report quantile losses for diffusion

codes/models/diffusion/gaussian_diffusion.py

@@ -870,6 +870,7 @@ class GaussianDiffusion:
             s_err = (target - model_output) ** 2
             if channel_balancing_fn is not None:
                 s_err = channel_balancing_fn(s_err)
+            terms["mse_by_batch"] = s_err.view(s_err.shape[0], -1).mean(dim=1)
             terms["mse"] = mean_flat(s_err)
             terms["x_start_predicted"] = x_start_pred
             if "vb" in terms:

codes/trainer/injectors/gaussian_diffusion_injector.py

@@ -56,12 +56,22 @@ class GaussianDiffusionInjector(Injector):
             self.channel_balancing_fn = None
         assert k <= 1, 'Only one channel filtering function can be applied.'
 
+        self.num_timesteps = opt['beta_schedule']['num_diffusion_timesteps']
+        self.latest_mse_by_batch = torch.tensor([0])
+        self.latest_timesteps = torch.tensor([0])
+
     def extra_metrics(self):
+        uqt = self.latest_timesteps > self.num_timesteps * 3 / 4
+        uql = (self.latest_mse_by_batch * uqt).sum() / uqt.sum()
+        muqt = (self.latest_timesteps > self.num_timesteps / 2) * (self.latest_timesteps < self.num_timesteps * 3 / 4)
+        muql = (self.latest_mse_by_batch * muqt).sum() / muqt.sum()
+        d = {
+            'upper_quantile_mse_loss': uql,
+            'mid_upper_quantile_mse_loss': muql,
+        }
         if hasattr(self, 'schedule_sampler') and isinstance(self.schedule_sampler, LossSecondMomentResampler):
-            return {
-                'sampler_warmed_up': torch.tensor(float(self.schedule_sampler._warmed_up()))
-            }
-        return {}
+            d['sampler_warmed_up'] = torch.tensor(float(self.schedule_sampler._warmed_up()))
+        return d
 
     def forward(self, state):
         gen = self.env['generators'][self.opt['generator']]
@@ -87,6 +97,8 @@ class GaussianDiffusionInjector(Injector):
             out.update({self.output: diffusion_outputs['mse'],
                     self.output_variational_bounds_key: diffusion_outputs['vb'],
                     self.output_x_start_key: diffusion_outputs['x_start_predicted']})
+            self.latest_mse_by_batch = diffusion_outputs['mse_by_batch'].detach().clone()
+            self.latest_timesteps = t.clone()
 
         return out