mmr/tkDNN
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Shortcat ok

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This commit is contained in:
Davide Sapienza
2019-10-29 15:08:44 +01:00
parent 058f9b795f
commit e9ec582223
5 changed files with 87 additions and 29 deletions
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tests/resnet101/resnet101.cpp
+16 -8
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@@ -148,7 +148,7 @@ const char *layer4_2_conv3_bin = "../tests/resnet101/layers/layer4-2-conv3.bin";
//final
const char *fc_bin = "../tests/resnet101/layers/fc.bin";
const char *output_bin = "../tests/resnet101/debug/layer1-0-conv3.bin";
const char *output_bin = "../tests/resnet101/debug/layer1-0-relu.bin";
int main()
{
@@ -161,16 +161,21 @@ int main()
tk::dnn::Activation relu3(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Pooling maxpool4(&net, 3, 3, 2, 2, 1, 1, tk::dnn::POOLING_MAX);
//layer 1
tk::dnn::Conv2d layer1_0_conv1(&net, 64, 1, 1, 1, 1, 0, 0, layer1_0_conv1_bin, true);
tk::dnn::Activation relu1_0_1(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d layer1_0_conv2(&net, 64, 3, 3, 1, 1, 1, 1, layer1_0_conv2_bin, true);
tk::dnn::Activation relu1_0_2(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d layer1_0_conv3(&net, 256, 1, 1, 1, 1, 0, 0, layer1_0_conv3_bin, true);
/*
tk::dnn::Layer *m83_layers[1] = { &maxpool4 };
tk::dnn::Route m83 (&net, m83_layers, 1);
tk::dnn::Conv2d layer1_0_downsample_0(&net, 256, 1, 1, 1, 1, 0, 0, layer1_0_downsample_0_bin, true);
tk::dnn::Shortcut s1_0 (&net, &layer1_0_conv3);
tk::dnn::Activation layer1_0_relu(&net, CUDNN_ACTIVATION_RELU);
tk::dnn::Conv2d layer1_0_downsample_0(&net, 256, 1, 1, 1, 1, 1, 1, layer1_0_downsample_0, true);
/*
tk::dnn::Conv2d layer1_1_conv1(&net, 64, 1, 1, 1, 1, 1, 1, layer1_1_conv1_bin, true);
tk::dnn::Conv2d layer1_1_conv2(&net, 64, 3, 3, 1, 1, 1, 1, layer1_1_conv2_bin, true);
tk::dnn::Conv2d layer1_1_conv3(&net, 256, 1, 1, 1, 1, 1, 1, layer1_1_conv3_bin, true);
@@ -347,6 +352,7 @@ int main()
dnnType *data;
dnnType *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
printDeviceVector(64, data, true);
//print network model
net.print();
@@ -356,7 +362,7 @@ int main()
*/
tk::dnn::dataDim_t out_dim;
out_dim = layer1_0_conv3.output_dim;
out_dim = net.layers[net.num_layers-1]->output_dim;
dnnType *cudnn_out, *rt_out;
tk::dnn::dataDim_t dim1 = dim; //input dim
@@ -368,7 +374,9 @@ int main()
TIMER_STOP
dim1.print();
}
cudnn_out = layer1_0_conv3.dstData;
cudnn_out = net.layers[net.num_layers-1]->dstData;
printDeviceVector(64, cudnn_out, true);
/*
tk::dnn::dataDim_t dim2 = dim;
printCenteredTitle(" TENSORRT inference ", '=', 30);