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better network model

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This commit is contained in:
Francesco Gatti
2017-08-01 23:03:02 +02:00
parent 300b0af5dd
commit e8355cee67
22 changed files with 166 additions and 179 deletions
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tests/yolo/yolo.cpp
+58 -58
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@@ -30,74 +30,74 @@ const char *output_bin = "../tests/yolo/layers/output.bin";
int main() {
// Network layout
tkDNN::Network net;
tkDNN::dataDim_t dim(1, 3, 608, 608, 1);
tkDNN::Layer *l;
l = new tkDNN::Conv2d (&net, dim, 32, 3, 3, 1, 1, 1, 1, c0_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Pooling (&net, l->output_dim, 2, 2, 2, 2, tkDNN::POOLING_MAX);
tkDNN::Network net(dim);
l = new tkDNN::Conv2d (&net, l->output_dim, 64, 3, 3, 1, 1, 1, 1, c2_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Pooling (&net, l->output_dim, 2, 2, 2, 2, tkDNN::POOLING_MAX);
tkDNN::Conv2d c0 (&net, 32, 3, 3, 1, 1, 1, 1, c0_bin, true);
tkDNN::Activation a0 (&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Pooling p1 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
l = new tkDNN::Conv2d (&net, l->output_dim, 128, 3, 3, 1, 1, 1, 1, c4_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Conv2d (&net, l->output_dim, 64, 1, 1, 1, 1, 0, 0, c5_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Conv2d (&net, l->output_dim, 128, 3, 3, 1, 1, 1, 1, c6_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Pooling (&net, l->output_dim, 2, 2, 2, 2, tkDNN::POOLING_MAX);
tkDNN::Conv2d c2 (&net, 64, 3, 3, 1, 1, 1, 1, c2_bin, true);
tkDNN::Activation a2 (&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Pooling p3 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
l = new tkDNN::Conv2d (&net, l->output_dim, 256, 3, 3, 1, 1, 1, 1, c8_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Conv2d (&net, l->output_dim, 128, 1, 1, 1, 1, 0, 0, c9_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Conv2d (&net, l->output_dim, 256, 3, 3, 1, 1, 1, 1, c10_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Pooling (&net, l->output_dim, 2, 2, 2, 2, tkDNN::POOLING_MAX);
tkDNN::Conv2d c4 (&net, 128, 3, 3, 1, 1, 1, 1, c4_bin, true);
tkDNN::Activation a4 (&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c5 (&net, 64, 1, 1, 1, 1, 0, 0, c5_bin, true);
tkDNN::Activation a5 (&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c6 (&net, 128, 3, 3, 1, 1, 1, 1, c6_bin, true);
tkDNN::Activation a6 (&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Pooling p7 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
l = new tkDNN::Conv2d (&net, l->output_dim, 512, 3, 3, 1, 1, 1, 1, c12_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Conv2d (&net, l->output_dim, 256, 1, 1, 1, 1, 0, 0, c13_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Conv2d (&net, l->output_dim, 512, 3, 3, 1, 1, 1, 1, c14_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Conv2d (&net, l->output_dim, 256, 1, 1, 1, 1, 0, 0, c15_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Conv2d (&net, l->output_dim, 512, 3, 3, 1, 1, 1, 1, c16_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY); //29
l = new tkDNN::Pooling (&net, l->output_dim, 2, 2, 2, 2, tkDNN::POOLING_MAX);
tkDNN::Conv2d c8 (&net, 256, 3, 3, 1, 1, 1, 1, c8_bin, true);
tkDNN::Activation a8 (&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c9 (&net, 128, 1, 1, 1, 1, 0, 0, c9_bin, true);
tkDNN::Activation a9 (&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c10(&net, 256, 3, 3, 1, 1, 1, 1, c10_bin, true);
tkDNN::Activation a10(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Pooling p11(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
l = new tkDNN::Conv2d (&net, l->output_dim, 1024, 3, 3, 1, 1, 1, 1, c18_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Conv2d (&net, l->output_dim, 512, 1, 1, 1, 1, 0, 0, c19_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Conv2d (&net, l->output_dim, 1024, 3, 3, 1, 1, 1, 1, c20_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Conv2d (&net, l->output_dim, 512, 1, 1, 1, 1, 0, 0, c21_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Conv2d (&net, l->output_dim, 1024, 3, 3, 1, 1, 1, 1, c22_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Conv2d (&net, l->output_dim, 1024, 3, 3, 1, 1, 1, 1, c23_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Conv2d (&net, l->output_dim, 1024, 3, 3, 1, 1, 1, 1, c24_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY); //44
tkDNN::Conv2d c12(&net, 512, 3, 3, 1, 1, 1, 1, c12_bin, true);
tkDNN::Activation a12(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c13(&net, 256, 1, 1, 1, 1, 0, 0, c13_bin, true);
tkDNN::Activation a13(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c14(&net, 512, 3, 3, 1, 1, 1, 1, c14_bin, true);
tkDNN::Activation a14(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c15(&net, 256, 1, 1, 1, 1, 0, 0, c15_bin, true);
tkDNN::Activation a15(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c16(&net, 512, 3, 3, 1, 1, 1, 1, c16_bin, true);
tkDNN::Activation a16(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Pooling p17(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
int rlayers[1] = {29};
l = new tkDNN::Route (&net, rlayers, 1);
l = new tkDNN::Conv2d (&net, l->output_dim, 64, 1, 1, 1, 1, 0, 0, c26_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Reorg (&net, l->output_dim, 2); //48
tkDNN::Conv2d c18(&net, 1024, 3, 3, 1, 1, 1, 1, c18_bin, true);
tkDNN::Activation a18(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c19(&net, 512, 1, 1, 1, 1, 0, 0, c19_bin, true);
tkDNN::Activation a19(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c20(&net, 1024, 3, 3, 1, 1, 1, 1, c20_bin, true);
tkDNN::Activation a20(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c21(&net, 512, 1, 1, 1, 1, 0, 0, c21_bin, true);
tkDNN::Activation a21(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c22(&net, 1024, 3, 3, 1, 1, 1, 1, c22_bin, true);
tkDNN::Activation a22(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c23(&net, 1024, 3, 3, 1, 1, 1, 1, c23_bin, true);
tkDNN::Activation a23(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c24(&net, 1024, 3, 3, 1, 1, 1, 1, c24_bin, true);
tkDNN::Activation a24(&net, tkDNN::ACTIVATION_LEAKY);
int rlayers2[2] = {48,44};
l = new tkDNN::Route (&net, rlayers2, 2);
tkDNN::Layer *m25_layers[1] = { &a16 };
tkDNN::Route m25(&net, m25_layers, 1);
tkDNN::Conv2d c26(&net, 64, 1, 1, 1, 1, 0, 0, c26_bin, true);
tkDNN::Activation a26(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Reorg r27(&net, 2);
l = new tkDNN::Conv2d (&net, l->output_dim, 1024, 3, 3, 1, 1, 1, 1, c29_bin, true);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_LEAKY);
l = new tkDNN::Conv2d (&net, l->output_dim, 425, 1, 1, 1, 1, 0, 0, c30_bin, false);
tkDNN::Layer *m28_layers[2] = { &r27, &a24 };
tkDNN::Route m28(&net, m28_layers, 2);
l = new tkDNN::Region (&net, l->output_dim, 80, 4, 5, 0.6f);
tkDNN::Conv2d c29(&net, 1024, 3, 3, 1, 1, 1, 1, c29_bin, true);
tkDNN::Activation a29(&net, tkDNN::ACTIVATION_LEAKY);
tkDNN::Conv2d c30(&net, 425, 1, 1, 1, 1, 0, 0, c30_bin, false);
tkDNN::Region g31(&net, 80, 4, 5, 0.6f);
// Load input
value_type *data;