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cudnn5 branch for tx2

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Francesco Gatti
2017-07-14 11:11:51 +02:00
parent 33513468e6
commit e4df86a07c
9 changed files with 49 additions and 276 deletions
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tests/test.cpp
+8 -25
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@@ -6,52 +6,35 @@ const char *c0_bin = "../tests/conv0.bin";
const char *c0_bias_bin = "../tests/conv0.bias.bin";
const char *c1_bin = "../tests/conv1.bin";
const char *c1_bias_bin = "../tests/conv1.bias.bin";
const char *c2_bin = "../tests/conv2.bin";
const char *c2_bias_bin = "../tests/conv2.bias.bin";
const char *d3_bin = "../tests/dense3.bin";
const char *d3_bias_bin = "../tests/dense3.bias.bin";
const char *d4_bin = "../tests/dense4.bin";
const char *d4_bias_bin = "../tests/dense4.bias.bin";
const char *d5_bin = "../tests/dense5.bin";
const char *d5_bias_bin = "../tests/dense5.bias.bin";
int main() {
// Network layout
tkDNN::Network net;
tkDNN::dataDim_t dim(1, 1, 100, 100, 4);
tkDNN::dataDim_t dim(1, 1, 10, 10, 1);
tkDNN::Layer *l;
l = new tkDNN::MulAdd (&net, dim, 2, -1);
l = new tkDNN::Conv3d (&net, l->output_dim, 16, 8, 8, 2, 4, 4, 1, c0_bin, c0_bias_bin);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_ELU);
l = new tkDNN::Pooling (&net, l->output_dim, 2, 2, 2, 2, tkDNN::POOLING_AVERAGE);
l = new tkDNN::Conv3d (&net, l->output_dim, 16, 4, 4, 2, 2, 2, 1, c1_bin, c1_bias_bin);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_ELU);
l = new tkDNN::Conv3d (&net, l->output_dim, 24, 3, 3, 2, 1, 1, 1, c2_bin, c2_bias_bin);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_ELU);
l = new tkDNN::Flatten (&net, l->output_dim);
l = new tkDNN::Dense (&net, l->output_dim, 256, d3_bin, d3_bias_bin);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_ELU);
l = new tkDNN::Dense (&net, l->output_dim, 32, d4_bin, d4_bias_bin);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_RELU);
l = new tkDNN::Dense (&net, l->output_dim, 2, d5_bin, d5_bias_bin);
l = new tkDNN::Conv2d (&net, dim, 2, 4, 4, 2, 2, c0_bin, c0_bias_bin);
l = new tkDNN::Activation (&net, l->output_dim, CUDNN_ACTIVATION_RELU);
l = new tkDNN::Conv2d (&net, l->output_dim, 4, 2, 2, 1, 1, c1_bin, c1_bias_bin);
l = new tkDNN::Activation (&net, l->output_dim, CUDNN_ACTIVATION_RELU);
// Load input
value_type *data;
value_type *input_h;
readBinaryFile(input_bin, dim.tot(), &input_h, &data);
printDeviceVector(dim.tot(), data);
dim.print(); //print initial dimension
TIMER_START
// Inference
data = net.infer(dim, data); dim.print();
TIMER_STOP
// Print result
printDeviceVector(dim.tot(), data);
return 0;
}
}