test(datamodules): Reduce boilerplate with toy_project

tests/test_datamodules.py

@@ -27,20 +27,13 @@
 from itertools import combinations
 from torch.utils.data import RandomSampler, SequentialSampler
 from aidsorb.utils import pcd_from_dir
-from aidsorb.data import prepare_data, Collator
+from aidsorb.data import prepare_data, Collator, get_names
 from aidsorb.transforms import Center, RandomRotation
 from aidsorb.datamodules import PCDDataModule
 
 
 class TestPCDDataModule(unittest.TestCase):
     def setUp(self):
-        self.tempdir = tempfile.TemporaryDirectory(dir='/tmp')
-        self.outname = os.path.join(self.tempdir.name, 'pcds.npz')
-        self.split_ratio = [3, 1, 2]
-
-        pcd_from_dir(dirname='tests/structures', outname=self.outname)
-        prepare_data(source=self.outname, split_ratio=self.split_ratio)
-
         # Arguments for the datamodule.
         self.train_size = 2
         self.train_trans_x = Center()
@@ -51,13 +44,13 @@ def setUp(self):
         self.eval_bs = 2
         self.config_dataloaders = {
                 'pin_memory': True,
-                'num_workers': 2,
+                'num_workers': 4,
                 'collate_fn': Collator()
                 }
 
         # Instantiate the datamodule.
         self.dm = PCDDataModule(
-                path_to_X=self.outname,
+                path_to_X='tests/dummy/toy_project/pcd_data',
                 path_to_Y='tests/dummy/toy_dataset.csv',
                 index_col='id',
                 labels=['y2', 'y3'],
@@ -76,9 +69,12 @@ def setUp(self):
 
     def test_datasets(self):
         # Check that the datasets have the correct size.
+        val_names = get_names('tests/dummy/toy_project/validation.json')
+        test_names = get_names('tests/dummy/toy_project/test.json')
+
         self.assertEqual(len(self.dm.train_dataset), self.train_size)
-        self.assertEqual(len(self.dm.validation_dataset), self.split_ratio[1])
-        self.assertEqual(len(self.dm.test_dataset), self.split_ratio[2])
+        self.assertEqual(len(self.dm.validation_dataset), len(val_names))
+        self.assertEqual(len(self.dm.test_dataset), len(test_names))
 
         # The pairwise intersections must be the empty set.
         for ds_comb in combinations([
@@ -101,7 +97,6 @@ def test_datasets(self):
 
             self.assertIs(ds.transform_y, self.trans_y)
 
-
     def test_dataloaders(self):
         dataloaders = [
                 self.dm.train_dataloader(),
@@ -114,8 +109,6 @@ def test_dataloaders(self):
                 self.dm.test_dataset,
                 ]
 
-        passed_collate_fn = self.config_dataloaders['collate_fn']
-
         for i, dl in enumerate(dataloaders):
             # Check that dataloaders use appropriate settings.
             if i == 0:
@@ -125,7 +118,7 @@ def test_dataloaders(self):
                 self.assertIsInstance(dl.sampler, SequentialSampler)
                 self.assertEqual(dl.batch_size, self.eval_bs)
 
-            self.assertEqual(dl.collate_fn, passed_collate_fn)
+            self.assertEqual(dl.collate_fn, self.config_dataloaders['collate_fn'])
 
             # Check that collate function is used properly.
             for x, y in dl:
@@ -137,4 +130,4 @@ def test_dataloaders(self):
             self.assertIs(dl.dataset, ds)
 
     def tearDown(self):
-        self.tempdir.cleanup()
+        ...