Hi, I am a beginner in deep learning. Some problem occurred when I was running the code. First, I use the command 「 tar -xvf encnet_jpu_res50_pcontext.pth.tar 」 to extract the tar file, but it fails. Second, if i successfully extract the file and get checkpoint, which file should I put my checkpoint in ? Where should I extract my checkpoint file to? Thank You!

Why does the code still execute without error when I delete the JPU module?（/FastFCN/encoding/nn/customize.py），I also can train model？ These are my commands :(I did load the JPU module) CUDA_VISIBLE_DEVICES=4,5,6,7 python train.py --dataset pcontext --model encnet --jpu --aux --se-loss --backbone resnet101 --checkname encnet_res101_pcontext

I think this problem is caused by my previous pytorch problem,so maybe i have to solve pytorch first.Could you give me some help? gcc:4.8 pytorch:1.1.0 python:3.5 and how could i change the pytorch version to 1.0.0?pip install torch==1.0?

Thanks for your work. I have tried this script: https://github.com/wuhuikai/FastFCN/blob/master/experiments/segmentation/scripts/encnet_res50_pcontext.sh with the hardware and software: 4xTitanXp, Ubuntu16.04, CUDA9.0, PyToch1.0

But I can't reproduce the performance reported in your paper. I got pixAcc: 0.7747, mIoU: 0.4785 for single-scale, and pixAcc: 0.7833, mIoU: 0.4898 for multi-scale.

I would appreciate your help. Thanks for your consideration.

Hi, right now FastFCN has been supported by MMSegmentation. We do find using JPU with smaller feature maps from backbone could get similar or higher performance than original models with larger feature maps.

There is still something to do for us, for example, we do not find obviously improvement about FPS in our implementation, thus we would try to figure it out in the future.

Anyway, thanks for your work and hope more people from community could use FastFCN.

Best,

Hi, thanks for your work. I try to run your code to train a model on the pascalContext dataset.But I got the following error: RuntimeError: Failed downloading url https://hangzh.s3.amazonaws.com/encoding/models/resnet50-ebb6acbb.zip I find the problem is I can not download the pretrained model. I find the author no longer provide the pretrained resnet model. https://github.com/zhanghang1989/PyTorch-Encoding/issues/273

So, How can I solve this problem. Thanks for your consideration.

Hello!

When I run test_single_image.py, I tried to set resume as path of resnet101-2a57e44d.pth and encountered an error.

File "G:/gitfolder/FastFCN/experiments/segmentation/test_single_image.py", line 43, in test model.load_state_dict(checkpoint['state_dict'], strict=False) KeyError: 'state_dict

I doubted that there existed a problem with "resume". Waiting for your reply.

Thank you!

Hi, first thank you for the great work. I just checked the codes and also had run some scripts. I am confused with the final loss which is composited with three individual losses. could you tell what is the se-loss and the aux-loss used for.

Download links for the backbone do not seem to work anymore.

I've tested with Resnet50 (https://hangzh.s3.amazonaws.com/encoding/models/resnet50-ebb6acbb.zip) and Resnet 101 (https://hangzh.s3.amazonaws.com/encoding/models/resnet101-2a57e44d.zip) too.

I also tried to use torchivision weights instead, but I got matching errors when trying to load them.

Could you consider reuploading the weights? That would be very helpful!

我執行以下 command 準備 train model 但是發生 segmentation fault 有人有這個問題嗎 ? 謝謝幫忙 !

run : CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset pcontext --model encnet --jpu --aux --se-loss --backbone resnet101 --checkname encnet_res101_pcontext

crashed : Using poly LR Scheduler! Starting Epoch: 0 Total Epoches: 80 0%| | 0/312 [00:00<?, ?it/s] =>Epoches 0, learning rate = 0.0010, previous best = 0.0000 Segmentation fault

//------------ Nvidia GPU : Tesla P100-PCIE 16G x 4 CPU : GenuineIntel x 18 , Memory 140G totally

Hi, i have designed this SPP module for my network. But i am also interested in your work to replace my his module with JPU. Would you like to give me any suggestions? here is my implementation

class SPP(nn.Module): def init(self, pool_sizes): super(SPP, self).init() self.pool_sizes = pool_sizes

def forward(self, x):
    h, w = x.shape[2:]
    k_sizes = []
    strides = []
    for pool_size in self.pool_sizes:
        k_sizes.append((int(h / pool_size), int(w / pool_size)))
        strides.append((int(h / pool_size), int(w / pool_size)))

    spp_sum = x

    for i in range(len(self.pool_sizes)):
        out = F.avg_pool2d(x, k_sizes[i], stride=strides[i], padding=0)
        out = F.upsample(out, size=(h, w), mode="bilinear")
        spp_sum = spp_sum + out

    return spp_sum  

Copy Resnest and xception65 from Pytorch-Encoding, and xception65 only can be used without pretrained models.

Pls be careful as there are many changes!!

I test it on my own server, and everything seems ok. As a caution, maybe you could test it by yourself first.My FastFCN

I don't change the Readme.md and *.sh. Maybe you can rectify it if you agree this request.

If the server resources are not tight, I will run the encnet+jpu+resnest101+pcontext and encnet+jpu_x+resnest101+pcontext, I will share you the results at issues or pull another request about Readme.md with my pth.tar.

Thanks for your work again.