Remove unnecessary use of comprehension (#8149)

Co-authored-by: deepsource-autofix[bot] <62050782+deepsource-autofix[bot]@users.noreply.github.com>

pytorch_lightning/loggers/base.py

@@ -366,7 +366,7 @@ def __init__(self, logger_iterable: Iterable[LightningLoggerBase]):
         self._logger_iterable = logger_iterable
 
     def __getitem__(self, index: int) -> LightningLoggerBase:
-        return [logger for logger in self._logger_iterable][index]
+        return list(self._logger_iterable)[index]
 
     def after_save_checkpoint(self, checkpoint_callback: 'ReferenceType[ModelCheckpoint]') -> None:
         for logger in self._logger_iterable:

pytorch_lightning/trainer/connectors/env_vars_connector.py

@@ -31,7 +31,7 @@ def insert_env_defaults(self, *args, **kwargs):
             # parse only the argument names
             cls_arg_names = [arg[0] for arg in get_init_arguments_and_types(cls)]
             # convert args to kwargs
-            kwargs.update({k: v for k, v in zip(cls_arg_names, args)})
+            kwargs.update(dict(zip(cls_arg_names, args)))
         env_variables = vars(parse_env_variables(cls))
         # update the kwargs by env variables
         kwargs = dict(list(env_variables.items()) + list(kwargs.items()))

pytorch_lightning/trainer/supporters.py

@@ -158,7 +158,7 @@ def to_disk(self) -> None:
             # Switch predictions so each entry has its own dict
             outputs = []
             for values in zip(*predictions.values()):
-                output_element = {k: v for k, v in zip(predictions.keys(), values)}
+                output_element = dict(zip(predictions.keys(), values))
                 outputs.append(output_element)
 
             # Write predictions for current file to disk

tests/callbacks/test_lr_monitor.py

@@ -288,7 +288,7 @@ def test_lr_monitor_custom_pg_name(tmpdir):
     class TestModel(BoringModel):
 
         def configure_optimizers(self):
-            optimizer = torch.optim.SGD([{'params': [p for p in self.layer.parameters()], 'name': 'linear'}], lr=0.1)
+            optimizer = torch.optim.SGD([{'params': list(self.layer.parameters()), 'name': 'linear'}], lr=0.1)
             lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
             return [optimizer], [lr_scheduler]
 
@@ -325,11 +325,11 @@ def forward(self, x):
         def configure_optimizers(self):
             param_groups = [
                 {
-                    'params': [p for p in self.linear_a.parameters()],
+                    'params': list(self.linear_a.parameters()),
                     'name': 'linear'
                 },
                 {
-                    'params': [p for p in self.linear_b.parameters()],
+                    'params': list(self.linear_b.parameters()),
                     'name': 'linear'
                 },
             ]

tests/checkpointing/test_model_checkpoint.py

@@ -340,7 +340,7 @@ def test_model_checkpoint_to_yaml(tmpdir, save_top_k: int):
     path_yaml = os.path.join(tmpdir, 'best_k_models.yaml')
     checkpoint.to_yaml(path_yaml)
     d = yaml.full_load(open(path_yaml, 'r'))
-    best_k = {k: v for k, v in checkpoint.best_k_models.items()}
+    best_k = dict(checkpoint.best_k_models.items())
     assert d == best_k
 
 

tests/models/test_horovod.py

@@ -264,7 +264,7 @@ def test_horovod_multi_optimizer(tmpdir):
         assert hasattr(optimizer, 'synchronize'), 'optimizer has not been wrapped into DistributedOptimizer'
 
     def get_model_params(model):
-        return set([p for p in model.parameters()])
+        return set(list(model.parameters()))
 
     def get_optimizer_params(optimizer):
         return set([p for group in optimizer.param_groups for p in group.get('params', [])])

tests/overrides/test_distributed.py

@@ -32,7 +32,7 @@ def test_unrepeated_distributed_sampler(shuffle, tmpdir):
     for rank in range(world_size):
         samplers.append(UnrepeatedDistributedSampler(dataset, rank=rank, num_replicas=world_size, shuffle=shuffle))
 
-    indices = [[v for v in s] for s in samplers]
+    indices = [list(s) for s in samplers]
     assert len(indices[0]) == 26
     assert len(indices[1]) == 26
     assert len(indices[2]) == 26

tests/trainer/test_dataloaders.py

@@ -813,11 +813,11 @@ def test_missing_worker_init_fn():
 
     seed_everything(0)
     dataloader = DataLoader(dataset, batch_size=2, num_workers=2, shuffle=False)
-    batches0 = torch.cat([batch for batch in dataloader])
+    batches0 = torch.cat(list(dataloader))
 
     seed_everything(0)
     dataloader = DataLoader(dataset, batch_size=2, num_workers=2, shuffle=False)
-    batches1 = torch.cat([batch for batch in dataloader])
+    batches1 = torch.cat(list(dataloader))
 
     is_duplicated = len(torch.unique(batches1, dim=0)) < len(dataset)
     is_deterministic = torch.eq(batches0, batches1).all()

tests/trainer/test_supporters.py

@@ -91,7 +91,7 @@ def __iter__(self):
 
     dataset = IterDataset()
     iterator = prefetch_iterator(dataset)
-    assert [item for item in iterator] == [(1, False), (2, False), (3, True)]
+    assert list(iterator) == [(1, False), (2, False), (3, True)]
 
     class EmptyIterDataset(IterableDataset):
 
@@ -100,7 +100,7 @@ def __iter__(self):
 
     dataset = EmptyIterDataset()
     iterator = prefetch_iterator(dataset)
-    assert [item for item in iterator] == []
+    assert list(iterator) == []
 
 
 @pytest.mark.parametrize(

tests/tuner/test_auto_gpu_select.py

@@ -51,7 +51,7 @@ def test_trainer_with_gpus_options_combination_at_available_gpus_env(auto_select
     ["nb", "expected_gpu_idxs", "expected_error"],
     [
         (0, [], MisconfigurationException),
-        (-1, [i for i in range(torch.cuda.device_count())], None),
+        (-1, list(range(torch.cuda.device_count())), None),
         (1, [0], None),
     ],
 )