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Add dynamic_range_quantize to generated op_test infra. 

Will add to all op_tests to get complete coverage in subsequent CLs.

PiperOrigin-RevId: 316177819
Change-Id: I3fe9d13e7116aa849111a27ab38b4c1815ee82e2
This commit is contained in:
Suharsh Sivakumar 2020-06-12 14:27:38 -07:00 committed by TensorFlower Gardener
parent e665a737f9
commit 7270ba4e6d
2 changed files with 34 additions and 28 deletions
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1
tensorflow/lite/testing/op_tests/abs.py
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@ -32,6 +32,7 @@ def make_abs_tests(options):
test_parameters = [{
"input_shape": [[], [1], [2, 3], [1, 1, 1, 1], [1, 3, 4, 3],
[3, 15, 14, 3], [3, 1, 2, 4, 6], [2, 2, 3, 4, 5, 6]],
"dynamic_range_quantize": [False, True]
}]
def build_graph(parameters):

61
tensorflow/lite/testing/toco_convert.py
View File

@ -103,10 +103,9 @@ def toco_convert(options, graph_def, input_tensors, output_tensors, **kwargs):
input_arrays = [x[0] for x in input_tensors]
data_types = [zip_test_utils.TF_TYPE_INFO[x[2]][1] for x in input_tensors]
if test_params.get("fully_quantize", False):
# Read the input range for the representative dataset from parameters.
min_value, max_value = test_params.get("input_range", (-1, 1))
fully_quantize = test_params.get("fully_quantize", False)
dynamic_range_quantize = test_params.get("dynamic_range_quantize", False)
if dynamic_range_quantize or fully_quantize:
with tempfile.NamedTemporaryFile() as graphdef_file:
graphdef_file.write(graph_def_str)
graphdef_file.flush()
@ -115,32 +114,38 @@ def toco_convert(options, graph_def, input_tensors, output_tensors, **kwargs):
converter = tf.compat.v1.lite.TFLiteConverter.from_frozen_graph(
graphdef_file.name, input_arrays, output_tensors, input_shapes)
def representative_dataset(input_tensors):
calibration_inputs = []
for _, shape, _ in input_tensors:
if shape:
dims = [dim.value for dim in shape.dims]
calibration_inputs.append(
np.random.uniform(min_value, max_value,
tuple(dims)).astype(np.float32))
return calibration_inputs
converter.optimizations = [tf.lite.Optimize.DEFAULT]
def representative_dataset_gen():
for _ in range(100):
yield representative_dataset(input_tensors)
if fully_quantize:
# Read the input range for the representative dataset from parameters.
min_value, max_value = test_params.get("input_range", (-1, 1))
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS_INT8
]
converter.representative_dataset = representative_dataset_gen
if extra_toco_options.inference_input_type:
converter.inference_input_type = (
extra_toco_options.inference_input_type)
if extra_toco_options.inference_output_type:
converter.inference_output_type = (
extra_toco_options.inference_output_type)
else:
converter.inference_output_type = tf.int8
def representative_dataset(input_tensors):
calibration_inputs = []
for _, shape, _ in input_tensors:
if shape:
dims = [dim.value for dim in shape.dims]
calibration_inputs.append(
np.random.uniform(min_value, max_value,
tuple(dims)).astype(np.float32))
return calibration_inputs
def representative_dataset_gen():
for _ in range(100):
yield representative_dataset(input_tensors)
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS_INT8
]
converter.representative_dataset = representative_dataset_gen
if extra_toco_options.inference_input_type:
converter.inference_input_type = (
extra_toco_options.inference_input_type)
if extra_toco_options.inference_output_type:
converter.inference_output_type = (
extra_toco_options.inference_output_type)
else:
converter.inference_output_type = tf.int8
try:
tflite_model = converter.convert()