def create_and_check_batch_inference(self, config, input_values, *args):
# test does not pass for models making use of `group_norm`
# check: https://github.com/pytorch/fairseq/issues/3227
config.layerdrop = 0.0
model = TFHubertModel(config)
input_values = input_values[:3]
attention_mask = tf.ones_like(input_values)
input_lengths = tf.constant(
[input_values.shape[-1] // i for i in [4, 2, 1]])
length_mask = tf.sequence_mask(input_lengths, dtype=tf.float32)
# convert values that are over input_lengths to padding
input_values = input_values * length_mask
attention_mask = attention_mask * length_mask
batch_outputs = model(input_values,
attention_mask=attention_mask,
training=False).last_hidden_state
for i in range(input_values.shape[0]):
input_slice = input_values[i:i + 1, :input_lengths[i]]
output = model(input_slice, training=False).last_hidden_state
batch_output = batch_outputs[i:i + 1, :output.shape[1]]
self.parent.assertTrue(np.allclose(output, batch_output,
atol=1e-3))
def test_model_from_pretrained(self):
model = TFHubertModel.from_pretrained("facebook/hubert-large-ls960-ft")
self.assertIsNotNone(model)
def create_and_check_model(self, config, input_values, attention_mask):
model = TFHubertModel(config)
result = model(input_values, attention_mask=attention_mask)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.output_seq_length, self.hidden_size))