When I lode the dataset,it raised Exception: Check dataroot.I changed the path in pix2pix_class.py and the dataroot in the command,but it still didn't work.

Hi, @hezhangsprinter . When I use the code to test my test dataset, it seems to resize the image to 512512. I tried to modify the orignalSize and imageSize of inputs, while there occurred some errors。I want to figure out whether the code can only tackle with image size of 512512, and the other size of image must resize/crop to 512*512. Because I want to compare your work with ours, I must test the other images. I am looking forward to your answer, thanks a lot.

when I run "python train_rain_class.py --dataroot ./facades/DID-MDN-training/Rain_Medium/train2018new --exp ./check_class" got this error. And I look into the train_rain_class.py. And I didn't find the definition of 'optimizerD'.

Hello, thanks for your work first. When I run test.py, there is an error RuntimeError: cuda runtime error (2) : out of memory at /pytorch/aten/src/THC/generic/THCStorage.cu:58 in File "test.py", line 215, in output = residue_net(val_inputv, label_cpu)

How can I handle this problem?

Hi there. I was following the Readme file to setup the environment, including ubuntu+python2.7 etc. And I have downloaded all pretrained models and put them in the corresponding directory. But when I tried to run the test.py file using the command python test.py --dataroot ./facades/github --valDataroot ./facades/github --netG ./pre_trained/netG_epoch_9.pth, an error occurred and said TypeError: __init__() got an unexpected keyword argument 'pretrained' Can you or anyone help me out? Really appreciate it!

Hi! I want to retrain DID-MDN using my dataset (without density-labels). I hope to only retrain multi-stream dense network instread of the whole network. Thank you very much!

When i run test.py ,the error came out:

Traceback (most recent call last): File "test.py", line 156, in net_label.load_state_dict(torch.load('./classification/netG_epoch_9.pth')) File "E:\Anaconda3\envs\pytorch\lib\site-packages\torch\nn\modules\module.py", line 522, in load_state_dict.format(name)) KeyError: 'unexpected key "conv_refin.weight" in state_dict'.

How can I solve the problem?

Thanks for sharing for your code. I have one problem： the readme show this method can process light、medium、heavy rain, but the training example is only Rain_Medium，if i want to train light、medium、heavy rain，Do I python derain_train_2018.py --dataroot ./facades/DID-MDN-training/Rain_Light/train2018new --valDataroot ./facades/github --exp ./check --netG ./pre_trained/netG_epoch_9.pth. python derain_train_2018.py --dataroot ./facades/DID-MDN-training/Rain_Heavy/train2018new --valDataroot ./facades/github --exp ./check --netG ./pre_trained/netG_epoch_9.pth. python derain_train_2018.py --dataroot ./facades/DID-MDN-training/Rain_Medium/train2018new --valDataroot ./facades/github --exp ./check --netG ./pre_trained/netG_epoch_9.pth. ？ waiting for your answers.thanks very much！

I am sorry to I am not understand the code. In derain_dense.py, I find that the 'Dense_base_down0' code is: x11 = self.upsample(self.relu((self.conv11(x1))), size=shape_out) x21 = self.upsample(self.relu((self.conv11(x1))), size=shape_out) x31 = self.upsample(self.relu((self.conv11(x1))), size=shape_out) x41 = self.upsample(self.relu((self.conv11(x1))), size=shape_out) x51 = self.upsample(self.relu((self.conv11(x1))), size=shape_out) why did you do the same operation? Does it conv to x1,x2,x3,x4,x5??

cheers

Guidance from README.md

python derain_train_2018.py  --dataroot ./facades/DID-MDN-training/Rain_Medium/train2018new  --valDataroot ./facades/github --exp ./check --netG ./pre_trained/netG_epoch_9.pth.
Make sure you download the training sample and put in the right folder

Code Snippet in derain_train_2018.py

netG=net.Dense_rain()

if opt.netG != '':
  netG.load_state_dict(torch.load(opt.netG))
print(netG)

Why fine-tune on top of a pre-trained model?

from test.py, it seems that there three sub-networks (Dense_rain_residual, vgg19ca, Dense_rain) are actually used, which are completely different architecture. It was never mentioned in the paper.

Seemingly, there is three training stage according to your paper:

• Stage 1: training residual extraction network only
• Stage 2: training density classification network based on residue output from the network in Stage 1
• Stage 3: joint training of both networks

quote: "To efficiently train the classifier, a two-stage training protocol is leveraged. A residual feature extraction network is firstly trained to estimate the residual part of the given rainy image, then a classification sub-network is trained using the estimated residual as the input and is optimized via the ground truth labels (rain-density). Finally, the two stages (feature extraction and classification) are jointly optimized."

It seems that the code of Stage 1 corresponds to file derain_residual.py, and Stage 3 corresponds to file train_rain_class.py (which directly loads two pre-trained models). How to get the density classifier trained in Stage 2?

Code snippet from train_rain_class.py:

netG = net1.vgg19ca()
residue_net = net2.Dense_rain_residual()
# ...
optimizerG = optim.Adam(netG.parameters(), ...)
# ...
optmizerG.step()

It seems that train_rain_class.py corresponds to the joint optimization stage in your paper, but only the density classifier network is updated here. Is it the expected behavior?

BTW, there is an undefined variable optimizer_D in line 156. I'm not sure what it's for.