def test_debugger_creation_counter_increase_multiple_same_topic_success(self):
try:
stub = metrics.TFLiteMetrics()
stub.increase_counter_debugger_creation()
self.assertEqual(metrics._counter_debugger_creation.get_cell().value(), 1)
stub2 = metrics.TFLiteMetrics()
stub2.increase_counter_debugger_creation()
self.assertEqual(metrics._counter_debugger_creation.get_cell().value(), 2)
del stub
gc.collect()
stub2.increase_counter_debugger_creation()
self.assertEqual(metrics._counter_debugger_creation.get_cell().value(), 3)
except:
raise Exception('No exception should be raised.')
def _initialize_stats(self):
"""Helper function initializes stats."""
# TODO(b/177749613) : Fix the dependency on tf.lite._get_ops_details()
# Following code is needed to get op's name from the output tensor index,
# since NumericVerify op only provides its quantized input tensor index.
self._defining_op = dict()
for op_info in self._quant_interpreter._get_ops_details(): # pylint: disable=protected-access
self._defining_op.update({
tensor_idx: op_info['index']
for tensor_idx in op_info['outputs']
})
self._numeric_verify_tensor_details = None
self._numeric_verify_op_details = None
if not self._get_numeric_verify_tensor_details():
raise ValueError(
'Please check if the quantized model is in debug mode')
self._layer_debug_metrics = _DEFAULT_LAYER_DEBUG_METRICS.copy()
if self._debug_options.layer_debug_metrics:
self._layer_debug_metrics.update(
self._debug_options.layer_debug_metrics)
self.layer_statistics = None
self.model_statistics = None
self._metrics = metrics_stub.TFLiteMetrics()
self._metrics.increase_counter_debugger_creation()
def test_converter_params_multiple_set_success(self):
stub = metrics.TFLiteMetrics()
stub.set_converter_param('name', 'value')
stub.set_converter_param('name', 'value1')
self.assertEqual(
metrics._gauge_conversion_params.get_cell('name').value(),
'value1')
def test_interpreter_creation_counter_increase_success(self):
stub = metrics.TFLiteMetrics()
stub.increase_counter_interpreter_creation()
self.assertEqual(
stub._counter_interpreter_creation.get_cell('python').value(), 1)
def test_TFLiteMetrics_creation_fail2_with_only_model_path(self):
with self.assertRaises(ValueError):
metrics.TFLiteMetrics(model_path='/path/to/model')
def test_TFLiteMetrics_creation_fails_with_only_md5(self):
with self.assertRaises(ValueError):
metrics.TFLiteMetrics(md5='md5')
def test_TFLiteMetrics_creation_arg_success(self):
metrics.TFLiteMetrics('md5', '/path/to/model')
def test_TFLiteMetrics_creation_no_arg_success(self):
metrics.TFLiteMetrics()
def test_converter_success_counter_increase_success(self):
stub = metrics.TFLiteMetrics()
stub.increase_counter_converter_success()
self.assertEqual(
metrics._counter_conversion_success.get_cell().value(), 1)
def test_TFLiteMetrics_creation_fails_with_only_hash(self):
with self.assertRaises(ValueError):
metrics.TFLiteMetrics(model_hash='hash')
def test_converter_params_set_latency(self):
stub = metrics.TFLiteMetrics()
stub.set_converter_latency(34566)
self.assertEqual(metrics._gauge_conversion_latency.get_cell().value(),
34566)
