Cosmetics from #1822

ignite/handlers/checkpoint.py

@@ -537,7 +537,7 @@ def load_objects(to_load: Mapping, checkpoint: Mapping, **kwargs: Any) -> None:
         if not isinstance(checkpoint, collections.Mapping):
             raise TypeError(f"Argument checkpoint should be a dictionary, but given {type(checkpoint)}")
 
-        if len(kwargs) > 1 or any(k for k in kwargs.keys() if k not in ["strict"]):
+        if len(kwargs) > 1 or any(k for k in kwargs if k not in ["strict"]):
             warnings.warn("kwargs contains keys other than strict and these will be ignored")
 
         is_state_dict_strict = kwargs.get("strict", True)

ignite/metrics/accumulation.py

@@ -58,7 +58,7 @@ def reset(self) -> None:
         self.num_examples = 0
 
     def _check_output_type(self, output: Union[float, torch.Tensor]) -> None:
-        if not (isinstance(output, numbers.Number) or isinstance(output, torch.Tensor)):
+        if not isinstance(output, (numbers.Number, torch.Tensor)):
             raise TypeError(f"Output should be a number or torch.Tensor, but given {type(output)}")
 
     @reinit__is_reduced

ignite/metrics/loss.py

@@ -38,7 +38,7 @@ def __init__(
         self,
         loss_fn: Callable,
         output_transform: Callable = lambda x: x,
-        batch_size: Callable = lambda x: len(x),
+        batch_size: Callable = len,
         device: Union[str, torch.device] = torch.device("cpu"),
     ):
         super(Loss, self).__init__(output_transform, device=device)

ignite/utils.py

@@ -263,7 +263,7 @@ def wrapper(*args: Any, **kwargs: Dict[str, Any]) -> Callable:
             warnings.warn(deprecation_warning, DeprecationWarning, stacklevel=2)
             return func(*args, **kwargs)
 
-        appended_doc = f".. deprecated:: {deprecated_in}" + ("\n\n\t" if len(reasons) else "")
+        appended_doc = f".. deprecated:: {deprecated_in}" + ("\n\n\t" if len(reasons) > 0 else "")
 
         for reason in reasons:
             appended_doc += "\n\t- " + reason

tests/ignite/contrib/handlers/test_mlflow_logger.py

@@ -340,7 +340,6 @@ def test_mlflow_bad_metric_name_handling(dirname):
 
 @pytest.fixture
 def no_site_packages():
-    import sys
 
     mlflow_client_modules = {}
     for k in sys.modules:

tests/ignite/contrib/handlers/test_visdom_logger.py

@@ -936,8 +936,6 @@ def update_fn(engine, batch):
 
 @pytest.fixture
 def no_site_packages():
-    import sys
-
     import visdom  # noqa: F401
 
     visdom_module = sys.modules["visdom"]

tests/ignite/distributed/test_auto.py

@@ -217,8 +217,6 @@ def test_auto_methods_xla():
 
 
 def test_dist_proxy_sampler():
-    import torch
-    from torch.utils.data import WeightedRandomSampler
 
     weights = torch.ones(100)
     weights[:50] += 1

tests/ignite/engine/test_deterministic.py

@@ -7,7 +7,8 @@
 import pytest
 import torch
 import torch.nn as nn
-from torch.utils.data import DataLoader
+from torch.optim import SGD
+from torch.utils.data import BatchSampler, DataLoader, RandomSampler
 
 import ignite.distributed as idist
 from ignite.engine import Events
@@ -92,8 +93,6 @@ def test_reproducible_batch_sampler_wrong_input():
 
 
 def test_reproducible_batch_sampler():
-    import torch
-    from torch.utils.data import DataLoader
 
     data = list(range(100))
     dataloader = DataLoader(data, batch_size=12, num_workers=0, shuffle=True, drop_last=True)
@@ -599,11 +598,12 @@ def test_multinode_distrib_gpu(distributed_context_multi_node_nccl):
 
 def test_concepts_snippet_resume():
 
-    import torch
-    from torch.utils.data import DataLoader
+    # Commented imports required in the snippet
+    # import torch
+    # from torch.utils.data import DataLoader
 
-    from ignite.engine import DeterministicEngine
-    from ignite.utils import manual_seed
+    # from ignite.engine import DeterministicEngine
+    # from ignite.utils import manual_seed
 
     seen_batches = []
     manual_seed(seed=15)
@@ -663,10 +663,7 @@ def _test_gradients_on_resume(
     dirname, device, with_dropout=True, with_dataaugs=True, data_size=24, batch_size=4, save_iter=None, save_epoch=None
 ):
 
-    debug = True
-
-    from torch.optim import SGD
-    from torch.utils.data import DataLoader
+    debug = False
 
     def random_train_data_loader(size):
         d = AugmentedData(torch.rand(size, 3, 32, 32), enabled=with_dataaugs)
@@ -820,7 +817,6 @@ def test_gradients_on_resume_on_cuda(dirname):
 
 def test_engine_with_dataloader_no_auto_batching():
     # tests https://github.com/pytorch/ignite/issues/941
-    from torch.utils.data import BatchSampler, DataLoader, RandomSampler
 
     data = torch.rand(64, 4, 10)
     data_loader = DataLoader(

tests/ignite/engine/test_engine.py

@@ -702,8 +702,7 @@ def restart_iter():
 
 def test_faq_inf_iterator_with_epoch_length():
     # Code snippet from FAQ
-
-    import torch
+    # import torch
 
     torch.manual_seed(12)
 
@@ -727,8 +726,7 @@ def train_step(trainer, batch):
 
 def test_faq_inf_iterator_no_epoch_length():
     # Code snippet from FAQ
-
-    import torch
+    # import torch
 
     torch.manual_seed(12)
 
@@ -756,8 +754,7 @@ def stop_training():
 
 def test_faq_fin_iterator_unknw_size():
     # Code snippet from FAQ
-
-    import torch
+    # import torch
 
     torch.manual_seed(12)
 
@@ -782,9 +779,8 @@ def restart_iter():
     assert trainer.state.epoch == 5
     assert trainer.state.iteration == 5 * 11
 
-    # # # # #
-
-    import torch
+    # Code snippet from FAQ
+    # import torch
 
     torch.manual_seed(12)
 
@@ -808,8 +804,7 @@ def val_step(evaluator, batch):
 
 def test_faq_fin_iterator():
     # Code snippet from FAQ
-
-    import torch
+    # import torch
 
     torch.manual_seed(12)
 
@@ -836,9 +831,8 @@ def restart_iter():
     assert trainer.state.epoch == 5
     assert trainer.state.iteration == 5 * size
 
-    # # # # #
-
-    import torch
+    # Code snippet from FAQ
+    # import torch
 
     torch.manual_seed(12)