Mathis/CUDANet
1
0
Fork 0
mirror of https://github.com/lordmathis/CUDANet.git synced 2025-11-06 09:44:28 +00:00
Code Issues Packages Projects Releases Wiki Activity

Add non square pooling and batch norm tests

Browse Source
This commit is contained in:
Matúš Námešný
2024-05-20 22:16:00 +02:00
parent 6dca8ccd3c
commit 10e73638b6
7 changed files with 533 additions and 173 deletions
Download Patch File Download Diff File Expand all files Collapse all files

34
test/layers/test_conv2d.cu
View File

@@ -82,8 +82,9 @@ TEST_F(Conv2dTest, SimpleTest) {
);
int outputHeight = (inputSize.first - kernelSize.first) / stride.first + 1;
int outputWidth = (inputSize.second - kernelSize.second) / stride.second + 1;
int outputSize = outputHeight * outputWidth * numFilters;
int outputWidth =
(inputSize.second - kernelSize.second) / stride.second + 1;
int outputSize = outputHeight * outputWidth * numFilters;
EXPECT_EQ(outputSize, conv2d.getOutputSize());
d_output = conv2d.forward(d_input);
@@ -112,9 +113,12 @@ TEST_F(Conv2dTest, PaddedTest) {
dim2d stride = {1, 1};
int numFilters = 2;
int paddingFirst = CUDANET_SAME_PADDING(inputSize.first, kernelSize.first, stride.first);
int paddingSecond = CUDANET_SAME_PADDING(inputSize.second, kernelSize.second, stride.second);
dim2d paddingSize = {paddingFirst, paddingSecond};
int paddingFirst =
CUDANET_SAME_PADDING(inputSize.first, kernelSize.first, stride.first);
int paddingSecond = CUDANET_SAME_PADDING(
inputSize.second, kernelSize.second, stride.second
);
dim2d paddingSize = {paddingFirst, paddingSecond};
CUDANet::Layers::ActivationType activationType =
CUDANet::Layers::ActivationType::NONE;
@@ -177,7 +181,9 @@ TEST_F(Conv2dTest, PaddedTest) {
activationType, input, kernels.data(), d_input
);
EXPECT_EQ(inputSize.first * inputSize.second * numFilters, conv2d.getOutputSize());
EXPECT_EQ(
inputSize.first * inputSize.second * numFilters, conv2d.getOutputSize()
);
d_output = conv2d.forward(d_input);
@@ -209,16 +215,18 @@ TEST_F(Conv2dTest, PaddedTest) {
TEST_F(Conv2dTest, StridedPaddedConvolution) {
dim2d inputSize = {5, 5};
int inputChannels = 2;
int inputChannels = 2;
dim2d kernelSize = {3, 3};
dim2d stride = {2, 2};
int numFilters = 2;
int numFilters = 2;
int paddingFirst = CUDANET_SAME_PADDING(inputSize.first, kernelSize.second, stride.first);
int paddingSecond = CUDANET_SAME_PADDING(inputSize.second, kernelSize.second, stride.second);
int paddingFirst =
CUDANET_SAME_PADDING(inputSize.first, kernelSize.second, stride.first);
int paddingSecond = CUDANET_SAME_PADDING(
inputSize.second, kernelSize.second, stride.second
);
dim2d paddingSize = {paddingFirst, paddingSecond};
CUDANet::Layers::ActivationType activationType =
CUDANet::Layers::ActivationType::RELU;
@@ -265,7 +273,9 @@ TEST_F(Conv2dTest, StridedPaddedConvolution) {
activationType, input, kernels.data(), d_input
);
EXPECT_EQ(inputSize.first * inputSize.second * numFilters, conv2d.getOutputSize());
EXPECT_EQ(
inputSize.first * inputSize.second * numFilters, conv2d.getOutputSize()
);
d_output = conv2d.forward(d_input);