ncnn之五：参数和模型文件结构

ncnn之五：参数和模型文件结构

net.param

7767517 # magic number158 183 # [layer count] [blob count]# [layer type] [layer name] [input count] [output count] [input blobs] [output blobs] [layer specific params]Input data 0 1 dataConvolution conv1 1 1 data conv1 0=64 1=3 11=3 3=2 13=2 4=0 14=0 5=1 6=1728ReLU relu_conv1 1 1 conv1 relu_conv1Convolution conv2 1 1 relu_conv1 conv2 0=64 1=3 11=3 3=2 13=2 4=0 14=0 5=1 6=36864

load_param代码:

int Net::load_param(FILE* fp){ int magic = 0; fscanf(fp, "%d", &magic); if (magic != 7767517) { fprintf(stderr, "param is too old, please regenerate\n"); return -1; } // parse int layer_count = 0; int blob_count = 0; fscanf(fp, "%d %d", &layer_count, &blob_count); layers.resize(layer_count); blobs.resize(blob_count); ParamDict pd; int layer_index = 0; int blob_index = 0; while (!feof(fp)) { int nscan = 0; char layer_type[257]; char layer_name[257]; int bottom_count = 0; int top_count = 0; nscan = fscanf(fp, "%256s %256s %d %d", layer_type, layer_name, &bottom_count, &top_count); if (nscan != 4) { continue; } Layer* layer = create_layer(layer_type); if (!layer) { layer = create_custom_layer(layer_type); } if (!layer) { fprintf(stderr, "layer %s not exists or registered\n", layer_type); clear(); return -1; } layer->type = std::string(layer_type); layer->name = std::string(layer_name);// fprintf(stderr, "new layer %d %s\n", layer_index, layer_name); layer->bottoms.resize(bottom_count); for (int i=0; ibottoms[i] = bottom_blob_index; } layer->tops.resize(top_count); for (int i=0; itops[i] = blob_index; blob_index++; } // layer specific params int pdlr = pd.load_param(fp); if (pdlr != 0) { fprintf(stderr, "ParamDict load_param failed\n"); continue; } int lr = layer->load_param(pd); if (lr != 0) { fprintf(stderr, "layer load_param failed\n"); continue; } layers[layer_index] = layer; layer_index++; } return 0;}int Net::load_param(const char* protopath){ FILE* fp = fopen(protopath, "rb"); if (!fp) { fprintf(stderr, "fopen %s failed\n", protopath); return -1; } int ret = load_param(fp); fclose(fp); return ret;}

参数字典 ParamDict

index：0~19 对应整形或浮点型数据,

查阅 ​​operation param weight table​​

index:-23000 减去0~19 对应整形或浮点型数组 , 属于特殊参数(可能没有)： 一种是k=v的类型；另一种是k=len,v1,v2,v3….（数组类型）。该层在ncnn中是存放到paramDict结构中. 根据读取的index判断，如果小于-23300，表示为数组，那么等号右边第一个参数就是数组长度，后面顺序就是数组内容，[array size],int,int,…,int或[array size],float,float,…,float，例如：

0=1 1=2.5 -23303=2,2.0,3.0

index为-23303，表明当前参数为数组，等号右边第一个参数为2，表明数组长度为2，后面2.0,3.0就是数组的内容

int ParamDict::load_param(const DataReader& dr){ clear();// 0=100 1=1.250000 -23303=5,0.1,0.2,0.4,0.8,1.0 // parse each key=value pair int id = 0; while (dr.scan("%d=", &id) == 1) { bool is_array = id <= -23300; if (is_array) { id = -id - 23300; } if (is_array) { int len = 0; int nscan = dr.scan("%d", &len); if (nscan != 1) { fprintf(stderr, "ParamDict read array length failed\n"); return -1; } params[id].v.create(len); for (int j = 0; j < len; j++) { char vstr[16]; nscan = dr.scan(",%15[^,\n ]", vstr); if (nscan != 1) { fprintf(stderr, "ParamDict read array element failed\n"); return -1; } bool is_float = vstr_is_float(vstr); if (is_float) { float* ptr = params[id].v; nscan = sscanf(vstr, "%f", &ptr[j]); } else { int* ptr = params[id].v; nscan = sscanf(vstr, "%d", &ptr[j]); } if (nscan != 1) { fprintf(stderr, "ParamDict parse array element failed\n"); return -1; } params[id].type = is_float ? 6 : 5; } } else { char vstr[16]; int nscan = dr.scan("%15s", vstr); if (nscan != 1) { fprintf(stderr, "ParamDict read value failed\n"); return -1; } bool is_float = vstr_is_float(vstr); if (is_float) nscan = sscanf(vstr, "%f", ¶ms[id].f); else nscan = sscanf(vstr, "%d", ¶ms[id].i); if (nscan != 1) { fprintf(stderr, "ParamDict parse value failed\n"); return -1; } params[id].type = is_float ? 3 : 2; } } return 0;}

其中 vstr_is_float函数，原理很简单，就是判断数字对应字符串中是否存在小数点’.‘或字母’e’，对应小数的两种写法，一种正常的小数点表示法，一种是科学计数法.

static bool vstr_is_float(const char vstr[16]){ // look ahead for determine isfloat for (int j=0; j<16; j++) { if (vstr[j] == '\0') break; if (vstr[j] == '.' || tolower(vstr[j]) == 'e') return true; } return false;}

net.bin

+---------+---------+---------+---------+---------+---------+ | weight1 | weight2 | weight3 | weight4 | ....... | weightN | +---------+---------+---------+---------+---------+---------+ ^ ^ ^ ^ 0x0 0x80 0x140 0x1C0

其中 weight buffer

[flag] (optional)[raw data][padding] (optional)

flag : unsigned int, little-endian, indicating the weight storage type, 0 => float32, 0x01306B47 => float16, otherwise => quantized int8, may be omitted if the layer implementation forced the storage type explicitly raw data : raw weight data, little-endian, float32 data or float16 data or quantized table and indexes depending on the storage type flag padding : padding space for 32bit alignment, may be omitted if already aligned

load_model代码:

int Net::load_model(FILE* fp){ if (layers.empty()) { fprintf(stderr, "network graph not ready\n"); return -1; } // load file int ret = 0; ModelBinFromStdio mb(fp); for (size_t i=0; iload_model(mb); if (lret != 0) { fprintf(stderr, "layer load_model %d failed\n", (int)i); ret = -1; break; } } return ret;}int Net::load_model(const char* modelpath){ FILE* fp = fopen(modelpath, "rb"); if (!fp) { fprintf(stderr, "fopen %s failed\n", modelpath); return -1; } int ret = load_model(fp); fclose(fp); return ret;}

参考: 1 ​​​param-and-model-file-structure​​​

2 ​​operation param weight table​​

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