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master lite train codex clean

This commit is contained in:
zhengjun10 2021-08-04 17:44:19 +08:00
parent 7b6822b9d4
commit a2ed372ccd
4 changed files with 14 additions and 20 deletions
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1
mindspore/lite/src/lite_model.cc
View File

@ -479,5 +479,4 @@ int Model::Export(Model *model, const char *filename) {
return chmod(filename, S_IRUSR);
#endif
}
} // namespace mindspore::lite

20
mindspore/lite/src/weight_decoder.cc
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@ -20,11 +20,13 @@
#include "src/huffman_decode.h"
namespace mindspore::lite {
constexpr int kBit8 = 8;
constexpr int kBit32 = 32;
std::vector<bool> StringToBitVector(const std::string &str) {
std::vector<bool> vec(str.size() * 8);
std::vector<bool> vec(str.size() * kBit8);
size_t index = 0;
for (auto ch : str) {
for (size_t shift = 8; shift > 0; shift--) {
for (size_t shift = kBit8; shift > 0; shift--) {
vec[index++] = (ch >> (shift - 1)) & 0x1;
}
}
@ -47,7 +49,7 @@ STATUS IndexingDecompress(const schema::Tensor &src_tensor, Tensor *dst_tensor)
if (unique_value_cnt == 0) {
unique_value_cnt = 1 << bit_num;
}
// parse unique_value_set;
// parse unique_value_set
std::vector<int> unique_values;
for (size_t i = 0; i < unique_value_cnt; i++) {
int unique_value = 0;
@ -81,7 +83,7 @@ STATUS IndexingDecompress(const schema::Tensor &src_tensor, Tensor *dst_tensor)
return RET_NULL_PTR;
}
auto dst_data = dst_tensor->data_c();
if (bit_num <= 8) {
if (bit_num <= kBit8) {
ret = UnIndexTensorData<int8_t>(unique_values, unique_value_index_vec, dst_data, dst_tensor->Size());
} else {
ret = UnIndexTensorData<int16_t>(unique_values, unique_value_index_vec, dst_data, dst_tensor->Size());
@ -102,15 +104,15 @@ STATUS SparseDecompress(const schema::Tensor &src_tensor, Tensor *dst_tensor) {
size_t index = 0;
// parse coor_best_bit
size_t coor_best_bit = 0;
for (size_t i = 0; i < 8; i++) {
for (size_t i = 0; i < kBit8; i++) {
bool bit = bit_vec[index++];
coor_best_bit |= bit << (8 - i - 1);
coor_best_bit |= bit << (kBit8 - i - 1);
}
// parse nz_cnt
size_t nz_cnt = 0;
for (size_t i = 0; i < 32; i++) {
for (size_t i = 0; i < kBit32; i++) {
bool bit = bit_vec[index++];
nz_cnt |= bit << (32 - i - 1);
nz_cnt |= bit << (kBit32 - i - 1);
}
// parse unique_value cnt
size_t unique_value_cnt = 0;
@ -167,7 +169,7 @@ STATUS SparseDecompress(const schema::Tensor &src_tensor, Tensor *dst_tensor) {
}
auto dst_data = dst_tensor->data_c();
if (bit_num <= 8) {
if (bit_num <= kBit8) {
ret = UnSparseTensorData<int8_t>(unique_values, unique_value_index_vec, coor_vec, src_tensor.quantParams(),
elem_cnt, coor_best_bit, dst_data, dst_tensor->Size());
} else {

11
mindspore/lite/tools/converter/legacy_optimizer/graph/batchnorm_convert_scale_pass.cc
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@ -230,15 +230,8 @@ STATUS BatchNormConvertScalePass::GetTransParam(MetaGraphT *graph, const std::un
return RET_OK;
}
// BatchNorm weight Tensor definition:
// caffe
// estimated_mean --0
// estimated_variance --1
// tensorflow
// scale -- 0
// bias --1
// estimated_mean --2
// estimated_variance --3
// caffe:estimated_mean:0 estimated_variance:1
// tensorflow scale:0,bias:1,estimated_mean:2,estimated_variance:3
STATUS BatchNormConvertScalePass::GetBnWeightTensors(MetaGraphT *graph, BNWeightTensors *bnWeightTensors,
const std::unique_ptr<CNodeT> &bnNode) {
MS_ASSERT(graph != nullptr);

2
mindspore/lite/tools/converter/quantizer/weight_quantizer.cc
View File

@ -143,7 +143,7 @@ STATUS WeightQuantizer::DoMulQuantize(const CNodePtr &cnode) {
auto status = RET_ERROR;
auto per_channel = true;
if (i == 3) {
if (i == kInputSize2) {
per_channel = false;
}
if (type_id_ == kNumberTypeInt8) {