From 159aa8aae8c8e21306f529ee1f469aaa5a58577b Mon Sep 17 00:00:00 2001 From: Francesco Gatti Date: Sun, 2 Jul 2017 21:02:12 +0200 Subject: [PATCH] new network declaration method --- tests/test.cpp | 47 +++++++++++++++-------------------------------- 1 file changed, 15 insertions(+), 32 deletions(-) diff --git a/tests/test.cpp b/tests/test.cpp index 661ab31..c91da28 100644 --- a/tests/test.cpp +++ b/tests/test.cpp @@ -20,20 +20,21 @@ int main() { // Network layout tkDNN::Network net; 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_ELU); - 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_ELU); - 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_ELU); - 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_ELU); - 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); + 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); // Load input @@ -48,24 +49,6 @@ int main() { // Inference data = net.infer(dim, data); dim.print(); - /* - //old 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 = 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