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[GPU] fixed a missing data type (#17200)

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* fixed missing data type

* updated the resolution for better accuracy check
This commit is contained in:
Eddy Kim
2023-04-26 17:28:18 +09:00
committed by GitHub
parent 7cf9d109e8
commit 09265083ed
3 changed files with 8 additions and 4 deletions
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2
src/plugins/intel_gpu/src/kernel_selector/cl_kernels/adaptive_pooling_gpu_ref.cl
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@@ -69,7 +69,7 @@ KERNEL(adaptive_pooling_gpu)(
const uint idx = INPUT0_GET_INDEX(b, f, j, i);
#endif
const current_input_value = TO_ACCUMULATOR_TYPE(input[idx]);
const ACCUMULATOR_TYPE current_input_value = TO_ACCUMULATOR_TYPE(input[idx]);
#if MAX_POOLING
if (current_input_value > result) {
result = current_input_value;

7
src/plugins/intel_gpu/tests/passes/prepare_primitive_fusing_test.cpp
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@@ -442,7 +442,7 @@ TEST(prepare_primitive_fusing, dont_remove_only_dep_reshape) {
ASSERT_TRUE(has_node(*prog, "reshape2"));
}
TEST(prepare_primitive_fusing, eltwise_fusing_residual_connection_taylor) {
TEST(prepare_primitive_fusing, eltwise_fusing_residual_connection) {
// Extended eltwise fusing pattern
// in w
// \ /
@@ -456,6 +456,9 @@ TEST(prepare_primitive_fusing, eltwise_fusing_residual_connection_taylor) {
// |
// reorder
auto& engine = get_test_engine();
if (engine.get_device_info().supports_immad)
return;
topology topology;
auto conv_in_layout = layout{ ov::PartialShape{1, 3, -1, -1}, data_types::f16, format::bfyx};
auto weight_layout = layout{ ov::PartialShape{10, 3, 3, 3}, data_types::f16, format::bfyx};
@@ -510,4 +513,4 @@ TEST(prepare_primitive_fusing, eltwise_fusing_residual_connection_taylor) {
net.set_input_data("elt1_input", elt_input_mem);
net.execute();
ASSERT_TRUE(conv_inst->has_unfused_subgraph());
}
}

3
src/plugins/intel_gpu/tests/test_cases/adaptive_avg_pooling_gpu_test.cpp
View File

@@ -66,7 +66,7 @@ ov::Shape tensorToShape(const tensor& t, const format f)
template<typename T>
void generateTestData(const AdaptiveAvgPoolingParams& p, const format fmt, std::vector<T>& inputs, std::vector<T>& outputs) {
const auto in = generate_random_1d<float>(p.inputTensor.count(), -127, 127, 1);
const auto in = generate_random_1d<float>(p.inputTensor.count(), -127, 127, 8);
std::vector<float> out(p.outputTensor.count());
const auto inShape = tensorToShape(p.inputTensor, fmt);
@@ -171,6 +171,7 @@ INSTANTIATE_TEST_SUITE_P(smoke_adaptive_avg_pooling_test_f32_2d,
::testing::ValuesIn(std::vector<AdaptiveAvgPoolingParams>{
{ tensor(1, 2, 7, 3), tensor(1, 2, 3, 3) },
{ tensor(2, 3, 7, 3), tensor(2, 3, 3, 3) },
{ tensor(1, 3, 16, 16), tensor(1, 3, 16, 16) },
}),
::testing::Values(format::bfyx),
::testing::Values(format::bfyx),