Comments (8)
your result is not good
Area Under the AR vs AN curve should be about 68%.
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your result is not good
Area Under the AR vs AN curve should be about 68%.
@lijiannuist thanks for your quick reply, but why.... I didn't modify any single line of your codes....
my software environment:
Ubuntu16.04, Titan xp*4, Driver Version: 418.87.00 CUDA Version: 10.1 Cudnn 7.6.4
Tensorflow-gpu 1.9.0, python 3.6.9, what else do i need to provide for comparasion...?
I think the AR@100 is also very low because the result in the paper is 76.65 ?
Do you have any advice to repeat your experiment results?
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@Light-- Hi, I rerun the code and got the results:
Area Under the AR vs AN curve: 68.37602852441032%
Do you try to recompile the proposal generation layer?
We did not compile the layer and test it in CUDA 10.1.
Our environment is CUDA 9.0.
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We did not compile the layer and test it in CUDA 10.1.
-
what do you mean by "recompile the PFG" layer"? Do i need to do anything else before run the code except for setting up the environment and preparing the dataset?
-
sorry, i didn't make myself clear. The CUDA Driver version(what the
nvidia-smi
shows) of my environment is 10.1, but the CUDA runtime version(what thenvcc -V
shows) is 9.0. All the outputs ofcat /usr/local/cuda/version.txt
,nvcc -V
,stat /usr/local/cuda
said my CUDA is 9.0.
from actiondetection-dbg.
cd custom_op/src
make
to recompile our proposal feature generation operation.
If the result is still incorrect, you can try to modify the file custom_op/src/Makefile
as below:
TF_CFLAGS:=$(shell python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_compile_flags()))')
TF_LFLAGS:=$(shell python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_link_flags()))')
CFLAGS = ${TF_CFLAGS} -fPIC -O2 -std=c++11
LDFLAGS = -shared ${TF_LFLAGS}
CUDA_ARCH =
all:
nvcc -std=c++11 -O2 -c -o prop_tcfg_op.cu.o prop_tcfg_op.cu.cc \
$(TF_CFLAGS) $(LDFLAGS) -I/usr/local -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC \
-DNDEBUG --expt-relaxed-constexpr -w $(CUDA_ARCH)
g++ $(CFLAGS) -o ../prop_tcfg.so prop_tcfg_op.cc prop_tcfg_op.cu.o \
$(LDFLAGS) -L/usr/local/cuda/lib64 -D GOOGLE_CUDA=1 \
-I/usr/local -I/usr/local/cuda/include -I/usr/local/cuda/targets/x86_64-linux/include \
-L/usr/local/cuda/targets/x86_64-linux/lib -lcudart
from actiondetection-dbg.
@lijiannuist @linchuming
Many thanks. The result seems correct now after i recompile PFG and re-run the auto_run.sh
:
[INIT] Loaded annotations from validation subset.▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒| 590/590 [02:55<00:00, 3.68it/s]
Number of ground truth instances: 7292
Number of proposals: 472700
Fixed threshold for tiou score: [0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95]
[RESULTS] Performance on ActivityNet proposal task.
Area Under the AR vs AN curve: 68.37602852441032%
AR@1 is 0.30814591332967634
AR@5 is 0.4914838178826111
AR@10 is 0.5723532638507954
AR@100 is 0.767937465715853
But may i ask what have changed after recompile the PFG layer? Did the layer structure change? or anything else changed....
PS. the AR@100
I got here is 0.767937465715853, but the published paper is 76.65, was my result correct? Since the Area Under the AR vs AN curve
result(68.37602852441032%) is also higher than that in the paper(68.23), should we ignore the difference after the decimal point in the result by default?
from actiondetection-dbg.
Because of randomness, you can ignore the difference after the decimal point in the result.
from actiondetection-dbg.
cd custom_op/src
maketo recompile our proposal feature generation operation.
If the result is still incorrect, you can try to modify the filecustom_op/src/Makefile
as below:TF_CFLAGS:=$(shell python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_compile_flags()))') TF_LFLAGS:=$(shell python -c 'import tensorflow as tf; print(" ".join(tf.sysconfig.get_link_flags()))') CFLAGS = ${TF_CFLAGS} -fPIC -O2 -std=c++11 LDFLAGS = -shared ${TF_LFLAGS} CUDA_ARCH = all: nvcc -std=c++11 -O2 -c -o prop_tcfg_op.cu.o prop_tcfg_op.cu.cc \ $(TF_CFLAGS) $(LDFLAGS) -I/usr/local -D GOOGLE_CUDA=1 -x cu -Xcompiler -fPIC \ -DNDEBUG --expt-relaxed-constexpr -w $(CUDA_ARCH) g++ $(CFLAGS) -o ../prop_tcfg.so prop_tcfg_op.cc prop_tcfg_op.cu.o \ $(LDFLAGS) -L/usr/local/cuda/lib64 -D GOOGLE_CUDA=1 \ -I/usr/local -I/usr/local/cuda/include -I/usr/local/cuda/targets/x86_64-linux/include \ -L/usr/local/cuda/targets/x86_64-linux/lib -lcudart
have you solved with the problem?
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Related Issues (20)
- The original ActivityNet TSN feature HOT 1
- 能否放一下thumos上的实验?
- features csv different from provided
- HOW ABOUT THE SPEED? HOT 1
- THUMOS14
- Question about PFG layer HOT 2
- About the provided features!! HOT 1
- run error HOT 1
- proposal HOT 1
- Segmentation fault when define the whole model with PFG layer compiled HOT 2
- why the "annotations" of testing is none?
- Compile tensorflow-version proposal feature generation layers HOT 1
- Can you release the THUMOS14 features? HOT 1
- gcc 7.5 可以?
- 作者能重新上下谷歌云特征文件吗?微云下载太慢了
- Can you release the THUMOS14 or ActivityNet1.3 features? HOT 1
- 关于连接失效问题。 HOT 1
- Thumos code
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- code for Thumos
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