Add InterpolateInjector

codes/models/steps/injectors.py

@@ -15,6 +15,8 @@ def create_injector(opt_inject, env):
         return AddNoiseInjector(opt_inject, env)
     elif type == 'greyscale':
         return GreyInjector(opt_inject, env)
+    elif type == 'interpolate':
+        return InterpolateInjector(opt_inject, env)
     else:
         raise NotImplementedError
 
@@ -101,5 +103,15 @@ class GreyInjector(Injector):
 
     def forward(self, state):
         mean = torch.mean(state[self.opt['in']], dim=1, keepdim=True)
-        mean = mean.repeat((1, 3, 1, 1))
+        mean = mean.repeat(1, 3, 1, 1)
         return {self.opt['out']: mean}
+
+
+class InterpolateInjector(Injector):
+    def __init__(self, opt, env):
+        super(InterpolateInjector, self).__init__(opt, env)
+
+    def forward(self, state):
+        scaled = torch.nn.functional.interpolate(state[self.opt['in']], scale_factor=self.opt['scale_factor'],
+                                                 mode=self.opt['mode'])
+        return {self.opt['out']: scaled}

codes/models/steps/losses.py

@@ -86,8 +86,7 @@ class InterpretedFeatureLoss(ConfigurableLoss):
             self.netF_gen = torch.nn.parallel.DataParallel(self.netF_gen)
 
     def forward(self, net, state):
-        with torch.no_grad():
-            logits_real = self.netF_real(state[self.opt['real']])
+        logits_real = self.netF_real(state[self.opt['real']])
         logits_fake = self.netF_gen(state[self.opt['fake']])
         return self.criterion(logits_fake, logits_real)