From 11c72b1132feca7c1252ea01d02da4cb497e723f Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 11 Jun 2015 22:38:58 +0000
Subject: [PATCH] testing on one image
---
src/network_kernels.cu | 286 ++++++++++++++++++--------------------------------------
1 files changed, 94 insertions(+), 192 deletions(-)
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index 1f3f2e0..5e353ae 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -6,200 +6,110 @@
#include "image.h"
#include "data.h"
#include "utils.h"
+#include "params.h"
+#include "parser.h"
#include "crop_layer.h"
#include "connected_layer.h"
+#include "detection_layer.h"
#include "convolutional_layer.h"
#include "deconvolutional_layer.h"
#include "maxpool_layer.h"
#include "cost_layer.h"
-#include "normalization_layer.h"
-#include "freeweight_layer.h"
#include "softmax_layer.h"
#include "dropout_layer.h"
+#include "route_layer.h"
}
-extern "C" float * get_network_output_gpu_layer(network net, int i);
-extern "C" float * get_network_delta_gpu_layer(network net, int i);
+float * get_network_output_gpu_layer(network net, int i);
+float * get_network_delta_gpu_layer(network net, int i);
+float * get_network_output_gpu(network net);
-void forward_network_gpu(network net, float * input, float * truth, int train)
+void forward_network_gpu(network net, network_state state)
{
int i;
for(i = 0; i < net.n; ++i){
- //clock_t time = clock();
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- forward_convolutional_layer_gpu(layer, input);
- input = layer.output_gpu;
+ layer l = net.layers[i];
+ if(l.type == CONVOLUTIONAL){
+ forward_convolutional_layer_gpu(l, state);
+ } else if(l.type == DECONVOLUTIONAL){
+ forward_deconvolutional_layer_gpu(l, state);
+ } else if(l.type == DETECTION){
+ forward_detection_layer_gpu(l, state);
+ } else if(l.type == CONNECTED){
+ forward_connected_layer_gpu(l, state);
+ } else if(l.type == CROP){
+ forward_crop_layer_gpu(l, state);
+ } else if(l.type == COST){
+ forward_cost_layer_gpu(l, state);
+ } else if(l.type == SOFTMAX){
+ forward_softmax_layer_gpu(l, state);
+ } else if(l.type == MAXPOOL){
+ forward_maxpool_layer_gpu(l, state);
+ } else if(l.type == DROPOUT){
+ forward_dropout_layer_gpu(l, state);
+ } else if(l.type == ROUTE){
+ forward_route_layer_gpu(l, net);
}
- else if(net.types[i] == DECONVOLUTIONAL){
- deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
- forward_deconvolutional_layer_gpu(layer, input);
- input = layer.output_gpu;
- }
- else if(net.types[i] == COST){
- cost_layer layer = *(cost_layer *)net.layers[i];
- forward_cost_layer_gpu(layer, input, truth);
- }
- else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- forward_connected_layer_gpu(layer, input);
- input = layer.output_gpu;
- }
- else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- forward_maxpool_layer_gpu(layer, input);
- input = layer.output_gpu;
- }
- else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- forward_softmax_layer_gpu(layer, input);
- input = layer.output_gpu;
- }
- else if(net.types[i] == DROPOUT){
- if(!train) continue;
- dropout_layer layer = *(dropout_layer *)net.layers[i];
- forward_dropout_layer_gpu(layer, input);
- input = layer.output_gpu;
- }
- else if(net.types[i] == CROP){
- crop_layer layer = *(crop_layer *)net.layers[i];
- forward_crop_layer_gpu(layer, train, input);
- input = layer.output_gpu;
- }
- //cudaDeviceSynchronize();
- //printf("Forward %d %s %f\n", i, get_layer_string(net.types[i]), sec(clock() - time));
+ state.input = l.output_gpu;
}
}
-void backward_network_gpu(network net, float * input)
+void backward_network_gpu(network net, network_state state)
{
int i;
- float * prev_input;
- float * prev_delta;
+ float * original_input = state.input;
for(i = net.n-1; i >= 0; --i){
- //clock_t time = clock();
+ layer l = net.layers[i];
if(i == 0){
- prev_input = input;
- prev_delta = 0;
+ state.input = original_input;
+ state.delta = 0;
}else{
- prev_input = get_network_output_gpu_layer(net, i-1);
- prev_delta = get_network_delta_gpu_layer(net, i-1);
+ layer prev = net.layers[i-1];
+ state.input = prev.output_gpu;
+ state.delta = prev.delta_gpu;
}
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- backward_convolutional_layer_gpu(layer, prev_input, prev_delta);
+ if(l.type == CONVOLUTIONAL){
+ backward_convolutional_layer_gpu(l, state);
+ } else if(l.type == DECONVOLUTIONAL){
+ backward_deconvolutional_layer_gpu(l, state);
+ } else if(l.type == MAXPOOL){
+ if(i != 0) backward_maxpool_layer_gpu(l, state);
+ } else if(l.type == DROPOUT){
+ backward_dropout_layer_gpu(l, state);
+ } else if(l.type == DETECTION){
+ backward_detection_layer_gpu(l, state);
+ } else if(l.type == SOFTMAX){
+ if(i != 0) backward_softmax_layer_gpu(l, state);
+ } else if(l.type == CONNECTED){
+ backward_connected_layer_gpu(l, state);
+ } else if(l.type == COST){
+ backward_cost_layer_gpu(l, state);
+ } else if(l.type == ROUTE){
+ backward_route_layer_gpu(l, net);
}
- else if(net.types[i] == DECONVOLUTIONAL){
- deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
- backward_deconvolutional_layer_gpu(layer, prev_input, prev_delta);
- }
- else if(net.types[i] == COST){
- cost_layer layer = *(cost_layer *)net.layers[i];
- backward_cost_layer_gpu(layer, prev_input, prev_delta);
- }
- else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- backward_connected_layer_gpu(layer, prev_input, prev_delta);
- }
- else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- backward_maxpool_layer_gpu(layer, prev_delta);
- }
- else if(net.types[i] == DROPOUT){
- dropout_layer layer = *(dropout_layer *)net.layers[i];
- backward_dropout_layer_gpu(layer, prev_delta);
- }
- else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- backward_softmax_layer_gpu(layer, prev_delta);
- }
- //printf("Backward %d %s %f\n", i, get_layer_string(net.types[i]), sec(clock() - time));
}
}
void update_network_gpu(network net)
{
int i;
+ int update_batch = net.batch*net.subdivisions;
for(i = 0; i < net.n; ++i){
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- update_convolutional_layer_gpu(layer);
- }
- else if(net.types[i] == DECONVOLUTIONAL){
- deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
- update_deconvolutional_layer_gpu(layer);
- }
- else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- update_connected_layer_gpu(layer);
+ layer l = net.layers[i];
+ if(l.type == CONVOLUTIONAL){
+ update_convolutional_layer_gpu(l, update_batch, net.learning_rate, net.momentum, net.decay);
+ } else if(l.type == DECONVOLUTIONAL){
+ update_deconvolutional_layer_gpu(l, net.learning_rate, net.momentum, net.decay);
+ } else if(l.type == CONNECTED){
+ update_connected_layer_gpu(l, update_batch, net.learning_rate, net.momentum, net.decay);
}
}
}
-float * get_network_output_gpu_layer(network net, int i)
-{
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- return layer.output_gpu;
- }
- else if(net.types[i] == DECONVOLUTIONAL){
- deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
- return layer.output_gpu;
- }
- else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- return layer.output_gpu;
- }
- else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- return layer.output_gpu;
- }
- else if(net.types[i] == CROP){
- crop_layer layer = *(crop_layer *)net.layers[i];
- return layer.output_gpu;
- }
- else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- return layer.output_gpu;
- } else if(net.types[i] == DROPOUT){
- dropout_layer layer = *(dropout_layer *)net.layers[i];
- return layer.output_gpu;
- }
- return 0;
-}
-
-float * get_network_delta_gpu_layer(network net, int i)
-{
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- return layer.delta_gpu;
- }
- else if(net.types[i] == DECONVOLUTIONAL){
- deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
- return layer.delta_gpu;
- }
- else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- return layer.delta_gpu;
- }
- else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- return layer.delta_gpu;
- }
- else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- return layer.delta_gpu;
- } else if(net.types[i] == DROPOUT){
- if(i == 0) return 0;
- return get_network_delta_gpu_layer(net, i-1);
- }
- return 0;
-}
-
float train_network_datum_gpu(network net, float *x, float *y)
{
- //clock_t time = clock();
+ network_state state;
int x_size = get_network_input_size(net)*net.batch;
int y_size = get_network_output_size(net)*net.batch;
if(!*net.input_gpu){
@@ -209,44 +119,33 @@
cuda_push_array(*net.input_gpu, x, x_size);
cuda_push_array(*net.truth_gpu, y, y_size);
}
- //printf("trans %f\n", sec(clock() - time));
- //time = clock();
- forward_network_gpu(net, *net.input_gpu, *net.truth_gpu, 1);
- //printf("forw %f\n", sec(clock() - time));
- //time = clock();
- backward_network_gpu(net, *net.input_gpu);
- //printf("back %f\n", sec(clock() - time));
- //time = clock();
- update_network_gpu(net);
+ state.input = *net.input_gpu;
+ state.truth = *net.truth_gpu;
+ state.train = 1;
+ forward_network_gpu(net, state);
+ backward_network_gpu(net, state);
float error = get_network_cost(net);
- //printf("updt %f\n", sec(clock() - time));
- //time = clock();
+ if ((net.seen / net.batch) % net.subdivisions == 0) update_network_gpu(net);
+
return error;
}
float *get_network_output_layer_gpu(network net, int i)
{
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- return layer.output;
- }
- else if(net.types[i] == DECONVOLUTIONAL){
- deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
- return layer.output;
- }
- else if(net.types[i] == CONNECTED){
- connected_layer layer = *(connected_layer *)net.layers[i];
- cuda_pull_array(layer.output_gpu, layer.output, layer.outputs*layer.batch);
- return layer.output;
- }
- else if(net.types[i] == MAXPOOL){
- maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- return layer.output;
- }
- else if(net.types[i] == SOFTMAX){
- softmax_layer layer = *(softmax_layer *)net.layers[i];
- pull_softmax_layer_output(layer);
- return layer.output;
+ layer l = net.layers[i];
+ cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
+ if(l.type == CONVOLUTIONAL){
+ return l.output;
+ } else if(l.type == DECONVOLUTIONAL){
+ return l.output;
+ } else if(l.type == CONNECTED){
+ return l.output;
+ } else if(l.type == DETECTION){
+ return l.output;
+ } else if(l.type == MAXPOOL){
+ return l.output;
+ } else if(l.type == SOFTMAX){
+ return l.output;
}
return 0;
}
@@ -254,18 +153,21 @@
float *get_network_output_gpu(network net)
{
int i;
- for(i = net.n-1; i > 0; --i) if(net.types[i] != COST) break;
+ for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
return get_network_output_layer_gpu(net, i);
}
float *network_predict_gpu(network net, float *input)
{
-
int size = get_network_input_size(net) * net.batch;
- float * input_gpu = cuda_make_array(input, size);
- forward_network_gpu(net, input_gpu, 0, 0);
+ network_state state;
+ state.input = cuda_make_array(input, size);
+ state.truth = 0;
+ state.train = 0;
+ state.delta = 0;
+ forward_network_gpu(net, state);
float *out = get_network_output_gpu(net);
- cuda_free(input_gpu);
+ cuda_free(state.input);
return out;
}
--
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