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Moved test_sequence_loss outside run_all_in_graph_and_eager_mode. #1384

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Merged
merged 1 commit into from
Mar 25, 2020
Merged

Moved test_sequence_loss outside run_all_in_graph_and_eager_mode. #1384

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84 changes: 34 additions & 50 deletions tensorflow_addons/seq2seq/loss_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -102,6 +102,40 @@ def test_sequence_loss(average_across_timesteps, average_across_batch, zero_weig
np.testing.assert_allclose(computed, expected, rtol=1e-6, atol=1e-6)


@pytest.mark.usefixtures("maybe_run_functions_eagerly")
@pytest.mark.parametrize("average_across_timesteps", [True, False])
@pytest.mark.parametrize("average_across_batch", [True, False])
def test_sequence_loss_class(average_across_timesteps, average_across_batch):

(
batch_size,
sequence_length,
_,
logits,
targets,
weights,
expected_loss,
) = get_test_data()
seq_loss = loss.SequenceLoss(
average_across_timesteps=average_across_timesteps,
average_across_batch=average_across_batch,
sum_over_timesteps=False,
sum_over_batch=False,
)
average_loss_per_example = seq_loss(targets, logits, weights)
res = average_loss_per_example.numpy()
if average_across_timesteps and average_across_batch:
expected = expected_loss
elif not average_across_timesteps and average_across_batch:
expected = np.full(sequence_length, expected_loss)
elif average_across_timesteps and not average_across_batch:
expected = np.full(batch_size, expected_loss)
elif not average_across_timesteps and not average_across_batch:
expected = np.full((batch_size, sequence_length), expected_loss)

np.testing.assert_allclose(res, expected, atol=1e-6, rtol=1e-6)


@test_utils.run_all_in_graph_and_eager_modes
class LossTest(tf.test.TestCase):
def setup(self):
Expand All @@ -128,56 +162,6 @@ def setup(self):
# and logits = [[0.5] * 5, [1.5] * 5, [2.5] * 5]
self.expected_loss = 1.60944

def testSequenceLossClass(self):
with self.cached_session(use_gpu=True):
self.setup()
seq_loss = loss.SequenceLoss(
average_across_timesteps=True,
average_across_batch=True,
sum_over_timesteps=False,
sum_over_batch=False,
)
average_loss_per_example = seq_loss(self.targets, self.logits, self.weights)
res = self.evaluate(average_loss_per_example)
self.assertAllClose(self.expected_loss, res)

seq_loss = loss.SequenceLoss(
average_across_timesteps=False,
average_across_batch=True,
sum_over_timesteps=False,
sum_over_batch=False,
)
average_loss_per_sequence = seq_loss(
self.targets, self.logits, self.weights
)
res = self.evaluate(average_loss_per_sequence)
compare_per_sequence = np.full((self.sequence_length), self.expected_loss)
self.assertAllClose(compare_per_sequence, res)

seq_loss = loss.SequenceLoss(
average_across_timesteps=True,
average_across_batch=False,
sum_over_timesteps=False,
sum_over_batch=False,
)
average_loss_per_batch = seq_loss(self.targets, self.logits, self.weights)
res = self.evaluate(average_loss_per_batch)
compare_per_batch = np.full((self.batch_size), self.expected_loss)
self.assertAllClose(compare_per_batch, res)

seq_loss = loss.SequenceLoss(
average_across_timesteps=False,
average_across_batch=False,
sum_over_timesteps=False,
sum_over_batch=False,
)
total_loss = seq_loss(self.targets, self.logits, self.weights)
res = self.evaluate(total_loss)
compare_total = np.full(
(self.batch_size, self.sequence_length), self.expected_loss
)
self.assertAllClose(compare_total, res)

def testSumReduction(self):
with self.cached_session(use_gpu=True):
self.setup()
Expand Down