def test_split_return_values_torch(self):
seq_length_output = numpy.array([10, 5])
output = torch.ones(seq_length_output.max(), 2, 4)
with unittest.mock.patch.object(ModelTrainer.logger, "error") as mock_logger:
with self.assertRaises(TypeError):
ModelTrainer._split_return_values(output, seq_length_output, None, False)
mock_logger.assert_called_with("No best model exists yet. Continue with the current one.")
def test_split_return_values(self):
seq_length_output = numpy.array([10, 6, 8])
batch_size = 3
feature_dim = 50
output = numpy.empty((seq_length_output.max(), batch_size, feature_dim))
hidden1 = numpy.empty((seq_length_output.max(), batch_size, 2))
hidden2 = numpy.empty((seq_length_output.max(), batch_size, 4))
for idx in range(batch_size):
output[:, idx] = idx
hidden1[:, idx] = idx * 10
hidden2[:, idx] = idx * 100
hidden = (hidden1, hidden2)
batch = (output, hidden)
split_batch = ModelTrainer._split_return_values(batch, seq_length_output, None, False)
for idx in range(batch_size):
b = split_batch[idx]
out = b[0]
h = b[1]
h1 = h[0]
h2 = h[1]
self.assertTrue((out == idx).all(), msg="Output of batch {} is wrong, expected was all values being {}.".format(idx, idx))
self.assertTrue((h1 == idx * 10).all(), msg="Hidden1 of batch {} is wrong, expected was all values being {}.".format(idx, idx * 10))
self.assertTrue((h2 == idx * 100).all(), msg="Hidden2 of batch {} is wrong, expected was all values being {}.".format(idx, idx * 100))