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Diffstat (limited to 'tesseract/unittest/stridemap_test.cc')
-rw-r--r-- | tesseract/unittest/stridemap_test.cc | 219 |
1 files changed, 219 insertions, 0 deletions
diff --git a/tesseract/unittest/stridemap_test.cc b/tesseract/unittest/stridemap_test.cc new file mode 100644 index 00000000..fa1ef234 --- /dev/null +++ b/tesseract/unittest/stridemap_test.cc @@ -0,0 +1,219 @@ +// (C) Copyright 2017, Google Inc. +// 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. + +#ifdef INCLUDE_TENSORFLOW +#include <tensorflow/compiler/xla/array2d.h> // for xla::Array2D +#else +#include <array> // std::array +#endif +#include "include_gunit.h" +#include "stridemap.h" + +namespace tesseract { + +#if !defined(INCLUDE_TENSORFLOW) && 0 +namespace xla { + +template <typename T> +class Array2D : public std::vector<T> { + public: + Array2D() : std::vector<T>(std::vector<int64_t>{0, 0}) {} + + Array2D(const int64_t n1, const int64_t n2) + : std::vector<T>(std::vector<int64_t>{n1, n2}) {} + + Array2D(const int64_t n1, const int64_t n2, const T value) + : std::vector<T>({n1, n2}, value) {} +}; +} +#endif + +class StridemapTest : public ::testing::Test { + protected: + void SetUp() { + std::locale::global(std::locale("")); + } + +#ifdef INCLUDE_TENSORFLOW + // Sets up an Array2d object of the given size, initialized to increasing + // values starting with start. + std::unique_ptr<xla::Array2D<int>> SetupArray(int ysize, int xsize, int start) { + std::unique_ptr<xla::Array2D<int>> a(new xla::Array2D<int>(ysize, xsize)); + int value = start; + for (int y = 0; y < ysize; ++y) { + for (int x = 0; x < xsize; ++x) { +#ifdef INCLUDE_TENSORFLOW + (*a)(y, x) = value++; +#else + a[y][x] = value++; +#endif + } + } + return a; + } +#endif +}; + +TEST_F(StridemapTest, Indexing) { + // This test verifies that with a batch of arrays of different sizes, the + // iteration index each of them in turn, without going out of bounds. +#ifdef INCLUDE_TENSORFLOW + std::vector<std::unique_ptr<xla::Array2D<int>>> arrays; + arrays.push_back(SetupArray(3, 4, 0)); + arrays.push_back(SetupArray(4, 5, 12)); + arrays.push_back(SetupArray(4, 4, 32)); + arrays.push_back(SetupArray(3, 5, 48)); + std::vector<std::pair<int, int>> h_w_sizes; + for (size_t i = 0; i < arrays.size(); ++i) { + h_w_sizes.emplace_back(arrays[i].get()->height(), arrays[i].get()->width()); + } + StrideMap stride_map; + stride_map.SetStride(h_w_sizes); + StrideMap::Index index(stride_map); + int pos = 0; + do { + EXPECT_GE(index.t(), pos); + EXPECT_EQ((*arrays.at(index.index(FD_BATCH)))(index.index(FD_HEIGHT), + index.index(FD_WIDTH)), + pos); + EXPECT_EQ(index.IsLast(FD_BATCH), + index.index(FD_BATCH) == arrays.size() - 1); + EXPECT_EQ( + index.IsLast(FD_HEIGHT), + index.index(FD_HEIGHT) == arrays[index.index(FD_BATCH)]->height() - 1); + EXPECT_EQ( + index.IsLast(FD_WIDTH), + index.index(FD_WIDTH) == arrays[index.index(FD_BATCH)]->width() - 1); + EXPECT_TRUE(index.IsValid()); + ++pos; + } while (index.Increment()); + LOG(INFO) << "pos=" << pos; + index.InitToLast(); + do { + --pos; + EXPECT_GE(index.t(), pos); + EXPECT_EQ((*arrays.at(index.index(FD_BATCH)))(index.index(FD_HEIGHT), + index.index(FD_WIDTH)), + pos); + StrideMap::Index copy(index); + // Since a change in batch index changes the height and width, it isn't + // necessarily true that the position is still valid, even when changing + // to another valid batch index. + if (index.IsLast(FD_BATCH)) { + EXPECT_FALSE(copy.AddOffset(1, FD_BATCH)); + } + copy = index; + EXPECT_EQ(index.IsLast(FD_HEIGHT), !copy.AddOffset(1, FD_HEIGHT)); + copy = index; + EXPECT_EQ(index.IsLast(FD_WIDTH), !copy.AddOffset(1, FD_WIDTH)); + copy = index; + if (index.index(FD_BATCH) == 0) { + EXPECT_FALSE(copy.AddOffset(-1, FD_BATCH)); + } + copy = index; + EXPECT_EQ(index.index(FD_HEIGHT) == 0, !copy.AddOffset(-1, FD_HEIGHT)); + copy = index; + EXPECT_EQ(index.index(FD_WIDTH) == 0, !copy.AddOffset(-1, FD_WIDTH)); + copy = index; + EXPECT_FALSE(copy.AddOffset(10, FD_WIDTH)); + copy = index; + EXPECT_FALSE(copy.AddOffset(-10, FD_HEIGHT)); + EXPECT_TRUE(index.IsValid()); + } while (index.Decrement()); +#else + LOG(INFO) << "Skip test because of missing xla::Array2D"; + GTEST_SKIP(); +#endif +} + +TEST_F(StridemapTest, Scaling) { + // This test verifies that with a batch of arrays of different sizes, the + // scaling/reduction functions work as expected. +#ifdef INCLUDE_TENSORFLOW + std::vector<std::unique_ptr<xla::Array2D<int>>> arrays; + arrays.push_back(SetupArray(3, 4, 0)); // 0-11 + arrays.push_back(SetupArray(4, 5, 12)); // 12-31 + arrays.push_back(SetupArray(4, 4, 32)); // 32-47 + arrays.push_back(SetupArray(3, 5, 48)); // 48-62 + std::vector<std::pair<int, int>> h_w_sizes; + for (size_t i = 0; i < arrays.size(); ++i) { + h_w_sizes.emplace_back(arrays[i].get()->height(), arrays[i].get()->width()); + } + StrideMap stride_map; + stride_map.SetStride(h_w_sizes); + + // Scale x by 2, keeping y the same. + std::vector<int> values_x2 = {0, 1, 4, 5, 8, 9, 12, 13, 17, 18, + 22, 23, 27, 28, 32, 33, 36, 37, 40, 41, + 44, 45, 48, 49, 53, 54, 58, 59}; + StrideMap test_map(stride_map); + test_map.ScaleXY(2, 1); + StrideMap::Index index(test_map); + int pos = 0; + do { + int expected_value = values_x2[pos++]; + EXPECT_EQ((*arrays.at(index.index(FD_BATCH)))(index.index(FD_HEIGHT), + index.index(FD_WIDTH)), + expected_value); + } while (index.Increment()); + EXPECT_EQ(pos, values_x2.size()); + + test_map = stride_map; + // Scale y by 2, keeping x the same. + std::vector<int> values_y2 = {0, 1, 2, 3, 12, 13, 14, 15, 16, + 17, 18, 19, 20, 21, 32, 33, 34, 35, + 36, 37, 38, 39, 48, 49, 50, 51, 52}; + test_map.ScaleXY(1, 2); + index.InitToFirst(); + pos = 0; + do { + int expected_value = values_y2[pos++]; + EXPECT_EQ((*arrays.at(index.index(FD_BATCH)))(index.index(FD_HEIGHT), + index.index(FD_WIDTH)), + expected_value); + } while (index.Increment()); + EXPECT_EQ(pos, values_y2.size()); + + test_map = stride_map; + // Scale x and y by 2. + std::vector<int> values_xy2 = {0, 1, 12, 13, 17, 18, 32, 33, 36, 37, 48, 49}; + test_map.ScaleXY(2, 2); + index.InitToFirst(); + pos = 0; + do { + int expected_value = values_xy2[pos++]; + EXPECT_EQ((*arrays.at(index.index(FD_BATCH)))(index.index(FD_HEIGHT), + index.index(FD_WIDTH)), + expected_value); + } while (index.Increment()); + EXPECT_EQ(pos, values_xy2.size()); + + test_map = stride_map; + // Reduce Width to 1. + std::vector<int> values_x_to_1 = {0, 4, 8, 12, 17, 22, 27, + 32, 36, 40, 44, 48, 53, 58}; + test_map.ReduceWidthTo1(); + index.InitToFirst(); + pos = 0; + do { + int expected_value = values_x_to_1[pos++]; + EXPECT_EQ((*arrays.at(index.index(FD_BATCH)))(index.index(FD_HEIGHT), + index.index(FD_WIDTH)), + expected_value); + } while (index.Increment()); + EXPECT_EQ(pos, values_x_to_1.size()); +#else + LOG(INFO) << "Skip test because of missing xla::Array2D"; + GTEST_SKIP(); +#endif +} + +} // namespace |