#
# Copyright (c) 2021, NVIDIA CORPORATION. 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.
#
import contextlib
import copy
import ctypes
import time
from polygraphy import config, constants, mod, util
from polygraphy.backend.base import BaseLoader
from polygraphy.backend.trt import util as trt_util
from polygraphy.backend.trt.profile import Profile
from polygraphy.logger import G_LOGGER
trt = mod.lazy_import("tensorrt")
gs = mod.lazy_import("onnx_graphsurgeon")
np = mod.lazy_import("numpy")
[docs]@mod.export(funcify=True)
class LoadPlugins(BaseLoader):
"""
A passthrough loader that loads plugins from the specified paths.
Passthrough here means that it can be used to wrap any other loader. The purpose of wrapping
another loader is that you can control the order of execution when lazily evaluating.
For immediate evaluation, use `load_plugins` instead:
::
load_plugins(plugins=["/path/to/my/plugin.so", "/path/to/my/other_plugin.so"])
"""
def __init__(self, plugins=None, obj=None):
"""
Loads plugins from the specified paths.
Args:
plugins (List[str]):
A list of paths to plugin libraries to load before inference.
obj (BaseLoader):
An object or callable to return or call respectively.
If ``obj`` is callable, extra parameters will be forwarded to ``obj``.
If ``obj`` is not callable, it will be returned.
"""
self.plugins = util.default(plugins, [])
self.obj = obj
[docs] def call_impl(self, *args, **kwargs):
"""
Returns:
object:
The provided ``obj`` argument, or its return value if it is
callable. Returns ``None`` if ``obj`` was not set.
"""
for plugin in self.plugins:
G_LOGGER.info("Loading plugin library: {:}".format(plugin))
ctypes.CDLL(plugin)
ret, _ = util.invoke_if_callable(self.obj, *args, **kwargs)
return ret
[docs]@mod.export(funcify=True)
class CreateNetwork(BaseLoader):
"""
Functor that creates an empty TensorRT network.
"""
def __init__(self, explicit_precision=None, explicit_batch=None):
"""
Creates an empty TensorRT network.
Args:
explicit_precision (bool):
Whether to create the network with explicit precision enabled. Defaults to False
explicit_batch (bool):
Whether to create the network with explicit batch mode. Defaults to True.
"""
self.explicit_precision = util.default(explicit_precision, False)
self.explicit_batch = util.default(explicit_batch, True)
[docs] def call_impl(self):
"""
Returns:
(trt.Builder, trt.INetworkDefinition): The builder and empty network.
"""
with util.FreeOnException([trt.Builder(trt_util.get_trt_logger())]) as (builder,):
network_flags = 0
if self.explicit_batch:
network_flags |= 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
if self.explicit_precision:
network_flags |= 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_PRECISION)
network = builder.create_network(flags=network_flags)
if network is None:
G_LOGGER.critical("Invalid network. See logging output above for details.")
return builder, network
class BaseNetworkFromOnnx(BaseLoader):
def __init__(self, explicit_precision, explicit_batch=None):
"""
Args:
explicit_precision (bool): Whether to create the network with explicit precision enabled.
"""
self.explicit_precision = util.default(explicit_precision, False)
self.explicit_batch = util.default(explicit_batch, True)
def call_impl(self):
with util.FreeOnException(
create_network(explicit_precision=self.explicit_precision, explicit_batch=self.explicit_batch)
) as (builder, network):
parser = trt.OnnxParser(network, trt_util.get_trt_logger())
return builder, network, parser
[docs]@mod.export(funcify=True)
class NetworkFromOnnxBytes(BaseNetworkFromOnnx):
"""
Functor that parses an ONNX model to create a trt.INetworkDefinition.
"""
def __init__(self, model_bytes, explicit_precision=None):
"""
Parses an ONNX model.
Args:
model_bytes (Callable() -> bytes): A loader that can supply a serialized ONNX model.
explicit_precision (bool): Whether to construct the TensorRT network with explicit precision enabled.
"""
super().__init__(explicit_precision)
self._model_bytes = model_bytes
[docs] def call_impl(self):
"""
Returns:
(trt.IBuilder, trt.INetworkDefinition, trt.OnnxParser):
A TensorRT network, as well as the builder used to create it, and the parser
used to populate it.
"""
with util.FreeOnException(super().call_impl()) as (builder, network, parser):
success = parser.parse(util.invoke_if_callable(self._model_bytes)[0])
trt_util.check_onnx_parser_errors(parser, success)
return builder, network, parser
[docs]@mod.export(funcify=True)
class NetworkFromOnnxPath(BaseNetworkFromOnnx):
"""
Functor that parses an ONNX model to create a trt.INetworkDefinition.
This loader supports models with weights stored in an external location.
"""
def __init__(self, path, explicit_precision=None):
"""
Parses an ONNX model from a file.
Args:
path (str): The path from which to load the model.
"""
super().__init__(explicit_precision)
self.path = path
[docs] def call_impl(self):
"""
Returns:
(trt.IBuilder, trt.INetworkDefinition, trt.OnnxParser):
A TensorRT network, as well as the builder used to create it, and the parser
used to populate it.
"""
path = util.invoke_if_callable(self.path)[0]
if mod.version(trt.__version__) >= mod.version("7.1"):
with util.FreeOnException(super().call_impl()) as (builder, network, parser):
# We need to use parse_from_file for the ONNX parser to keep track of the location of the ONNX file for
# potentially parsing any external weights.
success = parser.parse_from_file(path)
trt_util.check_onnx_parser_errors(parser, success)
return builder, network, parser
else:
from polygraphy.backend.common import bytes_from_path
return network_from_onnx_bytes(bytes_from_path(path), self.explicit_precision)
[docs]@mod.export_deprecated_alias("ModifyNetwork", remove_in="0.32.0")
@mod.export(funcify=True)
class ModifyNetworkOutputs(BaseLoader):
"""
Functor that modifies outputs in a TensorRT ``INetworkDefinition``.
"""
def __init__(self, network, outputs=None, exclude_outputs=None):
"""
Modifies outputs in a TensorRT ``INetworkDefinition``.
Args:
network (Callable() -> trt.Builder, trt.INetworkDefinition):
A callable capable of returning a TensorRT Builder and INetworkDefinition. The callable may
have at most 3 return values if another object needs to be kept alive for the duration of the network,
e.g., in the case of a parser. The first and second return values must
always be the builder and network respectively. ModifyNetworkOutputs will never take ownership of these.
outputs (Sequence[str]):
Names of tensors to mark as outputs. If provided, this will override the outputs
already marked in the network.
If a value of `constants.MARK_ALL` is used instead of a list, all tensors in the network are marked.
exclude_outputs (Sequence[str]):
Names of tensors to exclude as outputs. This can be useful in conjunction with
``outputs=constants.MARK_ALL`` to omit outputs.
"""
self._network = network
self.outputs = outputs
self.exclude_outputs = exclude_outputs
[docs] def call_impl(self):
"""
Returns:
trt.INetworkDefinition: The modified network.
"""
ret, owns_network = util.invoke_if_callable(self._network)
builder, network, parser = util.unpack_args(ret, num=3)
with contextlib.ExitStack() as stack:
if owns_network:
stack.enter_context(util.FreeOnException([builder, network, parser]))
if self.outputs == constants.MARK_ALL:
trt_util.mark_layerwise(network)
elif self.outputs is not None:
trt_util.mark_outputs(network, self.outputs)
if self.exclude_outputs is not None:
trt_util.unmark_outputs(network, self.exclude_outputs)
if parser is None:
return builder, network
return builder, network, parser
[docs]@mod.export(funcify=True)
class CreateConfig(BaseLoader):
"""
Functor that creates a TensorRT IBuilderConfig.
"""
def __init__(
self,
max_workspace_size=None,
tf32=None,
fp16=None,
int8=None,
profiles=None,
calibrator=None,
strict_types=None,
load_timing_cache=None,
algorithm_selector=None,
sparse_weights=None,
tactic_sources=None,
restricted=None,
):
"""
Creates a TensorRT IBuilderConfig that can be used by EngineFromNetwork.
Args:
max_workspace_size (int):
The maximum workspace size, in bytes, when building the engine.
Defaults to 16 MiB.
tf32 (bool):
Whether to build the engine with TF32 precision enabled.
Defaults to False.
fp16 (bool):
Whether to build the engine with FP16 precision enabled.
Defaults to False.
int8 (bool):
Whether to build the engine with INT8 precision enabled.
Defaults to False.
profiles (List[Profile]):
A list of optimization profiles to add to the configuration. Only needed for
networks with dynamic input shapes. If this is omitted for a network with
dynamic shapes, a default profile is created, where dynamic dimensions are
replaced with Polygraphy's DEFAULT_SHAPE_VALUE (defined in constants.py).
A partially populated profile will be automatically filled using values from ``Profile.fill_defaults()``
See ``Profile`` for details.
calibrator (trt.IInt8Calibrator):
An int8 calibrator. Only required in int8 mode when
the network does not have explicit precision. For networks with
dynamic shapes, the last profile provided (or default profile if
no profiles are provided) is used during calibration.
strict_types (bool):
Whether to enable strict types in the builder. This will constrain the builder from
using data types other than those specified in the network.
Defaults to False.
load_timing_cache (Union[str, file-like]):
A path or file-like object from which to load a tactic timing cache.
Providing a tactic timing cache can speed up the engine building process.
Caches can be generated while building an engine with, for example, EngineFromNetwork.
algorithm_selector (trt.IAlgorithmSelector):
An algorithm selector. Allows the user to control how tactics are selected
instead of letting TensorRT select them automatically.
sparse_weights (bool):
Whether to enable optimizations for sparse weights.
Defaults to False.
tactic_sources (List[trt.TacticSource]):
The tactic sources to enable. This controls which libraries (e.g. cudnn, cublas, etc.)
TensorRT is allowed to load tactics from.
Use an empty list to disable all tactic sources.
Defaults to TensorRT's default tactic sources.
restricted (bool):
Whether to enable safety scope checking in the builder. This will check if the network
and builder configuration are compatible with safety scope.
Defaults to False.
"""
self.max_workspace_size = util.default(max_workspace_size, 1 << 24)
self.tf32 = util.default(tf32, False)
self.fp16 = util.default(fp16, False)
self.int8 = util.default(int8, False)
self.profiles = util.default(profiles, [Profile()])
self.calibrator = calibrator
self.strict_types = util.default(strict_types, False)
self.restricted = util.default(restricted, False)
self.timing_cache_path = load_timing_cache
self.algorithm_selector = algorithm_selector
self.sparse_weights = util.default(sparse_weights, False)
self.tactic_sources = tactic_sources
if self.calibrator is not None and not self.int8:
G_LOGGER.warning(
"A calibrator was provided to `CreateConfig`, but int8 mode was not enabled. "
"Did you mean to set `int8=True` to enable building with int8 precision?"
)
[docs] def call_impl(self, builder, network):
"""
Args:
builder (trt.Builder):
The TensorRT builder to use to create the configuration.
network (trt.INetworkDefinition):
The TensorRT network for which to create the config. The network is used to
automatically create a default optimization profile if none are provided.
Returns:
trt.IBuilderConfig: The TensorRT builder configuration.
"""
with util.FreeOnException([builder.create_builder_config()]) as (config,):
def try_run(func, name):
try:
return func()
except AttributeError:
trt_util.fail_unavailable("{:} in CreateConfig".format(name))
def try_set_flag(flag_name):
return try_run(lambda: config.set_flag(getattr(trt.BuilderFlag, flag_name)), flag_name.lower())
with G_LOGGER.indent():
G_LOGGER.verbose("Setting TensorRT Optimization Profiles")
profiles = copy.deepcopy(self.profiles)
for profile in profiles:
# Last trt_profile is used for set_calibration_profile.
trt_profile = profile.fill_defaults(network).to_trt(builder, network)
config.add_optimization_profile(trt_profile)
G_LOGGER.info("Configuring with profiles: {:}".format(profiles))
config.max_workspace_size = int(self.max_workspace_size)
if self.strict_types:
try_set_flag("STRICT_TYPES")
if self.restricted:
try_set_flag("SAFETY_SCOPE")
if self.tf32:
try_set_flag("TF32")
else: # TF32 is on by default
with contextlib.suppress(AttributeError):
config.clear_flag(trt.BuilderFlag.TF32)
if self.fp16:
try_set_flag("FP16")
if self.int8:
try_set_flag("INT8")
if not network.has_explicit_precision:
if self.calibrator is not None:
input_metadata = trt_util.get_input_metadata_from_profile(trt_profile, network)
with contextlib.suppress(AttributeError): # Polygraphy calibrator has a reset method
self.calibrator.reset(input_metadata)
config.int8_calibrator = self.calibrator
try:
config.set_calibration_profile(trt_profile)
except:
G_LOGGER.extra_verbose("Cannot set calibration profile on TensorRT 7.0 and older.")
else:
G_LOGGER.warning(
"Network does not have explicit precision and no calibrator was provided. Please ensure "
"that tensors in the network have dynamic ranges set, or provide a calibrator in order to use int8 mode."
)
if self.sparse_weights:
try_set_flag("SPARSE_WEIGHTS")
if self.tactic_sources is not None:
tactic_sources_flag = 0
for source in self.tactic_sources:
tactic_sources_flag |= 1 << int(source)
try_run(lambda: config.set_tactic_sources(tactic_sources_flag), name="tactic_sources")
try:
if self.timing_cache_path:
timing_cache_data = util.load_file(self.timing_cache_path, description="tactic timing cache")
cache = config.create_timing_cache(timing_cache_data)
else:
# Create an empty timing cache by default so it will be populated during engine build.
# This way, consumers of CreateConfig have the option to use the cache later.
cache = config.create_timing_cache(b"")
except AttributeError:
if self.timing_cache_path:
trt_util.fail_unavailable("load_timing_cache in CreateConfig")
else:
config.set_timing_cache(cache, ignore_mismatch=False)
if self.algorithm_selector is not None:
def set_algo_selector():
config.algorithm_selector = self.algorithm_selector
try_run(set_algo_selector, "algorithm_selector")
return config
[docs]@mod.export(funcify=True)
class EngineBytesFromNetwork(BaseLoader):
"""
Functor that uses a TensorRT ``INetworkDefinition`` to build a serialized engine.
"""
def __init__(self, network, config=None, save_timing_cache=None):
"""
Builds and serializes TensorRT engine.
Args:
network (Callable() -> trt.Builder, trt.INetworkDefinition):
A callable capable of returning a TensorRT Builder and INetworkDefinition. The returned builder
and network are owned by EngineFromNetwork and should not be freed manually. The callable may
have at most 3 return values if another object needs to be kept alive for the duration of the network,
e.g., in the case of a parser. EngineFromNetwork will take ownership of the third return value, and,
like the network, it should not be freed by the callable. The first and second return values must
always be the builder and network respectively.
If instead of a loader, the network, builder, and optional parser arguments are provided directly,
then EngineFromNetwork will *not* deallocate them.
config (Callable(trt.Builder, trt.INetworkDefinition) -> trt.IBuilderConfig):
A callable that returns a TensorRT builder configuration. If not supplied,
a `CreateConfig` instance with default parameters is used.
save_timing_cache (Union[str, file-like]):
A path or file-like object at which to save a tactic timing cache.
Any existing cache will be overwritten. Note that if the provided config includes a tactic
timing cache, the data from that cache will be copied into the new cache.
"""
self._network = network
self._config = util.default(config, CreateConfig())
self.timing_cache_path = save_timing_cache
[docs] def call_impl(self):
"""
Returns:
bytes: The serialized engine that was created.
"""
# If network is a callable, then we own its return value
ret, owns_network = util.invoke_if_callable(self._network)
builder, network, parser = util.unpack_args(ret, num=3)
if builder is None or network is None:
G_LOGGER.critical(
"Expected to recevie a (builder, network) tuple for the `network` parameter, "
"but received: ({:}, {:})".format(builder, network)
)
with contextlib.ExitStack() as stack:
if owns_network:
stack.enter_context(builder)
stack.enter_context(network)
if parser is not None:
stack.enter_context(parser)
else:
provided = "Builder and Network" if parser is None else "Builder, Network, and Parser"
G_LOGGER.verbose(
"{:} were provided directly instead of via a Callable. This loader will not assume ownership. "
"Please ensure that they are freed.".format(provided)
)
config, owns_config = util.invoke_if_callable(self._config, builder, network)
if owns_config:
stack.enter_context(config)
else:
G_LOGGER.verbose(
"Builder configuration was provided directly instead of via a Callable. This loader will not assume "
"ownership. Please ensure it is freed."
)
try:
config.int8_calibrator.__enter__ # Polygraphy calibrator frees device buffers on exit.
except AttributeError:
pass
else:
stack.enter_context(config.int8_calibrator)
network_log_mode = "full" if G_LOGGER.severity <= G_LOGGER.ULTRA_VERBOSE else "attrs"
G_LOGGER.super_verbose(
lambda: ("Displaying TensorRT Network:\n" + trt_util.str_from_network(network, mode=network_log_mode))
)
G_LOGGER.start("Building engine with configuration:\n{:}".format(trt_util.str_from_config(config)))
start_time = time.time()
try:
engine_bytes = builder.build_serialized_network(network, config)
except AttributeError:
engine = builder.build_engine(network, config)
if not engine:
G_LOGGER.critical("Invalid Engine. Please ensure the engine was built correctly")
stack.enter_context(engine)
engine_bytes = engine.serialize()
end_time = time.time()
if not engine_bytes:
G_LOGGER.critical("Invalid Engine. Please ensure the engine was built correctly")
G_LOGGER.finish("Finished engine building in {:.3f} seconds".format(end_time - start_time))
try:
timing_cache = config.get_timing_cache()
except AttributeError:
if self.timing_cache_path:
trt_util.fail_unavailable("save_timing_cache in EngineBytesFromNetwork")
else:
if timing_cache and self.timing_cache_path:
with timing_cache.serialize() as buffer:
util.save_file(buffer, self.timing_cache_path, description="tactic timing cache")
return engine_bytes
[docs]@mod.export(funcify=True)
class EngineFromNetwork(EngineBytesFromNetwork):
"""
Similar to EngineBytesFromNetwork, but returns an ICudaEngine instance
instead of a serialized engine.
"""
[docs] def call_impl(self):
"""
Returns:
trt.ICudaEngine: The engine that was created.
"""
# We do not invoke super().call_impl here because we would otherwise be responsible
# for freeing it's return values.
return engine_from_bytes(super().call_impl)
[docs]@mod.export(funcify=True)
class EngineFromBytes(BaseLoader):
"""
Functor that deserializes an engine from a buffer.
"""
def __init__(self, serialized_engine):
"""
Deserializes an engine from a buffer.
Args:
serialized_engine (Callable() -> Union[str, bytes]):
Either a loader that can supply a memory buffer, or a memory buffer itself.
"""
self._serialized_engine = serialized_engine
[docs] def call_impl(self):
"""
Returns:
trt.ICudaEngine: The deserialized engine.
"""
buffer, owns_buffer = util.invoke_if_callable(self._serialized_engine)
trt.init_libnvinfer_plugins(trt_util.get_trt_logger(), "")
with contextlib.ExitStack() as stack, trt.Runtime(trt_util.get_trt_logger()) as runtime:
if owns_buffer:
try:
buffer.__enter__ # IHostMemory is freed only in __exit__
except AttributeError:
pass
else:
stack.enter_context(buffer)
engine = runtime.deserialize_cuda_engine(buffer)
if not engine:
G_LOGGER.critical("Could not deserialize engine. See log for details.")
return engine
[docs]@mod.export(funcify=True)
class BytesFromEngine(BaseLoader):
"""
Functor that serializes an engine.
"""
def __init__(self, engine):
"""
Serializes an engine.
Args:
engine (Callable() -> trt.ICudaEngine):
Either a loader that can supply an engine, or the engine itself.
"""
self._engine = engine
[docs] def call_impl(self):
"""
Returns:
bytes: The serialized engine.
"""
engine, owns_engine = util.invoke_if_callable(self._engine)
with contextlib.ExitStack() as stack:
if owns_engine:
stack.enter_context(util.FreeOnException([engine]))
with engine.serialize() as buffer:
return bytes(buffer)
[docs]@mod.export(funcify=True)
class SaveEngine(BaseLoader):
"""
Functor that saves an engine to the provided path.
"""
def __init__(self, engine, path):
"""
Saves an engine to the provided path.
Args:
engine (Callable() -> trt.ICudaEngine):
A callable that can supply a TensorRT engine.
path (str): The path at which to save the engine.
"""
self._engine = engine
self.path = path
[docs] def call_impl(self):
"""
Returns:
trt.ICudaEngine: The engine that was saved.
"""
engine, owns_engine = util.invoke_if_callable(self._engine)
with contextlib.ExitStack() as stack:
if owns_engine:
stack.enter_context(util.FreeOnException([engine]))
util.save_file(contents=bytes_from_engine(engine), dest=self.path, description="engine")
return engine
[docs]@mod.export(funcify=True)
class OnnxLikeFromNetwork(BaseLoader):
"""
Functor that creates an ONNX-like, but **not** valid ONNX, model based on a TensorRT network.
"""
def __init__(self, network) -> None:
"""
[HIGHLY EXPERIMENTAL] Creates an ONNX-like, but **not** valid ONNX, model from a TensorRT network.
This uses the ONNX format, but generates nodes that are **not** valid ONNX operators.
Hence, the resulting model is **not** valid ONNX.
This should be used **only** for visualization or debugging purposes.
The resulting model does **not** include enough information to faithfully reconstruct the TensorRT network.
Args:
network (Callable() -> trt.Builder, trt.INetworkDefinition):
A callable capable of returning a TensorRT Builder and INetworkDefinition. The callable may
have at most 3 return values if another object needs to be kept alive for the duration of the network,
e.g., in the case of a parser. The first and second return values must always be the builder and network respectively.
If instead of a loader, the network, builder, and optional parser arguments are provided directly,
then OnnxLikeFromNetwork will *not* deallocate them.
"""
self._network = network
[docs] def call_impl(self):
"""
Returns:
onnx.ModelProto: The ONNX-like, but **not** valid ONNX, model.
"""
ret, owns_network = util.invoke_if_callable(self._network)
builder, network, parser = util.unpack_args(ret, num=3)
if builder is None or network is None:
G_LOGGER.critical(
"Expected to recevie a (builder, network) tuple for the `network` parameter, "
"but received: ({:}, {:})".format(builder, network)
)
with contextlib.ExitStack() as stack:
if owns_network:
stack.enter_context(builder)
stack.enter_context(network)
if parser is not None:
stack.enter_context(parser)
tensor_map = {}
def tensors_from_meta(meta):
nonlocal tensor_map
tensors = []
for name, (dtype, shape) in meta.items():
if name not in tensor_map:
tensor_map[name] = gs.Variable(name=name, dtype=dtype, shape=shape)
tensors.append(tensor_map[name])
return tensors
nodes = []
graph_inputs = tensors_from_meta(trt_util.get_network_input_metadata(network))
graph_outputs = tensors_from_meta(trt_util.get_network_output_metadata(network))
LAYER_TYPE_CLASS_MAPPING = trt_util.get_layer_class_mapping()
for layer in network:
op_name = layer.type.name
if layer.type in LAYER_TYPE_CLASS_MAPPING:
layer.__class__ = LAYER_TYPE_CLASS_MAPPING[layer.type]
node_inputs = tensors_from_meta(trt_util.get_layer_input_metadata(layer))
node_outputs = tensors_from_meta(trt_util.get_layer_output_metadata(layer))
attrs = {}
attr_names = trt_util.get_layer_attribute_names(layer)
for name in attr_names:
with G_LOGGER.verbosity():
attr = getattr(layer, name)
if util.is_sequence(attr) or any(isinstance(attr, cls) for cls in [trt.Dims, trt.Permutation]):
try:
attr = list(attr)
except ValueError: # Invalid dims
attr = []
if hasattr(attr, "__entries"): # TensorRT Enums
attr = attr.name
if isinstance(attr, trt.ILoop):
attr = attr.name
VALID_TYPES = [np.ndarray, list, int, str, bool, float]
if not any(isinstance(attr, cls) for cls in VALID_TYPES):
G_LOGGER.internal_error(
"Unknown type: {:} for layer attribute: {:}.\n"
"Note: Layer was: {:}".format(type(attr), attr, layer)
)
try:
attr = str(attr)
except:
attr = "<error during conversion>"
attrs[name] = attr
nodes.append(
gs.Node(name=layer.name, op=op_name, attrs=attrs, inputs=node_inputs, outputs=node_outputs)
)
graph = gs.Graph(name=network.name, inputs=graph_inputs, outputs=graph_outputs, nodes=nodes)
return gs.export_onnx(graph)