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author | Grant Watson <grant.watson@arm.com> | 2023-08-28 16:34:28 +0100 |
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committer | Eric Kunze <eric.kunze@arm.com> | 2023-09-05 19:03:49 +0000 |
commit | e70d93175afb3bc053d264a02be2156a7354039c (patch) | |
tree | ce510859a642f617210d0190308a532fb7458df3 /reference_model/test | |
parent | 7935972b1ae427b597ce2e817c5071c44d7ba56e (diff) | |
download | reference_model-e70d93175afb3bc053d264a02be2156a7354039c.tar.gz |
Pass func_config to individual operator API
Updates the generate_api.py script and associated
templates to allow func_config and debug_config
to be passed when running individual operators
on the API.
This will allow us, for example, to set precise_mode
and abs_mode when running individual operators.
Signed-off-by: Grant Watson <grant.watson@arm.com>
Change-Id: Ia3e7ffc146f876daa307558433177c68285843b7
Diffstat (limited to 'reference_model/test')
-rw-r--r-- | reference_model/test/model_runner_tests.cpp | 61 |
1 files changed, 61 insertions, 0 deletions
diff --git a/reference_model/test/model_runner_tests.cpp b/reference_model/test/model_runner_tests.cpp index bb57657..0b73494 100644 --- a/reference_model/test/model_runner_tests.cpp +++ b/reference_model/test/model_runner_tests.cpp @@ -178,6 +178,67 @@ TEST_SUITE("model_runner") compareOutput(dstData, expectedData, expectedData.size()); } + TEST_CASE("op_entry_conv2d_abs_mode") + { + // Conv parameters + const int32_t stride[2] = { 1, 1 }; + const int32_t pad[4] = { 0, 0, 0, 0 }; + const int32_t dilation[2] = { 1, 1 }; + + // Inputs/Outputs + tosa_datatype_t dt = tosa_datatype_fp32_t; + std::vector<int32_t> input_shape = { 1, 32, 32, 8 }; + std::vector<int32_t> output_shape = { 1, 32, 32, 16 }; + std::vector<int32_t> weight_shape = { 16, 1, 1, 8 }; + std::vector<int32_t> bias_shape = { 16 }; + std::vector<float> srcData(32 * 32 * 8, -1.0f); + std::vector<float> dstData(32 * 32 * 16, 0.f); + std::vector<float> biasData(16, 0.f); + std::vector<float> weightData(16 * 8, 1.0f); + + tosa_tensor_t input; + input.shape = input_shape.data(); + input.num_dims = input_shape.size(); + input.data_type = dt; + input.data = reinterpret_cast<uint8_t*>(srcData.data()); + input.size = srcData.size() * sizeof(float); + + tosa_tensor_t weight; + weight.shape = weight_shape.data(); + weight.num_dims = weight_shape.size(); + weight.data_type = dt; + weight.data = reinterpret_cast<uint8_t*>(weightData.data()); + weight.size = weightData.size() * sizeof(float); + + tosa_tensor_t bias; + bias.shape = bias_shape.data(); + bias.num_dims = bias_shape.size(); + bias.data_type = dt; + bias.data = reinterpret_cast<uint8_t*>(biasData.data()); + bias.size = biasData.size() * sizeof(float); + + tosa_tensor_t output; + output.shape = output_shape.data(); + output.num_dims = output_shape.size(); + output.data_type = dt; + output.data = reinterpret_cast<uint8_t*>(dstData.data()); + output.size = dstData.size() * sizeof(float); + + const int32_t input_zp = 0; + const int32_t weight_zp = 0; + + // Execution + func_config_t func_config; + func_config.abs_mode = true; + auto status = + tosa_run_conv2d(input, weight, bias, pad, stride, dilation, input_zp, weight_zp, output, func_config); + CHECK((status == tosa_status_valid)); + + // Compare results + std::vector<float> expectedData(32 * 32 * 16, 8.0f); + compareOutput(dstData, expectedData, expectedData.size()); + } + TEST_CASE("op_entry_max_pool2d") { // Pool parameters |