def ensure_batch_predictions_are_consistent(self):
self.model.eval()
single_predictions = []
for i, instance in enumerate(self.instances):
dataset = Batch([instance])
tensors = dataset.as_tensor_dict(dataset.get_padding_lengths())
result = self.model(**tensors)
single_predictions.append(result)
full_dataset = Batch(self.instances)
batch_tensors = full_dataset.as_tensor_dict(
full_dataset.get_padding_lengths())
batch_predictions = self.model(**batch_tensors)
for i, instance_predictions in enumerate(single_predictions):
for key, single_predicted in instance_predictions.items():
tolerance = 1e-6
if 'loss' in key:
# Loss is particularly unstable; we'll just be satisfied if everything else is
# close.
continue
single_predicted = single_predicted[0]
batch_predicted = batch_predictions[key][i]
if isinstance(single_predicted, torch.Tensor):
if single_predicted.size() != batch_predicted.size():
slices = tuple(
slice(0, size) for size in single_predicted.size())
batch_predicted = batch_predicted[slices]
assert_allclose(single_predicted.data.numpy(),
batch_predicted.data.numpy(),
atol=tolerance,
err_msg=key)
else:
assert single_predicted == batch_predicted, key
def test_as_tensor_dict(self):
dataset = Batch(self.instances)
dataset.index_instances(self.vocab)
padding_lengths = dataset.get_padding_lengths()
tensors = dataset.as_tensor_dict(padding_lengths)
text1 = tensors["text1"]["tokens"].detach().cpu().numpy()
text2 = tensors["text2"]["tokens"].detach().cpu().numpy()
numpy.testing.assert_array_almost_equal(text1, numpy.array([[2, 3, 4, 5, 6],
[1, 3, 4, 5, 6]]))
numpy.testing.assert_array_almost_equal(text2, numpy.array([[2, 3, 4, 1, 5, 6],
[2, 3, 1, 0, 0, 0]]))
def test_padding_lengths_uses_max_instance_lengths(self):
dataset = Batch(self.instances)
dataset.index_instances(self.vocab)
padding_lengths = dataset.get_padding_lengths()
assert padding_lengths == {"text1": {"num_tokens": 5}, "text2": {"num_tokens": 6}}