Added epsilon cutoff

PULSE only returns an image when the downloss is within epsilon

Author: Alex Damian

PULSE.py

@@ -124,6 +124,7 @@ def forward(self, ref_im,
         loss_builder = LossBuilder(ref_im, loss_str, eps).cuda()
 
         min_loss = np.inf
+        min_l2 = np.inf
         best_summary = ""
         start_t = time.time()
         gen_im = None
@@ -156,6 +157,11 @@ def forward(self, ref_im,
                 [f'{x}: {y:.4f}' for x, y in loss_dict.items()])
                 best_im = gen_im.clone()
 
+            loss_l2 = loss_dict['L2']
+
+            if(loss_l2 < min_l2):
+                min_l2 = loss_l2
+
             # Save intermediate HR and LR images
             if(save_intermediate):
                 yield (best_im.cpu().detach().clamp(0, 1),loss_builder.D(best_im).cpu().detach().clamp(0, 1))
@@ -167,5 +173,7 @@ def forward(self, ref_im,
         total_t = time.time()-start_t
         current_info = f' | time: {total_t:.1f} | it/s: {(j+1)/total_t:.2f} | batchsize: {batch_size}'
         if self.verbose: print(best_summary+current_info)
-
-        yield (gen_im.clone().cpu().detach().clamp(0, 1),loss_builder.D(best_im).cpu().detach().clamp(0, 1))
+        if(min_l2 <= eps):
+            yield (gen_im.clone().cpu().detach().clamp(0, 1),loss_builder.D(best_im).cpu().detach().clamp(0, 1))
+        else:
+            print("Could not find a face that downscales correctly within epsilon")

run.py

@@ -36,7 +36,7 @@ def __getitem__(self, idx):
 #PULSE arguments
 parser.add_argument('-seed', type=int, help='manual seed to use')
 parser.add_argument('-loss_str', type=str, default="100*L2+0.05*GEOCROSS", help='Loss function to use')
-parser.add_argument('-eps', type=float, default=1e-3, help='Target for downscaling loss (L2)')
+parser.add_argument('-eps', type=float, default=2e-3, help='Target for downscaling loss (L2)')
 parser.add_argument('-noise_type', type=str, default='trainable', help='zero, fixed, or trainable')
 parser.add_argument('-num_trainable_noise_layers', type=int, default=5, help='Number of noise layers to optimize')
 parser.add_argument('-tile_latent', action='store_true', help='Whether to forcibly tile the same latent 18 times')