mmr/tkDNN
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Pooling and big test, ELU seem not to work

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This commit is contained in:
Francesco Gatti
2017-06-29 11:37:29 +00:00
parent cc99347560
commit ba215c35c0
5 changed files with 208 additions and 24 deletions
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tests/test.cpp
+35 -11
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@@ -6,20 +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 *d2_bin = "../tests/dense2.bin";
const char *d2_bias_bin = "../tests/dense2.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, 10, 10, 4);
tkDNN::Conv3d c0 (&net, dim, 2, 4, 4, 2, 2, 2, 1, c0_bin, c0_bias_bin);
tkDNN::dataDim_t dim(1, 1, 100, 100, 4);
tkDNN::MulAdd m0 (&net, dim, 2, -1);
tkDNN::Conv3d c0 (&net, m0.output_dim, 16, 8, 8, 2, 4, 4, 1, c0_bin, c0_bias_bin);
tkDNN::Activation a0 (&net, c0.output_dim, tkDNN::ACTIVATION_RELU);
tkDNN::Conv3d c1 (&net, a0.output_dim, 4, 2, 2, 2, 1, 1, 1, c1_bin, c1_bias_bin);
tkDNN::Activation a1 (&net, c1.output_dim, tkDNN::ACTIVATION_ELU);
tkDNN::Flatten f1 (&net, a1.output_dim);
tkDNN::MulAdd m1 (&net, f1.output_dim, 2, 1);
tkDNN::Pooling p0 (&net, a0.output_dim, 2, 2, 2, 2, tkDNN::POOLING_AVERAGE);
tkDNN::Conv3d c1 (&net, p0.output_dim, 16, 4, 4, 2, 2, 2, 1, c1_bin, c1_bias_bin);
tkDNN::Activation a1 (&net, c1.output_dim, tkDNN::ACTIVATION_RELU);
tkDNN::Conv3d c2 (&net, a1.output_dim, 24, 3, 3, 2, 1, 1, 1, c2_bin, c2_bias_bin);
tkDNN::Activation a2 (&net, c2.output_dim, tkDNN::ACTIVATION_RELU);
tkDNN::Flatten f2 (&net, a2.output_dim);
tkDNN::Dense d3 (&net, f2.output_dim, 256, d3_bin, d3_bias_bin);
tkDNN::Activation a3 (&net, d3.output_dim, tkDNN::ACTIVATION_RELU);
tkDNN::Dense d4 (&net, a3.output_dim, 32, d4_bin, d4_bias_bin);
tkDNN::Activation a4 (&net, d4.output_dim, tkDNN::ACTIVATION_RELU);
tkDNN::Dense d5 (&net, a4.output_dim, 2, d5_bin, d5_bias_bin);
// Load input
value_type *data;
@@ -31,14 +46,23 @@ int main() {
TIMER_START
// Inference
data = m0.infer(dim, data); dim.print();
data = c0.infer(dim, data); dim.print();
data = a0.infer(dim, data); dim.print();
data = p0.infer(dim, data); dim.print();
data = c1.infer(dim, data); dim.print();
data = a1.infer(dim, data); dim.print();
data = f1.infer(dim, data); dim.print();
data = m1.infer(dim, data); dim.print();
data = c2.infer(dim, data); dim.print();
data = a2.infer(dim, data); dim.print();
data = f2.infer(dim, data); dim.print();
data = d3.infer(dim, data); dim.print();
data = a3.infer(dim, data); dim.print();
data = d4.infer(dim, data); dim.print();
data = a4.infer(dim, data); dim.print();
data = d5.infer(dim, data); dim.print();
TIMER_STOP
// Print result
printDeviceVector(dim.tot(), data);
return 0;