From 5f4a5f59b072d4029107422d30b04941424c48b1 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 24 Feb 2015 02:52:05 +0000
Subject: [PATCH] captcha stuff
---
src/network_kernels.cu | 34 +++++++++++++++++++++++++++++++++-
1 files changed, 33 insertions(+), 1 deletions(-)
diff --git a/src/network_kernels.cu b/src/network_kernels.cu
index 7909e46..b83d056 100644
--- a/src/network_kernels.cu
+++ b/src/network_kernels.cu
@@ -10,6 +10,7 @@
#include "crop_layer.h"
#include "connected_layer.h"
#include "convolutional_layer.h"
+#include "deconvolutional_layer.h"
#include "maxpool_layer.h"
#include "cost_layer.h"
#include "normalization_layer.h"
@@ -20,6 +21,7 @@
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(network net);
void forward_network_gpu(network net, float * input, float * truth, int train)
{
@@ -31,6 +33,11 @@
forward_convolutional_layer_gpu(layer, input);
input = layer.output_gpu;
}
+ 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);
@@ -58,9 +65,10 @@
}
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
- forward_crop_layer_gpu(layer, input);
+ 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));
}
}
@@ -83,6 +91,10 @@
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
backward_convolutional_layer_gpu(layer, prev_input, prev_delta);
}
+ 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);
@@ -115,6 +127,10 @@
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);
@@ -128,6 +144,10 @@
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;
@@ -156,6 +176,10 @@
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;
@@ -196,6 +220,10 @@
//time = clock();
update_network_gpu(net);
float error = get_network_cost(net);
+
+ //print_letters(y, 50);
+ //float *out = get_network_output_gpu(net);
+ //print_letters(out, 50);
//printf("updt %f\n", sec(clock() - time));
//time = clock();
return error;
@@ -207,6 +235,10 @@
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);
--
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