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Integrate benchmark test of conv2d and depthwise conv2d (#276) 

Signed-off-by: Chen Xin <jack.chen@verisilicon.com>

Co-authored-by: Chen Xin <jack.chen@verisilicon.com>
This commit is contained in:
chxin66 2022-01-21 15:32:23 +08:00 committed by GitHub
parent 3b11a6a5b2
commit 9fdba427f7
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GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 65 additions and 21 deletions
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86
samples/benchmark_test/benchmark_test.cc
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@ -23,14 +23,14 @@ enum ConfigField {
kKernelW = 3,
kKernelH = 4,
kOutChannel = 5,
deptwise_option = 6,
kConfigFiledCnt,
kOutImageW = kInImageW,
kOutImageH = kInImageH,
};
static const uint32_t default_cfg[] = {256, 256, 128, 3, 3, 1};
static const uint32_t default_cfg[] = {256, 256, 128, 3, 3, 1, 0};
int main(int argc, char* argv[]) {
@ -44,6 +44,8 @@ int main(int argc, char* argv[]) {
uint32_t out_image_w = default_cfg[kOutImageW];
uint32_t out_image_h = default_cfg[kOutImageH];
uint32_t out_image_c = default_cfg[kOutChannel];
uint32_t is_depthwise = default_cfg[deptwise_option];
uint32_t multiplier;
if (argc != 0 && argc != kConfigFiledCnt + 1){
std::cout << "argc = " << argc << std::endl;
@ -58,12 +60,29 @@ int main(int argc, char* argv[]) {
out_image_c = atoi(argv[kOutChannel]);
out_image_h = atoi(argv[kOutImageH]);
out_image_w = atoi(argv[kOutImageW]);
is_depthwise = atoi(argv[deptwise_option]);
switch(is_depthwise){
case 1:
multiplier = out_image_c/in_image_c;
break;
default:
break;
}
}
if (!single_layer_test && in_image_c != out_image_c) {
std::cout << "Fatal error: multi-layer test only valid for ic = oc" << std::endl;
return -1;
}
// if (!single_layer_test && in_image_c != out_image_c) {
// std::cout << "Fatal error: multi-layer test only valid for ic = oc" << std::endl;
// return -1;
// }
tim::vx::ShapeType in_shape = {in_image_h, in_image_w, in_image_c, batch_sz};
tim::vx::ShapeType kernel_shape, bias_shape, out_shape;
std::vector<uint8_t> in_data(batch_sz * in_image_w * in_image_h * in_image_c);
fillRandomData(in_data);
std::vector<uint8_t> kernel_data;
std::vector<uint32_t> bias_data;
std::cout << "\n ===========================================================\n";
std::cout << "\t test config: \n";
@ -72,22 +91,37 @@ int main(int argc, char* argv[]) {
#endif
std::cout << "\t input image shape in (w,h,c): " << in_image_w << ", "
<< in_image_h << ", " << in_image_c << ", " << std::endl;
std::cout << "\t kernel shape in (w, h, ic, oc): " << kernel_w << ", "
<< kernel_h << ", " << in_image_c << ", " << out_image_c << ", "
<< std::endl;
std::cout << "\t output image shape in(w,h,c): " << out_image_w << ", " << out_image_h << ", " << out_image_c << ",\n";
switch(is_depthwise){
case 0:
std::cout << "\t kernel shape in (w, h, ic, oc): " << kernel_w << ", "
<< kernel_h << ", " << in_image_c << ", " << out_image_c << ", "
<< std::endl;
std::cout << "\t output image shape in(w,h,c): " << out_image_w << ", " << out_image_h << ", " << out_image_c << ",\n";
kernel_shape = {kernel_w, kernel_h, in_image_c, out_image_c};
bias_shape = {out_image_c};
out_shape = {out_image_h, out_image_w, out_image_c, batch_sz};
kernel_data.resize(kernel_w * kernel_h * in_image_c * out_image_c);
bias_data.resize(out_image_c);
break;
case 1:
std::cout << "\t kernel shape in (w, h, c*multipier, 1): " << kernel_w << ", "
<< kernel_h << ", " << in_image_c*multiplier << ", " << 1 << ", "
<< std::endl;
std::cout << "\t output image shape in(w,h,c): " << out_image_w << ", " << out_image_h << ", " << in_image_c*multiplier << ",\n";
kernel_shape = {kernel_w, kernel_h, in_image_c * multiplier, 1};
bias_shape = {in_image_c * multiplier};
out_shape = {out_image_w, out_image_h, in_image_c * multiplier, batch_sz};
kernel_data.resize(kernel_w * kernel_h * in_image_c * multiplier);
bias_data.resize(in_image_c * multiplier);
break;
default:
break;
}
std::cout << " ===========================================================" << std::endl;
tim::vx::ShapeType in_shape = {in_image_h, in_image_w, in_image_c, batch_sz};
tim::vx::ShapeType kernel_shape = {kernel_w, kernel_h, in_image_c, out_image_c};
tim::vx::ShapeType bias_shape = {out_image_c};
tim::vx::ShapeType out_shape = {out_image_h, out_image_w, out_image_c, batch_sz};
std::vector<uint8_t> in_data(batch_sz * in_image_w * in_image_h * in_image_c);
fillRandomData(in_data);
std::vector<uint8_t> kernel_data(kernel_w * kernel_h * in_image_c * out_image_c);
fillRandomData(kernel_data);
std::vector<uint32_t> bias_data(out_image_c);
fillRandomData(bias_data);
tim::vx::Quantization quant_type(tim::vx::QuantType::ASYMMETRIC, 1.0f, 0);
@ -109,7 +143,7 @@ int main(int argc, char* argv[]) {
auto relu6_input_tensor = graph->CreateTensor(relu6_spec);
#endif
auto output_tensor = graph->CreateTensor(output_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::array<uint32_t, 4> pad = {
(out_image_w - in_image_w + kernel_w - 1) / 2,
@ -119,7 +153,17 @@ int main(int argc, char* argv[]) {
};
std::array<uint32_t, 2> stride = {1,1};
std::array<uint32_t, 2> dilation = {1,1};
auto conv2d_op = graph->CreateOperation<tim::vx::ops::Conv2d>(pad, stride, dilation);
std::shared_ptr<tim::vx::ops::Conv2d> conv2d_op;
switch(is_depthwise){
case 0:
conv2d_op = graph->CreateOperation<tim::vx::ops::Conv2d>(pad, stride, dilation);
break;
case 1:
conv2d_op = graph->CreateOperation<tim::vx::ops::Conv2d>(pad, stride, dilation, multiplier);
break;
default:
break;
}
(*conv2d_op).BindInputs({input_tensor, kernel_tensor, bias_tensor});
#if ENABLE_RELU6
(*conv2d_op).BindOutput(relu6_input_tensor);