718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
5.1 KiB
5.1 KiB
current model load api
Cons
long and awful code
two functions
deal float32 float16 quantized-u8
deal alignment size
#if NCNN_STDIO
int Convolution::load_model(FILE* binfp)
{
int nread;
union
{
struct
{
unsigned char f0;
unsigned char f1;
unsigned char f2;
unsigned char f3;
};
unsigned int tag;
} flag_struct;
nread = fread(&flag_struct, sizeof(flag_struct), 1, binfp);
if (nread != 1)
{
fprintf(stderr, "Convolution read flag_struct failed %d\n", nread);
return -1;
}
unsigned int flag = flag_struct.f0 + flag_struct.f1 + flag_struct.f2 + flag_struct.f3;
weight_data.create(weight_data_size);
if (weight_data.empty())
return -100;
if (flag_struct.tag == 0x01306B47)
{
// half-precision weight data
int align_weight_data_size = alignSize(weight_data_size * sizeof(unsigned short), 4);
std::vector<unsigned short> float16_weights;
float16_weights.resize(align_weight_data_size);
nread = fread(float16_weights.data(), align_weight_data_size, 1, binfp);
if (nread != 1)
{
fprintf(stderr, "Convolution read float16_weights failed %d\n", nread);
return -1;
}
weight_data = Mat::from_float16(float16_weights.data(), weight_data_size);
if (weight_data.empty())
return -100;
}
else if (flag != 0)
{
// quantized weight data
float quantization_value[256];
nread = fread(quantization_value, 256 * sizeof(float), 1, binfp);
if (nread != 1)
{
fprintf(stderr, "Convolution read quantization_value failed %d\n", nread);
return -1;
}
int align_weight_data_size = alignSize(weight_data_size * sizeof(unsigned char), 4);
std::vector<unsigned char> index_array;
index_array.resize(align_weight_data_size);
nread = fread(index_array.data(), align_weight_data_size, 1, binfp);
if (nread != 1)
{
fprintf(stderr, "Convolution read index_array failed %d\n", nread);
return -1;
}
float* weight_data_ptr = weight_data;
for (int i = 0; i < weight_data_size; i++)
{
weight_data_ptr[i] = quantization_value[ index_array[i] ];
}
}
else if (flag_struct.f0 == 0)
{
// raw weight data
nread = fread(weight_data, weight_data_size * sizeof(float), 1, binfp);
if (nread != 1)
{
fprintf(stderr, "Convolution read weight_data failed %d\n", nread);
return -1;
}
}
if (bias_term)
{
bias_data.create(num_output);
if (bias_data.empty())
return -100;
nread = fread(bias_data, num_output * sizeof(float), 1, binfp);
if (nread != 1)
{
fprintf(stderr, "Convolution read bias_data failed %d\n", nread);
return -1;
}
}
return 0;
}
#endif // NCNN_STDIO
int Convolution::load_model(const unsigned char*& mem)
{
union
{
struct
{
unsigned char f0;
unsigned char f1;
unsigned char f2;
unsigned char f3;
};
unsigned int tag;
} flag_struct;
memcpy(&flag_struct, mem, sizeof(flag_struct));
mem += sizeof(flag_struct);
unsigned int flag = flag_struct.f0 + flag_struct.f1 + flag_struct.f2 + flag_struct.f3;
if (flag_struct.tag == 0x01306B47)
{
// half-precision weight data
weight_data = Mat::from_float16((unsigned short*)mem, weight_data_size);
mem += alignSize(weight_data_size * sizeof(unsigned short), 4);
if (weight_data.empty())
return -100;
}
else if (flag != 0)
{
// quantized weight data
const float* quantization_value = (const float*)mem;
mem += 256 * sizeof(float);
const unsigned char* index_array = (const unsigned char*)mem;
mem += alignSize(weight_data_size * sizeof(unsigned char), 4);
weight_data.create(weight_data_size);
if (weight_data.empty())
return -100;
float* weight_data_ptr = weight_data;
for (int i = 0; i < weight_data_size; i++)
{
weight_data_ptr[i] = quantization_value[ index_array[i] ];
}
}
else if (flag_struct.f0 == 0)
{
// raw weight data
weight_data = Mat(weight_data_size, (float*)mem);
mem += weight_data_size * sizeof(float);
}
if (bias_term)
{
bias_data = Mat(num_output, (float*)mem);
mem += num_output * sizeof(float);
}
return 0;
}
new model load api proposed
Pros
clean and simple api
element type detection
int Convolution::load_model(const ModelBin& mb)
{
// auto detect element type
weight_data = mb.load(weight_data_size, 0);
if (weight_data.empty())
return -100;
if (bias_term)
{
// certain type specified
bias_data = mb.load(num_output, 1);
if (bias_data.empty())
return -100;
}
return 0;
}