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So I have this new programming paradigm.......

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This commit is contained in:
Joseph Redmon
2016-09-24 23:12:54 -07:00
parent c899cc19f4
commit 481b57a96a
49 changed files with 629 additions and 914 deletions
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113
src/network.c
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@ -15,7 +15,6 @@
#include "local_layer.h"
#include "convolutional_layer.h"
#include "activation_layer.h"
#include "deconvolutional_layer.h"
#include "detection_layer.h"
#include "region_layer.h"
#include "normalization_layer.h"
@ -153,49 +152,7 @@ void forward_network(network net, network_state state)
if(l.delta){
scal_cpu(l.outputs * l.batch, 0, l.delta, 1);
}
if(l.type == CONVOLUTIONAL){
forward_convolutional_layer(l, state);
} else if(l.type == DECONVOLUTIONAL){
forward_deconvolutional_layer(l, state);
} else if(l.type == ACTIVE){
forward_activation_layer(l, state);
} else if(l.type == LOCAL){
forward_local_layer(l, state);
} else if(l.type == NORMALIZATION){
forward_normalization_layer(l, state);
} else if(l.type == BATCHNORM){
forward_batchnorm_layer(l, state);
} else if(l.type == DETECTION){
forward_detection_layer(l, state);
} else if(l.type == REGION){
forward_region_layer(l, state);
} else if(l.type == CONNECTED){
forward_connected_layer(l, state);
} else if(l.type == RNN){
forward_rnn_layer(l, state);
} else if(l.type == GRU){
forward_gru_layer(l, state);
} else if(l.type == CRNN){
forward_crnn_layer(l, state);
} else if(l.type == CROP){
forward_crop_layer(l, state);
} else if(l.type == COST){
forward_cost_layer(l, state);
} else if(l.type == SOFTMAX){
forward_softmax_layer(l, state);
} else if(l.type == MAXPOOL){
forward_maxpool_layer(l, state);
} else if(l.type == REORG){
forward_reorg_layer(l, state);
} else if(l.type == AVGPOOL){
forward_avgpool_layer(l, state);
} else if(l.type == DROPOUT){
forward_dropout_layer(l, state);
} else if(l.type == ROUTE){
forward_route_layer(l, net);
} else if(l.type == SHORTCUT){
forward_shortcut_layer(l, state);
}
l.forward(l, state);
state.input = l.output;
}
}
@ -207,29 +164,17 @@ void update_network(network net)
float rate = get_current_rate(net);
for(i = 0; i < net.n; ++i){
layer l = net.layers[i];
if(l.type == CONVOLUTIONAL){
update_convolutional_layer(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == DECONVOLUTIONAL){
update_deconvolutional_layer(l, rate, net.momentum, net.decay);
} else if(l.type == CONNECTED){
update_connected_layer(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == RNN){
update_rnn_layer(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == GRU){
update_gru_layer(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == CRNN){
update_crnn_layer(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == LOCAL){
update_local_layer(l, update_batch, rate, net.momentum, net.decay);
if(l.update){
l.update(l, update_batch, rate, net.momentum, net.decay);
}
}
}
float *get_network_output(network net)
{
#ifdef GPU
if (gpu_index >= 0) return get_network_output_gpu(net);
#endif
#ifdef GPU
if (gpu_index >= 0) return get_network_output_gpu(net);
#endif
int i;
for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
return net.layers[i].output;
@ -273,47 +218,7 @@ void backward_network(network net, network_state state)
state.delta = prev.delta;
}
layer l = net.layers[i];
if(l.type == CONVOLUTIONAL){
backward_convolutional_layer(l, state);
} else if(l.type == DECONVOLUTIONAL){
backward_deconvolutional_layer(l, state);
} else if(l.type == ACTIVE){
backward_activation_layer(l, state);
} else if(l.type == NORMALIZATION){
backward_normalization_layer(l, state);
} else if(l.type == BATCHNORM){
backward_batchnorm_layer(l, state);
} else if(l.type == MAXPOOL){
if(i != 0) backward_maxpool_layer(l, state);
} else if(l.type == REORG){
backward_reorg_layer(l, state);
} else if(l.type == AVGPOOL){
backward_avgpool_layer(l, state);
} else if(l.type == DROPOUT){
backward_dropout_layer(l, state);
} else if(l.type == DETECTION){
backward_detection_layer(l, state);
} else if(l.type == REGION){
backward_region_layer(l, state);
} else if(l.type == SOFTMAX){
if(i != 0) backward_softmax_layer(l, state);
} else if(l.type == CONNECTED){
backward_connected_layer(l, state);
} else if(l.type == RNN){
backward_rnn_layer(l, state);
} else if(l.type == GRU){
backward_gru_layer(l, state);
} else if(l.type == CRNN){
backward_crnn_layer(l, state);
} else if(l.type == LOCAL){
backward_local_layer(l, state);
} else if(l.type == COST){
backward_cost_layer(l, state);
} else if(l.type == ROUTE){
backward_route_layer(l, net);
} else if(l.type == SHORTCUT){
backward_shortcut_layer(l, state);
}
l.backward(l, state);
}
}
@ -406,11 +311,11 @@ void set_batch_network(network *net, int b)
int i;
for(i = 0; i < net->n; ++i){
net->layers[i].batch = b;
#ifdef CUDNN
#ifdef CUDNN
if(net->layers[i].type == CONVOLUTIONAL){
cudnn_convolutional_setup(net->layers + i);
}
#endif
#endif
}
}