Merge Changes from Base Repo

README.md

@@ -1,5 +1,5 @@
 # Yolo-v3 and Yolo-v2 for Windows and Linux
-### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-vcpkg)
+### (neural network for object detection) - Tensor Cores can be used on [Linux](https://github.com/AlexeyAB/darknet#how-to-compile-on-linux) and [Windows](https://github.com/AlexeyAB/darknet#how-to-compile-on-windows-using-cmake-gui)
 
 More details: http://pjreddie.com/darknet/yolo/
 
@@ -8,7 +8,8 @@ More details: http://pjreddie.com/darknet/yolo/
 [![TravisCI](https://travis-ci.org/AlexeyAB/darknet.svg?branch=master)](https://travis-ci.org/AlexeyAB/darknet)
 [![AppveyorCI](https://ci.appveyor.com/api/projects/status/594bwb5uoc1fxwiu/branch/master?svg=true)](https://ci.appveyor.com/project/AlexeyAB/darknet/branch/master)
 [![Contributors](https://img.shields.io/github/contributors/AlexeyAB/Darknet.svg)](https://github.com/AlexeyAB/darknet/graphs/contributors)
-[![License: Unlicense](https://img.shields.io/badge/license-Unlicense-blue.svg)](https://github.com/AlexeyAB/darknet/blob/master/LICENSE)  
+[![License: Unlicense](https://img.shields.io/badge/license-Unlicense-blue.svg)](https://github.com/AlexeyAB/darknet/blob/master/LICENSE)
+[![DOI](https://zenodo.org/badge/75388965.svg)](https://zenodo.org/badge/latestdoi/75388965)
 
 
 * [Requirements (and how to install dependecies)](#requirements)
@@ -395,7 +396,7 @@ Training Yolo v3:
       * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L610
       * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L696
       * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L783
-  * change [`filters=255`] to filters=(classes + 5)x3 in the 3 `[convolutional]` before each `[yolo]` layer
+  * change [`filters=255`] to filters=(classes + 5)x3 in the 3 `[convolutional]` before each `[yolo]` layer, keep in mind that it only has to be the last `[convolutional]` before each of the `[yolo]` layers.
       * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L603
       * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L689
       * https://github.com/AlexeyAB/darknet/blob/0039fd26786ab5f71d5af725fc18b3f521e7acfd/cfg/yolov3.cfg#L776

include/darknet.h

@@ -34,6 +34,8 @@
 
 #define SECRET_NUM -1234
 
+typedef enum { UNUSED_DEF_VAL } UNUSED_ENUM_TYPE;
+
 #ifdef GPU
 
 #include <cuda_runtime.h>
@@ -42,8 +44,8 @@
 
 #ifdef CUDNN
 #include <cudnn.h>
-#endif
-#endif
+#endif  // CUDNN
+#endif  // GPU
 
 #ifdef __cplusplus
 extern "C" {
@@ -216,6 +218,7 @@ struct layer {
     int batch_normalize;
     int shortcut;
     int batch;
+    int dynamic_minibatch;
     int forced;
     int flipped;
     int inputs;
@@ -495,7 +498,7 @@ struct layer {
 
     size_t workspace_size;
 
-#ifdef GPU
+//#ifdef GPU
     int *indexes_gpu;
 
     float *z_gpu;
@@ -610,8 +613,21 @@ struct layer {
     cudnnConvolutionBwdDataAlgo_t bd_algo, bd_algo16;
     cudnnConvolutionBwdFilterAlgo_t bf_algo, bf_algo16;
     cudnnPoolingDescriptor_t poolingDesc;
+#else   // CUDNN
+    void* srcTensorDesc, *dstTensorDesc;
+    void* srcTensorDesc16, *dstTensorDesc16;
+    void* dsrcTensorDesc, *ddstTensorDesc;
+    void* dsrcTensorDesc16, *ddstTensorDesc16;
+    void* normTensorDesc, *normDstTensorDesc, *normDstTensorDescF16;
+    void* weightDesc, *weightDesc16;
+    void* dweightDesc, *dweightDesc16;
+    void* convDesc;
+    UNUSED_ENUM_TYPE fw_algo, fw_algo16;
+    UNUSED_ENUM_TYPE bd_algo, bd_algo16;
+    UNUSED_ENUM_TYPE bf_algo, bf_algo16;
+    void* poolingDesc;
 #endif  // CUDNN
-#endif  // GPU
+//#endif  // GPU
 };
 
 
@@ -625,6 +641,7 @@ typedef struct network {
     int n;
     int batch;
     uint64_t *seen;
+    int *cur_iteration;
     int *t;
     float epoch;
     int subdivisions;
@@ -701,7 +718,7 @@ typedef struct network {
     float *cost;
     float clip;
 
-#ifdef GPU
+//#ifdef GPU
     //float *input_gpu;
     //float *truth_gpu;
     float *delta_gpu;
@@ -722,8 +739,9 @@ typedef struct network {
     float *global_delta_gpu;
     float *state_delta_gpu;
     size_t max_delta_gpu_size;
-#endif
+//#endif  // GPU
     int optimized_memory;
+    int dynamic_minibatch;
     size_t workspace_size_limit;
 } network;
 

src/batchnorm_layer.c

@@ -258,15 +258,17 @@ void forward_batchnorm_layer_gpu(layer l, network_state state)
             fast_mean_gpu(l.output_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.mean_gpu);
 
             //fast_v_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, l.v_cbn_gpu);
-            int minibatch_index = state.net.current_subdivision + 1;
-            float alpha = 0.01;
+            const int minibatch_index = state.net.current_subdivision + 1;
+            const int max_minibatch_index = state.net.subdivisions;
+            //printf("\n minibatch_index = %d, max_minibatch_index = %d \n", minibatch_index, max_minibatch_index);
+            const float alpha = 0.01;
 
             int inverse_variance = 0;
 #ifdef CUDNN
             inverse_variance = 1;
 #endif  // CUDNN
 
-            fast_v_cbn_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, minibatch_index, l.m_cbn_avg_gpu, l.v_cbn_avg_gpu, l.variance_gpu,
+            fast_v_cbn_gpu(l.output_gpu, l.mean_gpu, l.batch, l.out_c, l.out_h*l.out_w, minibatch_index, max_minibatch_index, l.m_cbn_avg_gpu, l.v_cbn_avg_gpu, l.variance_gpu,
                 alpha, l.rolling_mean_gpu, l.rolling_variance_gpu, inverse_variance, .00001);
 
             normalize_scale_bias_gpu(l.output_gpu, l.mean_gpu, l.variance_gpu, l.scales_gpu, l.biases_gpu, l.batch, l.out_c, l.out_h*l.out_w, inverse_variance, .00001f);

src/blas.h

@@ -87,7 +87,7 @@ void fast_variance_delta_gpu(float *x, float *delta, float *mean, float *varianc
 
 void fast_mean_gpu(float *x, int batch, int filters, int spatial, float *mean);
 void fast_variance_gpu(float *x, float *mean, int batch, int filters, int spatial, float *variance);
-void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, float *m_avg, float *v_avg, float *variance,
+void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, int max_minibatch_index, float *m_avg, float *v_avg, float *variance,
     const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon);
 void normalize_scale_bias_gpu(float *x, float *mean, float *variance, float *scales, float *biases, int batch, int filters, int spatial, int inverse_variance, float epsilon);
 void compare_2_arrays_gpu(float *one, float *two, int size);

src/blas_kernels.cu

@@ -572,7 +572,7 @@ extern "C" void fast_variance_gpu(float *x, float *mean, int batch, int filters,
 }
 
 
-__global__ void  fast_v_cbn_kernel(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, float *m_avg, float *v_avg, float *variance,
+__global__ void  fast_v_cbn_kernel(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, int max_minibatch_index, float *m_avg, float *v_avg, float *variance,
     const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon)
 {
     const int threads = BLOCK;
@@ -615,16 +615,19 @@ __global__ void  fast_v_cbn_kernel(const float *x, float *mean, int batch, int f
         if (inverse_variance) variance[filter] = 1.0f / sqrtf(variance_tmp + epsilon);
         else variance[filter] = variance_tmp;
 
-        rolling_mean_gpu[filter] = alpha * mean[filter] + (1 - alpha) * rolling_mean_gpu[filter];
+        //if (max_minibatch_index == minibatch_index)
+        {
+            rolling_mean_gpu[filter] = alpha * mean[filter] + (1 - alpha) * rolling_mean_gpu[filter];
 
-        rolling_variance_gpu[filter] = alpha * variance_tmp + (1 - alpha) * rolling_variance_gpu[filter];
+            rolling_variance_gpu[filter] = alpha * variance_tmp + (1 - alpha) * rolling_variance_gpu[filter];
+        }
     }
 }
 
-extern "C" void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, float *m_avg, float *v_avg, float *variance,
+extern "C" void fast_v_cbn_gpu(const float *x, float *mean, int batch, int filters, int spatial, int minibatch_index, int max_minibatch_index, float *m_avg, float *v_avg, float *variance,
     const float alpha, float *rolling_mean_gpu, float *rolling_variance_gpu, int inverse_variance, float epsilon)
 {
-    fast_v_cbn_kernel << <filters, BLOCK, 0, get_cuda_stream() >> >(x, mean, batch, filters, spatial, minibatch_index, m_avg, v_avg, variance, alpha, rolling_mean_gpu, rolling_variance_gpu, inverse_variance, epsilon);
+    fast_v_cbn_kernel << <filters, BLOCK, 0, get_cuda_stream() >> >(x, mean, batch, filters, spatial, minibatch_index, max_minibatch_index, m_avg, v_avg, variance, alpha, rolling_mean_gpu, rolling_variance_gpu, inverse_variance, epsilon);
     CHECK_CUDA(cudaPeekAtLastError());
 }
 

src/classifier.c

@@ -162,8 +162,8 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
         calc_topk_for_each = fmax(calc_topk_for_each, 100);
         if (i % 10 == 0) {
             if (calc_topk) {
-                fprintf(stderr, "\n (next TOP5 calculation at %d iterations) ", calc_topk_for_each);
-                if (topk > 0) fprintf(stderr, " Last accuracy TOP5 = %2.2f %% \n", topk * 100);
+                fprintf(stderr, "\n (next TOP%d calculation at %d iterations) ", topk_data, calc_topk_for_each);
+                if (topk > 0) fprintf(stderr, " Last accuracy TOP%d = %2.2f %% \n", topk_data, topk * 100);
             }
 
             if (net.cudnn_half) {
@@ -175,7 +175,7 @@ void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int *gpus,
         int draw_precision = 0;
         if (calc_topk && (i >= calc_topk_for_each || i == net.max_batches)) {
             iter_topk = i;
-            topk = validate_classifier_single(datacfg, cfgfile, weightfile, &net, topk_data); // calc TOP5
+            topk = validate_classifier_single(datacfg, cfgfile, weightfile, &net, topk_data); // calc TOP-n
             printf("\n accuracy %s = %f \n", topk_buff, topk);
             draw_precision = 1;
         }

src/convolutional_kernels.cu

@@ -419,7 +419,7 @@ void forward_convolutional_layer_gpu(convolutional_layer l, network_state state)
 
 //#ifdef CUDNN_HALF
     //if (state.use_mixed_precision) {
-    int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions);
+    int iteration_num = get_current_iteration(state.net); // (*state.net.seen) / (state.net.batch*state.net.subdivisions);
     if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) &&
         (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 1 && l.size > 1)
     {
@@ -671,7 +671,7 @@ void backward_convolutional_layer_gpu(convolutional_layer l, network_state state
     float alpha = 1, beta = 0;
 
 //#ifdef CUDNN_HALF
-    int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions);
+    int iteration_num = get_current_iteration(state.net); //(*state.net.seen) / (state.net.batch*state.net.subdivisions);
     if (state.index != 0 && state.net.cudnn_half && !l.xnor && (!state.train || iteration_num > 3*state.net.burn_in) &&
         (l.c / l.groups) % 8 == 0 && l.n % 8 == 0 && !state.train && l.groups <= 1 && l.size > 1)
     {
@@ -978,7 +978,7 @@ void assisted_activation2_gpu(float alpha, float *output, float *gt_gpu, float *
 
 void assisted_excitation_forward_gpu(convolutional_layer l, network_state state)
 {
-    const int iteration_num = (*state.net.seen) / (state.net.batch*state.net.subdivisions);
+    const int iteration_num = get_current_iteration(state.net); //(*state.net.seen) / (state.net.batch*state.net.subdivisions);
 
     // epoch
     //const float epoch = (float)(*state.net.seen) / state.net.train_images_num;

src/convolutional_layer.c

@@ -786,7 +786,7 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h)
 
     if (l->activation == SWISH || l->activation == MISH) l->activation_input = (float*)realloc(l->activation_input, total_batch*l->outputs * sizeof(float));
 #ifdef GPU
-    if (old_w < w || old_h < h) {
+    if (old_w < w || old_h < h || l->dynamic_minibatch) {
         if (l->train) {
             cuda_free(l->delta_gpu);
             l->delta_gpu = cuda_make_array(l->delta, total_batch*l->outputs);

src/darknet.c

@@ -455,6 +455,7 @@ int main(int argc, char **argv)
 
 #ifndef GPU
     gpu_index = -1;
+    printf(" GPU isn't used \n");
     init_cpu();
 #else
     if(gpu_index >= 0){