mmr/tkDNN
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conv2d implementation

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This commit is contained in:
Francesco Gatti
2017-06-28 14:04:43 +00:00
parent 8bf0b0257e
commit 3cb126420c
6 changed files with 199 additions and 34 deletions
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tests/test.cpp
+16 -15
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@@ -2,10 +2,10 @@
#include "Layer.h"
const char *input_bin = "../tests/input.bin";
const char *d0_bin = "../tests/dense0.bin";
const char *d0_bias_bin = "../tests/dense0.bias.bin";
const char *d1_bin = "../tests/dense1.bin";
const char *d1_bias_bin = "../tests/dense1.bias.bin";
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 *d2_bin = "../tests/dense2.bin";
const char *d2_bias_bin = "../tests/dense2.bias.bin";
@@ -13,13 +13,12 @@ int main() {
// Network layout
tkDNN::Network net;
tkDNN::dataDim_t dim(1, 512, 1, 1);
tkDNN::Dense d0 (&net, dim, 256, d0_bin, d0_bias_bin);
tkDNN::Activation a0 (&net, d0.output_dim, tkDNN::ACTIVATION_ELU);
tkDNN::Dense d1 (&net, a0.output_dim, 32, d1_bin, d1_bias_bin);
tkDNN::Activation a1 (&net, d1.output_dim, tkDNN::ACTIVATION_ELU);
tkDNN::Dense d2 (&net, a1.output_dim, 2, d2_bin, d2_bias_bin);
tkDNN::dataDim_t dim(1, 1, 10, 10);
tkDNN::Conv2d c0 (&net, dim, 2, 4, 4, 2, 2, c0_bin, c0_bias_bin);
tkDNN::Activation a0 (&net, c0.output_dim, tkDNN::ACTIVATION_ELU);
tkDNN::Conv2d c1 (&net, a0.output_dim, 4, 2, 2, 1, 1, c1_bin, c1_bias_bin);
tkDNN::Activation a1 (&net, c1.output_dim, tkDNN::ACTIVATION_RELU);
// Load input
value_type *data;
value_type *input_h;
@@ -27,13 +26,15 @@ int main() {
dim.print(); //print initial dimension
TIMER_START
// Inference
data = d0.infer(dim, data); dim.print();
data = c0.infer(dim, data); dim.print();
data = a0.infer(dim, data); dim.print();
data = d1.infer(dim, data); dim.print();
data = c1.infer(dim, data); dim.print();
data = a1.infer(dim, data); dim.print();
data = d2.infer(dim, data); dim.print();
TIMER_STOP
// Print result
printDeviceVector(dim.tot(), data);
return 0;