add avgpool layer

Makefile

@@ -34,9 +34,9 @@ CFLAGS+= -DGPU
 LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
 endif
 
-OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o detection.o route_layer.o writing.o box.o nightmare.o normalization_layer.o
+OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o detection.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o
 ifeq ($(GPU), 1) 
-OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o softmax_layer_kernels.o network_kernels.o
+OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o softmax_layer_kernels.o network_kernels.o avgpool_layer_kernels.o
 endif
 
 OBJS = $(addprefix $(OBJDIR), $(OBJ))

src/avgpool_layer.c

@@ -0,0 +1,66 @@
+#include "avgpool_layer.h"
+#include "cuda.h"
+#include <stdio.h>
+
+avgpool_layer make_avgpool_layer(int batch, int w, int h, int c)
+{
+    fprintf(stderr, "Avgpool Layer: %d x %d x %d image\n", w,h,c);
+    avgpool_layer l = {0};
+    l.type = AVGPOOL;
+    l.batch = batch;
+    l.h = h;
+    l.w = w;
+    l.c = c;
+    l.out_w = 1;
+    l.out_h = 1;
+    l.out_c = c;
+    l.outputs = l.out_c;
+    l.inputs = h*w*c;
+    int output_size = l.outputs * batch;
+    l.output =  calloc(output_size, sizeof(float));
+    l.delta =   calloc(output_size, sizeof(float));
+    #ifdef GPU
+    l.output_gpu  = cuda_make_array(l.output, output_size);
+    l.delta_gpu   = cuda_make_array(l.delta, output_size);
+    #endif
+    return l;
+}
+
+void resize_avgpool_layer(avgpool_layer *l, int w, int h)
+{
+    l->h = h;
+    l->w = w;
+}
+
+void forward_avgpool_layer(const avgpool_layer l, network_state state)
+{
+    int b,i,k;
+
+    for(b = 0; b < l.batch; ++b){
+        for(k = 0; k < l.c; ++k){
+            int out_index = k + b*l.c;
+            l.output[out_index] = 0;
+            for(i = 0; i < l.h*l.w; ++i){
+                int in_index = i + l.h*l.w*(k + b*l.c);
+                l.output[out_index] += state.input[in_index];
+            }
+            l.output[out_index] /= l.h*l.w;
+        }
+    }
+}
+
+void backward_avgpool_layer(const avgpool_layer l, network_state state)
+{
+    int b,i,k;
+
+    for(b = 0; b < l.batch; ++b){
+        for(k = 0; k < l.c; ++k){
+            int out_index = k + b*l.c;
+            for(i = 0; i < l.h*l.w; ++i){
+                int in_index = i + l.h*l.w*(k + b*l.c);
+                state.delta[in_index] = l.delta[out_index] / (l.h*l.w);
+            }
+        }
+    }
+}
+

src/avgpool_layer.h

@@ -0,0 +1,23 @@
+#ifndef AVGPOOL_LAYER_H
+#define AVGPOOL_LAYER_H
+
+#include "image.h"
+#include "params.h"
+#include "cuda.h"
+#include "layer.h"
+
+typedef layer avgpool_layer;
+
+image get_avgpool_image(avgpool_layer l);
+avgpool_layer make_avgpool_layer(int batch, int w, int h, int c);
+void resize_avgpool_layer(avgpool_layer *l, int w, int h);
+void forward_avgpool_layer(const avgpool_layer l, network_state state);
+void backward_avgpool_layer(const avgpool_layer l, network_state state);
+
+#ifdef GPU
+void forward_avgpool_layer_gpu(avgpool_layer l, network_state state);
+void backward_avgpool_layer_gpu(avgpool_layer l, network_state state);
+#endif
+
+#endif
+

src/avgpool_layer_kernels.cu

@@ -0,0 +1,57 @@
+extern "C" {
+#include "avgpool_layer.h"
+#include "cuda.h"
+}
+
+__global__ void forward_avgpool_layer_kernel(int n, int w, int h, int c, float *input, float *output)
+{
+    int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(id >= n) return;
+
+    int k = id % c;
+    id /= c;
+    int b = id;
+
+    int i;
+    int out_index = (k + c*b);
+    output[out_index] = 0;
+    for(i = 0; i < w*h; ++i){
+        int in_index = i + h*w*(k + b*c);
+        output[out_index] += input[in_index];
+    }
+    output[out_index] /= w*h;
+}
+
+__global__ void backward_avgpool_layer_kernel(int n, int w, int h, int c, float *in_delta, float *out_delta)
+{
+    int id = (blockIdx.x + blockIdx.y*gridDim.x) * blockDim.x + threadIdx.x;
+    if(id >= n) return;
+
+    int k = id % c;
+    id /= c;
+    int b = id;
+
+    int i;
+    int out_index = (k + c*b);
+    for(i = 0; i < w*h; ++i){
+        int in_index = i + h*w*(k + b*c);
+        in_delta[in_index] = out_delta[out_index] / (w*h);
+    }
+}
+
+extern "C" void forward_avgpool_layer_gpu(avgpool_layer layer, network_state state)
+{
+    size_t n = layer.c*layer.batch;
+
+    forward_avgpool_layer_kernel<<<cuda_gridsize(n), BLOCK>>>(n, layer.w, layer.h, layer.c, state.input, layer.output_gpu);
+    check_error(cudaPeekAtLastError());
+}
+
+extern "C" void backward_avgpool_layer_gpu(avgpool_layer layer, network_state state)
+{
+    size_t n = layer.c*layer.batch;
+
+    backward_avgpool_layer_kernel<<<cuda_gridsize(n), BLOCK>>>(n, layer.w, layer.h, layer.c, state.delta, layer.delta_gpu);
+    check_error(cudaPeekAtLastError());
+}
+

src/imagenet.c

@@ -25,7 +25,7 @@ void train_imagenet(char *cfgfile, char *weightfile)
     pthread_t load_thread;
     data train;
     data buffer;
-    load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 256, 256, &buffer);
+    load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, net.w, net.h, &buffer);
     while(1){
         ++i;
         time=clock();
@@ -38,7 +38,7 @@ void train_imagenet(char *cfgfile, char *weightfile)
         cvWaitKey(0);
         */
 
-        load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, 256, 256, &buffer);
+        load_thread = load_data_thread(paths, imgs, plist->size, labels, 1000, net.w, net.h, &buffer);
         printf("Loaded: %lf seconds\n", sec(clock()-time));
         time=clock();
         float loss = train_network(net, train);
@@ -47,7 +47,7 @@ void train_imagenet(char *cfgfile, char *weightfile)
         avg_loss = avg_loss*.9 + loss*.1;
         printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), net.seen);
         free_data(train);
-        if((i % 30000) == 0) net.learning_rate *= .1;
+        if((i % 20000) == 0) net.learning_rate *= .1;
         if(i%1000==0){
             char buff[256];
             sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);

src/layer.h

@@ -14,7 +14,8 @@ typedef enum {
     CROP,
     ROUTE,
     COST,
-    NORMALIZATION
+    NORMALIZATION,
+    AVGPOOL
 } LAYER_TYPE;
 
 typedef enum{

src/network.c

@@ -12,6 +12,7 @@
 #include "detection_layer.h"
 #include "normalization_layer.h"
 #include "maxpool_layer.h"
+#include "avgpool_layer.h"
 #include "cost_layer.h"
 #include "softmax_layer.h"
 #include "dropout_layer.h"
@@ -28,6 +29,8 @@ char *get_layer_string(LAYER_TYPE a)
             return "connected";
         case MAXPOOL:
             return "maxpool";
+        case AVGPOOL:
+            return "avgpool";
         case SOFTMAX:
             return "softmax";
         case DETECTION:
@@ -83,6 +86,8 @@ void forward_network(network net, network_state state)
             forward_softmax_layer(l, state);
         } else if(l.type == MAXPOOL){
             forward_maxpool_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){
@@ -156,6 +161,8 @@ void backward_network(network net, network_state state)
             backward_normalization_layer(l, state);
         } else if(l.type == MAXPOOL){
             if(i != 0) backward_maxpool_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){
@@ -273,6 +280,9 @@ int resize_network(network *net, int w, int h)
             resize_convolutional_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{

src/network_kernels.cu

@@ -15,6 +15,7 @@ extern "C" {
 #include "convolutional_layer.h"
 #include "deconvolutional_layer.h"
 #include "maxpool_layer.h"
+#include "avgpool_layer.h"
 #include "normalization_layer.h"
 #include "cost_layer.h"
 #include "softmax_layer.h"
@@ -49,6 +50,8 @@ void forward_network_gpu(network net, network_state state)
             forward_normalization_layer_gpu(l, state);
         } else if(l.type == MAXPOOL){
             forward_maxpool_layer_gpu(l, state);
+        } else if(l.type == AVGPOOL){
+            forward_avgpool_layer_gpu(l, state);
         } else if(l.type == DROPOUT){
             forward_dropout_layer_gpu(l, state);
         } else if(l.type == ROUTE){
@@ -79,6 +82,8 @@ void backward_network_gpu(network net, network_state state)
             backward_deconvolutional_layer_gpu(l, state);
         } else if(l.type == MAXPOOL){
             if(i != 0) backward_maxpool_layer_gpu(l, state);
+        } else if(l.type == AVGPOOL){
+            if(i != 0) backward_avgpool_layer_gpu(l, state);
         } else if(l.type == DROPOUT){
             backward_dropout_layer_gpu(l, state);
         } else if(l.type == DETECTION){

src/normalization_layer.c

@@ -40,10 +40,10 @@ void resize_normalization_layer(layer *layer, int w, int h)
     layer->out_w = w;
     layer->inputs = w*h*c;
     layer->outputs = layer->inputs;
-    layer->output = realloc(layer->output, h * w * layer->c * layer->batch * sizeof(float));
-    layer->delta = realloc(layer->delta, h * w * layer->c * layer->batch * sizeof(float));
-    layer->squared = realloc(layer->squared, h * w * layer->c * layer->batch * sizeof(float));
-    layer->norms = realloc(layer->norms, h * w * layer->c * layer->batch * sizeof(float));
+    layer->output = realloc(layer->output, h * w * c * batch * sizeof(float));
+    layer->delta = realloc(layer->delta, h * w * c * batch * sizeof(float));
+    layer->squared = realloc(layer->squared, h * w * c * batch * sizeof(float));
+    layer->norms = realloc(layer->norms, h * w * c * batch * sizeof(float));
 #ifdef GPU
     cuda_free(layer->output_gpu);
     cuda_free(layer->delta_gpu); 

src/parser.c

@@ -14,6 +14,7 @@
 #include "softmax_layer.h"
 #include "dropout_layer.h"
 #include "detection_layer.h"
+#include "avgpool_layer.h"
 #include "route_layer.h"
 #include "list.h"
 #include "option_list.h"
@@ -29,6 +30,7 @@ int is_convolutional(section *s);
 int is_deconvolutional(section *s);
 int is_connected(section *s);
 int is_maxpool(section *s);
+int is_avgpool(section *s);
 int is_dropout(section *s);
 int is_softmax(section *s);
 int is_normalization(section *s);
@@ -214,6 +216,19 @@ maxpool_layer parse_maxpool(list *options, size_params params)
     return layer;
 }
 
+avgpool_layer parse_avgpool(list *options, size_params params)
+{
+    int batch,w,h,c;
+    w = params.w;
+    h = params.h;
+    c = params.c;
+    batch=params.batch;
+    if(!(h && w && c)) error("Layer before avgpool layer must output image.");
+
+    avgpool_layer layer = make_avgpool_layer(batch,w,h,c);
+    return layer;
+}
+
 dropout_layer parse_dropout(list *options, size_params params)
 {
     float probability = option_find_float(options, "probability", .5);
@@ -333,6 +348,8 @@ network parse_network_cfg(char *filename)
             l = parse_normalization(options, params);
         }else if(is_maxpool(s)){
             l = parse_maxpool(options, params);
+        }else if(is_avgpool(s)){
+            l = parse_avgpool(options, params);
         }else if(is_route(s)){
             l = parse_route(options, params, net);
         }else if(is_dropout(s)){
@@ -402,6 +419,11 @@ int is_maxpool(section *s)
     return (strcmp(s->type, "[max]")==0
             || strcmp(s->type, "[maxpool]")==0);
 }
+int is_avgpool(section *s)
+{
+    return (strcmp(s->type, "[avg]")==0
+            || strcmp(s->type, "[avgpool]")==0);
+}
 int is_dropout(section *s)
 {
     return (strcmp(s->type, "[dropout]")==0);