Merge remote-tracking branch 'origin/gan_lab' into gan_lab

codes/models/steps/injectors.py

@@ -36,6 +36,8 @@ def create_injector(opt_inject, env):
         return MarginRemoval(opt_inject, env)
     elif type == 'foreach':
         return ForEachInjector(opt_inject, env)
+    elif type == 'constant':
+        return ConstantInjector(opt_inject, env)
     else:
         raise NotImplementedError
 
@@ -220,7 +222,6 @@ class MarginRemoval(Injector):
         input = state[self.input]
         return {self.opt['out']: input[:, :, self.margin:-self.margin, self.margin:-self.margin]}
 
-
 # Produces an injection which is composed of applying a single injector multiple times across a single dimension.
 class ForEachInjector(Injector):
     def __init__(self, opt, env):
@@ -238,4 +239,21 @@ class ForEachInjector(Injector):
         for i in range(inputs.shape[1]):
             st['_in'] = inputs[:, i]
             injs.append(self.injector(st)['_out'])
-        return {self.output: torch.stack(injs, dim=1)}
+        return {self.output: torch.stack(injs, dim=1)}        
+
+    
+class ConstantInjector(Injector):
+    def __init__(self, opt, env):
+        super(ConstantInjector, self).__init__(opt, env)
+        self.constant_type = opt['constant_type']
+        self.dim = opt['dim']
+        self.like = opt['like']  # This injector uses this tensor to determine what batch size and device to use.
+
+    def forward(self, state):
+        bs = state[self.like].shape[0]
+        dev = state[self.like].device
+        if self.constant_type == 'zeroes':
+            out = torch.zeros((bs,) + tuple(self.dim), device=dev)
+        else:
+            raise NotImplementedError
+        return { self.opt['out']: out }

codes/models/steps/tecogan_losses.py

@@ -247,9 +247,10 @@ class PingPongLoss(ConfigurableLoss):
     def forward(self, _, state):
         fake = state[self.opt['fake']]
         l_total = 0
-        for i in range((len(fake) - 1) // 2):
-            early = fake[i]
-            late = fake[-i]
+        img_count = fake.shape[1]
+        for i in range((img_count - 1) // 2):
+            early = fake[:, i]
+            late = fake[:, -i]
             l_total += self.criterion(early, late)
 
         if self.env['step'] % 50 == 0:
@@ -262,6 +263,7 @@ class PingPongLoss(ConfigurableLoss):
             return
         base_path = osp.join(self.env['base_path'], "..", "visual_dbg", "teco_pingpong", str(self.env['step']))
         os.makedirs(base_path, exist_ok=True)
-        assert isinstance(imglist, list)
-        for i, img in enumerate(imglist):
+        cnt = imglist.shape[1]
+        for i in range(cnt):
+            img = imglist[:, i]
             torchvision.utils.save_image(img, osp.join(base_path, "%s.png" % (i, )))