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convert_test.py
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convert_test.py
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""TensorFlow Lite Python Interface: Sanity check."""
from unittest import mock
import numpy as np
from tensorflow.lite.python import convert
from tensorflow.lite.python import op_hint
from tensorflow.lite.python.interpreter import Interpreter
from tensorflow.lite.python.metrics import converter_error_data_pb2
from tensorflow.lite.python.metrics.wrapper import metrics_wrapper
from tensorflow.lite.toco import toco_flags_pb2 as _conversion_flags_pb2
from tensorflow.python.client import session
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import test_util
from tensorflow.python.framework.graph_util_impl import _bfs_for_reachable_nodes
from tensorflow.python.framework.graph_util_impl import _extract_graph_summary
from tensorflow.python.framework.graph_util_impl import _node_name
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import array_ops_stack
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
def _mock_wrapped_convert(
unused_model_flags_str="",
conversion_flags_str="",
unused_input_data_str="",
unused_debug_info_str="",
unused_enable_mlir_converter=True,
):
# Simulate the converter throwing and error when
# `guarantee_all_funcs_one_use` is not set.
if not _conversion_flags_pb2.TocoFlags.FromString(
conversion_flags_str
).guarantee_all_funcs_one_use:
raise Exception()
else:
return bytes("A model", encoding="utf-8")
def _mock_retrieve_errors():
err_data = converter_error_data_pb2.ConverterErrorData(
error_code=converter_error_data_pb2.ConverterErrorData.ERROR_STATEFUL_PARTITIONED_CALL_IN_FINAL_IR
)
return [err_data]
class ConvertTest(test_util.TensorFlowTestCase):
def testBasic(self):
with ops.Graph().as_default():
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.convert_graphdef(
sess.graph_def, input_tensors=[in_tensor], output_tensors=[out_tensor])
self.assertTrue(tflite_model)
@mock.patch.object(
convert,
"_deprecated_conversion_binary",
new="tocos_from_proto",
)
@mock.patch.object(
convert,
"_run_deprecated_conversion_binary",
autospec=True,
)
def testBasicDeprecatedConversionBinary(self, mock_func):
with ops.Graph().as_default():
in_tensor = array_ops.placeholder(
shape=[1, 16, 16, 3], dtype=dtypes.float32
)
out_tensor = in_tensor + in_tensor
sess = session.Session()
convert.convert_graphdef(
sess.graph_def,
input_tensors=[in_tensor],
output_tensors=[out_tensor],
enable_mlir_converter=False,
)
mock_func.assert_called_once()
@mock.patch.object(
convert.wrap_converter, "wrapped_convert", new=_mock_wrapped_convert
)
@mock.patch.object(
metrics_wrapper, "retrieve_collected_errors", new=_mock_retrieve_errors
)
# This test wants to check that in the case of the converter throwing an
# `ERROR_STATEFUL_PARTITIONED_CALL_IN_FINAL_IR` error, it will
# retry conversion with the `guarantee_all_funcs_one_use` flag.
# We can wrap the convert call in order to assert it is called appropriately.
@mock.patch.object(convert, "convert", wraps=convert.convert)
def testConversionStatefulPartitionRetry(self, mock_convert):
with ops.Graph().as_default():
in_tensor = array_ops.placeholder(
shape=[1, 16, 16, 3], dtype=dtypes.float32
)
out_tensor = in_tensor + in_tensor
sess = session.Session()
model = convert.convert_graphdef(
sess.graph_def,
input_tensors=[in_tensor],
output_tensors=[out_tensor],
enable_mlir_converter=True,
guarantee_all_funcs_one_use=False,
)
self.assertTrue(str(model, encoding="utf-8"), "A model")
self.assertEqual(mock_convert.call_count, 2)
def testQuantization(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()
tflite_model = convert.convert_graphdef(
sess.graph_def,
input_tensors=[in_tensor],
output_tensors=[out_tensor],
inference_type=dtypes.uint8,
quantized_input_stats=[(0., 1.)])
self.assertTrue(tflite_model)
def testGraphDefBasic(self):
with ops.Graph().as_default():
in_tensor = array_ops.placeholder(
shape=[1, 16, 16, 3], dtype=dtypes.float32, name="input")
_ = in_tensor + in_tensor
sess = session.Session()
tflite_model = convert.convert_graphdef_with_arrays(
sess.graph_def,
input_arrays_with_shape=[("input", [1, 16, 16, 3])],
output_arrays=["add"],
control_output_arrays=None,
inference_type=dtypes.float32,
enable_mlir_converter=False)
self.assertTrue(tflite_model)
# Check values from converted model.
interpreter = Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
self.assertEqual(1, len(input_details))
self.assertEqual("input", input_details[0]["name"])
self.assertEqual(np.float32, input_details[0]["dtype"])
self.assertTrue(([1, 16, 16, 3] == input_details[0]["shape"]).all()) # type: ignore
self.assertEqual((0., 0.), input_details[0]["quantization"])
output_details = interpreter.get_output_details()
self.assertEqual(1, len(output_details))
self.assertEqual("add", output_details[0]["name"])
self.assertEqual(np.float32, output_details[0]["dtype"])
self.assertTrue(([1, 16, 16, 3] == output_details[0]["shape"]).all()) # type: ignore
self.assertEqual((0., 0.), output_details[0]["quantization"])
def testGraphDefQuantization(self):
with ops.Graph().as_default():
in_tensor_1 = array_ops.placeholder(
shape=[1, 16, 16, 3], dtype=dtypes.float32, name="inputA")
in_tensor_2 = array_ops.placeholder(
shape=[1, 16, 16, 3], dtype=dtypes.float32, name="inputB")
_ = array_ops.fake_quant_with_min_max_args(
in_tensor_1 + in_tensor_2, min=0., max=1., name="output")
sess = session.Session()
tflite_model = convert.convert_graphdef_with_arrays(
sess.graph_def,
input_arrays_with_shape=[("inputA", [1, 16, 16, 3]),
("inputB", [1, 16, 16, 3])],
output_arrays=["output"],
control_output_arrays=None,
inference_type=dtypes.uint8,
quantized_input_stats=[(0., 1.), (0., 1.)],
enable_mlir_converter=False,
)
self.assertTrue(tflite_model)
# Check values from converted model.
interpreter = Interpreter(model_content=tflite_model)
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
self.assertEqual(2, len(input_details))
self.assertEqual("inputA", input_details[0]["name"])
self.assertEqual(np.uint8, input_details[0]["dtype"])
self.assertTrue(([1, 16, 16, 3] == input_details[0]["shape"]).all()) # type: ignore
self.assertEqual((1., 0.),
input_details[0]["quantization"]) # scale, zero_point
self.assertEqual("inputB", input_details[1]["name"])
self.assertEqual(np.uint8, input_details[1]["dtype"])
self.assertTrue(([1, 16, 16, 3] == input_details[1]["shape"]).all()) # type: ignore
self.assertEqual((1., 0.),
input_details[1]["quantization"]) # scale, zero_point
output_details = interpreter.get_output_details()
self.assertEqual(1, len(output_details))
self.assertEqual("output", output_details[0]["name"])
self.assertEqual(np.uint8, output_details[0]["dtype"])
self.assertTrue(([1, 16, 16, 3] == output_details[0]["shape"]).all()) # type: ignore
self.assertGreater(output_details[0]["quantization"][0], 0) # scale
def testGraphDefQuantizationInvalid(self):
with ops.Graph().as_default():
in_tensor_1 = array_ops.placeholder(
shape=[1, 16, 16, 3], dtype=dtypes.float32, name="inputA")
in_tensor_2 = array_ops.placeholder(
shape=[1, 16, 16, 3], dtype=dtypes.float32, name="inputB")
_ = array_ops.fake_quant_with_min_max_args(
in_tensor_1 + in_tensor_2, min=0., max=1., name="output")
sess = session.Session()
with self.assertRaises(ValueError) as error:
convert.convert_graphdef_with_arrays(
sess.graph_def,
input_arrays_with_shape=[("inputA", [1, 16, 16, 3]),
("inputB", [1, 16, 16, 3])],
output_arrays=["output"],
control_output_arrays=None,
inference_type=dtypes.uint8,
enable_mlir_converter=False)
self.assertEqual(
"The `quantized_input_stats` flag must be defined when either "
"`inference_type` flag or `inference_input_type` flag is set to "
"tf.int8 or tf.uint8.", str(error.exception))
class ConvertTestOpHint(test_util.TensorFlowTestCase):
"""Test the hint to stub functionality."""
def _getGraphOpTypes(self, graphdef, output_nodes):
"""Returns used op types in `graphdef` reachable from `output_nodes`.
This is used to check that after the stub transformation the expected
nodes are there.
NOTE: this is not a exact test that the graph is the correct output, but
it balances compact expressibility of test with sanity checking.
Args:
graphdef: TensorFlow proto graphdef.
output_nodes: A list of output node names that we need to reach.
Returns:
A set of node types reachable from `output_nodes`.
"""
name_to_input_name, name_to_node, _ = (
_extract_graph_summary(graphdef))
# Find all nodes that are needed by the outputs
used_node_names = _bfs_for_reachable_nodes(output_nodes, name_to_input_name)
return set([name_to_node[node_name].op for node_name in used_node_names])
def _countIdentities(self, nodes):
"""Count the number of "Identity" op types in the list of proto nodes.
Args:
nodes: NodeDefs of the graph.
Returns:
The number of nodes with op type "Identity" found.
"""
return len([x for x in nodes if x.op == "Identity"])
def testSwishLiteHint(self):
"""Makes a custom op swish and makes sure it gets converted as a unit."""
with ops.Graph().as_default():
image = array_ops.constant([1., 2., 3., 4.])
swish_scale = array_ops.constant(1.0)
def _swish(input_tensor, scale):
custom = op_hint.OpHint("cool_activation")
input_tensor, scale = custom.add_inputs(input_tensor, scale)
output = math_ops.sigmoid(input_tensor) * input_tensor * scale
output, = custom.add_outputs(output)
return output
output = array_ops.identity(
_swish(image, swish_scale), name="ModelOutput")
with self.cached_session() as sess:
# check if identities have been put into the graph (2 input, 1 output,
# and 1 final output).
self.assertEqual(self._countIdentities(sess.graph_def.node), 4)
stubbed_graphdef = op_hint.convert_op_hints_to_stubs(
graph_def=sess.graph_def)
self.assertEqual(
self._getGraphOpTypes(
stubbed_graphdef,
output_nodes=[op_hint._tensor_name_base(output.name)]),
set(["cool_activation", "Const", "Identity"]))
def testScaleAndBiasAndIdentity(self):
"""This tests a scaled add which has 3 inputs and 2 outputs."""
with ops.Graph().as_default():
a = array_ops.constant(1.)
x = array_ops.constant([2., 3.])
b = array_ops.constant([4., 5.])
def _scaled_and_bias_and_identity(a, x, b):
custom = op_hint.OpHint("scale_and_bias_and_identity")
a, x, b = custom.add_inputs(a, x, b)
return custom.add_outputs(a * x + b, x)
output = array_ops.identity(
_scaled_and_bias_and_identity(a, x, b), name="ModelOutput")
with self.cached_session() as sess:
# make sure one identity for each input (3) and output (2) => 3 + 2 = 5
# +1 for the final output
self.assertEqual(self._countIdentities(sess.graph_def.node), 6)
stubbed_graphdef = op_hint.convert_op_hints_to_stubs(
graph_def=sess.graph_def)
self.assertEqual(
self._getGraphOpTypes(
stubbed_graphdef,
output_nodes=[op_hint._tensor_name_base(output.name)]),
set(["scale_and_bias_and_identity", "Const", "Identity", "Pack"]))
def testTwoFunctions(self):
"""Tests if two functions are converted correctly."""
with ops.Graph().as_default():
a = array_ops.constant([1.])
b = array_ops.constant([1.])
def _double_values(x):
custom = op_hint.OpHint("add_test")
x, = custom.add_inputs(x)
output = math_ops.multiply(x, x)
output, = custom.add_outputs(output)
return output
output = array_ops.identity(
math_ops.add(_double_values(a), _double_values(b)),
name="ModelOutput")
with self.cached_session() as sess:
# make sure one identity for each input (2) and output (2) => 2 + 2
# +1 for the final output
self.assertEqual(self._countIdentities(sess.graph_def.node), 5)
stubbed_graphdef = op_hint.convert_op_hints_to_stubs(
graph_def=sess.graph_def)
self.assertEqual(
self._getGraphOpTypes(
stubbed_graphdef,
output_nodes=[op_hint._tensor_name_base(output.name)]),
set(["add_test", "Const", "Identity", "AddV2"]))
def _get_input_index(self, x):
return x.op.node_def.attr[op_hint.OpHint.FUNCTION_INPUT_INDEX_ATTR].i
def _get_output_index(self, x):
return x.op.node_def.attr[op_hint.OpHint.FUNCTION_OUTPUT_INDEX_ATTR].i
def _get_sort_index(self, x):
return x.op.node_def.attr[op_hint.OpHint.FUNCTION_SORT_INDEX_ATTR].i
def testTags(self):
"""Test if multiple args with the same tag are grouped."""
with ops.Graph().as_default():
a = array_ops.constant([1.])
b = array_ops.constant([2.])
c = array_ops.constant([3.])
d = array_ops.constant([4.])
custom = op_hint.OpHint("test_tag")
a = custom.add_input(
a, tag="mytag", aggregate=op_hint.OpHint.AGGREGATE_STACK)
b, = custom.add_inputs(b)
c = custom.add_input(
c, tag="mytag", aggregate=op_hint.OpHint.AGGREGATE_STACK)
d = custom.add_input(
d, tag="mytag2", aggregate=op_hint.OpHint.AGGREGATE_STACK)
res = math_ops.add(math_ops.mul(a, b), math_ops.mul(c, b))
custom.add_outputs([res])
with self.cached_session():
self.assertEqual(self._get_input_index(a), 0)
self.assertEqual(self._get_sort_index(a), 0)
self.assertEqual(self._get_input_index(b), 1)
self.assertEqual(self._get_sort_index(b), 0)
self.assertEqual(self._get_input_index(c), 0)
self.assertEqual(self._get_sort_index(c), 1)
def testOverrideIndex(self):
with ops.Graph().as_default():
a = array_ops.constant([1.])
b = array_ops.constant([2.])
c = array_ops.constant([3.])
custom = op_hint.OpHint("test_override")
b = custom.add_input(b) # should auto assign 0
a = custom.add_input(a, index_override=1)
c = custom.add_input(c) # should auto assign 2
with self.cached_session():
self.assertEqual(self._get_input_index(a), 1)
self.assertEqual(self._get_input_index(b), 0)
self.assertEqual(self._get_input_index(c), 2)
def testAggregate(self):
with ops.Graph().as_default():
a = array_ops.constant([3., 4.])
b = array_ops.constant([5., 6.])
hint = op_hint.OpHint("agg")
a0, a1 = array_ops_stack.unstack(a)
b0, b1 = array_ops_stack.unstack(b)
a0 = hint.add_input(a0, tag="c", aggregate=op_hint.OpHint.AGGREGATE_STACK)
b0 = hint.add_input(b0, tag="n", aggregate=op_hint.OpHint.AGGREGATE_STACK)
a1 = hint.add_input(a1, tag="c", aggregate=op_hint.OpHint.AGGREGATE_STACK)
b1 = hint.add_input(b1, tag="n", aggregate=op_hint.OpHint.AGGREGATE_STACK)
c0 = math_ops.add(a0, b0, name="addleft")
c1 = math_ops.add(a1, b1, name="addright")
c0 = hint.add_output(
c0, tag="out", aggregate=op_hint.OpHint.AGGREGATE_STACK)
c1 = hint.add_output(
c1, tag="out", aggregate=op_hint.OpHint.AGGREGATE_STACK)
curr = array_ops_stack.stack([c0, c1])
output = array_ops.identity(curr, name="FINAL_OUTPUT")
with self.cached_session() as sess:
stubbed_graphdef = op_hint.convert_op_hints_to_stubs(
graph_def=sess.graph_def)
self.assertEqual(
self._getGraphOpTypes(
stubbed_graphdef,
output_nodes=[op_hint._tensor_name_base(output.name)]),
set(["agg", "Const", "Identity"]))
def testFindHintedOutputNodes(self):
"""Test if all hinted output nodes are correctly found."""
with ops.Graph().as_default():
def _build_ophinted_op(name, input1, input2):
custom_op = op_hint.OpHint(name)
input1 = custom_op.add_input(input1)
input2 = custom_op.add_input(input2)
output = math_ops.mul(input1, input2)
return custom_op.add_output(output)
output_1 = _build_ophinted_op("custom_op_1", array_ops.constant([1.]),
array_ops.constant([2.]))
output_2 = _build_ophinted_op("custom_op_2", array_ops.constant([3.]),
array_ops.constant([4.]))
with self.cached_session() as sess:
hinted_outputs_nodes = op_hint.find_all_hinted_output_nodes(sess)
expected_hinted_output_nodes = [
_node_name(output_1.name),
_node_name(output_2.name)
]
self.assertEqual(
len(hinted_outputs_nodes), len(expected_hinted_output_nodes))
if __name__ == "__main__":
test.main()