Minor Python and C API improvement

build/darknet/x64/darknet.py

@@ -125,7 +125,7 @@ lib.network_width.restype = c_int
 lib.network_height.argtypes = [c_void_p]
 lib.network_height.restype = c_int
 
-predict = lib.network_predict
+predict = lib.network_predict_ptr
 predict.argtypes = [c_void_p, POINTER(c_float)]
 predict.restype = POINTER(c_float)
 
@@ -151,7 +151,7 @@ free_detections.argtypes = [POINTER(DETECTION), c_int]
 free_ptrs = lib.free_ptrs
 free_ptrs.argtypes = [POINTER(c_void_p), c_int]
 
-network_predict = lib.network_predict
+network_predict = lib.network_predict_ptr
 network_predict.argtypes = [c_void_p, POINTER(c_float)]
 
 reset_rnn = lib.reset_rnn

build/darknet/x64/darknet_video.py

@@ -60,7 +60,7 @@ lib.network_width.restype = c_int
 lib.network_height.argtypes = [c_void_p]
 lib.network_height.restype = c_int
 
-predict = lib.network_predict
+predict = lib.network_predict_ptr
 predict.argtypes = [c_void_p, POINTER(c_float)]
 predict.restype = POINTER(c_float)
 
@@ -88,7 +88,7 @@ free_detections.argtypes = [POINTER(DETECTION), c_int]
 free_ptrs = lib.free_ptrs
 free_ptrs.argtypes = [POINTER(c_void_p), c_int]
 
-network_predict = lib.network_predict
+network_predict = lib.network_predict_ptr
 network_predict.argtypes = [c_void_p, POINTER(c_float)]
 
 reset_rnn = lib.reset_rnn

darknet.py

@@ -151,7 +151,7 @@ free_detections.argtypes = [POINTER(DETECTION), c_int]
 free_ptrs = lib.free_ptrs
 free_ptrs.argtypes = [POINTER(c_void_p), c_int]
 
-network_predict = lib.network_predict
+network_predict = lib.network_predict_ptr
 network_predict.argtypes = [c_void_p, POINTER(c_float)]
 
 reset_rnn = lib.reset_rnn

darknet_video.py

@@ -60,7 +60,7 @@ lib.network_width.restype = c_int
 lib.network_height.argtypes = [c_void_p]
 lib.network_height.restype = c_int
 
-predict = lib.network_predict
+predict = lib.network_predict_ptr
 predict.argtypes = [c_void_p, POINTER(c_float)]
 predict.restype = POINTER(c_float)
 
@@ -88,7 +88,7 @@ free_detections.argtypes = [POINTER(DETECTION), c_int]
 free_ptrs = lib.free_ptrs
 free_ptrs.argtypes = [POINTER(c_void_p), c_int]
 
-network_predict = lib.network_predict
+network_predict = lib.network_predict_ptr
 network_predict.argtypes = [c_void_p, POINTER(c_float)]
 
 reset_rnn = lib.reset_rnn

include/darknet.h

@@ -745,6 +745,7 @@ LIB_API void do_nms_obj(detection *dets, int total, int classes, float thresh);
 
 // network.h
 LIB_API float *network_predict(network net, float *input);
+LIB_API float *network_predict_ptr(network *net, float *input);
 LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter);
 LIB_API void free_detections(detection *dets, int n);
 LIB_API void fuse_conv_batchnorm(network net);
@@ -766,6 +767,7 @@ LIB_API void optimize_picture(network *net, image orig, int max_layer, float sca
 
 // image.h
 LIB_API image resize_image(image im, int w, int h);
+LIB_API void copy_image_from_bytes(image im, char *pdata);
 LIB_API image letterbox_image(image im, int w, int h);
 LIB_API void rgbgr_image(image im);
 LIB_API image make_image(int w, int h, int c);

src/detector.c

@@ -247,6 +247,9 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
         if (mean_average_precision > 0) {
             printf("\n Last accuracy mAP@0.5 = %2.2f %% ", mean_average_precision*100);
         }
+        int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions);  // calculate mAP for each 4 Epochs
+        if (calc_map) printf("\n (next mAP calculation at %d iterations) ", (iter_map + calc_map_for_each));
+
         if (net.cudnn_half) {
             if (i < net.burn_in * 3) printf("\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in);
             else printf("\n Tensor Cores are used.");
@@ -254,8 +257,6 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
         printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", get_current_batch(net), loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), i*imgs);
 
         int draw_precision = 0;
-        int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions);  // calculate mAP for each 4 Epochs
-        if (calc_map) printf(" Next mAP calculation at %d iterations \n", (iter_map + calc_map_for_each));
         if (calc_map && (i >= (iter_map + calc_map_for_each) || i == net.max_batches) && i >= net.burn_in && i >= 1000) {
             if (l.random) {
                 printf("Resizing to initial size: %d x %d \n", init_w, init_h);

src/image.h

@@ -33,7 +33,7 @@ image random_crop_image(image im, int w, int h);
 image random_augment_image(image im, float angle, float aspect, int low, int high, int size);
 void random_distort_image(image im, float hue, float saturation, float exposure);
 //LIB_API image resize_image(image im, int w, int h);
-LIB_API void copy_image_from_bytes(image im, char *pdata);
+//LIB_API void copy_image_from_bytes(image im, char *pdata);
 void fill_image(image m, float s);
 void letterbox_image_into(image im, int w, int h, image boxed);
 //LIB_API image letterbox_image(image im, int w, int h);

src/network.c

@@ -738,6 +738,7 @@ float *network_predict_image(network *net, image im)
 {
     //image imr = letterbox_image(im, net->w, net->h);
     float *p;
+    if(net->batch != 1) set_batch_network(net, 1);
     if (im.w == net->w && im.h == net->h) {
         // Input image is the same size as our net, predict on that image
         p = network_predict(*net, im.data);

src/network.h

@@ -122,7 +122,7 @@ float train_network_datum(network net, float *x, float *y);
 
 matrix network_predict_data(network net, data test);
 //LIB_API float *network_predict(network net, float *input);
-LIB_API float *network_predict_ptr(network *net, float *input);
+//LIB_API float *network_predict_ptr(network *net, float *input);
 float network_accuracy(network net, data d);
 float *network_accuracies(network net, data d, int n);
 float network_accuracy_multi(network net, data d, int n);