Refactor datasets and use float32 instead of float64 in Tecator

prototorch/datasets/__init__.py

@@ -0,0 +1,7 @@
+"""ProtoTorch datasets."""
+
+from .tecator import Tecator
+
+__all__ = [
+    'Tecator',
+]

prototorch/datasets/tecator.py

@@ -1,4 +1,4 @@
-"""Tecator dataset for classification
+"""Tecator dataset for classification.
 
 URL:
     http://lib.stat.cmu.edu/datasets/tecator
@@ -46,9 +46,11 @@ from prototorch.datasets.abstract import ProtoDataset
 
 
 class Tecator(ProtoDataset):
-    """Tecator dataset for classification"""
-    resources = [('1MMuUK8V41IgNpnPDbg3E-QAL6wlErTk0',
-                  'ba5607c580d0f91bb27dc29d13c2f8df')]
+    """Tecator dataset for classification."""
+    resources = [
+        ('1MMuUK8V41IgNpnPDbg3E-QAL6wlErTk0',
+         'ba5607c580d0f91bb27dc29d13c2f8df'),
+    ]  # (google_storage_id, md5hash)
     classes = ['0 - low_fat', '1 - high_fat']
 
     def __getitem__(self, index):
@@ -80,8 +82,14 @@ class Tecator(ProtoDataset):
                      allow_pickle=False) as f:
             x_train, y_train = f['x_train'], f['y_train']
             x_test, y_test = f['x_test'], f['y_test']
-        training_set = [torch.as_tensor(x_train), torch.as_tensor(y_train)]
-        test_set = [torch.as_tensor(x_test), torch.as_tensor(y_test)]
+        training_set = [
+            torch.tensor(x_train, dtype=torch.float32),
+            torch.tensor(y_train),
+        ]
+        test_set = [
+            torch.tensor(x_test, dtype=torch.float32),
+            torch.tensor(y_test),
+        ]
 
         with open(os.path.join(self.processed_folder, self.training_file),
                   'wb') as f: