Add the shape_inference_test folder for CPU. (#8189)
This commit is contained in:
Luwei Zhou
GitHub
parent
96af987c46
commit
05074efb9b
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// Copyright (C) 2018-2021 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include "utils/shape_inference/static_shape.hpp"
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#include <convolution_shape_inference.hpp>
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#include <experimental_detectron_detection_output_shape_inference.hpp>
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#include <gtest/gtest.h>
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#include <openvino/core/coordinate_diff.hpp>
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#include <openvino/op/convolution.hpp>
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#include <openvino/op/ops.hpp>
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#include <openvino/op/parameter.hpp>
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using namespace ov;
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TEST(StaticShapeInferenceTest, ConvolutionTest) {
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Strides strides{1, 1};
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CoordinateDiff pads_begin{0, 0};
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CoordinateDiff pads_end{0, 0};
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Strides dilations{1, 1};
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const auto auto_pad = op::PadType::SAME_LOWER;
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auto data = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1, -1});
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auto filters = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1, -1});
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auto conv =
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std::make_shared<op::v1::Convolution>(data, filters, strides, pads_begin, pads_end, dilations, auto_pad);
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std::vector<PartialShape> input_shapes = {PartialShape{3, 6, 5, 5}, PartialShape{7, 6, 3, 3}}, output_shapes = {PartialShape{}};
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shape_infer(conv.get(), input_shapes, output_shapes);
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ASSERT_EQ(output_shapes[0], PartialShape({3, 7, 5, 5}));
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ASSERT_EQ(conv->get_pads_begin(), (CoordinateDiff{1, 1}));
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ASSERT_EQ(conv->get_pads_end(), (CoordinateDiff{1, 1}));
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std::vector<StaticShape> static_input_shapes = {StaticShape{3, 6, 5, 5}, StaticShape{7, 6, 3, 3}}, static_output_shapes = {StaticShape{}};
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shape_infer(conv.get(), static_input_shapes, static_output_shapes);
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ASSERT_EQ(static_output_shapes[0], StaticShape({3, 7, 5, 5}));
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ASSERT_EQ(conv->get_pads_begin(), (CoordinateDiff{1, 1}));
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ASSERT_EQ(conv->get_pads_end(), (CoordinateDiff{1, 1}));
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}
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TEST(StaticShapeInferenceTest, ExperimentalDetectronDetectionOutputTest) {
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using Attrs = op::v6::ExperimentalDetectronDetectionOutput::Attributes;
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Attrs attrs;
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attrs.class_agnostic_box_regression = true;
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attrs.deltas_weights = {10.0f, 10.0f, 5.0f, 5.0f};
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attrs.max_delta_log_wh = 2.0f;
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attrs.max_detections_per_image = 5;
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attrs.nms_threshold = 0.2f;
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attrs.num_classes = 2;
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attrs.post_nms_count = 500;
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attrs.score_threshold = 0.01000000074505806f;
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int64_t rois_num = static_cast<int64_t>(attrs.max_detections_per_image);
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auto rois = std::make_shared<ov::op::v0::Parameter>(element::f32,
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PartialShape{-1, -1});
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auto deltas = std::make_shared<ov::op::v0::Parameter>(element::f32,
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PartialShape{-1, -1});
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auto scores = std::make_shared<ov::op::v0::Parameter>(element::f32,
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PartialShape{-1, -1});
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auto im_info = std::make_shared<ov::op::v0::Parameter>(element::f32,
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PartialShape{-1, -1});
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auto detection =
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std::make_shared<ov::op::v6::ExperimentalDetectronDetectionOutput>(
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rois, deltas, scores, im_info, attrs);
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std::vector<PartialShape> input_shapes = {
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PartialShape::dynamic(), PartialShape::dynamic(), PartialShape::dynamic(),
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PartialShape::dynamic()};
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std::vector<PartialShape> output_shapes = {PartialShape::dynamic(),
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PartialShape::dynamic(),
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PartialShape::dynamic()};
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shape_infer(detection.get(), input_shapes, output_shapes);
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ASSERT_EQ(output_shapes[0], (PartialShape{rois_num, 4}));
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ASSERT_EQ(output_shapes[1], (PartialShape{rois_num}));
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ASSERT_EQ(output_shapes[2], (PartialShape{rois_num}));
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input_shapes = {PartialShape{-1, -1}, PartialShape{-1, -1},
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PartialShape{-1, -1}, PartialShape{-1, -1}};
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output_shapes = {PartialShape{}, PartialShape{}, PartialShape{}};
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shape_infer(detection.get(), input_shapes, output_shapes);
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ASSERT_EQ(output_shapes[0], (PartialShape{rois_num, 4}));
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ASSERT_EQ(output_shapes[1], (PartialShape{rois_num}));
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ASSERT_EQ(output_shapes[2], (PartialShape{rois_num}));
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input_shapes = {PartialShape{16, 4}, PartialShape{16, 8}, PartialShape{16, 2}, PartialShape{1, 3}};
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output_shapes = {PartialShape{}, PartialShape{}, PartialShape{}};
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shape_infer(detection.get(), input_shapes, output_shapes);
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ASSERT_EQ(output_shapes[0], (PartialShape{rois_num, 4}));
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ASSERT_EQ(output_shapes[1], (PartialShape{rois_num}));
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ASSERT_EQ(output_shapes[2], (PartialShape{rois_num}));
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std::vector<StaticShape> static_input_shapes = {StaticShape{16, 4}, StaticShape{16, 8}, StaticShape{16, 2}, StaticShape{1, 3}};
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std::vector<StaticShape> static_output_shapes = {StaticShape{}, StaticShape{},
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StaticShape{}};
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shape_infer(detection.get(), static_input_shapes, static_output_shapes);
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ASSERT_EQ(static_output_shapes[0],
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StaticShape({attrs.max_detections_per_image, 4}));
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ASSERT_EQ(static_output_shapes[1], StaticShape({attrs.max_detections_per_image}));
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ASSERT_EQ(static_output_shapes[2], StaticShape({attrs.max_detections_per_image}));
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}
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#if 0
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TEST(StaticShapeInferenceTest, ConvolutionTimeTest) {
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Strides strides{1, 1};
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CoordinateDiff pads_begin{0, 0};
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CoordinateDiff pads_end{0, 0};
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Strides dilations{1, 1};
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const auto auto_pad = op::PadType::SAME_LOWER;
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auto data = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{3, 6, 5, 5});
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auto filters = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{7, 6, 3, 3});
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auto conv =
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std::make_shared<op::v1::Convolution>(data, filters, strides, pads_begin, pads_end, dilations, auto_pad);
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std::vector<StaticShape> static_input_shapes = {StaticShape{3, 6, 5, 5}, StaticShape{7, 6, 3, 3}}, static_output_shapes = {StaticShape{}};
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auto before = std::chrono::high_resolution_clock::now();
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auto after = std::chrono::high_resolution_clock::now();
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std::cout << conv << std::endl;
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auto convolution_time_sum = 0;
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for (size_t i = 0; i < 10; ++i) {
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before = std::chrono::high_resolution_clock::now();
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shape_infer(conv.get(), static_input_shapes, static_output_shapes);
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after = std::chrono::high_resolution_clock::now();
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auto diff = std::chrono::duration_cast<std::chrono::nanoseconds>(after - before).count();
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std::cout << diff << " ns" << std::endl;
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convolution_time_sum += diff;
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}
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// other operation creation and time measurements: ReLU is an example
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auto relu = std::make_shared<op::v0::Relu>(data);
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std::cout << relu << std::endl;
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auto other_op_time_sum = 0;
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for (size_t i = 0; i < 10; ++i) {
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before = std::chrono::high_resolution_clock::now();
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relu->validate_and_infer_types();
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after = std::chrono::high_resolution_clock::now();
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auto diff = std::chrono::duration_cast<std::chrono::nanoseconds>(after - before).count();
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std::cout << diff << " ns" << std::endl;
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other_op_time_sum += diff;
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}
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std::cout << (convolution_time_sum >= other_op_time_sum ? "ON PAR WITH CONVOLUTION: " : "LONGER THAN CONVOLUTION ")
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<< 1. * other_op_time_sum / convolution_time_sum << std::endl;
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}
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#endif
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@ -0,0 +1,28 @@
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BasedOnStyle: Google
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IndentWidth: 4
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UseTab: Never
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ColumnLimit: 120
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Language: Cpp
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Standard: Cpp11
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AccessModifierOffset: -4
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AlignConsecutiveMacros: true
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AllowAllArgumentsOnNextLine: false
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AllowAllConstructorInitializersOnNextLine: false
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AllowAllParametersOfDeclarationOnNextLine: false
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AllowShortFunctionsOnASingleLine: Empty
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AllowShortIfStatementsOnASingleLine: Never
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AllowShortLambdasOnASingleLine: Empty
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AllowShortLoopsOnASingleLine: false
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AlwaysBreakBeforeMultilineStrings: false
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BinPackArguments: false
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BinPackParameters: false
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CommentPragmas: '^#'
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DerivePointerAlignment: false
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FixNamespaceComments: true
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IndentCaseLabels: false
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IndentPPDirectives: AfterHash
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ForEachMacros:
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- foreach
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- FOREACH_CHILD
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@ -0,0 +1,88 @@
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// Copyright (C) 2018-2021 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include <gtest/gtest.h>
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#include <convolution_shape_inference.hpp>
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#include <openvino/core/coordinate_diff.hpp>
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#include <openvino/op/convolution.hpp>
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#include <openvino/op/ops.hpp>
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#include <openvino/op/parameter.hpp>
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#include "utils/shape_inference/static_shape.hpp"
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using namespace ov;
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TEST(StaticShapeInferenceTest, ConvolutionTest) {
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Strides strides{1, 1};
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CoordinateDiff pads_begin{0, 0};
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CoordinateDiff pads_end{0, 0};
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Strides dilations{1, 1};
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const auto auto_pad = op::PadType::SAME_LOWER;
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auto data = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1, -1});
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auto filters = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1, -1, -1});
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auto conv =
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std::make_shared<op::v1::Convolution>(data, filters, strides, pads_begin, pads_end, dilations, auto_pad);
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std::vector<PartialShape> input_shapes = {PartialShape{3, 6, 5, 5}, PartialShape{7, 6, 3, 3}},
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output_shapes = {PartialShape{}};
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shape_infer(conv.get(), input_shapes, output_shapes);
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ASSERT_EQ(output_shapes[0], PartialShape({3, 7, 5, 5}));
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ASSERT_EQ(conv->get_pads_begin(), (CoordinateDiff{1, 1}));
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ASSERT_EQ(conv->get_pads_end(), (CoordinateDiff{1, 1}));
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std::vector<StaticShape> static_input_shapes = {StaticShape{3, 6, 5, 5}, StaticShape{7, 6, 3, 3}},
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static_output_shapes = {StaticShape{}};
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shape_infer(conv.get(), static_input_shapes, static_output_shapes);
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ASSERT_EQ(static_output_shapes[0], StaticShape({3, 7, 5, 5}));
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ASSERT_EQ(conv->get_pads_begin(), (CoordinateDiff{1, 1}));
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ASSERT_EQ(conv->get_pads_end(), (CoordinateDiff{1, 1}));
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}
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#if 0
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TEST(StaticShapeInferenceTest, ConvolutionTimeTest) {
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Strides strides{1, 1};
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CoordinateDiff pads_begin{0, 0};
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CoordinateDiff pads_end{0, 0};
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Strides dilations{1, 1};
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const auto auto_pad = op::PadType::SAME_LOWER;
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auto data = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{3, 6, 5, 5});
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auto filters = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{7, 6, 3, 3});
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auto conv =
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std::make_shared<op::v1::Convolution>(data, filters, strides, pads_begin, pads_end, dilations, auto_pad);
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std::vector<StaticShape> static_input_shapes = {StaticShape{3, 6, 5, 5}, StaticShape{7, 6, 3, 3}}, static_output_shapes = {StaticShape{}};
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auto before = std::chrono::high_resolution_clock::now();
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auto after = std::chrono::high_resolution_clock::now();
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std::cout << conv << std::endl;
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auto convolution_time_sum = 0;
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for (size_t i = 0; i < 10; ++i) {
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before = std::chrono::high_resolution_clock::now();
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shape_infer(conv.get(), static_input_shapes, static_output_shapes);
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after = std::chrono::high_resolution_clock::now();
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auto diff = std::chrono::duration_cast<std::chrono::nanoseconds>(after - before).count();
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std::cout << diff << " ns" << std::endl;
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convolution_time_sum += diff;
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}
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// other operation creation and time measurements: ReLU is an example
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auto relu = std::make_shared<op::v0::Relu>(data);
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std::cout << relu << std::endl;
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auto other_op_time_sum = 0;
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for (size_t i = 0; i < 10; ++i) {
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before = std::chrono::high_resolution_clock::now();
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relu->validate_and_infer_types();
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after = std::chrono::high_resolution_clock::now();
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auto diff = std::chrono::duration_cast<std::chrono::nanoseconds>(after - before).count();
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std::cout << diff << " ns" << std::endl;
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other_op_time_sum += diff;
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}
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std::cout << (convolution_time_sum >= other_op_time_sum ? "ON PAR WITH CONVOLUTION: " : "LONGER THAN CONVOLUTION ")
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<< 1. * other_op_time_sum / convolution_time_sum << std::endl;
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}
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#endif
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@ -0,0 +1,72 @@
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// Copyright (C) 2018-2021 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#include <gtest/gtest.h>
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#include <experimental_detectron_detection_output_shape_inference.hpp>
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#include <openvino/core/coordinate_diff.hpp>
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#include <openvino/op/convolution.hpp>
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#include <openvino/op/ops.hpp>
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#include <openvino/op/parameter.hpp>
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#include "utils/shape_inference/static_shape.hpp"
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using namespace ov;
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TEST(StaticShapeInferenceTest, ExperimentalDetectronDetectionOutputTest) {
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using Attrs = op::v6::ExperimentalDetectronDetectionOutput::Attributes;
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Attrs attrs;
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attrs.class_agnostic_box_regression = true;
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attrs.deltas_weights = {10.0f, 10.0f, 5.0f, 5.0f};
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attrs.max_delta_log_wh = 2.0f;
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attrs.max_detections_per_image = 5;
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attrs.nms_threshold = 0.2f;
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attrs.num_classes = 2;
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attrs.post_nms_count = 500;
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attrs.score_threshold = 0.01000000074505806f;
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int64_t rois_num = static_cast<int64_t>(attrs.max_detections_per_image);
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auto rois = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1});
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auto deltas = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1});
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auto scores = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1});
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auto im_info = std::make_shared<ov::op::v0::Parameter>(element::f32, PartialShape{-1, -1});
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auto detection =
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std::make_shared<ov::op::v6::ExperimentalDetectronDetectionOutput>(rois, deltas, scores, im_info, attrs);
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std::vector<PartialShape> input_shapes = {PartialShape::dynamic(),
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PartialShape::dynamic(),
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PartialShape::dynamic(),
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PartialShape::dynamic()};
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std::vector<PartialShape> output_shapes = {PartialShape::dynamic(),
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PartialShape::dynamic(),
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PartialShape::dynamic()};
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shape_infer(detection.get(), input_shapes, output_shapes);
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ASSERT_EQ(output_shapes[0], (PartialShape{rois_num, 4}));
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ASSERT_EQ(output_shapes[1], (PartialShape{rois_num}));
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ASSERT_EQ(output_shapes[2], (PartialShape{rois_num}));
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input_shapes = {PartialShape{-1, -1}, PartialShape{-1, -1}, PartialShape{-1, -1}, PartialShape{-1, -1}};
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output_shapes = {PartialShape{}, PartialShape{}, PartialShape{}};
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shape_infer(detection.get(), input_shapes, output_shapes);
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ASSERT_EQ(output_shapes[0], (PartialShape{rois_num, 4}));
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ASSERT_EQ(output_shapes[1], (PartialShape{rois_num}));
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ASSERT_EQ(output_shapes[2], (PartialShape{rois_num}));
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input_shapes = {PartialShape{16, 4}, PartialShape{16, 8}, PartialShape{16, 2}, PartialShape{1, 3}};
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output_shapes = {PartialShape{}, PartialShape{}, PartialShape{}};
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shape_infer(detection.get(), input_shapes, output_shapes);
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ASSERT_EQ(output_shapes[0], (PartialShape{rois_num, 4}));
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ASSERT_EQ(output_shapes[1], (PartialShape{rois_num}));
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ASSERT_EQ(output_shapes[2], (PartialShape{rois_num}));
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std::vector<StaticShape> static_input_shapes = {StaticShape{16, 4},
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StaticShape{16, 8},
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StaticShape{16, 2},
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StaticShape{1, 3}};
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std::vector<StaticShape> static_output_shapes = {StaticShape{}, StaticShape{}, StaticShape{}};
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shape_infer(detection.get(), static_input_shapes, static_output_shapes);
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ASSERT_EQ(static_output_shapes[0], StaticShape({attrs.max_detections_per_image, 4}));
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ASSERT_EQ(static_output_shapes[1], StaticShape({attrs.max_detections_per_image}));
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ASSERT_EQ(static_output_shapes[2], StaticShape({attrs.max_detections_per_image}));
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}
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