0c9abf43a9
* Updated copyright headers
* Revert "Fixed linker warnings in docs snippets on Windows (#15119)"
This reverts commit 372699ec49.
342 lines
15 KiB
Python
342 lines
15 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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import pytest
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from common.onnx_layer_test_class import OnnxRuntimeLayerTest
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class TestSum(OnnxRuntimeLayerTest):
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def create_net(self, dyn_shapes, const_shapes, precision, ir_version, opset=None):
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"""
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ONNX net IR net
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Inputs->Sum with consts->Output => Input->Eltwise
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"""
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#
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# Create ONNX model
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#
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from onnx import helper
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from onnx import TensorProto
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inputs = list()
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input_names = list()
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out_shape_len = 0
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for i, shape in enumerate(dyn_shapes):
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input_name = 'input{}'.format(i + 1)
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inputs.append(helper.make_tensor_value_info(input_name, TensorProto.FLOAT, shape))
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input_names.append(input_name)
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if len(shape) > out_shape_len:
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out_shape_len = len(shape)
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output_shape = shape
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output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
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nodes = list()
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consts = list()
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for i, shape in enumerate(const_shapes):
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const = np.random.randint(-127, 127, shape).astype(float)
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const_name = 'const{}'.format(i + 1)
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nodes.append(helper.make_node(
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'Constant',
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inputs=[],
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outputs=[const_name],
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value=helper.make_tensor(
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name='const_tensor',
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data_type=TensorProto.FLOAT,
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dims=const.shape,
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vals=const.flatten(),
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),
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))
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input_names.append(const_name)
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consts.append(const)
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nodes.append(helper.make_node(
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'Sum',
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inputs=input_names,
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outputs=['output']
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))
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# Create the graph (GraphProto)
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graph_def = helper.make_graph(
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nodes,
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'test_model',
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inputs,
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[output],
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)
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# Create the model (ModelProto)
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args = dict(producer_name='test_model')
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if opset:
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args['opset_imports'] = [helper.make_opsetid("", opset)]
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onnx_net = helper.make_model(graph_def, **args)
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# Create reference IR net
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ref_net = None
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# Too complicated IR to generate by hand
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return onnx_net, ref_net
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def create_const_net(self, const_shapes, ir_version, opset=None):
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"""
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ONNX net IR net
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Inputs->Concat with Sum of consts->Output => Input->Concat with consts
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"""
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#
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# Create ONNX model
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#
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from onnx import helper
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from onnx import TensorProto
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shape_len = 0
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for shape in const_shapes:
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if len(shape) > shape_len:
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shape_len = len(shape)
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input_shape = shape
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concat_axis = 0
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output_shape = input_shape.copy()
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output_shape[concat_axis] *= 2
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input = helper.make_tensor_value_info('input', TensorProto.FLOAT, input_shape)
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output = helper.make_tensor_value_info('output', TensorProto.FLOAT, output_shape)
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nodes = list()
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input_names = list()
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consts = list()
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for i, shape in enumerate(const_shapes):
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const = np.random.randint(-127, 127, shape).astype(float)
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const_name = 'const{}'.format(i + 1)
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nodes.append(helper.make_node(
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'Constant',
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inputs=[],
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outputs=[const_name],
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value=helper.make_tensor(
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name='const_tensor',
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data_type=TensorProto.FLOAT,
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dims=const.shape,
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vals=const.flatten(),
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),
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))
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input_names.append(const_name)
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consts.append(const)
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nodes.append(helper.make_node(
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'Sum',
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inputs=input_names,
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outputs=['sum']
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))
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nodes.append(helper.make_node(
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'Concat',
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inputs=['input', 'sum'],
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outputs=['output'],
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axis=concat_axis
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))
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# Create the graph (GraphProto)
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graph_def = helper.make_graph(
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nodes,
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'test_model',
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[input],
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[output],
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)
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# Create the model (ModelProto)
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args = dict(producer_name='test_model')
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if opset:
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args['opset_imports'] = [helper.make_opsetid("", opset)]
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onnx_net = helper.make_model(graph_def, **args)
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# Create reference IR net
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ref_net = None
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return onnx_net, ref_net
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test_data_precommit = [
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dict(dyn_shapes=[[4, 6, 8], [4, 6, 8], [4, 6, 8]],
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const_shapes=[[4, 6, 8], [4, 6, 8], [4, 6, 8]]),
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dict(dyn_shapes=[[4, 6, 8, 10, 12]],
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const_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12]]),
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dict(dyn_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [4, 6, 8, 10, 12]],
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const_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [4, 6, 8, 10, 12]])]
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test_data = [
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# TODO: Add broadcasting tests. Note: Sum-6 doesn't support broadcasting
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dict(dyn_shapes=[[4, 6]], const_shapes=[[4, 6]]),
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dict(dyn_shapes=[[4, 6]], const_shapes=[[4, 6], [4, 6]]),
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dict(dyn_shapes=[[4, 6]], const_shapes=[[4, 6], [4, 6], [4, 6]]),
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dict(dyn_shapes=[[4, 6], [4, 6]], const_shapes=[]),
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dict(dyn_shapes=[[4, 6], [4, 6]], const_shapes=[[4, 6]]),
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dict(dyn_shapes=[[4, 6], [4, 6]], const_shapes=[[4, 6], [4, 6]]),
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dict(dyn_shapes=[[4, 6], [4, 6]], const_shapes=[[4, 6], [4, 6], [4, 6]]),
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dict(dyn_shapes=[[4, 6], [4, 6], [4, 6]], const_shapes=[]),
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dict(dyn_shapes=[[4, 6], [4, 6], [4, 6]], const_shapes=[[4, 6]]),
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dict(dyn_shapes=[[4, 6], [4, 6], [4, 6]], const_shapes=[[4, 6], [4, 6]]),
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dict(dyn_shapes=[[4, 6], [4, 6], [4, 6]], const_shapes=[[4, 6], [4, 6], [4, 6]]),
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dict(dyn_shapes=[[4, 6, 8]], const_shapes=[[4, 6, 8]]),
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dict(dyn_shapes=[[4, 6, 8]], const_shapes=[[4, 6, 8], [4, 6, 8]]),
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dict(dyn_shapes=[[4, 6, 8]], const_shapes=[[4, 6, 8], [4, 6, 8], [4, 6, 8]]),
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dict(dyn_shapes=[[4, 6, 8], [4, 6, 8]], const_shapes=[]),
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dict(dyn_shapes=[[4, 6, 8], [4, 6, 8]], const_shapes=[[4, 6, 8]]),
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dict(dyn_shapes=[[4, 6, 8], [4, 6, 8]], const_shapes=[[4, 6, 8], [4, 6, 8]]),
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dict(dyn_shapes=[[4, 6, 8], [4, 6, 8]], const_shapes=[[4, 6, 8], [4, 6, 8], [4, 6, 8]]),
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dict(dyn_shapes=[[4, 6, 8], [4, 6, 8], [4, 6, 8]], const_shapes=[]),
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dict(dyn_shapes=[[4, 6, 8], [4, 6, 8], [4, 6, 8]], const_shapes=[[4, 6, 8]]),
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dict(dyn_shapes=[[4, 6, 8], [4, 6, 8], [4, 6, 8]], const_shapes=[[4, 6, 8], [4, 6, 8]]),
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dict(dyn_shapes=[[4, 6, 8], [4, 6, 8], [4, 6, 8]],
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const_shapes=[[4, 6, 8], [4, 6, 8], [4, 6, 8]]),
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dict(dyn_shapes=[[4, 6, 8, 10]], const_shapes=[[4, 6, 8, 10]]),
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dict(dyn_shapes=[[4, 6, 8, 10]], const_shapes=[[4, 6, 8, 10], [4, 6, 8, 10]]),
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dict(dyn_shapes=[[4, 6, 8, 10]],
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const_shapes=[[4, 6, 8, 10], [4, 6, 8, 10], [4, 6, 8, 10]]),
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dict(dyn_shapes=[[4, 6, 8, 10], [4, 6, 8, 10]], const_shapes=[]),
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dict(dyn_shapes=[[4, 6, 8, 10], [4, 6, 8, 10]], const_shapes=[[4, 6, 8, 10]]),
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dict(dyn_shapes=[[4, 6, 8, 10], [4, 6, 8, 10]],
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const_shapes=[[4, 6, 8, 10], [4, 6, 8, 10]]),
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dict(dyn_shapes=[[4, 6, 8, 10], [4, 6, 8, 10]],
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const_shapes=[[4, 6, 8, 10], [4, 6, 8, 10], [4, 6, 8, 10]]),
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dict(dyn_shapes=[[4, 6, 8, 10], [4, 6, 8, 10], [4, 6, 8, 10]], const_shapes=[]),
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dict(dyn_shapes=[[4, 6, 8, 10], [4, 6, 8, 10], [4, 6, 8, 10]],
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const_shapes=[[4, 6, 8, 10]]),
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dict(dyn_shapes=[[4, 6, 8, 10], [4, 6, 8, 10], [4, 6, 8, 10]],
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const_shapes=[[4, 6, 8, 10], [4, 6, 8, 10]]),
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dict(dyn_shapes=[[4, 6, 8, 10], [4, 6, 8, 10], [4, 6, 8, 10]],
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const_shapes=[[4, 6, 8, 10], [4, 6, 8, 10], [4, 6, 8, 10]]),
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dict(dyn_shapes=[[4, 6, 8, 10, 12]], const_shapes=[[4, 6, 8, 10, 12]]),
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dict(dyn_shapes=[[4, 6, 8, 10, 12]], const_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12]]),
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dict(dyn_shapes=[[4, 6, 8, 10, 12]],
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const_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [4, 6, 8, 10, 12]]),
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dict(dyn_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12]], const_shapes=[]),
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dict(dyn_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12]], const_shapes=[[4, 6, 8, 10, 12]]),
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dict(dyn_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12]],
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const_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12]]),
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dict(dyn_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12]],
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const_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [4, 6, 8, 10, 12]]),
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dict(dyn_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [4, 6, 8, 10, 12]], const_shapes=[]),
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dict(dyn_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [4, 6, 8, 10, 12]],
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const_shapes=[[4, 6, 8, 10, 12]]),
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dict(dyn_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [4, 6, 8, 10, 12]],
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const_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12]]),
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dict(dyn_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [4, 6, 8, 10, 12]],
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const_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [4, 6, 8, 10, 12]])]
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const_test_data_precommit = [
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dict(const_shapes=[[4, 6, 8, 10], [4, 6, 8, 10], [4, 6, 8, 10]]),
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dict(const_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [4, 6, 8, 10, 12],
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[4, 6, 8, 10, 12]])
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]
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const_test_data = [
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dict(const_shapes=[[4, 6], [4, 6]]),
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dict(const_shapes=[[4, 6], [4, 6], [4, 6]]),
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dict(const_shapes=[[4, 6], [4, 6], [4, 6], [4, 6]]),
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dict(const_shapes=[[4, 6, 8], [4, 6, 8]]),
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dict(const_shapes=[[4, 6, 8], [4, 6, 8], [4, 6, 8]]),
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dict(const_shapes=[[4, 6, 8], [4, 6, 8], [4, 6, 8], [4, 6, 8]]),
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dict(const_shapes=[[4, 6, 8, 10], [4, 6, 8, 10]]),
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dict(const_shapes=[[4, 6, 8, 10], [4, 6, 8, 10], [4, 6, 8, 10]]),
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dict(const_shapes=[[4, 6, 8, 10], [4, 6, 8, 10], [4, 6, 8, 10], [4, 6, 8, 10]]),
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dict(const_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12]]),
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dict(const_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [4, 6, 8, 10, 12]]),
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dict(const_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [4, 6, 8, 10, 12],
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[4, 6, 8, 10, 12]])
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]
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const_test_data_broadcasting_precommit = [
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dict(const_shapes=[[4, 6, 8, 10], [10], [10], [10]]),
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dict(const_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [12]]),
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dict(const_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [12]])
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]
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const_test_data_broadcasting = [
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dict(const_shapes=[[4, 6], [6]]),
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dict(const_shapes=[[4, 6], [6], [6]]),
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dict(const_shapes=[[4, 6], [4, 6], [6]]),
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dict(const_shapes=[[4, 6], [6], [6], [6]]),
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dict(const_shapes=[[4, 6], [4, 6], [6], [6]]),
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dict(const_shapes=[[4, 6], [4, 6], [4, 6], [6]]),
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dict(const_shapes=[[4, 6, 8], [8]]),
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dict(const_shapes=[[4, 6, 8], [8], [8]]),
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dict(const_shapes=[[4, 6, 8], [4, 6, 8], [8]]),
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dict(const_shapes=[[4, 6, 8], [8], [8], [8]]),
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dict(const_shapes=[[4, 6, 8], [4, 6, 8], [8], [8]]),
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dict(const_shapes=[[4, 6, 8], [4, 6, 8], [4, 6, 8], [8]]),
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dict(const_shapes=[[4, 6, 8, 10], [10]]),
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dict(const_shapes=[[4, 6, 8, 10], [10], [10]]),
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dict(const_shapes=[[4, 6, 8, 10], [4, 6, 8, 10], [10]]),
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dict(const_shapes=[[4, 6, 8, 10], [10], [10], [10]]),
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dict(const_shapes=[[4, 6, 8, 10], [4, 6, 8, 10], [10], [10]]),
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dict(const_shapes=[[4, 6, 8, 10], [4, 6, 8, 10], [4, 6, 8, 10], [10]]),
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dict(const_shapes=[[4, 6, 8, 10, 12], [12]]),
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dict(const_shapes=[[4, 6, 8, 10, 12], [12], [12]]),
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dict(const_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [12]]),
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dict(const_shapes=[[4, 6, 8, 10, 12], [12], [12], [12]]),
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dict(const_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [12], [12]]),
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dict(const_shapes=[[4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [4, 6, 8, 10, 12], [12]])
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]
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.nightly
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def test_sum_opset6(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_net(**params, precision=precision, opset=6, ir_version=ir_version),
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ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data_precommit)
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@pytest.mark.precommit
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def test_sum_precommit(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_net(**params, precision=precision, ir_version=ir_version),
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ie_device, precision, ir_version, temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.nightly
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def test_sum(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(
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*self.create_net(**params, precision=precision, ir_version=ir_version), ie_device,
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precision, ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", const_test_data)
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@pytest.mark.nightly
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def test_sum_const_opset6(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_const_net(**params, opset=6, ir_version=ir_version), ie_device,
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precision, ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", const_test_data_precommit)
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@pytest.mark.precommit
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def test_sum_const_precommit(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_const_net(**params, ir_version=ir_version), ie_device, precision,
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ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", const_test_data)
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@pytest.mark.nightly
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def test_sum_const(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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self._test(*self.create_const_net(**params, ir_version=ir_version), ie_device, precision,
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ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", const_test_data_broadcasting_precommit)
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@pytest.mark.precommit
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def test_sum_const_broadcasting_precommit(self, params, ie_device, precision, ir_version,
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temp_dir, use_old_api):
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self._test(*self.create_const_net(**params, ir_version=ir_version), ie_device, precision,
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ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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@pytest.mark.parametrize("params", const_test_data_broadcasting)
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@pytest.mark.nightly
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def test_sum_const_broadcasting(self, params, ie_device, precision, ir_version, temp_dir,
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use_old_api):
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self._test(*self.create_const_net(**params, ir_version=ir_version), ie_device, precision,
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ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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