Addressed comments from francisco

autoPyTorch/pipeline/components/training/losses.py

@@ -14,14 +14,14 @@
 
 losses = dict(classification=dict(
     CrossEntropyLoss=dict(
-        module=CrossEntropyLoss, supported_output_types=(MULTICLASS, BINARY)),
+        module=CrossEntropyLoss, supported_output_types=[MULTICLASS, BINARY]),
     BCEWithLogitsLoss=dict(
-        module=BCEWithLogitsLoss, supported_output_types=(BINARY,))),
+        module=BCEWithLogitsLoss, supported_output_types=[BINARY])),
     regression=dict(
         MSELoss=dict(
-            module=MSELoss, supported_output_types=(CONTINUOUS,)),
+            module=MSELoss, supported_output_types=[CONTINUOUS]),
         L1Loss=dict(
-            module=L1Loss, supported_output_types=(CONTINUOUS,))))
+            module=L1Loss, supported_output_types=[CONTINUOUS])))
 
 default_losses = dict(classification=CrossEntropyLoss, regression=MSELoss)
 

test/conftest.py

@@ -367,7 +367,12 @@ def loss_cross_entropy_multiclass():
     predictions = torch.randn(4, 4, requires_grad=True)
     name = 'CrossEntropyLoss'
     targets = torch.empty(4, dtype=torch.long).random_(4)
-    labels = torch.empty(20, dtype=torch.long).random_(4)
+    # to ensure we have all classes in the labels
+    while True:
+        labels = torch.empty(20, dtype=torch.long).random_(4)
+        if len(torch.unique(labels)) == 4:
+            break
+
     return dataset_properties, predictions, name, targets, labels
 
 
@@ -377,7 +382,11 @@ def loss_cross_entropy_binary():
     predictions = torch.randn(4, 2, requires_grad=True)
     name = 'CrossEntropyLoss'
     targets = torch.empty(4, dtype=torch.long).random_(2)
-    labels = torch.empty(20, dtype=torch.long).random_(2)
+    # to ensure we have all classes in the labels
+    while True:
+        labels = torch.empty(20, dtype=torch.long).random_(2)
+        if len(torch.unique(labels)) == 2:
+            break
     return dataset_properties, predictions, name, targets, labels
 
 
@@ -387,7 +396,11 @@ def loss_bce():
     predictions = torch.empty(4).random_(2)
     name = 'BCEWithLogitsLoss'
     targets = torch.empty(4).random_(2)
-    labels = torch.empty(20, dtype=torch.long).random_(2)
+    # to ensure we have all classes in the labels
+    while True:
+        labels = torch.empty(20, dtype=torch.long).random_(2)
+        if len(torch.unique(labels)) == 2:
+            break
     return dataset_properties, predictions, name, targets, labels
 
 

test/test_pipeline/test_losses.py

@@ -2,8 +2,9 @@
 
 import torch
 from torch import nn
+from torch.nn.modules.loss import _Loss as Loss
 
-from autoPyTorch.pipeline.components.training.losses import get_loss
+from autoPyTorch.pipeline.components.training.losses import get_loss, losses
 from autoPyTorch.utils.implementations import get_loss_weight_strategy
 
 
@@ -52,3 +53,16 @@ def test_losses(weighted, loss_details):
     loss = loss() if weights is None else loss(**kwargs)
     score = loss(predictions, targets)
     assert isinstance(score, torch.Tensor)
+    # Ensure it is a one element tensor
+    assert len(score.size()) == 0
+
+
+def test_loss_dict():
+    assert 'classification' in losses.keys()
+    assert 'regression' in losses.keys()
+    for task in losses.values():
+        for loss in task.values():
+            assert 'module' in loss.keys()
+            assert isinstance(loss['module'](), Loss)
+            assert 'supported_output_types' in loss.keys()
+            assert isinstance(loss['supported_output_types'], list)