def testQuantizationInvalid(self):
with ops.Graph().as_default():
in_tensor = array_ops.placeholder(shape=[1, 16, 16, 3],
dtype=dtypes.float32)
out_tensor = array_ops.fake_quant_with_min_max_args(in_tensor +
in_tensor,
min=0.,
max=1.)
sess = session.Session()
with self.assertRaises(ValueError) as error:
convert.toco_convert(sess.graph_def, [in_tensor], [out_tensor],
inference_type=lite_constants.QUANTIZED_UINT8)
self.assertEqual(
"std_dev and mean must be defined when inference_type or "
"inference_input_type is QUANTIZED_UINT8 or INT8.",
str(error.exception))
with self.assertRaises(ValueError) as error:
convert.toco_convert(sess.graph_def, [in_tensor], [out_tensor],
inference_type=lite_constants.QUANTIZED_UINT8,
inference_input_type=lite_constants.FLOAT)
self.assertEqual(
"std_dev and mean must be defined when inference_type or "
"inference_input_type is QUANTIZED_UINT8 or INT8.",
str(error.exception))
def testQuantizationInvalid(self):
in_tensor = array_ops.placeholder(
shape=[1, 16, 16, 3], dtype=dtypes.float32)
out_tensor = array_ops.fake_quant_with_min_max_args(
in_tensor + in_tensor, min=0., max=1.)
sess = session.Session()
with self.assertRaises(ValueError) as error:
convert.toco_convert(
sess.graph_def, [in_tensor], [out_tensor],
inference_type=lite_constants.QUANTIZED_UINT8)
self.assertEqual(
"std_dev and mean must be defined when inference_input_type is "
"QUANTIZED_UINT8.", str(error.exception))
def testBasic(self):
in_tensor = array_ops.placeholder(shape=[1, 16, 16, 3],
dtype=dtypes.float32)
out_tensor = in_tensor + in_tensor
sess = session.Session()
# Try running on valid graph
tflite_model = convert.toco_convert(sess.graph_def, [in_tensor],
[out_tensor])
self.assertTrue(tflite_model)
def testBasic(self):
in_tensor = array_ops.placeholder(shape=[1, 16, 16, 3],
dtype=dtypes.float32)
out_tensor = in_tensor + in_tensor
sess = session.Session()
# Try running on valid graph
tflite_model = convert.toco_convert(sess.graph_def, [in_tensor],
[out_tensor])
self.assertTrue(tflite_model)
def testQuantization(self):
in_tensor = array_ops.placeholder(shape=[1, 16, 16, 3],
dtype=dtypes.float32)
out_tensor = array_ops.fake_quant_with_min_max_args(in_tensor + in_tensor,
min=0., max=1.)
sess = session.Session()
tflite_model = convert.toco_convert(
sess.graph_def, [in_tensor], [out_tensor],
inference_type=lite_constants.QUANTIZED_UINT8,
quantized_input_stats=[(0., 1.)])
self.assertTrue(tflite_model)
def testQuantization(self):
in_tensor = array_ops.placeholder(shape=[1, 16, 16, 3],
dtype=dtypes.float32)
out_tensor = array_ops.fake_quant_with_min_max_args(in_tensor + in_tensor,
min=0., max=1.)
sess = session.Session()
tflite_model = convert.toco_convert(
sess.graph_def, [in_tensor], [out_tensor],
inference_type=lite_constants.QUANTIZED_UINT8,
quantized_input_stats=[(0., 1.)])
self.assertTrue(tflite_model)
