Tensorboard Setting has inserted into the project

[hinofafa]

Tensorboard feature has inserted to the repository.

add a tensorboard logging to the train.py by inserting the following code at line 180. This needs to be modified because l4 does not exist if imsize is less than 64 and the network fails if imsize is greater than 64. When the larger imsize is fixed I will issue a pull request. Also build_tensorboard should be changed. Hope this helps!

def build_tensorboard(self):
    from logger import Logger
    if os.path.exists(self.log_path):
        shutil.rmtree(self.log_path)
    os.makedirs(self.log_path)
    self.logger = Logger(self.log_path)

Insert at line 180 in train.py

Print out log info

if (step + 1) % self.log_step == 0: elapsed = time.time() - start_time elapsed = str(datetime.timedelta(seconds=elapsed)) print("Elapsed [{}], G_step [{}/{}], D_step[{}/{}], d_out_real: {:.4f}, " " ave_gamma_l3: {:.4f}, ave_gamma_l4: {:.4f}". format(elapsed, step + 1, self.total_step, (step + 1), self.total_step, d_loss_real.item(), self.G.attn1.gamma.mean().item(), self.G.attn2.gamma.mean().item()))

            # (1) Log values of the losses (scalars)
            info = {
                'd_loss_real': d_loss_real.item(),
                'd_loss_fake': d_loss_fake.item(),
                'd_loss': d_loss.item(),
                'g_loss_fake': g_loss_fake.item(),
                'ave_gamma_l3': self.G.attn1.gamma.mean().item(),
                'ave_gamma_l4': self.G.attn2.gamma.mean().item(),
            }

            for tag, value in info.items():
                self.logger.scalar_summary(tag, value, step + 1)

        # Sample images / Save and log
        if (step + 1) % self.sample_step == 0:

            # (2) Log values and gradients of the parameters (histogram)
            for net, name in zip([self.G, self.D], ['G_', 'D_']):
                for tag, value in net.named_parameters():
                    tag = name + tag.replace('.', '/')
                    self.logger.histo_summary(tag, self.to_np(value), step + 1)

            # (3) Log the images
            info = {

                'fake_images': self.to_np(fake_images.view(*display_vars)[:10, :, :, :]),
                'real_images': self.to_np(real_images.view(*display_vars)[:10, :, :, :]),
            }

            fake_images, _, _ = self.G(fixed_z)
            save_image(denorm(fake_images.data),
                       os.path.join(self.sample_path, '{}_fake.png'.format(step + 1)))

            info['fixed_fake_images'] = self.to_np(denorm(real_images.data).view(*display_vars)[:10, :, :, :])

            for tag, image in info.items():
                self.logger.image_summary(tag, image, step + 1)

[hinofafa]

For remarks:

• logger module (logger.py)
• item() -> data[0]
• self.to_np -> .data.cpu().numpy() (How torch tensor to numpy)
• display_vars -> images.size()

[hinofafa]

Temporarily, below codes are commented.

info['fixed_fake_images'] = self.to_np(denorm(real_images.data).view(*display_vars)[:10, :, :, :])

[hinofafa]

For https://github.com/hinofafa/Self-Attention-GAN/issues/1#issuecomment-462315109, the code has uncommented by fixing the code at commit cf99d8bc4307168e8f05188474c1875441c4fd33