def add_trt_config_loader(script, args, data_loader_name):
profiles = []
for (min_shape, opt_shape, max_shape) in args_util.get(args, "profiles"):
profile_str = "Profile()"
for name in min_shape.keys():
profile_str += Script.format_str(".add({:}, min={:}, opt={:}, max={:})", name, min_shape[name], opt_shape[name], max_shape[name])
profiles.append(Inline(profile_str))
if profiles:
script.add_import(imports=["Profile"], frm="polygraphy.backend.trt")
sep = Inline("\n{:}".format(constants.TAB))
profiles = Script.format_str("[{:}{:}\n]", sep, Inline((",{:}".format(sep)).join(profiles)))
profile_name = script.add_loader(profiles, "profiles")
else:
profile_name = None
calibrator = None
if args_util.get(args, "int8"):
script.add_import(imports=["DataLoader"], frm="polygraphy.comparator")
script.add_import(imports=["Calibrator"], frm="polygraphy.backend.trt")
calibrator = Script.invoke("Calibrator", data_loader=Inline(data_loader_name) if data_loader_name else Inline("DataLoader()"),
cache=args_util.get(args, "calibration_cache"))
config_loader_str = Script.invoke_if_nondefault("CreateTrtConfig", max_workspace_size=args_util.get(args, "workspace"), tf32=args_util.get(args, "tf32"),
fp16=args_util.get(args, "fp16"), int8=args_util.get(args, "int8"), strict_types=args_util.get(args, "strict_types"),
profiles=profile_name, calibrator=Inline(calibrator) if calibrator else None)
if config_loader_str is not None:
script.add_import(imports=["CreateConfig as CreateTrtConfig"], frm="polygraphy.backend.trt")
config_loader_name = script.add_loader(config_loader_str, "create_trt_config")
else:
config_loader_name = None
return config_loader_name
def _make_data_loader(script):
data_loader_name = Inline("data_loader")
input_metadata_str = Inline(
repr(self.model_args.input_shapes
)) if self.model_args.input_shapes else None
if input_metadata_str:
script.add_import(imports=["TensorMetadata"],
frm="polygraphy.common")
data_loader = Script.invoke_if_nondefault(
"DataLoader",
seed=self.seed,
iterations=self.iterations,
input_metadata=input_metadata_str,
int_range=self.int_range,
float_range=self.float_range)
if data_loader is not None:
script.add_import(imports=["DataLoader"],
frm="polygraphy.comparator")
script.append_prefix(
Script.format_str("\n# Inference Inputs Loader\n{:} = {:}",
data_loader_name, Inline(data_loader)))
else:
data_loader_name = None
return data_loader_name
def add_to_script(self, script, data_loader_name):
script.add_import(imports=["Comparator"], frm="polygraphy.comparator")
script.add_import(imports=["sys"])
RESULTS_VAR_NAME = Inline("results")
comparator_run = Script.invoke("Comparator.run",
script.get_runners(),
warm_up=self.warm_up,
data_loader=data_loader_name,
use_subprocess=self.use_subprocess,
save_inputs_path=self.save_inputs)
script.append_suffix(
Script.format_str("\n# Runner Execution\n{results} = {:}",
Inline(comparator_run),
results=RESULTS_VAR_NAME))
if self.save_results:
G_LOGGER.verbose("Will save runner results to: {:}".format(
self.save_results))
script.add_import(imports=["misc"], frm="polygraphy.util")
script.append_suffix(
Script.format_str(
"\n# Save results\nmisc.pickle_save({:}, {results})",
self.save_results,
results=RESULTS_VAR_NAME))
return RESULTS_VAR_NAME
def add_to_script(self, script):
def _make_data_loader(script):
data_loader_name = Inline("data_loader")
input_metadata_str = Inline(
repr(self.model_args.input_shapes
)) if self.model_args.input_shapes else None
if input_metadata_str:
script.add_import(imports=["TensorMetadata"],
frm="polygraphy.common")
data_loader = Script.invoke_if_nondefault(
"DataLoader",
seed=self.seed,
iterations=self.iterations,
input_metadata=input_metadata_str,
int_range=self.int_range,
float_range=self.float_range)
if data_loader is not None:
script.add_import(imports=["DataLoader"],
frm="polygraphy.comparator")
script.append_prefix(
Script.format_str("\n# Inference Inputs Loader\n{:} = {:}",
data_loader_name, Inline(data_loader)))
else:
data_loader_name = None
return data_loader_name
if self.load_inputs:
script.add_import(imports=["misc"], frm="polygraphy.util")
data_loader_name = Inline("data_loader")
script.append_prefix(
Script.format_str(
"# Load inputs\n{data_loader} = []\nfor input_data_path in {load_inputs}:"
"\n{tab}{data_loader}.extend(misc.pickle_load(input_data_path))",
data_loader=data_loader_name,
load_inputs=self.load_inputs,
tab=Inline(constants.TAB)))
else:
data_loader_name = _make_data_loader(script)
script.append_prefix("") # Newline
return data_loader_name
def build_script(self, args):
script = Script(
summary=generate_summary(self.makers[ModelArgs].model_file,
args.runners, args.load_results))
self.makers[LoggerArgs].add_to_script(script)
data_loader_name = self.makers[DataLoaderArgs].add_to_script(script)
for runner_arg in args.runners:
add_runner_func = {
"tf":
self.makers[TfRunnerArgs].add_to_script,
"onnxrt":
self.makers[OnnxrtRunnerArgs].add_to_script,
"onnxtf":
self.makers[OnnxtfRunnerArgs].add_to_script,
"trt":
lambda script: self.makers[TrtRunnerArgs].add_to_script(
script, data_loader_name),
"trt_legacy":
self.makers[TrtLegacyArgs].add_to_script,
}[runner_arg]
add_runner_func(script)
RESULTS_VAR_NAME = self.makers[ComparatorRunArgs].add_to_script(
script, data_loader_name=data_loader_name)
SUCCESS_VAR_NAME = self.makers[ComparatorCompareArgs].add_to_script(
script, results_name=RESULTS_VAR_NAME)
cmd_run = Inline("' '.join(sys.argv)")
script.append_suffix(
Script.format_str(
'# Report Results\ncmd_run={cmd}\nif {success}:\n{tab}G_LOGGER.finish("PASSED | Command: {{}}".format(cmd_run))\nelse:\n{tab}G_LOGGER.error("FAILED | Command: {{}}".format(cmd_run))',
cmd=cmd_run,
success=SUCCESS_VAR_NAME,
tab=Inline(constants.TAB)))
script.append_suffix(
"sys.exit(0 if {success} else 1)".format(success=SUCCESS_VAR_NAME))
return str(script)
def add_data_loader(script, args):
def omit_none_tuple(tup):
if all([elem is None for elem in tup]):
return None
return tup
int_range = omit_none_tuple(tup=(args_util.get(args, "int_min"), args_util.get(args, "int_max")))
float_range = omit_none_tuple(tup=(args_util.get(args, "float_min"), args_util.get(args, "float_max")))
input_metadata_str = Inline(repr(args_util.get(args, "inputs"))) if args_util.get(args, "inputs") else None
if input_metadata_str:
script.add_import(imports=["TensorMetadata"], frm="polygraphy.common")
data_loader = Script.invoke_if_nondefault("DataLoader", seed=args_util.get(args, "seed"), iterations=args_util.get(args, "iterations"),
input_metadata=input_metadata_str, int_range=int_range, float_range=float_range)
if data_loader is not None:
data_loader_name = Inline("data_loader")
script.add_import(imports=["DataLoader"], frm="polygraphy.comparator")
script.append_prefix(Script.format_str("\n# Inference Inputs Loader\n{:} = {:}\n", data_loader_name, Inline(data_loader)))
else:
data_loader_name = None
return data_loader_name
def add_trt_config_loader(self, script, data_loader_name):
profiles = []
profile_args = tools_util.parse_profile_shapes(
self.model_args.input_shapes, self.trt_min_shapes,
self.trt_opt_shapes, self.trt_max_shapes)
for (min_shape, opt_shape, max_shape) in profile_args:
profile_str = "Profile()"
for name in min_shape.keys():
profile_str += Script.format_str(
".add({:}, min={:}, opt={:}, max={:})", name,
min_shape[name], opt_shape[name], max_shape[name])
profiles.append(Inline(profile_str))
if profiles:
script.add_import(imports=["Profile"],
frm="polygraphy.backend.trt")
sep = Inline("\n{:}".format(constants.TAB))
profiles = Script.format_str(
"[{:}{:}\n]", sep, Inline((",{:}".format(sep)).join(profiles)))
profile_name = script.add_loader(profiles, "profiles")
else:
profile_name = None
calibrator = None
if self.int8:
script.add_import(imports=["Calibrator"],
frm="polygraphy.backend.trt")
script.add_import(imports=["DataLoader"],
frm="polygraphy.comparator")
calibrator = Script.invoke(
"Calibrator",
data_loader=Inline(data_loader_name)
if data_loader_name else Inline("DataLoader()"),
cache=self.calibration_cache)
config_loader_str = Script.invoke_if_nondefault(
"CreateTrtConfig",
max_workspace_size=self.workspace,
tf32=self.tf32,
fp16=self.fp16,
int8=self.int8,
strict_types=self.strict_types,
profiles=profile_name,
calibrator=Inline(calibrator) if calibrator else None)
if config_loader_str is not None:
script.add_import(imports=["CreateConfig as CreateTrtConfig"],
frm="polygraphy.backend.trt")
config_loader_name = script.add_loader(config_loader_str,
"create_trt_config")
else:
config_loader_name = None
return config_loader_name
def add_trt_network_loader(script, args):
model_file = args_util.get(args, "model_file")
outputs = _get_outputs_arg(script, args, "trt_outputs")
if args_util.get(args, "network_api"):
CREATE_NETWORK_FUNC = Inline("create_network")
script.add_import(imports=["CreateNetwork"], frm="polygraphy.backend.trt")
script.add_import(imports=["extend"], frm="polygraphy.common.func")
script.append_prefix("# Manual TensorRT network creation")
script.append_prefix("@extend(CreateNetwork())")
script.append_prefix("def {:}(builder, network):".format(CREATE_NETWORK_FUNC))
script.append_prefix("{tab}import tensorrt as trt\n".format(tab=constants.TAB))
script.append_prefix("{tab}# Define your network here. Make sure to mark outputs!".format(tab=constants.TAB))
net_inputs = args_util.get(args, "inputs")
if net_inputs:
for name, (dtype, shape) in net_inputs.items():
script.append_prefix("{tab}{name} = network.add_input(name='{name}', shape={shape}, dtype=trt.float32) # TODO: Set dtype".format(
name=name, shape=shape, tab=constants.TAB))
script.append_prefix("{tab}# TODO: network.mark_output(...)\n".format(tab=constants.TAB))
return CREATE_NETWORK_FUNC
if args_util.get(args, "ext"):
script.add_import(imports=["NetworkFromOnnxPath"], frm="polygraphy.backend.trt")
loader_str = Script.invoke("NetworkFromOnnxPath", _wrap_if_plugins(script, args, model_file), explicit_precision=args_util.get(args, "explicit_precision"))
loader_name = script.add_loader(loader_str, "parse_network_from_onnx")
else:
script.add_import(imports=["NetworkFromOnnxBytes"], frm="polygraphy.backend.trt")
onnx_loader = add_serialized_onnx_loader(script, args, disable_outputs=True)
loader_str = Script.invoke("NetworkFromOnnxBytes", _wrap_if_plugins(script, args, onnx_loader), explicit_precision=args_util.get(args, "explicit_precision"))
loader_name = script.add_loader(loader_str, "parse_network_from_onnx")
MODIFY_NETWORK = "ModifyNetwork"
modify_network_str = Script.invoke(MODIFY_NETWORK, loader_name, outputs=outputs, exclude_outputs=args_util.get(args, "trt_exclude_outputs"))
if modify_network_str != Script.invoke(MODIFY_NETWORK, loader_name):
script.add_import(imports=[MODIFY_NETWORK], frm="polygraphy.backend.trt")
loader_name = script.add_loader(modify_network_str, "modify_network")
return loader_name
def add_comparator(script, args, data_loader_name, cmd_run):
script.add_import(imports=["Comparator"], frm="polygraphy.comparator")
script.add_import(imports=["sys"])
comparator_run = Script.invoke("Comparator.run",
script.get_runners(),
warm_up=args.warm_up,
data_loader=data_loader_name,
use_subprocess=args.use_subprocess)
script.append_suffix(
Script.format_str("\n# Runner Execution\nresults = {:}",
Inline(comparator_run)))
if args.load_results:
G_LOGGER.verbose("Will load runner results from: {:}".format(
args.load_results))
script.add_import(imports=["misc"], frm="polygraphy.util")
script.append_suffix(
Script.format_str(
"\n# Load results\nfor load_output in {:}:\n{:}results.update(misc.pickle_load(load_output))",
args.load_results, Inline(constants.TAB)))
if args.save_results:
G_LOGGER.verbose("Will save runner results to: {:}".format(
args.save_results))
script.add_import(imports=["misc"], frm="polygraphy.util")
script.append_suffix(
Script.format_str(
"\n# Save results\nmisc.pickle_save({:}, results)",
args.save_results))
top_k = args_util.get(args, "top_k")
if top_k is not None:
script.add_import(imports=["PostprocessFunc"],
frm="polygraphy.comparator")
script.append_suffix(
Script.format_str(
"\n# Postprocessing - Apply Top-{:}\nresults = Comparator.postprocess(results, PostprocessFunc.topk_func(k={:}))",
top_k, top_k))
script.append_suffix("\nsuccess = True")
if len(
args.runners
) > 1 or args.load_results: # Only do comparisons if there's actually something to compare.
script.append_suffix("# Accuracy Comparison")
compare_func_str = Script.invoke_if_nondefault(
"CompareFunc.basic_compare_func",
rtol=args.rtol,
atol=args.atol,
check_shapes=False if args.no_shape_check else None,
fail_fast=args.fail_fast)
compare_func = None
if compare_func_str:
script.add_import(imports=["CompareFunc"],
frm="polygraphy.comparator")
compare_func = "compare_func"
script.append_suffix(
Script.format_str("{:} = {:}", Inline(compare_func),
Inline(compare_func_str)))
compare_accuracy = Script.invoke("Comparator.compare_accuracy",
Inline("results"),
compare_func=Inline(compare_func)
if compare_func is not None else None,
fail_fast=args.fail_fast)
script.append_suffix(
Script.format_str("success &= bool({:})\n",
Inline(compare_accuracy)))
if args.validate:
script.append_suffix(
"# Validation\nsuccess &= Comparator.validate(results)\n")
if cmd_run is None:
cmd_run = Inline("' '.join(sys.argv)")
script.append_suffix(
Script.format_str(
'# Report Results\ncmd_run={cmd}\nif success:\n G_LOGGER.success("PASSED | Command: {{}}".format(cmd_run))\nelse:\n G_LOGGER.error("FAILED | Command: {{}}".format(cmd_run))',
cmd=cmd_run))
script.append_suffix("sys.exit(0 if success else 1)")
def add_trt_network_loader(self, script):
model_file = self.model_args.model_file
outputs = tools_util.get_outputs_for_script(script, self.outputs)
if self.network_api:
CREATE_NETWORK_FUNC = Inline("create_network")
script.add_import(imports=["CreateNetwork"],
frm="polygraphy.backend.trt")
script.add_import(imports=["func"], frm="polygraphy.common")
script.append_prefix("# Manual TensorRT network creation")
script.append_prefix("@func.extend(CreateNetwork())")
script.append_prefix(
"def {:}(builder, network):".format(CREATE_NETWORK_FUNC))
script.append_prefix(
"{tab}import tensorrt as trt\n".format(tab=constants.TAB))
script.append_prefix(
"{tab}# Define your network here. Make sure to mark outputs!".
format(tab=constants.TAB))
net_inputs = self.model_args.input_shapes
if net_inputs:
for name, (dtype, shape) in net_inputs.items():
script.append_prefix(
"{tab}{name} = network.add_input(name='{name}', shape={shape}, dtype=trt.float32) # TODO: Set dtype"
.format(name=name, shape=shape, tab=constants.TAB))
script.append_prefix(
"{tab}# TODO: network.mark_output(...)\n".format(
tab=constants.TAB))
return CREATE_NETWORK_FUNC
should_use_onnx_loader = not self.ext and self.onnx_loader_args is not None
if should_use_onnx_loader:
script.add_import(imports=["NetworkFromOnnxBytes"],
frm="polygraphy.backend.trt")
onnx_loader = self.onnx_loader_args.add_serialized_onnx_loader(
script, disable_outputs=True)
loader_str = Script.invoke(
"NetworkFromOnnxBytes",
self._wrap_if_plugins(script, onnx_loader),
explicit_precision=self.explicit_precision)
loader_name = script.add_loader(loader_str,
"parse_network_from_onnx")
else:
script.add_import(imports=["NetworkFromOnnxPath"],
frm="polygraphy.backend.trt")
loader_str = Script.invoke(
"NetworkFromOnnxPath",
self._wrap_if_plugins(script, model_file),
explicit_precision=self.explicit_precision)
loader_name = script.add_loader(loader_str,
"parse_network_from_onnx")
MODIFY_NETWORK = "ModifyNetwork"
modify_network_str = Script.invoke(
MODIFY_NETWORK,
loader_name,
outputs=outputs,
exclude_outputs=self.exclude_outputs)
if modify_network_str != Script.invoke(MODIFY_NETWORK, loader_name):
script.add_import(imports=[MODIFY_NETWORK],
frm="polygraphy.backend.trt")
loader_name = script.add_loader(modify_network_str,
"modify_network")
return loader_name
def add_to_script(self, script, results_name):
if self.load_results:
G_LOGGER.verbose("Will load runner results from: {:}".format(
self.load_results))
script.add_import(imports=["misc"], frm="polygraphy.util")
script.append_suffix(
Script.format_str(
"\n# Load results\nfor load_output in {:}:\n{:}{results}.extend(misc.pickle_load(load_output))",
self.load_results,
Inline(constants.TAB),
results=results_name))
if self.top_k is not None:
script.add_import(imports=["PostprocessFunc"],
frm="polygraphy.comparator")
script.append_suffix(
Script.format_str(
"\n# Postprocessing - Apply Top-{top_k}\n{results} = Comparator.postprocess({results}, PostprocessFunc.topk_func(k={top_k}))",
top_k=self.top_k,
results=results_name))
SUCCESS_VAR_NAME = Inline("success")
script.append_suffix(
"\n{success} = True".format(success=SUCCESS_VAR_NAME))
if len(
self.runners
) > 1 or self.load_results: # Only do comparisons if there's actually something to compare.
script.append_suffix("# Accuracy Comparison")
compare_func_str = Script.invoke_if_nondefault(
"CompareFunc.basic_compare_func",
rtol=self.rtol,
atol=self.atol,
check_shapes=False if self.no_shape_check else None,
fail_fast=self.fail_fast)
compare_func = None
if compare_func_str:
script.add_import(imports=["CompareFunc"],
frm="polygraphy.comparator")
compare_func = "compare_func"
script.append_suffix(
Script.format_str("{:} = {:}", Inline(compare_func),
Inline(compare_func_str)))
compare_accuracy = Script.invoke(
"Comparator.compare_accuracy",
results_name,
compare_func=Inline(compare_func)
if compare_func is not None else None,
fail_fast=self.fail_fast)
script.append_suffix(
Script.format_str("{success} &= bool({:})\n",
Inline(compare_accuracy),
success=SUCCESS_VAR_NAME))
if self.validate:
script.append_suffix(
"# Validation\n{success} &= Comparator.validate({results})\n".
format(success=SUCCESS_VAR_NAME, results=results_name))
return SUCCESS_VAR_NAME
def get_outputs_for_script(script, outputs):
if outputs == constants.MARK_ALL:
outputs = Inline("constants.MARK_ALL")
script.add_import(["constants"], frm="polygraphy.common")
return outputs
def _get_outputs_arg(script, args, name):
outputs = args_util.get(args, name)
if outputs == constants.MARK_ALL:
outputs = Inline("constants.MARK_ALL")
script.add_import(["constants"], frm="polygraphy.common")
return outputs