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

tests/create_tests.cpp
101 строка4 KB
kolkir
Add broadcast test, rename tags
02 янв 2025, 15:33
02 янв 2025, 15:33ee1048d
#include <adept/tensor.hpp>

#include "adept/types.hpp"
#include "catch.hpp"

using namespace adept;

TEST_CASE("Create empty CPU tensor", "[create]") {
  REQUIRE_NOTHROW(
      Tensor::empty({.shape = {4, 4, 4}, .device = device_t::CPU, .dtype = dtype_t::Float32}));
  REQUIRE_NOTHROW(
      Tensor::empty({.shape = {4, 4, 4}, .device = device_t::CPU, .dtype = dtype_t::Float64}));
  REQUIRE_NOTHROW(
      Tensor::empty({.shape = {4, 4, 4}, .device = device_t::CPU, .dtype = dtype_t::Int32}));
  REQUIRE_NOTHROW(
      Tensor::empty({.shape = {4, 4, 4}, .device = device_t::CPU, .dtype = dtype_t::Int8}));
}

TEST_CASE("Check empty CPU tensor props", "[create]") {
  auto a = Tensor::empty({.shape = {4, 4, 4}, .device = device_t::CPU, .dtype = dtype_t::Float32});
  REQUIRE(a.properties().device == device_t::CPU);
  REQUIRE(a.properties().dtype == dtype_t::Float32);
  REQUIRE(a.properties().shape.numel() == 64);
}

TEST_CASE("Check zero CPU tensor props", "[create]") {
  auto a = Tensor::zero({.shape = {3, 3}, .device = device_t::CPU, .dtype = dtype_t::Int32});
  REQUIRE(a.properties().device == device_t::CPU);
  REQUIRE(a.properties().dtype == dtype_t::Int32);
  REQUIRE(a.properties().shape.numel() == 9);

  REQUIRE(a.at<int32_t>({0, 0}) == 0);
  REQUIRE(a.at<int32_t>({1, 0}) == 0);
  REQUIRE(a.at<int32_t>({2, 0}) == 0);
  REQUIRE(a.at<int32_t>({0, 1}) == 0);
  REQUIRE(a.at<int32_t>({1, 1}) == 0);
  REQUIRE(a.at<int32_t>({2, 1}) == 0);
  REQUIRE(a.at<int32_t>({0, 2}) == 0);
  REQUIRE(a.at<int32_t>({1, 2}) == 0);
  REQUIRE(a.at<int32_t>({2, 2}) == 0);
}

TEST_CASE("Create zero CPU tensor", "[create]") {
  REQUIRE_NOTHROW(
      Tensor::zero({.shape = {4, 4, 4}, .device = device_t::CPU, .dtype = dtype_t::Float32}));
  REQUIRE_NOTHROW(
      Tensor::zero({.shape = {4, 4, 4}, .device = device_t::CPU, .dtype = dtype_t::Float64}));
  REQUIRE_NOTHROW(
      Tensor::zero({.shape = {4, 4, 4}, .device = device_t::CPU, .dtype = dtype_t::Int32}));
  REQUIRE_NOTHROW(
      Tensor::zero({.shape = {4, 4, 4}, .device = device_t::CPU, .dtype = dtype_t::Int8}));
}

TEST_CASE("Create CPU tensor from blob", "[create]") {
  std::vector<int32_t> data = {1, 2, 3, 4, 5, 6, 7, 8, 9};

  REQUIRE_NOTHROW(Tensor::from_blob(
      data.data(), {.shape = {3, 3}, .device = device_t::CPU, .dtype = dtype_t::Int32}));
}

TEST_CASE("Check CPU tensor from blob", "[create]") {
  std::vector<int32_t> data = {1, 2, 3, 4, 5, 6, 7, 8, 9};

  auto a = Tensor::from_blob(data.data(),
                             {.shape = {3, 3}, .device = device_t::CPU, .dtype = dtype_t::Int32});

  REQUIRE(a.properties().device == device_t::CPU);
  REQUIRE(a.properties().dtype == dtype_t::Int32);
  REQUIRE(a.properties().shape.numel() == 9);

  REQUIRE(a.at<int32_t>({0, 0}) == 1);
  REQUIRE(a.at<int32_t>({1, 0}) == 4);
  REQUIRE(a.at<int32_t>({2, 0}) == 7);
  REQUIRE(a.at<int32_t>({0, 1}) == 2);
  REQUIRE(a.at<int32_t>({1, 1}) == 5);
  REQUIRE(a.at<int32_t>({2, 1}) == 8);
  REQUIRE(a.at<int32_t>({0, 2}) == 3);
  REQUIRE(a.at<int32_t>({1, 2}) == 6);
  REQUIRE(a.at<int32_t>({2, 2}) == 9);
}

// --- Fail cases ------------------------------------

TEST_CASE("Create empty GPU tensor", "[create]") {
  REQUIRE_THROWS(
      Tensor::empty({.shape = {4, 4, 4}, .device = device_t::GPU, .dtype = dtype_t::Float32}));
}

TEST_CASE("Create tensor with empty shape", "[create]") {
  REQUIRE_THROWS(Tensor::empty({.shape = {}, .device = device_t::CPU, .dtype = dtype_t::Float32}));
}

TEST_CASE("Access tensor with incorrect type", "[create]") {
  auto a = Tensor::empty({.shape = {4, 4, 4}, .device = device_t::CPU, .dtype = dtype_t::Float32});
  REQUIRE_THROWS(a.at<int32_t>({0}));
}

TEST_CASE("Access tensor with incorrect index", "[create]") {
  auto a = Tensor::empty({.shape = {4, 4, 4}, .device = device_t::CPU, .dtype = dtype_t::Float32});
  REQUIRE_THROWS(a.at<float32_t>({0}));
}