vuron/adept: Платформа для обучения и вывода нейронных сетей, с API приближенным к PyTorch.

tests/avgpool2d_tests.cpp

37 строк2 KB

kolkir

AvgPool2d implementation

08 фев 2025, 20:12

08 фев 2025, 20:1261cfbe7

#include <adept/nn/avgpool2d.hpp>

#include "catch.hpp"

using namespace adept;

TEST_CASE("AvgPool2d construction", "[avgpool2d]") {
  AvgPool2d pool(AvgPool2dOptions(3));
}

TEST_CASE("AvgPool2d im3x3", "[avgpool2d]") {
  std::vector<float32_t> im_data = {1, 2, 3, 4, 5, 6, 7, 8, 9};

  auto im = Variable(Tensor::from_blob(
      im_data.data(),
      {.shape = {1, 1, 3, 3}, .device = device_t::CPU, .dtype = to_dtype<float32_t>()}));

  AvgPool2d pool(AvgPool2dOptions(2).with_stride(1));
  auto res = pool(im);

  REQUIRE_THAT(res.data().at<float32_t>({0, 0, 0, 0}), Catch::WithinRel(3.f, 0.001f));
  REQUIRE_THAT(res.data().at<float32_t>({0, 0, 0, 1}), Catch::WithinRel(4.f, 0.001f));
  REQUIRE_THAT(res.data().at<float32_t>({0, 0, 1, 0}), Catch::WithinRel(6.f, 0.001f));
  REQUIRE_THAT(res.data().at<float32_t>({0, 0, 1, 1}), Catch::WithinRel(7.f, 0.001f));

  mean(res).backward();

  REQUIRE_THAT(im.grad().at<float32_t>({0, 0, 0, 0}), Catch::WithinRel(0.0625f, 0.001f));
  REQUIRE_THAT(im.grad().at<float32_t>({0, 0, 0, 1}), Catch::WithinRel(0.1250f, 0.001f));
  REQUIRE_THAT(im.grad().at<float32_t>({0, 0, 0, 2}), Catch::WithinRel(0.0625f, 0.001f));
  REQUIRE_THAT(im.grad().at<float32_t>({0, 0, 1, 0}), Catch::WithinRel(0.1250f, 0.001f));
  REQUIRE_THAT(im.grad().at<float32_t>({0, 0, 1, 1}), Catch::WithinRel(0.25f, 0.001f));
  REQUIRE_THAT(im.grad().at<float32_t>({0, 0, 1, 2}), Catch::WithinRel(0.1250f, 0.001f));
  REQUIRE_THAT(im.grad().at<float32_t>({0, 0, 2, 0}), Catch::WithinRel(0.0625f, 0.001f));
  REQUIRE_THAT(im.grad().at<float32_t>({0, 0, 2, 1}), Catch::WithinRel(0.1250f, 0.001f));
  REQUIRE_THAT(im.grad().at<float32_t>({0, 0, 2, 2}), Catch::WithinRel(0.0625f, 0.001f));
}