fix by comment

mmgen/core/evaluation/evaluation.py

@@ -280,7 +280,7 @@ def single_gpu_online_evaluation(model, data_loader, metrics, logger,
                                    'channels in the first, '
                                    'not % d' % reals.shape[1])
             if reals.shape[1] == 1:
-                reals = torch.cat([reals] * 3, dim=1)
+                reals = reals.repeat(1, 3, 1, 1)
             num_feed = metric.feed(reals, 'reals')
             if num_feed <= 0:
                 break
@@ -342,7 +342,7 @@ def single_gpu_online_evaluation(model, data_loader, metrics, logger,
     for metric in probabilistic_metrics:
         metric.prepare()
         pbar = mmcv.ProgressBar(len(data_loader))
-        # here we assert probabilistic model have reconstruction mode
+        # here we assume probabilistic model have reconstruction mode
         kwargs['mode'] = 'reconstruction'
         for data in data_loader:
             # key for unconditional GAN
@@ -362,7 +362,7 @@ def single_gpu_online_evaluation(model, data_loader, metrics, logger,
                                    'channels in the first, '
                                    'not % d' % reals.shape[1])
             if reals.shape[1] == 1:
-                reals = torch.cat([reals] * 3, dim=1)
+                reals = reals.repeat(1, 3, 1, 1)
 
             prob_dict = model(reals, return_loss=False, **kwargs)
             num_feed = metric.feed(prob_dict, 'reals')

mmgen/core/evaluation/metrics.py

@@ -385,7 +385,7 @@ def feed(self, batch, mode):
         operation in 'feed_op' function.
 
         Args:
-            batch (Tensor|dict[Tensor]): Images or dict to be feeded into
+            batch (Tensor | dict): Images or dict to be feeded into
                 metric object. If ``Tensor`` is passed, the order of ``Tensor``
                 should be "NCHW". If ``dict`` is passed, each term in the
                 ``dict`` are ``Tensor`` with order "NCHW".
@@ -1405,11 +1405,12 @@ def __init__(self, num_images, base='e', reduction='batchmean'):
                         '2'], ('We only support log_base for \'e\' and \'2\'')
         self.reduction = reduction
         self.num_fake_feeded = self.num_images
-        self.kld = partial(gaussian_kld, weight=1, reduction='none', base=base)
+        self.cal_kld = partial(
+            gaussian_kld, weight=1, reduction='none', base=base)
 
     def prepare(self):
         """Prepare for evaluating models with this metric."""
-        self.KLD = []
+        self.kld = []
         self.num_real_feeded = 0
 
     @torch.no_grad()
@@ -1434,8 +1435,8 @@ def feed_op(self, batch, mode):
                            'the same time. But keys in the given dict are '
                            f'{batch.keys()}. Some of the requirements are '
                            'missing.')
-        kld = self.kld(batch['mean_target'], batch['mean_pred'],
-                       batch['logvar_target'], batch['logvar_pred'])
+        kld = self.cal_kld(batch['mean_target'], batch['mean_pred'],
+                           batch['logvar_target'], batch['logvar_pred'])
         if dist.is_initialized():
             ws = dist.get_world_size()
             placeholder = [torch.zeros_like(kld) for _ in range(ws)]
@@ -1445,7 +1446,7 @@ def feed_op(self, batch, mode):
         # in distributed training, we only collect features at rank-0.
         if (dist.is_initialized()
                 and dist.get_rank() == 0) or not dist.is_initialized():
-            self.KLD.append(kld.cpu())
+            self.kld.append(kld.cpu())
 
     @torch.no_grad()
     def summary(self):
@@ -1454,7 +1455,7 @@ def summary(self):
         Returns:
             dict | list: Summarized results.
         """
-        kld = torch.cat(self.KLD, dim=0)
+        kld = torch.cat(self.kld, dim=0)
         assert kld.shape[0] >= self.num_images
         kld_np = kld.numpy()
         if self.reduction == 'sum':