Misc changes

codes/models/SRGAN_model.py

@@ -307,6 +307,8 @@ class SRGANModel(BaseModel):
             noise = torch.randn_like(var_ref[0]) * noise_theta
             noise.to(self.device)
             self.optimizer_D.zero_grad()
+            real_disc_images = []
+            fake_disc_images = []
             for var_L, var_H, var_ref, pix in zip(self.var_L, self.var_H, self.var_ref, self.pix):
                 # Re-compute generator outputs (post-update).
                 with torch.no_grad():
@@ -347,7 +349,6 @@ class SRGANModel(BaseModel):
 
                     # randomly determine portions of the image to swap to keep the discriminator honest.
                     if random.random() > .25:
-
                         # Make the swap across fake_H and var_ref
                         SWAP_MAX_DIM = var_ref[0].shape[2] // (2 * PIXDISC_MAX_REDUCTION)
                         assert SWAP_MAX_DIM > 0
@@ -378,6 +379,14 @@ class SRGANModel(BaseModel):
                     l_d_fake_log = l_d_fake * self.mega_batch_factor
                     with amp.scale_loss(l_d_fake, self.optimizer_D, loss_id=1) as l_d_fake_scaled:
                         l_d_fake_scaled.backward()
+
+                    pdr = pred_d_real.detach() + torch.abs(torch.min(pred_d_real))
+                    pdr = pdr / torch.max(pdr)
+                    real_disc_images.append(pdr.view(disc_output_shape))
+                    pdf = pred_d_fake.detach() + torch.abs(torch.min(pred_d_fake))
+                    pdf = pdf / torch.max(pdf)
+                    fake_disc_images.append(pdf.view(disc_output_shape))
+
                 elif self.opt['train']['gan_type'] == 'ragan':
                     pred_d_fake = self.netD(fake_H).detach()
                     pred_d_real = self.netD(var_ref)
@@ -423,6 +432,7 @@ class SRGANModel(BaseModel):
             os.makedirs(os.path.join(sample_save_path, "gen"), exist_ok=True)
             os.makedirs(os.path.join(sample_save_path, "disc_fake"), exist_ok=True)
             os.makedirs(os.path.join(sample_save_path, "pix"), exist_ok=True)
+            os.makedirs(os.path.join(sample_save_path, "disc"), exist_ok=True)
             multi_gen = False
             if isinstance(self.fake_GenOut[0], tuple):
                 os.makedirs(os.path.join(sample_save_path, "ref"), exist_ok=True)
@@ -435,9 +445,11 @@ class SRGANModel(BaseModel):
                 utils.save_image(self.pix[i].cpu(), os.path.join(sample_save_path, "pix", "%05i_%02i.png" % (step, i)))
                 if multi_gen:
                     utils.save_image(self.fake_GenOut[i][0].cpu(), os.path.join(sample_save_path, "gen", "%05i_%02i.png" % (step, i)))
-                    if self.l_gan_w > 0 and step > self.G_warmup:
+                    if self.l_gan_w > 0 and step > self.G_warmup and self.opt['train']['gan_type'] == 'pixgan':
                         utils.save_image(var_ref_skips[i].cpu(), os.path.join(sample_save_path, "ref", "%05i_%02i.png" % (step, i)))
-                        utils.save_image(self.fake_H[i], os.path.join(sample_save_path, "disc_fake", "%05i_%02i.png" % (step, i)))
+                        utils.save_image(self.fake_H[i], os.path.join(sample_save_path, "disc_fake", "fake%05i_%02i.png" % (step, i)))
+                        utils.save_image(F.interpolate(fake_disc_images[i], scale_factor=4), os.path.join(sample_save_path, "disc", "fake%05i_%02i.png" % (step, i)))
+                        utils.save_image(F.interpolate(real_disc_images[i], scale_factor=4), os.path.join(sample_save_path, "disc", "real%05i_%02i.png" % (step, i)))
                 else:
                     utils.save_image(self.fake_GenOut[i].cpu(), os.path.join(sample_save_path, "gen", "%05i_%02i.png" % (step, i)))
 

codes/models/archs/SwitchedResidualGenerator_arch.py

@@ -230,7 +230,7 @@ class ConfigurableSwitchedResidualGenerator2(nn.Module):
                 temp = 1 / temp
             self.set_temperature(temp)
             if step % 50 == 0:
-                [save_attention_to_image(experiments_path, self.attentions[i], self.transformation_counts[i], step, "a%i" % (i+1,)) for i in range(len(self.switches))]
+                [save_attention_to_image(experiments_path, self.attentions[i], self.transformation_counts, step, "a%i" % (i+1,)) for i in range(len(self.switches))]
 
     def get_debug_values(self, step):
         temp = self.switches[0].switch.temperature

codes/utils/distill_torchscript.py

@@ -92,14 +92,14 @@ class TorchCustomTrace:
 if __name__ == "__main__":
 
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/debug.yml')
+    parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../../options/train_div2k_pixgan_srg2.yml')
     opt = option.parse(parser.parse_args().opt, is_train=False)
     opt = option.dict_to_nonedict(opt)
 
     netG = define_G(opt)
     dummyInput = torch.rand(1,3,32,32)
 
-    mode = 'memtrace'
+    mode = 'onnx'
     if mode == 'torchscript':
         print("Tracing generator network..")
         traced_netG = torch.jit.trace(netG, dummyInput)