def setUp(self):
path, _ = os.path.split(os.path.dirname(__file__))
self.xml = os.path.join(path, os.pardir, os.pardir,
"utils", "test_data", "mxnet_synthetic_gru_bidirectional_FP16_1_v6.xml")
self.xml_negative = os.path.join(path, os.pardir, os.pardir,
"utils", "test_data",
"mxnet_synthetic_gru_bidirectional_FP16_1_v6_negative.xml")
self.bin = os.path.splitext(self.xml)[0] + '.bin'
self.assertTrue(os.path.exists(self.xml), 'XML file not found: {}'.format(self.xml))
self.assertTrue(os.path.exists(self.bin), 'BIN file not found: {}'.format(self.bin))
self.IR = IREngine(path_to_xml=str(self.xml), path_to_bin=str(self.bin))
self.IR_ref = IREngine(path_to_xml=str(self.xml), path_to_bin=str(self.bin))
self.IR_negative = IREngine(path_to_xml=str(self.xml_negative), path_to_bin=str(self.bin))
def test_load_bin_hashes(self):
path_for_file = self.IR.generate_bin_hashes_file()
IR = IREngine(path_to_xml=str(self.xml), path_to_bin=str(path_for_file))
is_ok = True
# Check for constant nodes
const_nodes = IR.graph.get_op_nodes(type='Const')
for node in const_nodes:
if not node.has_valid('hashes'):
log.error('Constant node {} do not include hashes'.format(node.name))
is_ok = False
# Check for TensorIterator Body
ti_nodes = IR.graph.get_op_nodes(type='TensorIterator')
for ti in ti_nodes:
if not ti.has_valid('body'):
log.error("TensorIterator doesn't have body attribute for node: {}".format(ti.name))
else:
const_ti_nodes = ti.body.graph.get_op_nodes(type='Const')
for node in const_ti_nodes:
if not node.has_valid('hashes'):
log.error('Constant node {} do not include hashes'.format(node.name))
is_ok = False
self.assertTrue(is_ok, 'Test for function load_bin_hashes failed')
os.remove(path_for_file)
def test_is_float(self, test_data, result):
test_data = test_data
self.assertEqual(
IREngine._IREngine__isfloat(test_data), result,
"Function __isfloat is not working with value: {}".format(
test_data))
log.info(
'Test for function __is_float passed with value: {}, expected result: {}'
.format(test_data, result))
def check_graph(tmp_path,
graph,
model_name,
model_framework,
check_weights=False):
"""
Checking that two graphs are equal by comparing topologies and
all weights if check_weights is specified as True.
"""
model_name = '_'.join([model_name, model_framework])
ir_name_xml = model_name + '.xml'
path_to_ir_xml = tmp_path.joinpath(ir_name_xml)
save_graph(graph, tmp_path.as_posix(), model_name)
path_to_ref_ir_xml = REFERENCE_MODELS_PATH.joinpath(ir_name_xml)
if check_weights:
ir_name_bin = model_name + '.bin'
path_to_ir_bin = tmp_path.joinpath(ir_name_bin).as_posix()
path_to_ref_ir_bin = REFERENCE_MODELS_PATH.joinpath(
ir_name_bin).as_posix()
else:
path_to_ir_bin = None
path_to_ref_ir_bin = None
if not path_to_ref_ir_xml.exists():
shutil.copyfile(path_to_ir_xml.as_posix(),
path_to_ref_ir_xml.as_posix())
if check_weights:
shutil.copyfile(path_to_ir_bin, path_to_ref_ir_bin)
ref_graph = IREngine(path_to_ref_ir_xml.as_posix(), path_to_ref_ir_bin)
test_graph = IREngine(path_to_ir_xml.as_posix(), path_to_ir_bin)
result, stderr = ref_graph.compare(test_graph)
if stderr:
print(stderr)
assert result
def restore_graph_from_ir(path_to_xml: str, path_to_bin: str = None) -> (Graph, dict):
"""
Function to make valid graph and metadata for MO back stage from IR.
:param path_to_xml:
:param path_to_bin:
:return: (restored graph, meta data)
"""
ir = IREngine(path_to_xml, path_to_bin)
assert ir.graph.graph.get('ir_version') >= 10, 'IR version {} is not supported, ' \
'please generate actual IR for your model and use it.'.format(ir.graph.graph.get('ir_version'))
path = get_mo_root_dir()
collect_ops(path)
collect_extenders(path)
# Create a new copy of graph with correct attributes (shape & type infer, backend attrs etc.)
new_graph = copy_graph_with_ops(ir.graph)
return new_graph, copy(ir.meta_data)
def _test(self, framework_model, ref_net, ie_device, precision, ir_version, temp_dir, infer_timeout=60,
enabled_transforms='', disabled_transforms='', **kwargs):
"""
:param enabled_transforms/disabled_transforms: string with idxs of transforms that should be enabled/disabled.
Example: "transform_1,transform_2"
"""
model_path = self.produce_model_path(framework_model=framework_model, save_path=temp_dir)
# TODO Pass environment variables via subprocess environment
os.environ['MO_ENABLED_TRANSFORMS'] = enabled_transforms
os.environ['MO_DISABLED_TRANSFORMS'] = disabled_transforms
mo_params = {self.input_model_key: model_path,
"output_dir": temp_dir,
"data_type": precision, "model_name": 'model'
}
if 'input_shapes' in kwargs and len(kwargs['input_shapes']):
input_shapes_str = []
for ishape in kwargs['input_shapes']:
input_shapes_str.append('[' + ','.join([str(i) for i in ishape]) + ']')
mo_params.update(dict(input_shape=','.join(input_shapes_str)))
if 'input_names' in kwargs and len(kwargs['input_names']):
mo_params.update(dict(input=','.join(kwargs['input_names'])))
exit_code, stderr = generate_ir(**mo_params)
del os.environ['MO_ENABLED_TRANSFORMS']
del os.environ['MO_DISABLED_TRANSFORMS']
assert not exit_code, ("IR generation failed with {} exit code: {}".format(exit_code, stderr))
path_to_xml = Path(temp_dir, 'model.xml')
path_to_bin = Path(temp_dir, 'model.bin')
ir = IREngine(path_to_xml, path_to_bin, precision=precision)
if ref_net is not None:
(flag, resp) = ir.compare(ref_net)
assert flag, '\n'.join(resp)
# Prepare feed dict
if 'kwargs_to_prepare_input' in kwargs and kwargs['kwargs_to_prepare_input']:
inputs_dict = self._prepare_input(ir.get_inputs(), kwargs['kwargs_to_prepare_input'])
else:
inputs_dict = self._prepare_input(ir.get_inputs())
# IE infer:
ie_engine = IEInfer(model=path_to_xml,
weights=path_to_bin,
device=ie_device)
infer_res = ie_engine.infer(input_data=inputs_dict, infer_timeout=infer_timeout)
if hasattr(self, 'skip_framework') and self.skip_framework:
warnings.warn('Framework is skipped')
return
# Framework infer:
fw_res = self.get_framework_results(inputs_dict=inputs_dict, model_path=model_path)
if len(fw_res) == len(infer_res) == 1:
# match output layers directly
mapping_dict = {next(iter(fw_res)): next(iter(infer_res))}
else:
# Load mapping file
mapping_dict = mapping_parser(path_to_xml.with_suffix('.mapping'))
if 'custom_eps' in kwargs and kwargs['custom_eps'] is not None:
custom_eps = kwargs['custom_eps']
else:
custom_eps = 1e-4
# Compare Ie results with Framework results
fw_eps = custom_eps if precision == 'FP32' else 5e-2
assert self.compare_ie_results_with_framework(infer_res=infer_res, framework_res=fw_res,
mapping_dict=mapping_dict, framework_eps=fw_eps), \
"Comparing with Framework failed: ie_res={}; framework_res={}.".format(infer_res, fw_res)
def test_normalize_attr(self, test_data, reference):
result_dict = IREngine._IREngine__normalize_attrs(attrs=test_data)
self.assertTrue(reference == result_dict, 'Test on function normalize_attr failed')
log.info('Test for function normalize_attr passed')
def test_find_input(self):
# Create references for this test:
ref_nodes = [Node(self.IR.graph, '0')]
# Check function:
a = IREngine._IREngine__find_input(self.IR.graph)
self.assertTrue(a == ref_nodes, 'Error')
class TestFunction(unittest.TestCase):
def setUp(self):
path, _ = os.path.split(os.path.dirname(__file__))
self.xml = os.path.join(path, os.pardir, os.pardir,
"utils", "test_data", "mxnet_synthetic_gru_bidirectional_FP16_1_v6.xml")
self.xml_negative = os.path.join(path, os.pardir, os.pardir,
"utils", "test_data",
"mxnet_synthetic_gru_bidirectional_FP16_1_v6_negative.xml")
self.bin = os.path.splitext(self.xml)[0] + '.bin'
self.assertTrue(os.path.exists(self.xml), 'XML file not found: {}'.format(self.xml))
self.assertTrue(os.path.exists(self.bin), 'BIN file not found: {}'.format(self.bin))
self.IR = IREngine(path_to_xml=str(self.xml), path_to_bin=str(self.bin))
self.IR_ref = IREngine(path_to_xml=str(self.xml), path_to_bin=str(self.bin))
self.IR_negative = IREngine(path_to_xml=str(self.xml_negative), path_to_bin=str(self.bin))
@generate(*[(4.4, True), ('aaaa', False)])
def test_is_float(self, test_data, result):
test_data = test_data
self.assertEqual(IREngine._IREngine__isfloat(test_data), result,
"Function __isfloat is not working with value: {}".format(test_data))
log.info('Test for function __is_float passed with value: {}, expected result: {}'.format(test_data, result))
# TODO add comparison not for type IREngine
def test_compare(self):
flag, msg = self.IR.compare(self.IR_ref)
self.assertTrue(flag, 'Comparing false, test compare function failed')
log.info('Test for function compare passed')
def test_comare_negative(self):
# Reference data for test:
reference_msg = 'Current node "2" with type Const and reference node "2" with type Input have different attr "type" : Const and Input'
# Check function:
flag, msg = self.IR.compare(self.IR_negative)
self.assertFalse(flag, 'Comparing flag failed, test compare function failed')
self.assertEqual('\n'.join(msg), reference_msg, 'Comparing message failed, test compare negative failed')
log.info('Test for function compare passed')
def test_find_input(self):
# Create references for this test:
ref_nodes = [Node(self.IR.graph, '0')]
# Check function:
a = IREngine._IREngine__find_input(self.IR.graph)
self.assertTrue(a == ref_nodes, 'Error')
def test_get_inputs(self):
# Reference data for test:
ref_input_dict = {'data': [1, 10, 16]}
# Check function:
inputs_dict = self.IR.get_inputs()
# is_equal = compare_dictionaries(ref_input_dict, inputs_dict)
self.assertTrue(ref_input_dict == inputs_dict, 'Test on function get_inputs failed')
log.info('Test for function get_inputs passed')
def test_eq_function(self):
self.assertTrue(self.IR == self.IR_ref, 'Comparing false, test eq function failed')
log.info('Test for function eq passed')
@unittest.mock.patch('numpy.savez_compressed')
def test_generate_bin_hashes_file(self, numpy_savez):
# Generate bin_hashes file in default directory
self.IR.generate_bin_hashes_file()
numpy_savez.assert_called_once()
log.info('Test for function generate_bin_hashes_file with default folder passed')
@unittest.mock.patch('numpy.savez_compressed')
def test_generate_bin_hashes_file_custom_directory(self, numpy_savez):
# Generate bin_hashes file in custom directory
directory_for_file = os.path.join(os.path.split(os.path.dirname(__file__))[0], "utils", "test_data",
"bin_hash")
self.IR.generate_bin_hashes_file(path_for_file=directory_for_file)
numpy_savez.assert_called_once()
log.info('Test for function generate_bin_hashes_file with custom folder passed')
@generate(*[({'order': '1,0,2'}, {'order': [1, 0, 2]}),
({'order': '1'}, {'order': 1})])
def test_normalize_attr(self, test_data, reference):
result_dict = IREngine._IREngine__normalize_attrs(attrs=test_data)
self.assertTrue(reference == result_dict, 'Test on function normalize_attr failed')
log.info('Test for function normalize_attr passed')
def test_load_bin_hashes(self):
path_for_file = self.IR.generate_bin_hashes_file()
IR = IREngine(path_to_xml=str(self.xml), path_to_bin=str(path_for_file))
is_ok = True
# Check for constant nodes
const_nodes = IR.graph.get_op_nodes(type='Const')
for node in const_nodes:
if not node.has_valid('hashes'):
log.error('Constant node {} do not include hashes'.format(node.name))
is_ok = False
# Check for TensorIterator Body
ti_nodes = IR.graph.get_op_nodes(type='TensorIterator')
for ti in ti_nodes:
if not ti.has_valid('body'):
log.error("TensorIterator doesn't have body attribute for node: {}".format(ti.name))
else:
const_ti_nodes = ti.body.graph.get_op_nodes(type='Const')
for node in const_ti_nodes:
if not node.has_valid('hashes'):
log.error('Constant node {} do not include hashes'.format(node.name))
is_ok = False
self.assertTrue(is_ok, 'Test for function load_bin_hashes failed')
os.remove(path_for_file)
@generate(*[
("0", True),
("1", True),
("-1", True),
("-", False),
("+1", True),
("+", False),
("1.0", False),
("-1.0", False),
("1.5", False),
("+1.5", False),
("abracadabra", False),
])
def test_isint(self, value, result):
self.assertEqual(IREngine._IREngine__isint(value), result)
def test_isint(self, value, result):
self.assertEqual(IREngine._IREngine__isint(value), result)