test with path provided

tests/test_mask.py

@@ -1,10 +1,11 @@
 import os
+import shutil
 
 import numpy as np
-import pydicom as pyd
 import pytest
+import torch
 
-from lungmask.mask import LMInferer, apply, apply_fused
+from lungmask.mask import MODEL_URLS, LMInferer
 from lungmask.utils import read_dicoms
 
 
@@ -13,8 +14,32 @@ def fixture_testvol():
     return read_dicoms(os.path.join(os.path.dirname(__file__), "testdata"))[0]
 
 
-def test_LMInferer(fixture_testvol):
+@pytest.fixture(scope="session")
+def fixture_weights_path_R231(tmpdir_factory):
+    # we make sure the model is there
+    torch.hub.load_state_dict_from_url(
+        MODEL_URLS["R231"][0], progress=True, map_location=torch.device("cpu")
+    )
+    modelbasename = os.path.basename(MODEL_URLS["R231"][0])
+    modelpath = os.path.join(torch.hub.get_dir(), "checkpoints", modelbasename)
+    tmppath = str(tmpdir_factory.mktemp("weights").join(modelbasename))
+    shutil.copy(modelpath, tmppath)
+    return tmppath
+
+
+def test_LMInferer(fixture_testvol, fixture_weights_path_R231):
+    inferer = LMInferer(
+        force_cpu=True,
+        tqdm_disable=True,
+    )
+    res = inferer.apply(fixture_testvol)
+    assert np.all(np.unique(res, return_counts=True)[1] == [423000, 64752, 36536])
+
+    # here, we provide a path to the R231 weights but specify LTRCLobes (6 channel) as modelname
+    # The modelname should be ignored and a 3 channel output should be generated
     inferer = LMInferer(
+        modelname="LTRCLobes",
+        modelpath=fixture_weights_path_R231,
         force_cpu=True,
         tqdm_disable=True,
     )