forgot to push these files...

Author: justusschock

pytorch_lightning/metrics/metric.py

@@ -4,7 +4,7 @@
 import torch
 import torch.distributed
 
-from pytorch_lightning.metrics.convertors import tensor_metric, numpy_metric
+from pytorch_lightning.metrics.converters import tensor_metric, numpy_metric
 from pytorch_lightning.utilities.apply_func import apply_to_collection
 
 __all__ = ['Metric', 'TensorMetric', 'NumpyMetric']
@@ -35,7 +35,7 @@ def dtype(self) -> Union[str, torch.dtype]:
 
     @dtype.setter
     def dtype(self, new_dtype: Union[str, torch.dtype]):
-        # Necessary to avoid infinite recursion
+        # necessary to avoid infinite recursion
         raise RuntimeError('Cannot set the dtype explicitly. Please use metric.to(new_dtype).')
 
     @property
@@ -107,25 +107,19 @@ def to(self, *args, **kwargs) -> torch.nn.Module:
             tensor([[0.4963, 0.7682, 0.0885, 0.1320],
                     [0.3074, 0.6341, 0.4901, 0.8964],
                     [0.4556, 0.6323, 0.3489, 0.4017]])
-            >>> metric.to(torch.double)
+            >>> metric.to(torch.double) #doctest: +ELLIPSIS
             ExampleMetric()
             >>> metric.weight
-            tensor([[0.4963, 0.7682, 0.0885, 0.1320],
-                    [0.3074, 0.6341, 0.4901, 0.8964],
-                    [0.4556, 0.6323, 0.3489, 0.4017]], dtype=torch.float64)
+            tensor([[...]], dtype=torch.float64)
             >>> cpu = torch.device('cpu')
             >>> metric.to(cpu, dtype=torch.half, non_blocking=True)
             ExampleMetric()
-            >>> metric.weight
-            tensor([[0.4963, 0.7682, 0.0885, 0.1320],
-                    [0.3074, 0.6341, 0.4901, 0.8964],
-                    [0.4556, 0.6323, 0.3489, 0.4017]], dtype=torch.float16)
+            >>> metric.weight #doctest: +ELLIPSIS
+            tensor([[...]], dtype=torch.float16)
             >>> metric.to(cpu)
             ExampleMetric()
-            >>> metric.weight
-            tensor([[0.4963, 0.7682, 0.0885, 0.1320],
-                    [0.3074, 0.6341, 0.4901, 0.8964],
-                    [0.4556, 0.6323, 0.3489, 0.4017]], dtype=torch.float16)
+            >>> metric.weight #doctest: +ELLIPSIS
+            tensor([[...]], dtype=torch.float16)
 
 
         """

tests/metrics/test_convertors.py renamed to tests/metrics/test_converters.py

@@ -4,9 +4,9 @@
 import torch.distributed as dist
 
 import tests.base.utils as tutils
-from pytorch_lightning.metrics.convertors import (
+from pytorch_lightning.metrics.converters import (
     _apply_to_inputs, _apply_to_outputs, _convert_to_tensor, _convert_to_numpy,
-    _numpy_metric_conversion, _tensor_metric_conversion, _sync_ddp, tensor_metric, numpy_metric)
+    _numpy_metric_conversion, _tensor_metric_conversion, _sync_ddp_if_available, tensor_metric, numpy_metric)
 
 
 @pytest.mark.parametrize(['args', 'kwargs'],
@@ -109,7 +109,7 @@ def test_sync_reduce_ddp():
 
     tensor = torch.tensor([1.], device='cuda:0')
 
-    reduced_tensor = _sync_ddp(tensor)
+    reduced_tensor = _sync_ddp_if_available(tensor)
 
     assert reduced_tensor.item() == dist.get_world_size(), \
         'Sync-Reduce does not work properly with DDP and Tensors'
@@ -121,7 +121,7 @@ def test_sync_reduce_simple():
     """Make sure sync-reduce works without DDP"""
     tensor = torch.tensor([1.], device='cpu')
 
-    reduced_tensor = _sync_ddp(tensor)
+    reduced_tensor = _sync_ddp_if_available(tensor)
 
     assert torch.allclose(tensor, reduced_tensor), \
         'Sync-Reduce does not work properly without DDP and Tensors'