The poolnet from swoook

Request a processing time on desktop/server CPU

We need a model for real-time SOD on CPU
First of all, define a baseline
We currently use backseason/PoolNet
Measure its processing time on desktop/server CPU
Refer to #6 from swoook/ucnet (github) for more details

We can't achieve the MAE stated in the paper when we train PoolNet ourselves

Issue description

We trained PoolNet with pre-trained weights on ImageNet ourselves on union set of MSRA-B and HKU-IS datasets
However, the MAE achived by ours is too lower than one stated in the paper

Code example

Configuration defined in launch.json for vscode

        {
            "name": "Python: Test system environments",
            "type": "python",
            "request": "launch",
            "program": "${workspaceRoot}/main.py",
            "console": "integratedTerminal",
            "args": [
                "--mode", "train",
                "--arch", "resnet",
                "--model", "/data/swook/models/poolnet/resnet50_caffe.pth",
                "--pretrained_model", "/data/swook/models/poolnet/resnet50_caffe.pth",
                "--train_root", "/data/swook/dataset/poolnet/msrab_hkuis",
                "--train_list", "/data/swook/dataset/poolnet/msrab_hkuis/msrab_hkuis_train_no_small.lst",
                "--test_root", "/data/swook/dataset/duts/DUTS-SOD100K/DUTS-TE",
                "--test_list", "/data/swook/dataset/duts/DUTS-SOD100K/DUTS-TE/test.lst",
                // "--cpu"
                ]
        },

System Info

PyTorch version: 1.8.1
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A

OS: Ubuntu 16.04.3 LTS (x86_64)
GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609
Clang version: Could not collect
CMake version: version 3.5.1

Python version: 3.8 (64-bit runtime)
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: 
GPU 0: GeForce RTX 2080 Ti
GPU 1: GeForce RTX 2080 Ti

Nvidia driver version: 440.33.01
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A

Versions of relevant libraries:
[pip3] numpy==1.20.2
[pip3] torch==1.8.1
[pip3] torchvision==0.9.1
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               10.2.89              hfd86e86_1  
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] mkl                       2021.2.0           h06a4308_296  
[conda] mkl-service               2.3.0            py38h27cfd23_1  
[conda] mkl_fft                   1.3.0            py38h42c9631_2  
[conda] mkl_random                1.2.1            py38ha9443f7_2  
[conda] numpy                     1.20.2           py38h2d18471_0  
[conda] numpy-base                1.20.2           py38hfae3a4d_0  
[conda] pytorch                   1.8.1           py3.8_cuda10.2_cudnn7.6.5_0    pytorch
[conda] torchvision               0.9.1                py38_cu102    pytorch

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poolnet's Issues

Request a processing time on desktop/server CPU

We can't achieve the MAE stated in the paper when we train PoolNet ourselves

Issue description

Code example

System Info

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