cov: add tests for ignite.contrib.engines

tests/ignite/contrib/engines/test_common.py

@@ -39,7 +39,14 @@ def forward(self, x):
 
 
 def _test_setup_common_training_handlers(
-    dirname, device, rank=0, local_rank=0, distributed=False, lr_scheduler=None, save_handler=None
+    dirname,
+    device,
+    rank=0,
+    local_rank=0,
+    distributed=False,
+    lr_scheduler=None,
+    save_handler=None,
+    output_transform=lambda loss: loss,
 ):
 
     lr = 0.01
@@ -74,7 +81,7 @@ def update_fn(engine, batch):
         loss = y_pred.mean()
         loss.backward()
         optimizer.step()
-        return loss
+        return output_transform(loss)
 
     train_sampler = None
     if distributed and idist.get_world_size() > 1:
@@ -141,6 +148,22 @@ def test_asserts_setup_common_training_handlers():
         train_sampler = MagicMock(spec=DistributedSampler)
         setup_common_training_handlers(trainer, train_sampler=train_sampler)
 
+    with pytest.raises(RuntimeError, match=r"This contrib module requires available GPU"):
+        setup_common_training_handlers(trainer, with_gpu_stats=True)
+
+    with pytest.raises(TypeError, match=r"Unhandled type of update_function's output."):
+        trainer = Engine(lambda e, b: None)
+        setup_common_training_handlers(
+            trainer,
+            output_names=["loss"],
+            with_pbar_on_iters=False,
+            with_pbars=False,
+            with_gpu_stats=False,
+            stop_on_nan=False,
+            clear_cuda_cache=False,
+        )
+        trainer.run([1])
+
 
 def test_no_warning_with_train_sampler(recwarn):
     from torch.utils.data import RandomSampler
@@ -165,7 +188,6 @@ def test_assert_setup_common_training_handlers_wrong_train_sampler(distributed_c
 
 
 def test_setup_common_training_handlers(dirname, capsys):
-
     _test_setup_common_training_handlers(dirname, device="cpu")
 
     # Check epoch-wise pbar
@@ -175,6 +197,24 @@ def test_setup_common_training_handlers(dirname, capsys):
     out = list(filter(None, out))
     assert "Epoch" in out[-1] or "Epoch" in out[-2], f"{out[-2]}, {out[-1]}"
 
+    _test_setup_common_training_handlers(dirname, device="cpu", output_transform=lambda loss: [loss])
+
+    # Check epoch-wise pbar
+    captured = capsys.readouterr()
+    out = captured.err.split("\r")
+    out = list(map(lambda x: x.strip(), out))
+    out = list(filter(None, out))
+    assert "Epoch" in out[-1] or "Epoch" in out[-2], f"{out[-2]}, {out[-1]}"
+
+    _test_setup_common_training_handlers(dirname, device="cpu", output_transform=lambda loss: {"batch_loss": loss})
+
+    # Check epoch-wise pbar
+    captured = capsys.readouterr()
+    out = captured.err.split("\r")
+    out = list(map(lambda x: x.strip(), out))
+    out = list(filter(None, out))
+    assert "Epoch" in out[-1] or "Epoch" in out[-2], f"{out[-2]}, {out[-1]}"
+
 
 def test_setup_common_training_handlers_using_save_handler(dirname, capsys):
 

tests/ignite/contrib/metrics/test_average_precision.py

@@ -91,9 +91,9 @@ def get_test_cases():
             (torch.randint(0, 2, size=(10, 1)).long(), torch.randint(0, 2, size=(10, 1)).long(), 1),
             (torch.randint(0, 2, size=(100, 1)).long(), torch.randint(0, 2, size=(100, 1)).long(), 1),
             # updated batches
-            (torch.randint(0, 2, size=(10,)).long(), torch.randint(0, 2, size=(10,)).long(), 16),
+            (torch.randint(0, 2, size=(16,)).long(), torch.randint(0, 2, size=(16,)).long(), 16),
             (torch.randint(0, 2, size=(100,)).long(), torch.randint(0, 2, size=(100,)).long(), 16),
-            (torch.randint(0, 2, size=(10, 1)).long(), torch.randint(0, 2, size=(10, 1)).long(), 16),
+            (torch.randint(0, 2, size=(16, 1)).long(), torch.randint(0, 2, size=(16, 1)).long(), 16),
             (torch.randint(0, 2, size=(100, 1)).long(), torch.randint(0, 2, size=(100, 1)).long(), 16),
         ]
         return test_cases
@@ -134,7 +134,7 @@ def get_test_cases():
             (torch.randint(0, 2, size=(100, 4)).long(), torch.randint(0, 2, size=(100, 4)).long(), 1),
             (torch.randint(0, 2, size=(200, 6)).long(), torch.randint(0, 2, size=(200, 6)).long(), 1),
             # updated batches
-            (torch.randint(0, 2, size=(10, 4)).long(), torch.randint(0, 2, size=(10, 4)).long(), 16),
+            (torch.randint(0, 2, size=(16, 4)).long(), torch.randint(0, 2, size=(16, 4)).long(), 16),
             (torch.randint(0, 2, size=(50, 7)).long(), torch.randint(0, 2, size=(50, 7)).long(), 16),
             (torch.randint(0, 2, size=(100, 4)).long(), torch.randint(0, 2, size=(100, 4)).long(), 16),
             (torch.randint(0, 2, size=(200, 6)).long(), torch.randint(0, 2, size=(200, 6)).long(), 16),

tests/ignite/contrib/metrics/test_gpu_info.py

@@ -1,4 +1,3 @@
-import sys
 from unittest.mock import Mock, patch
 
 import pytest
@@ -7,35 +6,15 @@
 from ignite.contrib.metrics import GpuInfo
 from ignite.engine import Engine, State
 
-python_below_36 = (sys.version[0] == "3" and int(sys.version[2]) < 6) or int(sys.version[0]) < 2
 
+def test_no_pynvml_package():
+    with patch.dict("sys.modules", {"pynvml.smi": None}):
+        with pytest.raises(RuntimeError, match="This contrib module requires pynvml to be installed."):
+            GpuInfo()
 
-@pytest.fixture
-def no_site_packages():
-    import sys
-
-    import pynvml  # noqa: F401
-
-    assert "pynvml" in sys.modules
-    pynvml_module = sys.modules["pynvml"]
-    del sys.modules["pynvml"]
-    prev_path = list(sys.path)
-    sys.path = [p for p in sys.path if "site-packages" not in p]
-    yield "no_site_packages"
-    sys.path = prev_path
-    sys.modules["pynvml"] = pynvml_module
 
-
-@pytest.mark.skipif(python_below_36, reason="No pynvml for python < 3.6")
-def test_no_pynvml_package(no_site_packages):
-
-    with pytest.raises(RuntimeError, match="This contrib module requires pynvml to be installed."):
-        GpuInfo()
-
-
-@pytest.mark.skipif(python_below_36 or torch.cuda.is_available(), reason="No pynvml for python < 3.6")
+@pytest.mark.skipif(torch.cuda.is_available(), reason="Skip if GPU")
 def test_no_gpu():
-
     with pytest.raises(RuntimeError, match="This contrib module requires available GPU"):
         GpuInfo()
 
@@ -79,7 +58,7 @@ def _test_gpu_info(device="cpu"):
         assert "gpu:0 util(%)" not in engine.state.metrics
 
 
-@pytest.mark.skipif(python_below_36 or not (torch.cuda.is_available()), reason="No pynvml for python < 3.6 and no GPU")
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="Skip if no GPU")
 def test_gpu_info_on_cuda():
     _test_gpu_info(device="cuda")
 
@@ -89,7 +68,6 @@ def test_gpu_info_on_cuda():
 
 @pytest.fixture
 def mock_pynvml_module():
-
     with patch.dict(
         "sys.modules",
         {
@@ -115,7 +93,6 @@ def getInstance():
 
 @pytest.fixture
 def mock_gpu_is_available():
-
     with patch("ignite.contrib.metrics.gpu_info.torch.cuda") as mock_cuda:
         mock_cuda.is_available.return_value = True
         yield mock_cuda