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bug fixes for dla networks and ported optimization from different pull request

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This commit is contained in:
perseusdg
2021-11-13 02:00:49 +05:30
parent 9e328c0daa
commit ee5000ccca
7 changed files with 122 additions and 192 deletions
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tests/shelfnet/shelfnet_berkeley.cpp
+81 -81
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@@ -9,77 +9,77 @@
const char *input_bin = "shelfnet_berkeley/debug/input.bin";
const char *backbone[] = {
"shelfnet_berkeley/layers/backbone-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer1-0-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer1-0-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer1-1-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer1-1-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer2-0-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer2-0-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer2-0-downsample-0.bin",
"shelfnet_berkeley/layers/backbone-layer2-1-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer2-1-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer3-0-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer3-0-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer3-0-downsample-0.bin",
"shelfnet_berkeley/layers/backbone-layer3-1-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer3-1-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer4-0-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer4-0-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer4-0-downsample-0.bin",
"shelfnet_berkeley/layers/backbone-layer4-1-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer4-1-conv2.bin"};
"shelfnet_berkeley/layers/backbone-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer1-0-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer1-0-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer1-1-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer1-1-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer2-0-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer2-0-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer2-0-downsample-0.bin",
"shelfnet_berkeley/layers/backbone-layer2-1-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer2-1-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer3-0-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer3-0-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer3-0-downsample-0.bin",
"shelfnet_berkeley/layers/backbone-layer3-1-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer3-1-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer4-0-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer4-0-conv2.bin",
"shelfnet_berkeley/layers/backbone-layer4-0-downsample-0.bin",
"shelfnet_berkeley/layers/backbone-layer4-1-conv1.bin",
"shelfnet_berkeley/layers/backbone-layer4-1-conv2.bin"};
const char *conv_out[] = {
"shelfnet_berkeley/layers/conv_out-conv-conv.bin",
"shelfnet_berkeley/layers/conv_out-conv_out.bin",
"shelfnet_berkeley/layers/conv_out16-conv-conv.bin",
"shelfnet_berkeley/layers/conv_out16-conv_out.bin",
"shelfnet_berkeley/layers/conv_out32-conv-conv.bin",
"shelfnet_berkeley/layers/conv_out32-conv_out.bin"
};
"shelfnet_berkeley/layers/conv_out-conv-conv.bin",
"shelfnet_berkeley/layers/conv_out-conv_out.bin",
"shelfnet_berkeley/layers/conv_out16-conv-conv.bin",
"shelfnet_berkeley/layers/conv_out16-conv_out.bin",
"shelfnet_berkeley/layers/conv_out32-conv-conv.bin",
"shelfnet_berkeley/layers/conv_out32-conv_out.bin"
};
const char *decoder[] = {
"shelfnet_berkeley/layers/decoder-bottom-conv1.bin",
"shelfnet_berkeley/layers/decoder-bottom-conv12.bin",
"shelfnet_berkeley/layers/decoder-up_conv_list-0-conv-conv.bin",
"shelfnet_berkeley/layers/decoder-up_conv_list-0-conv_atten.bin",
"shelfnet_berkeley/layers/decoder-up_dense_list-0-conv.bin",
"shelfnet_berkeley/layers/decoder-up_conv_list-1-conv-conv.bin",
"shelfnet_berkeley/layers/decoder-up_conv_list-1-conv_atten.bin",
"shelfnet_berkeley/layers/decoder-up_dense_list-1-conv.bin"
};
"shelfnet_berkeley/layers/decoder-bottom-conv1.bin",
"shelfnet_berkeley/layers/decoder-bottom-conv12.bin",
"shelfnet_berkeley/layers/decoder-up_conv_list-0-conv-conv.bin",
"shelfnet_berkeley/layers/decoder-up_conv_list-0-conv_atten.bin",
"shelfnet_berkeley/layers/decoder-up_dense_list-0-conv.bin",
"shelfnet_berkeley/layers/decoder-up_conv_list-1-conv-conv.bin",
"shelfnet_berkeley/layers/decoder-up_conv_list-1-conv_atten.bin",
"shelfnet_berkeley/layers/decoder-up_dense_list-1-conv.bin"
};
const char *ladder[] = {
"shelfnet_berkeley/layers/ladder-inconv-conv1.bin",
"shelfnet_berkeley/layers/ladder-inconv-conv12.bin",
"shelfnet_berkeley/layers/ladder-down_module_list-0-conv1.bin",
"shelfnet_berkeley/layers/ladder-down_module_list-0-conv12.bin",
"shelfnet_berkeley/layers/ladder-down_conv_list-0.bin",
"shelfnet_berkeley/layers/ladder-inconv-conv1.bin",
"shelfnet_berkeley/layers/ladder-inconv-conv12.bin",
"shelfnet_berkeley/layers/ladder-down_module_list-0-conv1.bin",
"shelfnet_berkeley/layers/ladder-down_module_list-0-conv12.bin",
"shelfnet_berkeley/layers/ladder-down_conv_list-0.bin",
"shelfnet_berkeley/layers/ladder-down_module_list-1-conv1.bin",
"shelfnet_berkeley/layers/ladder-down_module_list-1-conv12.bin",
"shelfnet_berkeley/layers/ladder-down_conv_list-1.bin",
"shelfnet_berkeley/layers/ladder-down_module_list-1-conv1.bin",
"shelfnet_berkeley/layers/ladder-down_module_list-1-conv12.bin",
"shelfnet_berkeley/layers/ladder-down_conv_list-1.bin",
"shelfnet_berkeley/layers/ladder-bottom-conv1.bin",
"shelfnet_berkeley/layers/ladder-bottom-conv12.bin",
"shelfnet_berkeley/layers/ladder-up_conv_list-0-conv-conv.bin",
"shelfnet_berkeley/layers/ladder-up_conv_list-0-conv_atten.bin",
"shelfnet_berkeley/layers/ladder-up_dense_list-0-conv.bin",
"shelfnet_berkeley/layers/ladder-bottom-conv1.bin",
"shelfnet_berkeley/layers/ladder-bottom-conv12.bin",
"shelfnet_berkeley/layers/ladder-up_conv_list-1-conv-conv.bin",
"shelfnet_berkeley/layers/ladder-up_conv_list-1-conv_atten.bin",
"shelfnet_berkeley/layers/ladder-up_dense_list-1-conv.bin"};
"shelfnet_berkeley/layers/ladder-up_conv_list-0-conv-conv.bin",
"shelfnet_berkeley/layers/ladder-up_conv_list-0-conv_atten.bin",
"shelfnet_berkeley/layers/ladder-up_dense_list-0-conv.bin",
"shelfnet_berkeley/layers/ladder-up_conv_list-1-conv-conv.bin",
"shelfnet_berkeley/layers/ladder-up_conv_list-1-conv_atten.bin",
"shelfnet_berkeley/layers/ladder-up_dense_list-1-conv.bin"};
const char *trans[] = {
"shelfnet_berkeley/layers/trans1-conv.bin",
"shelfnet_berkeley/layers/trans2-conv.bin",
"shelfnet_berkeley/layers/trans3-conv.bin"};
"shelfnet_berkeley/layers/trans1-conv.bin",
"shelfnet_berkeley/layers/trans2-conv.bin",
"shelfnet_berkeley/layers/trans3-conv.bin"};
int main()
{
@@ -87,7 +87,7 @@ int main()
int classes = 20;
// Network layout
// Network layout
tk::dnn::dataDim_t dim(1, 3, 736, 1280, 1);
tk::dnn::Network net(dim);
@@ -97,7 +97,7 @@ int main()
tk::dnn::Layer* last = new tk::dnn::Pooling (&net, 3, 3, 2, 2, 1, 1, tk::dnn::POOLING_MAX);
for(int i=0; i<2; ++i){
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
@@ -121,7 +121,7 @@ int main()
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
new tk::dnn::Shortcut(&net, last);
last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
features.push_back(last);
@@ -135,7 +135,7 @@ int main()
}
//DECODER
last = features[2];
std::vector<tk::dnn::Layer*> up_out;
//bottom
@@ -152,10 +152,10 @@ int main()
std::cout<<out_channel<<std::endl;
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, decoder[di++], true);
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Pooling(&net, last->output_dim.w, last->output_dim.h, last->output_dim.w, last->output_dim.h, 0, 0, tk::dnn::POOLING_AVERAGE);
new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, decoder[di++], true);
tk::dnn::Layer* act = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_SIGMOID);
new tk::dnn::Route(&net, &last, 1);
new tk::dnn::Shortcut(&net, act, true);
@@ -178,11 +178,11 @@ int main()
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
new tk::dnn::Shortcut(&net, last);
new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
for(int i=0; i<2;++i){
int out_channel = pow(2,6+i);
tk::dnn::Layer* l_last = new tk::dnn::Shortcut(&net, up_out[2-i]);
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
@@ -207,10 +207,10 @@ int main()
//up-conv
new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true);
last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Pooling(&net, last->output_dim.w, last->output_dim.h, last->output_dim.w, last->output_dim.h, 0, 0, tk::dnn::POOLING_AVERAGE);
new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, ladder[li++], true);
tk::dnn::Layer* act = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_SIGMOID);
new tk::dnn::Route(&net, &last, 1);
new tk::dnn::Shortcut(&net, act, true);
@@ -227,17 +227,17 @@ int main()
// for(int i=2;i>=0;--i){
// new tk::dnn::Route(&net, &up_out[i], 1);
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, conv_out[ci++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, classes, 3, 3, 1, 1, 1, 1, conv_out[ci++], false);
/*up_out[i] =*/ new tk::dnn::Resize(&net, classes, net.input_dim.h, net.input_dim.w, true, tk::dnn::ResizeMode_t::LINEAR);
// new tk::dnn::Route(&net, &up_out[i], 1);
new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, conv_out[ci++], true);
new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
new tk::dnn::Conv2d (&net, classes, 3, 3, 1, 1, 1, 1, conv_out[ci++], false);
/*up_out[i] =*/ new tk::dnn::Resize(&net, classes, net.input_dim.h, net.input_dim.w, true, tk::dnn::ResizeMode_t::LINEAR);
// }
new tk::dnn::Softmax(&net);
const char *output_bin = "shelfnet_berkeley/debug/softmax.bin";
// Load input
dnnType *data;
dnnType *input_h;
@@ -278,7 +278,7 @@ int main()
int odim1 = dim1.tot();
readBinaryFile(output_bin, odim1, &out1_h, &out1);
int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0;
int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0;
std::cout << "CUDNN vs correct" << std::endl;
ret_cudnn |= checkResult(odim1, cudnn_out, out1, true, 20) == 0 ? 0 : ERROR_CUDNN;
@@ -287,9 +287,9 @@ int main()
std::cout << "CUDNN vs TRT " << std::endl;
ret_cudnn_tensorrt |= checkResult(odim1, cudnn_out, rt_out1) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
cv::Mat viz = vizLayer2Mat(&net, net.num_layers-1);
cv::imwrite("test.png", viz);
netRT.destroy();
return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
}
}