Clear metrics properly

Holy cow, what a PITA bug.

codes/models/steps/losses.py

@@ -64,6 +64,9 @@ class ConfigurableLoss(nn.Module):
     def extra_metrics(self):
         return self.metrics
 
+    def clear_metrics(self):
+        self.metrics = []
+
 
 def get_basic_criterion_for_name(name, device):
     if name == 'l1':
@@ -192,7 +195,6 @@ class DiscriminatorGanLoss(ConfigurableLoss):
         self.losses_computed = 0
 
     def forward(self, net, state):
-        self.metrics = []
         real = extract_params_from_state(self.opt['real'], state)
         real = [r.detach() for r in real]
         fake = extract_params_from_state(self.opt['fake'], state)
@@ -258,7 +260,6 @@ class GeometricSimilarityGeneratorLoss(ConfigurableLoss):
                               (functools.partial(torch.rot90, k=3, dims=[2,3]), functools.partial(torch.rot90, k=1, dims=[2,3]))])
 
     def forward(self, net, state):
-        self.metrics = []
         net = self.env['generators'][self.generator]  # Get the network from an explicit parameter.
                                                     # The <net> parameter is not reliable for generator losses since often they are combined with many networks.
         fake = extract_params_from_state(self.opt['fake'], state)
@@ -305,7 +306,6 @@ class TranslationInvarianceLoss(ConfigurableLoss):
         assert(self.patch_size > self.overlap)
 
     def forward(self, net, state):
-        self.metrics = []
         net = self.env['generators'][self.generator]  # Get the network from an explicit parameter.
         # The <net> parameter is not reliable for generator losses since often they are combined with many networks.
 
@@ -356,7 +356,6 @@ class RecursiveInvarianceLoss(ConfigurableLoss):
         assert(self.recursive_depth > 0)
 
     def forward(self, net, state):
-        self.metrics = []
         net = self.env['generators'][self.generator]  # Get the network from an explicit parameter.
         # The <net> parameter is not reliable for generator losses since they can be combined with many networks.
 

codes/models/steps/steps.py

@@ -156,6 +156,7 @@ class ConfigurableStep(Module):
                     self.loss_accumulator.add_loss(loss_name, l)
                 for n, v in loss.extra_metrics():
                     self.loss_accumulator.add_loss("%s_%s" % (loss_name, n), v)
+                loss.clear_metrics()
 
             # In some cases, the loss could not be set (e.g. all losses have 'after'
             if isinstance(total_loss, torch.Tensor):

codes/models/steps/tecogan_losses.py

@@ -61,7 +61,7 @@ class RecurrentImageGeneratorSequenceInjector(Injector):
         super(RecurrentImageGeneratorSequenceInjector, self).__init__(opt, env)
         self.flow = opt['flow_network']
         self.input_lq_index = opt['input_lq_index'] if 'input_lq_index' in opt.keys() else 0
-        self.output_hq_index = opt['output_hq_index'] if 'output_index' in opt.keys() else 0
+        self.output_hq_index = opt['output_hq_index'] if 'output_hq_index' in opt.keys() else 0
         self.recurrent_index = opt['recurrent_index']
         self.scale = opt['scale']
         self.resample = Resample2d()
@@ -271,3 +271,5 @@ class PingPongLoss(ConfigurableLoss):
         for i in range(cnt):
             img = imglist[:, i]
             torchvision.utils.save_image(img, osp.join(base_path, "%s.png" % (i, )))
+
+