From c7b10ceadb1a78e7480d281444a31ae2a7dc1b05 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 06 May 2016 23:25:16 +0000
Subject: [PATCH] so much need to commit
---
src/network.c | 86 ++++++++++++++++++++++++++++++++++++++++++-
1 files changed, 84 insertions(+), 2 deletions(-)
diff --git a/src/network.c b/src/network.c
index 9bcb264..ca485d6 100644
--- a/src/network.c
+++ b/src/network.c
@@ -8,16 +8,23 @@
#include "crop_layer.h"
#include "connected_layer.h"
+#include "gru_layer.h"
+#include "rnn_layer.h"
+#include "crnn_layer.h"
+#include "local_layer.h"
#include "convolutional_layer.h"
+#include "activation_layer.h"
#include "deconvolutional_layer.h"
#include "detection_layer.h"
#include "normalization_layer.h"
+#include "batchnorm_layer.h"
#include "maxpool_layer.h"
#include "avgpool_layer.h"
#include "cost_layer.h"
#include "softmax_layer.h"
#include "dropout_layer.h"
#include "route_layer.h"
+#include "shortcut_layer.h"
int get_current_batch(network net)
{
@@ -25,6 +32,17 @@
return batch_num;
}
+void reset_momentum(network net)
+{
+ if (net.momentum == 0) return;
+ net.learning_rate = 0;
+ net.momentum = 0;
+ net.decay = 0;
+ #ifdef GPU
+ if(gpu_index >= 0) update_network_gpu(net);
+ #endif
+}
+
float get_current_rate(network net)
{
int batch_num = get_current_batch(net);
@@ -40,6 +58,7 @@
for(i = 0; i < net.num_steps; ++i){
if(net.steps[i] > batch_num) return rate;
rate *= net.scales[i];
+ if(net.steps[i] > batch_num - 1) reset_momentum(net);
}
return rate;
case EXP:
@@ -59,10 +78,20 @@
switch(a){
case CONVOLUTIONAL:
return "convolutional";
+ case ACTIVE:
+ return "activation";
+ case LOCAL:
+ return "local";
case DECONVOLUTIONAL:
return "deconvolutional";
case CONNECTED:
return "connected";
+ case RNN:
+ return "rnn";
+ case GRU:
+ return "gru";
+ case CRNN:
+ return "crnn";
case MAXPOOL:
return "maxpool";
case AVGPOOL:
@@ -79,8 +108,12 @@
return "cost";
case ROUTE:
return "route";
+ case SHORTCUT:
+ return "shortcut";
case NORMALIZATION:
return "normalization";
+ case BATCHNORM:
+ return "batchnorm";
default:
break;
}
@@ -104,6 +137,7 @@
{
int i;
for(i = 0; i < net.n; ++i){
+ state.index = i;
layer l = net.layers[i];
if(l.delta){
scal_cpu(l.outputs * l.batch, 0, l.delta, 1);
@@ -112,12 +146,24 @@
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 == 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){
@@ -132,6 +178,8 @@
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);
}
state.input = l.output;
}
@@ -150,12 +198,23 @@
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);
}
}
}
float *get_network_output(network net)
{
+ #ifdef GPU
+ 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;
@@ -168,7 +227,7 @@
int count = 0;
for(i = 0; i < net.n; ++i){
if(net.layers[i].type == COST){
- sum += net.layers[i].output[0];
+ sum += net.layers[i].cost[0];
++count;
}
if(net.layers[i].type == DETECTION){
@@ -192,6 +251,7 @@
float *original_input = state.input;
float *original_delta = state.delta;
for(i = net.n-1; i >= 0; --i){
+ state.index = i;
if(i == 0){
state.input = original_input;
state.delta = original_delta;
@@ -205,8 +265,12 @@
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 == AVGPOOL){
@@ -219,10 +283,20 @@
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);
}
}
}
@@ -234,6 +308,8 @@
if(gpu_index >= 0) return train_network_datum_gpu(net, x, y);
#endif
network_state state;
+ state.index = 0;
+ state.net = net;
state.input = x;
state.delta = 0;
state.truth = y;
@@ -286,6 +362,8 @@
{
int i,j;
network_state state;
+ state.index = 0;
+ state.net = net;
state.train = 1;
state.delta = 0;
float sum = 0;
@@ -326,11 +404,12 @@
layer l = net->layers[i];
if(l.type == CONVOLUTIONAL){
resize_convolutional_layer(&l, w, h);
+ }else if(l.type == CROP){
+ resize_crop_layer(&l, w, h);
}else if(l.type == MAXPOOL){
resize_maxpool_layer(&l, w, h);
}else if(l.type == AVGPOOL){
resize_avgpool_layer(&l, w, h);
- break;
}else if(l.type == NORMALIZATION){
resize_normalization_layer(&l, w, h);
}else if(l.type == COST){
@@ -342,6 +421,7 @@
net->layers[i] = l;
w = l.out_w;
h = l.out_h;
+ if(l.type == AVGPOOL) break;
}
//fprintf(stderr, " Done!\n");
return 0;
@@ -422,6 +502,8 @@
#endif
network_state state;
+ state.net = net;
+ state.index = 0;
state.input = input;
state.truth = 0;
state.train = 0;
--
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