本文整理汇总了Python中onnx.load_from_string方法的典型用法代码示例。如果您正苦于以下问题:Python onnx.load_from_string方法的具体用法?Python onnx.load_from_string怎么用?Python onnx.load_from_string使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类onnx
的用法示例。
在下文中一共展示了onnx.load_from_string方法的10个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: import onnx [as 别名]
# 或者: from onnx import load_from_string [as 别名]
def __init__(self, onnx_model_proto, make_deepcopy=False):
"""Creates a ModelWrapper instance.
onnx_model_proto can be either a ModelProto instance, or a string
with the path to a stored .onnx file on disk, or serialized bytes.
The make_deepcopy option controls whether a deep copy of the ModelProto
is made internally.
"""
if isinstance(onnx_model_proto, str):
self._model_proto = onnx.load(onnx_model_proto)
elif isinstance(onnx_model_proto, bytes):
self._model_proto = onnx.load_from_string(onnx_model_proto)
else:
if make_deepcopy:
self._model_proto = copy.deepcopy(onnx_model_proto)
else:
self._model_proto = onnx_model_proto
示例2: export_onnx_model
# 需要导入模块: import onnx [as 别名]
# 或者: from onnx import load_from_string [as 别名]
def export_onnx_model(model, inputs, passes):
"""Trace and export a model to onnx format. Modified from
https://github.com/facebookresearch/detectron2/
Args:
model (nn.Module):
inputs (tuple[args]): the model will be called by `model(*inputs)`
passes (None or list[str]): the optimization passed for ONNX model
Returns:
an onnx model
"""
assert isinstance(model, torch.nn.Module)
# make sure all modules are in eval mode, onnx may change the training
# state of the module if the states are not consistent
def _check_eval(module):
assert not module.training
model.apply(_check_eval)
# Export the model to ONNX
with torch.no_grad():
with io.BytesIO() as f:
torch.onnx.export(
model,
inputs,
f,
operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK,
# verbose=True, # NOTE: uncomment this for debugging
# export_params=True,
)
onnx_model = onnx.load_from_string(f.getvalue())
# Apply ONNX's Optimization
if passes is not None:
all_passes = optimizer.get_available_passes()
assert all(p in all_passes for p in passes), \
f'Only {all_passes} are supported'
onnx_model = optimizer.optimize(onnx_model, passes)
return onnx_model
示例3: convert_version
# 需要导入模块: import onnx [as 别名]
# 或者: from onnx import load_from_string [as 别名]
def convert_version(model, target_version): # type: (ModelProto, int) -> ModelProto
if not isinstance(model, ModelProto):
raise ValueError('VersionConverter only accepts ModelProto as model, incorrect type: {}'.format(type(model)))
if not isinstance(target_version, int):
raise ValueError('VersionConverter only accepts int as target_version, incorrect type: {}'.format(type(target_version)))
model_str = model.SerializeToString()
converted_model_str = C.convert_version(model_str, target_version)
return onnx.load_from_string(converted_model_str)
示例4: infer_shapes
# 需要导入模块: import onnx [as 别名]
# 或者: from onnx import load_from_string [as 别名]
def infer_shapes(model): # type: (ModelProto) -> ModelProto
if not isinstance(model, ModelProto):
raise ValueError('Shape inference only accepts ModelProto, '
'incorrect type: {}'.format(type(model)))
model_str = model.SerializeToString()
inferred_model_str = C.infer_shapes(model_str)
return onnx.load_from_string(inferred_model_str)
示例5: optimize
# 需要导入模块: import onnx [as 别名]
# 或者: from onnx import load_from_string [as 别名]
def optimize(model, passes=[]): # type: (ModelProto, Sequence[Text]) -> ModelProto
if len(passes) == 0:
passes = ['eliminate_nop_transpose',
'fuse_consecutive_transposes',
'fuse_transpose_into_gemm']
if not isinstance(model, ModelProto):
raise ValueError('Optimizer only accepts ModelProto, incorrect type: {}'.format(type(model)))
model_str = model.SerializeToString()
optimized_model_str = C.optimize(model_str, passes)
return onnx.load_from_string(optimized_model_str)
示例6: load_and_parse_binary
# 需要导入模块: import onnx [as 别名]
# 或者: from onnx import load_from_string [as 别名]
def load_and_parse_binary(binary: onnx.ModelProto) -> OnnxModel:
model = onnx.load_from_string(binary)
model = OnnxModel.create_from_onnx_model(model)
model = clean_model(model)
return model
示例7: load_model_only
# 需要导入模块: import onnx [as 别名]
# 或者: from onnx import load_from_string [as 别名]
def load_model_only(path: str) -> onnx.ModelProto:
"""
@param path path to file
@return deserialized onnx model
"""
with open(path, 'rb') as model_file:
binary = model_file.read()
model = onnx.load_from_string(binary)
return model
示例8: export_onnx_model
# 需要导入模块: import onnx [as 别名]
# 或者: from onnx import load_from_string [as 别名]
def export_onnx_model(model, inputs):
"""
Trace and export a model to onnx format.
Args:
model (nn.Module):
inputs (tuple[args]): the model will be called by `model(*inputs)`
Returns:
an onnx model
"""
assert isinstance(model, torch.nn.Module)
# make sure all modules are in eval mode, onnx may change the training state
# of the module if the states are not consistent
def _check_eval(module):
assert not module.training
model.apply(_check_eval)
# Export the model to ONNX
with torch.no_grad():
with io.BytesIO() as f:
torch.onnx.export(
model,
inputs,
f,
operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK,
# verbose=True, # NOTE: uncomment this for debugging
# export_params=True,
)
onnx_model = onnx.load_from_string(f.getvalue())
# Apply ONNX's Optimization
all_passes = onnx.optimizer.get_available_passes()
passes = ["fuse_bn_into_conv"]
assert all(p in all_passes for p in passes)
onnx_model = onnx.optimizer.optimize(onnx_model, passes)
return onnx_model
示例9: _export_via_onnx
# 需要导入模块: import onnx [as 别名]
# 或者: from onnx import load_from_string [as 别名]
def _export_via_onnx(model, inputs):
from ipdb import set_trace;
set_trace()
def _check_val(module):
assert not module.training
model.apply(_check_val)
# Export the model to ONNX
with torch.no_grad():
with io.BytesIO() as f:
torch.onnx.export(
model,
inputs,
f,
# verbose=True, # NOTE: uncomment this for debugging
export_params=True,
)
onnx_model = onnx.load_from_string(f.getvalue())
# torch.onnx.export(model, # model being run
# inputs, # model input (or a tuple for multiple inputs)
# "reid_test.onnx", # where to save the model (can be a file or file-like object)
# export_params=True, # store the trained parameter weights inside the model file
# opset_version=10, # the ONNX version to export the model to
# do_constant_folding=True, # whether to execute constant folding for optimization
# input_names=['input'], # the model's input names
# output_names=['output'], # the model's output names
# dynamic_axes={'input': {0: 'batch_size'}, # variable lenght axes
# 'output': {0: 'batch_size'}})
# )
# Apply ONNX's Optimization
# all_passes = optimizer.get_available_passes()
# passes = ["fuse_bn_into_conv"]
# assert all(p in all_passes for p in passes)
# onnx_model = optimizer.optimize(onnx_model, passes)
# Convert ONNX Model to Tensorflow Model
tf_rep = prepare(onnx_model, strict=False) # Import the ONNX model to Tensorflow
print(tf_rep.inputs) # Input nodes to the model
print('-----')
print(tf_rep.outputs) # Output nodes from the model
print('-----')
# print(tf_rep.tensor_dict) # All nodes in the model
# """
# install onnx-tensorflow from github,and tf_rep = prepare(onnx_model, strict=False)
# Reference https://github.com/onnx/onnx-tensorflow/issues/167
# tf_rep = prepare(onnx_model) # whthout strict=False leads to KeyError: 'pyfunc_0'
# debug, here using the same input to check onnx and tf.
# output_onnx_tf = tf_rep.run(to_numpy(img))
# print('output_onnx_tf = {}'.format(output_onnx_tf))
# onnx --> tf.graph.pb
# tf_pb_path = 'reid_tf_graph.pb'
# tf_rep.export_graph(tf_pb_path)
return tf_rep
示例10: convert_tests
# 需要导入模块: import onnx [as 别名]
# 或者: from onnx import load_from_string [as 别名]
def convert_tests(testcases, sets=1):
print("Collect {} test cases from PyTorch.".format(len(testcases)))
failed = 0
ops = set()
for t in testcases:
test_name = get_test_name(t)
module = gen_module(t)
try:
input = gen_input(t)
f = io.BytesIO()
torch.onnx._export(module, input, f)
onnx_model = onnx.load_from_string(f.getvalue())
onnx.checker.check_model(onnx_model)
onnx.helper.strip_doc_string(onnx_model)
output_dir = os.path.join(test_onnx_common.pytorch_converted_dir, test_name)
if os.path.exists(output_dir):
shutil.rmtree(output_dir)
os.makedirs(output_dir)
with open(os.path.join(output_dir, "model.onnx"), "wb") as file:
file.write(onnx_model.SerializeToString())
for i in range(sets):
output = module(input)
data_dir = os.path.join(output_dir, "test_data_set_{}".format(i))
os.makedirs(data_dir)
for index, var in enumerate([input]):
tensor = numpy_helper.from_array(var.data.numpy())
with open(os.path.join(data_dir, "input_{}.pb".format(index)), "wb") as file:
file.write(tensor.SerializeToString())
for index, var in enumerate([output]):
tensor = numpy_helper.from_array(var.data.numpy())
with open(os.path.join(data_dir, "output_{}.pb".format(index)), "wb") as file:
file.write(tensor.SerializeToString())
input = gen_input(t)
except:
traceback.print_exc()
failed += 1
print("Collect {} test cases from PyTorch repo, failed to export {} cases.".format(
len(testcases), failed))
print("PyTorch converted cases are stored in {}.".format(test_onnx_common.pytorch_converted_dir))