Fix metrics to work with grayscale datasets (#9)

Author: tkarras

metrics/metric_utils.py

@@ -213,6 +213,8 @@ def compute_feature_stats_for_dataset(opts, detector_url, detector_kwargs, rel_l
     # Main loop.
     item_subset = [(i * opts.num_gpus + opts.rank) % num_items for i in range((num_items - 1) // opts.num_gpus + 1)]
     for images, _labels in torch.utils.data.DataLoader(dataset=dataset, sampler=item_subset, batch_size=batch_size, **data_loader_kwargs):
+        if images.shape[1] == 1:
+            images = images.repeat([1, 3, 1, 1])
         features = detector(images.to(opts.device), **detector_kwargs)
         stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank)
         progress.update(stats.num_items)
@@ -262,7 +264,10 @@ def run_generator(z, c):
             c = [dataset.get_label(np.random.randint(len(dataset))) for _i in range(batch_gen)]
             c = torch.from_numpy(np.stack(c)).pin_memory().to(opts.device)
             images.append(run_generator(z, c))
-        features = detector(torch.cat(images), **detector_kwargs)
+        images = torch.cat(images)
+        if images.shape[1] == 1:
+            images = images.repeat([1, 3, 1, 1])
+        features = detector(images, **detector_kwargs)
         stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank)
         progress.update(stats.num_items)
     return stats