For CPU and GPU structures in the darknet.h have the same size

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  // CUDNN
-#endif
+#endif  // GPU
 
 #ifdef __cplusplus
 extern "C" {
@@ -495,7 +497,7 @@ struct layer {
 
     size_t workspace_size;
 
-#ifdef GPU
+//#ifdef GPU
     int *indexes_gpu;
 
     float *z_gpu;
@@ -610,8 +612,21 @@ 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
 };
 
 
@@ -701,7 +716,7 @@ typedef struct network {
     float *cost;
     float clip;
 
-#ifdef GPU
+//#ifdef GPU
     //float *input_gpu;
     //float *truth_gpu;
     float *delta_gpu;
@@ -722,7 +737,7 @@ typedef struct network {
     float *global_delta_gpu;
     float *state_delta_gpu;
     size_t max_delta_gpu_size;
-#endif
+//#endif  // GPU
     int optimized_memory;
     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;
+            const int minibatch_index = state.net.current_subdivision + 1;
-            float alpha = 0.01;
+            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/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){