IntenseWebs/openvino
3
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Reference ROIAlign-3 implementation (#2128)

Browse Source
This commit is contained in:
Bartosz Lesniewski
2020-09-21 14:04:39 +02:00
committed by GitHub
parent 3f52e64c25
commit 9af49a7f95
8 changed files with 669 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
ngraph/test/CMakeLists.txt
View File

@@ -77,6 +77,7 @@ set(SRC
op_eval/non_zero.cpp
op_eval/reduce_l1.cpp
op_eval/reduce_l2.cpp
op_eval/roi_align.cpp
op_eval/softplus.cpp
op_eval/split.cpp
op_eval/swish.cpp

110
ngraph/test/models/onnx/roi_align_f32.prototxt Normal file
View File

@@ -0,0 +1,110 @@
ir_version: 3
producer_name: "onnx-importer-test"
graph {
node{
input: "feature_maps"
input: "rois"
input: "batch_indices"
output: "Y"
op_type: "RoiAlign"
attribute{
name: "output_height"
i: 3
type: INT
}
attribute{
name: "output_width"
i: 4
type: INT
}
attribute{
name: "sampling_ratio"
i: 2
type: INT
}
attribute{
name: "spatial_scale"
f: 0.0625
type: FLOAT
}
attribute{
name: "mode"
s: "avg"
type: STRING
}
}
name: "test-model"
input{
name: "feature_maps"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 1
}
dim {
dim_value: 3
}
dim {
dim_value: 5
}
dim {
dim_value: 5
}
}
}
}
}
input{
name: "rois"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 5
}
dim {
dim_value: 4
}
}
}
}
}
input{
name:"batch_indices"
type{
tensor_type{
elem_type: 6
shape {
dim {
dim_value: 5
}
}
}
}
}
output{
name: "Y"
type {
tensor_type {
elem_type: 1
shape {
dim {
dim_value: 5
}
dim {
dim_value: 3
}
dim {
dim_value: 3
}
dim {
dim_value: 4
}
}
}
}
}
}

44
ngraph/test/onnx/onnx_import.in.cpp
View File

@@ -748,7 +748,7 @@ namespace
-1.21722605, 0.82919357, 0.55765697, 0.12657686, 0.63432172, 0.75425957, -2.43721014,
-1.24478184, 2.65316853, 1.19509542, -0.95523998, 0.5149006, -0.01151649, 0.68327026,
-0.4589638, -0.46554745, 0.21055324, 0.39266729, 2.05098086, 1.83207919};
}
} // namespace
NGRAPH_TEST(${BACKEND_NAME}, onnx_model_softmax_axis_0)
{
@@ -2468,6 +2468,48 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_empty_initializers_handling)
test_case.run_with_tolerance_as_fp(2.0e-5f);
}
NGRAPH_TEST(${BACKEND_NAME}, onnx_roi_align_f32)
{
const auto function = onnx_import::import_onnx_model(
file_util::path_join(SERIALIZED_ZOO, "onnx/roi_align_f32.prototxt"));
auto test_case = test::TestCase<TestEngine>(function);
test_case.add_input<float>({0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.,
13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38.,
39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51.,
52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64.,
65., 66., 67., 68., 69., 70., 71., 72., 73., 74.});
test_case.add_input<float>({7., 5., 7., 5., -15., -15., -15., -15., -10., 21.,
-10., 21., 13., 8., 13., 8., -14., 19., -14., 19.});
test_case.add_input<int32_t>({0, 0, 0, 0, 0});
test_case.add_expected_output<float>(
Shape{5, 3, 3, 4},
{2.95833f, 3.20833f, 3.45833f, 3.70833f, 4.625f, 4.875f, 5.125f, 5.375f, 6.29167f,
6.54167f, 6.79167f, 7.04167f, 27.9583f, 28.2083f, 28.4583f, 28.7083f, 29.625f, 29.875f,
30.125f, 30.375f, 31.2917f, 31.5417f, 31.7917f, 32.0417f, 52.9583f, 53.2083f, 53.4583f,
53.7083f, 54.625f, 54.875f, 55.125f, 55.375f, 56.2917f, 56.5417f, 56.7917f, 57.0417f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 25.f, 25.f, 25.f, 25.f, 25.f, 25.f,
25.f, 25.f, 25.f, 25.f, 25.f, 25.f, 50.f, 50.f, 50.f,
50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f,
7.39583f, 7.39583f, 7.42708f, 7.64583f, 9.0625f, 9.0625f, 9.09375f, 9.3125f, 10.7292f,
10.7292f, 10.7604f, 10.9792f, 32.3958f, 32.3958f, 32.4271f, 32.6458f, 34.0625f, 34.0625f,
34.0938f, 34.3125f, 35.7292f, 35.7292f, 35.7604f, 35.9792f, 57.3958f, 57.3958f, 57.4271f,
57.6458f, 59.0625f, 59.0625f, 59.0938f, 59.3125f, 60.7292f, 60.7292f, 60.7604f, 60.9792f,
4.27083f, 4.52083f, 4.77083f, 5.02083f, 5.9375f, 6.1875f, 6.4375f, 6.6875f, 7.60417f,
7.85417f, 8.10417f, 8.35417f, 29.2708f, 29.5208f, 29.7708f, 30.0208f, 30.9375f, 31.1875f,
31.4375f, 31.6875f, 32.6042f, 32.8542f, 33.1042f, 33.3542f, 54.2708f, 54.5208f, 54.7708f,
55.0208f, 55.9375f, 56.1875f, 56.4375f, 56.6875f, 57.6042f, 57.8542f, 58.1042f, 58.3542f,
6.77083f, 6.77083f, 6.77083f, 6.80208f, 8.4375f, 8.4375f, 8.4375f, 8.46875f, 10.1042f,
10.1042f, 10.1042f, 10.1354f, 31.7708f, 31.7708f, 31.7708f, 31.8021f, 33.4375f, 33.4375f,
33.4375f, 33.4688f, 35.1042f, 35.1042f, 35.1042f, 35.1354f, 56.7708f, 56.7708f, 56.7708f,
56.8021f, 58.4375f, 58.4375f, 58.4375f, 58.4688f, 60.1042f, 60.1042f, 60.1042f, 60.1354f});
test_case.run_with_tolerance_as_fp(1.0e-4f);
}
NGRAPH_TEST(${BACKEND_NAME}, quant_dequant_pattern)
{
const auto function = onnx_import::import_onnx_model(

166
ngraph/test/op_eval/roi_align.cpp Normal file
View File

@@ -0,0 +1,166 @@
//*****************************************************************************
// Copyright 2017-2020 Intel Corporation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//*****************************************************************************
#include <numeric>
#include <vector>
#include "gtest/gtest.h"
#include "ngraph/op/roi_align.hpp"
#include "ngraph/runtime/host_tensor.hpp"
#include "ngraph/validation_util.hpp"
#include "runtime/backend.hpp"
#include "util/all_close_f.hpp"
#include "util/test_tools.hpp"
#include "util/type_prop.hpp"
using namespace std;
using namespace ngraph;
TEST(op_eval, roi_align_avg_pool)
{
const int N = 1;
const int C = 3;
const int H = 5;
const int W = 5;
const int num_rois = 5;
const int pooled_height = 3;
const int pooled_width = 4;
const auto data_shape = Shape{N, C, H, W};
const auto rois_shape = Shape{num_rois, 4};
const auto data = make_shared<op::Parameter>(element::f32, data_shape);
const auto rois = make_shared<op::Parameter>(element::f32, rois_shape);
const auto batch_indices = make_shared<op::Parameter>(element::i32, Shape{num_rois});
auto roi_align = make_shared<op::v3::ROIAlign>(
data, rois, batch_indices, pooled_height, pooled_width, 2, 1.0f / 16.0f, "avg");
auto f = make_shared<Function>(roi_align, ParameterVector{data, rois, batch_indices});
std::vector<float> data_vec{0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.,
13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38.,
39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51.,
52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64.,
65., 66., 67., 68., 69., 70., 71., 72., 73., 74.};
std::vector<float> rois_vec{7., 5., 7., 5., -15., -15., -15., -15., -10., 21.,
-10., 21., 13., 8., 13., 8., -14., 19., -14., 19.};
std::vector<int64_t> batch_indices_vec{0, 0, 0, 0, 0};
auto result = make_shared<HostTensor>();
ASSERT_TRUE(f->evaluate({result},
{make_host_tensor<element::Type_t::f32>(data_shape, data_vec),
make_host_tensor<element::Type_t::f32>(rois_shape, rois_vec),
make_host_tensor<element::Type_t::i64>(Shape{num_rois})}));
std::vector<float> expected_vec{
2.95833f, 3.20833f, 3.45833f, 3.70833f, 4.625f, 4.875f, 5.125f, 5.375f, 6.29167f,
6.54167f, 6.79167f, 7.04167f, 27.9583f, 28.2083f, 28.4583f, 28.7083f, 29.625f, 29.875f,
30.125f, 30.375f, 31.2917f, 31.5417f, 31.7917f, 32.0417f, 52.9583f, 53.2083f, 53.4583f,
53.7083f, 54.625f, 54.875f, 55.125f, 55.375f, 56.2917f, 56.5417f, 56.7917f, 57.0417f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 25.f, 25.f, 25.f, 25.f, 25.f, 25.f,
25.f, 25.f, 25.f, 25.f, 25.f, 25.f, 50.f, 50.f, 50.f,
50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f,
7.39583f, 7.39583f, 7.42708f, 7.64583f, 9.0625f, 9.0625f, 9.09375f, 9.3125f, 10.7292f,
10.7292f, 10.7604f, 10.9792f, 32.3958f, 32.3958f, 32.4271f, 32.6458f, 34.0625f, 34.0625f,
34.0938f, 34.3125f, 35.7292f, 35.7292f, 35.7604f, 35.9792f, 57.3958f, 57.3958f, 57.4271f,
57.6458f, 59.0625f, 59.0625f, 59.0938f, 59.3125f, 60.7292f, 60.7292f, 60.7604f, 60.9792f,
4.27083f, 4.52083f, 4.77083f, 5.02083f, 5.9375f, 6.1875f, 6.4375f, 6.6875f, 7.60417f,
7.85417f, 8.10417f, 8.35417f, 29.2708f, 29.5208f, 29.7708f, 30.0208f, 30.9375f, 31.1875f,
31.4375f, 31.6875f, 32.6042f, 32.8542f, 33.1042f, 33.3542f, 54.2708f, 54.5208f, 54.7708f,
55.0208f, 55.9375f, 56.1875f, 56.4375f, 56.6875f, 57.6042f, 57.8542f, 58.1042f, 58.3542f,
6.77083f, 6.77083f, 6.77083f, 6.80208f, 8.4375f, 8.4375f, 8.4375f, 8.46875f, 10.1042f,
10.1042f, 10.1042f, 10.1354f, 31.7708f, 31.7708f, 31.7708f, 31.8021f, 33.4375f, 33.4375f,
33.4375f, 33.4688f, 35.1042f, 35.1042f, 35.1042f, 35.1354f, 56.7708f, 56.7708f, 56.7708f,
56.8021f, 58.4375f, 58.4375f, 58.4375f, 58.4688f, 60.1042f, 60.1042f, 60.1042f, 60.1354f};
const auto expected_shape = Shape{num_rois, C, pooled_height, pooled_width};
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_shape(), expected_shape);
ASSERT_TRUE(test::all_close_f(read_vector<float>(result), expected_vec, 6, 0.001));
}
TEST(op_eval, roi_align_max_pool)
{
const int N = 1;
const int C = 3;
const int H = 5;
const int W = 5;
const int num_rois = 5;
const int pooled_height = 3;
const int pooled_width = 4;
const auto data_shape = Shape{N, C, H, W};
const auto rois_shape = Shape{num_rois, 4};
const auto data = make_shared<op::Parameter>(element::f32, data_shape);
const auto rois = make_shared<op::Parameter>(element::f32, rois_shape);
const auto batch_indices = make_shared<op::Parameter>(element::i32, Shape{num_rois});
auto roi_align = make_shared<op::v3::ROIAlign>(
data, rois, batch_indices, pooled_height, pooled_width, 2, 1.0f / 16.0f, "max");
auto f = make_shared<Function>(roi_align, ParameterVector{data, rois, batch_indices});
std::vector<float> data_vec{0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10., 11., 12.,
13., 14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25.,
26., 27., 28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38.,
39., 40., 41., 42., 43., 44., 45., 46., 47., 48., 49., 50., 51.,
52., 53., 54., 55., 56., 57., 58., 59., 60., 61., 62., 63., 64.,
65., 66., 67., 68., 69., 70., 71., 72., 73., 74.};
std::vector<float> rois_vec{7., 5., 7., 5., -15., -15., -15., -15., -10., 21.,
-10., 21., 13., 8., 13., 8., -14., 19., -14., 19.};
std::vector<int64_t> batch_indices_vec{0, 0, 0, 0, 0};
auto result = make_shared<HostTensor>();
ASSERT_TRUE(f->evaluate({result},
{make_host_tensor<element::Type_t::f32>(data_shape, data_vec),
make_host_tensor<element::Type_t::f32>(rois_shape, rois_vec),
make_host_tensor<element::Type_t::i64>(Shape{num_rois})}));
std::vector<float> expected_vec{
2.10938f, 2.95313f, 3.375f, 2.53125f, 3.35938f, 4.70313f, 5.375f, 4.03125f, 3.51563f,
4.92188f, 5.625f, 4.21875f, 10.8984f, 15.2578f, 17.4375f, 13.0781f, 17.3568f, 24.2995f,
27.7708f, 20.8281f, 18.1641f, 25.4297f, 29.0625f, 21.7969f, 19.6875f, 27.5625f, 31.5f,
23.625f, 31.3542f, 43.8958f, 50.1667f, 37.625f, 32.8125f, 45.9375f, 52.5f, 39.375f,
0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f,
0.f, 0.f, 0.f, 25.f, 25.f, 25.f, 25.f, 25.f, 25.f,
25.f, 25.f, 25.f, 25.f, 25.f, 25.f, 50.f, 50.f, 50.f,
50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f, 50.f,
5.625f, 5.625f, 5.625f, 4.57031f, 8.95833f, 8.95833f, 8.95833f, 7.27865f, 9.375f,
9.375f, 9.375f, 7.61719f, 19.6875f, 19.6875f, 19.6875f, 15.9961f, 31.3542f, 31.3542f,
31.3542f, 25.4753f, 32.8125f, 32.8125f, 32.8125f, 26.6602f, 33.75f, 33.75f, 33.75f,
27.4219f, 53.75f, 53.75f, 53.75f, 43.6719f, 56.25f, 56.25f, 56.25f, 45.7031f,
4.5f, 3.9375f, 2.8125f, 3.9375f, 5.5f, 4.8125f, 3.4375f, 4.8125f, 4.58333f,
4.01042f, 2.86458f, 3.9375f, 23.25f, 20.3438f, 14.5313f, 18.f, 28.4167f, 24.86458f,
17.76042f, 22.f, 23.25f, 20.3437f, 14.5312f, 18.f, 42.f, 36.75f, 26.25f,
32.0625f, 51.3333f, 44.9167f, 32.08333f, 39.1875f, 42.f, 36.75f, 26.25f, 32.0625f,
4.375f, 4.375f, 4.375f, 4.375f, 7.70833f, 7.70833f, 7.70833f, 7.70833f, 9.375f,
9.375f, 9.375f, 9.375f, 21.875f, 21.875f, 21.875f, 21.875f, 26.9792f, 26.9792f,
26.9792f, 26.9792f, 32.8125f, 32.8125f, 32.8125f, 32.8125f, 40.1042f, 40.1042f, 40.1042f,
40.1042f, 46.25f, 46.25f, 46.25f, 46.25f, 56.25f, 56.25f, 56.25f, 56.25f};
const auto expected_shape = Shape{num_rois, C, pooled_height, pooled_width};
EXPECT_EQ(result->get_element_type(), element::f32);
EXPECT_EQ(result->get_shape(), expected_shape);
ASSERT_TRUE(test::all_close_f(read_vector<float>(result), expected_vec, 6, 0.001));
}

5
ngraph/test/runtime/ie/unit_test.manifest
View File

@@ -552,6 +552,9 @@ abc_int64
# Unsupported primitive of type: SigmoidBackprop
sigmoid_bprop_n1c1h4
# Unsupported primitive of type: ROIAlign
IE_CPU.onnx_roi_align_f32
# [NOT_IMPLEMENTED] Input image format BOOL is not supported yet...
select
not
@@ -1201,7 +1204,7 @@ IE_GPU.onnx_dyn_shapes_avg_pool_dyn_shape
IE_GPU.onnx_dyn_shapes_max_pool_dyn_shape
IE_GPU.onnx_dyn_shapes_global_avg_pool_dyn_shape
IE_GPU.onnx_dyn_shapes_global_max_pool_dyn_shape
IE_GPU.onnx_roi_align_f32
IE_GPU.tanh
IE_GPU.tan
IE_GPU.sum_to_scalar