/test/test_all.testbin
009ec000-00a0d000 rw-p 003ec000 00:16 589966976 /public/home/kli/C3D/build/test/test_all.testbin
00a0d000-00a0e000 rw-p 00000000 00:00 0
0240c000-06bdb000 rw-p 00000000 00:00 0 [heap]
200000000-200100000 rw-s 84accb000 00:05 17983 /dev/nvidiactl
200100000-204100000 ---p 00000000 00:00 0
204100000-204200000 rw-s 100894000 00:05 17983 /dev/nvidiactl
204200000-204300000 ---p 00000000 00:00 0
204300000-204400000 rw-s 100abf000 00:05 17983 /dev/nvidiactl
204400000-204500000 ---p 00000000 00:00 0
204500000-204600000 rw-s 1668d9000 00:05 17983 /dev/nvidiactl
204600000-1900000000 ---p 00000000 00:00 0
33ed000000-33ed020000 r-xp 00000000 103:02 991240 /lib64/ld-2.12.so
33ed21f000-33ed220000 r--p 0001f000 103:02 991240 /lib64/ld-2.12.so
33ed220000-33ed221000 rw-p 00020000 103:02 991240 /lib64/ld-2.12.so
33ed221000-33ed222000 rw-p 00000000 00:00 0
33ed400000-33ed58a000 r-xp 00000000 103:02 991241 /lib64/libc-2.12.so
33ed58a000-33ed78a000 ---p 0018a000 103:02 991241 /lib64/libc-2.12.so
33ed78a000-33ed78e000 r--p 0018a000 103:02 991241 /lib64/libc-2.12.so
33ed78e000-33ed78f000 rw-p 0018e000 103:02 991241 /lib64/libc-2.12.so
33ed78f000-33ed794000 rw-p 00000000 00:00 0
33ed800000-33ed817000 r-xp 00000000 103:02 1826817 /lib64/libpthread-2.12.so
33ed817000-33eda17000 ---p 00017000 103:02 1826817 /lib64/libpthread-2.12.so
33eda17000-33eda18000 r--p 00017000 103:02 1826817 /lib64/libpthread-2.12.so
33eda18000-33eda19000 rw-p 00018000 103:02 1826817 /lib64/libpthread-2.12.so
33eda19000-33eda1d000 rw-p 00000000 00:00 0
33edc00000-33edc02000 r-xp 00000000 103:02 1826818 /lib64/libdl-2.12.so
33edc02000-33ede02000 ---p 00002000 103:02 1826818 /lib64/libdl-2.12.so
33ede02000-33ede03000 r--p 00002000 103:02 1826818 /lib64/libdl-2.12.so
33ede03000-33ede04000 rw-p 00003000 103:02 1826818 /lib64/libdl-2.12.so
33ee000000-33ee083000 r-xp 00000000 103:02 1826820 /lib64/libm-2.12.so
33ee083000-33ee282000 ---p 00083000 103:02 1826820 /lib64/libm-2.12.so
33ee282000-33ee283000 r--p 00082000 103:02 1826820 /lib64/libm-2.12.so
33ee283000-33ee284000 rw-p 00083000 103:02 1826820 /lib64/libm-2.12.so
33ee400000-33ee415000 r-xp 00000000 103:02 1826819 /lib64/libz.so.1.2.3
33ee415000-33ee614000 ---p 00015000 103:02 1826819 /lib64/libz.so.1.2.3
33ee614000-33ee615000 r--p 00014000 103:02 1826819 /lib64/libz.so.1.2.3
33ee615000-33ee616000 rw-p 00015000 103:02 1826819 /lib64/libz.so.1.2.3
33ee800000-33ee807000 r-xp 00000000 103:02 1826840 /lib64/librt-2.12.so
33ee807000-33eea06000 ---p 00007000 103:02 1826840 /lib64/librt-2.12.so
33eea06000-33eea07000 r--p 00006000 103:02 1826840 /lib64/librt-2.12.so
33eea07000-33eea08000 rw-p 00007000 103:02 1826840 /lib64/librt-2.12.so
33eec00000-33eec16000 r-xp 00000000 103:02 1826828 /lib64/libresolv-2.12.so
33eec16000-33eee16000 ---p 00016000 103:02 1826828 /lib64/libresolv-2.12.so
33eee16000-33eee17000 r--p 00016000 103:02 1826828 /lib64/libresolv-2.12.so
33eee17000-33eee18000 rw-p 00017000 103:02 1826828 /lib64/libresolv-2.12.so
33eee18000-33eee1a000 rw-p 00000000 00:00 0
33ef000000-33ef01d000 r-xp 00000000 103:02 1826829 /lib64/libselinux.so.1
33ef01d000-33ef21c000 ---p 0001d000 103:02 1826829 /lib64/libselinux.so.1
33ef21c000-33ef21d000 r--p 0001c000 103:02 1826829 /lib64/libselinux.so.1
33ef21d000-33ef21e000 rw-p 0001d000 103:02 1826829 /lib64/libselinux.so.1
33ef21e000-33ef21f000 rw-p 00000000 00:00 0
33ef400000-33ef515000 r-xp 00000000 103:02 1826846 /lib64/libglib-2.0.so.0.2800.8
33ef515000-33ef715000 ---p 00115000 103:02 1826846 /lib64/libglib-2.0.so.0.2800.8
33ef715000-33ef716000 rw-p 00115000 103:02 1826846 /lib64/libglib-2.0.so.0.2800.8
33ef716000-33ef717000 rw-p 00000000 00:00 0
33ef800000-33ef804000 r-xp 00000000 103:02 1826851 /lib64/libgthread-2.0.so.0.2800.8
33ef804000-33efa03000 ---p 00004000 103:02 1826851 /lib64/libgthread-2.0.so.0.2800.8
33efa03000-33efa04000 rw-p 00003000 103:02 1826851 /lib64/libgthread-2.0.so.0.2800.8
33efc00000-33efc4a000 r-xp 00000000 103:02 1826852 /lib64/libgobject-2.0.so.0.2800.8
33efc4a000-33efe49000 ---p 0004a000 103:02 1826852 /lib64/libgobject-2.0.so.0.2800.8
33efe49000-33efe4b000 rw-p 00049000 103:02 1826852 /lib64/libgobject-2.0.so.0.2800.8
33efe4b000-33efe4c000 rw-p 00000000 00:00 0 make: *** [runtest] Aborted (core dumped)
[kli@node2 C3D]$
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dutran
commented on August 24, 2024
@whjxnyzh Try it again now. Please make clean, then compile again, then test. Thanks
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whjxnyzh123
commented on August 24, 2024
@dutran Great! it worked!!! Thank you very much!!!
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dutran
commented on August 24, 2024
Glad to hear! Cheers.
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Related Issues (20)
- Random crop is bad for this model? HOT 1
- Features are not being extracted for all the frames HOT 5
- static link error with anaconda HOT 1
- Couldn't found the result in the output folder HOT 1
- Make all fails at examples/mnist/convert_mnist_data.cpp HOT 2
- Error with no data at frame xx always appears while set sample_rate > 1
- Check Failed while training from scratch in 1.0 HOT 1
- using more gpu HOT 1
- ERROR : video_data_layer.cpp:252] Check failed: ReadImageSequenceToVolumeDatum
- File not found: conv3d_deepnetA_sport1m_iter_1900000 HOT 2
- [C3D v.1.0] Failed to execute: sh c3d_sport1m_feature_extraction_frm.sh HOT 4
- C3D Google Colab - Extract feature from raw video (mp4)
- Action similarity labeling - Similarity is NAN
- compilation error in C3D v1.0
- recipe for target '.build_release/src/caffe/proto/caffe.pb.o' failed,...Error while make in both C3D1.0 and 1.1
- GPU Configuration Details
- image_io.cpp:339] Could not open or find file HOT 1
- _make clean
- F0905 11:26:43.151420 12241 layer_factory.hpp:81] Check failed: registry.count(type) == 1 (0 vs. 1) Unknown layer type: Video_Data
- Have some error for running extract_C3D_feature.py in C3D-v1.0
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