no comment

src/convolutional_kernels.cu

@@ -11,15 +11,15 @@ extern "C" {
 __global__ void bias_output_kernel(float *output, float *biases, int n, int size)
 {
     int offset = blockIdx.x * blockDim.x + threadIdx.x;
-    int filter = blockIdx.y % n;
+    int filter = blockIdx.y;
-    int batch = blockIdx.y / n;
+    int batch = blockIdx.z;
 
     if(offset < size) output[(batch*n+filter)*size + offset] = biases[filter];
 }
 
 void bias_output_gpu(float *output, float *biases, int batch, int n, int size)
 {
-    dim3 dimGrid((size-1)/BLOCK + 1, n*batch, 1);
+    dim3 dimGrid((size-1)/BLOCK + 1, n, batch);
     dim3 dimBlock(BLOCK, 1, 1);
 
     bias_output_kernel<<<dimGrid, dimBlock>>>(output, biases, n, size);

src/data.c

@@ -66,6 +66,7 @@ matrix load_image_paths(char **paths, int n, int h, int w)
 typedef struct box{
     int id;
     float x,y,w,h;
+    float left, right, top, bottom;
 } box;
 
 box *read_boxes(char *filename, int *n)
@@ -83,6 +84,10 @@ box *read_boxes(char *filename, int *n)
         boxes[count].y = y;
         boxes[count].h = h;
         boxes[count].w = w;
+        boxes[count].left   = x - w/2;
+        boxes[count].right  = x + w/2;
+        boxes[count].top    = y - h/2;
+        boxes[count].bottom = y + h/2;
         ++count;
     }
     fclose(file);

src/image.c

@@ -395,6 +395,26 @@ image ipl_to_image(IplImage* src)
     return out;
 }
 
+image crop_image(image im, int dr, int dc, int h, int w)
+{
+    image cropped = make_image(h, w, im.c);
+    int i, j, k;
+    for(k = 0; k < im.c; ++k){
+        for(j = 0; j < h; ++j){
+            for(i = 0; i < w; ++i){
+                int r = j + dr;
+                int c = i + dc;
+                float val = 128;
+                if (r >= 0 && r < im.h && c >= 0 && c < im.w) {
+                    val = get_pixel(im, r, c, k);
+                }
+                set_pixel(cropped, j, i, k, val);
+            }
+        }
+    }
+    return cropped;
+}
+
 // #wikipedia
 image resize_image(image im, int h, int w)
 {
@@ -427,9 +447,13 @@ void test_resize(char *filename)
     image im = load_image(filename, 0,0);
     image small = resize_image(im, 63, 65);
     image big = resize_image(im, 512, 513);
+    image crop = crop_image(im, 10, 50, 100, 100);
+    image crop2 = crop_image(im, -50, -30, 400, 291);
     show_image(im, "original");
     show_image(small, "smaller");
     show_image(big, "bigger");
+    show_image(crop, "crop");
+    show_image(crop2, "crop2");
     cvWaitKey(0);
 }
 

src/imagenet.c

@@ -38,6 +38,7 @@ 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%100 == 0 && net.learning_rate > .00001) net.learning_rate *= .97;
         if(i%100==0){
             char buff[256];
             sprintf(buff, "/home/pjreddie/imagenet_backup/%s_%d.weights",base, i);