def test_mmf_loss(self):
get_loss_class_mock = MagicMock(side_effect=build_loss_side_effect())
registry.get_loss_class = get_loss_class_mock
# Test if MMFLoss accepts empty parameters
self.assertRaises(ValueError, losses.MMFLoss)
self.assertTrue(
losses.MMFLoss({
"type": "cross_entropy"
}).name, "cross_entropy")
self.assertTrue(losses.MMFLoss("cross_entropy").name, "cross_entropy")
self.assertRaises(AssertionError, losses.MMFLoss, [])
# Multi requires dict
self.assertRaises(AssertionError, losses.MMFLoss, "multi")
cross_entropy = losses.MMFLoss("cross_entropy")
cross_entropy_from_dict = losses.MMFLoss({"type": "cross_entropy"})
sample_list = SampleList()
sample_list.dataset_type = "val"
sample_list.dataset_name = "vqa2"
output = cross_entropy(sample_list, {})
output_from_dict = cross_entropy_from_dict(sample_list, {})
self.assertEqual(output, {"val/vqa2/cross_entropy": torch.tensor(1.0)})
self.assertEqual(output_from_dict, output)
get_loss_class_mock.side_effect = build_loss_side_effect(1.0)
output = cross_entropy(sample_list, {})
self.assertEqual(output, {"val/vqa2/cross_entropy": torch.tensor(1.0)})
self.assertEqual(output_from_dict, output)
self.assertTrue(get_loss_class_mock.called)
self.assertEqual(get_loss_class_mock.call_count, 5)
def test_mmf_dict_loss(self):
mse_mae_loss = losses.MMFLoss("mse_mae")
torch.manual_seed(1234)
random_tensor = torch.rand((1, 768))
sample_list = SampleList()
sample_list.dataset_type = "val"
sample_list.dataset_name = "vqa2"
sample_list["targets"] = random_tensor
model_output = {"scores": random_tensor}
output = mse_mae_loss(sample_list, model_output)
self.assertEqual(output["val/vqa2/mse_mae/mse"].item(), 0.0)
self.assertEqual(output["val/vqa2/mse_mae/mae"].item(), 0.0)