Merge branch 'release/1.2-dev' into fix/yaml

pytorch_lightning/loggers/wandb.py

@@ -29,8 +29,9 @@
 _WANDB_AVAILABLE = _module_available("wandb")
 
 try:
-    import wandb
     from wandb.wandb_run import Run
+
+    import wandb
 except ImportError:
     # needed for test mocks, these tests shall be updated
     wandb, Run = None, None

pytorch_lightning/trainer/supporters.py

@@ -14,7 +14,7 @@
 
 import os
 from collections.abc import Iterable, Iterator, Mapping, Sequence
-from typing import Any, Optional, Union
+from typing import Any, Callable, Optional, Union
 
 import torch
 from torch import Tensor
@@ -306,12 +306,8 @@ def _calc_num_data(datasets: Union[Sequence, Mapping], mode: str) -> Union[int,
 
         if isinstance(all_lengths, (int, float)):
             length = all_lengths
-
-        elif isinstance(all_lengths, Mapping):
-            length = compute_func(all_lengths.values())
-
-        elif isinstance(all_lengths, Sequence):
-            length = compute_func(all_lengths)
+        else:
+            length = _nested_calc_num_data(all_lengths, compute_func)
 
         return length
 
@@ -437,13 +433,8 @@ def _calc_num_batches(loaders: Any) -> Union[int, float]:
         if isinstance(all_lengths, (int, float)):
             return all_lengths
 
-        elif isinstance(all_lengths, Mapping):
-            return min(all_lengths.values())
-
-        elif isinstance(all_lengths, Sequence):
-            return min(all_lengths)
-
-        raise TypeError(f'Got Type {type(all_lengths).__name__}, but expected one of Sequence, int or Mapping')
+        else:
+            return _nested_calc_num_data(all_lengths, min)
 
     def __len__(self) -> int:
         return self._calc_num_batches(self.loaders)
@@ -516,3 +507,25 @@ def create_loader_iters(
         """
         # dataloaders are Iterable but not Sequences. Need this to specifically exclude sequences
         return apply_to_collection(loaders, Iterable, iter, wrong_dtype=(Sequence, Mapping))
+
+
+def _nested_calc_num_data(data: Union[Mapping, Sequence], compute_func: Callable):
+
+    if isinstance(data, int):
+        return data
+
+    if isinstance(data, Mapping):
+        data = list(data.values())
+
+    if not isinstance(data, Sequence):
+        raise TypeError(f'Expected data to be int, Sequence or Mapping, but got {type(data).__name__}')
+
+    new_data = []
+
+    for x in data:
+        if isinstance(x, (Mapping, Sequence)):
+            new_data.append(_nested_calc_num_data(x, compute_func))
+        else:
+            new_data.append(x)
+
+    return compute_func(new_data)

tests/trainer/test_supporters.py

@@ -18,6 +18,7 @@
 from torch.utils.data import TensorDataset
 
 from pytorch_lightning.trainer.supporters import (
+    _nested_calc_num_data,
     CombinedDataset,
     CombinedLoader,
     CombinedLoaderIterator,
@@ -61,7 +62,7 @@ def test_cycle_iterator():
 def test_none_length_cycle_iterator():
     """Test the infinite cycling function of `CycleIterator`"""
     iterator = CycleIterator(range(100))
-    assert iterator.__len__() == float('inf')
+    assert iterator.__len__() == float("inf")
 
     # test infinite loop
     for idx, item in enumerate(iterator):
@@ -70,12 +71,15 @@ def test_none_length_cycle_iterator():
     assert item == 0
 
 
-@pytest.mark.parametrize(['dataset_1', 'dataset_2'], [
-    ([list(range(10)), list(range(20))]),
-    ([range(10), range(20)]),
-    ([torch.randn(10, 3, 2), torch.randn(20, 5, 6)]),
-    ([TensorDataset(torch.randn(10, 3, 2)), TensorDataset(torch.randn(20, 5, 6))])
-])
+@pytest.mark.parametrize(
+    ["dataset_1", "dataset_2"],
+    [
+        ([list(range(10)), list(range(20))]),
+        ([range(10), range(20)]),
+        ([torch.randn(10, 3, 2), torch.randn(20, 5, 6)]),
+        ([TensorDataset(torch.randn(10, 3, 2)), TensorDataset(torch.randn(20, 5, 6))]),
+    ],
+)
 def test_combined_dataset(dataset_1, dataset_2):
     """Verify the length of the CombinedDataset"""
     datasets = [dataset_1, dataset_2]
@@ -86,83 +90,91 @@ def test_combined_dataset(dataset_1, dataset_2):
 
 
 def test_combined_dataset_length_mode_error():
-    with pytest.raises(MisconfigurationException, match='Invalid Mode'):
-        CombinedDataset._calc_num_data([range(10)], 'test')
+    with pytest.raises(MisconfigurationException, match="Invalid Mode"):
+        CombinedDataset._calc_num_data([range(10)], "test")
 
 
 def test_combined_loader_iterator_dict_min_size():
     """Test `CombinedLoaderIterator` given mapping loaders"""
-    loaders = {'a': torch.utils.data.DataLoader(range(10), batch_size=4),
-               'b': torch.utils.data.DataLoader(range(20), batch_size=5)}
+    loaders = {
+        "a": torch.utils.data.DataLoader(range(10), batch_size=4),
+        "b": torch.utils.data.DataLoader(range(20), batch_size=5),
+    }
 
     combined_iter = CombinedLoaderIterator(loaders)
 
     for idx, item in enumerate(combined_iter):
         assert isinstance(item, dict)
         assert len(item) == 2
-        assert 'a' in item and 'b' in item
+        assert "a" in item and "b" in item
 
-    assert idx == min(len(loaders['a']), len(loaders['b'])) - 1
+    assert idx == min(len(loaders["a"]), len(loaders["b"])) - 1
 
 
 def test_combined_loader_init_mode_error():
     """Test the ValueError when constructing `CombinedLoader`"""
-    with pytest.raises(MisconfigurationException, match='selected unsupported mode'):
-        CombinedLoader([range(10)], 'testtt')
+    with pytest.raises(MisconfigurationException, match="selected unsupported mode"):
+        CombinedLoader([range(10)], "testtt")
 
 
 def test_combined_loader_loader_type_error():
     """Test the ValueError when wrapping the loaders"""
-    with pytest.raises(ValueError, match='Invalid Datatype'):
-        CombinedLoader(None, 'max_size_cycle')
+    with pytest.raises(ValueError, match="Invalid Datatype"):
+        CombinedLoader(None, "max_size_cycle")
 
 
 def test_combined_loader_calc_length_mode_error():
     """Test the ValueError when calculating the number of batches"""
-    with pytest.raises(TypeError, match='Got Type NoneType, but expected one of Sequence, int or Mapping'):
+    with pytest.raises(TypeError, match="Expected data to be int, Sequence or Mapping, but got NoneType"):
         CombinedLoader._calc_num_batches(None)
 
 
 def test_combined_loader_dict_min_size():
     """Test `CombinedLoader` of mode 'min_size' given mapping loaders"""
-    loaders = {'a': torch.utils.data.DataLoader(range(10), batch_size=4),
-               'b': torch.utils.data.DataLoader(range(20), batch_size=5)}
+    loaders = {
+        "a": torch.utils.data.DataLoader(range(10), batch_size=4),
+        "b": torch.utils.data.DataLoader(range(20), batch_size=5),
+    }
 
-    combined_loader = CombinedLoader(loaders, 'min_size')
+    combined_loader = CombinedLoader(loaders, "min_size")
 
     assert len(combined_loader) == min([len(v) for v in loaders.values()])
 
     for idx, item in enumerate(combined_loader):
         assert isinstance(item, dict)
         assert len(item) == 2
-        assert 'a' in item and 'b' in item
+        assert "a" in item and "b" in item
 
     assert idx == len(combined_loader) - 1
 
 
 def test_combined_loader_dict_max_size_cycle():
     """Test `CombinedLoader` of mode 'max_size_cycle' given mapping loaders"""
-    loaders = {'a': torch.utils.data.DataLoader(range(10), batch_size=4),
-               'b': torch.utils.data.DataLoader(range(20), batch_size=5)}
+    loaders = {
+        "a": torch.utils.data.DataLoader(range(10), batch_size=4),
+        "b": torch.utils.data.DataLoader(range(20), batch_size=5),
+    }
 
-    combined_loader = CombinedLoader(loaders, 'max_size_cycle')
+    combined_loader = CombinedLoader(loaders, "max_size_cycle")
 
     assert len(combined_loader) == max([len(v) for v in loaders.values()])
 
     for idx, item in enumerate(combined_loader):
         assert isinstance(item, dict)
         assert len(item) == 2
-        assert 'a' in item and 'b' in item
+        assert "a" in item and "b" in item
 
     assert idx == len(combined_loader) - 1
 
 
 def test_combined_loader_sequence_min_size():
     """Test `CombinedLoader` of mode 'min_size' given sequence loaders"""
-    loaders = [torch.utils.data.DataLoader(range(10), batch_size=4),
-               torch.utils.data.DataLoader(range(20), batch_size=5)]
+    loaders = [
+        torch.utils.data.DataLoader(range(10), batch_size=4),
+        torch.utils.data.DataLoader(range(20), batch_size=5),
+    ]
 
-    combined_loader = CombinedLoader(loaders, 'min_size')
+    combined_loader = CombinedLoader(loaders, "min_size")
 
     assert len(combined_loader) == min([len(v) for v in loaders])
 
@@ -175,10 +187,12 @@ def test_combined_loader_sequence_min_size():
 
 def test_combined_loader_sequence_max_size_cycle():
     """Test `CombinedLoader` of mode 'max_size_cycle' given sequence loaders"""
-    loaders = [torch.utils.data.DataLoader(range(10), batch_size=4),
-               torch.utils.data.DataLoader(range(20), batch_size=5)]
+    loaders = [
+        torch.utils.data.DataLoader(range(10), batch_size=4),
+        torch.utils.data.DataLoader(range(20), batch_size=5),
+    ]
 
-    combined_loader = CombinedLoader(loaders, 'max_size_cycle')
+    combined_loader = CombinedLoader(loaders, "max_size_cycle")
 
     assert len(combined_loader) == max([len(v) for v in loaders])
 
@@ -187,3 +201,22 @@ def test_combined_loader_sequence_max_size_cycle():
         assert len(item) == 2
 
     assert idx == len(combined_loader) - 1
+
+
+@pytest.mark.parametrize(
+    ["input_data", "compute_func", "expected_length"],
+    [
+        ([*range(10), list(range(1, 20))], min, 0),
+        ([*range(10), list(range(1, 20))], max, 19),
+        ([*range(10), {str(i): i for i in range(1, 20)}], min, 0),
+        ([*range(10), {str(i): i for i in range(1, 20)}], max, 19),
+        ({**{str(i): i for i in range(10)}, "nested": {str(i): i for i in range(1, 20)}}, min, 0),
+        ({**{str(i): i for i in range(10)}, "nested": {str(i): i for i in range(1, 20)}}, max, 19),
+        ({**{str(i): i for i in range(10)}, "nested": list(range(20))}, min, 0),
+        ({**{str(i): i for i in range(10)}, "nested": list(range(20))}, max, 19),
+    ],
+)
+def test_nested_calc_num_data(input_data, compute_func, expected_length):
+    calculated_length = _nested_calc_num_data(input_data, compute_func)
+
+    assert calculated_length == expected_length