Showing content from https://github.com/Tencent/ncnn/wiki/param-and-model-file-structure below:
param and model file structure · Tencent/ncnn Wiki · GitHub
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3 3
Input input 0 1 data 0=4 1=4 2=1
InnerProduct ip 1 1 data fc 0=10 1=1 2=80
Softmax softmax 1 1 fc prob 0=0
[layer count] [blob count]
- layer count : count of the layer line follows, should be exactly the count of all layer names
- blob count : count of all blobs, usually greater than or equals to the layer count
[layer type] [layer name] [input count] [output count] [input blobs] [output blobs] [layer specific params]
- layer type : type name, such as Convolution Softmax etc
- layer name : name of this layer, must be unique among all layer names
- input count : count of the blobs this layer needs as input
- output count : count of the blobs this layer produces as output
- input blobs : name list of all the input blob names, separated by space, must be unique among input blob names of all layers
- output blobs : name list of all the output blob names, separated by space, must be unique among output blob names of all layers
- layer specific params : key=value pair list, separated by space
0=1 1=2.5 -23303=2,2.0,3.0
key index should be unique in each layer line, pair can be omitted if the default value used
the meaning of existing param key index can be looked up at operation-param-weight-table
- integer or float key : index 0 ~ 19
- integer value : int
- float value : float
- integer array or float array key : -23300 minus index 0 ~ 19
- integer array value : [array size],int,int,...,int
- float array value : [array size],float,float,...,float
In modern ncnn param file
- array could be represented as
3=2.0,3.0 that is much more human friendly
- string typed value:
4=hello and the string is no longer than 255
+---------+---------+---------+---------+---------+---------+
| weight1 | weight2 | weight3 | weight4 | ....... | weightN |
+---------+---------+---------+---------+---------+---------+
^ ^ ^ ^
0x0 0x80 0x140 0x1C0
the model binary is the concatenation of all weight data, each weight buffer is aligned by 32bit
[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
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