def _test_export_to_tflite(self,
model,
validation_data,
threshold=0.0,
atol=1e-04):
tflite_output_file = os.path.join(self.get_temp_dir(), 'model.tflite')
model.export(self.get_temp_dir(), export_format=ExportFormat.TFLITE)
self.assertTrue(os.path.isfile(tflite_output_file))
self.assertGreater(os.path.getsize(tflite_output_file), 0)
metric = model.evaluate_tflite(tflite_output_file, validation_data)
self.assertGreaterEqual(metric['final_f1'], threshold)
spec = model.model_spec
input_word_ids = np.random.randint(low=0,
high=len(spec.tokenizer.vocab),
size=(1, spec.seq_len),
dtype=np.int32)
input_mask = np.random.randint(low=0,
high=2,
size=(1, spec.seq_len),
dtype=np.int32)
input_type_ids = np.random.randint(low=0,
high=2,
size=(1, spec.seq_len),
dtype=np.int32)
random_inputs = (input_word_ids, input_mask, input_type_ids)
self.assertTrue(
test_util.is_same_output(tflite_output_file,
model.model,
random_inputs,
model.model_spec,
atol=atol))
def _test_export_to_tflite(self,
model,
threshold=1.0,
atol=1e-04,
expected_json_file=None):
tflite_output_file = os.path.join(self.get_temp_dir(), 'model.tflite')
model.export(self.get_temp_dir(),
export_format=ExportFormat.TFLITE,
quantization_config=None,
export_metadata_json_file=expected_json_file is not None)
self.assertTrue(tf.io.gfile.exists(tflite_output_file))
self.assertGreater(os.path.getsize(tflite_output_file), 0)
result = model.evaluate_tflite(tflite_output_file, self.test_data)
self.assertGreaterEqual(result['accuracy'], threshold)
spec = model.model_spec
if isinstance(spec, text_spec.AverageWordVecModelSpec):
random_inputs = np.random.randint(low=0,
high=len(spec.vocab),
size=(1, spec.seq_len),
dtype=np.int32)
elif isinstance(spec, text_spec.BertClassifierModelSpec):
input_word_ids = np.random.randint(low=0,
high=len(spec.tokenizer.vocab),
size=(1, spec.seq_len),
dtype=np.int32)
input_mask = np.random.randint(low=0,
high=2,
size=(1, spec.seq_len),
dtype=np.int32)
input_type_ids = np.random.randint(low=0,
high=2,
size=(1, spec.seq_len),
dtype=np.int32)
random_inputs = (input_word_ids, input_mask, input_type_ids)
else:
raise ValueError('Unsupported model_spec type: %s' %
str(type(spec)))
self.assertTrue(
test_util.is_same_output(tflite_output_file,
model.model,
random_inputs,
spec,
atol=atol))
if expected_json_file is not None:
json_output_file = os.path.join(self.get_temp_dir(), 'model.json')
self.assertTrue(os.path.isfile(json_output_file))
self.assertGreater(os.path.getsize(json_output_file), 0)
expected_json_file = test_util.get_test_data_path(
expected_json_file)
self.assertTrue(filecmp.cmp(json_output_file, expected_json_file))
def _test_export_to_tflite(self, model, threshold=0.0):
tflite_output_file = os.path.join(self.get_temp_dir(), 'model.tflite')
model.export(self.get_temp_dir(), export_format=ExportFormat.TFLITE)
result = model.evaluate_tflite(tflite_output_file, self.test_data)
self.assertGreaterEqual(result['accuracy'], threshold)
random_input = np.random.uniform(
size=[1] + model.model_spec.input_image_shape + [3]).astype(np.float32)
self.assertTrue(
test_util.is_same_output(tflite_output_file, model.model, random_input,
model.model_spec))
def _test_tflite(self,
keras_model,
tflite_model_file,
input_dim,
max_input_value=1000,
atol=1e-04):
np.random.seed(0)
random_input = np.random.uniform(
low=0, high=max_input_value, size=(1, input_dim)).astype(np.float32)
self.assertTrue(
test_util.is_same_output(
tflite_model_file, keras_model, random_input, atol=atol))
def _test_export_to_tflite(self, model, threshold=1.0):
tflite_output_file = os.path.join(self.get_temp_dir(), 'model.tflite')
model.export(self.get_temp_dir(), export_format=ExportFormat.TFLITE)
self.assertTrue(tf.io.gfile.exists(tflite_output_file))
self.assertGreater(os.path.getsize(tflite_output_file), 0)
result = model.evaluate_tflite(tflite_output_file, self.test_data)
self.assertGreaterEqual(result['accuracy'], threshold)
spec = model.model_spec
if isinstance(spec, ms.AverageWordVecModelSpec):
random_inputs = np.random.randint(low=0,
high=len(spec.vocab),
size=(1, spec.seq_len),
dtype=np.int32)
elif isinstance(spec, ms.BertClassifierModelSpec):
input_word_ids = np.random.randint(low=0,
high=len(spec.tokenizer.vocab),
size=(1, spec.seq_len),
dtype=np.int32)
input_mask = np.random.randint(low=0,
high=2,
size=(1, spec.seq_len),
dtype=np.int32)
input_type_ids = np.random.randint(low=0,
high=2,
size=(1, spec.seq_len),
dtype=np.int32)
random_inputs = (input_word_ids, input_mask, input_type_ids)
else:
raise ValueError('Unsupported model_spec type: %s' %
str(type(spec)))
self.assertTrue(
test_util.is_same_output(tflite_output_file, model.model,
random_inputs, spec))
