Switch padding kernel to row major

This commit is contained in:
2024-03-05 21:04:11 +01:00
parent f4257afd5a
commit 07f231a30b
2 changed files with 43 additions and 24 deletions

View File

@@ -2,7 +2,7 @@
/*
Pads matrix width x height x n_channels to width + 2 * padding x height + 2 *
padding x n_channels Matrix is represented as a pointer to column major vector
padding x n_channels Matrix is represented as a pointer to a vector
For example:
@@ -22,20 +22,29 @@ Channel 1:
Is represented as:
0 2 4 1 3 5 6 8 10 7 9 11
0 1 2 3 4 5 6 7 8 9 10 11
Padded result (as a continuous vector):
0 0 0 0 0 0 0 2 4 0
0 1 3 5 0 0 0 0 0 0
0 0 0 0 0 0 6 8 10 0
0 7 9 11 0 0 0 0 0 0
0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f,
0.0f, 2.0f, 3.0f, 0.0f,
0.0f, 4.0f, 5.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 6.0f, 7.0f, 0.0f,
0.0f, 8.0f, 9.0f, 0.0f,
9.0f, 10.0f, 11.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f
Args:
d_input: Pointer to input vector representing matrix
d_padded: Pointer to output vector representing padded matrix (needs to be
pre-allocated) w: Width of input matrix h: Height of input matrix n: Number of
channels in input matrix p: Padding
pre-allocated)
w: Width of input matrix
h: Height of input matrix
n: Number of channels in input matrix
p: Padding
*/
__global__ void pad_matrix_kernel(
const float* d_input,
@@ -53,21 +62,17 @@ __global__ void pad_matrix_kernel(
int idx = tid;
// unravel index
int i_h = idx % (h + 2 * p);
idx /= (h + 2 * p);
// unravel index into padded matrix
int i_n = idx / ((w + 2 * p) * (h + 2 * p));
int i_h = idx % ((w + 2 * p) * (h + 2 * p)) / (w + 2 * p);
int i_w = idx % (w + 2 * p);
idx /= (w + 2 * p);
int i_n = idx % n;
// if i is in the padding region
if (i_w < p || i_w >= (w + p) || i_h < p || i_h >= (h + p)) {
d_padded[tid] = 0.0f;
} else {
// Get index into input vector
int i_orig = i_n * w * h + (i_w - p) * h + (i_h - p);
int i_orig = i_n * w * h + (i_h - p) * w + (i_w - p);
d_padded[tid] = d_input[i_orig];
}
}

View File

@@ -35,13 +35,13 @@ TEST(PaddingTest, SimplePaddingTest) {
8 9
10 11
Represented as column major vector:
Represented as a vector:
0 2 4 1 3 5 6 8 10 7 9 11
0 1 2 3 4 5 6 7 8 9 10 11
*/
std::vector<float> input = {0.0f, 2.0f, 4.0f, 1.0f, 3.0f, 5.0f,
6.0f, 8.0f, 10.0f, 7.0f, 9.0f, 11.0f};
std::vector<float> input = {0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f,
6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f};
cudaStatus = cudaMemcpy(
d_input, input.data(), sizeof(float) * inputSize, cudaMemcpyHostToDevice
@@ -57,12 +57,22 @@ TEST(PaddingTest, SimplePaddingTest) {
cudaStatus = cudaDeviceSynchronize();
EXPECT_EQ(cudaStatus, cudaSuccess);
// clang-format off
std::vector<float> expectedOutput = {
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 2.0f, 4.0f, 0.0f,
0.0f, 1.0f, 3.0f, 5.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 6.0f, 8.0f, 10.0f, 0.0f,
0.0f, 7.0f, 9.0f, 11.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f
// channel 0
0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 1.0f, 0.0f,
0.0f, 2.0f, 3.0f, 0.0f,
0.0f, 4.0f, 5.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f,
// channel 1
0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 6.0f, 7.0f, 0.0f,
0.0f, 8.0f, 9.0f, 0.0f,
0.0f, 10.0f, 11.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f
};
// clang-format on
std::vector<float> output(paddedSize);
@@ -75,4 +85,8 @@ TEST(PaddingTest, SimplePaddingTest) {
for (int i = 0; i < paddedSize; i++) {
EXPECT_NEAR(expectedOutput[i], output[i], 1e-5);
}
cudaFree(d_input);
cudaFree(d_padded);
}