clean-up

score.py

@@ -353,37 +353,7 @@ def score(config, train_checkpoint_id, train_config):
 
         return {key_prefix: {k: scores[k] for k in ['seen', 'unseen', 'harmonic', 'overall']}}
 
-    elif config.test_dataset == 'fss1000':
-        from datasets.fss1000 import FSS1000
-        dataset_cls = FSS1000
-        _, dataset_args, _ = filter_args(config, inspect.signature(dataset_cls).parameters)
-        dataset = dataset_cls(**dataset_args)
-
-        loader = DataLoader(dataset, batch_size=config.batch_size, num_workers=2, shuffle=False, drop_last=False)
-        metric = FixedIntervalMetrics(resize_pred=True, **metric_args)
-
-        with torch.no_grad():
-
-            i, losses = 0, []
-            for i_all, (data_x, data_y) in enumerate(loader):
-                data_x = [v.cuda(non_blocking=True) if isinstance(v, torch.Tensor) else v for v in data_x]
-                data_y = [v.cuda(non_blocking=True) if isinstance(v, torch.Tensor) else v for v in data_y]
-
-                pred, = model(data_x[0], data_x[1], data_x[2])
-                metric.add([pred], data_y)
-
-                i += 1
-                if config.max_iterations and i >= config.max_iterations:
-                    break           
-
-        prefix = 'fss'
-        if 'ratio_negative' in dataset_args:
-            prefix += f'-neg{dataset_args["ratio_negative"]}'
-
-        return {prefix: metric.scores()}
-
-
-    elif config.test_dataset in {'same_as_training', 'lvis', 'affordance', 'fss1000'}:
+    elif config.test_dataset in {'same_as_training', 'affordance'}:
         loss_fn = get_attribute(train_config.loss)
 
         metric_cls = get_attribute(config.metric)