派生自 Algorithm/baseDetector
lib/detecter_tools/darknet/parser.c
@@ -1,2140 +1,2352 @@ #include <stdio.h> #include <string.h> #include <stdlib.h> #include <stdint.h> #include "activation_layer.h" #include "activations.h" #include "assert.h" #include "avgpool_layer.h" #include "batchnorm_layer.h" #include "blas.h" #include "connected_layer.h" #include "convolutional_layer.h" #include "cost_layer.h" #include "crnn_layer.h" #include "crop_layer.h" #include "detection_layer.h" #include "dropout_layer.h" #include "gru_layer.h" #include "list.h" #include "local_layer.h" #include "lstm_layer.h" #include "conv_lstm_layer.h" #include "maxpool_layer.h" #include "normalization_layer.h" #include "option_list.h" #include "parser.h" #include "region_layer.h" #include "reorg_layer.h" #include "reorg_old_layer.h" #include "rnn_layer.h" #include "route_layer.h" #include "shortcut_layer.h" #include "scale_channels_layer.h" #include "sam_layer.h" #include "softmax_layer.h" #include "utils.h" #include "upsample_layer.h" #include "version.h" #include "yolo_layer.h" #include "gaussian_yolo_layer.h" typedef struct{ char *type; list *options; }section; list *read_cfg(char *filename); LAYER_TYPE string_to_layer_type(char * type) { if (strcmp(type, "[shortcut]")==0) return SHORTCUT; if (strcmp(type, "[scale_channels]") == 0) return SCALE_CHANNELS; if (strcmp(type, "[sam]") == 0) return SAM; if (strcmp(type, "[crop]")==0) return CROP; if (strcmp(type, "[cost]")==0) return COST; if (strcmp(type, "[detection]")==0) return DETECTION; if (strcmp(type, "[region]")==0) return REGION; if (strcmp(type, "[yolo]") == 0) return YOLO; if (strcmp(type, "[Gaussian_yolo]") == 0) return GAUSSIAN_YOLO; if (strcmp(type, "[local]")==0) return LOCAL; if (strcmp(type, "[conv]")==0 || strcmp(type, "[convolutional]")==0) return CONVOLUTIONAL; if (strcmp(type, "[activation]")==0) return ACTIVE; if (strcmp(type, "[net]")==0 || strcmp(type, "[network]")==0) return NETWORK; if (strcmp(type, "[crnn]")==0) return CRNN; if (strcmp(type, "[gru]")==0) return GRU; if (strcmp(type, "[lstm]")==0) return LSTM; if (strcmp(type, "[conv_lstm]") == 0) return CONV_LSTM; if (strcmp(type, "[rnn]")==0) return RNN; if (strcmp(type, "[conn]")==0 || strcmp(type, "[connected]")==0) return CONNECTED; if (strcmp(type, "[max]")==0 || strcmp(type, "[maxpool]")==0) return MAXPOOL; if (strcmp(type, "[local_avg]") == 0 || strcmp(type, "[local_avgpool]") == 0) return LOCAL_AVGPOOL; if (strcmp(type, "[reorg3d]")==0) return REORG; if (strcmp(type, "[reorg]") == 0) return REORG_OLD; if (strcmp(type, "[avg]")==0 || strcmp(type, "[avgpool]")==0) return AVGPOOL; if (strcmp(type, "[dropout]")==0) return DROPOUT; if (strcmp(type, "[lrn]")==0 || strcmp(type, "[normalization]")==0) return NORMALIZATION; if (strcmp(type, "[batchnorm]")==0) return BATCHNORM; if (strcmp(type, "[soft]")==0 || strcmp(type, "[softmax]")==0) return SOFTMAX; if (strcmp(type, "[route]")==0) return ROUTE; if (strcmp(type, "[upsample]") == 0) return UPSAMPLE; if (strcmp(type, "[empty]") == 0) return EMPTY; return BLANK; } void free_section(section *s) { free(s->type); node *n = s->options->front; while(n){ kvp *pair = (kvp *)n->val; free(pair->key); free(pair); node *next = n->next; free(n); n = next; } free(s->options); free(s); } void parse_data(char *data, float *a, int n) { int i; if(!data) return; char *curr = data; char *next = data; int done = 0; for(i = 0; i < n && !done; ++i){ while(*++next !='\0' && *next != ','); if(*next == '\0') done = 1; *next = '\0'; sscanf(curr, "%g", &a[i]); curr = next+1; } } typedef struct size_params{ int batch; int inputs; int h; int w; int c; int index; int time_steps; int train; network net; } size_params; local_layer parse_local(list *options, size_params params) { int n = option_find_int(options, "filters",1); int size = option_find_int(options, "size",1); int stride = option_find_int(options, "stride",1); int pad = option_find_int(options, "pad",0); char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); int batch,h,w,c; h = params.h; w = params.w; c = params.c; batch=params.batch; if(!(h && w && c)) error("Layer before local layer must output image."); local_layer layer = make_local_layer(batch,h,w,c,n,size,stride,pad,activation); return layer; } convolutional_layer parse_convolutional(list *options, size_params params) { int n = option_find_int(options, "filters",1); int groups = option_find_int_quiet(options, "groups", 1); int size = option_find_int(options, "size",1); int stride = -1; //int stride = option_find_int(options, "stride",1); int stride_x = option_find_int_quiet(options, "stride_x", -1); int stride_y = option_find_int_quiet(options, "stride_y", -1); if (stride_x < 1 || stride_y < 1) { stride = option_find_int(options, "stride", 1); if (stride_x < 1) stride_x = stride; if (stride_y < 1) stride_y = stride; } else { stride = option_find_int_quiet(options, "stride", 1); } int dilation = option_find_int_quiet(options, "dilation", 1); int antialiasing = option_find_int_quiet(options, "antialiasing", 0); if (size == 1) dilation = 1; int pad = option_find_int_quiet(options, "pad",0); int padding = option_find_int_quiet(options, "padding",0); if(pad) padding = size/2; char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); int assisted_excitation = option_find_float_quiet(options, "assisted_excitation", 0); int share_index = option_find_int_quiet(options, "share_index", -1000000000); convolutional_layer *share_layer = NULL; if(share_index >= 0) share_layer = ¶ms.net.layers[share_index]; else if(share_index != -1000000000) share_layer = ¶ms.net.layers[params.index + share_index]; int batch,h,w,c; h = params.h; w = params.w; c = params.c; batch=params.batch; if(!(h && w && c)) error("Layer before convolutional layer must output image."); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); int cbn = option_find_int_quiet(options, "cbn", 0); if (cbn) batch_normalize = 2; int binary = option_find_int_quiet(options, "binary", 0); int xnor = option_find_int_quiet(options, "xnor", 0); int use_bin_output = option_find_int_quiet(options, "bin_output", 0); int sway = option_find_int_quiet(options, "sway", 0); int rotate = option_find_int_quiet(options, "rotate", 0); int stretch = option_find_int_quiet(options, "stretch", 0); int stretch_sway = option_find_int_quiet(options, "stretch_sway", 0); if ((sway + rotate + stretch + stretch_sway) > 1) { printf(" Error: should be used only 1 param: sway=1, rotate=1 or stretch=1 in the [convolutional] layer \n"); exit(0); } int deform = sway || rotate || stretch || stretch_sway; if (deform && size == 1) { printf(" Error: params (sway=1, rotate=1 or stretch=1) should be used only with size >=3 in the [convolutional] layer \n"); exit(0); } convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride_x,stride_y,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, antialiasing, share_layer, assisted_excitation, deform, params.train); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); layer.sway = sway; layer.rotate = rotate; layer.stretch = stretch; layer.stretch_sway = stretch_sway; layer.angle = option_find_float_quiet(options, "angle", 15); layer.grad_centr = option_find_int_quiet(options, "grad_centr", 0); if(params.net.adam){ layer.B1 = params.net.B1; layer.B2 = params.net.B2; layer.eps = params.net.eps; } return layer; } layer parse_crnn(list *options, size_params params) { int size = option_find_int_quiet(options, "size", 3); int stride = option_find_int_quiet(options, "stride", 1); int dilation = option_find_int_quiet(options, "dilation", 1); int pad = option_find_int_quiet(options, "pad", 0); int padding = option_find_int_quiet(options, "padding", 0); if (pad) padding = size / 2; int output_filters = option_find_int(options, "output",1); int hidden_filters = option_find_int(options, "hidden",1); int groups = option_find_int_quiet(options, "groups", 1); char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); int xnor = option_find_int_quiet(options, "xnor", 0); layer l = make_crnn_layer(params.batch, params.h, params.w, params.c, hidden_filters, output_filters, groups, params.time_steps, size, stride, dilation, padding, activation, batch_normalize, xnor, params.train); l.shortcut = option_find_int_quiet(options, "shortcut", 0); return l; } layer parse_rnn(list *options, size_params params) { int output = option_find_int(options, "output",1); int hidden = option_find_int(options, "hidden",1); char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); int logistic = option_find_int_quiet(options, "logistic", 0); layer l = make_rnn_layer(params.batch, params.inputs, hidden, output, params.time_steps, activation, batch_normalize, logistic); l.shortcut = option_find_int_quiet(options, "shortcut", 0); return l; } layer parse_gru(list *options, size_params params) { int output = option_find_int(options, "output",1); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); layer l = make_gru_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize); return l; } layer parse_lstm(list *options, size_params params) { int output = option_find_int(options, "output",1); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); layer l = make_lstm_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize); return l; } layer parse_conv_lstm(list *options, size_params params) { // a ConvLSTM with a larger transitional kernel should be able to capture faster motions int size = option_find_int_quiet(options, "size", 3); int stride = option_find_int_quiet(options, "stride", 1); int dilation = option_find_int_quiet(options, "dilation", 1); int pad = option_find_int_quiet(options, "pad", 0); int padding = option_find_int_quiet(options, "padding", 0); if (pad) padding = size / 2; int output_filters = option_find_int(options, "output", 1); int groups = option_find_int_quiet(options, "groups", 1); char *activation_s = option_find_str(options, "activation", "LINEAR"); ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); int xnor = option_find_int_quiet(options, "xnor", 0); int peephole = option_find_int_quiet(options, "peephole", 0); layer l = make_conv_lstm_layer(params.batch, params.h, params.w, params.c, output_filters, groups, params.time_steps, size, stride, dilation, padding, activation, batch_normalize, peephole, xnor, params.train); l.state_constrain = option_find_int_quiet(options, "state_constrain", params.time_steps * 32); l.shortcut = option_find_int_quiet(options, "shortcut", 0); return l; } connected_layer parse_connected(list *options, size_params params) { int output = option_find_int(options, "output",1); char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); connected_layer layer = make_connected_layer(params.batch, 1, params.inputs, output, activation, batch_normalize); return layer; } softmax_layer parse_softmax(list *options, size_params params) { int groups = option_find_int_quiet(options, "groups", 1); softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups); layer.temperature = option_find_float_quiet(options, "temperature", 1); char *tree_file = option_find_str(options, "tree", 0); if (tree_file) layer.softmax_tree = read_tree(tree_file); layer.w = params.w; layer.h = params.h; layer.c = params.c; layer.spatial = option_find_float_quiet(options, "spatial", 0); layer.noloss = option_find_int_quiet(options, "noloss", 0); return layer; } int *parse_yolo_mask(char *a, int *num) { int *mask = 0; if (a) { int len = strlen(a); int n = 1; int i; for (i = 0; i < len; ++i) { if (a[i] == ',') ++n; } mask = (int*)xcalloc(n, sizeof(int)); for (i = 0; i < n; ++i) { int val = atoi(a); mask[i] = val; a = strchr(a, ',') + 1; } *num = n; } return mask; } float *get_classes_multipliers(char *cpc, const int classes) { float *classes_multipliers = NULL; if (cpc) { int classes_counters = classes; int *counters_per_class = parse_yolo_mask(cpc, &classes_counters); if (classes_counters != classes) { printf(" number of values in counters_per_class = %d doesn't match with classes = %d \n", classes_counters, classes); exit(0); } float max_counter = 0; int i; for (i = 0; i < classes_counters; ++i) if (max_counter < counters_per_class[i]) max_counter = counters_per_class[i]; classes_multipliers = (float *)calloc(classes_counters, sizeof(float)); for (i = 0; i < classes_counters; ++i) classes_multipliers[i] = max_counter / counters_per_class[i]; free(counters_per_class); printf(" classes_multipliers: "); for (i = 0; i < classes_counters; ++i) printf("%.1f, ", classes_multipliers[i]); printf("\n"); } return classes_multipliers; } layer parse_yolo(list *options, size_params params) { int classes = option_find_int(options, "classes", 20); int total = option_find_int(options, "num", 1); int num = total; char *a = option_find_str(options, "mask", 0); int *mask = parse_yolo_mask(a, &num); int max_boxes = option_find_int_quiet(options, "max", 90); layer l = make_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes); if (l.outputs != params.inputs) { printf("Error: l.outputs == params.inputs \n"); printf("filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [yolo]-layer \n"); exit(EXIT_FAILURE); } //assert(l.outputs == params.inputs); char *cpc = option_find_str(options, "counters_per_class", 0); l.classes_multipliers = get_classes_multipliers(cpc, classes); l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); l.objectness_smooth = option_find_int_quiet(options, "objectness_smooth", 0); l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1); char *iou_loss = option_find_str_quiet(options, "iou_loss", "mse"); // "iou"); if (strcmp(iou_loss, "mse") == 0) l.iou_loss = MSE; else if (strcmp(iou_loss, "giou") == 0) l.iou_loss = GIOU; else if (strcmp(iou_loss, "diou") == 0) l.iou_loss = DIOU; else if (strcmp(iou_loss, "ciou") == 0) l.iou_loss = CIOU; else l.iou_loss = IOU; fprintf(stderr, "[yolo] params: iou loss: %s (%d), iou_norm: %2.2f, cls_norm: %2.2f, scale_x_y: %2.2f\n", iou_loss, l.iou_loss, l.iou_normalizer, l.cls_normalizer, l.scale_x_y); char *iou_thresh_kind_str = option_find_str_quiet(options, "iou_thresh_kind", "iou"); if (strcmp(iou_thresh_kind_str, "iou") == 0) l.iou_thresh_kind = IOU; else if (strcmp(iou_thresh_kind_str, "giou") == 0) l.iou_thresh_kind = GIOU; else if (strcmp(iou_thresh_kind_str, "diou") == 0) l.iou_thresh_kind = DIOU; else if (strcmp(iou_thresh_kind_str, "ciou") == 0) l.iou_thresh_kind = CIOU; else { fprintf(stderr, " Wrong iou_thresh_kind = %s \n", iou_thresh_kind_str); l.iou_thresh_kind = IOU; } l.beta_nms = option_find_float_quiet(options, "beta_nms", 0.6); char *nms_kind = option_find_str_quiet(options, "nms_kind", "default"); if (strcmp(nms_kind, "default") == 0) l.nms_kind = DEFAULT_NMS; else { if (strcmp(nms_kind, "greedynms") == 0) l.nms_kind = GREEDY_NMS; else if (strcmp(nms_kind, "diounms") == 0) l.nms_kind = DIOU_NMS; else l.nms_kind = DEFAULT_NMS; printf("nms_kind: %s (%d), beta = %f \n", nms_kind, l.nms_kind, l.beta_nms); } l.jitter = option_find_float(options, "jitter", .2); l.resize = option_find_float_quiet(options, "resize", 1.0); l.focal_loss = option_find_int_quiet(options, "focal_loss", 0); l.ignore_thresh = option_find_float(options, "ignore_thresh", .5); l.truth_thresh = option_find_float(options, "truth_thresh", 1); l.iou_thresh = option_find_float_quiet(options, "iou_thresh", 1); // recommended to use iou_thresh=0.213 in [yolo] l.random = option_find_float_quiet(options, "random", 0); char *map_file = option_find_str(options, "map", 0); if (map_file) l.map = read_map(map_file); a = option_find_str(options, "anchors", 0); if (a) { int len = strlen(a); int n = 1; int i; for (i = 0; i < len; ++i) { if (a[i] == ',') ++n; } for (i = 0; i < n && i < total*2; ++i) { float bias = atof(a); l.biases[i] = bias; a = strchr(a, ',') + 1; } } return l; } int *parse_gaussian_yolo_mask(char *a, int *num) // Gaussian_YOLOv3 { int *mask = 0; if (a) { int len = strlen(a); int n = 1; int i; for (i = 0; i < len; ++i) { if (a[i] == ',') ++n; } mask = (int *)calloc(n, sizeof(int)); for (i = 0; i < n; ++i) { int val = atoi(a); mask[i] = val; a = strchr(a, ',') + 1; } *num = n; } return mask; } layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 { int classes = option_find_int(options, "classes", 20); int max_boxes = option_find_int_quiet(options, "max", 90); int total = option_find_int(options, "num", 1); int num = total; char *a = option_find_str(options, "mask", 0); int *mask = parse_gaussian_yolo_mask(a, &num); layer l = make_gaussian_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes); if (l.outputs != params.inputs) { printf("Error: l.outputs == params.inputs \n"); printf("filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [Gaussian_yolo]-layer \n"); exit(EXIT_FAILURE); } //assert(l.outputs == params.inputs); char *cpc = option_find_str(options, "counters_per_class", 0); l.classes_multipliers = get_classes_multipliers(cpc, classes); l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); l.objectness_smooth = option_find_int_quiet(options, "objectness_smooth", 0); l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 l.uc_normalizer = option_find_float_quiet(options, "uc_normalizer", 1.0); l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1.0); char *iou_loss = option_find_str_quiet(options, "iou_loss", "mse"); // "iou"); if (strcmp(iou_loss, "mse") == 0) l.iou_loss = MSE; else if (strcmp(iou_loss, "giou") == 0) l.iou_loss = GIOU; else if (strcmp(iou_loss, "diou") == 0) l.iou_loss = DIOU; else if (strcmp(iou_loss, "ciou") == 0) l.iou_loss = CIOU; else l.iou_loss = IOU; char *iou_thresh_kind_str = option_find_str_quiet(options, "iou_thresh_kind", "iou"); if (strcmp(iou_thresh_kind_str, "iou") == 0) l.iou_thresh_kind = IOU; else if (strcmp(iou_thresh_kind_str, "giou") == 0) l.iou_thresh_kind = GIOU; else if (strcmp(iou_thresh_kind_str, "diou") == 0) l.iou_thresh_kind = DIOU; else if (strcmp(iou_thresh_kind_str, "ciou") == 0) l.iou_thresh_kind = CIOU; else { fprintf(stderr, " Wrong iou_thresh_kind = %s \n", iou_thresh_kind_str); l.iou_thresh_kind = IOU; } l.beta_nms = option_find_float_quiet(options, "beta_nms", 0.6); char *nms_kind = option_find_str_quiet(options, "nms_kind", "default"); if (strcmp(nms_kind, "default") == 0) l.nms_kind = DEFAULT_NMS; else { if (strcmp(nms_kind, "greedynms") == 0) l.nms_kind = GREEDY_NMS; else if (strcmp(nms_kind, "diounms") == 0) l.nms_kind = DIOU_NMS; else if (strcmp(nms_kind, "cornersnms") == 0) l.nms_kind = CORNERS_NMS; else l.nms_kind = DEFAULT_NMS; printf("nms_kind: %s (%d), beta = %f \n", nms_kind, l.nms_kind, l.beta_nms); } char *yolo_point = option_find_str_quiet(options, "yolo_point", "center"); if (strcmp(yolo_point, "left_top") == 0) l.yolo_point = YOLO_LEFT_TOP; else if (strcmp(yolo_point, "right_bottom") == 0) l.yolo_point = YOLO_RIGHT_BOTTOM; else l.yolo_point = YOLO_CENTER; fprintf(stderr, "[Gaussian_yolo] iou loss: %s (%d), iou_norm: %2.2f, cls_norm: %2.2f, scale: %2.2f, point: %d\n", iou_loss, l.iou_loss, l.iou_normalizer, l.cls_normalizer, l.scale_x_y, l.yolo_point); l.jitter = option_find_float(options, "jitter", .2); l.resize = option_find_float_quiet(options, "resize", 1.0); l.ignore_thresh = option_find_float(options, "ignore_thresh", .5); l.truth_thresh = option_find_float(options, "truth_thresh", 1); l.iou_thresh = option_find_float_quiet(options, "iou_thresh", 1); // recommended to use iou_thresh=0.213 in [yolo] l.random = option_find_float_quiet(options, "random", 0); char *map_file = option_find_str(options, "map", 0); if (map_file) l.map = read_map(map_file); a = option_find_str(options, "anchors", 0); if (a) { int len = strlen(a); int n = 1; int i; for (i = 0; i < len; ++i) { if (a[i] == ',') ++n; } for (i = 0; i < n; ++i) { float bias = atof(a); l.biases[i] = bias; a = strchr(a, ',') + 1; } } return l; } layer parse_region(list *options, size_params params) { int coords = option_find_int(options, "coords", 4); int classes = option_find_int(options, "classes", 20); int num = option_find_int(options, "num", 1); int max_boxes = option_find_int_quiet(options, "max", 90); layer l = make_region_layer(params.batch, params.w, params.h, num, classes, coords, max_boxes); if (l.outputs != params.inputs) { printf("Error: l.outputs == params.inputs \n"); printf("filters= in the [convolutional]-layer doesn't correspond to classes= or num= in [region]-layer \n"); exit(EXIT_FAILURE); } //assert(l.outputs == params.inputs); l.log = option_find_int_quiet(options, "log", 0); l.sqrt = option_find_int_quiet(options, "sqrt", 0); l.softmax = option_find_int(options, "softmax", 0); l.focal_loss = option_find_int_quiet(options, "focal_loss", 0); //l.max_boxes = option_find_int_quiet(options, "max",30); l.jitter = option_find_float(options, "jitter", .2); l.resize = option_find_float_quiet(options, "resize", 1.0); l.rescore = option_find_int_quiet(options, "rescore",0); l.thresh = option_find_float(options, "thresh", .5); l.classfix = option_find_int_quiet(options, "classfix", 0); l.absolute = option_find_int_quiet(options, "absolute", 0); l.random = option_find_float_quiet(options, "random", 0); l.coord_scale = option_find_float(options, "coord_scale", 1); l.object_scale = option_find_float(options, "object_scale", 1); l.noobject_scale = option_find_float(options, "noobject_scale", 1); l.mask_scale = option_find_float(options, "mask_scale", 1); l.class_scale = option_find_float(options, "class_scale", 1); l.bias_match = option_find_int_quiet(options, "bias_match",0); char *tree_file = option_find_str(options, "tree", 0); if (tree_file) l.softmax_tree = read_tree(tree_file); char *map_file = option_find_str(options, "map", 0); if (map_file) l.map = read_map(map_file); char *a = option_find_str(options, "anchors", 0); if(a){ int len = strlen(a); int n = 1; int i; for(i = 0; i < len; ++i){ if (a[i] == ',') ++n; } for(i = 0; i < n && i < num*2; ++i){ float bias = atof(a); l.biases[i] = bias; a = strchr(a, ',')+1; } } return l; } detection_layer parse_detection(list *options, size_params params) { int coords = option_find_int(options, "coords", 1); int classes = option_find_int(options, "classes", 1); int rescore = option_find_int(options, "rescore", 0); int num = option_find_int(options, "num", 1); int side = option_find_int(options, "side", 7); detection_layer layer = make_detection_layer(params.batch, params.inputs, num, side, classes, coords, rescore); layer.softmax = option_find_int(options, "softmax", 0); layer.sqrt = option_find_int(options, "sqrt", 0); layer.max_boxes = option_find_int_quiet(options, "max",30); layer.coord_scale = option_find_float(options, "coord_scale", 1); layer.forced = option_find_int(options, "forced", 0); layer.object_scale = option_find_float(options, "object_scale", 1); layer.noobject_scale = option_find_float(options, "noobject_scale", 1); layer.class_scale = option_find_float(options, "class_scale", 1); layer.jitter = option_find_float(options, "jitter", .2); layer.resize = option_find_float_quiet(options, "resize", 1.0); layer.random = option_find_float_quiet(options, "random", 0); layer.reorg = option_find_int_quiet(options, "reorg", 0); return layer; } cost_layer parse_cost(list *options, size_params params) { char *type_s = option_find_str(options, "type", "sse"); COST_TYPE type = get_cost_type(type_s); float scale = option_find_float_quiet(options, "scale",1); cost_layer layer = make_cost_layer(params.batch, params.inputs, type, scale); layer.ratio = option_find_float_quiet(options, "ratio",0); return layer; } crop_layer parse_crop(list *options, size_params params) { int crop_height = option_find_int(options, "crop_height",1); int crop_width = option_find_int(options, "crop_width",1); int flip = option_find_int(options, "flip",0); float angle = option_find_float(options, "angle",0); float saturation = option_find_float(options, "saturation",1); float exposure = option_find_float(options, "exposure",1); int batch,h,w,c; h = params.h; w = params.w; c = params.c; batch=params.batch; if(!(h && w && c)) error("Layer before crop layer must output image."); int noadjust = option_find_int_quiet(options, "noadjust",0); crop_layer l = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip, angle, saturation, exposure); l.shift = option_find_float(options, "shift", 0); l.noadjust = noadjust; return l; } layer parse_reorg(list *options, size_params params) { int stride = option_find_int(options, "stride",1); int reverse = option_find_int_quiet(options, "reverse",0); int batch,h,w,c; h = params.h; w = params.w; c = params.c; batch=params.batch; if(!(h && w && c)) error("Layer before reorg layer must output image."); layer layer = make_reorg_layer(batch,w,h,c,stride,reverse); return layer; } layer parse_reorg_old(list *options, size_params params) { printf("\n reorg_old \n"); int stride = option_find_int(options, "stride", 1); int reverse = option_find_int_quiet(options, "reverse", 0); int batch, h, w, c; h = params.h; w = params.w; c = params.c; batch = params.batch; if (!(h && w && c)) error("Layer before reorg layer must output image."); layer layer = make_reorg_old_layer(batch, w, h, c, stride, reverse); return layer; } maxpool_layer parse_local_avgpool(list *options, size_params params) { int stride = option_find_int(options, "stride", 1); int stride_x = option_find_int_quiet(options, "stride_x", stride); int stride_y = option_find_int_quiet(options, "stride_y", stride); int size = option_find_int(options, "size", stride); int padding = option_find_int_quiet(options, "padding", size - 1); int maxpool_depth = 0; int out_channels = 1; int antialiasing = 0; const int avgpool = 1; int batch, h, w, c; h = params.h; w = params.w; c = params.c; batch = params.batch; if (!(h && w && c)) error("Layer before [local_avgpool] layer must output image."); maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels, antialiasing, avgpool, params.train); return layer; } maxpool_layer parse_maxpool(list *options, size_params params) { int stride = option_find_int(options, "stride",1); int stride_x = option_find_int_quiet(options, "stride_x", stride); int stride_y = option_find_int_quiet(options, "stride_y", stride); int size = option_find_int(options, "size",stride); int padding = option_find_int_quiet(options, "padding", size-1); int maxpool_depth = option_find_int_quiet(options, "maxpool_depth", 0); int out_channels = option_find_int_quiet(options, "out_channels", 1); int antialiasing = option_find_int_quiet(options, "antialiasing", 0); const int avgpool = 0; int batch,h,w,c; h = params.h; w = params.w; c = params.c; batch=params.batch; if(!(h && w && c)) error("Layer before [maxpool] layer must output image."); maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels, antialiasing, avgpool, params.train); return layer; } avgpool_layer parse_avgpool(list *options, size_params params) { int batch,w,h,c; w = params.w; h = params.h; c = params.c; batch=params.batch; if(!(h && w && c)) error("Layer before avgpool layer must output image."); avgpool_layer layer = make_avgpool_layer(batch,w,h,c); return layer; } dropout_layer parse_dropout(list *options, size_params params) { float probability = option_find_float(options, "probability", .2); int dropblock = option_find_int_quiet(options, "dropblock", 0); float dropblock_size_rel = option_find_float_quiet(options, "dropblock_size_rel", 0); int dropblock_size_abs = option_find_float_quiet(options, "dropblock_size_abs", 0); if (dropblock_size_abs > params.w || dropblock_size_abs > params.h) { printf(" [dropout] - dropblock_size_abs = %d that is bigger than layer size %d x %d \n", dropblock_size_abs, params.w, params.h); dropblock_size_abs = min_val_cmp(params.w, params.h); } if (dropblock && !dropblock_size_rel && !dropblock_size_abs) { printf(" [dropout] - None of the parameters (dropblock_size_rel or dropblock_size_abs) are set, will be used: dropblock_size_abs = 7 \n"); dropblock_size_abs = 7; } if (dropblock_size_rel && dropblock_size_abs) { printf(" [dropout] - Both parameters are set, only the parameter will be used: dropblock_size_abs = %d \n", dropblock_size_abs); dropblock_size_rel = 0; } dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability, dropblock, dropblock_size_rel, dropblock_size_abs, params.w, params.h, params.c); layer.out_w = params.w; layer.out_h = params.h; layer.out_c = params.c; return layer; } layer parse_normalization(list *options, size_params params) { float alpha = option_find_float(options, "alpha", .0001); float beta = option_find_float(options, "beta" , .75); float kappa = option_find_float(options, "kappa", 1); int size = option_find_int(options, "size", 5); layer l = make_normalization_layer(params.batch, params.w, params.h, params.c, size, alpha, beta, kappa); return l; } layer parse_batchnorm(list *options, size_params params) { layer l = make_batchnorm_layer(params.batch, params.w, params.h, params.c, params.train); return l; } layer parse_shortcut(list *options, size_params params, network net) { char *activation_s = option_find_str(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); char *weights_type_str = option_find_str_quiet(options, "weights_type", "none"); WEIGHTS_TYPE_T weights_type = NO_WEIGHTS; if(strcmp(weights_type_str, "per_feature") == 0 || strcmp(weights_type_str, "per_layer") == 0) weights_type = PER_FEATURE; else if (strcmp(weights_type_str, "per_channel") == 0) weights_type = PER_CHANNEL; else if (strcmp(weights_type_str, "none") != 0) { printf("Error: Incorrect weights_type = %s \n Use one of: none, per_feature, per_channel \n", weights_type_str); getchar(); exit(0); } char *weights_normalization_str = option_find_str_quiet(options, "weights_normalization", "none"); WEIGHTS_NORMALIZATION_T weights_normalization = NO_NORMALIZATION; if (strcmp(weights_normalization_str, "relu") == 0 || strcmp(weights_normalization_str, "avg_relu") == 0) weights_normalization = RELU_NORMALIZATION; else if (strcmp(weights_normalization_str, "softmax") == 0) weights_normalization = SOFTMAX_NORMALIZATION; else if (strcmp(weights_type_str, "none") != 0) { printf("Error: Incorrect weights_normalization = %s \n Use one of: none, relu, softmax \n", weights_normalization_str); getchar(); exit(0); } char *l = option_find(options, "from"); int len = strlen(l); if (!l) error("Route Layer must specify input layers: from = ..."); int n = 1; int i; for (i = 0; i < len; ++i) { if (l[i] == ',') ++n; } int* layers = (int*)calloc(n, sizeof(int)); int* sizes = (int*)calloc(n, sizeof(int)); float **layers_output = (float **)calloc(n, sizeof(float *)); float **layers_delta = (float **)calloc(n, sizeof(float *)); float **layers_output_gpu = (float **)calloc(n, sizeof(float *)); float **layers_delta_gpu = (float **)calloc(n, sizeof(float *)); for (i = 0; i < n; ++i) { int index = atoi(l); l = strchr(l, ',') + 1; if (index < 0) index = params.index + index; layers[i] = index; sizes[i] = params.net.layers[index].outputs; layers_output[i] = params.net.layers[index].output; layers_delta[i] = params.net.layers[index].delta; } #ifdef GPU for (i = 0; i < n; ++i) { layers_output_gpu[i] = params.net.layers[layers[i]].output_gpu; layers_delta_gpu[i] = params.net.layers[layers[i]].delta_gpu; } #endif// GPU layer s = make_shortcut_layer(params.batch, n, layers, sizes, params.w, params.h, params.c, layers_output, layers_delta, layers_output_gpu, layers_delta_gpu, weights_type, weights_normalization, activation, params.train); free(layers_output_gpu); free(layers_delta_gpu); for (i = 0; i < n; ++i) { int index = layers[i]; assert(params.w == net.layers[index].out_w && params.h == net.layers[index].out_h); if (params.w != net.layers[index].out_w || params.h != net.layers[index].out_h || params.c != net.layers[index].out_c) fprintf(stderr, " (%4d x%4d x%4d) + (%4d x%4d x%4d) \n", params.w, params.h, params.c, net.layers[index].out_w, net.layers[index].out_h, params.net.layers[index].out_c); } return s; } layer parse_scale_channels(list *options, size_params params, network net) { char *l = option_find(options, "from"); int index = atoi(l); if (index < 0) index = params.index + index; int scale_wh = option_find_int_quiet(options, "scale_wh", 0); int batch = params.batch; layer from = net.layers[index]; layer s = make_scale_channels_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c, scale_wh); char *activation_s = option_find_str_quiet(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); s.activation = activation; if (activation == SWISH || activation == MISH) { printf(" [scale_channels] layer doesn't support SWISH or MISH activations \n"); } return s; } layer parse_sam(list *options, size_params params, network net) { char *l = option_find(options, "from"); int index = atoi(l); if (index < 0) index = params.index + index; int batch = params.batch; layer from = net.layers[index]; layer s = make_sam_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c); char *activation_s = option_find_str_quiet(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); s.activation = activation; if (activation == SWISH || activation == MISH) { printf(" [sam] layer doesn't support SWISH or MISH activations \n"); } return s; } layer parse_activation(list *options, size_params params) { char *activation_s = option_find_str(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); layer l = make_activation_layer(params.batch, params.inputs, activation); l.out_h = params.h; l.out_w = params.w; l.out_c = params.c; l.h = params.h; l.w = params.w; l.c = params.c; return l; } layer parse_upsample(list *options, size_params params, network net) { int stride = option_find_int(options, "stride", 2); layer l = make_upsample_layer(params.batch, params.w, params.h, params.c, stride); l.scale = option_find_float_quiet(options, "scale", 1); return l; } route_layer parse_route(list *options, size_params params) { char *l = option_find(options, "layers"); if(!l) error("Route Layer must specify input layers"); int len = strlen(l); int n = 1; int i; for(i = 0; i < len; ++i){ if (l[i] == ',') ++n; } int* layers = (int*)xcalloc(n, sizeof(int)); int* sizes = (int*)xcalloc(n, sizeof(int)); for(i = 0; i < n; ++i){ int index = atoi(l); l = strchr(l, ',')+1; if(index < 0) index = params.index + index; layers[i] = index; sizes[i] = params.net.layers[index].outputs; } int batch = params.batch; int groups = option_find_int_quiet(options, "groups", 1); int group_id = option_find_int_quiet(options, "group_id", 0); route_layer layer = make_route_layer(batch, n, layers, sizes, groups, group_id); convolutional_layer first = params.net.layers[layers[0]]; layer.out_w = first.out_w; layer.out_h = first.out_h; layer.out_c = first.out_c; for(i = 1; i < n; ++i){ int index = layers[i]; convolutional_layer next = params.net.layers[index]; if(next.out_w == first.out_w && next.out_h == first.out_h){ layer.out_c += next.out_c; }else{ fprintf(stderr, " The width and height of the input layers are different. \n"); layer.out_h = layer.out_w = layer.out_c = 0; } } layer.out_c = layer.out_c / layer.groups; layer.w = first.w; layer.h = first.h; layer.c = layer.out_c; if (n > 3) fprintf(stderr, " \t "); else if (n > 1) fprintf(stderr, " \t "); else fprintf(stderr, " \t\t "); fprintf(stderr, " "); if (layer.groups > 1) fprintf(stderr, "%d/%d", layer.group_id, layer.groups); else fprintf(stderr, " "); fprintf(stderr, " -> %4d x%4d x%4d \n", layer.out_w, layer.out_h, layer.out_c); return layer; } learning_rate_policy get_policy(char *s) { if (strcmp(s, "random")==0) return RANDOM; if (strcmp(s, "poly")==0) return POLY; if (strcmp(s, "constant")==0) return CONSTANT; if (strcmp(s, "step")==0) return STEP; if (strcmp(s, "exp")==0) return EXP; if (strcmp(s, "sigmoid")==0) return SIG; if (strcmp(s, "steps")==0) return STEPS; if (strcmp(s, "sgdr")==0) return SGDR; fprintf(stderr, "Couldn't find policy %s, going with constant\n", s); return CONSTANT; } void parse_net_options(list *options, network *net) { net->max_batches = option_find_int(options, "max_batches", 0); net->batch = option_find_int(options, "batch",1); net->learning_rate = option_find_float(options, "learning_rate", .001); net->learning_rate_min = option_find_float_quiet(options, "learning_rate_min", .00001); net->batches_per_cycle = option_find_int_quiet(options, "sgdr_cycle", net->max_batches); net->batches_cycle_mult = option_find_int_quiet(options, "sgdr_mult", 2); net->momentum = option_find_float(options, "momentum", .9); net->decay = option_find_float(options, "decay", .0001); int subdivs = option_find_int(options, "subdivisions",1); net->time_steps = option_find_int_quiet(options, "time_steps",1); net->track = option_find_int_quiet(options, "track", 0); net->augment_speed = option_find_int_quiet(options, "augment_speed", 2); net->init_sequential_subdivisions = net->sequential_subdivisions = option_find_int_quiet(options, "sequential_subdivisions", subdivs); if (net->sequential_subdivisions > subdivs) net->init_sequential_subdivisions = net->sequential_subdivisions = subdivs; net->try_fix_nan = option_find_int_quiet(options, "try_fix_nan", 0); net->batch /= subdivs; net->batch *= net->time_steps; net->subdivisions = subdivs; *net->seen = 0; *net->cur_iteration = 0; net->loss_scale = option_find_float_quiet(options, "loss_scale", 1); net->dynamic_minibatch = option_find_int_quiet(options, "dynamic_minibatch", 0); net->optimized_memory = option_find_int_quiet(options, "optimized_memory", 0); net->workspace_size_limit = (size_t)1024*1024 * option_find_float_quiet(options, "workspace_size_limit_MB", 1024); // 1024 MB by default net->adam = option_find_int_quiet(options, "adam", 0); if(net->adam){ net->B1 = option_find_float(options, "B1", .9); net->B2 = option_find_float(options, "B2", .999); net->eps = option_find_float(options, "eps", .000001); } net->h = option_find_int_quiet(options, "height",0); net->w = option_find_int_quiet(options, "width",0); net->c = option_find_int_quiet(options, "channels",0); net->inputs = option_find_int_quiet(options, "inputs", net->h * net->w * net->c); net->max_crop = option_find_int_quiet(options, "max_crop",net->w*2); net->min_crop = option_find_int_quiet(options, "min_crop",net->w); net->flip = option_find_int_quiet(options, "flip", 1); net->blur = option_find_int_quiet(options, "blur", 0); net->gaussian_noise = option_find_int_quiet(options, "gaussian_noise", 0); net->mixup = option_find_int_quiet(options, "mixup", 0); int cutmix = option_find_int_quiet(options, "cutmix", 0); int mosaic = option_find_int_quiet(options, "mosaic", 0); if (mosaic && cutmix) net->mixup = 4; else if (cutmix) net->mixup = 2; else if (mosaic) net->mixup = 3; net->letter_box = option_find_int_quiet(options, "letter_box", 0); net->label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); net->resize_step = option_find_float_quiet(options, "resize_step", 32); net->attention = option_find_int_quiet(options, "attention", 0); net->adversarial_lr = option_find_float_quiet(options, "adversarial_lr", 0); net->angle = option_find_float_quiet(options, "angle", 0); net->aspect = option_find_float_quiet(options, "aspect", 1); net->saturation = option_find_float_quiet(options, "saturation", 1); net->exposure = option_find_float_quiet(options, "exposure", 1); net->hue = option_find_float_quiet(options, "hue", 0); net->power = option_find_float_quiet(options, "power", 4); if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied"); char *policy_s = option_find_str(options, "policy", "constant"); net->policy = get_policy(policy_s); net->burn_in = option_find_int_quiet(options, "burn_in", 0); #ifdef GPU if (net->gpu_index >= 0) { char device_name[1024]; int compute_capability = get_gpu_compute_capability(net->gpu_index, device_name); #ifdef CUDNN_HALF if (compute_capability >= 700) net->cudnn_half = 1; else net->cudnn_half = 0; #endif// CUDNN_HALF fprintf(stderr, " %d : compute_capability = %d, cudnn_half = %d, GPU: %s \n", net->gpu_index, compute_capability, net->cudnn_half, device_name); } else fprintf(stderr, " GPU isn't used \n"); #endif// GPU if(net->policy == STEP){ net->step = option_find_int(options, "step", 1); net->scale = option_find_float(options, "scale", 1); } else if (net->policy == STEPS || net->policy == SGDR){ char *l = option_find(options, "steps"); char *p = option_find(options, "scales"); char *s = option_find(options, "seq_scales"); if(net->policy == STEPS && (!l || !p)) error("STEPS policy must have steps and scales in cfg file"); if (l) { int len = strlen(l); int n = 1; int i; for (i = 0; i < len; ++i) { if (l[i] == ',') ++n; } int* steps = (int*)xcalloc(n, sizeof(int)); float* scales = (float*)xcalloc(n, sizeof(float)); float* seq_scales = (float*)xcalloc(n, sizeof(float)); for (i = 0; i < n; ++i) { float scale = 1.0; if (p) { scale = atof(p); p = strchr(p, ',') + 1; } float sequence_scale = 1.0; if (s) { sequence_scale = atof(s); s = strchr(s, ',') + 1; } int step = atoi(l); l = strchr(l, ',') + 1; steps[i] = step; scales[i] = scale; seq_scales[i] = sequence_scale; } net->scales = scales; net->steps = steps; net->seq_scales = seq_scales; net->num_steps = n; } } else if (net->policy == EXP){ net->gamma = option_find_float(options, "gamma", 1); } else if (net->policy == SIG){ net->gamma = option_find_float(options, "gamma", 1); net->step = option_find_int(options, "step", 1); } else if (net->policy == POLY || net->policy == RANDOM){ //net->power = option_find_float(options, "power", 1); } } int is_network(section *s) { return (strcmp(s->type, "[net]")==0 || strcmp(s->type, "[network]")==0); } void set_train_only_bn(network net) { int train_only_bn = 0; int i; for (i = net.n - 1; i >= 0; --i) { if (net.layers[i].train_only_bn) train_only_bn = net.layers[i].train_only_bn; // set l.train_only_bn for all previous layers if (train_only_bn) { net.layers[i].train_only_bn = train_only_bn; if (net.layers[i].type == CONV_LSTM) { net.layers[i].wf->train_only_bn = train_only_bn; net.layers[i].wi->train_only_bn = train_only_bn; net.layers[i].wg->train_only_bn = train_only_bn; net.layers[i].wo->train_only_bn = train_only_bn; net.layers[i].uf->train_only_bn = train_only_bn; net.layers[i].ui->train_only_bn = train_only_bn; net.layers[i].ug->train_only_bn = train_only_bn; net.layers[i].uo->train_only_bn = train_only_bn; if (net.layers[i].peephole) { net.layers[i].vf->train_only_bn = train_only_bn; net.layers[i].vi->train_only_bn = train_only_bn; net.layers[i].vo->train_only_bn = train_only_bn; } } else if (net.layers[i].type == CRNN) { net.layers[i].input_layer->train_only_bn = train_only_bn; net.layers[i].self_layer->train_only_bn = train_only_bn; net.layers[i].output_layer->train_only_bn = train_only_bn; } } } } network parse_network_cfg(char *filename) { return parse_network_cfg_custom(filename, 0, 0); } network parse_network_cfg_custom(char *filename, int batch, int time_steps) { list *sections = read_cfg(filename); node *n = sections->front; if(!n) error("Config file has no sections"); network net = make_network(sections->size - 1); net.gpu_index = gpu_index; size_params params; if (batch > 0) params.train = 0; // allocates memory for Detection only else params.train = 1; // allocates memory for Detection & Training section *s = (section *)n->val; list *options = s->options; if(!is_network(s)) error("First section must be [net] or [network]"); parse_net_options(options, &net); #ifdef GPU printf("net.optimized_memory = %d \n", net.optimized_memory); if (net.optimized_memory >= 2 && params.train) { pre_allocate_pinned_memory((size_t)1024 * 1024 * 1024 * 8); // pre-allocate 8 GB CPU-RAM for pinned memory } #endif // GPU params.h = net.h; params.w = net.w; params.c = net.c; params.inputs = net.inputs; if (batch > 0) net.batch = batch; if (time_steps > 0) net.time_steps = time_steps; if (net.batch < 1) net.batch = 1; if (net.time_steps < 1) net.time_steps = 1; if (net.batch < net.time_steps) net.batch = net.time_steps; params.batch = net.batch; params.time_steps = net.time_steps; params.net = net; printf("mini_batch = %d, batch = %d, time_steps = %d, train = %d \n", net.batch, net.batch * net.subdivisions, net.time_steps, params.train); int avg_outputs = 0; int avg_counter = 0; float bflops = 0; size_t workspace_size = 0; size_t max_inputs = 0; size_t max_outputs = 0; int receptive_w = 1, receptive_h = 1; int receptive_w_scale = 1, receptive_h_scale = 1; const int show_receptive_field = option_find_float_quiet(options, "show_receptive_field", 0); n = n->next; int count = 0; free_section(s); fprintf(stderr, " layer filters size/strd(dil) input output\n"); while(n){ params.index = count; fprintf(stderr, "%4d ", count); s = (section *)n->val; options = s->options; layer l = { (LAYER_TYPE)0 }; LAYER_TYPE lt = string_to_layer_type(s->type); if(lt == CONVOLUTIONAL){ l = parse_convolutional(options, params); }else if(lt == LOCAL){ l = parse_local(options, params); }else if(lt == ACTIVE){ l = parse_activation(options, params); }else if(lt == RNN){ l = parse_rnn(options, params); }else if(lt == GRU){ l = parse_gru(options, params); }else if(lt == LSTM){ l = parse_lstm(options, params); }else if (lt == CONV_LSTM) { l = parse_conv_lstm(options, params); }else if(lt == CRNN){ l = parse_crnn(options, params); }else if(lt == CONNECTED){ l = parse_connected(options, params); }else if(lt == CROP){ l = parse_crop(options, params); }else if(lt == COST){ l = parse_cost(options, params); l.keep_delta_gpu = 1; }else if(lt == REGION){ l = parse_region(options, params); l.keep_delta_gpu = 1; }else if (lt == YOLO) { l = parse_yolo(options, params); l.keep_delta_gpu = 1; }else if (lt == GAUSSIAN_YOLO) { l = parse_gaussian_yolo(options, params); l.keep_delta_gpu = 1; }else if(lt == DETECTION){ l = parse_detection(options, params); }else if(lt == SOFTMAX){ l = parse_softmax(options, params); net.hierarchy = l.softmax_tree; l.keep_delta_gpu = 1; }else if(lt == NORMALIZATION){ l = parse_normalization(options, params); }else if(lt == BATCHNORM){ l = parse_batchnorm(options, params); }else if(lt == MAXPOOL){ l = parse_maxpool(options, params); }else if (lt == LOCAL_AVGPOOL) { l = parse_local_avgpool(options, params); }else if(lt == REORG){ l = parse_reorg(options, params); } else if (lt == REORG_OLD) { l = parse_reorg_old(options, params); }else if(lt == AVGPOOL){ l = parse_avgpool(options, params); }else if(lt == ROUTE){ l = parse_route(options, params); int k; for (k = 0; k < l.n; ++k) { net.layers[l.input_layers[k]].use_bin_output = 0; net.layers[l.input_layers[k]].keep_delta_gpu = 1; } }else if (lt == UPSAMPLE) { l = parse_upsample(options, params, net); }else if(lt == SHORTCUT){ l = parse_shortcut(options, params, net); net.layers[count - 1].use_bin_output = 0; net.layers[l.index].use_bin_output = 0; net.layers[l.index].keep_delta_gpu = 1; }else if (lt == SCALE_CHANNELS) { l = parse_scale_channels(options, params, net); net.layers[count - 1].use_bin_output = 0; net.layers[l.index].use_bin_output = 0; net.layers[l.index].keep_delta_gpu = 1; } else if (lt == SAM) { l = parse_sam(options, params, net); net.layers[count - 1].use_bin_output = 0; net.layers[l.index].use_bin_output = 0; net.layers[l.index].keep_delta_gpu = 1; }else if(lt == DROPOUT){ l = parse_dropout(options, params); l.output = net.layers[count-1].output; l.delta = net.layers[count-1].delta; #ifdef GPU l.output_gpu = net.layers[count-1].output_gpu; l.delta_gpu = net.layers[count-1].delta_gpu; l.keep_delta_gpu = 1; #endif } else if (lt == EMPTY) { layer empty_layer = {(LAYER_TYPE)0}; empty_layer.out_w = params.w; empty_layer.out_h = params.h; empty_layer.out_c = params.c; l = empty_layer; l.output = net.layers[count - 1].output; l.delta = net.layers[count - 1].delta; #ifdef GPU l.output_gpu = net.layers[count - 1].output_gpu; l.delta_gpu = net.layers[count - 1].delta_gpu; #endif }else{ fprintf(stderr, "Type not recognized: %s\n", s->type); } // calculate receptive field if(show_receptive_field) { int dilation = max_val_cmp(1, l.dilation); int stride = max_val_cmp(1, l.stride); int size = max_val_cmp(1, l.size); if (l.type == UPSAMPLE || (l.type == REORG)) { l.receptive_w = receptive_w; l.receptive_h = receptive_h; l.receptive_w_scale = receptive_w_scale = receptive_w_scale / stride; l.receptive_h_scale = receptive_h_scale = receptive_h_scale / stride; } else { if (l.type == ROUTE) { receptive_w = receptive_h = receptive_w_scale = receptive_h_scale = 0; int k; for (k = 0; k < l.n; ++k) { layer route_l = net.layers[l.input_layers[k]]; receptive_w = max_val_cmp(receptive_w, route_l.receptive_w); receptive_h = max_val_cmp(receptive_h, route_l.receptive_h); receptive_w_scale = max_val_cmp(receptive_w_scale, route_l.receptive_w_scale); receptive_h_scale = max_val_cmp(receptive_h_scale, route_l.receptive_h_scale); } } else { int increase_receptive = size + (dilation - 1) * 2 - 1;// stride; increase_receptive = max_val_cmp(0, increase_receptive); receptive_w += increase_receptive * receptive_w_scale; receptive_h += increase_receptive * receptive_h_scale; receptive_w_scale *= stride; receptive_h_scale *= stride; } l.receptive_w = receptive_w; l.receptive_h = receptive_h; l.receptive_w_scale = receptive_w_scale; l.receptive_h_scale = receptive_h_scale; } //printf(" size = %d, dilation = %d, stride = %d, receptive_w = %d, receptive_w_scale = %d - ", size, dilation, stride, receptive_w, receptive_w_scale); int cur_receptive_w = receptive_w; int cur_receptive_h = receptive_h; fprintf(stderr, "%4d - receptive field: %d x %d \n", count, cur_receptive_w, cur_receptive_h); } #ifdef GPU // futher GPU-memory optimization: net.optimized_memory == 2 if (net.optimized_memory >= 2 && params.train && l.type != DROPOUT) { l.optimized_memory = net.optimized_memory; if (l.output_gpu) { cuda_free(l.output_gpu); //l.output_gpu = cuda_make_array_pinned(l.output, l.batch*l.outputs); // l.steps l.output_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps } if (l.activation_input_gpu) { cuda_free(l.activation_input_gpu); l.activation_input_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps } if (l.x_gpu) { cuda_free(l.x_gpu); l.x_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps } // maximum optimization if (net.optimized_memory >= 3 && l.type != DROPOUT) { if (l.delta_gpu) { cuda_free(l.delta_gpu); //l.delta_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps //printf("\n\n PINNED DELTA GPU = %d \n", l.batch*l.outputs); } } if (l.type == CONVOLUTIONAL) { set_specified_workspace_limit(&l, net.workspace_size_limit); // workspace size limit 1 GB } } #endif // GPU l.clip = option_find_float_quiet(options, "clip", 0); l.dynamic_minibatch = net.dynamic_minibatch; l.onlyforward = option_find_int_quiet(options, "onlyforward", 0); l.dont_update = option_find_int_quiet(options, "dont_update", 0); l.burnin_update = option_find_int_quiet(options, "burnin_update", 0); l.stopbackward = option_find_int_quiet(options, "stopbackward", 0); l.train_only_bn = option_find_int_quiet(options, "train_only_bn", 0); l.dontload = option_find_int_quiet(options, "dontload", 0); l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0); l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1); option_unused(options); net.layers[count] = l; if (l.workspace_size > workspace_size) workspace_size = l.workspace_size; if (l.inputs > max_inputs) max_inputs = l.inputs; if (l.outputs > max_outputs) max_outputs = l.outputs; free_section(s); n = n->next; ++count; if(n){ if (l.antialiasing) { params.h = l.input_layer->out_h; params.w = l.input_layer->out_w; params.c = l.input_layer->out_c; params.inputs = l.input_layer->outputs; } else { params.h = l.out_h; params.w = l.out_w; params.c = l.out_c; params.inputs = l.outputs; } } if (l.bflops > 0) bflops += l.bflops; if (l.w > 1 && l.h > 1) { avg_outputs += l.outputs; avg_counter++; } } free_list(sections); #ifdef GPU if (net.optimized_memory && params.train) { int k; for (k = 0; k < net.n; ++k) { layer l = net.layers[k]; // delta GPU-memory optimization: net.optimized_memory == 1 if (!l.keep_delta_gpu) { const size_t delta_size = l.outputs*l.batch; // l.steps if (net.max_delta_gpu_size < delta_size) { net.max_delta_gpu_size = delta_size; if (net.global_delta_gpu) cuda_free(net.global_delta_gpu); if (net.state_delta_gpu) cuda_free(net.state_delta_gpu); assert(net.max_delta_gpu_size > 0); net.global_delta_gpu = (float *)cuda_make_array(NULL, net.max_delta_gpu_size); net.state_delta_gpu = (float *)cuda_make_array(NULL, net.max_delta_gpu_size); } if (l.delta_gpu) { if (net.optimized_memory >= 3) {} else cuda_free(l.delta_gpu); } l.delta_gpu = net.global_delta_gpu; } // maximum optimization if (net.optimized_memory >= 3 && l.type != DROPOUT) { if (l.delta_gpu && l.keep_delta_gpu) { //cuda_free(l.delta_gpu); // already called above l.delta_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps //printf("\n\n PINNED DELTA GPU = %d \n", l.batch*l.outputs); } } net.layers[k] = l; } } #endif set_train_only_bn(net); // set l.train_only_bn for all required layers net.outputs = get_network_output_size(net); net.output = get_network_output(net); avg_outputs = avg_outputs / avg_counter; fprintf(stderr, "Total BFLOPS %5.3f \n", bflops); fprintf(stderr, "avg_outputs = %d \n", avg_outputs); #ifdef GPU get_cuda_stream(); get_cuda_memcpy_stream(); if (gpu_index >= 0) { int size = get_network_input_size(net) * net.batch; net.input_state_gpu = cuda_make_array(0, size); if (cudaSuccess == cudaHostAlloc(&net.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1; else { cudaGetLastError(); // reset CUDA-error net.input_pinned_cpu = (float*)xcalloc(size, sizeof(float)); } // pre-allocate memory for inference on Tensor Cores (fp16) if (net.cudnn_half) { *net.max_input16_size = max_inputs; CHECK_CUDA(cudaMalloc((void **)net.input16_gpu, *net.max_input16_size * sizeof(short))); //sizeof(half) *net.max_output16_size = max_outputs; CHECK_CUDA(cudaMalloc((void **)net.output16_gpu, *net.max_output16_size * sizeof(short))); //sizeof(half) } if (workspace_size) { fprintf(stderr, " Allocate additional workspace_size = %1.2f MB \n", (float)workspace_size/1000000); net.workspace = cuda_make_array(0, workspace_size / sizeof(float) + 1); } else { net.workspace = (float*)xcalloc(1, workspace_size); } } #else if (workspace_size) { net.workspace = (float*)xcalloc(1, workspace_size); } #endif LAYER_TYPE lt = net.layers[net.n - 1].type; if ((net.w % 32 != 0 || net.h % 32 != 0) && (lt == YOLO || lt == REGION || lt == DETECTION)) { printf("\n Warning: width=%d and height=%d in cfg-file must be divisible by 32 for default networks Yolo v1/v2/v3!!! \n\n", net.w, net.h); } return net; } list *read_cfg(char *filename) { FILE *file = fopen(filename, "r"); if(file == 0) file_error(filename); char *line; int nu = 0; list *sections = make_list(); section *current = 0; while((line=fgetl(file)) != 0){ ++ nu; strip(line); switch(line[0]){ case '[': current = (section*)xmalloc(sizeof(section)); list_insert(sections, current); current->options = make_list(); current->type = line; break; case '\0': case '#': case ';': free(line); break; default: if(!read_option(line, current->options)){ fprintf(stderr, "Config file error line %d, could parse: %s\n", nu, line); free(line); } break; } } fclose(file); return sections; } void save_convolutional_weights_binary(layer l, FILE *fp) { #ifdef GPU if(gpu_index >= 0){ pull_convolutional_layer(l); } #endif int size = (l.c/l.groups)*l.size*l.size; binarize_weights(l.weights, l.n, size, l.binary_weights); int i, j, k; fwrite(l.biases, sizeof(float), l.n, fp); if (l.batch_normalize){ fwrite(l.scales, sizeof(float), l.n, fp); fwrite(l.rolling_mean, sizeof(float), l.n, fp); fwrite(l.rolling_variance, sizeof(float), l.n, fp); } for(i = 0; i < l.n; ++i){ float mean = l.binary_weights[i*size]; if(mean < 0) mean = -mean; fwrite(&mean, sizeof(float), 1, fp); for(j = 0; j < size/8; ++j){ int index = i*size + j*8; unsigned char c = 0; for(k = 0; k < 8; ++k){ if (j*8 + k >= size) break; if (l.binary_weights[index + k] > 0) c = (c | 1<<k); } fwrite(&c, sizeof(char), 1, fp); } } } void save_shortcut_weights(layer l, FILE *fp) { #ifdef GPU if (gpu_index >= 0) { pull_shortcut_layer(l); printf("\n pull_shortcut_layer \n"); } #endif int i; for (i = 0; i < l.nweights; ++i) printf(" %f, ", l.weight_updates[i]); printf(" l.nweights = %d - update \n", l.nweights); for (i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]); printf(" l.nweights = %d \n\n", l.nweights); int num = l.nweights; fwrite(l.weights, sizeof(float), num, fp); } void save_convolutional_weights(layer l, FILE *fp) { if(l.binary){ //save_convolutional_weights_binary(l, fp); //return; } #ifdef GPU if(gpu_index >= 0){ pull_convolutional_layer(l); } #endif int num = l.nweights; fwrite(l.biases, sizeof(float), l.n, fp); if (l.batch_normalize){ fwrite(l.scales, sizeof(float), l.n, fp); fwrite(l.rolling_mean, sizeof(float), l.n, fp); fwrite(l.rolling_variance, sizeof(float), l.n, fp); } fwrite(l.weights, sizeof(float), num, fp); //if(l.adam){ // fwrite(l.m, sizeof(float), num, fp); // fwrite(l.v, sizeof(float), num, fp); //} } void save_batchnorm_weights(layer l, FILE *fp) { #ifdef GPU if(gpu_index >= 0){ pull_batchnorm_layer(l); } #endif fwrite(l.biases, sizeof(float), l.c, fp); fwrite(l.scales, sizeof(float), l.c, fp); fwrite(l.rolling_mean, sizeof(float), l.c, fp); fwrite(l.rolling_variance, sizeof(float), l.c, fp); } void save_connected_weights(layer l, FILE *fp) { #ifdef GPU if(gpu_index >= 0){ pull_connected_layer(l); } #endif fwrite(l.biases, sizeof(float), l.outputs, fp); fwrite(l.weights, sizeof(float), l.outputs*l.inputs, fp); if (l.batch_normalize){ fwrite(l.scales, sizeof(float), l.outputs, fp); fwrite(l.rolling_mean, sizeof(float), l.outputs, fp); fwrite(l.rolling_variance, sizeof(float), l.outputs, fp); } } void save_weights_upto(network net, char *filename, int cutoff) { #ifdef GPU if(net.gpu_index >= 0){ cuda_set_device(net.gpu_index); } #endif fprintf(stderr, "Saving weights to %s\n", filename); FILE *fp = fopen(filename, "wb"); if(!fp) file_error(filename); int major = MAJOR_VERSION; int minor = MINOR_VERSION; int revision = PATCH_VERSION; fwrite(&major, sizeof(int), 1, fp); fwrite(&minor, sizeof(int), 1, fp); fwrite(&revision, sizeof(int), 1, fp); (*net.seen) = get_current_iteration(net) * net.batch * net.subdivisions; // remove this line, when you will save to weights-file both: seen & cur_iteration fwrite(net.seen, sizeof(uint64_t), 1, fp); int i; for(i = 0; i < net.n && i < cutoff; ++i){ layer l = net.layers[i]; if (l.type == CONVOLUTIONAL && l.share_layer == NULL) { save_convolutional_weights(l, fp); } if (l.type == SHORTCUT && l.nweights > 0) { save_shortcut_weights(l, fp); } if(l.type == CONNECTED){ save_connected_weights(l, fp); } if(l.type == BATCHNORM){ save_batchnorm_weights(l, fp); } if(l.type == RNN){ save_connected_weights(*(l.input_layer), fp); save_connected_weights(*(l.self_layer), fp); save_connected_weights(*(l.output_layer), fp); } if(l.type == GRU){ save_connected_weights(*(l.input_z_layer), fp); save_connected_weights(*(l.input_r_layer), fp); save_connected_weights(*(l.input_h_layer), fp); save_connected_weights(*(l.state_z_layer), fp); save_connected_weights(*(l.state_r_layer), fp); save_connected_weights(*(l.state_h_layer), fp); } if(l.type == LSTM){ save_connected_weights(*(l.wf), fp); save_connected_weights(*(l.wi), fp); save_connected_weights(*(l.wg), fp); save_connected_weights(*(l.wo), fp); save_connected_weights(*(l.uf), fp); save_connected_weights(*(l.ui), fp); save_connected_weights(*(l.ug), fp); save_connected_weights(*(l.uo), fp); } if (l.type == CONV_LSTM) { if (l.peephole) { save_convolutional_weights(*(l.vf), fp); save_convolutional_weights(*(l.vi), fp); save_convolutional_weights(*(l.vo), fp); } save_convolutional_weights(*(l.wf), fp); save_convolutional_weights(*(l.wi), fp); save_convolutional_weights(*(l.wg), fp); save_convolutional_weights(*(l.wo), fp); save_convolutional_weights(*(l.uf), fp); save_convolutional_weights(*(l.ui), fp); save_convolutional_weights(*(l.ug), fp); save_convolutional_weights(*(l.uo), fp); } if(l.type == CRNN){ save_convolutional_weights(*(l.input_layer), fp); save_convolutional_weights(*(l.self_layer), fp); save_convolutional_weights(*(l.output_layer), fp); } if(l.type == LOCAL){ #ifdef GPU if(gpu_index >= 0){ pull_local_layer(l); } #endif int locations = l.out_w*l.out_h; int size = l.size*l.size*l.c*l.n*locations; fwrite(l.biases, sizeof(float), l.outputs, fp); fwrite(l.weights, sizeof(float), size, fp); } } fclose(fp); } void save_weights(network net, char *filename) { save_weights_upto(net, filename, net.n); } void transpose_matrix(float *a, int rows, int cols) { float* transpose = (float*)xcalloc(rows * cols, sizeof(float)); int x, y; for(x = 0; x < rows; ++x){ for(y = 0; y < cols; ++y){ transpose[y*rows + x] = a[x*cols + y]; } } memcpy(a, transpose, rows*cols*sizeof(float)); free(transpose); } void load_connected_weights(layer l, FILE *fp, int transpose) { fread(l.biases, sizeof(float), l.outputs, fp); fread(l.weights, sizeof(float), l.outputs*l.inputs, fp); if(transpose){ transpose_matrix(l.weights, l.inputs, l.outputs); } //printf("Biases: %f mean %f variance\n", mean_array(l.biases, l.outputs), variance_array(l.biases, l.outputs)); //printf("Weights: %f mean %f variance\n", mean_array(l.weights, l.outputs*l.inputs), variance_array(l.weights, l.outputs*l.inputs)); if (l.batch_normalize && (!l.dontloadscales)){ fread(l.scales, sizeof(float), l.outputs, fp); fread(l.rolling_mean, sizeof(float), l.outputs, fp); fread(l.rolling_variance, sizeof(float), l.outputs, fp); //printf("Scales: %f mean %f variance\n", mean_array(l.scales, l.outputs), variance_array(l.scales, l.outputs)); //printf("rolling_mean: %f mean %f variance\n", mean_array(l.rolling_mean, l.outputs), variance_array(l.rolling_mean, l.outputs)); //printf("rolling_variance: %f mean %f variance\n", mean_array(l.rolling_variance, l.outputs), variance_array(l.rolling_variance, l.outputs)); } #ifdef GPU if(gpu_index >= 0){ push_connected_layer(l); } #endif } void load_batchnorm_weights(layer l, FILE *fp) { fread(l.biases, sizeof(float), l.c, fp); fread(l.scales, sizeof(float), l.c, fp); fread(l.rolling_mean, sizeof(float), l.c, fp); fread(l.rolling_variance, sizeof(float), l.c, fp); #ifdef GPU if(gpu_index >= 0){ push_batchnorm_layer(l); } #endif } void load_convolutional_weights_binary(layer l, FILE *fp) { fread(l.biases, sizeof(float), l.n, fp); if (l.batch_normalize && (!l.dontloadscales)){ fread(l.scales, sizeof(float), l.n, fp); fread(l.rolling_mean, sizeof(float), l.n, fp); fread(l.rolling_variance, sizeof(float), l.n, fp); } int size = (l.c / l.groups)*l.size*l.size; int i, j, k; for(i = 0; i < l.n; ++i){ float mean = 0; fread(&mean, sizeof(float), 1, fp); for(j = 0; j < size/8; ++j){ int index = i*size + j*8; unsigned char c = 0; fread(&c, sizeof(char), 1, fp); for(k = 0; k < 8; ++k){ if (j*8 + k >= size) break; l.weights[index + k] = (c & 1<<k) ? mean : -mean; } } } #ifdef GPU if(gpu_index >= 0){ push_convolutional_layer(l); } #endif } void load_convolutional_weights(layer l, FILE *fp) { if(l.binary){ //load_convolutional_weights_binary(l, fp); //return; } int num = l.nweights; int read_bytes; read_bytes = fread(l.biases, sizeof(float), l.n, fp); if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.biases - l.index = %d \n", l.index); //fread(l.weights, sizeof(float), num, fp); // as in connected layer if (l.batch_normalize && (!l.dontloadscales)){ read_bytes = fread(l.scales, sizeof(float), l.n, fp); if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.scales - l.index = %d \n", l.index); read_bytes = fread(l.rolling_mean, sizeof(float), l.n, fp); if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.rolling_mean - l.index = %d \n", l.index); read_bytes = fread(l.rolling_variance, sizeof(float), l.n, fp); if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.rolling_variance - l.index = %d \n", l.index); if(0){ int i; for(i = 0; i < l.n; ++i){ printf("%g, ", l.rolling_mean[i]); } printf("\n"); for(i = 0; i < l.n; ++i){ printf("%g, ", l.rolling_variance[i]); } printf("\n"); } if(0){ fill_cpu(l.n, 0, l.rolling_mean, 1); fill_cpu(l.n, 0, l.rolling_variance, 1); } } read_bytes = fread(l.weights, sizeof(float), num, fp); if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.weights - l.index = %d \n", l.index); //if(l.adam){ // fread(l.m, sizeof(float), num, fp); // fread(l.v, sizeof(float), num, fp); //} //if(l.c == 3) scal_cpu(num, 1./256, l.weights, 1); if (l.flipped) { transpose_matrix(l.weights, (l.c/l.groups)*l.size*l.size, l.n); } //if (l.binary) binarize_weights(l.weights, l.n, (l.c/l.groups)*l.size*l.size, l.weights); #ifdef GPU if(gpu_index >= 0){ push_convolutional_layer(l); } #endif } void load_shortcut_weights(layer l, FILE *fp) { int num = l.nweights; int read_bytes; read_bytes = fread(l.weights, sizeof(float), num, fp); if (read_bytes > 0 && read_bytes < num) printf("\n Warning: Unexpected end of wights-file! l.weights - l.index = %d \n", l.index); //for (int i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]); //printf(" read_bytes = %d \n\n", read_bytes); #ifdef GPU if (gpu_index >= 0) { push_shortcut_layer(l); } #endif } void load_weights_upto(network *net, char *filename, int cutoff) { #ifdef GPU if(net->gpu_index >= 0){ cuda_set_device(net->gpu_index); } #endif fprintf(stderr, "Loading weights from %s...", filename); fflush(stdout); FILE *fp = fopen(filename, "rb"); if(!fp) file_error(filename); int major; int minor; int revision; fread(&major, sizeof(int), 1, fp); fread(&minor, sizeof(int), 1, fp); fread(&revision, sizeof(int), 1, fp); if ((major * 10 + minor) >= 2) { printf("\n seen 64"); uint64_t iseen = 0; fread(&iseen, sizeof(uint64_t), 1, fp); *net->seen = iseen; } else { printf("\n seen 32"); uint32_t iseen = 0; fread(&iseen, sizeof(uint32_t), 1, fp); *net->seen = iseen; } *net->cur_iteration = get_current_batch(*net); printf(", trained: %.0f K-images (%.0f Kilo-batches_64) \n", (float)(*net->seen / 1000), (float)(*net->seen / 64000)); int transpose = (major > 1000) || (minor > 1000); int i; for(i = 0; i < net->n && i < cutoff; ++i){ layer l = net->layers[i]; if (l.dontload) continue; if(l.type == CONVOLUTIONAL && l.share_layer == NULL){ load_convolutional_weights(l, fp); } if (l.type == SHORTCUT && l.nweights > 0) { load_shortcut_weights(l, fp); } if(l.type == CONNECTED){ load_connected_weights(l, fp, transpose); } if(l.type == BATCHNORM){ load_batchnorm_weights(l, fp); } if(l.type == CRNN){ load_convolutional_weights(*(l.input_layer), fp); load_convolutional_weights(*(l.self_layer), fp); load_convolutional_weights(*(l.output_layer), fp); } if(l.type == RNN){ load_connected_weights(*(l.input_layer), fp, transpose); load_connected_weights(*(l.self_layer), fp, transpose); load_connected_weights(*(l.output_layer), fp, transpose); } if(l.type == GRU){ load_connected_weights(*(l.input_z_layer), fp, transpose); load_connected_weights(*(l.input_r_layer), fp, transpose); load_connected_weights(*(l.input_h_layer), fp, transpose); load_connected_weights(*(l.state_z_layer), fp, transpose); load_connected_weights(*(l.state_r_layer), fp, transpose); load_connected_weights(*(l.state_h_layer), fp, transpose); } if(l.type == LSTM){ load_connected_weights(*(l.wf), fp, transpose); load_connected_weights(*(l.wi), fp, transpose); load_connected_weights(*(l.wg), fp, transpose); load_connected_weights(*(l.wo), fp, transpose); load_connected_weights(*(l.uf), fp, transpose); load_connected_weights(*(l.ui), fp, transpose); load_connected_weights(*(l.ug), fp, transpose); load_connected_weights(*(l.uo), fp, transpose); } if (l.type == CONV_LSTM) { if (l.peephole) { load_convolutional_weights(*(l.vf), fp); load_convolutional_weights(*(l.vi), fp); load_convolutional_weights(*(l.vo), fp); } load_convolutional_weights(*(l.wf), fp); load_convolutional_weights(*(l.wi), fp); load_convolutional_weights(*(l.wg), fp); load_convolutional_weights(*(l.wo), fp); load_convolutional_weights(*(l.uf), fp); load_convolutional_weights(*(l.ui), fp); load_convolutional_weights(*(l.ug), fp); load_convolutional_weights(*(l.uo), fp); } if(l.type == LOCAL){ int locations = l.out_w*l.out_h; int size = l.size*l.size*l.c*l.n*locations; fread(l.biases, sizeof(float), l.outputs, fp); fread(l.weights, sizeof(float), size, fp); #ifdef GPU if(gpu_index >= 0){ push_local_layer(l); } #endif } if (feof(fp)) break; } fprintf(stderr, "Done! Loaded %d layers from weights-file \n", i); fclose(fp); } void load_weights(network *net, char *filename) { load_weights_upto(net, filename, net->n); } // load network & force - set batch size network *load_network_custom(char *cfg, char *weights, int clear, int batch) { printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear); network* net = (network*)xcalloc(1, sizeof(network)); *net = parse_network_cfg_custom(cfg, batch, 1); if (weights && weights[0] != 0) { printf(" Try to load weights: %s \n", weights); load_weights(net, weights); } fuse_conv_batchnorm(*net); if (clear) { (*net->seen) = 0; (*net->cur_iteration) = 0; } return net; } // load network & get batch size from cfg-file network *load_network(char *cfg, char *weights, int clear) { printf(" Try to load cfg: %s, clear = %d \n", cfg, clear); network* net = (network*)xcalloc(1, sizeof(network)); *net = parse_network_cfg(cfg); if (weights && weights[0] != 0) { printf(" Try to load weights: %s \n", weights); load_weights(net, weights); } if (clear) { (*net->seen) = 0; (*net->cur_iteration) = 0; } return net; } #include <stdio.h> #include <string.h> #include <stdlib.h> #include <stdint.h> #include "activation_layer.h" #include "activations.h" #include "assert.h" #include "avgpool_layer.h" #include "batchnorm_layer.h" #include "blas.h" #include "connected_layer.h" #include "convolutional_layer.h" #include "cost_layer.h" #include "crnn_layer.h" #include "crop_layer.h" #include "detection_layer.h" #include "dropout_layer.h" #include "gru_layer.h" #include "list.h" #include "local_layer.h" #include "lstm_layer.h" #include "conv_lstm_layer.h" #include "maxpool_layer.h" #include "normalization_layer.h" #include "option_list.h" #include "parser.h" #include "region_layer.h" #include "reorg_layer.h" #include "reorg_old_layer.h" #include "rnn_layer.h" #include "route_layer.h" #include "shortcut_layer.h" #include "scale_channels_layer.h" #include "sam_layer.h" #include "softmax_layer.h" #include "utils.h" #include "upsample_layer.h" #include "version.h" #include "yolo_layer.h" #include "gaussian_yolo_layer.h" #include "representation_layer.h" void empty_func(dropout_layer l, network_state state) { //l.output_gpu = state.input; } typedef struct{ char *type; list *options; }section; list *read_cfg(char *filename); LAYER_TYPE string_to_layer_type(char * type) { if (strcmp(type, "[shortcut]")==0) return SHORTCUT; if (strcmp(type, "[scale_channels]") == 0) return SCALE_CHANNELS; if (strcmp(type, "[sam]") == 0) return SAM; if (strcmp(type, "[crop]")==0) return CROP; if (strcmp(type, "[cost]")==0) return COST; if (strcmp(type, "[detection]")==0) return DETECTION; if (strcmp(type, "[region]")==0) return REGION; if (strcmp(type, "[yolo]") == 0) return YOLO; if (strcmp(type, "[Gaussian_yolo]") == 0) return GAUSSIAN_YOLO; if (strcmp(type, "[local]")==0) return LOCAL; if (strcmp(type, "[conv]")==0 || strcmp(type, "[convolutional]")==0) return CONVOLUTIONAL; if (strcmp(type, "[activation]")==0) return ACTIVE; if (strcmp(type, "[net]")==0 || strcmp(type, "[network]")==0) return NETWORK; if (strcmp(type, "[crnn]")==0) return CRNN; if (strcmp(type, "[gru]")==0) return GRU; if (strcmp(type, "[lstm]")==0) return LSTM; if (strcmp(type, "[conv_lstm]") == 0) return CONV_LSTM; if (strcmp(type, "[history]") == 0) return HISTORY; if (strcmp(type, "[rnn]")==0) return RNN; if (strcmp(type, "[conn]")==0 || strcmp(type, "[connected]")==0) return CONNECTED; if (strcmp(type, "[max]")==0 || strcmp(type, "[maxpool]")==0) return MAXPOOL; if (strcmp(type, "[local_avg]") == 0 || strcmp(type, "[local_avgpool]") == 0) return LOCAL_AVGPOOL; if (strcmp(type, "[reorg3d]")==0) return REORG; if (strcmp(type, "[reorg]") == 0) return REORG_OLD; if (strcmp(type, "[avg]")==0 || strcmp(type, "[avgpool]")==0) return AVGPOOL; if (strcmp(type, "[dropout]")==0) return DROPOUT; if (strcmp(type, "[lrn]")==0 || strcmp(type, "[normalization]")==0) return NORMALIZATION; if (strcmp(type, "[batchnorm]")==0) return BATCHNORM; if (strcmp(type, "[soft]")==0 || strcmp(type, "[softmax]")==0) return SOFTMAX; if (strcmp(type, "[contrastive]") == 0) return CONTRASTIVE; if (strcmp(type, "[route]")==0) return ROUTE; if (strcmp(type, "[upsample]") == 0) return UPSAMPLE; if (strcmp(type, "[empty]") == 0 || strcmp(type, "[silence]") == 0) return EMPTY; if (strcmp(type, "[implicit]") == 0) return IMPLICIT; return BLANK; } void free_section(section *s) { free(s->type); node *n = s->options->front; while(n){ kvp *pair = (kvp *)n->val; free(pair->key); free(pair); node *next = n->next; free(n); n = next; } free(s->options); free(s); } void parse_data(char *data, float *a, int n) { int i; if(!data) return; char *curr = data; char *next = data; int done = 0; for(i = 0; i < n && !done; ++i){ while(*++next !='\0' && *next != ','); if(*next == '\0') done = 1; *next = '\0'; sscanf(curr, "%g", &a[i]); curr = next+1; } } typedef struct size_params{ int batch; int inputs; int h; int w; int c; int index; int time_steps; int train; network net; } size_params; local_layer parse_local(list *options, size_params params) { int n = option_find_int(options, "filters",1); int size = option_find_int(options, "size",1); int stride = option_find_int(options, "stride",1); int pad = option_find_int(options, "pad",0); char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); int batch,h,w,c; h = params.h; w = params.w; c = params.c; batch=params.batch; if(!(h && w && c)) error("Layer before local layer must output image."); local_layer layer = make_local_layer(batch,h,w,c,n,size,stride,pad,activation); return layer; } convolutional_layer parse_convolutional(list *options, size_params params) { int n = option_find_int(options, "filters",1); int groups = option_find_int_quiet(options, "groups", 1); int size = option_find_int(options, "size",1); int stride = -1; //int stride = option_find_int(options, "stride",1); int stride_x = option_find_int_quiet(options, "stride_x", -1); int stride_y = option_find_int_quiet(options, "stride_y", -1); if (stride_x < 1 || stride_y < 1) { stride = option_find_int(options, "stride", 1); if (stride_x < 1) stride_x = stride; if (stride_y < 1) stride_y = stride; } else { stride = option_find_int_quiet(options, "stride", 1); } int dilation = option_find_int_quiet(options, "dilation", 1); int antialiasing = option_find_int_quiet(options, "antialiasing", 0); if (size == 1) dilation = 1; int pad = option_find_int_quiet(options, "pad",0); int padding = option_find_int_quiet(options, "padding",0); if(pad) padding = size/2; char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); int assisted_excitation = option_find_float_quiet(options, "assisted_excitation", 0); int share_index = option_find_int_quiet(options, "share_index", -1000000000); convolutional_layer *share_layer = NULL; if(share_index >= 0) share_layer = ¶ms.net.layers[share_index]; else if(share_index != -1000000000) share_layer = ¶ms.net.layers[params.index + share_index]; int batch,h,w,c; h = params.h; w = params.w; c = params.c; batch=params.batch; if(!(h && w && c)) error("Layer before convolutional layer must output image."); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); int cbn = option_find_int_quiet(options, "cbn", 0); if (cbn) batch_normalize = 2; int binary = option_find_int_quiet(options, "binary", 0); int xnor = option_find_int_quiet(options, "xnor", 0); int use_bin_output = option_find_int_quiet(options, "bin_output", 0); int sway = option_find_int_quiet(options, "sway", 0); int rotate = option_find_int_quiet(options, "rotate", 0); int stretch = option_find_int_quiet(options, "stretch", 0); int stretch_sway = option_find_int_quiet(options, "stretch_sway", 0); if ((sway + rotate + stretch + stretch_sway) > 1) { printf(" Error: should be used only 1 param: sway=1, rotate=1 or stretch=1 in the [convolutional] layer \n"); exit(0); } int deform = sway || rotate || stretch || stretch_sway; if (deform && size == 1) { printf(" Error: params (sway=1, rotate=1 or stretch=1) should be used only with size >=3 in the [convolutional] layer \n"); exit(0); } convolutional_layer layer = make_convolutional_layer(batch,1,h,w,c,n,groups,size,stride_x,stride_y,dilation,padding,activation, batch_normalize, binary, xnor, params.net.adam, use_bin_output, params.index, antialiasing, share_layer, assisted_excitation, deform, params.train); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); layer.sway = sway; layer.rotate = rotate; layer.stretch = stretch; layer.stretch_sway = stretch_sway; layer.angle = option_find_float_quiet(options, "angle", 15); layer.grad_centr = option_find_int_quiet(options, "grad_centr", 0); layer.reverse = option_find_float_quiet(options, "reverse", 0); layer.coordconv = option_find_int_quiet(options, "coordconv", 0); layer.stream = option_find_int_quiet(options, "stream", -1); layer.wait_stream_id = option_find_int_quiet(options, "wait_stream", -1); if(params.net.adam){ layer.B1 = params.net.B1; layer.B2 = params.net.B2; layer.eps = params.net.eps; } return layer; } layer parse_crnn(list *options, size_params params) { int size = option_find_int_quiet(options, "size", 3); int stride = option_find_int_quiet(options, "stride", 1); int dilation = option_find_int_quiet(options, "dilation", 1); int pad = option_find_int_quiet(options, "pad", 0); int padding = option_find_int_quiet(options, "padding", 0); if (pad) padding = size / 2; int output_filters = option_find_int(options, "output",1); int hidden_filters = option_find_int(options, "hidden",1); int groups = option_find_int_quiet(options, "groups", 1); char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); int xnor = option_find_int_quiet(options, "xnor", 0); layer l = make_crnn_layer(params.batch, params.h, params.w, params.c, hidden_filters, output_filters, groups, params.time_steps, size, stride, dilation, padding, activation, batch_normalize, xnor, params.train); l.shortcut = option_find_int_quiet(options, "shortcut", 0); return l; } layer parse_rnn(list *options, size_params params) { int output = option_find_int(options, "output",1); int hidden = option_find_int(options, "hidden",1); char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); int logistic = option_find_int_quiet(options, "logistic", 0); layer l = make_rnn_layer(params.batch, params.inputs, hidden, output, params.time_steps, activation, batch_normalize, logistic); l.shortcut = option_find_int_quiet(options, "shortcut", 0); return l; } layer parse_gru(list *options, size_params params) { int output = option_find_int(options, "output",1); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); layer l = make_gru_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize); return l; } layer parse_lstm(list *options, size_params params) { int output = option_find_int(options, "output",1); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); layer l = make_lstm_layer(params.batch, params.inputs, output, params.time_steps, batch_normalize); return l; } layer parse_conv_lstm(list *options, size_params params) { // a ConvLSTM with a larger transitional kernel should be able to capture faster motions int size = option_find_int_quiet(options, "size", 3); int stride = option_find_int_quiet(options, "stride", 1); int dilation = option_find_int_quiet(options, "dilation", 1); int pad = option_find_int_quiet(options, "pad", 0); int padding = option_find_int_quiet(options, "padding", 0); if (pad) padding = size / 2; int output_filters = option_find_int(options, "output", 1); int groups = option_find_int_quiet(options, "groups", 1); char *activation_s = option_find_str(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); int xnor = option_find_int_quiet(options, "xnor", 0); int peephole = option_find_int_quiet(options, "peephole", 0); int bottleneck = option_find_int_quiet(options, "bottleneck", 0); layer l = make_conv_lstm_layer(params.batch, params.h, params.w, params.c, output_filters, groups, params.time_steps, size, stride, dilation, padding, activation, batch_normalize, peephole, xnor, bottleneck, params.train); l.state_constrain = option_find_int_quiet(options, "state_constrain", params.time_steps * 32); l.shortcut = option_find_int_quiet(options, "shortcut", 0); char *lstm_activation_s = option_find_str(options, "lstm_activation", "tanh"); l.lstm_activation = get_activation(lstm_activation_s); l.time_normalizer = option_find_float_quiet(options, "time_normalizer", 1.0); return l; } layer parse_history(list *options, size_params params) { int history_size = option_find_int(options, "history_size", 4); layer l = make_history_layer(params.batch, params.h, params.w, params.c, history_size, params.time_steps, params.train); return l; } connected_layer parse_connected(list *options, size_params params) { int output = option_find_int(options, "output",1); char *activation_s = option_find_str(options, "activation", "logistic"); ACTIVATION activation = get_activation(activation_s); int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0); connected_layer layer = make_connected_layer(params.batch, 1, params.inputs, output, activation, batch_normalize); return layer; } softmax_layer parse_softmax(list *options, size_params params) { int groups = option_find_int_quiet(options, "groups", 1); softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups); layer.temperature = option_find_float_quiet(options, "temperature", 1); char *tree_file = option_find_str(options, "tree", 0); if (tree_file) layer.softmax_tree = read_tree(tree_file); layer.w = params.w; layer.h = params.h; layer.c = params.c; layer.spatial = option_find_float_quiet(options, "spatial", 0); layer.noloss = option_find_int_quiet(options, "noloss", 0); return layer; } contrastive_layer parse_contrastive(list *options, size_params params) { int classes = option_find_int(options, "classes", 1000); layer *yolo_layer = NULL; int yolo_layer_id = option_find_int_quiet(options, "yolo_layer", 0); if (yolo_layer_id < 0) yolo_layer_id = params.index + yolo_layer_id; if(yolo_layer_id != 0) yolo_layer = params.net.layers + yolo_layer_id; if (yolo_layer->type != YOLO) { printf(" Error: [contrastive] layer should point to the [yolo] layer instead of %d layer! \n", yolo_layer_id); getchar(); exit(0); } contrastive_layer layer = make_contrastive_layer(params.batch, params.w, params.h, params.c, classes, params.inputs, yolo_layer); layer.temperature = option_find_float_quiet(options, "temperature", 1); layer.steps = params.time_steps; layer.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1); layer.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 layer.contrastive_neg_max = option_find_int_quiet(options, "contrastive_neg_max", 3); return layer; } int *parse_yolo_mask(char *a, int *num) { int *mask = 0; if (a) { int len = strlen(a); int n = 1; int i; for (i = 0; i < len; ++i) { if (a[i] == '#') break; if (a[i] == ',') ++n; } mask = (int*)xcalloc(n, sizeof(int)); for (i = 0; i < n; ++i) { int val = atoi(a); mask[i] = val; a = strchr(a, ',') + 1; } *num = n; } return mask; } float *get_classes_multipliers(char *cpc, const int classes, const float max_delta) { float *classes_multipliers = NULL; if (cpc) { int classes_counters = classes; int *counters_per_class = parse_yolo_mask(cpc, &classes_counters); if (classes_counters != classes) { printf(" number of values in counters_per_class = %d doesn't match with classes = %d \n", classes_counters, classes); exit(0); } float max_counter = 0; int i; for (i = 0; i < classes_counters; ++i) { if (counters_per_class[i] < 1) counters_per_class[i] = 1; if (max_counter < counters_per_class[i]) max_counter = counters_per_class[i]; } classes_multipliers = (float *)calloc(classes_counters, sizeof(float)); for (i = 0; i < classes_counters; ++i) { classes_multipliers[i] = max_counter / counters_per_class[i]; if(classes_multipliers[i] > max_delta) classes_multipliers[i] = max_delta; } free(counters_per_class); printf(" classes_multipliers: "); for (i = 0; i < classes_counters; ++i) printf("%.1f, ", classes_multipliers[i]); printf("\n"); } return classes_multipliers; } layer parse_yolo(list *options, size_params params) { int classes = option_find_int(options, "classes", 20); int total = option_find_int(options, "num", 1); int num = total; char *a = option_find_str(options, "mask", 0); int *mask = parse_yolo_mask(a, &num); int max_boxes = option_find_int_quiet(options, "max", 200); layer l = make_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes); if (l.outputs != params.inputs) { printf("Error: l.outputs == params.inputs \n"); printf("filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [yolo]-layer \n"); exit(EXIT_FAILURE); } //assert(l.outputs == params.inputs); l.show_details = option_find_int_quiet(options, "show_details", 1); l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 char *cpc = option_find_str(options, "counters_per_class", 0); l.classes_multipliers = get_classes_multipliers(cpc, classes, l.max_delta); l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); l.objectness_smooth = option_find_int_quiet(options, "objectness_smooth", 0); l.new_coords = option_find_int_quiet(options, "new_coords", 0); l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); l.obj_normalizer = option_find_float_quiet(options, "obj_normalizer", 1); l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1); l.delta_normalizer = option_find_float_quiet(options, "delta_normalizer", 1); char *iou_loss = option_find_str_quiet(options, "iou_loss", "mse"); // "iou"); if (strcmp(iou_loss, "mse") == 0) l.iou_loss = MSE; else if (strcmp(iou_loss, "giou") == 0) l.iou_loss = GIOU; else if (strcmp(iou_loss, "diou") == 0) l.iou_loss = DIOU; else if (strcmp(iou_loss, "ciou") == 0) l.iou_loss = CIOU; else l.iou_loss = IOU; fprintf(stderr, "[yolo] params: iou loss: %s (%d), iou_norm: %2.2f, obj_norm: %2.2f, cls_norm: %2.2f, delta_norm: %2.2f, scale_x_y: %2.2f\n", iou_loss, l.iou_loss, l.iou_normalizer, l.obj_normalizer, l.cls_normalizer, l.delta_normalizer, l.scale_x_y); char *iou_thresh_kind_str = option_find_str_quiet(options, "iou_thresh_kind", "iou"); if (strcmp(iou_thresh_kind_str, "iou") == 0) l.iou_thresh_kind = IOU; else if (strcmp(iou_thresh_kind_str, "giou") == 0) l.iou_thresh_kind = GIOU; else if (strcmp(iou_thresh_kind_str, "diou") == 0) l.iou_thresh_kind = DIOU; else if (strcmp(iou_thresh_kind_str, "ciou") == 0) l.iou_thresh_kind = CIOU; else { fprintf(stderr, " Wrong iou_thresh_kind = %s \n", iou_thresh_kind_str); l.iou_thresh_kind = IOU; } l.beta_nms = option_find_float_quiet(options, "beta_nms", 0.6); char *nms_kind = option_find_str_quiet(options, "nms_kind", "default"); if (strcmp(nms_kind, "default") == 0) l.nms_kind = DEFAULT_NMS; else { if (strcmp(nms_kind, "greedynms") == 0) l.nms_kind = GREEDY_NMS; else if (strcmp(nms_kind, "diounms") == 0) l.nms_kind = DIOU_NMS; else l.nms_kind = DEFAULT_NMS; printf("nms_kind: %s (%d), beta = %f \n", nms_kind, l.nms_kind, l.beta_nms); } l.jitter = option_find_float(options, "jitter", .2); l.resize = option_find_float_quiet(options, "resize", 1.0); l.focal_loss = option_find_int_quiet(options, "focal_loss", 0); l.ignore_thresh = option_find_float(options, "ignore_thresh", .5); l.truth_thresh = option_find_float(options, "truth_thresh", 1); l.iou_thresh = option_find_float_quiet(options, "iou_thresh", 1); // recommended to use iou_thresh=0.213 in [yolo] l.random = option_find_float_quiet(options, "random", 0); l.track_history_size = option_find_int_quiet(options, "track_history_size", 5); l.sim_thresh = option_find_int_quiet(options, "sim_thresh", 0.8); l.dets_for_track = option_find_int_quiet(options, "dets_for_track", 1); l.dets_for_show = option_find_int_quiet(options, "dets_for_show", 1); l.track_ciou_norm = option_find_float_quiet(options, "track_ciou_norm", 0.01); int embedding_layer_id = option_find_int_quiet(options, "embedding_layer", 999999); if (embedding_layer_id < 0) embedding_layer_id = params.index + embedding_layer_id; if (embedding_layer_id != 999999) { printf(" embedding_layer_id = %d, ", embedding_layer_id); layer le = params.net.layers[embedding_layer_id]; l.embedding_layer_id = embedding_layer_id; l.embedding_output = (float*)xcalloc(le.batch * le.outputs, sizeof(float)); l.embedding_size = le.n / l.n; printf(" embedding_size = %d \n", l.embedding_size); if (le.n % l.n != 0) { printf(" Warning: filters=%d number in embedding_layer=%d isn't divisable by number of anchors %d \n", le.n, embedding_layer_id, l.n); getchar(); } } char *map_file = option_find_str(options, "map", 0); if (map_file) l.map = read_map(map_file); a = option_find_str(options, "anchors", 0); if (a) { int len = strlen(a); int n = 1; int i; for (i = 0; i < len; ++i) { if (a[i] == '#') break; if (a[i] == ',') ++n; } for (i = 0; i < n && i < total*2; ++i) { float bias = atof(a); l.biases[i] = bias; a = strchr(a, ',') + 1; } } return l; } int *parse_gaussian_yolo_mask(char *a, int *num) // Gaussian_YOLOv3 { int *mask = 0; if (a) { int len = strlen(a); int n = 1; int i; for (i = 0; i < len; ++i) { if (a[i] == '#') break; if (a[i] == ',') ++n; } mask = (int *)calloc(n, sizeof(int)); for (i = 0; i < n; ++i) { int val = atoi(a); mask[i] = val; a = strchr(a, ',') + 1; } *num = n; } return mask; } layer parse_gaussian_yolo(list *options, size_params params) // Gaussian_YOLOv3 { int classes = option_find_int(options, "classes", 20); int max_boxes = option_find_int_quiet(options, "max", 200); int total = option_find_int(options, "num", 1); int num = total; char *a = option_find_str(options, "mask", 0); int *mask = parse_gaussian_yolo_mask(a, &num); layer l = make_gaussian_yolo_layer(params.batch, params.w, params.h, num, total, mask, classes, max_boxes); if (l.outputs != params.inputs) { printf("Error: l.outputs == params.inputs \n"); printf("filters= in the [convolutional]-layer doesn't correspond to classes= or mask= in [Gaussian_yolo]-layer \n"); exit(EXIT_FAILURE); } //assert(l.outputs == params.inputs); l.max_delta = option_find_float_quiet(options, "max_delta", FLT_MAX); // set 10 char *cpc = option_find_str(options, "counters_per_class", 0); l.classes_multipliers = get_classes_multipliers(cpc, classes, l.max_delta); l.label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); l.scale_x_y = option_find_float_quiet(options, "scale_x_y", 1); l.objectness_smooth = option_find_int_quiet(options, "objectness_smooth", 0); l.uc_normalizer = option_find_float_quiet(options, "uc_normalizer", 1.0); l.iou_normalizer = option_find_float_quiet(options, "iou_normalizer", 0.75); l.obj_normalizer = option_find_float_quiet(options, "obj_normalizer", 1.0); l.cls_normalizer = option_find_float_quiet(options, "cls_normalizer", 1); l.delta_normalizer = option_find_float_quiet(options, "delta_normalizer", 1); char *iou_loss = option_find_str_quiet(options, "iou_loss", "mse"); // "iou"); if (strcmp(iou_loss, "mse") == 0) l.iou_loss = MSE; else if (strcmp(iou_loss, "giou") == 0) l.iou_loss = GIOU; else if (strcmp(iou_loss, "diou") == 0) l.iou_loss = DIOU; else if (strcmp(iou_loss, "ciou") == 0) l.iou_loss = CIOU; else l.iou_loss = IOU; char *iou_thresh_kind_str = option_find_str_quiet(options, "iou_thresh_kind", "iou"); if (strcmp(iou_thresh_kind_str, "iou") == 0) l.iou_thresh_kind = IOU; else if (strcmp(iou_thresh_kind_str, "giou") == 0) l.iou_thresh_kind = GIOU; else if (strcmp(iou_thresh_kind_str, "diou") == 0) l.iou_thresh_kind = DIOU; else if (strcmp(iou_thresh_kind_str, "ciou") == 0) l.iou_thresh_kind = CIOU; else { fprintf(stderr, " Wrong iou_thresh_kind = %s \n", iou_thresh_kind_str); l.iou_thresh_kind = IOU; } l.beta_nms = option_find_float_quiet(options, "beta_nms", 0.6); char *nms_kind = option_find_str_quiet(options, "nms_kind", "default"); if (strcmp(nms_kind, "default") == 0) l.nms_kind = DEFAULT_NMS; else { if (strcmp(nms_kind, "greedynms") == 0) l.nms_kind = GREEDY_NMS; else if (strcmp(nms_kind, "diounms") == 0) l.nms_kind = DIOU_NMS; else if (strcmp(nms_kind, "cornersnms") == 0) l.nms_kind = CORNERS_NMS; else l.nms_kind = DEFAULT_NMS; printf("nms_kind: %s (%d), beta = %f \n", nms_kind, l.nms_kind, l.beta_nms); } char *yolo_point = option_find_str_quiet(options, "yolo_point", "center"); if (strcmp(yolo_point, "left_top") == 0) l.yolo_point = YOLO_LEFT_TOP; else if (strcmp(yolo_point, "right_bottom") == 0) l.yolo_point = YOLO_RIGHT_BOTTOM; else l.yolo_point = YOLO_CENTER; fprintf(stderr, "[Gaussian_yolo] iou loss: %s (%d), iou_norm: %2.2f, obj_norm: %2.2f, cls_norm: %2.2f, delta_norm: %2.2f, scale: %2.2f, point: %d\n", iou_loss, l.iou_loss, l.iou_normalizer, l.obj_normalizer, l.cls_normalizer, l.delta_normalizer, l.scale_x_y, l.yolo_point); l.jitter = option_find_float(options, "jitter", .2); l.resize = option_find_float_quiet(options, "resize", 1.0); l.ignore_thresh = option_find_float(options, "ignore_thresh", .5); l.truth_thresh = option_find_float(options, "truth_thresh", 1); l.iou_thresh = option_find_float_quiet(options, "iou_thresh", 1); // recommended to use iou_thresh=0.213 in [yolo] l.random = option_find_float_quiet(options, "random", 0); char *map_file = option_find_str(options, "map", 0); if (map_file) l.map = read_map(map_file); a = option_find_str(options, "anchors", 0); if (a) { int len = strlen(a); int n = 1; int i; for (i = 0; i < len; ++i) { if (a[i] == ',') ++n; } for (i = 0; i < n; ++i) { float bias = atof(a); l.biases[i] = bias; a = strchr(a, ',') + 1; } } return l; } layer parse_region(list *options, size_params params) { int coords = option_find_int(options, "coords", 4); int classes = option_find_int(options, "classes", 20); int num = option_find_int(options, "num", 1); int max_boxes = option_find_int_quiet(options, "max", 200); layer l = make_region_layer(params.batch, params.w, params.h, num, classes, coords, max_boxes); if (l.outputs != params.inputs) { printf("Error: l.outputs == params.inputs \n"); printf("filters= in the [convolutional]-layer doesn't correspond to classes= or num= in [region]-layer \n"); exit(EXIT_FAILURE); } //assert(l.outputs == params.inputs); l.log = option_find_int_quiet(options, "log", 0); l.sqrt = option_find_int_quiet(options, "sqrt", 0); l.softmax = option_find_int(options, "softmax", 0); l.focal_loss = option_find_int_quiet(options, "focal_loss", 0); //l.max_boxes = option_find_int_quiet(options, "max",30); l.jitter = option_find_float(options, "jitter", .2); l.resize = option_find_float_quiet(options, "resize", 1.0); l.rescore = option_find_int_quiet(options, "rescore",0); l.thresh = option_find_float(options, "thresh", .5); l.classfix = option_find_int_quiet(options, "classfix", 0); l.absolute = option_find_int_quiet(options, "absolute", 0); l.random = option_find_float_quiet(options, "random", 0); l.coord_scale = option_find_float(options, "coord_scale", 1); l.object_scale = option_find_float(options, "object_scale", 1); l.noobject_scale = option_find_float(options, "noobject_scale", 1); l.mask_scale = option_find_float(options, "mask_scale", 1); l.class_scale = option_find_float(options, "class_scale", 1); l.bias_match = option_find_int_quiet(options, "bias_match",0); char *tree_file = option_find_str(options, "tree", 0); if (tree_file) l.softmax_tree = read_tree(tree_file); char *map_file = option_find_str(options, "map", 0); if (map_file) l.map = read_map(map_file); char *a = option_find_str(options, "anchors", 0); if(a){ int len = strlen(a); int n = 1; int i; for(i = 0; i < len; ++i){ if (a[i] == ',') ++n; } for(i = 0; i < n && i < num*2; ++i){ float bias = atof(a); l.biases[i] = bias; a = strchr(a, ',')+1; } } return l; } detection_layer parse_detection(list *options, size_params params) { int coords = option_find_int(options, "coords", 1); int classes = option_find_int(options, "classes", 1); int rescore = option_find_int(options, "rescore", 0); int num = option_find_int(options, "num", 1); int side = option_find_int(options, "side", 7); detection_layer layer = make_detection_layer(params.batch, params.inputs, num, side, classes, coords, rescore); layer.softmax = option_find_int(options, "softmax", 0); layer.sqrt = option_find_int(options, "sqrt", 0); layer.max_boxes = option_find_int_quiet(options, "max",200); layer.coord_scale = option_find_float(options, "coord_scale", 1); layer.forced = option_find_int(options, "forced", 0); layer.object_scale = option_find_float(options, "object_scale", 1); layer.noobject_scale = option_find_float(options, "noobject_scale", 1); layer.class_scale = option_find_float(options, "class_scale", 1); layer.jitter = option_find_float(options, "jitter", .2); layer.resize = option_find_float_quiet(options, "resize", 1.0); layer.random = option_find_float_quiet(options, "random", 0); layer.reorg = option_find_int_quiet(options, "reorg", 0); return layer; } cost_layer parse_cost(list *options, size_params params) { char *type_s = option_find_str(options, "type", "sse"); COST_TYPE type = get_cost_type(type_s); float scale = option_find_float_quiet(options, "scale",1); cost_layer layer = make_cost_layer(params.batch, params.inputs, type, scale); layer.ratio = option_find_float_quiet(options, "ratio",0); return layer; } crop_layer parse_crop(list *options, size_params params) { int crop_height = option_find_int(options, "crop_height",1); int crop_width = option_find_int(options, "crop_width",1); int flip = option_find_int(options, "flip",0); float angle = option_find_float(options, "angle",0); float saturation = option_find_float(options, "saturation",1); float exposure = option_find_float(options, "exposure",1); int batch,h,w,c; h = params.h; w = params.w; c = params.c; batch=params.batch; if(!(h && w && c)) error("Layer before crop layer must output image."); int noadjust = option_find_int_quiet(options, "noadjust",0); crop_layer l = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip, angle, saturation, exposure); l.shift = option_find_float(options, "shift", 0); l.noadjust = noadjust; return l; } layer parse_reorg(list *options, size_params params) { int stride = option_find_int(options, "stride",1); int reverse = option_find_int_quiet(options, "reverse",0); int batch,h,w,c; h = params.h; w = params.w; c = params.c; batch=params.batch; if(!(h && w && c)) error("Layer before reorg layer must output image."); layer layer = make_reorg_layer(batch,w,h,c,stride,reverse); return layer; } layer parse_reorg_old(list *options, size_params params) { printf("\n reorg_old \n"); int stride = option_find_int(options, "stride", 1); int reverse = option_find_int_quiet(options, "reverse", 0); int batch, h, w, c; h = params.h; w = params.w; c = params.c; batch = params.batch; if (!(h && w && c)) error("Layer before reorg layer must output image."); layer layer = make_reorg_old_layer(batch, w, h, c, stride, reverse); return layer; } maxpool_layer parse_local_avgpool(list *options, size_params params) { int stride = option_find_int(options, "stride", 1); int stride_x = option_find_int_quiet(options, "stride_x", stride); int stride_y = option_find_int_quiet(options, "stride_y", stride); int size = option_find_int(options, "size", stride); int padding = option_find_int_quiet(options, "padding", size - 1); int maxpool_depth = 0; int out_channels = 1; int antialiasing = 0; const int avgpool = 1; int batch, h, w, c; h = params.h; w = params.w; c = params.c; batch = params.batch; if (!(h && w && c)) error("Layer before [local_avgpool] layer must output image."); maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels, antialiasing, avgpool, params.train); return layer; } maxpool_layer parse_maxpool(list *options, size_params params) { int stride = option_find_int(options, "stride",1); int stride_x = option_find_int_quiet(options, "stride_x", stride); int stride_y = option_find_int_quiet(options, "stride_y", stride); int size = option_find_int(options, "size",stride); int padding = option_find_int_quiet(options, "padding", size-1); int maxpool_depth = option_find_int_quiet(options, "maxpool_depth", 0); int out_channels = option_find_int_quiet(options, "out_channels", 1); int antialiasing = option_find_int_quiet(options, "antialiasing", 0); const int avgpool = 0; int batch,h,w,c; h = params.h; w = params.w; c = params.c; batch=params.batch; if(!(h && w && c)) error("Layer before [maxpool] layer must output image."); maxpool_layer layer = make_maxpool_layer(batch, h, w, c, size, stride_x, stride_y, padding, maxpool_depth, out_channels, antialiasing, avgpool, params.train); layer.maxpool_zero_nonmax = option_find_int_quiet(options, "maxpool_zero_nonmax", 0); return layer; } avgpool_layer parse_avgpool(list *options, size_params params) { int batch,w,h,c; w = params.w; h = params.h; c = params.c; batch=params.batch; if(!(h && w && c)) error("Layer before avgpool layer must output image."); avgpool_layer layer = make_avgpool_layer(batch,w,h,c); return layer; } dropout_layer parse_dropout(list *options, size_params params) { float probability = option_find_float(options, "probability", .2); int dropblock = option_find_int_quiet(options, "dropblock", 0); float dropblock_size_rel = option_find_float_quiet(options, "dropblock_size_rel", 0); int dropblock_size_abs = option_find_float_quiet(options, "dropblock_size_abs", 0); if (dropblock_size_abs > params.w || dropblock_size_abs > params.h) { printf(" [dropout] - dropblock_size_abs = %d that is bigger than layer size %d x %d \n", dropblock_size_abs, params.w, params.h); dropblock_size_abs = min_val_cmp(params.w, params.h); } if (dropblock && !dropblock_size_rel && !dropblock_size_abs) { printf(" [dropout] - None of the parameters (dropblock_size_rel or dropblock_size_abs) are set, will be used: dropblock_size_abs = 7 \n"); dropblock_size_abs = 7; } if (dropblock_size_rel && dropblock_size_abs) { printf(" [dropout] - Both parameters are set, only the parameter will be used: dropblock_size_abs = %d \n", dropblock_size_abs); dropblock_size_rel = 0; } dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability, dropblock, dropblock_size_rel, dropblock_size_abs, params.w, params.h, params.c); layer.out_w = params.w; layer.out_h = params.h; layer.out_c = params.c; return layer; } layer parse_normalization(list *options, size_params params) { float alpha = option_find_float(options, "alpha", .0001); float beta = option_find_float(options, "beta" , .75); float kappa = option_find_float(options, "kappa", 1); int size = option_find_int(options, "size", 5); layer l = make_normalization_layer(params.batch, params.w, params.h, params.c, size, alpha, beta, kappa); return l; } layer parse_batchnorm(list *options, size_params params) { layer l = make_batchnorm_layer(params.batch, params.w, params.h, params.c, params.train); return l; } layer parse_shortcut(list *options, size_params params, network net) { char *activation_s = option_find_str(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); char *weights_type_str = option_find_str_quiet(options, "weights_type", "none"); WEIGHTS_TYPE_T weights_type = NO_WEIGHTS; if(strcmp(weights_type_str, "per_feature") == 0 || strcmp(weights_type_str, "per_layer") == 0) weights_type = PER_FEATURE; else if (strcmp(weights_type_str, "per_channel") == 0) weights_type = PER_CHANNEL; else if (strcmp(weights_type_str, "none") != 0) { printf("Error: Incorrect weights_type = %s \n Use one of: none, per_feature, per_channel \n", weights_type_str); getchar(); exit(0); } char *weights_normalization_str = option_find_str_quiet(options, "weights_normalization", "none"); WEIGHTS_NORMALIZATION_T weights_normalization = NO_NORMALIZATION; if (strcmp(weights_normalization_str, "relu") == 0 || strcmp(weights_normalization_str, "avg_relu") == 0) weights_normalization = RELU_NORMALIZATION; else if (strcmp(weights_normalization_str, "softmax") == 0) weights_normalization = SOFTMAX_NORMALIZATION; else if (strcmp(weights_type_str, "none") != 0) { printf("Error: Incorrect weights_normalization = %s \n Use one of: none, relu, softmax \n", weights_normalization_str); getchar(); exit(0); } char *l = option_find(options, "from"); int len = strlen(l); if (!l) error("Route Layer must specify input layers: from = ..."); int n = 1; int i; for (i = 0; i < len; ++i) { if (l[i] == ',') ++n; } int* layers = (int*)calloc(n, sizeof(int)); int* sizes = (int*)calloc(n, sizeof(int)); float **layers_output = (float **)calloc(n, sizeof(float *)); float **layers_delta = (float **)calloc(n, sizeof(float *)); float **layers_output_gpu = (float **)calloc(n, sizeof(float *)); float **layers_delta_gpu = (float **)calloc(n, sizeof(float *)); for (i = 0; i < n; ++i) { int index = atoi(l); l = strchr(l, ',') + 1; if (index < 0) index = params.index + index; layers[i] = index; sizes[i] = params.net.layers[index].outputs; layers_output[i] = params.net.layers[index].output; layers_delta[i] = params.net.layers[index].delta; } #ifdef GPU for (i = 0; i < n; ++i) { layers_output_gpu[i] = params.net.layers[layers[i]].output_gpu; layers_delta_gpu[i] = params.net.layers[layers[i]].delta_gpu; } #endif// GPU layer s = make_shortcut_layer(params.batch, n, layers, sizes, params.w, params.h, params.c, layers_output, layers_delta, layers_output_gpu, layers_delta_gpu, weights_type, weights_normalization, activation, params.train); free(layers_output_gpu); free(layers_delta_gpu); for (i = 0; i < n; ++i) { int index = layers[i]; assert(params.w == net.layers[index].out_w && params.h == net.layers[index].out_h); if (params.w != net.layers[index].out_w || params.h != net.layers[index].out_h || params.c != net.layers[index].out_c) fprintf(stderr, " (%4d x%4d x%4d) + (%4d x%4d x%4d) \n", params.w, params.h, params.c, net.layers[index].out_w, net.layers[index].out_h, params.net.layers[index].out_c); } return s; } layer parse_scale_channels(list *options, size_params params, network net) { char *l = option_find(options, "from"); int index = atoi(l); if (index < 0) index = params.index + index; int scale_wh = option_find_int_quiet(options, "scale_wh", 0); int batch = params.batch; layer from = net.layers[index]; layer s = make_scale_channels_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c, scale_wh); char *activation_s = option_find_str_quiet(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); s.activation = activation; if (activation == SWISH || activation == MISH) { printf(" [scale_channels] layer doesn't support SWISH or MISH activations \n"); } return s; } layer parse_sam(list *options, size_params params, network net) { char *l = option_find(options, "from"); int index = atoi(l); if (index < 0) index = params.index + index; int batch = params.batch; layer from = net.layers[index]; layer s = make_sam_layer(batch, index, params.w, params.h, params.c, from.out_w, from.out_h, from.out_c); char *activation_s = option_find_str_quiet(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); s.activation = activation; if (activation == SWISH || activation == MISH) { printf(" [sam] layer doesn't support SWISH or MISH activations \n"); } return s; } layer parse_implicit(list *options, size_params params, network net) { float mean_init = option_find_float(options, "mean", 0.0); float std_init = option_find_float(options, "std", 0.2); int filters = option_find_int(options, "filters", 128); int atoms = option_find_int_quiet(options, "atoms", 1); layer s = make_implicit_layer(params.batch, params.index, mean_init, std_init, filters, atoms); return s; } layer parse_activation(list *options, size_params params) { char *activation_s = option_find_str(options, "activation", "linear"); ACTIVATION activation = get_activation(activation_s); layer l = make_activation_layer(params.batch, params.inputs, activation); l.out_h = params.h; l.out_w = params.w; l.out_c = params.c; l.h = params.h; l.w = params.w; l.c = params.c; return l; } layer parse_upsample(list *options, size_params params, network net) { int stride = option_find_int(options, "stride", 2); layer l = make_upsample_layer(params.batch, params.w, params.h, params.c, stride); l.scale = option_find_float_quiet(options, "scale", 1); return l; } route_layer parse_route(list *options, size_params params) { char *l = option_find(options, "layers"); if(!l) error("Route Layer must specify input layers"); int len = strlen(l); int n = 1; int i; for(i = 0; i < len; ++i){ if (l[i] == ',') ++n; } int* layers = (int*)xcalloc(n, sizeof(int)); int* sizes = (int*)xcalloc(n, sizeof(int)); for(i = 0; i < n; ++i){ int index = atoi(l); l = strchr(l, ',')+1; if(index < 0) index = params.index + index; layers[i] = index; sizes[i] = params.net.layers[index].outputs; } int batch = params.batch; int groups = option_find_int_quiet(options, "groups", 1); int group_id = option_find_int_quiet(options, "group_id", 0); route_layer layer = make_route_layer(batch, n, layers, sizes, groups, group_id); convolutional_layer first = params.net.layers[layers[0]]; layer.out_w = first.out_w; layer.out_h = first.out_h; layer.out_c = first.out_c; for(i = 1; i < n; ++i){ int index = layers[i]; convolutional_layer next = params.net.layers[index]; if(next.out_w == first.out_w && next.out_h == first.out_h){ layer.out_c += next.out_c; }else{ fprintf(stderr, " The width and height of the input layers are different. \n"); layer.out_h = layer.out_w = layer.out_c = 0; } } layer.out_c = layer.out_c / layer.groups; layer.w = first.w; layer.h = first.h; layer.c = layer.out_c; layer.stream = option_find_int_quiet(options, "stream", -1); layer.wait_stream_id = option_find_int_quiet(options, "wait_stream", -1); if (n > 3) fprintf(stderr, " \t "); else if (n > 1) fprintf(stderr, " \t "); else fprintf(stderr, " \t\t "); fprintf(stderr, " "); if (layer.groups > 1) fprintf(stderr, "%d/%d", layer.group_id, layer.groups); else fprintf(stderr, " "); fprintf(stderr, " -> %4d x%4d x%4d \n", layer.out_w, layer.out_h, layer.out_c); return layer; } learning_rate_policy get_policy(char *s) { if (strcmp(s, "random")==0) return RANDOM; if (strcmp(s, "poly")==0) return POLY; if (strcmp(s, "constant")==0) return CONSTANT; if (strcmp(s, "step")==0) return STEP; if (strcmp(s, "exp")==0) return EXP; if (strcmp(s, "sigmoid")==0) return SIG; if (strcmp(s, "steps")==0) return STEPS; if (strcmp(s, "sgdr")==0) return SGDR; fprintf(stderr, "Couldn't find policy %s, going with constant\n", s); return CONSTANT; } void parse_net_options(list *options, network *net) { net->max_batches = option_find_int(options, "max_batches", 0); net->batch = option_find_int(options, "batch",1); net->learning_rate = option_find_float(options, "learning_rate", .001); net->learning_rate_min = option_find_float_quiet(options, "learning_rate_min", .00001); net->batches_per_cycle = option_find_int_quiet(options, "sgdr_cycle", net->max_batches); net->batches_cycle_mult = option_find_int_quiet(options, "sgdr_mult", 2); net->momentum = option_find_float(options, "momentum", .9); net->decay = option_find_float(options, "decay", .0001); int subdivs = option_find_int(options, "subdivisions",1); net->time_steps = option_find_int_quiet(options, "time_steps",1); net->track = option_find_int_quiet(options, "track", 0); net->augment_speed = option_find_int_quiet(options, "augment_speed", 2); net->init_sequential_subdivisions = net->sequential_subdivisions = option_find_int_quiet(options, "sequential_subdivisions", subdivs); if (net->sequential_subdivisions > subdivs) net->init_sequential_subdivisions = net->sequential_subdivisions = subdivs; net->try_fix_nan = option_find_int_quiet(options, "try_fix_nan", 0); net->batch /= subdivs; // mini_batch const int mini_batch = net->batch; net->batch *= net->time_steps; // mini_batch * time_steps net->subdivisions = subdivs; // number of mini_batches net->weights_reject_freq = option_find_int_quiet(options, "weights_reject_freq", 0); net->equidistant_point = option_find_int_quiet(options, "equidistant_point", 0); net->badlabels_rejection_percentage = option_find_float_quiet(options, "badlabels_rejection_percentage", 0); net->num_sigmas_reject_badlabels = option_find_float_quiet(options, "num_sigmas_reject_badlabels", 0); net->ema_alpha = option_find_float_quiet(options, "ema_alpha", 0); *net->badlabels_reject_threshold = 0; *net->delta_rolling_max = 0; *net->delta_rolling_avg = 0; *net->delta_rolling_std = 0; *net->seen = 0; *net->cur_iteration = 0; *net->cuda_graph_ready = 0; net->use_cuda_graph = option_find_int_quiet(options, "use_cuda_graph", 0); net->loss_scale = option_find_float_quiet(options, "loss_scale", 1); net->dynamic_minibatch = option_find_int_quiet(options, "dynamic_minibatch", 0); net->optimized_memory = option_find_int_quiet(options, "optimized_memory", 0); net->workspace_size_limit = (size_t)1024*1024 * option_find_float_quiet(options, "workspace_size_limit_MB", 1024); // 1024 MB by default net->adam = option_find_int_quiet(options, "adam", 0); if(net->adam){ net->B1 = option_find_float(options, "B1", .9); net->B2 = option_find_float(options, "B2", .999); net->eps = option_find_float(options, "eps", .000001); } net->h = option_find_int_quiet(options, "height",0); net->w = option_find_int_quiet(options, "width",0); net->c = option_find_int_quiet(options, "channels",0); net->inputs = option_find_int_quiet(options, "inputs", net->h * net->w * net->c); net->max_crop = option_find_int_quiet(options, "max_crop",net->w*2); net->min_crop = option_find_int_quiet(options, "min_crop",net->w); net->flip = option_find_int_quiet(options, "flip", 1); net->blur = option_find_int_quiet(options, "blur", 0); net->gaussian_noise = option_find_int_quiet(options, "gaussian_noise", 0); net->mixup = option_find_int_quiet(options, "mixup", 0); int cutmix = option_find_int_quiet(options, "cutmix", 0); int mosaic = option_find_int_quiet(options, "mosaic", 0); if (mosaic && cutmix) net->mixup = 4; else if (cutmix) net->mixup = 2; else if (mosaic) net->mixup = 3; net->letter_box = option_find_int_quiet(options, "letter_box", 0); net->mosaic_bound = option_find_int_quiet(options, "mosaic_bound", 0); net->contrastive = option_find_int_quiet(options, "contrastive", 0); net->contrastive_jit_flip = option_find_int_quiet(options, "contrastive_jit_flip", 0); net->contrastive_color = option_find_int_quiet(options, "contrastive_color", 0); net->unsupervised = option_find_int_quiet(options, "unsupervised", 0); if (net->contrastive && mini_batch < 2) { printf(" Error: mini_batch size (batch/subdivisions) should be higher than 1 for Contrastive loss \n"); exit(0); } net->label_smooth_eps = option_find_float_quiet(options, "label_smooth_eps", 0.0f); net->resize_step = option_find_float_quiet(options, "resize_step", 32); net->attention = option_find_int_quiet(options, "attention", 0); net->adversarial_lr = option_find_float_quiet(options, "adversarial_lr", 0); net->max_chart_loss = option_find_float_quiet(options, "max_chart_loss", 20.0); net->angle = option_find_float_quiet(options, "angle", 0); net->aspect = option_find_float_quiet(options, "aspect", 1); net->saturation = option_find_float_quiet(options, "saturation", 1); net->exposure = option_find_float_quiet(options, "exposure", 1); net->hue = option_find_float_quiet(options, "hue", 0); net->power = option_find_float_quiet(options, "power", 4); if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied"); char *policy_s = option_find_str(options, "policy", "constant"); net->policy = get_policy(policy_s); net->burn_in = option_find_int_quiet(options, "burn_in", 0); #ifdef GPU if (net->gpu_index >= 0) { char device_name[1024]; int compute_capability = get_gpu_compute_capability(net->gpu_index, device_name); #ifdef CUDNN_HALF if (compute_capability >= 700) net->cudnn_half = 1; else net->cudnn_half = 0; #endif// CUDNN_HALF fprintf(stderr, " %d : compute_capability = %d, cudnn_half = %d, GPU: %s \n", net->gpu_index, compute_capability, net->cudnn_half, device_name); } else fprintf(stderr, " GPU isn't used \n"); #endif// GPU if(net->policy == STEP){ net->step = option_find_int(options, "step", 1); net->scale = option_find_float(options, "scale", 1); } else if (net->policy == STEPS || net->policy == SGDR){ char *l = option_find(options, "steps"); char *p = option_find(options, "scales"); char *s = option_find(options, "seq_scales"); if(net->policy == STEPS && (!l || !p)) error("STEPS policy must have steps and scales in cfg file"); if (l) { int len = strlen(l); int n = 1; int i; for (i = 0; i < len; ++i) { if (l[i] == '#') break; if (l[i] == ',') ++n; } int* steps = (int*)xcalloc(n, sizeof(int)); float* scales = (float*)xcalloc(n, sizeof(float)); float* seq_scales = (float*)xcalloc(n, sizeof(float)); for (i = 0; i < n; ++i) { float scale = 1.0; if (p) { scale = atof(p); p = strchr(p, ',') + 1; } float sequence_scale = 1.0; if (s) { sequence_scale = atof(s); s = strchr(s, ',') + 1; } int step = atoi(l); l = strchr(l, ',') + 1; steps[i] = step; scales[i] = scale; seq_scales[i] = sequence_scale; } net->scales = scales; net->steps = steps; net->seq_scales = seq_scales; net->num_steps = n; } } else if (net->policy == EXP){ net->gamma = option_find_float(options, "gamma", 1); } else if (net->policy == SIG){ net->gamma = option_find_float(options, "gamma", 1); net->step = option_find_int(options, "step", 1); } else if (net->policy == POLY || net->policy == RANDOM){ //net->power = option_find_float(options, "power", 1); } } int is_network(section *s) { return (strcmp(s->type, "[net]")==0 || strcmp(s->type, "[network]")==0); } void set_train_only_bn(network net) { int train_only_bn = 0; int i; for (i = net.n - 1; i >= 0; --i) { if (net.layers[i].train_only_bn) train_only_bn = net.layers[i].train_only_bn; // set l.train_only_bn for all previous layers if (train_only_bn) { net.layers[i].train_only_bn = train_only_bn; if (net.layers[i].type == CONV_LSTM) { net.layers[i].wf->train_only_bn = train_only_bn; net.layers[i].wi->train_only_bn = train_only_bn; net.layers[i].wg->train_only_bn = train_only_bn; net.layers[i].wo->train_only_bn = train_only_bn; net.layers[i].uf->train_only_bn = train_only_bn; net.layers[i].ui->train_only_bn = train_only_bn; net.layers[i].ug->train_only_bn = train_only_bn; net.layers[i].uo->train_only_bn = train_only_bn; if (net.layers[i].peephole) { net.layers[i].vf->train_only_bn = train_only_bn; net.layers[i].vi->train_only_bn = train_only_bn; net.layers[i].vo->train_only_bn = train_only_bn; } } else if (net.layers[i].type == CRNN) { net.layers[i].input_layer->train_only_bn = train_only_bn; net.layers[i].self_layer->train_only_bn = train_only_bn; net.layers[i].output_layer->train_only_bn = train_only_bn; } } } } network parse_network_cfg(char *filename) { return parse_network_cfg_custom(filename, 0, 0); } network parse_network_cfg_custom(char *filename, int batch, int time_steps) { list *sections = read_cfg(filename); node *n = sections->front; if(!n) error("Config file has no sections"); network net = make_network(sections->size - 1); net.gpu_index = gpu_index; size_params params; if (batch > 0) params.train = 0; // allocates memory for Detection only else params.train = 1; // allocates memory for Detection & Training section *s = (section *)n->val; list *options = s->options; if(!is_network(s)) error("First section must be [net] or [network]"); parse_net_options(options, &net); #ifdef GPU printf("net.optimized_memory = %d \n", net.optimized_memory); if (net.optimized_memory >= 2 && params.train) { pre_allocate_pinned_memory((size_t)1024 * 1024 * 1024 * 8); // pre-allocate 8 GB CPU-RAM for pinned memory } #endif // GPU params.h = net.h; params.w = net.w; params.c = net.c; params.inputs = net.inputs; if (batch > 0) net.batch = batch; if (time_steps > 0) net.time_steps = time_steps; if (net.batch < 1) net.batch = 1; if (net.time_steps < 1) net.time_steps = 1; if (net.batch < net.time_steps) net.batch = net.time_steps; params.batch = net.batch; params.time_steps = net.time_steps; params.net = net; printf("mini_batch = %d, batch = %d, time_steps = %d, train = %d \n", net.batch, net.batch * net.subdivisions, net.time_steps, params.train); int avg_outputs = 0; int avg_counter = 0; float bflops = 0; size_t workspace_size = 0; size_t max_inputs = 0; size_t max_outputs = 0; int receptive_w = 1, receptive_h = 1; int receptive_w_scale = 1, receptive_h_scale = 1; const int show_receptive_field = option_find_float_quiet(options, "show_receptive_field", 0); n = n->next; int count = 0; free_section(s); fprintf(stderr, " layer filters size/strd(dil) input output\n"); while(n){ params.index = count; fprintf(stderr, "%4d ", count); s = (section *)n->val; options = s->options; layer l = { (LAYER_TYPE)0 }; LAYER_TYPE lt = string_to_layer_type(s->type); if(lt == CONVOLUTIONAL){ l = parse_convolutional(options, params); }else if(lt == LOCAL){ l = parse_local(options, params); }else if(lt == ACTIVE){ l = parse_activation(options, params); }else if(lt == RNN){ l = parse_rnn(options, params); }else if(lt == GRU){ l = parse_gru(options, params); }else if(lt == LSTM){ l = parse_lstm(options, params); }else if (lt == CONV_LSTM) { l = parse_conv_lstm(options, params); }else if (lt == HISTORY) { l = parse_history(options, params); }else if(lt == CRNN){ l = parse_crnn(options, params); }else if(lt == CONNECTED){ l = parse_connected(options, params); }else if(lt == CROP){ l = parse_crop(options, params); }else if(lt == COST){ l = parse_cost(options, params); l.keep_delta_gpu = 1; }else if(lt == REGION){ l = parse_region(options, params); l.keep_delta_gpu = 1; }else if (lt == YOLO) { l = parse_yolo(options, params); l.keep_delta_gpu = 1; }else if (lt == GAUSSIAN_YOLO) { l = parse_gaussian_yolo(options, params); l.keep_delta_gpu = 1; }else if(lt == DETECTION){ l = parse_detection(options, params); }else if(lt == SOFTMAX){ l = parse_softmax(options, params); net.hierarchy = l.softmax_tree; l.keep_delta_gpu = 1; }else if (lt == CONTRASTIVE) { l = parse_contrastive(options, params); l.keep_delta_gpu = 1; }else if(lt == NORMALIZATION){ l = parse_normalization(options, params); }else if(lt == BATCHNORM){ l = parse_batchnorm(options, params); }else if(lt == MAXPOOL){ l = parse_maxpool(options, params); }else if (lt == LOCAL_AVGPOOL) { l = parse_local_avgpool(options, params); }else if(lt == REORG){ l = parse_reorg(options, params); } else if (lt == REORG_OLD) { l = parse_reorg_old(options, params); }else if(lt == AVGPOOL){ l = parse_avgpool(options, params); }else if(lt == ROUTE){ l = parse_route(options, params); int k; for (k = 0; k < l.n; ++k) { net.layers[l.input_layers[k]].use_bin_output = 0; net.layers[l.input_layers[k]].keep_delta_gpu = 1; } }else if (lt == UPSAMPLE) { l = parse_upsample(options, params, net); }else if(lt == SHORTCUT){ l = parse_shortcut(options, params, net); net.layers[count - 1].use_bin_output = 0; net.layers[l.index].use_bin_output = 0; net.layers[l.index].keep_delta_gpu = 1; }else if (lt == SCALE_CHANNELS) { l = parse_scale_channels(options, params, net); net.layers[count - 1].use_bin_output = 0; net.layers[l.index].use_bin_output = 0; net.layers[l.index].keep_delta_gpu = 1; } else if (lt == SAM) { l = parse_sam(options, params, net); net.layers[count - 1].use_bin_output = 0; net.layers[l.index].use_bin_output = 0; net.layers[l.index].keep_delta_gpu = 1; } else if (lt == IMPLICIT) { l = parse_implicit(options, params, net); }else if(lt == DROPOUT){ l = parse_dropout(options, params); l.output = net.layers[count-1].output; l.delta = net.layers[count-1].delta; #ifdef GPU l.output_gpu = net.layers[count-1].output_gpu; l.delta_gpu = net.layers[count-1].delta_gpu; l.keep_delta_gpu = 1; #endif } else if (lt == EMPTY) { layer empty_layer = {(LAYER_TYPE)0}; l = empty_layer; l.type = EMPTY; l.w = l.out_w = params.w; l.h = l.out_h = params.h; l.c = l.out_c = params.c; l.batch = params.batch; l.inputs = l.outputs = params.inputs; l.output = net.layers[count - 1].output; l.delta = net.layers[count - 1].delta; l.forward = empty_func; l.backward = empty_func; #ifdef GPU l.output_gpu = net.layers[count - 1].output_gpu; l.delta_gpu = net.layers[count - 1].delta_gpu; l.keep_delta_gpu = 1; l.forward_gpu = empty_func; l.backward_gpu = empty_func; #endif fprintf(stderr, "empty \n"); }else{ fprintf(stderr, "Type not recognized: %s\n", s->type); } // calculate receptive field if(show_receptive_field) { int dilation = max_val_cmp(1, l.dilation); int stride = max_val_cmp(1, l.stride); int size = max_val_cmp(1, l.size); if (l.type == UPSAMPLE || (l.type == REORG)) { l.receptive_w = receptive_w; l.receptive_h = receptive_h; l.receptive_w_scale = receptive_w_scale = receptive_w_scale / stride; l.receptive_h_scale = receptive_h_scale = receptive_h_scale / stride; } else { if (l.type == ROUTE) { receptive_w = receptive_h = receptive_w_scale = receptive_h_scale = 0; int k; for (k = 0; k < l.n; ++k) { layer route_l = net.layers[l.input_layers[k]]; receptive_w = max_val_cmp(receptive_w, route_l.receptive_w); receptive_h = max_val_cmp(receptive_h, route_l.receptive_h); receptive_w_scale = max_val_cmp(receptive_w_scale, route_l.receptive_w_scale); receptive_h_scale = max_val_cmp(receptive_h_scale, route_l.receptive_h_scale); } } else { int increase_receptive = size + (dilation - 1) * 2 - 1;// stride; increase_receptive = max_val_cmp(0, increase_receptive); receptive_w += increase_receptive * receptive_w_scale; receptive_h += increase_receptive * receptive_h_scale; receptive_w_scale *= stride; receptive_h_scale *= stride; } l.receptive_w = receptive_w; l.receptive_h = receptive_h; l.receptive_w_scale = receptive_w_scale; l.receptive_h_scale = receptive_h_scale; } //printf(" size = %d, dilation = %d, stride = %d, receptive_w = %d, receptive_w_scale = %d - ", size, dilation, stride, receptive_w, receptive_w_scale); int cur_receptive_w = receptive_w; int cur_receptive_h = receptive_h; fprintf(stderr, "%4d - receptive field: %d x %d \n", count, cur_receptive_w, cur_receptive_h); } #ifdef GPU // futher GPU-memory optimization: net.optimized_memory == 2 l.optimized_memory = net.optimized_memory; if (net.optimized_memory >= 2 && params.train && l.type != DROPOUT) { if (l.output_gpu) { cuda_free(l.output_gpu); //l.output_gpu = cuda_make_array_pinned(l.output, l.batch*l.outputs); // l.steps l.output_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps } if (l.activation_input_gpu) { cuda_free(l.activation_input_gpu); l.activation_input_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps } if (l.x_gpu) { cuda_free(l.x_gpu); l.x_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps } // maximum optimization if (net.optimized_memory >= 3 && l.type != DROPOUT) { if (l.delta_gpu) { cuda_free(l.delta_gpu); //l.delta_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps //printf("\n\n PINNED DELTA GPU = %d \n", l.batch*l.outputs); } } if (l.type == CONVOLUTIONAL) { set_specified_workspace_limit(&l, net.workspace_size_limit); // workspace size limit 1 GB } } #endif // GPU l.clip = option_find_float_quiet(options, "clip", 0); l.dynamic_minibatch = net.dynamic_minibatch; l.onlyforward = option_find_int_quiet(options, "onlyforward", 0); l.dont_update = option_find_int_quiet(options, "dont_update", 0); l.burnin_update = option_find_int_quiet(options, "burnin_update", 0); l.stopbackward = option_find_int_quiet(options, "stopbackward", 0); l.train_only_bn = option_find_int_quiet(options, "train_only_bn", 0); l.dontload = option_find_int_quiet(options, "dontload", 0); l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0); l.learning_rate_scale = option_find_float_quiet(options, "learning_rate", 1); option_unused(options); net.layers[count] = l; if (l.workspace_size > workspace_size) workspace_size = l.workspace_size; if (l.inputs > max_inputs) max_inputs = l.inputs; if (l.outputs > max_outputs) max_outputs = l.outputs; free_section(s); n = n->next; ++count; if(n){ if (l.antialiasing) { params.h = l.input_layer->out_h; params.w = l.input_layer->out_w; params.c = l.input_layer->out_c; params.inputs = l.input_layer->outputs; } else { params.h = l.out_h; params.w = l.out_w; params.c = l.out_c; params.inputs = l.outputs; } } if (l.bflops > 0) bflops += l.bflops; if (l.w > 1 && l.h > 1) { avg_outputs += l.outputs; avg_counter++; } } free_list(sections); #ifdef GPU if (net.optimized_memory && params.train) { int k; for (k = 0; k < net.n; ++k) { layer l = net.layers[k]; // delta GPU-memory optimization: net.optimized_memory == 1 if (!l.keep_delta_gpu) { const size_t delta_size = l.outputs*l.batch; // l.steps if (net.max_delta_gpu_size < delta_size) { net.max_delta_gpu_size = delta_size; if (net.global_delta_gpu) cuda_free(net.global_delta_gpu); if (net.state_delta_gpu) cuda_free(net.state_delta_gpu); assert(net.max_delta_gpu_size > 0); net.global_delta_gpu = (float *)cuda_make_array(NULL, net.max_delta_gpu_size); net.state_delta_gpu = (float *)cuda_make_array(NULL, net.max_delta_gpu_size); } if (l.delta_gpu) { if (net.optimized_memory >= 3) {} else cuda_free(l.delta_gpu); } l.delta_gpu = net.global_delta_gpu; } // maximum optimization if (net.optimized_memory >= 3 && l.type != DROPOUT) { if (l.delta_gpu && l.keep_delta_gpu) { //cuda_free(l.delta_gpu); // already called above l.delta_gpu = cuda_make_array_pinned_preallocated(NULL, l.batch*l.outputs); // l.steps //printf("\n\n PINNED DELTA GPU = %d \n", l.batch*l.outputs); } } net.layers[k] = l; } } #endif set_train_only_bn(net); // set l.train_only_bn for all required layers net.outputs = get_network_output_size(net); net.output = get_network_output(net); avg_outputs = avg_outputs / avg_counter; fprintf(stderr, "Total BFLOPS %5.3f \n", bflops); fprintf(stderr, "avg_outputs = %d \n", avg_outputs); #ifdef GPU get_cuda_stream(); //get_cuda_memcpy_stream(); if (gpu_index >= 0) { int size = get_network_input_size(net) * net.batch; net.input_state_gpu = cuda_make_array(0, size); if (cudaSuccess == cudaHostAlloc(&net.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1; else { cudaGetLastError(); // reset CUDA-error net.input_pinned_cpu = (float*)xcalloc(size, sizeof(float)); } // pre-allocate memory for inference on Tensor Cores (fp16) *net.max_input16_size = 0; *net.max_output16_size = 0; if (net.cudnn_half) { *net.max_input16_size = max_inputs; CHECK_CUDA(cudaMalloc((void **)net.input16_gpu, *net.max_input16_size * sizeof(short))); //sizeof(half) *net.max_output16_size = max_outputs; CHECK_CUDA(cudaMalloc((void **)net.output16_gpu, *net.max_output16_size * sizeof(short))); //sizeof(half) } if (workspace_size) { fprintf(stderr, " Allocate additional workspace_size = %1.2f MB \n", (float)workspace_size/1000000); net.workspace = cuda_make_array(0, workspace_size / sizeof(float) + 1); } else { net.workspace = (float*)xcalloc(1, workspace_size); } } #else if (workspace_size) { net.workspace = (float*)xcalloc(1, workspace_size); } #endif LAYER_TYPE lt = net.layers[net.n - 1].type; if ((net.w % 32 != 0 || net.h % 32 != 0) && (lt == YOLO || lt == REGION || lt == DETECTION)) { printf("\n Warning: width=%d and height=%d in cfg-file must be divisible by 32 for default networks Yolo v1/v2/v3!!! \n\n", net.w, net.h); } return net; } list *read_cfg(char *filename) { FILE *file = fopen(filename, "r"); if(file == 0) file_error(filename); char *line; int nu = 0; list *sections = make_list(); section *current = 0; while((line=fgetl(file)) != 0){ ++ nu; strip(line); switch(line[0]){ case '[': current = (section*)xmalloc(sizeof(section)); list_insert(sections, current); current->options = make_list(); current->type = line; break; case '\0': case '#': case ';': free(line); break; default: if(!read_option(line, current->options)){ fprintf(stderr, "Config file error line %d, could parse: %s\n", nu, line); free(line); } break; } } fclose(file); return sections; } void save_convolutional_weights_binary(layer l, FILE *fp) { #ifdef GPU if(gpu_index >= 0){ pull_convolutional_layer(l); } #endif int size = (l.c/l.groups)*l.size*l.size; binarize_weights(l.weights, l.n, size, l.binary_weights); int i, j, k; fwrite(l.biases, sizeof(float), l.n, fp); if (l.batch_normalize){ fwrite(l.scales, sizeof(float), l.n, fp); fwrite(l.rolling_mean, sizeof(float), l.n, fp); fwrite(l.rolling_variance, sizeof(float), l.n, fp); } for(i = 0; i < l.n; ++i){ float mean = l.binary_weights[i*size]; if(mean < 0) mean = -mean; fwrite(&mean, sizeof(float), 1, fp); for(j = 0; j < size/8; ++j){ int index = i*size + j*8; unsigned char c = 0; for(k = 0; k < 8; ++k){ if (j*8 + k >= size) break; if (l.binary_weights[index + k] > 0) c = (c | 1<<k); } fwrite(&c, sizeof(char), 1, fp); } } } void save_shortcut_weights(layer l, FILE *fp) { #ifdef GPU if (gpu_index >= 0) { pull_shortcut_layer(l); printf("\n pull_shortcut_layer \n"); } #endif int i; //if(l.weight_updates) for (i = 0; i < l.nweights; ++i) printf(" %f, ", l.weight_updates[i]); //printf(" l.nweights = %d - update \n", l.nweights); for (i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]); printf(" l.nweights = %d \n\n", l.nweights); int num = l.nweights; fwrite(l.weights, sizeof(float), num, fp); } void save_implicit_weights(layer l, FILE *fp) { #ifdef GPU if (gpu_index >= 0) { pull_implicit_layer(l); //printf("\n pull_implicit_layer \n"); } #endif int i; //if(l.weight_updates) for (i = 0; i < l.nweights; ++i) printf(" %f, ", l.weight_updates[i]); //printf(" l.nweights = %d - update \n", l.nweights); //for (i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]); //printf(" l.nweights = %d \n\n", l.nweights); int num = l.nweights; fwrite(l.weights, sizeof(float), num, fp); } void save_convolutional_weights(layer l, FILE *fp) { if(l.binary){ //save_convolutional_weights_binary(l, fp); //return; } #ifdef GPU if(gpu_index >= 0){ pull_convolutional_layer(l); } #endif int num = l.nweights; fwrite(l.biases, sizeof(float), l.n, fp); if (l.batch_normalize){ fwrite(l.scales, sizeof(float), l.n, fp); fwrite(l.rolling_mean, sizeof(float), l.n, fp); fwrite(l.rolling_variance, sizeof(float), l.n, fp); } fwrite(l.weights, sizeof(float), num, fp); //if(l.adam){ // fwrite(l.m, sizeof(float), num, fp); // fwrite(l.v, sizeof(float), num, fp); //} } void save_convolutional_weights_ema(layer l, FILE *fp) { if (l.binary) { //save_convolutional_weights_binary(l, fp); //return; } #ifdef GPU if (gpu_index >= 0) { pull_convolutional_layer(l); } #endif int num = l.nweights; fwrite(l.biases_ema, sizeof(float), l.n, fp); if (l.batch_normalize) { fwrite(l.scales_ema, sizeof(float), l.n, fp); fwrite(l.rolling_mean, sizeof(float), l.n, fp); fwrite(l.rolling_variance, sizeof(float), l.n, fp); } fwrite(l.weights_ema, sizeof(float), num, fp); //if(l.adam){ // fwrite(l.m, sizeof(float), num, fp); // fwrite(l.v, sizeof(float), num, fp); //} } void save_batchnorm_weights(layer l, FILE *fp) { #ifdef GPU if(gpu_index >= 0){ pull_batchnorm_layer(l); } #endif fwrite(l.biases, sizeof(float), l.c, fp); fwrite(l.scales, sizeof(float), l.c, fp); fwrite(l.rolling_mean, sizeof(float), l.c, fp); fwrite(l.rolling_variance, sizeof(float), l.c, fp); } void save_connected_weights(layer l, FILE *fp) { #ifdef GPU if(gpu_index >= 0){ pull_connected_layer(l); } #endif fwrite(l.biases, sizeof(float), l.outputs, fp); fwrite(l.weights, sizeof(float), l.outputs*l.inputs, fp); if (l.batch_normalize){ fwrite(l.scales, sizeof(float), l.outputs, fp); fwrite(l.rolling_mean, sizeof(float), l.outputs, fp); fwrite(l.rolling_variance, sizeof(float), l.outputs, fp); } } void save_weights_upto(network net, char *filename, int cutoff, int save_ema) { #ifdef GPU if(net.gpu_index >= 0){ cuda_set_device(net.gpu_index); } #endif fprintf(stderr, "Saving weights to %s\n", filename); FILE *fp = fopen(filename, "wb"); if(!fp) file_error(filename); int major = MAJOR_VERSION; int minor = MINOR_VERSION; int revision = PATCH_VERSION; fwrite(&major, sizeof(int), 1, fp); fwrite(&minor, sizeof(int), 1, fp); fwrite(&revision, sizeof(int), 1, fp); (*net.seen) = get_current_iteration(net) * net.batch * net.subdivisions; // remove this line, when you will save to weights-file both: seen & cur_iteration fwrite(net.seen, sizeof(uint64_t), 1, fp); int i; for(i = 0; i < net.n && i < cutoff; ++i){ layer l = net.layers[i]; if (l.type == CONVOLUTIONAL && l.share_layer == NULL) { if (save_ema) { save_convolutional_weights_ema(l, fp); } else { save_convolutional_weights(l, fp); } } if (l.type == SHORTCUT && l.nweights > 0) { save_shortcut_weights(l, fp); } if (l.type == IMPLICIT) { save_implicit_weights(l, fp); } if(l.type == CONNECTED){ save_connected_weights(l, fp); } if(l.type == BATCHNORM){ save_batchnorm_weights(l, fp); } if(l.type == RNN){ save_connected_weights(*(l.input_layer), fp); save_connected_weights(*(l.self_layer), fp); save_connected_weights(*(l.output_layer), fp); } if(l.type == GRU){ save_connected_weights(*(l.input_z_layer), fp); save_connected_weights(*(l.input_r_layer), fp); save_connected_weights(*(l.input_h_layer), fp); save_connected_weights(*(l.state_z_layer), fp); save_connected_weights(*(l.state_r_layer), fp); save_connected_weights(*(l.state_h_layer), fp); } if(l.type == LSTM){ save_connected_weights(*(l.wf), fp); save_connected_weights(*(l.wi), fp); save_connected_weights(*(l.wg), fp); save_connected_weights(*(l.wo), fp); save_connected_weights(*(l.uf), fp); save_connected_weights(*(l.ui), fp); save_connected_weights(*(l.ug), fp); save_connected_weights(*(l.uo), fp); } if (l.type == CONV_LSTM) { if (l.peephole) { save_convolutional_weights(*(l.vf), fp); save_convolutional_weights(*(l.vi), fp); save_convolutional_weights(*(l.vo), fp); } save_convolutional_weights(*(l.wf), fp); if (!l.bottleneck) { save_convolutional_weights(*(l.wi), fp); save_convolutional_weights(*(l.wg), fp); save_convolutional_weights(*(l.wo), fp); } save_convolutional_weights(*(l.uf), fp); save_convolutional_weights(*(l.ui), fp); save_convolutional_weights(*(l.ug), fp); save_convolutional_weights(*(l.uo), fp); } if(l.type == CRNN){ save_convolutional_weights(*(l.input_layer), fp); save_convolutional_weights(*(l.self_layer), fp); save_convolutional_weights(*(l.output_layer), fp); } if(l.type == LOCAL){ #ifdef GPU if(gpu_index >= 0){ pull_local_layer(l); } #endif int locations = l.out_w*l.out_h; int size = l.size*l.size*l.c*l.n*locations; fwrite(l.biases, sizeof(float), l.outputs, fp); fwrite(l.weights, sizeof(float), size, fp); } } fclose(fp); } void save_weights(network net, char *filename) { save_weights_upto(net, filename, net.n, 0); } void transpose_matrix(float *a, int rows, int cols) { float* transpose = (float*)xcalloc(rows * cols, sizeof(float)); int x, y; for(x = 0; x < rows; ++x){ for(y = 0; y < cols; ++y){ transpose[y*rows + x] = a[x*cols + y]; } } memcpy(a, transpose, rows*cols*sizeof(float)); free(transpose); } void load_connected_weights(layer l, FILE *fp, int transpose) { fread(l.biases, sizeof(float), l.outputs, fp); fread(l.weights, sizeof(float), l.outputs*l.inputs, fp); if(transpose){ transpose_matrix(l.weights, l.inputs, l.outputs); } //printf("Biases: %f mean %f variance\n", mean_array(l.biases, l.outputs), variance_array(l.biases, l.outputs)); //printf("Weights: %f mean %f variance\n", mean_array(l.weights, l.outputs*l.inputs), variance_array(l.weights, l.outputs*l.inputs)); if (l.batch_normalize && (!l.dontloadscales)){ fread(l.scales, sizeof(float), l.outputs, fp); fread(l.rolling_mean, sizeof(float), l.outputs, fp); fread(l.rolling_variance, sizeof(float), l.outputs, fp); //printf("Scales: %f mean %f variance\n", mean_array(l.scales, l.outputs), variance_array(l.scales, l.outputs)); //printf("rolling_mean: %f mean %f variance\n", mean_array(l.rolling_mean, l.outputs), variance_array(l.rolling_mean, l.outputs)); //printf("rolling_variance: %f mean %f variance\n", mean_array(l.rolling_variance, l.outputs), variance_array(l.rolling_variance, l.outputs)); } #ifdef GPU if(gpu_index >= 0){ push_connected_layer(l); } #endif } void load_batchnorm_weights(layer l, FILE *fp) { fread(l.biases, sizeof(float), l.c, fp); fread(l.scales, sizeof(float), l.c, fp); fread(l.rolling_mean, sizeof(float), l.c, fp); fread(l.rolling_variance, sizeof(float), l.c, fp); #ifdef GPU if(gpu_index >= 0){ push_batchnorm_layer(l); } #endif } void load_convolutional_weights_binary(layer l, FILE *fp) { fread(l.biases, sizeof(float), l.n, fp); if (l.batch_normalize && (!l.dontloadscales)){ fread(l.scales, sizeof(float), l.n, fp); fread(l.rolling_mean, sizeof(float), l.n, fp); fread(l.rolling_variance, sizeof(float), l.n, fp); } int size = (l.c / l.groups)*l.size*l.size; int i, j, k; for(i = 0; i < l.n; ++i){ float mean = 0; fread(&mean, sizeof(float), 1, fp); for(j = 0; j < size/8; ++j){ int index = i*size + j*8; unsigned char c = 0; fread(&c, sizeof(char), 1, fp); for(k = 0; k < 8; ++k){ if (j*8 + k >= size) break; l.weights[index + k] = (c & 1<<k) ? mean : -mean; } } } #ifdef GPU if(gpu_index >= 0){ push_convolutional_layer(l); } #endif } void load_convolutional_weights(layer l, FILE *fp) { if(l.binary){ //load_convolutional_weights_binary(l, fp); //return; } int num = l.nweights; int read_bytes; read_bytes = fread(l.biases, sizeof(float), l.n, fp); if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.biases - l.index = %d \n", l.index); //fread(l.weights, sizeof(float), num, fp); // as in connected layer if (l.batch_normalize && (!l.dontloadscales)){ read_bytes = fread(l.scales, sizeof(float), l.n, fp); if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.scales - l.index = %d \n", l.index); read_bytes = fread(l.rolling_mean, sizeof(float), l.n, fp); if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.rolling_mean - l.index = %d \n", l.index); read_bytes = fread(l.rolling_variance, sizeof(float), l.n, fp); if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.rolling_variance - l.index = %d \n", l.index); if(0){ int i; for(i = 0; i < l.n; ++i){ printf("%g, ", l.rolling_mean[i]); } printf("\n"); for(i = 0; i < l.n; ++i){ printf("%g, ", l.rolling_variance[i]); } printf("\n"); } if(0){ fill_cpu(l.n, 0, l.rolling_mean, 1); fill_cpu(l.n, 0, l.rolling_variance, 1); } } read_bytes = fread(l.weights, sizeof(float), num, fp); if (read_bytes > 0 && read_bytes < l.n) printf("\n Warning: Unexpected end of wights-file! l.weights - l.index = %d \n", l.index); //if(l.adam){ // fread(l.m, sizeof(float), num, fp); // fread(l.v, sizeof(float), num, fp); //} //if(l.c == 3) scal_cpu(num, 1./256, l.weights, 1); if (l.flipped) { transpose_matrix(l.weights, (l.c/l.groups)*l.size*l.size, l.n); } //if (l.binary) binarize_weights(l.weights, l.n, (l.c/l.groups)*l.size*l.size, l.weights); #ifdef GPU if(gpu_index >= 0){ push_convolutional_layer(l); } #endif } void load_shortcut_weights(layer l, FILE *fp) { int num = l.nweights; int read_bytes; read_bytes = fread(l.weights, sizeof(float), num, fp); if (read_bytes > 0 && read_bytes < num) printf("\n Warning: Unexpected end of wights-file! l.weights - l.index = %d \n", l.index); //for (int i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]); //printf(" read_bytes = %d \n\n", read_bytes); #ifdef GPU if (gpu_index >= 0) { push_shortcut_layer(l); } #endif } void load_implicit_weights(layer l, FILE *fp) { int num = l.nweights; int read_bytes; read_bytes = fread(l.weights, sizeof(float), num, fp); if (read_bytes > 0 && read_bytes < num) printf("\n Warning: Unexpected end of wights-file! l.weights - l.index = %d \n", l.index); //for (int i = 0; i < l.nweights; ++i) printf(" %f, ", l.weights[i]); //printf(" read_bytes = %d \n\n", read_bytes); #ifdef GPU if (gpu_index >= 0) { push_implicit_layer(l); } #endif } void load_weights_upto(network *net, char *filename, int cutoff) { #ifdef GPU if(net->gpu_index >= 0){ cuda_set_device(net->gpu_index); } #endif fprintf(stderr, "Loading weights from %s...", filename); fflush(stdout); FILE *fp = fopen(filename, "rb"); if(!fp) file_error(filename); int major; int minor; int revision; fread(&major, sizeof(int), 1, fp); fread(&minor, sizeof(int), 1, fp); fread(&revision, sizeof(int), 1, fp); if ((major * 10 + minor) >= 2) { printf("\n seen 64"); uint64_t iseen = 0; fread(&iseen, sizeof(uint64_t), 1, fp); *net->seen = iseen; } else { printf("\n seen 32"); uint32_t iseen = 0; fread(&iseen, sizeof(uint32_t), 1, fp); *net->seen = iseen; } *net->cur_iteration = get_current_batch(*net); printf(", trained: %.0f K-images (%.0f Kilo-batches_64) \n", (float)(*net->seen / 1000), (float)(*net->seen / 64000)); int transpose = (major > 1000) || (minor > 1000); int i; for(i = 0; i < net->n && i < cutoff; ++i){ layer l = net->layers[i]; if (l.dontload) continue; if(l.type == CONVOLUTIONAL && l.share_layer == NULL){ load_convolutional_weights(l, fp); } if (l.type == SHORTCUT && l.nweights > 0) { load_shortcut_weights(l, fp); } if (l.type == IMPLICIT) { load_implicit_weights(l, fp); } if(l.type == CONNECTED){ load_connected_weights(l, fp, transpose); } if(l.type == BATCHNORM){ load_batchnorm_weights(l, fp); } if(l.type == CRNN){ load_convolutional_weights(*(l.input_layer), fp); load_convolutional_weights(*(l.self_layer), fp); load_convolutional_weights(*(l.output_layer), fp); } if(l.type == RNN){ load_connected_weights(*(l.input_layer), fp, transpose); load_connected_weights(*(l.self_layer), fp, transpose); load_connected_weights(*(l.output_layer), fp, transpose); } if(l.type == GRU){ load_connected_weights(*(l.input_z_layer), fp, transpose); load_connected_weights(*(l.input_r_layer), fp, transpose); load_connected_weights(*(l.input_h_layer), fp, transpose); load_connected_weights(*(l.state_z_layer), fp, transpose); load_connected_weights(*(l.state_r_layer), fp, transpose); load_connected_weights(*(l.state_h_layer), fp, transpose); } if(l.type == LSTM){ load_connected_weights(*(l.wf), fp, transpose); load_connected_weights(*(l.wi), fp, transpose); load_connected_weights(*(l.wg), fp, transpose); load_connected_weights(*(l.wo), fp, transpose); load_connected_weights(*(l.uf), fp, transpose); load_connected_weights(*(l.ui), fp, transpose); load_connected_weights(*(l.ug), fp, transpose); load_connected_weights(*(l.uo), fp, transpose); } if (l.type == CONV_LSTM) { if (l.peephole) { load_convolutional_weights(*(l.vf), fp); load_convolutional_weights(*(l.vi), fp); load_convolutional_weights(*(l.vo), fp); } load_convolutional_weights(*(l.wf), fp); if (!l.bottleneck) { load_convolutional_weights(*(l.wi), fp); load_convolutional_weights(*(l.wg), fp); load_convolutional_weights(*(l.wo), fp); } load_convolutional_weights(*(l.uf), fp); load_convolutional_weights(*(l.ui), fp); load_convolutional_weights(*(l.ug), fp); load_convolutional_weights(*(l.uo), fp); } if(l.type == LOCAL){ int locations = l.out_w*l.out_h; int size = l.size*l.size*l.c*l.n*locations; fread(l.biases, sizeof(float), l.outputs, fp); fread(l.weights, sizeof(float), size, fp); #ifdef GPU if(gpu_index >= 0){ push_local_layer(l); } #endif } if (feof(fp)) break; } fprintf(stderr, "Done! Loaded %d layers from weights-file \n", i); fclose(fp); } void load_weights(network *net, char *filename) { load_weights_upto(net, filename, net->n); } // load network & force - set batch size network *load_network_custom(char *cfg, char *weights, int clear, int batch) { printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear); network* net = (network*)xcalloc(1, sizeof(network)); *net = parse_network_cfg_custom(cfg, batch, 1); if (weights && weights[0] != 0) { printf(" Try to load weights: %s \n", weights); load_weights(net, weights); } fuse_conv_batchnorm(*net); if (clear) { (*net->seen) = 0; (*net->cur_iteration) = 0; } return net; } // load network & get batch size from cfg-file network *load_network(char *cfg, char *weights, int clear) { printf(" Try to load cfg: %s, clear = %d \n", cfg, clear); network* net = (network*)xcalloc(1, sizeof(network)); *net = parse_network_cfg(cfg); if (weights && weights[0] != 0) { printf(" Try to load weights: %s \n", weights); load_weights(net, weights); } if (clear) { (*net->seen) = 0; (*net->cur_iteration) = 0; } return net; }
