def RunTest(self, run_params):
if not self.ShouldRunTest(run_params):
return
assert run_params.precision_mode in PRECISION_MODES
np.random.seed(12345)
params = self._GetParamsCached()
input_gdef = params.gdef
input_dtypes = {}
for node in input_gdef.node:
if self._ToString(node.name) in params.input_names:
assert self._ToString(node.op) == "Placeholder"
input_dtypes[self._ToString(node.name)] = (dtypes.as_dtype(
node.attr["dtype"].type).as_numpy_dtype())
assert len(params.input_names) == len(input_dtypes)
input_data = []
for i in range(len(params.input_names)):
dtype = input_dtypes[params.input_names[i]]
# Multiply the input by some constant to avoid all zeros input for integer
# types.
scale = 10.0 if np.issubdtype(dtype, np.integer) else 1.0
dims = params.input_dims[i]
input_data.append(
(scale * np.random.random_sample(dims)).astype(dtype))
self._VerifyGraphDef(run_params, input_gdef, GraphState.ORIGINAL)
# Get reference result without running trt.
config_no_trt = self._GetConfigProto(run_params, GraphState.ORIGINAL)
logging.info("Running original graph w/o trt, config:\n%s",
str(config_no_trt))
ref_result = self._RunGraph(run_params, input_gdef, input_data,
config_no_trt, GraphState.ORIGINAL)
# Run calibration if necessary.
if IsQuantizationMode(run_params.precision_mode):
calib_config = self._GetConfigProto(run_params,
GraphState.CALIBRATE)
logging.info("Running calibration graph, config:\n%s",
str(calib_config))
if run_params.use_optimizer:
result = self._RunCalibration(run_params, input_gdef,
input_data, calib_config)
else:
calib_gdef = self._GetTrtGraphDef(run_params, input_gdef)
self._VerifyGraphDef(run_params, calib_gdef,
GraphState.CALIBRATE)
result = self._RunCalibration(run_params, calib_gdef,
input_data, calib_config)
infer_gdef = trt_convert.calib_graph_to_infer_graph(
calib_gdef, run_params.dynamic_engine)
self._VerifyGraphDef(run_params, infer_gdef, GraphState.INFERENCE)
self.assertAllClose(
ref_result,
result,
atol=self.ExpectedAbsoluteTolerance(run_params),
rtol=self.ExpectedRelativeTolerance(run_params))
else:
infer_gdef = input_gdef
# Run inference.
infer_config = self._GetConfigProto(run_params, GraphState.INFERENCE)
logging.info("Running final inference graph, config:\n%s",
str(infer_config))
if not run_params.use_optimizer:
infer_gdef = self._GetTrtGraphDef(run_params, infer_gdef)
self._VerifyGraphDef(run_params, infer_gdef, GraphState.INFERENCE)
result = self._RunGraph(run_params, infer_gdef, input_data,
infer_config, GraphState.INFERENCE)
self.assertAllClose(ref_result,
result,
atol=self.ExpectedAbsoluteTolerance(run_params),
rtol=self.ExpectedRelativeTolerance(run_params))
def RunTest(self, params, use_optimizer, precision_mode,
dynamic_infer_engine, dynamic_calib_engine):
assert precision_mode in PRECISION_MODES
input_data = [np.random.random_sample(dims) for dims in params.input_dims]
input_gdef = params.gdef
self._VerifyGraphDef(params, input_gdef)
# Get reference result without running trt.
config_no_trt = self._GetConfigProto(params, False)
logging.info("Running original graph w/o trt, config:\n%s",
str(config_no_trt))
ref_result = self._RunGraph(params, input_gdef, input_data, config_no_trt)
# Run calibration if necessary.
if _IsQuantizationMode(precision_mode):
calib_config = self._GetConfigProto(params, use_optimizer, precision_mode,
dynamic_calib_engine)
logging.info("Running calibration graph, config:\n%s", str(calib_config))
if use_optimizer:
self.assertTrue(False)
# TODO(aaroey): uncomment this and get infer_gdef when this mode is
# supported.
# result = self._RunCalibration(params, input_gdef, input_data,
# calib_config)
else:
calib_gdef = self._GetTrtGraphDef(params, input_gdef, precision_mode,
dynamic_calib_engine)
self._VerifyGraphDef(params, calib_gdef, precision_mode, False,
dynamic_calib_engine)
result = self._RunCalibration(params, calib_gdef, input_data,
calib_config)
infer_gdef = trt_convert.calib_graph_to_infer_graph(calib_gdef)
self._VerifyGraphDef(params, infer_gdef, precision_mode, True,
dynamic_calib_engine)
self.assertAllClose(
ref_result,
result,
atol=params.allclose_atol,
rtol=params.allclose_rtol)
else:
infer_gdef = input_gdef
# Run inference.
infer_config = self._GetConfigProto(params, use_optimizer, precision_mode,
dynamic_infer_engine)
logging.info("Running final inference graph, config:\n%s",
str(infer_config))
if use_optimizer:
result = self._RunGraph(params, infer_gdef, input_data, infer_config)
else:
trt_infer_gdef = self._GetTrtGraphDef(params, infer_gdef, precision_mode,
dynamic_infer_engine)
self._VerifyGraphDef(params, trt_infer_gdef, precision_mode, True,
dynamic_infer_engine)
result = self._RunGraph(params, trt_infer_gdef, input_data, infer_config)
self.assertAllClose(
ref_result,
result,
atol=params.allclose_atol,
rtol=params.allclose_rtol)
def RunTest(self, run_params):
if not self.ShouldRunTest(run_params):
return
assert run_params.precision_mode in PRECISION_MODES
np.random.seed(12345)
params = self._GetParamsCached()
input_gdef = params.gdef
input_dtypes = {}
for node in input_gdef.node:
if self._ToString(node.name) in params.input_names:
assert self._ToString(node.op) == "Placeholder"
input_dtypes[self._ToString(node.name)] = (
dtypes.as_dtype(node.attr["dtype"].type).as_numpy_dtype())
assert len(params.input_names) == len(input_dtypes)
input_data = []
for i in range(len(params.input_names)):
dtype = input_dtypes[params.input_names[i]]
# Multiply the input by some constant to avoid all zeros input for integer
# types.
scale = 10.0 if np.issubdtype(dtype, np.integer) else 1.0
dims = params.input_dims[i]
input_data.append((scale * np.random.random_sample(dims)).astype(dtype))
self._VerifyGraphDef(run_params, input_gdef, GraphState.ORIGINAL)
# Get reference result without running trt.
config_no_trt = self._GetConfigProto(run_params, GraphState.ORIGINAL)
logging.info("Running original graph w/o trt, config:\n%s",
str(config_no_trt))
ref_result = self._RunGraph(run_params, input_gdef, input_data,
config_no_trt, GraphState.ORIGINAL)
# Run calibration if necessary.
if IsQuantizationMode(run_params.precision_mode):
calib_config = self._GetConfigProto(run_params, GraphState.CALIBRATE)
logging.info("Running calibration graph, config:\n%s", str(calib_config))
if run_params.use_optimizer:
result = self._RunCalibration(run_params, input_gdef, input_data,
calib_config)
else:
calib_gdef = self._GetTrtGraphDef(run_params, input_gdef)
self._VerifyGraphDef(run_params, calib_gdef, GraphState.CALIBRATE)
result = self._RunCalibration(run_params, calib_gdef, input_data,
calib_config)
infer_gdef = trt_convert.calib_graph_to_infer_graph(
calib_gdef, run_params.dynamic_engine)
self._VerifyGraphDef(run_params, infer_gdef, GraphState.INFERENCE)
self.assertAllClose(
ref_result,
result,
atol=self.ExpectedAbsoluteTolerance(run_params),
rtol=self.ExpectedRelativeTolerance(run_params))
else:
infer_gdef = input_gdef
# Run inference.
infer_config = self._GetConfigProto(run_params, GraphState.INFERENCE)
logging.info("Running final inference graph, config:\n%s",
str(infer_config))
if not run_params.use_optimizer:
infer_gdef = self._GetTrtGraphDef(run_params, infer_gdef)
self._VerifyGraphDef(run_params, infer_gdef, GraphState.INFERENCE)
result = self._RunGraph(run_params, infer_gdef, input_data, infer_config,
GraphState.INFERENCE)
self.assertAllClose(
ref_result,
result,
atol=self.ExpectedAbsoluteTolerance(run_params),
rtol=self.ExpectedRelativeTolerance(run_params))
def RunTest(self, params, run_params):
assert run_params.precision_mode in PRECISION_MODES
input_data = [
np.random.random_sample(dims) for dims in params.input_dims
]
input_gdef = params.gdef
self._VerifyGraphDef(params, run_params, input_gdef,
GraphState.ORIGINAL)
# Get reference result without running trt.
config_no_trt = self._GetConfigProto(params, run_params,
GraphState.ORIGINAL)
logging.info("Running original graph w/o trt, config:\n%s",
str(config_no_trt))
ref_result = self._RunGraph(params, input_gdef, input_data,
config_no_trt, GraphState.ORIGINAL)
# Run calibration if necessary.
if _IsQuantizationMode(run_params.precision_mode):
calib_config = self._GetConfigProto(params, run_params,
GraphState.CALIBRATE)
logging.info("Running calibration graph, config:\n%s",
str(calib_config))
if run_params.use_optimizer:
result = self._RunCalibration(params, input_gdef, input_data,
calib_config)
else:
calib_gdef = self._GetTrtGraphDef(params, run_params,
input_gdef)
self._VerifyGraphDef(params, run_params, calib_gdef,
GraphState.CALIBRATE)
result = self._RunCalibration(params, calib_gdef, input_data,
calib_config)
infer_gdef = trt_convert.calib_graph_to_infer_graph(calib_gdef)
self._VerifyGraphDef(params, run_params, infer_gdef,
GraphState.INFERENCE)
self.assertAllClose(ref_result,
result,
atol=params.allclose_atol,
rtol=params.allclose_rtol)
else:
infer_gdef = input_gdef
# Run inference.
infer_config = self._GetConfigProto(params, run_params,
GraphState.INFERENCE)
logging.info("Running final inference graph, config:\n%s",
str(infer_config))
if run_params.use_optimizer:
result = self._RunGraph(params, infer_gdef, input_data,
infer_config, GraphState.INFERENCE)
else:
trt_infer_gdef = self._GetTrtGraphDef(params, run_params,
infer_gdef)
self._VerifyGraphDef(params, run_params, trt_infer_gdef,
GraphState.INFERENCE)
result = self._RunGraph(params, trt_infer_gdef, input_data,
infer_config, GraphState.INFERENCE)
self.assertAllClose(ref_result,
result,
atol=params.allclose_atol,
rtol=params.allclose_rtol)
