rename variables to be same with the paper

Author: tuttyfrutyee

README.md

@@ -44,19 +44,21 @@ where .mat files has shape of (#images, width, height) and image values are betw
 ## Train
 
 <br />
+
 ```
 python3 train.py --image_size 256 --exp exp_syndiff --num_channels 2 --num_channels_dae 64 --ch_mult 1 1 2 2 4 4 --num_timesteps 4 --num_res_blocks 2 --batch_size 1 --contrast1 T1 --contrast2 T2 --num_epoch 500 --ngf 64 --embedding_type positional --use_ema --ema_decay 0.999 --r1_gamma 1. --z_emb_dim 256 --lr_d 1e-4 --lr_g 1.6e-4 --lazy_reg 10 --num_process_per_node 1 --save_content --local_rank 0 --input_path /input/path/for/data --output_path /output/for/results
 ```
-<br />
-<br />
 
+<br />
 
 ## Test
 
 <br />
+
 ```
 python test.py --image_size 256 --exp exp_syndiff --num_channels 2 --num_channels_dae 64 --ch_mult 1 1 2 2 4 4 --num_timesteps 4 --num_res_blocks 2 --batch_size 1 --embedding_type positional  --z_emb_dim 256 --contrast1 T1  --contrast2 T2 --which_epoch 50 --gpu_chose 0 --input_path /input/path/for/data --output_path /output/for/results
 ```
+
 <br />
 <br />
 
@@ -74,4 +76,10 @@ You are encouraged to modify/distribute this code. However, please acknowledge t
 ```
 For any questions, comments and contributions, please contact Muzaffer Özbey (muzafferozbey94[at]gmail.com ) <br />
 
-(c) ICON Lab 2022
+(c) ICON Lab 2022
+
+<br />
+
+# Acknowledgements
+
+This code uses libraries from the StyleGAN-2 (https://github.com/NVlabs/stylegan2) and DD-GAN (https://github.com/NVlabs/denoising-diffusion-gan) repositories.

test.py

@@ -162,16 +162,16 @@ def sample_and_test(args):
                                                shuffle=False,
                                                num_workers=4)
     #Initializing and loading network
-    netG1 = NCSNpp(args).to(device)
-    netG2 = NCSNpp(args).to(device)
+    gen_diffusive_1 = NCSNpp(args).to(device)
+    gen_diffusive_2 = NCSNpp(args).to(device)
 
     exp = args.exp
     output_dir = args.output_path
     exp_path = os.path.join(output_dir,exp)
 
     checkpoint_file = exp_path + "/{}_{}.pth"
-    load_checkpoint(checkpoint_file, netG1,'netG1',epoch=str(epoch_chosen), device = device)
-    load_checkpoint(checkpoint_file, netG2,'netG2',epoch=str(epoch_chosen), device = device)
+    load_checkpoint(checkpoint_file, gen_diffusive_1,'gen_diffusive_1',epoch=str(epoch_chosen), device = device)
+    load_checkpoint(checkpoint_file, gen_diffusive_2,'gen_diffusive_2',epoch=str(epoch_chosen), device = device)
 
 
     T = get_time_schedule(args, device)
@@ -194,7 +194,7 @@ def sample_and_test(args):
 
         x1_t = torch.cat((torch.randn_like(real_data),source_data),axis=1)
         #diffusion steps
-        fake_sample1 = sample_from_model(pos_coeff, netG1, args.num_timesteps, x1_t, T, args)
+        fake_sample1 = sample_from_model(pos_coeff, gen_diffusive_1, args.num_timesteps, x1_t, T, args)
 
         fake_sample1 = to_range_0_1(fake_sample1) ; fake_sample1 = fake_sample1/fake_sample1.max()
         real_data = to_range_0_1(real_data) ; real_data = real_data/real_data.max()
@@ -218,7 +218,7 @@ def sample_and_test(args):
 
         x2_t = torch.cat((torch.randn_like(real_data),source_data),axis=1)
         #diffusion steps
-        fake_sample2 = sample_from_model(pos_coeff, netG2, args.num_timesteps, x2_t, T, args)
+        fake_sample2 = sample_from_model(pos_coeff, gen_diffusive_2, args.num_timesteps, x2_t, T, args)
 
 
         fake_sample2 = to_range_0_1(fake_sample2) ; fake_sample2 = fake_sample2/fake_sample2.max()