Fix tests for native torch metrics

CHANGELOG.md

@@ -4,9 +4,6 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
-## Metrics (will be added to unreleased once the metric branch was finished)
-- Add Metric Base Classes ([#1326](https://github.com/PyTorchLightning/pytorch-lightning/pull/1326))
-
 ## [unreleased] - YYYY-MM-DD
 
 ### Added

pytorch_lightning/metrics/converters.py

@@ -18,12 +18,13 @@
 def _apply_to_inputs(func_to_apply: Callable, *dec_args, **dec_kwargs) -> Callable:
     """
     Decorator function to apply a function to all inputs of a function.
+
     Args:
         func_to_apply: the function to apply to the inputs
         *dec_args: positional arguments for the function to be applied
         **dec_kwargs: keyword arguments for the function to be applied
 
-    Returns:
+    Return:
         the decorated function
     """
 
@@ -42,12 +43,13 @@ def new_func(*args, **kwargs):
 def _apply_to_outputs(func_to_apply: Callable, *dec_args, **dec_kwargs) -> Callable:
     """
     Decorator function to apply a function to all outputs of a function.
+
     Args:
         func_to_apply: the function to apply to the outputs
         *dec_args: positional arguments for the function to be applied
         **dec_kwargs: keyword arguments for the function to be applied
 
-    Returns:
+    Return:
         the decorated function
     """
 
@@ -64,40 +66,40 @@ def new_func(*args, **kwargs):
 
 def _convert_to_tensor(data: Any) -> Any:
     """
-    Maps all kind of collections and numbers to tensors
+    Maps all kind of collections and numbers to tensors.
 
     Args:
         data: the data to convert to tensor
 
-    Returns:
+    Return:
         the converted data
-
     """
     if isinstance(data, numbers.Number):
         return torch.tensor([data])
     # is not array of object
     elif isinstance(data, np.ndarray) and np_str_obj_array_pattern.search(data.dtype.str) is None:
         return torch.from_numpy(data)
-
-    raise TypeError("The given type ('%s') cannot be converted to a tensor!" % type(data).__name__)
+    elif isinstance(data, torch.Tensor):
+        return data
 
     raise TypeError(f"The given type ('{type(data).__name__}') cannot be converted to a tensor!")
 
 
 def _convert_to_numpy(data: Union[torch.Tensor, np.ndarray, numbers.Number]) -> np.ndarray:
-    """
-    converts all tensors and numpy arrays to numpy arrays
+    """Convert all tensors and numpy arrays to numpy arrays.
+
     Args:
         data: the tensor or array to convert to numpy
 
-    Returns:
+    Return:
         the resulting numpy array
-
     """
     if isinstance(data, torch.Tensor):
         return data.cpu().detach().numpy()
     elif isinstance(data, numbers.Number):
         return np.array([data])
+    elif isinstance(data, np.ndarray):
+        return data
 
     raise TypeError("The given type ('%s') cannot be converted to a numpy array!" % type(data).__name__)
 
@@ -111,9 +113,8 @@ def _numpy_metric_conversion(func_to_decorate: Callable) -> Callable:
     Args:
         func_to_decorate: the function whose inputs and outputs shall be converted
 
-    Returns:
+    Return:
         the decorated function
-
     """
     # applies collection conversion from tensor to numpy to all inputs
     # we need to include numpy arrays here, since otherwise they will also be treated as sequences
@@ -132,9 +133,8 @@ def _tensor_metric_conversion(func_to_decorate: Callable) -> Callable:
     Args:
         func_to_decorate: the function whose inputs and outputs shall be converted
 
-    Returns:
+    Return:
         the decorated function
-
     """
     # converts all inputs to tensor if possible
     # we need to include tensors here, since otherwise they will also be treated as sequences
@@ -156,9 +156,8 @@ def _sync_ddp_if_available(result: Union[torch.Tensor],
         group: the process group to gather results from. Defaults to all processes (world)
         reduce_op: the reduction operation. Defaults to sum.
 
-    Returns:
+    Return:
         reduced value
-
     """
 
     if torch.distributed.is_available() and torch.distributed.is_initialized():
@@ -180,7 +179,6 @@ def numpy_metric(group: Optional[Any] = None,
                  reduce_op: Optional[torch.distributed.ReduceOp] = None) -> Callable:
     """
     This decorator shall be used on all function metrics working on numpy arrays.
-
     It handles the argument conversion and DDP reduction for metrics working on numpy.
     All inputs of the decorated function will be converted to numpy and all
     outputs will be converted to tensors.
@@ -190,13 +188,12 @@ def numpy_metric(group: Optional[Any] = None,
         group: the process group to gather results from. Defaults to all processes (world)
         reduce_op: the reduction operation. Defaults to sum
 
-    Returns:
+    Return:
         the decorated function
-
     """
 
     def decorator_fn(func_to_decorate):
-        return _apply_to_outputs(apply_to_collection, torch.Tensor, _sync_ddp,
+        return _apply_to_outputs(apply_to_collection, torch.Tensor, _sync_ddp_if_available,
                                  group=group,
                                  reduce_op=reduce_op)(_numpy_metric_conversion(func_to_decorate))
 
@@ -207,7 +204,6 @@ def tensor_metric(group: Optional[Any] = None,
                   reduce_op: Optional[torch.distributed.ReduceOp] = None) -> Callable:
     """
     This decorator shall be used on all function metrics working on tensors.
-
     It handles the argument conversion and DDP reduction for metrics working on tensors.
     All inputs and outputs of the decorated function will be converted to tensors.
     In DDP Training all output tensors will be reduced according to the given rules.
@@ -216,14 +212,12 @@ def tensor_metric(group: Optional[Any] = None,
        group: the process group to gather results from. Defaults to all processes (world)
        reduce_op: the reduction operation. Defaults to sum
 
-    Returns:
+    Return:
        the decorated function
-
     """
 
-
     def decorator_fn(func_to_decorate):
-        return _apply_to_outputs(apply_to_collection, torch.Tensor, _sync_ddp,
+        return _apply_to_outputs(apply_to_collection, torch.Tensor, _sync_ddp_if_available,
                                  group=group,
                                  reduce_op=reduce_op)(_tensor_metric_conversion(func_to_decorate))
 

pytorch_lightning/metrics/functional/classification.py

@@ -1,5 +1,6 @@
 from collections import Sequence
 from typing import Optional, Tuple, Callable
+from functools import wraps
 
 import torch
 
@@ -326,6 +327,7 @@ def auc(x: torch.Tensor, y: torch.Tensor, reorder: bool = True):
 
 def auc_decorator(reorder: bool = True) -> Callable:
     def wrapper(func_to_decorate: Callable) -> Callable:
+        @wraps(func_to_decorate)
         def new_func(*args, **kwargs) -> torch.Tensor:
             x, y = func_to_decorate(*args, **kwargs)[:2]
 

pytorch_lightning/metrics/functional/reduction.py

@@ -3,22 +3,17 @@
 
 def reduce(to_reduce: torch.Tensor, reduction: str) -> torch.Tensor:
     """
-    reduces a given tensor by a given reduction method
-    Parameters
-    ----------
-    to_reduce : torch.Tensor
-        the tensor, which shall be reduced
-    reduction : str
-        a string specifying the reduction method.
-        should be one of 'elementwise_mean' | 'none' | 'sum'
-    Returns
-    -------
-    torch.Tensor
+    Reduces a given tensor by a given reduction method
+
+    Args:
+        to_reduce : the tensor, which shall be reduced
+       reduction :  a string specifying the reduction method ('elementwise_mean', 'none', 'sum')
+
+    Return:
         reduced Tensor
-    Raises
-    ------
-    ValueError
-        if an invalid reduction parameter was given
+
+    Raise:
+        ValueError if an invalid reduction parameter was given
     """
     if reduction == 'elementwise_mean':
         return torch.mean(to_reduce)