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Fix bug of the loss of quantization parameters in quantization models.

This commit is contained in:
wsc 2020-09-04 11:16:28 +08:00
parent fbf8a3bbcc
commit 5170065722
4 changed files with 91 additions and 63 deletions
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3
mindspore/lite/tools/anf_exporter/anf_exporter.cc
View File

@ -64,8 +64,6 @@ int AnfExporter::ConvertQuantParam(const std::unique_ptr<schema::MetaGraphT> &me
MS_ASSERT(dst_node != nullptr);
// add quant param
dst_node->quantType = primitive->GetQuantType();
if (dst_node->quantType == schema::QuantType_PostTraining || dst_node->quantType == schema::QuantType_AwareTraining
|| dst_node->quantType == schema::QuantType_WeightQuant) {
MS_LOG(DEBUG) << "node: " << dst_node->name << " add QuantParam";
// activation
auto input_quant_params = primitive->GetInputQuantParams();
@ -124,7 +122,6 @@ int AnfExporter::ConvertQuantParam(const std::unique_ptr<schema::MetaGraphT> &me
}
}
}
}
return RET_OK;
}

5
mindspore/lite/tools/anf_importer/import_from_meta_graphT.cc
View File

@ -83,12 +83,15 @@ ValueNodePtr AnfImporterFromMetaGraphT::ConvertPrimitive(const std::unique_ptr<s
auto primitiveCValue = PrimitiveC::UnPackFromSchemaPrimitiveT(cNode->primitive.release());
cNode->primitive = nullptr;
// add quant parameter
if (cNode->quantType == schema::QuantType_AwareTraining) {
if (cNode->quantType != schema::QuantType_PostTraining) {
primitiveCValue->SetQuantType(cNode->quantType);
for (int index : cNode->inputIndex) {
if (meta_graph_->allTensors[index]->quantParams.size() > 0) {
std::vector<schema::QuantParamT> quant_params = {*(meta_graph_->allTensors[index]->quantParams[0])};
primitiveCValue->AddInputQuantParam(quant_params);
} else {
std::vector<schema::QuantParamT> empty_quant_params;
primitiveCValue->AddInputQuantParam(empty_quant_params);
}
}
for (int index : cNode->outputIndex) {

28
mindspore/lite/tools/converter/parser/tflite/tflite_dequantize_parser.cc
View File

@ -38,27 +38,37 @@ STATUS TfliteDequantizeParser::Parse(const std::unique_ptr<tflite::OperatorT> &t
return RET_NULL_PTR;
}
std::unique_ptr<schema::CastT> attr = std::make_unique<schema::CastT>();
if (attr == nullptr) {
MS_LOG(ERROR) << "new op failed";
return RET_NULL_PTR;
}
const auto &in_tensor = tflite_tensors[tflite_op->inputs[0]];
if (in_tensor == nullptr) {
MS_LOG(ERROR) << "input tensor is null";
return RET_NULL_PTR;
}
attr->srcT = GetTfliteDataType(in_tensor->type);
const auto &out_tensor = tflite_tensors[tflite_op->outputs[0]];
if (out_tensor == nullptr) {
MS_LOG(ERROR) << "output tensor is null";
return RET_NULL_PTR;
}
if (GetTfliteDataType(in_tensor->type) == kNumberTypeInt8) {
std::unique_ptr<schema::QuantDTypeCastT> attr = std::make_unique<schema::QuantDTypeCastT>();
if (attr == nullptr) {
MS_LOG(ERROR) << "new op failed";
return RET_NULL_PTR;
}
attr->srcT = GetTfliteDataType(in_tensor->type);
attr->dstT = GetTfliteDataType(out_tensor->type);
op->primitive->value.type = schema::PrimitiveType_Cast;
op->primitive->value.value = attr.release();
op->primitive->value.type = schema::PrimitiveType_QuantDTypeCast;
} else {
std::unique_ptr<schema::CastT> attr = std::make_unique<schema::CastT>();
if (attr == nullptr) {
MS_LOG(ERROR) << "new op failed";
return RET_NULL_PTR;
}
attr->srcT = GetTfliteDataType(in_tensor->type);
attr->dstT = GetTfliteDataType(out_tensor->type);
op->primitive->value.value = attr.release();
op->primitive->value.type = schema::PrimitiveType_Cast;
}
AddOpInput(op, tensors_id, tensors_format, tensors_id_map,
tflite_op->inputs[0], tensors_id->size(), tflite_tensors.size(), schema::Format_NHWC);

18
mindspore/lite/tools/optimizer/fusion/constant_folding_fusion.cc
View File

@ -200,6 +200,24 @@ const AnfNodePtr ConstFoldPass::Process(const FuncGraphPtr &func_graph, const An
FreeTensors(&input_tensors, &output_tensors);
return nullptr;
}
auto inputQuantParams = lite_primitive->GetInputQuantParams();
for (size_t m = 0; m < inputQuantParams.size(); m++) {
for (auto inputQuantParam : inputQuantParams[m]) {
lite::tensor::QuantArg quant_arg{};
quant_arg.scale = inputQuantParam.scale;
quant_arg.zeroPoint = inputQuantParam.zeroPoint;
input_tensors[m]->AddQuantParam(quant_arg);
}
}
auto outputQuantParams = lite_primitive->GetOutputQuantParams();
for (size_t m = 0; m < outputQuantParams.size(); m++) {
for (auto outputQuantParam : outputQuantParams[m]) {
lite::tensor::QuantArg quant_arg{};
quant_arg.scale = outputQuantParam.scale;
quant_arg.zeroPoint = outputQuantParam.zeroPoint;
output_tensors[m]->AddQuantParam(quant_arg);
}
}
// here, input_tensor's format need to be transposed nhwc according to fmkType,
// but for the time being, we only transpose the tensor with 0/1/2/3D.
// Others should be added in future.