def testEval(self):
if not trt_convert.is_tensorrt_enabled():
return
model_dir = test.test_src_dir_path('contrib/tensorrt/test/testdata')
accuracy_tf_native = self._Run(
is_training=False,
use_trt=False,
batch_size=128,
num_epochs=None,
model_dir=model_dir)['accuracy']
logging.info('accuracy_tf_native: %f', accuracy_tf_native)
self.assertAllClose(accuracy_tf_native, 0.9662)
if trt_convert.get_linked_tensorrt_version()[0] < 5:
return
accuracy_tf_trt = self._Run(
is_training=False,
use_trt=True,
batch_size=128,
num_epochs=None,
model_dir=model_dir)['accuracy']
logging.info('accuracy_tf_trt: %f', accuracy_tf_trt)
self.assertAllClose(accuracy_tf_trt, 0.9677)
def testEval(self):
if not trt_convert.is_tensorrt_enabled():
return
model_dir = test.test_src_dir_path('contrib/tensorrt/test/testdata')
accuracy_tf_native = self._Run(
is_training=False,
use_trt=False,
batch_size=128,
num_epochs=None,
model_dir=model_dir)['accuracy']
logging.info('accuracy_tf_native: %f', accuracy_tf_native)
self.assertAllClose(0.9662, accuracy_tf_native, rtol=1e-3, atol=1e-3)
if trt_convert.get_linked_tensorrt_version()[0] < 5:
return
accuracy_tf_trt = self._Run(
is_training=False,
use_trt=True,
batch_size=128,
num_epochs=None,
model_dir=model_dir)['accuracy']
logging.info('accuracy_tf_trt: %f', accuracy_tf_trt)
self.assertAllClose(0.9675, accuracy_tf_trt, rtol=1e-3, atol=1e-3)
def testCreateInferenceGraph_BasicConversion(self):
"""Test case for trt_convert.create_inference_graph()."""
if not trt_convert.is_tensorrt_enabled():
return
# Use GraphDef as input.
self._TestCreateInferenceGraph()
# Use SavedModel as input.
tmp_dir = self.get_temp_dir()
input_saved_model_dir = os.path.join(tmp_dir, "in_dir1")
output_saved_model_dir = os.path.join(tmp_dir, "out_dir1")
self._WriteInputSavedModel(input_saved_model_dir)
self._TestCreateInferenceGraph(input_saved_model_dir,
output_saved_model_dir)
def testCreateInferenceGraph_BasicConversion(self):
"""Test case for trt_convert.create_inference_graph()."""
if not trt_convert.is_tensorrt_enabled():
return
# Use GraphDef as input.
self._TestCreateInferenceGraph()
# Use SavedModel as input.
tmp_dir = self.get_temp_dir()
input_saved_model_dir = os.path.join(tmp_dir, "in_dir1")
output_saved_model_dir = os.path.join(tmp_dir, "out_dir1")
self._WriteInputSavedModel(input_saved_model_dir)
self._TestCreateInferenceGraph(input_saved_model_dir,
output_saved_model_dir)
def testCreateInferenceGraph_MinimumSegmentSize(self):
if not trt_convert.is_tensorrt_enabled():
return
output_graph_def = trt_convert.create_inference_graph(
self._GetGraphDef(), ["output"],
minimum_segment_size=5,
is_dynamic_op=False)
node_name_to_op = {node.name: node.op for node in output_graph_def.node}
self.assertEqual({
"v1/read": "Const",
"input": "Placeholder",
"add": "Add",
"mul": "Mul",
"add_1": "Add",
"output": "Identity"
}, node_name_to_op)
def testCreateInferenceGraph_MinimumSegmentSize(self):
if not trt_convert.is_tensorrt_enabled():
return
output_graph_def = trt_convert.create_inference_graph(
self._GetGraphDef(), ["output"],
minimum_segment_size=5,
is_dynamic_op=False)
node_name_to_op = {node.name: node.op for node in output_graph_def.node}
self.assertEqual({
"v1/read": "Const",
"input": "Placeholder",
"add": "Add",
"mul": "Mul",
"add_1": "Add",
"output": "Identity"
}, node_name_to_op)
def testCreateInferenceGraph_DynamicOp(self):
if not trt_convert.is_tensorrt_enabled():
return
trt_convert.enable_test_value()
tmp_dir = self.get_temp_dir()
input_saved_model_dir = os.path.join(tmp_dir, "in_dir2")
output_saved_model_dir = os.path.join(tmp_dir, "out_dir2")
self._WriteInputSavedModel(input_saved_model_dir)
output_graph_def = trt_convert.create_inference_graph(
None,
None,
is_dynamic_op=True,
maximum_cached_engines=2,
input_saved_model_dir=input_saved_model_dir,
output_saved_model_dir=output_saved_model_dir,
session_config=self._GetConfigProto())
# Test the output GraphDef.
with ops.Graph().as_default():
importer.import_graph_def(output_graph_def, name="")
with self.test_session(config=self._GetConfigProto()) as sess:
# Run with batch size 1, a new engine is created and cached.
self._TestRun(sess, 1, True)
# Run with batch size 2, a new engine is created and cached.
self._TestRun(sess, 2, True)
# Run with batch size 3, since the number of cached engines has reached
# the max, it should fall back to TF function.
self._TestRun(sess, 3, False)
# Test the output SavedModel
with ops.Graph().as_default():
with self.test_session(config=self._GetConfigProto()) as sess:
loader.load(sess, [tag_constants.SERVING],
output_saved_model_dir)
# Run with batch size 1, a new engine is created and cached.
self._TestRun(sess, 1, True)
# Run with batch size 2, a new engine is created and cached.
self._TestRun(sess, 2, True)
# Run with batch size 3, since the number of cached engines has reached
# the max, it should fall back to TF function.
self._TestRun(sess, 3, False)
def testCreateInferenceGraph_DynamicOp(self):
if not trt_convert.is_tensorrt_enabled():
return
trt_convert.enable_test_value()
tmp_dir = self.get_temp_dir()
input_saved_model_dir = os.path.join(tmp_dir, "in_dir2")
output_saved_model_dir = os.path.join(tmp_dir, "out_dir2")
self._WriteInputSavedModel(input_saved_model_dir)
output_graph_def = trt_convert.create_inference_graph(
None,
None,
is_dynamic_op=True,
maximum_cached_engines=2,
input_saved_model_dir=input_saved_model_dir,
output_saved_model_dir=output_saved_model_dir,
session_config=self._GetConfigProto())
# Test the output GraphDef.
with ops.Graph().as_default():
importer.import_graph_def(output_graph_def, name="")
with self.test_session(config=self._GetConfigProto()) as sess:
# Run with batch size 1, a new engine is created and cached.
self._TestRun(sess, 1, True)
# Run with batch size 2, a new engine is created and cached.
self._TestRun(sess, 2, True)
# Run with batch size 3, since the number of cached engines has reached
# the max, it should fall back to TF function.
self._TestRun(sess, 3, False)
# Test the output SavedModel
with ops.Graph().as_default():
with self.test_session(config=self._GetConfigProto()) as sess:
loader.load(sess, [tag_constants.SERVING], output_saved_model_dir)
# Run with batch size 1, a new engine is created and cached.
self._TestRun(sess, 1, True)
# Run with batch size 2, a new engine is created and cached.
self._TestRun(sess, 2, True)
# Run with batch size 3, since the number of cached engines has reached
# the max, it should fall back to TF function.
self._TestRun(sess, 3, False)
def testCreateInferenceGraph_StaticOp(self):
if not trt_convert.is_tensorrt_enabled():
return
trt_convert.enable_test_value()
tmp_dir = self.get_temp_dir()
input_saved_model_dir = os.path.join(tmp_dir, "in_dir3")
output_saved_model_dir = os.path.join(tmp_dir, "out_dir3")
self._WriteInputSavedModel(input_saved_model_dir)
output_graph_def = trt_convert.create_inference_graph(
None,
None,
max_batch_size=1,
is_dynamic_op=False,
maximum_cached_engines=2, # This is noop, added just for testing.
input_saved_model_dir=input_saved_model_dir,
output_saved_model_dir=output_saved_model_dir,
session_config=self._GetConfigProto())
# Test the output GraphDef.
with ops.Graph().as_default():
importer.import_graph_def(output_graph_def, name="")
with self.test_session(config=self._GetConfigProto()) as sess:
# Run with batch size 1, the default engine embedded in the graphdef
# will be used.
self._TestRun(sess, 1, True)
# Run with batch size 2, which exceed the max_batch_size, it should fall
# back to TF function.
self._TestRun(sess, 2, False)
# Test the output SavedModel
with ops.Graph().as_default():
with self.test_session(config=self._GetConfigProto()) as sess:
loader.load(sess, [tag_constants.SERVING],
output_saved_model_dir)
# Run with batch size 1, the default engine embedded in the graphdef
# will be used.
self._TestRun(sess, 1, True)
# Run with batch size 2, which exceed the max_batch_size, it should fall
# back to TF function.
self._TestRun(sess, 2, False)
def testCreateInferenceGraph_StaticOp(self):
if not trt_convert.is_tensorrt_enabled():
return
trt_convert.enable_test_value()
tmp_dir = self.get_temp_dir()
input_saved_model_dir = os.path.join(tmp_dir, "in_dir3")
output_saved_model_dir = os.path.join(tmp_dir, "out_dir3")
self._WriteInputSavedModel(input_saved_model_dir)
output_graph_def = trt_convert.create_inference_graph(
None,
None,
max_batch_size=1,
is_dynamic_op=False,
maximum_cached_engines=2, # This is noop, added just for testing.
input_saved_model_dir=input_saved_model_dir,
output_saved_model_dir=output_saved_model_dir,
session_config=self._GetConfigProto())
# Test the output GraphDef.
with ops.Graph().as_default():
importer.import_graph_def(output_graph_def, name="")
with self.test_session(config=self._GetConfigProto()) as sess:
# Run with batch size 1, the default engine embedded in the graphdef
# will be used.
self._TestRun(sess, 1, True)
# Run with batch size 2, which exceed the max_batch_size, it should fall
# back to TF function.
self._TestRun(sess, 2, False)
# Test the output SavedModel
with ops.Graph().as_default():
with self.test_session(config=self._GetConfigProto()) as sess:
loader.load(sess, [tag_constants.SERVING], output_saved_model_dir)
# Run with batch size 1, the default engine embedded in the graphdef
# will be used.
self._TestRun(sess, 1, True)
# Run with batch size 2, which exceed the max_batch_size, it should fall
# back to TF function.
self._TestRun(sess, 2, False)
use_optimizer_options = [False, True]
dynamic_engine_options = [False, True]
for (use_optimizer, precision_mode,
dynamic_engine) in itertools.product(use_optimizer_options,
PRECISION_MODES,
dynamic_engine_options):
if IsQuantizationMode(precision_mode):
if use_optimizer:
# TODO(aaroey): if use_optimizer is True we need to get the inference
# graphdef using custom python wrapper class, which is not currently
# supported yet.
continue
if not dynamic_engine:
# TODO(aaroey): construction of static calibration engine is not
# supported yet.
continue
conversion = "OptimizerConversion" if use_optimizer else "ToolConversion"
engine_type = ("DynamicEngine" if dynamic_engine else "StaticEngine")
test_name = "%s_%s_%s" % (conversion, precision_mode, engine_type)
run_params = RunParams(use_optimizer=use_optimizer,
precision_mode=precision_mode,
dynamic_engine=dynamic_engine,
test_name=test_name)
setattr(test_class, "testTfTrt_" + test_name, _GetTest(run_params))
if trt_convert.is_tensorrt_enabled():
_AddTests(TfTrtIntegrationTestBase)
return _Test
use_optimizer_options = [False, True]
dynamic_engine_options = [False, True]
for (use_optimizer, precision_mode, dynamic_engine) in itertools.product(
use_optimizer_options, PRECISION_MODES, dynamic_engine_options):
if IsQuantizationMode(precision_mode):
if use_optimizer:
# TODO(aaroey): if use_optimizer is True we need to get the inference
# graphdef using custom python wrapper class, which is not currently
# supported yet.
continue
if not dynamic_engine:
# TODO(aaroey): construction of static calibration engine is not
# supported yet.
continue
conversion = "OptimizerConversion" if use_optimizer else "ToolConversion"
engine_type = ("DynamicEngine" if dynamic_engine else "StaticEngine")
test_name = "%s_%s_%s" % (conversion, precision_mode, engine_type)
run_params = RunParams(
use_optimizer=use_optimizer,
precision_mode=precision_mode,
dynamic_engine=dynamic_engine,
test_name=test_name)
setattr(test_class, "testTfTrt_" + test_name, _GetTest(run_params))
if trt_convert.is_tensorrt_enabled():
_AddTests(TfTrtIntegrationTestBase)
