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Added regression tests for tensor convolutions

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This commit is contained in:
Benoit Steiner
2015-03-31 09:08:08 -07:00
parent 68d4afe985
commit 678207e02a
2 changed files with 212 additions and 197 deletions
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58
unsupported/test/cxx11_tensor_convolution.cpp
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@@ -14,15 +14,16 @@
using Eigen::Tensor;
using Eigen::DefaultDevice;
template <int DataLayout>
static void test_evals()
{
Tensor<float, 2> input(3, 3);
Tensor<float, 1> kernel(2);
Tensor<float, 2, DataLayout> input(3, 3);
Tensor<float, 1, DataLayout> kernel(2);
input.setRandom();
kernel.setRandom();
Tensor<float, 2> result(2,3);
Tensor<float, 2, DataLayout> result(2,3);
result.setZero();
Eigen::array<Tensor<float, 2>::Index, 1> dims3({0});
@@ -41,15 +42,15 @@ static void test_evals()
VERIFY_IS_APPROX(result(1,2), input(1,2)*kernel(0) + input(2,2)*kernel(1)); // index 5
}
template <int DataLayout>
static void test_expr()
{
Tensor<float, 2> input(3, 3);
Tensor<float, 2> kernel(2, 2);
Tensor<float, 2, DataLayout> input(3, 3);
Tensor<float, 2, DataLayout> kernel(2, 2);
input.setRandom();
kernel.setRandom();
Tensor<float, 2> result(2,2);
Tensor<float, 2, DataLayout> result(2,2);
Eigen::array<ptrdiff_t, 2> dims({0, 1});
result = input.convolve(kernel, dims);
@@ -63,10 +64,10 @@ static void test_expr()
input(2,1)*kernel(1,0) + input(2,2)*kernel(1,1));
}
template <int DataLayout>
static void test_modes() {
Tensor<float, 1> input(3);
Tensor<float, 1> kernel(3);
Tensor<float, 1, DataLayout> input(3);
Tensor<float, 1, DataLayout> kernel(3);
input(0) = 1.0f;
input(1) = 2.0f;
input(2) = 3.0f;
@@ -74,13 +75,13 @@ static void test_modes() {
kernel(1) = 1.0f;
kernel(2) = 0.0f;
const Eigen::array<ptrdiff_t, 1> dims{{0}};
const Eigen::array<ptrdiff_t, 1> dims({0});
Eigen::array<std::pair<ptrdiff_t, ptrdiff_t>, 1> padding;
// Emulate VALID mode (as defined in
// http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html).
padding[0] = std::make_pair(0, 0);
Tensor<float, 1> valid(1);
Tensor<float, 1, DataLayout> valid(1);
valid = input.pad(padding).convolve(kernel, dims);
VERIFY_IS_EQUAL(valid.dimension(0), 1);
VERIFY_IS_APPROX(valid(0), 2.5f);
@@ -88,7 +89,7 @@ static void test_modes() {
// Emulate SAME mode (as defined in
// http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html).
padding[0] = std::make_pair(1, 1);
Tensor<float, 1> same(3);
Tensor<float, 1, DataLayout> same(3);
same = input.pad(padding).convolve(kernel, dims);
VERIFY_IS_EQUAL(same.dimension(0), 3);
VERIFY_IS_APPROX(same(0), 1.0f);
@@ -98,7 +99,7 @@ static void test_modes() {
// Emulate FULL mode (as defined in
// http://docs.scipy.org/doc/numpy/reference/generated/numpy.convolve.html).
padding[0] = std::make_pair(2, 2);
Tensor<float, 1> full(5);
Tensor<float, 1, DataLayout> full(5);
full = input.pad(padding).convolve(kernel, dims);
VERIFY_IS_EQUAL(full.dimension(0), 5);
VERIFY_IS_APPROX(full(0), 0.0f);
@@ -108,18 +109,18 @@ static void test_modes() {
VERIFY_IS_APPROX(full(4), 1.5f);
}
template <int DataLayout>
static void test_strides() {
Tensor<float, 1> input(13);
Tensor<float, 1> kernel(3);
Tensor<float, 1, DataLayout> input(13);
Tensor<float, 1, DataLayout> kernel(3);
input.setRandom();
kernel.setRandom();
const Eigen::array<ptrdiff_t, 1> dims{{0}};
const Eigen::array<ptrdiff_t, 1> stride_of_3{{3}};
const Eigen::array<ptrdiff_t, 1> stride_of_2{{2}};
const Eigen::array<ptrdiff_t, 1> dims({0});
const Eigen::array<ptrdiff_t, 1> stride_of_3({3});
const Eigen::array<ptrdiff_t, 1> stride_of_2({2});
Tensor<float, 1> result;
Tensor<float, 1, DataLayout> result;
result = input.stride(stride_of_3).convolve(kernel, dims).stride(stride_of_2);
VERIFY_IS_EQUAL(result.dimension(0), 2);
@@ -129,13 +130,14 @@ static void test_strides() {
input(12)*kernel(2)));
}
void test_cxx11_tensor_convolution()
{
CALL_SUBTEST(test_evals());
CALL_SUBTEST(test_expr());
CALL_SUBTEST(test_modes());
CALL_SUBTEST(test_strides());
CALL_SUBTEST(test_evals<ColMajor>());
CALL_SUBTEST(test_evals<RowMajor>());
CALL_SUBTEST(test_expr<ColMajor>());
CALL_SUBTEST(test_expr<RowMajor>());
CALL_SUBTEST(test_modes<ColMajor>());
CALL_SUBTEST(test_modes<RowMajor>());
CALL_SUBTEST(test_strides<ColMajor>());
CALL_SUBTEST(test_strides<RowMajor>());
}