Intorduce json model analysis feature on the new path (#9618)
This commit is contained in:
Mateusz Bencer
GitHub
parent
a8381f71f9
commit
5fe228bc14
@ -837,6 +837,7 @@ openvino/tools/mo/mo_onnx.py
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openvino/tools/mo/mo_paddle.py
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openvino/tools/mo/mo_tf.py
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openvino/tools/mo/moc_frontend/__init__.py
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openvino/tools/mo/moc_frontend/analysis.py
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openvino/tools/mo/moc_frontend/extractor.py
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openvino/tools/mo/moc_frontend/pipeline.py
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openvino/tools/mo/moc_frontend/serialize.py
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45
tools/mo/openvino/tools/mo/moc_frontend/analysis.py
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45
tools/mo/openvino/tools/mo/moc_frontend/analysis.py
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@ -0,0 +1,45 @@
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# Copyright (C) 2022 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import json
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from openvino.runtime import PartialShape, Model, Type # pylint: disable=no-name-in-module,import-error
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from openvino.runtime.utils.types import get_dtype
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def json_model_analysis_dump(framework_model: Model):
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def dump_partial_shape(shape: PartialShape):
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if shape.rank.is_dynamic:
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return 'None'
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return [dim.get_length() if dim.is_static else 0 for dim in shape]
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def dump_element_type(ov_type: Type):
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try:
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return str(get_dtype(ov_type))
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except:
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return 'None'
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json_dump = {}
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json_dump['inputs'] = {}
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for param in framework_model.get_parameters():
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param_name = param.get_friendly_name()
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json_dump['inputs'][param_name] = {}
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json_dump['inputs'][param_name]['shape'] = dump_partial_shape(param.get_partial_shape())
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json_dump['inputs'][param_name]['data_type'] = dump_element_type(param.get_element_type())
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json_dump['inputs'][param_name]['value'] = 'None' # not supported in 22.1
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json_dump['intermediate'] = {}
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#TODO: extend model analysis dump for operations with body graphs (If, Loop, and TensorIterator)
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for op in framework_model.get_ordered_ops():
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for out_idx in range(op.get_output_size()):
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output = op.output(out_idx)
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tensor_name = output.get_any_name()
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json_dump['intermediate'][tensor_name] = {}
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json_dump['intermediate'][tensor_name]['shape'] = dump_partial_shape(output.get_partial_shape())
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json_dump['intermediate'][tensor_name]['data_type'] = dump_element_type(output.get_element_type())
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json_dump['intermediate'][tensor_name]['value'] = 'None' # not supported in 22.1
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json_model_analysis_print(json_dump)
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def json_model_analysis_print(json_dump:str):
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print(json_dump)
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@ -4,9 +4,13 @@
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import argparse
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import logging as log
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from typing import List
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import sys
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from os import environ
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from openvino.tools.mo.moc_frontend.analysis import json_model_analysis_dump
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from openvino.tools.mo.moc_frontend.extractor import fe_user_data_repack
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from openvino.tools.mo.middle.passes.infer import validate_batch_in_shape
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from openvino.tools.mo.utils.class_registration import get_enabled_and_disabled_transforms
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from openvino.runtime import Dimension, PartialShape # pylint: disable=no-name-in-module,import-error
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from openvino.frontend import FrontEnd, InputModel, NotImplementedFailure, Place # pylint: disable=no-name-in-module,import-error
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@ -57,6 +61,14 @@ def moc_pipeline(argv: argparse.Namespace, moc_front_end: FrontEnd):
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log.warn('Could not add an additional name to a tensor pointed to by \'{}\'. Details: {}'.format(
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new_input['input_name'], str(e)))
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enabled_transforms, disabled_transforms = get_enabled_and_disabled_transforms()
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if 'ANALYSIS_JSON_PRINT' in enabled_transforms:
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# NOTE that model analysis is performed before applying user's settings (inputs's shapes etc.)
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framework_model = moc_front_end.decode(input_model)
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json_model_analysis_dump(framework_model)
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# a model is not processed further in json analysis mode
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sys.exit(0)
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inputs_equal = True
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if user_shapes:
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inputs_equal = check_places_are_same(input_model.get_inputs(), user_shapes)
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@ -68,3 +68,12 @@ def test_mo_fallback_test():
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status = subprocess.run(args, env=os.environ)
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assert not status.returncode
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def test_mo_model_analysis():
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setup_env()
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args = [sys.executable, '-m', 'pytest',
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os.path.join(os.path.dirname(__file__), 'utils/test_mo_model_analysis_actual.py'), '-s']
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status = subprocess.run(args, env=os.environ)
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assert not status.returncode
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189
tools/mo/unit_tests/mo/utils/test_mo_model_analysis_actual.py
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tools/mo/unit_tests/mo/utils/test_mo_model_analysis_actual.py
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@ -0,0 +1,189 @@
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# Copyright (C) 2022 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import unittest
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from unittest.mock import patch, Mock
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import onnx
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from onnx.helper import make_graph, make_model, make_tensor_value_info
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import os
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from os import environ
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import json
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import argparse
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from openvino.tools.mo.main import prepare_ir
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from openvino.frontend import FrontEndManager # pylint: disable=no-name-in-module,import-error
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from openvino.tools.mo.moc_frontend.analysis import json_model_analysis_dump
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try:
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import openvino_telemetry as tm
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except ImportError:
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import openvino.tools.mo.utils.telemetry_stub as tm
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def base_args_config():
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args = argparse.Namespace()
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args.feManager = FrontEndManager()
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args.extensions = None
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args.use_legacy_frontend = False
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args.use_new_frontend = True
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args.framework = 'onnx'
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args.model_name = None
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args.input_model = None
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args.silent = True
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args.transform=[]
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args.legacy_ir_generation = False
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args.scale = None
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args.output=None
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args.input=None
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args.input_shape=None
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args.batch=None
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args.mean_values=None
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args.scale_values=None
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args.output_dir=os.getcwd()
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args.freeze_placeholder_with_value = None
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args.transformations_config = None
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args.disable_fusing = None
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args.finegrain_fusing = None
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args.disable_gfusing = None
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args.disable_resnet_optimization = None
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args.enable_concat_optimization = None
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args.static_shape = None
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args.disable_weights_compression = None
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args.reverse_input_channels = None
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args.data_type = None
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args.layout = None
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args.source_layout = None
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args.target_layout = None
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args.frontend_defaults = {
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'onnx': 'legacy',
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'tf': 'legacy'
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}
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return args
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class TestMoFallback(unittest.TestCase):
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def setUp(self):
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environ.update({'MO_ENABLED_TRANSFORMS': 'ANALYSIS_JSON_PRINT'})
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tm.Telemetry.__init__ = Mock(return_value=None)
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tm.Telemetry.send_event = Mock()
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self.models = {}
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add = onnx.helper.make_node("Add", inputs=["in1", "in2"], outputs=["add_out"])
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input_tensors = [
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make_tensor_value_info("in1", onnx.TensorProto.FLOAT, (1, 2)),
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make_tensor_value_info("in2", onnx.TensorProto.FLOAT, (1, 2)),
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]
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output_tensors = [
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make_tensor_value_info("add_out", onnx.TensorProto.FLOAT, (1, 2)),
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]
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graph = make_graph([add], "test_graph", input_tensors, output_tensors)
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model = make_model(graph, producer_name="MO tests",
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opset_imports=[onnx.helper.make_opsetid("", 13)])
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self.models["test_model.onnx"] = model
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input_tensors_2 = [
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make_tensor_value_info("in1", onnx.TensorProto.INT64, (1, 'dyn_dim', 3)),
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make_tensor_value_info("in2", onnx.TensorProto.INT64, None),
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make_tensor_value_info("in3", onnx.TensorProto.INT64, ()),
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]
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output_tensors_2 = [
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make_tensor_value_info("mul_out", onnx.TensorProto.FLOAT, None),
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]
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mul = onnx.helper.make_node("Mul", inputs=["add_out", "in3"], outputs=["mul_out"])
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graph_2 = make_graph([add, mul], "test_graph_2", input_tensors_2, output_tensors_2)
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model_2 = make_model(graph_2, producer_name="MO tests",
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opset_imports=[onnx.helper.make_opsetid("", 13)])
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self.models["test_model_2.onnx"] = model_2
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split_1 = onnx.helper.make_node("Split", inputs=["add_out"],
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outputs=["out1", "out2"], axis=0)
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split_2 = onnx.helper.make_node("Split", inputs=["mul_out"],
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outputs=["out3", "out4"], axis=0)
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output_tensors_3 = [
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make_tensor_value_info("out1", onnx.TensorProto.FLOAT, 'dyn_dim'),
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make_tensor_value_info("out2", onnx.TensorProto.FLOAT, 'dyn_dim'),
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make_tensor_value_info("out3", onnx.TensorProto.FLOAT, 'dyn_dim'),
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make_tensor_value_info("out4", onnx.TensorProto.FLOAT, 'dyn_dim'),
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]
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graph_3 = make_graph([add, mul, split_1, split_2], "test_graph_3", input_tensors_2, output_tensors_3)
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model_3 = make_model(graph_3, producer_name="MO tests",
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opset_imports=[onnx.helper.make_opsetid("", 13)])
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self.models["test_model_3.onnx"] = model_3
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for name, model in self.models.items():
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onnx.save(model, name)
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def tearDown(self):
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del environ['MO_ENABLED_TRANSFORMS']
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for name in self.models.keys():
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os.remove(name)
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@patch('openvino.tools.mo.moc_frontend.analysis.json_model_analysis_print')
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def test_model(self, json_print):
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args = base_args_config()
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args.input_model = "test_model.onnx"
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with patch('sys.exit') as exit_mock: # do not exit execution
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prepare_ir(args)
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result = json_print.call_args.args[0]
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assert 'inputs' in result
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assert result['inputs'] == json.loads('{"in1": {"shape": [1, 2], "data_type": "float32", "value": "None"}, \
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"in2": {"shape": [1, 2], "data_type": "float32", "value": "None"}}')
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assert 'intermediate' in result
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assert result['intermediate'] == json.loads('{"in1": {"shape": [1, 2], "data_type": "float32", "value": "None"}, \
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"in2": {"shape": [1, 2], "data_type": "float32", "value": "None"}, \
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"add_out": {"shape": "None", "data_type": "None", "value": "None"}}')
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@patch('openvino.tools.mo.moc_frontend.analysis.json_model_analysis_print')
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def test_model_with_dyn_shapes(self, json_print):
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args = base_args_config()
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args.input_model = "test_model_2.onnx"
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with patch('sys.exit') as exit_mock: # do not exit execution
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prepare_ir(args)
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result = json_print.call_args.args[0]
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assert 'inputs' in result
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print(result['inputs'])
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assert result['inputs'] == json.loads('{"in1": {"shape": [1, 0, 3], "data_type": "int64", "value": "None"}, \
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"in2": {"shape": "None", "data_type": "int64", "value": "None"}, \
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"in3": {"shape": [], "data_type": "int64", "value": "None"}}')
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assert 'intermediate' in result
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assert result['intermediate'] == json.loads('{"in1": {"shape": [1, 0, 3], "data_type": "int64", "value": "None"}, \
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"in2": {"shape": "None", "data_type": "int64", "value": "None"}, \
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"in3": {"shape": [], "data_type": "int64", "value": "None"}, \
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"mul_out": {"shape": "None", "data_type": "None", "value": "None"}, \
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"add_out": {"shape": "None", "data_type": "None", "value": "None"}}')
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@patch('openvino.tools.mo.moc_frontend.analysis.json_model_analysis_print')
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def test_multi_outputs_model(self, json_print):
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args = base_args_config()
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args.input_model = "test_model_3.onnx"
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with patch('sys.exit') as exit_mock: # do not exit execution
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prepare_ir(args)
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result = json_print.call_args.args[0]
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assert 'inputs' in result
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assert result['inputs'] == json.loads('{"in1": {"shape": [1, 0, 3], "data_type": "int64", "value": "None"}, \
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"in2": {"shape": "None", "data_type": "int64", "value": "None"}, \
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"in3": {"shape": [], "data_type": "int64", "value": "None"}}')
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assert 'intermediate' in result
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assert result['intermediate'] == json.loads('{"in1": {"shape": [1, 0, 3], "data_type": "int64", "value": "None"}, \
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"in2": {"shape": "None", "data_type": "int64", "value": "None"}, \
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"in3": {"shape": [], "data_type": "int64", "value": "None"}, \
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"mul_out": {"shape": "None", "data_type": "None", "value": "None"}, \
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"add_out": {"shape": "None", "data_type": "None", "value": "None"}, \
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"out1": {"shape": "None", "data_type": "None", "value": "None"}, \
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"out2": {"shape": "None", "data_type": "None", "value": "None"}, \
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"out3": {"shape": "None", "data_type": "None", "value": "None"}, \
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"out4": {"shape": "None", "data_type": "None", "value": "None"}}')
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