Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaption

Related tags

Deep Learning SG-GAN
Overview

SG-GAN

TensorFlow implementation of SG-GAN.

Prerequisites

  • TensorFlow (implemented in v1.3)
  • numpy
  • scipy
  • pillow

Getting Started

Train

  • Prepare dataset. We present example images in datasets folder for reference as data format, and scripts prepare_data.py and segment_class.py for reference in preparing dataset.

  • Train a model:

CUDA_VISIBLE_DEVICES=0 python main.py

Models are saved to ./checkpoints/ (can be changed by passing --checkpoint_dir=your_dir).

  • Continue training a model (useful for updating parameters)
CUDA_VISIBLE_DEVICES=0 python main.py --continue_train 1

Test

  • Finally, test the model:
CUDA_VISIBLE_DEVICES=0 python main.py --phase test --img_width 2048 --img_height 1024

Adapted test images will be outputted to ./test/ (can be changed by passing --test_dir=your_dir).

Reference

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Comments
  • Loss for generators

    Loss for generators

    Great paper and neat implementation, nice done!

    I have a question regarding the loss of the generators: I did not really get why the losses of the two generators are identical:

        self.g_loss_a2b = self.criterionGAN(self.DB_fake, tf.ones_like(self.DB_fake)) \
        + self.L1_lambda * abs_criterion(self.real_A, self.fake_A_) \
        + self.L1_lambda * abs_criterion(self.real_B, self.fake_B_) \
        + self.Lg_lambda * gradloss_criterion(self.real_A, self.fake_B, self.weighted_seg_A) \
        + self.Lg_lambda * gradloss_criterion(self.real_B, self.fake_A, self.weighted_seg_B)
    self.g_loss_b2a = self.criterionGAN(self.DA_fake, tf.ones_like(self.DA_fake)) \
        + self.L1_lambda * abs_criterion(self.real_A, self.fake_A_) \
        + self.L1_lambda * abs_criterion(self.real_B, self.fake_B_) \
        + self.Lg_lambda * gradloss_criterion(self.real_A, self.fake_B, self.weighted_seg_A) \
        + self.Lg_lambda * gradloss_criterion(self.real_B, self.fake_A, self.weighted_seg_B)

    I might be wrong, but that is not what I would expect. Thanks

    opened by andrearama 5
Owner
lplcor
Machine Learning Artist 👩‍🎨 (CV, DL, AI)
lplcor
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