Added [net] dynamic_minibatch=1 for increasing mini_batch_size when r…

…andom=1 is used

include/darknet.h

@@ -218,6 +218,7 @@ struct layer {
     int batch_normalize;
     int shortcut;
     int batch;
+    int dynamic_minibatch;
     int forced;
     int flipped;
     int inputs;
@@ -640,6 +641,7 @@ typedef struct network {
     int n;
     int batch;
     uint64_t *seen;
+    int *cur_iteration;
     int *t;
     float epoch;
     int subdivisions;
@@ -739,6 +741,7 @@ typedef struct network {
     size_t max_delta_gpu_size;
 //#endif  // GPU
     int optimized_memory;
+    int dynamic_minibatch;
     size_t workspace_size_limit;
 } network;
 

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/detector.c

@@ -66,19 +66,19 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
 
     srand(time(0));
     int seed = rand();
-    int i;
-    for (i = 0; i < ngpus; ++i) {
+    int k;
+    for (k = 0; k < ngpus; ++k) {
         srand(seed);
 #ifdef GPU
-        cuda_set_device(gpus[i]);
+        cuda_set_device(gpus[k]);
 #endif
-        nets[i] = parse_network_cfg(cfgfile);
-        nets[i].benchmark_layers = benchmark_layers;
+        nets[k] = parse_network_cfg(cfgfile);
+        nets[k].benchmark_layers = benchmark_layers;
         if (weightfile) {
-            load_weights(&nets[i], weightfile);
+            load_weights(&nets[k], weightfile);
         }
-        if (clear) *nets[i].seen = 0;
-        nets[i].learning_rate *= ngpus;
+        if (clear) *nets[k].seen = 0;
+        nets[k].learning_rate *= ngpus;
     }
     srand(time(0));
     network net = nets[0];
@@ -105,12 +105,13 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
     int train_images_num = plist->size;
     char **paths = (char **)list_to_array(plist);
 
-    int init_w = net.w;
-    int init_h = net.h;
+    const int init_w = net.w;
+    const int init_h = net.h;
+    const int init_b = net.batch;
     int iter_save, iter_save_last, iter_map;
-    iter_save = get_current_batch(net);
-    iter_save_last = get_current_batch(net);
-    iter_map = get_current_batch(net);
+    iter_save = get_current_iteration(net);
+    iter_save_last = get_current_iteration(net);
+    iter_map = get_current_iteration(net);
     float mean_average_precision = -1;
     float best_map = mean_average_precision;
 
@@ -165,7 +166,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
     pthread_t load_thread = load_data(args);
     int count = 0;
     //while(i*imgs < N*120){
-    while (get_current_batch(net) < net.max_batches) {
+    while (get_current_iteration(net) < net.max_batches) {
         if (l.random && count++ % 10 == 0) {
             float rand_coef = 1.4;
             if (l.random != 1.0) rand_coef = l.random;
@@ -175,26 +176,48 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
             int dim_h = roundl(random_val*init_h / net.resize_step + 1) * net.resize_step;
             if (random_val < 1 && (dim_w > init_w || dim_h > init_h)) dim_w = init_w, dim_h = init_h;
 
-            // at the beginning
-            if (avg_loss < 0) {
-                dim_w = roundl(rand_coef*init_w / net.resize_step + 1) * net.resize_step;
-                dim_h = roundl(rand_coef*init_h / net.resize_step + 1) * net.resize_step;
+            int max_dim_w = roundl(rand_coef*init_w / net.resize_step + 1) * net.resize_step;
+            int max_dim_h = roundl(rand_coef*init_h / net.resize_step + 1) * net.resize_step;
+
+            // at the beginning (check if enough memory) and at the end (calc rolling mean/variance)
+            if (avg_loss < 0 || get_current_iteration(net) > net.max_batches - 100) {
+                dim_w = max_dim_w;
+                dim_h = max_dim_h;
             }
 
             if (dim_w < net.resize_step) dim_w = net.resize_step;
             if (dim_h < net.resize_step) dim_h = net.resize_step;
+            int dim_b = (init_b * max_dim_w * max_dim_h) / (dim_w * dim_h);
+            int new_dim_b = (int)(dim_b * 0.8);
+            if (new_dim_b > init_b) dim_b = new_dim_b;
 
-            printf("%d x %d \n", dim_w, dim_h);
             args.w = dim_w;
             args.h = dim_h;
 
+            int k;
+            if (net.dynamic_minibatch) {
+                for (k = 0; k < ngpus; ++k) {
+                    (*nets[k].seen) = init_b * net.subdivisions * get_current_iteration(net); // remove this line, when you will save to weights-file both: seen & cur_iteration
+                    nets[k].batch = dim_b;
+                    int j;
+                    for (j = 0; j < nets[k].n; ++j)
+                        nets[k].layers[j].batch = dim_b;
+                }
+                net.batch = dim_b;
+                imgs = net.batch * net.subdivisions * ngpus;
+                args.n = imgs;
+                printf("\n %d x %d  (batch = %d) \n", dim_w, dim_h, net.batch);
+            }
+            else
+                printf("\n %d x %d \n", dim_w, dim_h);
+
             pthread_join(load_thread, 0);
             train = buffer;
             free_data(train);
             load_thread = load_data(args);
 
-            for (i = 0; i < ngpus; ++i) {
-                resize_network(nets + i, dim_w, dim_h);
+            for (k = 0; k < ngpus; ++k) {
+                resize_network(nets + k, dim_w, dim_h);
             }
             net = nets[0];
         }
@@ -246,7 +269,8 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
         if (avg_loss < 0 || avg_loss != avg_loss) avg_loss = loss;    // if(-inf or nan)
         avg_loss = avg_loss*.9 + loss*.1;
 
-        i = get_current_batch(net);
+        const int iteration = get_current_iteration(net);
+        //i = get_current_batch(net);
 
         int calc_map_for_each = 4 * train_images_num / (net.batch * net.subdivisions);  // calculate mAP for each 4 Epochs
         calc_map_for_each = fmax(calc_map_for_each, 100);
@@ -259,22 +283,36 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
         }
 
         if (net.cudnn_half) {
-            if (i < net.burn_in * 3) fprintf(stderr, "\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in);
+            if (iteration < net.burn_in * 3) fprintf(stderr, "\n Tensor Cores are disabled until the first %d iterations are reached.", 3 * net.burn_in);
             else fprintf(stderr, "\n Tensor Cores are used.");
         }
-        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);
+        printf("\n %d: %f, %f avg loss, %f rate, %lf seconds, %d images\n", iteration, loss, avg_loss, get_current_rate(net), (what_time_is_it_now() - time), iteration*imgs);
 
         int draw_precision = 0;
-        if (calc_map && (i >= next_map_calc || i == net.max_batches)) {
+        if (calc_map && (iteration >= next_map_calc || iteration == net.max_batches)) {
             if (l.random) {
-                printf("Resizing to initial size: %d x %d \n", init_w, init_h);
+                printf("Resizing to initial size: %d x %d ", init_w, init_h);
                 args.w = init_w;
                 args.h = init_h;
+                int k;
+                if (net.dynamic_minibatch) {
+                    for (k = 0; k < ngpus; ++k) {
+                        for (k = 0; k < ngpus; ++k) {
+                            nets[k].batch = init_b;
+                            int j;
+                            for (j = 0; j < nets[k].n; ++j)
+                                nets[k].layers[j].batch = init_b;
+                        }
+                    }
+                    net.batch = init_b;
+                    imgs = init_b * net.subdivisions * ngpus;
+                    args.n = imgs;
+                    printf("\n %d x %d  (batch = %d) \n", init_w, init_h, init_b);
+                }
                 pthread_join(load_thread, 0);
                 free_data(train);
                 train = buffer;
                 load_thread = load_data(args);
-                int k;
                 for (k = 0; k < ngpus; ++k) {
                     resize_network(nets + k, init_w, init_h);
                 }
@@ -286,7 +324,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
             // combine Training and Validation networks
             //network net_combined = combine_train_valid_networks(net, net_map);
 
-            iter_map = i;
+            iter_map = iteration;
             mean_average_precision = validate_detector_map(datacfg, cfgfile, weightfile, 0.25, 0.5, 0, net.letter_box, &net_map);// &net_combined);
             printf("\n mean_average_precision ([email protected]) = %f \n", mean_average_precision);
             if (mean_average_precision > best_map) {
@@ -300,23 +338,23 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
             draw_precision = 1;
         }
 #ifdef OPENCV
-        draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, i, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port);
+        draw_train_loss(windows_name, img, img_size, avg_loss, max_img_loss, iteration, net.max_batches, mean_average_precision, draw_precision, "mAP%", dont_show, mjpeg_port);
 #endif    // OPENCV
 
         //if (i % 1000 == 0 || (i < 1000 && i % 100 == 0)) {
         //if (i % 100 == 0) {
-        if (i >= (iter_save + 1000) || i % 1000 == 0) {
-            iter_save = i;
+        if (iteration >= (iter_save + 1000) || iteration % 1000 == 0) {
+            iter_save = iteration;
 #ifdef GPU
             if (ngpus != 1) sync_nets(nets, ngpus, 0);
 #endif
             char buff[256];
-            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
+            sprintf(buff, "%s/%s_%d.weights", backup_directory, base, iteration);
             save_weights(net, buff);
         }
 
-        if (i >= (iter_save_last + 100) || i % 100 == 0) {
-            iter_save_last = i;
+        if (iteration >= (iter_save_last + 100) || iteration % 100 == 0) {
+            iter_save_last = iteration;
 #ifdef GPU
             if (ngpus != 1) sync_nets(nets, ngpus, 0);
 #endif
@@ -350,7 +388,7 @@ void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, i
     free_list_contents_kvp(options);
     free_list(options);
 
-    for (i = 0; i < ngpus; ++i) free_network(nets[i]);
+    for (k = 0; k < ngpus; ++k) free_network(nets[k]);
     free(nets);
     //free_network(net);
 

src/dropout_layer.c

@@ -33,8 +33,10 @@ dropout_layer make_dropout_layer(int batch, int inputs, float probability, int d
     l.forward_gpu = forward_dropout_layer_gpu;
     l.backward_gpu = backward_dropout_layer_gpu;
     l.rand_gpu = cuda_make_array(l.rand, inputs*batch);
-    l.drop_blocks_scale = cuda_make_array_pinned(l.rand, l.batch);
-    l.drop_blocks_scale_gpu = cuda_make_array(l.rand, l.batch);
+    if (l.dropblock) {
+        l.drop_blocks_scale = cuda_make_array_pinned(l.rand, l.batch);
+        l.drop_blocks_scale_gpu = cuda_make_array(l.rand, l.batch);
+    }
 #endif
     if (l.dropblock) {
         if(l.dropblock_size_abs) fprintf(stderr, "dropblock    p = %.3f   l.dropblock_size_abs = %d    %4d  ->   %4d\n", probability, l.dropblock_size_abs, inputs, inputs);
@@ -48,11 +50,18 @@ void resize_dropout_layer(dropout_layer *l, int inputs)
 {
     l->inputs = l->outputs = inputs;
     l->rand = (float*)xrealloc(l->rand, l->inputs * l->batch * sizeof(float));
-    #ifdef GPU
+#ifdef GPU
     cuda_free(l->rand_gpu);
-
     l->rand_gpu = cuda_make_array(l->rand, l->inputs*l->batch);
-    #endif
+
+    if (l->dropblock) {
+        cudaFreeHost(l->drop_blocks_scale);
+        l->drop_blocks_scale = cuda_make_array_pinned(l->rand, l->batch);
+
+        cuda_free(l->drop_blocks_scale_gpu);
+        l->drop_blocks_scale_gpu = cuda_make_array(l->rand, l->batch);
+    }
+#endif
 }
 
 void forward_dropout_layer(dropout_layer l, network_state state)

src/dropout_layer_kernels.cu

@@ -95,7 +95,7 @@ __global__ void yoloswag420blazeit360noscope(float *input, int size, float *rand
 void forward_dropout_layer_gpu(dropout_layer l, network_state state)
 {
     if (!state.train) return;
-    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 (iteration_num < state.net.burn_in) return;
 
     // We gradually increase the block size and the probability of dropout - during the first half of the training
@@ -141,9 +141,9 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state)
         for (int b = 0; b < l.batch; ++b) {
             const float prob = l.drop_blocks_scale[b] * block_size * block_size / (float)l.outputs;
             const float scale = 1.0f / (1.0f - prob);
-            printf(" %d x %d - block_size = %d, block_size*block_size = %d , ", l.w, l.h, block_size, block_size*block_size);
-            printf(" , l.drop_blocks_scale[b] = %f, prob = %f, calc scale = %f \t cur_prob = %f, cur_scale = %f \n",
-                l.drop_blocks_scale[b], prob, scale, cur_prob, cur_scale);
+            //printf(" %d x %d - block_size = %d, block_size*block_size = %d , ", l.w, l.h, block_size, block_size*block_size);
+            //printf(" , l.drop_blocks_scale[b] = %f, prob = %f, calc scale = %f \t cur_prob = %f, cur_scale = %f \n",
+            //    l.drop_blocks_scale[b], prob, scale, cur_prob, cur_scale);
             l.drop_blocks_scale[b] = scale;
         }
 
@@ -176,14 +176,14 @@ void forward_dropout_layer_gpu(dropout_layer l, network_state state)
 void backward_dropout_layer_gpu(dropout_layer l, network_state state)
 {
     if(!state.delta) return;
-    //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 (iteration_num < state.net.burn_in) return;
 
     int size = l.inputs*l.batch;
 
     // dropblock
     if (l.dropblock) {
-        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);
         float multiplier = 1.0;
         if (iteration_num < (state.net.max_batches*0.85))
             multiplier = (iteration_num / (float)(state.net.max_batches*0.85));