Add 'before' and 'after' defs to injections, steps and optimizers

codes/models/ExtensibleTrainer.py

@@ -180,6 +180,9 @@ class ExtensibleTrainer(BaseModel):
             # Skip steps if mod_step doesn't line up.
             if 'mod_step' in s.opt.keys() and step % s.opt['mod_step'] != 0:
                 continue
+            # Steps can opt out of early (or late) training, make sure that happens here.
+            if 'after' in s.opt.keys() and step < s.opt['after'] or 'before' in s.opt.keys() and step > s.opt['before']:
+                continue
 
             # Only set requires_grad=True for the network being trained.
             nets_to_train = s.get_networks_trained()
@@ -226,6 +229,8 @@ class ExtensibleTrainer(BaseModel):
         if 'visuals' in self.opt['logger'].keys():
             sample_save_path = os.path.join(self.opt['path']['models'], "..", "visual_dbg")
             for v in self.opt['logger']['visuals']:
+                if v not in state.keys():
+                    continue   # This can happen for several reasons (ex: 'after' defs), just ignore it.
                 if step % self.opt['logger']['visual_debug_rate'] == 0:
                     for i, dbgv in enumerate(state[v]):
                         if dbgv.shape[1] > 3:

codes/models/archs/StructuredSwitchedGenerator.py

@@ -344,8 +344,7 @@ class SSGNoEmbedding(nn.Module):
             x_grad = self.get_g_nopadding(x)
 
             x = self.model_fea_conv(x)
-            x1 = x
-            x1, a1 = self.sw1(x1, True, identity=x)
+            x1, a1 = self.sw1(x, True)
 
             x_grad = self.grad_conv(x_grad)
             x_grad_identity = x_grad

codes/models/steps/steps.py

@@ -76,6 +76,7 @@ class ConfigurableStep(Module):
             elif self.step_opt['optimizer'] == 'novograd':
                 opt = NovoGrad(optim_params, lr=opt_config['lr'], weight_decay=opt_config['weight_decay'],
                                        betas=(opt_config['beta1'], opt_config['beta2']))
+            opt._config = opt_config  # This is a bit seedy, but we will need these configs later.
             self.optimizers.append(opt)
 
     # Returns all optimizers used in this step.
@@ -116,6 +117,10 @@ class ConfigurableStep(Module):
             # Likewise, don't do injections tagged with train unless we are not in eval.
             if not train and 'train' in inj.opt.keys() and inj.opt['train']:
                 continue
+            # Don't do injections tagged with 'after' or 'before' when we are out of spec.
+            if 'after' in inj.opt.keys() and self.env['step'] < inj.opt['after'] or \
+                'before' in inj.opt.keys() and self.env['step'] > inj.opt['before']:
+                continue
             injected = inj(local_state)
             local_state.update(injected)
             new_state.update(injected)
@@ -155,6 +160,13 @@ class ConfigurableStep(Module):
     # all self.optimizers.
     def do_step(self):
         for opt in self.optimizers:
+            # Optimizers can be opted out in the early stages of training.
+            after = opt._config['after'] if 'after' in opt._config.keys() else 0
+            if self.env['step'] < after:
+                continue
+            before = opt._config['before'] if 'before' in opt._config.keys() else -1
+            if before != -1 and self.env['step'] > before:
+                continue
             opt.step()
 
     def get_metrics(self):