Add pytorch test

tests/benzina/torch/meson.build

@@ -0,0 +1,9 @@
+#
+# A few tests using this executable
+#
+
+pytest = find_program('pytest')
+
+test_devsubset = files('test_devsubset.py')
+
+test('Parse devsubset', pytest, suite: 'torch', args: [test_devsubset], timeout: 120, workdir: meson.source_root())

tests/benzina/torch/test_devsubset.py

@@ -0,0 +1,68 @@
+import os
+
+import torch
+
+import benzina.torch as bz
+
+
+class Subset(torch.utils.data.Dataset):
+    def __init__(self, dataset, indices):
+        self.dataset = dataset
+        self.indices = indices
+
+    def __getitem__(self, idx):
+        return self.dataset[self.indices[idx]]
+
+    def __len__(self):
+        return len(self.indices)
+
+    @property
+    def shape(self):
+        return self.dataset.shape
+
+    @property
+    def root(self):
+        return self._root
+
+    @property
+    def _core(self):
+        return self.dataset._core
+
+    @property
+    def _root(self):
+        return self.dataset._root 
+
+
+def test_devsubset_pytorch_loading():
+    dataset_path = os.environ["DATASET_PATH"]
+
+    with open("tests/data/devsubset_files", 'r') as devsubset_list:
+        # skip the end line following the filename
+        subset_targets_map = {line.rstrip()[5:]: int(line[:4]) for line in devsubset_list if line}
+
+    subset_filenames = set(subset_targets_map.keys())
+
+    with open(os.path.join(dataset_path, "data.filenames"), 'r') as filenames:
+        subset_indices = []
+        subset_targets = []
+        for i, filename in enumerate(filenames):
+            # skip the end line following the filename
+            filename = filename.rstrip()
+            if filename in subset_filenames:
+                subset_indices.append(i)
+                subset_targets.append(subset_targets_map[filename])
+
+    dataset = bz.ImageNet(dataset_path)
+
+    subset = Subset(dataset, subset_indices)
+
+    subset_loader = bz.DataLoader(subset,
+                                  batch_size=100,
+                                  seed=0,
+                                  shape=(256,256))
+
+    for start_i, (images, targets) in zip(range(0, len(subset), 100), subset_loader):
+        for i, (image, target) in enumerate(zip(images, targets)):
+            assert image.size() == (3, 256, 256)
+            assert image.sum().item() > 0
+            assert target.item() == subset_targets[start_i + i]