# darknet-mobilenet **Repository Path**: ccstl/darknet-mobilenet ## Basic Information - **Project Name**: darknet-mobilenet - **Description**: Recurrent mobilenet based on darknet - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 1 - **Created**: 2023-11-07 - **Last Updated**: 2023-11-07 ## Categories & Tags **Categories**: Uncategorized **Tags**: YOLO改进 ## README # Install 1 add depthwise convolutional layer into darknet First, open this file /src/parser.c. 1) add below into it #include "utils.h" ++ #include "depthwise_convolutional_layer.h" //added by chen 2) find function 'string_to_layer_type' added below into it if (strcmp(type, "[upsample]")==0) return UPSAMPLE; ++ if (strcmp(type, "[depthwise_convolutional]") == 0) return DEPTHWISE_CONVOLUTIONAL; //added by chen return BLANK; 3) find function 'parse_network_cfg' added below into it if(lt == CONVOLUTIONAL){ l = parse_convolutional(options, params); } ++ else if (lt == DEPTHWISE_CONVOLUTIONAL) { ++ l = parse_depthwise_convolutional(options, params); //added by chen ++ } else if(lt == DECONVOLUTIONAL){ l = parse_deconvolutional(options, params); } 4) add this function 'parse_depthwise_convolutional' into it //added by chen depthwise_convolutional_layer parse_depthwise_convolutional(list *options, size_params params) { int size = option_find_int(options, "size", 1); int stride = option_find_int(options, "stride", 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 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); depthwise_convolutional_layer layer = make_depthwise_convolutional_layer(batch, h, w, c, size, stride, padding, activation, batch_normalize); layer.flipped = option_find_int_quiet(options, "flipped", 0); layer.dot = option_find_float_quiet(options, "dot", 0); return layer; } 5) find function 'load_weights_upto' added below into it if (l.dontload) continue; ++ if (l.type == DEPTHWISE_CONVOLUTIONAL) { ++ load_depthwise_convolutional_weights(l, fp);//added by chen ++ } 6) add this function 'load_depthwise_convolutional_weights' into it //added by chen void load_depthwise_convolutional_weights(layer l, FILE *fp) { int num = l.n*l.size*l.size; 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); 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); } } fread(l.weights, sizeof(float), num, fp); if (l.flipped) { //transpose_matrix(l.weights, l.c*l.size*l.size, l.n); } #ifdef GPU if (gpu_index >= 0) { push_depthwise_convolutional_layer(l); } #endif } 7) find function 'save_weights_upto' added below into it if (l.dontsave) continue; ++ if (l.type == DEPTHWISE_CONVOLUTIONAL) { ++　　　　save_depthwise_convolutional_weights(l, fp); //added by chen ++ } 8) add this function 'save_depthwise_convolutional_weights' into it //added by chen void save_depthwise_convolutional_weights(layer l, FILE *fp) { #ifdef GPU if (gpu_index >= 0) { pull_depthwise_convolutional_layer(l); } #endif int num = l.n*l.size*l.size; 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); } Second, open this file /src/network.c . 1) add below into it #include "data.h" ++ #include "depthwise_convolutional_layer.h" // added by chen 2) add below into it if(l.type == CONVOLUTIONAL){ resize_convolutional_layer(&l, w, h); } ++ else if(l.type == DEPTHWISE_CONVOLUTIONAL){ ++ resize_depthwise_convolutional_layer(&l, w, h); //added by chen ++ } Third,open this file /include/darknet.h and add below into it. BLANK, ++ DEPTHWISE_CONVOLUTIONAL //added by chen Final, put these files 'depthwise_convolutional_layer.h depthwise_convolutional_layer.c depthwise_convolutional_kernels.cu' into /src． # Compile Open Makefile add below into it. ++ OBJ= depthwise_convolutional_layer.o ifeq ($(GPU), 1) LDFLAGS+= -lstdc++ ++ OBJ+=depthwise_convolutional_kernels.o endif Make $ make -j8 # Test cd darknet ./darknet classifier predict cfg/imagenet1k.data cfg/mobilenet_v1.cfg mobilenet_v1_72.weights data/cat.jpg layer filters size input output 0 conv 32 3 x 3 / 2 256 x 256 x 3 -> 128 x 128 x 32 0.028 BFLOPs 1 dw conv 32 3 x 3 / 1 128 x 128 x 32 -> 128 x 128 x 32 0.009 BFLOPs 2 conv 64 1 x 1 / 1 128 x 128 x 32 -> 128 x 128 x 64 0.067 BFLOPs 3 dw conv 64 3 x 3 / 2 128 x 128 x 64 -> 64 x 64 x 64 0.005 BFLOPs 4 conv 128 1 x 1 / 1 64 x 64 x 64 -> 64 x 64 x 128 0.067 BFLOPs 5 dw conv 128 3 x 3 / 1 64 x 64 x 128 -> 64 x 64 x 128 0.009 BFLOPs 6 conv 128 1 x 1 / 1 64 x 64 x 128 -> 64 x 64 x 128 0.134 BFLOPs 7 dw conv 128 3 x 3 / 2 64 x 64 x 128 -> 32 x 32 x 128 0.002 BFLOPs 8 conv 256 1 x 1 / 1 32 x 32 x 128 -> 32 x 32 x 256 0.067 BFLOPs 9 dw conv 256 3 x 3 / 1 32 x 32 x 256 -> 32 x 32 x 256 0.005 BFLOPs 10 conv 256 1 x 1 / 1 32 x 32 x 256 -> 32 x 32 x 256 0.134 BFLOPs 11 dw conv 256 3 x 3 / 2 32 x 32 x 256 -> 16 x 16 x 256 0.001 BFLOPs 12 conv 512 1 x 1 / 1 16 x 16 x 256 -> 16 x 16 x 512 0.067 BFLOPs 13 dw conv 512 3 x 3 / 1 16 x 16 x 512 -> 16 x 16 x 512 0.002 BFLOPs 14 conv 512 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 512 0.134 BFLOPs 15 dw conv 512 3 x 3 / 1 16 x 16 x 512 -> 16 x 16 x 512 0.002 BFLOPs 16 conv 512 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 512 0.134 BFLOPs 17 dw conv 512 3 x 3 / 1 16 x 16 x 512 -> 16 x 16 x 512 0.002 BFLOPs 18 conv 512 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 512 0.134 BFLOPs 19 dw conv 512 3 x 3 / 1 16 x 16 x 512 -> 16 x 16 x 512 0.002 BFLOPs 20 conv 512 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 512 0.134 BFLOPs 21 dw conv 512 3 x 3 / 1 16 x 16 x 512 -> 16 x 16 x 512 0.002 BFLOPs 22 conv 512 1 x 1 / 1 16 x 16 x 512 -> 16 x 16 x 512 0.134 BFLOPs 23 dw conv 512 3 x 3 / 2 16 x 16 x 512 -> 8 x 8 x 512 0.001 BFLOPs 24 conv 1024 1 x 1 / 1 8 x 8 x 512 -> 8 x 8 x1024 0.067 BFLOPs 25 dw conv 1024 3 x 3 / 1 8 x 8 x1024 -> 8 x 8 x1024 0.001 BFLOPs 26 conv 1024 1 x 1 / 1 8 x 8 x1024 -> 8 x 8 x1024 0.134 BFLOPs 27 avg 8 x 8 x1024 -> 1024 28 conv 1000 1 x 1 / 1 1 x 1 x1024 -> 1 x 1 x1000 0.002 BFLOPs 29 softmax 1000 Loading weights from ../darknet-mod/mobilenet_v1_72.weights...Done! data/cat.jpg: Predicted in 0.005337 seconds. 43.06%: tiger cat 17.93%: tabby 9.49%: Egyptian cat 3.43%: lynx 1.38%: bucket # Valid ./darknet classifier valid cfg/imagenet1k.data cfg/mobilenet_v1_416.cfg mobilenet_v1_72.weights # Precision network|top1|top5 --------|-----|------------- Mobilenet_v1|0.7203|0.90514