PyTorch implementation of "Image-to-Image Translation Using Conditional Adversarial Networks".

Related tags

Deep Learning pix2pix-pytorch
Overview

pix2pix-pytorch

PyTorch implementation of Image-to-Image Translation Using Conditional Adversarial Networks.

Based on pix2pix by Phillip Isola et al.

The examples from the paper:

examples

Prerequisites

  • Linux
  • Python, Numpy, PIL
  • pytorch 0.4.0
  • torchvision 0.2.1

Getting Started

  • Clone this repo:

    git clone [email protected]:mrzhu-cool/pix2pix-pytorch.git cd pix2pix-pytorch

  • Get dataset

    unzip dataset/facades.zip

  • Train the model:

    python train.py --dataset facades --cuda

  • Test the model:

    python test.py --dataset facades --cuda

Acknowledgments

This code is a simple implementation of pix2pix. Easier to understand. Note that we use a downsampling-resblocks-upsampling structure instead of the unet structure in this code, therefore the results of this code may inconsistent with the results presented in the paper.

Highly recommend the more sophisticated and organized code pytorch-CycleGAN-and-pix2pix by Jun-Yan Zhu.

Comments
  • Minor changes

    Minor changes

    Nice work. Regarding not getting similar result check the section 3.4 of the paper. I am made some changes accordingly.

    1. Efficient backward from discriminator while training generator.
    2. The Generator model does not have a batchNorm between encoder and decoder.
    3. Adjusted discriminator such that the receptive field is 70x70. This is what they use in the best performing result in the paper.

    Kind regards, Sagar M. Waghmare

    opened by sagarwaghmare69 5
  • Models' forward are incorrect

    Models' forward are incorrect

    Hey just realized that the model forward/sequence is wrong. E.g Current code: input is (nc) x 256 x 256

    e1 = self.conv1(input)
# input is (ngf) x 128 x 128
e2 = self.batch_norm2(self.conv2(self.leaky_relu(e1)))

    This should be input is (nc) x 256 x 256

    e1 = self.conv1(input)
# input is (ngf) x 128 x 128
e2 = self.conv2(self.leaky_relu(self.batch_norm2(e1)))

    Hence both the discriminator and generator models are incorrect.

    opened by sagarwaghmare69 3
  • Fixed saving and volatility

    Fixed saving and volatility

    Thanks for the repo! Two quick improvements that stood out to me:

    1. When you save/load models in pytorch, you should be saving and loading their state_dict, and not the model itself
    2. Rather than setting and resetting requires_grad on all of the parameters, you can use the volatile keyword to get the same effect
    opened by atgambardella 0
  • should I add set_requires_grad(net_d, True/False) for discriminator during training?

    should I add set_requires_grad(net_d, True/False) for discriminator during training?

    Hello, Thank you for your great work. However, I think you should add set_requires_grad(net_d, True/False) for discriminator during training. Is it true?

    modified code:

        # (1) Update D network
    ######################
    set_requires_grad(net_d, True) # add it here
    optimizer_d.zero_grad()        
    # train with fake
    fake_ab = torch.cat((real_a, fake_b), 1)
    pdb.set_trace()
    pred_fake = net_d.forward(fake_ab.detach())
    loss_d_fake = criterionGAN(pred_fake, False)

    # train with real
    real_ab = torch.cat((reala, real_b), 1)
    pred_real = net_d.forward(real_ab)
    loss_d_real = criterionGAN(pred_real, True)
    
    # Combined D loss
    loss_d = (loss_d_fake + loss_d_real) * 0.5

    loss_d.backward()
   
    optimizer_d.step()

    set_requires_grad(net_d, False) # add it here

    ######################

    I am looking forward to hearing from you. Thank you in advance!

    opened by vince2003 0
  • Input and output sizes of images？

    Input and output sizes of images？

    @mrzhu-cool Why do I find that the output size will be smaller in the training results？ How can I modify the code to customize the output size or the same size as the input image...

    opened by ArtScanner 0
  • Consider only L1 loss

    Consider only L1 loss

    I want to consider only L1 loss.Is it correct to comment out the generator loss as

    #loss_g = loss_g_gan + loss_g_l1 and consider only, loss_g = loss_g_l1

    Do I need to change anything in discriminator part?

    Thanks in advance

    opened by riktimmondal 3
  • PatchGAN part in the implementation

    PatchGAN part in the implementation

    Hi, I had hard time finding the PatchGAN part of code,Although you have commented before writing discriminator.Can you please point out where is it exactly there and how are you using it? Thanks

    opened by subham913 1
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