Add basic model testing. (#811)

* Add basic model testing.

Also fixes flaky test

* Fix flake8

test/test_models.py

+import torch
+from torchvision import models
+import unittest
+
+
+def get_available_models():
+    # TODO add a registration mechanism to torchvision.models
+    return [k for k, v in models.__dict__.items() if callable(v) and k[0].lower() == k[0]]
+
+
+class Tester(unittest.TestCase):
+    def _test_model(self, name, input_shape):
+        model = models.__dict__[name]()
+        model.eval()
+        x = torch.rand(input_shape)
+        out = model(x)
+        self.assertEqual(out.shape[-1], 1000)
+
+
+for model_name in get_available_models():
+    # for-loop bodies don't define scopes, so we have to save the variables
+    # we want to close over in some way
+    def do_test(self, model_name=model_name):
+        input_shape = (1, 3, 224, 224)
+        if model_name in ['inception_v3']:
+            input_shape = (1, 3, 299, 299)
+        self._test_model(model_name, input_shape)
+
+    setattr(Tester, "test_" + model_name, do_test)
+
+
+if __name__ == '__main__':
+    unittest.main()

test/test_transforms.py

@@ -142,7 +142,7 @@ class Tester(unittest.TestCase):
         for i in range(10):
             scale_min = round(random.random(), 2)
             scale_range = (scale_min, scale_min + round(random.random(), 2))
-            aspect_min = round(random.random(), 2)
+            aspect_min = max(round(random.random(), 2), epsilon)
             aspect_ratio_range = (aspect_min, aspect_min + round(random.random(), 2))
             randresizecrop = transforms.RandomResizedCrop(size, scale_range, aspect_ratio_range)
             _, _, h, w = randresizecrop.get_params(img, scale_range, aspect_ratio_range)