Open manwu1994 opened 3 years ago
fangwei123456
commented
3 years ago No, I have not tried CIFAR. The origin resnet is designed for ImageNet, which is much complex than CIFAR. Using the origin resnet on CIFAR may cause overfitting. I suggest that you use the resnet structure in https://github.com/weiaicunzai/pytorch-cifar100 and change residual block to sew block.
JumpAndShoot
commented
2 years ago Which version of torch do u use? I wanna replicate your code,but I get many bug.
fangwei123456
commented
2 years ago Which version of torch do u use? I wanna replicate your code,but I get many bug.
# Name Version Build Channel
pytorch 1.8.0 py3.8_cuda11.1_cudnn8.0.5_0 pytorch
torchaudio 0.8.0 py38 pytorch
torchvision 0.9.0 py38_cu111 pytorch@JumpAndShoot Codes in this repo with SpikingJelly need to build CUDA extension for neurons.
JumpAndShoot
commented
2 years ago Which version of torch do u use? I wanna replicate your code,but I get many bug.
# Name Version Build Channel pytorch 1.8.0 py3.8_cuda11.1_cudnn8.0.5_0 pytorch torchaudio 0.8.0 py38 pytorch torchvision 0.9.0 py38_cu111 pytorch@JumpAndShoot Codes in this repo with SpikingJelly need to build CUDA extension for neurons.
How about the spikingjelly?whether it is 0.0.0.0.6
fangwei123456
commented
2 years ago It is a speicific verison, not 0.0.0.0.6:
git reset --hard 2958519df84ad77c316c6e6fbfac96fb2e5f59a3
JumpAndShoot
commented
2 years ago May I get your email or qq or WeChat
It is a speicific verison, not 0.0.0.0.6:
git reset --hard 2958519df84ad77c316c6e6fbfac96fb2e5f59a3
May I get your email or qq or WeChat
fangwei123456
commented
2 years ago Hi, I suggest to use Github, and our discussion can be available and helpful to others.
git clone https://github.com/fangwei123456/spikingjelly.git
cd spikingjelly
git reset --hard 2958519df84ad77c316c6e6fbfac96fb2e5f59a3
python setup.py installWhat is the error message?
JumpAndShoot
commented
2 years ago Hi, I suggest to use Github, and our discussion can be available and helpful to others.
git clone https://github.com/fangwei123456/spikingjelly.git cd spikingjelly git reset --hard 2958519df84ad77c316c6e6fbfac96fb2e5f59a3 python setup.py installWhat is the error message?
If my system is windows,I download the spkingjelly.where I need to put the file to?
JumpAndShoot
commented
2 years ago Hi, I suggest to use Github, and our discussion can be available and helpful to others.
git clone https://github.com/fangwei123456/spikingjelly.git cd spikingjelly git reset --hard 2958519df84ad77c316c6e6fbfac96fb2e5f59a3 python setup.py installWhat is the error message?
If my system is windows,I download the spkingjelly.where I need to put the file to?
And where can I download the special version
fangwei123456
commented
2 years ago Download SpikingJelly's source codes:
git clone https://github.com/fangwei123456/spikingjelly.git
CD to the root dir:
cd spikingjelly
Revert to the specific version:
git reset --hard 2958519df84ad77c316c6e6fbfac96fb2e5f59a3
Install the specific version:
python setup.py install
BarCodeReader
commented
2 years ago Hi,
Firstly thanks for your contribution.
I tested the code on dvsgestrue...and the best accuracy(top1) I can obtain here is 95.48 instead of 97 in the paper...which i think is a relatively large gap...
thoughts to provide a docker image so everyone is on the same platform?
fangwei123456
commented
2 years ago @BarCodeReader Sorry, I do not use the docker image. The identical results (e.g., the same tensorboard logs) can not be guaranteed (see https://pytorch.org/docs/stable/notes/randomness.html). But your result 95.48 is too low. In most cases, you will get nearly 97 top1 accuracy.
I just train a network with new version of SpikingJelly and LIF neuron (you only need to change cext.neuron to neuron.MultiStepNeuron(backend='cupy') in smodel.py. The top1 accuracy is 97.22, which only has one more wrong sample than the paper. Here is the training logs, models and codes. You can try it with the new version of SpikingJelly (I recommend to install from source codes, rather than pip).
dvsgesture.zip
BarCodeReader
commented
2 years ago Hi @fangwei123456, thanks for your zip file.
I reinstalled the SpikingJelly from source, and its latest now. The MultiStepLIFNode provided in your zip is better than MultiStepParametricLIFNode in the repo, and now my best accuracy is 96.88, which is 1% behind your best score, :)
To have a fair comparison, I ran the experiment 5 times with arbitrary random seeds, without using amp mode.
Below is the log compared with yours (with smoothing factor 0.6 in tensorboard). The top navy color one is from your log file, which has 97.22.
To me, it seems like it's my machine's issue, maybe some libraries version, etc...and sorry to bother again...do you mind sharing your "pip list", so that I can try to find the reason a bit?
fangwei123456
commented
2 years ago @BarCodeReader
(pytorch-env) wfang@Precision-5820-Tower-X-Series:~$ conda list
# packages in environment at /home/wfang/anaconda3/envs/pytorch-env:
#
# Name Version Build Channel
_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 1_llvm conda-forge
absl-py 0.13.0 pypi_0 pypi
blas 1.0 mkl defaults
blessings 1.7 pypi_0 pypi
bzip2 1.0.8 h7f98852_4 conda-forge
ca-certificates 2021.10.26 h06a4308_2 defaults
cachetools 4.2.2 pypi_0 pypi
certifi 2021.10.8 py39h06a4308_0 defaults
chardet 4.0.0 pypi_0 pypi
cudatoolkit 11.3.1 h2bc3f7f_2 defaults
cupy-cuda111 9.4.0 pypi_0 pypi
cycler 0.10.0 pypi_0 pypi
docopt 0.6.2 pypi_0 pypi
fastrlock 0.6 pypi_0 pypi
ffmpeg 4.3 hf484d3e_0 pytorch
flatbuffers 2.0 pypi_0 pypi
freetype 2.10.4 h0708190_1 conda-forge
gmp 6.2.1 h58526e2_0 conda-forge
gnutls 3.6.13 h85f3911_1 conda-forge
google-auth 1.32.0 pypi_0 pypi
google-auth-oauthlib 0.4.4 pypi_0 pypi
gpustat 0.6.0 pypi_0 pypi
grpcio 1.38.1 pypi_0 pypi
idna 2.10 pypi_0 pypi
joblib 1.0.1 pypi_0 pypi
jpeg 9b h024ee3a_2 defaults
kiwisolver 1.3.1 pypi_0 pypi
lame 3.100 h7f98852_1001 conda-forge
lcms2 2.12 h3be6417_0 defaults
ld_impl_linux-64 2.35.1 hea4e1c9_2 conda-forge
libffi 3.3 h58526e2_2 conda-forge
libgcc-ng 9.3.0 h2828fa1_19 conda-forge
libiconv 1.16 h516909a_0 conda-forge
libpng 1.6.37 h21135ba_2 conda-forge
libstdcxx-ng 9.3.0 h6de172a_19 conda-forge
libtiff 4.2.0 h85742a9_0 defaults
libuv 1.40.0 h7b6447c_0 defaults
libwebp-base 1.2.0 h7f98852_2 conda-forge
llvm-openmp 11.1.0 h4bd325d_1 conda-forge
lz4-c 1.9.3 h9c3ff4c_0 conda-forge
markdown 3.3.4 pypi_0 pypi
matplotlib 3.4.2 pypi_0 pypi
mkl 2021.2.0 h726a3e6_389 conda-forge
mkl-service 2.4.0 py39h3811e60_0 conda-forge
mkl_fft 1.3.0 py39h42c9631_2 defaults
mkl_random 1.2.2 py39hde0f152_0 conda-forge
ncurses 6.2 h58526e2_4 conda-forge
nettle 3.6 he412f7d_0 conda-forge
ninja 1.10.2 h4bd325d_0 conda-forge
numpy 1.20.2 py39h2d18471_0 defaults
numpy-base 1.20.2 py39hfae3a4d_0 defaults
nvidia-ml-py3 7.352.0 pypi_0 pypi
oauthlib 3.1.1 pypi_0 pypi
olefile 0.46 pyh9f0ad1d_1 conda-forge
onnx 1.8.0 pypi_0 pypi
onnxruntime 1.9.0 pypi_0 pypi
onnxruntime-gpu 1.9.0 pypi_0 pypi
openh264 2.1.1 h780b84a_0 conda-forge
openssl 1.1.1l h7f8727e_0 defaults
pillow 8.2.0 py39he98fc37_0 defaults
pip 21.1.3 pyhd8ed1ab_0 conda-forge
pqi 2.0.6 pypi_0 pypi
protobuf 3.8.0 pypi_0 pypi
psutil 5.8.0 pypi_0 pypi
pyasn1 0.4.8 pypi_0 pypi
pyasn1-modules 0.2.8 pypi_0 pypi
pynvml 11.4.1 pypi_0 pypi
pyparsing 2.4.7 pypi_0 pypi
python 3.9.5 h49503c6_0_cpython conda-forge
python-dateutil 2.8.1 pypi_0 pypi
python_abi 3.9 2_cp39 conda-forge
pytorch 1.10.1 py3.9_cuda11.3_cudnn8.2.0_0 pytorch
pytorch-mutex 1.0 cuda pytorch
rarfile 4.0 pypi_0 pypi
readline 8.1 h46c0cb4_0 conda-forge
requests 2.25.1 pypi_0 pypi
requests-oauthlib 1.3.0 pypi_0 pypi
rsa 4.7.2 pypi_0 pypi
scikit-learn 0.24.2 pypi_0 pypi
scipy 1.7.0 pypi_0 pypi
setuptools 57.0.0 pypi_0 pypi
six 1.16.0 pyh6c4a22f_0 conda-forge
sklearn 0.0 pypi_0 pypi
spikingjelly 0.0.0.0.9 dev_0 <develop>
sqlite 3.36.0 h9cd32fc_0 conda-forge
tbb 2021.2.0 h4bd325d_1 conda-forge
tensorboard 2.5.0 pypi_0 pypi
tensorboard-data-server 0.6.1 pypi_0 pypi
tensorboard-plugin-wit 1.8.0 pypi_0 pypi
threadpoolctl 2.2.0 pypi_0 pypi
tk 8.6.10 h21135ba_1 conda-forge
torchaudio 0.10.1 py39_cu113 pytorch
torchvision 0.11.2 py39_cu113 pytorch
tqdm 4.61.1 pypi_0 pypi
typing_extensions 3.10.0.0 pyha770c72_0 conda-forge
tzdata 2021a he74cb21_0 conda-forge
urllib3 1.26.6 pypi_0 pypi
werkzeug 2.0.1 pypi_0 pypi
wheel 0.36.2 pyhd3deb0d_0 conda-forge
xz 5.2.5 h516909a_1 conda-forge
zlib 1.2.11 h516909a_1010 conda-forge
zstd 1.4.9 ha95c52a_0 conda-forge
(pytorch-env) wfang@Precision-5820-Tower-X-Series:~$ nvidia-smi
Fri Feb 18 09:34:35 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 465.19.01 Driver Version: 465.19.01 CUDA Version: 11.3 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... On | 00000000:17:00.0 Off | N/A |
| 19% 33C P8 16W / 250W | 0MiB / 11011MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
| 1 NVIDIA GeForce ... On | 00000000:B3:00.0 Off | N/A |
| 18% 34C P8 21W / 250W | 0MiB / 11019MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
You can also compare with the origin logs https://github.com/fangwei123456/Spike-Element-Wise-ResNet/tree/main/origin_logs/dvsgesture/DVSGNetSEW32_b16_lr0.001_T16_Ttrain12_zi_steplr64_0.1_sgd_cnf_add_logs . But the origin env of this machine is no longer available because I have updated pytorch. This pt file checkpoint_max_val_acc1.zip might be the origin model file.
BarCodeReader
commented
2 years ago @fangwei123456 thanks so much for your help and your kindness!
have a nice day!
mountains-high
commented
2 years ago It is a speicific verison, not 0.0.0.0.6:
git reset --hard 2958519df84ad77c316c6e6fbfac96fb2e5f59a3
Hello! Thank you for your work. Currently, I am using this specific version. I wanted to use pre-trained Imagenet weights. Thus, I need the network architecture, because I found that weights were saved via ['model'] (state_dict). Could you help me please? I faced with this error message:
File "test.py", line 40, in <module>
import sew_resnet, spiking_resnet
File "/home/Desktop/SEW/spikingjelly/sew_resnet.py", line 4, in <module>
from spikingjelly.cext import neuron as cext_neuron
File "/home/Desktop/SEW/spikingjelly/spikingjelly/cext/neuron.py", line 5, in <module>
import _C_neuron
ModuleNotFoundError: No module named '_C_neuron'Thank you for your time
fangwei123456
commented
2 years ago @mountains-high
_C_neuron is a old CUDA module used in SpikingJelly. You need to use git reset --hard 2958519df84ad77c316c6e6fbfac96fb2e5f59a3 and install SpikingJelly. Refer to this: https://github.com/fangwei123456/spikingjelly/issues/46 .
mountains-high
commented
2 years ago Thank you for your response. I'm sorry to ask an unrelated question under this opened issue.
Traceback (most recent call last):
File "setup.py", line 5, in <module>
import setuptools
ImportError: No module named setuptoolsI tried to run "python setup.py install" before git reset --hard 2958519df84ad77c316c6e6fbfac96fb2e5f59a3 and after. But the same error message occurs
fangwei123456
commented
2 years ago pip install setuptools
mountains-high
commented
2 years ago Thank you. I tried that way, I thought it might be spikingjelly's setuptools.
fangwei123456
commented
2 years ago I suggest that you use anaconda to create a virtual environment, and install pytorch, spikingjelly into this environment to avoid the permission and dependency problem.
mountains-high
commented
2 years ago Thank you very much for your considerations I will try.
I try to replicate your code over Cifar100 and Cifar10. But I can not achieve acceptable accuracy. Did you perform the spike-element-wise-ResNet over Cifar100?