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add eltwise layer ocl implementation

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Signed-off-by: Li Peng <peng.li@intel.com>
This commit is contained in:
Li Peng 2018-01-04 02:21:04 +08:00
parent 16821b877c
commit f99a135eda
3 changed files with 81 additions and 16 deletions
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52
modules/dnn/src/layers/eltwise_layer.cpp
View File

@ -259,11 +259,63 @@ public:
}
};
#ifdef HAVE_OPENCL
bool forward_ocl(InputArrayOfArrays inputs_, OutputArrayOfArrays outputs_, OutputArrayOfArrays internals_)
{
std::vector<UMat> inputs;
std::vector<UMat> outputs;
inputs_.getUMatVector(inputs);
outputs_.getUMatVector(outputs);
switch (op)
{
case SUM:
if (coeffs.empty())
{
add(inputs[0], inputs[1], outputs[0]);
for (int i = 2; i < inputs.size(); ++i)
add(outputs[0], inputs[i], outputs[0]);
}
else
{
UMat mul0, mul1;
multiply(coeffs[0], inputs[0], mul0);
multiply(coeffs[1], inputs[1], mul1);
add(mul0, mul1, outputs[0]);
for (int i = 2; i < inputs.size(); ++i)
{
multiply(coeffs[i], inputs[i], mul0);
add(mul0, outputs[0], outputs[0]);
}
}
break;
case PROD:
multiply(inputs[0], inputs[1], outputs[0]);
for (int i = 2; i < inputs.size(); ++i)
multiply(inputs[i], outputs[0], outputs[0]);
break;
case MAX:
max(inputs[0], inputs[1], outputs[0]);
for (int i = 2; i < inputs.size(); ++i)
max(inputs[i], outputs[0], outputs[0]);
break;
default:
return false;
}
return true;
}
#endif
void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr)
{
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))
Layer::forward_fallback(inputs_arr, outputs_arr, internals_arr);
}

5
modules/dnn/test/test_layers.cpp
View File

@ -320,6 +320,11 @@ TEST(Layer_Test_Eltwise, Accuracy)
testLayerUsingCaffeModels("layer_eltwise");
}
OCL_TEST(Layer_Test_Eltwise, Accuracy)
{
testLayerUsingCaffeModels("layer_eltwise", DNN_TARGET_OPENCL);
}
TEST(Layer_Test_PReLU, Accuracy)
{
testLayerUsingCaffeModels("layer_prelu", DNN_TARGET_CPU, true);

40
modules/dnn/test/test_tf_importer.cpp
View File

@ -76,7 +76,7 @@ static std::string path(const std::string& file)
return findDataFile("dnn/tensorflow/" + file, false);
}
static void runTensorFlowNet(const std::string& prefix, bool hasText = false,
static void runTensorFlowNet(const std::string& prefix, int targetId = DNN_TARGET_CPU, bool hasText = false,
double l1 = 1e-5, double lInf = 1e-4,
bool memoryLoad = false)
{
@ -104,6 +104,9 @@ static void runTensorFlowNet(const std::string& prefix, bool hasText = false,
ASSERT_FALSE(net.empty());
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
net.setPreferableTarget(targetId);
cv::Mat input = blobFromNPY(inpPath);
cv::Mat target = blobFromNPY(outPath);
@ -132,6 +135,11 @@ TEST(Test_TensorFlow, eltwise_add_mul)
runTensorFlowNet("eltwise_add_mul");
}
OCL_TEST(Test_TensorFlow, eltwise_add_mul)
{
runTensorFlowNet("eltwise_add_mul", DNN_TARGET_OPENCL);
}
TEST(Test_TensorFlow, pad_and_concat)
{
runTensorFlowNet("pad_and_concat");
@ -141,7 +149,7 @@ TEST(Test_TensorFlow, batch_norm)
{
runTensorFlowNet("batch_norm");
runTensorFlowNet("fused_batch_norm");
runTensorFlowNet("batch_norm_text", true);
runTensorFlowNet("batch_norm_text", DNN_TARGET_CPU, true);
}
TEST(Test_TensorFlow, pooling)
@ -179,15 +187,15 @@ TEST(Test_TensorFlow, fp16)
{
const float l1 = 1e-3;
const float lInf = 1e-2;
runTensorFlowNet("fp16_single_conv", false, l1, lInf);
runTensorFlowNet("fp16_deconvolution", false, l1, lInf);
runTensorFlowNet("fp16_max_pool_odd_same", false, l1, lInf);
runTensorFlowNet("fp16_padding_valid", false, l1, lInf);
runTensorFlowNet("fp16_eltwise_add_mul", false, l1, lInf);
runTensorFlowNet("fp16_max_pool_odd_valid", false, l1, lInf);
runTensorFlowNet("fp16_pad_and_concat", false, l1, lInf);
runTensorFlowNet("fp16_max_pool_even", false, l1, lInf);
runTensorFlowNet("fp16_padding_same", false, l1, lInf);
runTensorFlowNet("fp16_single_conv", DNN_TARGET_CPU, false, l1, lInf);
runTensorFlowNet("fp16_deconvolution", DNN_TARGET_CPU, false, l1, lInf);
runTensorFlowNet("fp16_max_pool_odd_same", DNN_TARGET_CPU, false, l1, lInf);
runTensorFlowNet("fp16_padding_valid", DNN_TARGET_CPU, false, l1, lInf);
runTensorFlowNet("fp16_eltwise_add_mul", DNN_TARGET_CPU, false, l1, lInf);
runTensorFlowNet("fp16_max_pool_odd_valid", DNN_TARGET_CPU, false, l1, lInf);
runTensorFlowNet("fp16_pad_and_concat", DNN_TARGET_CPU, false, l1, lInf);
runTensorFlowNet("fp16_max_pool_even", DNN_TARGET_CPU, false, l1, lInf);
runTensorFlowNet("fp16_padding_same", DNN_TARGET_CPU, false, l1, lInf);
}
TEST(Test_TensorFlow, quantized)
@ -267,7 +275,7 @@ OCL_TEST(Test_TensorFlow, MobileNet_SSD)
TEST(Test_TensorFlow, lstm)
{
runTensorFlowNet("lstm", true);
runTensorFlowNet("lstm", DNN_TARGET_CPU, true);
}
TEST(Test_TensorFlow, split)
@ -284,11 +292,11 @@ TEST(Test_TensorFlow, memory_read)
{
double l1 = 1e-5;
double lInf = 1e-4;
runTensorFlowNet("lstm", true, l1, lInf, true);
runTensorFlowNet("lstm", DNN_TARGET_CPU, true, l1, lInf, true);
runTensorFlowNet("batch_norm", false, l1, lInf, true);
runTensorFlowNet("fused_batch_norm", false, l1, lInf, true);
runTensorFlowNet("batch_norm_text", true, l1, lInf, true);
runTensorFlowNet("batch_norm", DNN_TARGET_CPU, false, l1, lInf, true);
runTensorFlowNet("fused_batch_norm", DNN_TARGET_CPU, false, l1, lInf, true);
runTensorFlowNet("batch_norm_text", DNN_TARGET_CPU, true, l1, lInf, true);
}
}