def post_processing(outputs_path, outputs_path_other):
# Compare outputs with e.g. fp16 and fp32
record = {}
if_close = {}
import glob
for filename in glob.glob(os.path.join(outputs_path, '*.tensorproto')):
filename_other = os.path.join(outputs_path_other, Path(filename).name)
if not os.path.exists(filename_other):
continue
with open(filename, 'rb') as f:
tensor = TensorProto()
tensor.ParseFromString(f.read())
array = numpy_helper.to_array(tensor)
with open(filename_other, 'rb') as f:
tensor_other = TensorProto()
tensor_other.ParseFromString(f.read())
array_other = numpy_helper.to_array(tensor_other)
if array_other.size == 0:
continue
diff = numpy.average(
numpy.abs(array_other - array) /
(numpy.abs(array_other) + 1e-6))
if math.isnan(diff):
continue
record[Path(filename).name.split(".")[0]] = diff
if_close[Path(filename).name.split(".")[0]] = numpy.allclose(
array, array_other, rtol=1e-04, atol=1e-04)
results = [f"Node\tDiff\tClose"]
for k, v in sorted(record.items(), key=lambda x: x[1], reverse=True):
results.append(f"{k}\t{v}\t{if_close[k]}")
for line in results:
print(line)
def get_test_files(name):
"""Extract tar file and returns model path and input, output data"""
tar_name = download(URLS.get(name), dirname=CURR_PATH.__str__())
# extract tar file
tar_path = os.path.join(CURR_PATH, tar_name)
tar = tarfile.open(tar_path.__str__(), "r:*")
tar.extractall(path=CURR_PATH.__str__())
tar.close()
data_dir = os.path.join(CURR_PATH, name)
model_path = os.path.join(data_dir, 'model.onnx')
inputs = []
outputs = []
# get test files
for test_file in os.listdir(data_dir):
case_dir = os.path.join(data_dir, test_file)
# skip the non-dir files
if not os.path.isdir(case_dir):
continue
input_file = os.path.join(case_dir, 'input_0.pb')
input_tensor = TensorProto()
with open(input_file, 'rb') as proto_file:
input_tensor.ParseFromString(proto_file.read())
inputs.append(numpy_helper.to_array(input_tensor))
output_tensor = TensorProto()
output_file = os.path.join(case_dir, 'output_0.pb')
with open(output_file, 'rb') as proto_file:
output_tensor.ParseFromString(proto_file.read())
outputs.append(numpy_helper.to_array(output_tensor))
return model_path, inputs, outputs
def caffe2_op_to_onnx_node(cls, op_def, shapes):
if C.support_onnx_export(op_def.type):
shape_list = list(shapes.values())
node_strs, tensor_strs = C.export_to_onnx(
op_def.SerializeToString(), shapes)
nodes = []
for s in node_strs:
node = NodeProto()
node.ParseFromString(s)
nodes.append(node)
const_tensors = []
for s in tensor_strs:
tensor = TensorProto()
tensor.ParseFromString(s)
const_tensors.append(tensor)
return nodes, const_tensors
elif op_def.type in cls._special_operators:
translator = getattr(cls, cls._special_operators[op_def.type])
else:
translator = cls._common_caffe2_op_to_onnx_node
nodes = translator(op_def, shapes)
const_tensors = []
if isinstance(nodes, tuple):
nodes, const_tensors = nodes
if not isinstance(nodes, collections.Iterable):
nodes = [nodes]
return nodes, const_tensors
def from_bytes(b):
"""
Retrieves an array from bytes then protobuf.
@param b bytes
@return array
.. exref::
:title: Converts bytes into an array (serialization)
Useful to deserialize.
.. runpython::
:showcode:
import numpy
from mlprodict.onnxrt.onnx2py_helper import to_bytes, from_bytes
data = numpy.array([[0, 1], [2, 3], [4, 5]], dtype=numpy.float32)
pb = to_bytes(data)
data2 = from_bytes(pb)
print(data2)
"""
if isinstance(b, bytes):
pb = TensorProto()
pb.ParseFromString(b)
else:
pb = b
return to_array(pb)
def load_data(folder):
"""
Restores protobuf data stored in a folder.
:param folder: folder
:return: dictionary
"""
res = OrderedDict()
res['in'] = OrderedDict()
res['out'] = OrderedDict()
files = os.listdir(folder)
for name in files:
noext, ext = os.path.splitext(name)
if ext == '.pb':
data = TensorProto()
with open(os.path.join(folder, name), 'rb') as f:
data.ParseFromString(f.read())
if noext.startswith('input'):
res['in'][noext] = numpy_helper.to_array(data)
elif noext.startswith('output'):
res['out'][noext] = numpy_helper.to_array(data)
else:
raise ValueError( # pragma: no cover
"Unable to guess anything about %r." % noext)
return res
def create_onnx_components():
"""创建onnx的基本组件：node, graph, model，可以看到onnx是如何本onnx.proto文件对应的
参考：https://github.com/onnx/onnx/blob/master/onnx/examples/Protobufs.ipynb
"""
# ------ 创建int变量: 传入数值和描述即可 ------
arg1 = helper.make_attribute("this is INT", 64)
arg2 = helper.make_attribute("this is float/1", 3.14)
arg3 = helper.make_attribute("this is STRING", "helloworld")
arg4 = helper.make_attribute("this is INTS", [1,2,3,4])
# ------ 创建TensorProto ------
tensor0 = helper.make_tensor_value_info() # ？
array1 = np.array([[1,2,3],[4,5,6]])
tensor1 = numpy_helper.from_array(array1) # 从numpy获取tensorProto
with open('ts.pb', 'wb') as f:
f.write(tensor1.SerializeToString()) # 保存tensorProto
tensor2 = TensorProto()
with open('ts.pb', 'rb') as f:
tensor2.ParseFromString(f.read()) # 读取tensorProto
with
# ------ 创建node ------
node1 = helper.make_node("Relu", ["X"], ["Y"]) # op_type="Relu"
node2 = helper.make_node("Conv", ["X", "W", "Y"], kernel=3, stride=1, pad=1)
print(node2)
print(helper.printable_node(node2)) # 这就是常看到的onnx形式：%Y = Conv[]
# ------ 创建graph ------
node_list = []
arg_list = []
graph1 = helper.make_graph(
[
helper.make_node("FC", ["X", "W1", "B1"], ["H1"]),
helper.make_node("Relu", ["H1"], ["R1"]),
helper.make_node("FC", ["R1", "W2", "B2"], ["Y"]),
],
"MLP",
[
helper.make_tensor_value_info('X' , TensorProto.FLOAT, [1]),
helper.make_tensor_value_info('W1', TensorProto.FLOAT, [1]),
helper.make_tensor_value_info('B1', TensorProto.FLOAT, [1]),
helper.make_tensor_value_info('W2', TensorProto.FLOAT, [1]),
helper.make_tensor_value_info('B2', TensorProto.FLOAT, [1]),
],
[
helper.make_tensor_value_info('Y', TensorProto.FLOAT, [1]),
])
def load_checkpoint_to_model(path_to_checkpoint, model):
"""Loads the checkpoint to an onnx inference model."""
# Load the parameters from the checkpoint
parameters = _internal_load_checkpoint(path_to_checkpoint)
parameters_dict = {}
for param in parameters:
param_proto = TensorProto()
param_proto.ParseFromString(param)
parameters_dict[param_proto.name] = param_proto
for initializer in model.graph.initializer:
initializer.CopyFrom(parameters_dict[initializer.name])
def add_onnx_graph(scope, operator, container, onx):
"""
Adds a whole ONNX graph to an existing one following
:epkg:`skl2onnx` API assuming this ONNX graph implements
an `operator <http://onnx.ai/sklearn-onnx/api_summary.html?
highlight=operator#skl2onnx.common._topology.Operator>`_.
:param scope: scope (to get unique names)
:param operator: operator
:param container: container
:param onx: ONNX graph
"""
graph = onx.graph
name_mapping = {}
node_mapping = {}
for node in graph.node:
name = node.name
if name is not None:
node_mapping[node.name] = _clean_initializer_name(node.name, scope)
for o in node.input:
name_mapping[o] = _clean_variable_name(o, scope)
for o in node.output:
name_mapping[o] = _clean_variable_name(o, scope)
for o in graph.initializer:
name_mapping[o.name] = _clean_operator_name(o.name, scope)
inputs = [_copy_inout(o, scope, name_mapping[o.name]) for o in graph.input]
outputs = [
_copy_inout(o, scope, name_mapping[o.name]) for o in graph.output
]
for inp, to in zip(operator.inputs, inputs):
n = helper.make_node('Identity', [inp.onnx_name], [to.name],
name=_clean_operator_name('Identity', scope))
container.nodes.append(n)
for inp, to in zip(outputs, operator.outputs):
n = helper.make_node('Identity', [inp.name], [to.onnx_name],
name=_clean_operator_name('Identity', scope))
container.nodes.append(n)
for node in graph.node:
n = helper.make_node(
node.op_type, [name_mapping[o] for o in node.input],
[name_mapping[o] for o in node.output],
name=node_mapping[node.name] if node.name else None,
domain=node.domain if node.domain else None)
n.attribute.extend(node.attribute) # pylint: disable=E1101
container.nodes.append(n)
for o in graph.initializer:
as_str = o.SerializeToString()
tensor = TensorProto()
tensor.ParseFromString(as_str)
tensor.name = name_mapping[o.name]
container.initializers.append(tensor)
# opset
for oimp in onx.opset_import:
container.node_domain_version_pair_sets.add(
(oimp.domain, oimp.version))
import os
import mxnet as mx
from mxnet.contrib import onnx as onnx_mxnet
import onnx
import numpy as np
from onnx import TensorProto
from onnx import numpy_helper
curr_dir = os.path.dirname(__file__)
sym, arg_params, aux_params = onnx_mxnet.import_model(curr_dir + "/model.onnx")
input_tensor = TensorProto()
with open(curr_dir + "/input_0.pb", 'rb') as proto_file:
input_tensor.ParseFromString(proto_file.read())
input_array = numpy_helper.to_array(input_tensor)
x = mx.nd.array(input_array)
mod = mx.mod.Module(symbol=sym,
data_names=['0'],
context=mx.cpu(),
label_names=None)
mod.bind(for_training=False,
data_shapes=[('0', (2, 4, 6, 6))],
label_shapes=None)
mod.set_params(arg_params=arg_params, aux_params=aux_params)
mod.forward(mx.io.DataBatch([x]))
result = mod.get_outputs()[0].asnumpy()
