def _GetConfigProto(self, run_params, graph_state):
"""Get config proto based on specific settings."""
if graph_state != GraphState.ORIGINAL and run_params.use_optimizer:
conversion_params = self.GetConversionParams(run_params)
rewriter_cfg = trt_convert.tensorrt_rewriter_config(
conversion_params.rewriter_config,
conversion_params.max_batch_size,
conversion_params.max_workspace_size_bytes,
conversion_params.precision_mode,
conversion_params.minimum_segment_size,
conversion_params.is_dynamic_op,
conversion_params.maximum_cached_engines,
conversion_params.cached_engine_batch_sizes)
graph_options = config_pb2.GraphOptions(
rewrite_options=rewriter_cfg)
else:
graph_options = config_pb2.GraphOptions()
gpu_options = config_pb2.GPUOptions()
gpu_options.allow_growth = True
if trt_convert.get_linked_tensorrt_version()[0] == 3:
gpu_options.per_process_gpu_memory_fraction = 0.50
config = config_pb2.ConfigProto(gpu_options=gpu_options,
graph_options=graph_options)
return config
def testTensorrtRewriterConfig(self):
"""Test case for trt_convert.tensorrt_rewriter_config()."""
rewriter_cfg = trt_convert.tensorrt_rewriter_config(
max_batch_size=128,
max_workspace_size_bytes=1234,
precision_mode="INT8",
minimum_segment_size=10,
is_dynamic_op=True,
maximum_cached_engines=2,
cached_engine_batch_sizes=[1, 128])
trt_optimizer = None
for optimizer in rewriter_cfg.custom_optimizers:
if optimizer.name == "TensorRTOptimizer":
self.assertTrue(trt_optimizer is None)
trt_optimizer = optimizer
self.assertTrue(trt_optimizer is not None)
for key in [
"minimum_segment_size", "max_batch_size", "is_dynamic_op",
"max_workspace_size_bytes", "precision_mode",
"maximum_cached_engines", "cached_engine_batches"
]:
self.assertTrue(key in trt_optimizer.parameter_map)
self.assertEqual(10,
trt_optimizer.parameter_map["minimum_segment_size"].i)
self.assertEqual(128, trt_optimizer.parameter_map["max_batch_size"].i)
self.assertEqual(True, trt_optimizer.parameter_map["is_dynamic_op"].b)
self.assertEqual(
1234, trt_optimizer.parameter_map["max_workspace_size_bytes"].i)
self.assertEqual(trt_convert._to_bytes("INT8"),
trt_optimizer.parameter_map["precision_mode"].s)
self.assertEqual(
2, trt_optimizer.parameter_map["maximum_cached_engines"].i)
self.assertEqual(
[1, 128],
trt_optimizer.parameter_map["cached_engine_batches"].list.i)
def testTensorrtRewriterConfig(self):
"""Test case for trt_convert.tensorrt_rewriter_config()."""
rewriter_cfg = trt_convert.tensorrt_rewriter_config(
max_batch_size=128,
max_workspace_size_bytes=1234,
precision_mode="INT8",
minimum_segment_size=10,
is_dynamic_op=True,
maximum_cached_engines=2,
cached_engine_batch_sizes=[1, 128])
trt_optimizer = None
for optimizer in rewriter_cfg.custom_optimizers:
if optimizer.name == "TensorRTOptimizer":
self.assertTrue(trt_optimizer is None)
trt_optimizer = optimizer
self.assertTrue(trt_optimizer is not None)
for key in [
"minimum_segment_size", "max_batch_size", "is_dynamic_op",
"max_workspace_size_bytes", "precision_mode", "maximum_cached_engines",
"cached_engine_batches"
]:
self.assertTrue(key in trt_optimizer.parameter_map)
self.assertEqual(10, trt_optimizer.parameter_map["minimum_segment_size"].i)
self.assertEqual(128, trt_optimizer.parameter_map["max_batch_size"].i)
self.assertEqual(True, trt_optimizer.parameter_map["is_dynamic_op"].b)
self.assertEqual(1234,
trt_optimizer.parameter_map["max_workspace_size_bytes"].i)
self.assertEqual(
trt_convert._to_bytes("INT8"),
trt_optimizer.parameter_map["precision_mode"].s)
self.assertEqual(2, trt_optimizer.parameter_map["maximum_cached_engines"].i)
self.assertEqual(
[1, 128],
trt_optimizer.parameter_map["cached_engine_batches"].list.i)
def _GetConfigProto(self, run_params, graph_state):
"""Get config proto based on specific settings."""
if graph_state != GraphState.ORIGINAL and run_params.use_optimizer:
conversion_params = self.GetConversionParams(run_params)
rewriter_cfg = trt_convert.tensorrt_rewriter_config(
conversion_params.rewriter_config, conversion_params.max_batch_size,
conversion_params.max_workspace_size_bytes,
conversion_params.precision_mode,
conversion_params.minimum_segment_size,
conversion_params.is_dynamic_op,
conversion_params.maximum_cached_engines,
conversion_params.cached_engine_batch_sizes)
graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_cfg)
else:
graph_options = config_pb2.GraphOptions()
gpu_options = config_pb2.GPUOptions()
gpu_options.allow_growth = True
if trt_convert.get_linked_tensorrt_version()[0] == 3:
gpu_options.per_process_gpu_memory_fraction = 0.50
config = config_pb2.ConfigProto(
gpu_options=gpu_options, graph_options=graph_options)
return config