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Fixed type check in convert_model(). (#14472)

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* Fixed passing Path to convert_model().

* Update tools/mo/openvino/tools/mo/convert_impl.py

Co-authored-by: Ekaterina Aidova <ekaterina.aidova@intel.com>

Co-authored-by: Ekaterina Aidova <ekaterina.aidova@intel.com>
This commit is contained in:
Anastasiia Pnevskaia 2022-12-08 09:51:11 +01:00 committed by GitHub
parent 532000a0ce
commit 60bb9e7b7c
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2 changed files with 81 additions and 59 deletions
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3
tools/mo/openvino/tools/mo/convert_impl.py
View File

@ -9,6 +9,7 @@ import platform
import sys
from collections import OrderedDict
from copy import deepcopy
from pathlib import Path
import numpy as np
@ -831,7 +832,7 @@ def show_mo_convert_help():
def input_model_is_object(argv):
if isinstance(argv['input_model'], str):
if isinstance(argv['input_model'], (str, Path)):
return False
if argv['input_model'] is None:
return False

137
tools/mo/unit_tests/mo/convert/import_from_mo_test.py
View File

@ -3,8 +3,8 @@
import os
import tempfile
from pathlib import Path
import numpy as np
from generator import generator, generate
from openvino.runtime import serialize
@ -19,6 +19,66 @@ from utils import create_onnx_model, save_to_onnx
class ConvertImportMOTest(UnitTestWithMockedTelemetry):
test_directory = os.path.dirname(os.path.realpath(__file__))
@staticmethod
def create_onnx_model():
#
# Create ONNX model
#
import onnx
from onnx import helper
from onnx import TensorProto
shape = [1, 2, 3]
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, shape)
node_def = onnx.helper.make_node(
'Relu',
inputs=['input'],
outputs=['Relu_out'],
)
node_def2 = onnx.helper.make_node(
'Sigmoid',
inputs=['Relu_out'],
outputs=['output'],
)
# Create the graph (GraphProto)
graph_def = helper.make_graph(
[node_def, node_def2],
'test_model',
[input],
[output],
)
# Create the model (ModelProto)
onnx_net = helper.make_model(graph_def, producer_name='test_model')
return onnx_net
@staticmethod
def create_model_ref():
nodes_attributes = {
'input': {'kind': 'op', 'type': 'Parameter'},
'input_data': {'shape': [1, 2, 3], 'kind': 'data'},
'relu': {'kind': 'op', 'type': 'ReLU'},
'relu_data': {'shape': [1, 2, 3], 'kind': 'data'},
'sigmoid': {'kind': 'op', 'type': 'Sigmoid'},
'sigmoid_data': {'shape': [1, 2, 3], 'kind': 'data'},
'result': {'kind': 'op', 'type': 'Result'}
}
ref_graph = build_graph(nodes_attributes,
[('input', 'input_data'),
('input_data', 'relu'),
('relu', 'relu_data'),
('relu_data', 'sigmoid'),
('sigmoid', 'sigmoid_data'),
('sigmoid_data', 'result'),
])
return ref_graph
@generate(*[
({}),
({'input': InputCutInfo(name='LeakyRelu_out', shape=None, type=None, value=None)}),
@ -37,66 +97,26 @@ class ConvertImportMOTest(UnitTestWithMockedTelemetry):
serialize(ov_model, out_xml.encode('utf-8'), out_xml.replace('.xml', '.bin').encode('utf-8'))
assert os.path.exists(out_xml)
def test_input_model_path(self):
from openvino.tools.mo import convert_model
with tempfile.TemporaryDirectory(dir=self.test_directory) as tmpdir:
model = self.create_onnx_model()
model_path = save_to_onnx(model, tmpdir)
out_xml = os.path.join(tmpdir, Path("model.xml"))
ov_model = convert_model(input_model=model_path)
serialize(ov_model, out_xml.encode('utf-8'), out_xml.replace('.xml', '.bin').encode('utf-8'))
ir = IREngine(out_xml, out_xml.replace('.xml', '.bin'))
ref_graph = self.create_model_ref()
flag, resp = ir.compare(ref_graph)
assert flag, '\n'.join(resp)
def test_unnamed_input_model(self):
def create_onnx_model():
#
# Create ONNX model
#
import onnx
from onnx import helper
from onnx import TensorProto
shape = [1, 2, 3]
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, shape)
node_def = onnx.helper.make_node(
'Relu',
inputs=['input'],
outputs=['Relu_out'],
)
node_def2 = onnx.helper.make_node(
'Sigmoid',
inputs=['Relu_out'],
outputs=['output'],
)
# Create the graph (GraphProto)
graph_def = helper.make_graph(
[node_def, node_def2],
'test_model',
[input],
[output],
)
# Create the model (ModelProto)
onnx_net = helper.make_model(graph_def, producer_name='test_model')
return onnx_net
nodes_attributes = {
'input': {'kind': 'op', 'type': 'Parameter'},
'input_data': {'shape': [1, 2, 3], 'kind': 'data'},
'relu': {'kind': 'op', 'type': 'ReLU'},
'relu_data': {'shape': [1, 2, 3], 'kind': 'data'},
'sigmoid': {'kind': 'op', 'type': 'Sigmoid'},
'sigmoid_data': {'shape': [1, 2, 3], 'kind': 'data'},
'result': {'kind': 'op', 'type': 'Result'}
}
ref_graph = build_graph(nodes_attributes,
[('input', 'input_data'),
('input_data', 'relu'),
('relu', 'relu_data'),
('relu_data', 'sigmoid'),
('sigmoid', 'sigmoid_data'),
('sigmoid_data', 'result'),
])
from openvino.tools.mo import convert_model
with tempfile.TemporaryDirectory(dir=self.test_directory) as tmpdir:
model = create_onnx_model()
model = self.create_onnx_model()
model_path = save_to_onnx(model, tmpdir)
out_xml = os.path.join(tmpdir, "model.xml")
@ -104,5 +124,6 @@ class ConvertImportMOTest(UnitTestWithMockedTelemetry):
serialize(ov_model, out_xml.encode('utf-8'), out_xml.replace('.xml', '.bin').encode('utf-8'))
ir = IREngine(out_xml, out_xml.replace('.xml', '.bin'))
ref_graph = self.create_model_ref()
flag, resp = ir.compare(ref_graph)
assert flag, '\n'.join(resp)