Skip to content

N
ngraph
Commit 3f68b549 authored by Adam Procter's avatar Adam Procter
Browse files
Options

Add more tests (one not passing)

parent f8c4a44e
Show whitespace changes
Inline Side-by-side
Showing with 912 additions and 17 deletions
test/dyn_slice_test.in.cpp
View file @ 3f68b549
......@@ -30,6 +30,7 @@
//
// clang-format off
#include <algorithm>
#include <cmath>
#include "gtest/gtest.h"
......@@ -53,7 +54,8 @@ NGRAPH_TEST(${BACKEND_NAME}, dyn_slice)
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{2});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{2});
std::vector<int32_t> input_values{0,1,2,3};
std::vector<int32_t> input_values(4);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{0,3};
std::vector<int64_t> ub_values{0,0};
std::vector<int64_t> strides_values{1,-1};
......@@ -65,21 +67,21 @@ NGRAPH_TEST(${BACKEND_NAME}, dyn_slice)
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice},ParameterVector{arg, lb, ub, strides});
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32,Shape{4});
auto input_lb = backend->create_tensor(element::i64,Shape{2});
auto input_ub = backend->create_tensor(element::i64,Shape{2});
auto input_strides = backend->create_tensor(element::i64,Shape{2});
auto input_arg = backend->create_tensor(element::i32, Shape{4});
auto input_lb = backend->create_tensor(element::i64, Shape{2});
auto input_ub = backend->create_tensor(element::i64, Shape{2});
auto input_strides = backend->create_tensor(element::i64, Shape{2});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32,PartialShape::dynamic());
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
......@@ -91,6 +93,861 @@ NGRAPH_TEST(${BACKEND_NAME}, dyn_slice)
std::vector<int32_t> expected_values{3,2,1};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{4});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{1});
std::vector<int32_t> input_values(4);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{0};
std::vector<int64_t> ub_values{0};
std::vector<int64_t> strides_values{1};
AxisSet lb_mask{};
AxisSet ub_mask{};
AxisSet new_mask{};
AxisSet shrink_mask{};
AxisSet ellipsis_mask{0};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{4});
auto input_lb = backend->create_tensor(element::i64, Shape{1});
auto input_ub = backend->create_tensor(element::i64, Shape{1});
auto input_strides = backend->create_tensor(element::i64, Shape{1});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{4}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{0,1,2,3};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{4});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{1});
std::vector<int32_t> input_values(4);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{1};
std::vector<int64_t> ub_values{3};
std::vector<int64_t> strides_values{1};
AxisSet lb_mask{};
AxisSet ub_mask{};
AxisSet new_mask{};
AxisSet shrink_mask{};
AxisSet ellipsis_mask{};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{4});
auto input_lb = backend->create_tensor(element::i64, Shape{1});
auto input_ub = backend->create_tensor(element::i64, Shape{1});
auto input_strides = backend->create_tensor(element::i64, Shape{1});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{2}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{1,2};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{4});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{1});
std::vector<int32_t> input_values(4);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{2};
std::vector<int64_t> ub_values{0};
std::vector<int64_t> strides_values{1};
AxisSet lb_mask{};
AxisSet ub_mask{};
AxisSet new_mask{};
AxisSet shrink_mask{0};
AxisSet ellipsis_mask{};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{4});
auto input_lb = backend->create_tensor(element::i64, Shape{1});
auto input_ub = backend->create_tensor(element::i64, Shape{1});
auto input_strides = backend->create_tensor(element::i64, Shape{1});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{2};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{4});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{1});
std::vector<int32_t> input_values(4);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{3};
std::vector<int64_t> ub_values{0};
std::vector<int64_t> strides_values{-2};
AxisSet lb_mask{};
AxisSet ub_mask{};
AxisSet new_mask{};
AxisSet shrink_mask{};
AxisSet ellipsis_mask{};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{4});
auto input_lb = backend->create_tensor(element::i64, Shape{1});
auto input_ub = backend->create_tensor(element::i64, Shape{1});
auto input_strides = backend->create_tensor(element::i64, Shape{1});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{2}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{3,1};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{4});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{1});
std::vector<int32_t> input_values(4);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{0};
std::vector<int64_t> ub_values{0};
std::vector<int64_t> strides_values{1};
AxisSet lb_mask{};
AxisSet ub_mask{};
AxisSet new_mask{0};
AxisSet shrink_mask{};
AxisSet ellipsis_mask{};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{4});
auto input_lb = backend->create_tensor(element::i64, Shape{1});
auto input_ub = backend->create_tensor(element::i64, Shape{1});
auto input_strides = backend->create_tensor(element::i64, Shape{1});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{1,4}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{0,1,2,3};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{4});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{2});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{2});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{2});
std::vector<int32_t> input_values(4);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{0,0};
std::vector<int64_t> ub_values{0,0};
std::vector<int64_t> strides_values{1,1};
AxisSet lb_mask{};
AxisSet ub_mask{};
AxisSet new_mask{0,1};
AxisSet shrink_mask{};
AxisSet ellipsis_mask{};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{4});
auto input_lb = backend->create_tensor(element::i64, Shape{2});
auto input_ub = backend->create_tensor(element::i64, Shape{2});
auto input_strides = backend->create_tensor(element::i64, Shape{2});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{1,1,4}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{0,1,2,3};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{4});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{4});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{4});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{4});
std::vector<int32_t> input_values(4);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{0,0,0,0};
std::vector<int64_t> ub_values{0,0,0,0};
std::vector<int64_t> strides_values{1,1,1,1};
AxisSet lb_mask{};
AxisSet ub_mask{};
AxisSet new_mask{0,1,3};
AxisSet shrink_mask{};
AxisSet ellipsis_mask{2};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{4});
auto input_lb = backend->create_tensor(element::i64, Shape{4});
auto input_ub = backend->create_tensor(element::i64, Shape{4});
auto input_strides = backend->create_tensor(element::i64, Shape{4});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{1,1,4,1}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{0,1,2,3};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{5});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{1});
std::vector<int32_t> input_values(5);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{3};
std::vector<int64_t> ub_values{0};
std::vector<int64_t> strides_values{-2};
AxisSet lb_mask{};
AxisSet ub_mask{};
AxisSet new_mask{};
AxisSet shrink_mask{};
AxisSet ellipsis_mask{};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{5});
auto input_lb = backend->create_tensor(element::i64, Shape{1});
auto input_ub = backend->create_tensor(element::i64, Shape{1});
auto input_strides = backend->create_tensor(element::i64, Shape{1});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{2}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{3,1};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{5});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{1});
std::vector<int32_t> input_values(5);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{0};
std::vector<int64_t> ub_values{3};
std::vector<int64_t> strides_values{2};
AxisSet lb_mask{};
AxisSet ub_mask{};
AxisSet new_mask{};
AxisSet shrink_mask{};
AxisSet ellipsis_mask{};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{5});
auto input_lb = backend->create_tensor(element::i64, Shape{1});
auto input_ub = backend->create_tensor(element::i64, Shape{1});
auto input_strides = backend->create_tensor(element::i64, Shape{1});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{2}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{0,2};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{5});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{1});
std::vector<int32_t> input_values(5);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{0};
std::vector<int64_t> ub_values{4};
std::vector<int64_t> strides_values{2};
AxisSet lb_mask{};
AxisSet ub_mask{};
AxisSet new_mask{};
AxisSet shrink_mask{};
AxisSet ellipsis_mask{};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{5});
auto input_lb = backend->create_tensor(element::i64, Shape{1});
auto input_ub = backend->create_tensor(element::i64, Shape{1});
auto input_strides = backend->create_tensor(element::i64, Shape{1});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{2}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{0,2};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{5});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{1});
std::vector<int32_t> input_values(5);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{0};
std::vector<int64_t> ub_values{5};
std::vector<int64_t> strides_values{2};
AxisSet lb_mask{};
AxisSet ub_mask{};
AxisSet new_mask{};
AxisSet shrink_mask{};
AxisSet ellipsis_mask{};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{5});
auto input_lb = backend->create_tensor(element::i64, Shape{1});
auto input_ub = backend->create_tensor(element::i64, Shape{1});
auto input_strides = backend->create_tensor(element::i64, Shape{1});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{3}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{0,2,4};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{5});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{1});
std::vector<int32_t> input_values(5);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{0};
std::vector<int64_t> ub_values{6};
std::vector<int64_t> strides_values{2};
AxisSet lb_mask{};
AxisSet ub_mask{};
AxisSet new_mask{};
AxisSet shrink_mask{};
AxisSet ellipsis_mask{};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{5});
auto input_lb = backend->create_tensor(element::i64, Shape{1});
auto input_ub = backend->create_tensor(element::i64, Shape{1});
auto input_strides = backend->create_tensor(element::i64, Shape{1});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{3}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{0,2,4};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{5});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{1});
std::vector<int32_t> input_values(5);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{0};
std::vector<int64_t> ub_values{100};
std::vector<int64_t> strides_values{2};
AxisSet lb_mask{};
AxisSet ub_mask{};
AxisSet new_mask{};
AxisSet shrink_mask{};
AxisSet ellipsis_mask{};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{5});
auto input_lb = backend->create_tensor(element::i64, Shape{1});
auto input_ub = backend->create_tensor(element::i64, Shape{1});
auto input_strides = backend->create_tensor(element::i64, Shape{1});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{3}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{0,2,4};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{5});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{1});
std::vector<int32_t> input_values(5);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{4};
std::vector<int64_t> ub_values{0};
std::vector<int64_t> strides_values{-2};
AxisSet lb_mask{};
AxisSet ub_mask{};
AxisSet new_mask{};
AxisSet shrink_mask{};
AxisSet ellipsis_mask{};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{5});
auto input_lb = backend->create_tensor(element::i64, Shape{1});
auto input_ub = backend->create_tensor(element::i64, Shape{1});
auto input_strides = backend->create_tensor(element::i64, Shape{1});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{2}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{4,2};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{5});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{1});
std::vector<int32_t> input_values(5);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{4};
std::vector<int64_t> ub_values{0};
std::vector<int64_t> strides_values{-3};
AxisSet lb_mask{};
AxisSet ub_mask{};
AxisSet new_mask{};
AxisSet shrink_mask{};
AxisSet ellipsis_mask{};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{5});
auto input_lb = backend->create_tensor(element::i64, Shape{1});
auto input_ub = backend->create_tensor(element::i64, Shape{1});
auto input_strides = backend->create_tensor(element::i64, Shape{1});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{2}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{4,1};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{5});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{1});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{1});
std::vector<int32_t> input_values(5);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{4};
std::vector<int64_t> ub_values{0};
std::vector<int64_t> strides_values{-2};
AxisSet lb_mask{};
AxisSet ub_mask{0};
AxisSet new_mask{};
AxisSet shrink_mask{};
AxisSet ellipsis_mask{};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{5});
auto input_lb = backend->create_tensor(element::i64, Shape{1});
auto input_ub = backend->create_tensor(element::i64, Shape{1});
auto input_strides = backend->create_tensor(element::i64, Shape{1});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{3}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{4,2,0};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{2,3,4});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{2});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{2});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{2});
std::vector<int32_t> input_values(24);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{1,0};
std::vector<int64_t> ub_values{0,0};
std::vector<int64_t> strides_values{1,1};
AxisSet lb_mask{};
AxisSet ub_mask{};
AxisSet new_mask{1};
AxisSet shrink_mask{0};
AxisSet ellipsis_mask{};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{2,3,4});
auto input_lb = backend->create_tensor(element::i64, Shape{2});
auto input_ub = backend->create_tensor(element::i64, Shape{2});
auto input_strides = backend->create_tensor(element::i64, Shape{2});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{1,3,4}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{12,13,14,15,16,17,18,19,20,21,22,23};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{2,3,4});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{3});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{3});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{3});
std::vector<int32_t> input_values(24);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{-1,-1,0};
std::vector<int64_t> ub_values{0,0,0};
std::vector<int64_t> strides_values{1,1,1};
AxisSet lb_mask{};
AxisSet ub_mask{};
AxisSet new_mask{2};
AxisSet shrink_mask{0,1};
AxisSet ellipsis_mask{};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{2,3,4});
auto input_lb = backend->create_tensor(element::i64, Shape{3});
auto input_ub = backend->create_tensor(element::i64, Shape{3});
auto input_strides = backend->create_tensor(element::i64, Shape{3});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{1,4}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{20,21,22,23};
EXPECT_EQ(output_values, expected_values);
}
{
auto arg = std::make_shared<op::Parameter>(element::i32, Shape{2,4,6,8,2,2,2});
auto lb = std::make_shared<op::Parameter>(element::i64, Shape{7});
auto ub = std::make_shared<op::Parameter>(element::i64, Shape{7});
auto strides = std::make_shared<op::Parameter>(element::i64, Shape{7});
std::vector<int32_t> input_values(3072);
std::iota(input_values.begin(), input_values.end(), static_cast<int32_t>(0));
std::vector<int64_t> lb_values{0,0,2,7,0,0,1};
std::vector<int64_t> ub_values{0,4,6,3,0,0,0};
std::vector<int64_t> strides_values{1,1,2,-2,1,1,1};
AxisSet lb_mask{1};
AxisSet ub_mask{0};
AxisSet new_mask{4};
AxisSet shrink_mask{6};
AxisSet ellipsis_mask{5};
auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);
auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});
auto backend = runtime::Backend::create("${BACKEND_NAME}",true);
auto ex = backend->compile(f);
auto input_arg = backend->create_tensor(element::i32, Shape{2,4,6,8,2,2,2});
auto input_lb = backend->create_tensor(element::i64, Shape{7});
auto input_ub = backend->create_tensor(element::i64, Shape{7});
auto input_strides = backend->create_tensor(element::i64, Shape{7});
copy_data(input_arg, input_values);
copy_data(input_lb, lb_values);
copy_data(input_ub, ub_values);
copy_data(input_strides, strides_values);
auto output = backend->create_dynamic_tensor(element::i32, PartialShape::dynamic());
ex->call({output}, {input_arg, input_lb, input_ub, input_strides});
EXPECT_EQ(output->get_element_type(), (element::i32));
EXPECT_EQ(output->get_shape(), (Shape{2,4,2,2,1,2,2}));
auto output_values = read_vector<int32_t>(output);
std::vector<int32_t> expected_values{185,187,189,191,169,171,173,175,313,315,317,319,297,299,301,303,569,571,573,575,553,555,557,559,697,699,701,703,681,683,685,687,953,955,957,959,937,939,941,943,1081,1083,1085,1087,1065,1067,1069,1071,1337,1339,1341,1343,1321,1323,1325,1327,1465,1467,1469,1471,1449,1451,1453,1455,1721,1723,1725,1727,1705,1707,1709,1711,1849,1851,1853,1855,1833,1835,1837,1839,2105,2107,2109,2111,2089,2091,2093,2095,2233,2235,2237,2239,2217,2219,2221,2223,2489,2491,2493,2495,2473,2475,2477,2479,2617,2619,2621,2623,2601,2603,2605,2607,2873,2875,2877,2879,2857,2859,2861,2863,3001,3003,3005,3007,2985,2987,2989,2991};
EXPECT_EQ(output_values, expected_values);
}
}
// clang-format on
test/ref_generators/generate_dyn_slice_ref.py
View file @ 3f68b549
......@@ -210,15 +210,24 @@ class SliceTestWriter:
self._stream = stream
def __getitem__(self, slices):
self.write_test(slices)
def write_test(self, slices):
data_in = np.linspace(0,np.prod(self._shape)-1,np.prod(self._shape),dtype=self._dtype).reshape(self._shape)
data_out = data_in.__getitem__(slices)
n_slices = 1
try:
n_slices = len(slices)
except TypeError:
pass
self._stream.write(' {\n')
self._stream.write(' auto arg = std::make_shared<op::Parameter>(%s, %s);\n' % (np_dt_to_ng(self._dtype), print_shape(self._shape)))
self._stream.write(' auto lb = std::make_shared<op::Parameter>(element::i64, %s);\n' % print_shape((len(slices),)))
self._stream.write(' auto ub = std::make_shared<op::Parameter>(element::i64, %s);\n' % print_shape((len(slices),)))
self._stream.write(' auto strides = std::make_shared<op::Parameter>(element::i64, %s);\n' % print_shape((len(slices),)))
self._stream.write(' auto lb = std::make_shared<op::Parameter>(element::i64, %s);\n' % print_shape((n_slices,)))
self._stream.write(' auto ub = std::make_shared<op::Parameter>(element::i64, %s);\n' % print_shape((n_slices,)))
self._stream.write(' auto strides = std::make_shared<op::Parameter>(element::i64, %s);\n' % print_shape((n_slices,)))
self._stream.write('\n')
self._stream.write(' std::vector<%s> input_values{%s};\n' % (np_dt_to_c(self._dtype), print_values(data_in.reshape(-1))))
self._stream.write(' std::vector<%s> input_values(%s);\n' % (np_dt_to_c(self._dtype), np.prod(self._shape)))
self._stream.write(' std::iota(input_values.begin(), input_values.end(), static_cast<%s>(0));\n' % np_dt_to_c(self._dtype))
self._stream.write(' std::vector<int64_t> lb_values{%s};\n' % print_lb_values(slices))
self._stream.write(' std::vector<int64_t> ub_values{%s};\n' % print_ub_values(slices))
self._stream.write(' std::vector<int64_t> strides_values{%s};\n' % print_stride_values(slices))
......@@ -230,21 +239,21 @@ class SliceTestWriter:
self._stream.write('\n')
self._stream.write(' auto slice = std::make_shared<op::DynSlice>(arg, lb, ub, strides, lb_mask, ub_mask, new_mask, shrink_mask, ellipsis_mask);\n')
self._stream.write('\n')
self._stream.write(' auto f = std::make_shared<Function>(NodeVector{slice},ParameterVector{arg, lb, ub, strides});\n')
self._stream.write(' auto f = std::make_shared<Function>(NodeVector{slice}, ParameterVector{arg, lb, ub, strides});\n')
self._stream.write('\n')
self._stream.write(' auto backend = runtime::Backend::create("${BACKEND_NAME}",true);\n')
self._stream.write(' auto ex = backend->compile(f);\n')
self._stream.write('\n')
self._stream.write(' auto input_arg = backend->create_tensor(%s,%s);\n' % (np_dt_to_ng(self._dtype), print_shape(self._shape)))
self._stream.write(' auto input_lb = backend->create_tensor(element::i64,%s);\n' % print_shape((len(slices),)))
self._stream.write(' auto input_ub = backend->create_tensor(element::i64,%s);\n' % print_shape((len(slices),)))
self._stream.write(' auto input_strides = backend->create_tensor(element::i64,%s);\n' % print_shape((len(slices),)))
self._stream.write(' auto input_arg = backend->create_tensor(%s, %s);\n' % (np_dt_to_ng(self._dtype), print_shape(self._shape)))
self._stream.write(' auto input_lb = backend->create_tensor(element::i64, %s);\n' % print_shape((n_slices,)))
self._stream.write(' auto input_ub = backend->create_tensor(element::i64, %s);\n' % print_shape((n_slices,)))
self._stream.write(' auto input_strides = backend->create_tensor(element::i64, %s);\n' % print_shape((n_slices,)))
self._stream.write(' copy_data(input_arg, input_values);\n')
self._stream.write(' copy_data(input_lb, lb_values);\n')
self._stream.write(' copy_data(input_ub, ub_values);\n')
self._stream.write(' copy_data(input_strides, strides_values);\n')
self._stream.write('\n')
self._stream.write(' auto output = backend->create_dynamic_tensor(%s,PartialShape::dynamic());\n' % np_dt_to_ng(self._dtype))
self._stream.write(' auto output = backend->create_dynamic_tensor(%s, PartialShape::dynamic());\n' % np_dt_to_ng(self._dtype))
self._stream.write('\n')
self._stream.write(' ex->call({output}, {input_arg, input_lb, input_ub, input_strides});\n')
self._stream.write('\n')
......@@ -299,6 +308,7 @@ def main():
//
// clang-format off
#include <algorithm>
#include <cmath>
#include "gtest/gtest.h"
......@@ -321,8 +331,36 @@ NGRAPH_TEST(${BACKEND_NAME}, dyn_slice)
t = SliceTestWriter(stream=f)
t.set_dtype('int32')
t.set_shape((4,))
t[np.newaxis,3:0:-1]
t[...]
t[1:3]
t[2]
t[3:0:-2]
t[np.newaxis]
t[np.newaxis,np.newaxis]
t[np.newaxis,np.newaxis,...,np.newaxis]
t.set_shape((5,))
t[3:0:-2]
t[0:3:2]
t[0:4:2]
t[0:5:2]
t[0:6:2]
t[0:100:2]
t[4:0:-2]
t[4:0:-3]
# FIXME: DynElimination is not properly handling this (end < begin).
# t[3:2:1]
t[4::-2]
t.set_shape((2,3,4))
t[1,np.newaxis]
t[-1,-1,np.newaxis]
t.set_shape((2,4,6,8,2,2,2))
t[0:,:4,2:6:2,7:3:-2,np.newaxis,...,1]
f.write('''
}
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment