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load fp16 as fp32 and align fp16 and double in onnx_graph_simplifie

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This commit is contained in:
Zihao Mu 2022-08-26 10:04:44 +08:00
parent fc3e393516
commit 7eaec9dd22
2 changed files with 70 additions and 1 deletions
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66
modules/dnn/src/onnx/onnx_graph_simplifier.cpp
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@ -18,6 +18,17 @@ CV__DNN_INLINE_NS_BEGIN
extern bool DNN_DIAGNOSTICS_RUN;
static int isLittleEndianCPU()
{
int x = 7;
char *ptr = (char *)&x;
if(ptr[0] == 0)
return 0;
else
return 1;
}
// This wrapper can behave differently for fake input nodes and real graph nodes.
class ONNXNodeWrapper : public ImportNodeWrapper
{
@ -767,11 +778,64 @@ Mat getMatFromTensor(const opencv_onnx::TensorProto& tensor_proto)
Mat(sizes, CV_32FC1, val).copyTo(blob);
}
}
else if (datatype == opencv_onnx::TensorProto_DataType_FLOAT16)
{
// FIXME, for now, we only load FP16 Tensor as FP32 Mat, full support for FP16 is required in the future.
CV_LOG_ONCE_WARNING(NULL, "DNN: load FP16 model as FP32 model, and it takes twice the FP16 RAM requirement.");
// ONNX saves float 16 data in two format: int32 and raw_data.
// Link: https://github.com/onnx/onnx/issues/4460#issuecomment-1224373746
if (!tensor_proto.int32_data().empty())
{
const int offset = isLittleEndianCPU() ? 0 : 1;
const ::google::protobuf::RepeatedField<int32_t> field = tensor_proto.int32_data();
AutoBuffer<float16_t, 16> aligned_val;
size_t sz = tensor_proto.int32_data().size();
aligned_val.allocate(sz);
float16_t* bufPtr = aligned_val.data();
float16_t *fp16Ptr = (float16_t *)field.data();
for (int i = 0; i < sz; i++)
{
bufPtr[i] = fp16Ptr[i*2 + offset];
}
Mat(sizes, CV_16FC1, bufPtr).convertTo(blob, CV_32FC1);
}
else
{
char* val = const_cast<char*>(tensor_proto.raw_data().c_str());
#if CV_STRONG_ALIGNMENT
// Aligned pointer is required.
AutoBuffer<float16_t, 16> aligned_val;
if (!isAligned<sizeof(float16_t)>(val))
{
size_t sz = tensor_proto.raw_data().size();
aligned_val.allocate(divUp(sz, sizeof(float16_t)));
memcpy(aligned_val.data(), val, sz);
val = (char*)aligned_val.data();
}
#endif
Mat(sizes, CV_16FC1, val).convertTo(blob, CV_32FC1);
}
}
else if (datatype == opencv_onnx::TensorProto_DataType_DOUBLE)
{
const ::google::protobuf::RepeatedField<double> field = tensor_proto.double_data();
CV_Assert(!field.empty());
Mat(sizes, CV_64FC1, (void*)field.data()).convertTo(blob, CV_32FC1);
char* val = (char *)field.data();
#if CV_STRONG_ALIGNMENT
// Aligned pointer is required.
AutoBuffer<double, 16> aligned_val;
if (!isAligned<sizeof(double)>(val))
{
size_t sz = tensor_proto.raw_data().size();
aligned_val.allocate(divUp(sz, sizeof(double)));
memcpy(aligned_val.data(), val, sz);
val = (char*)aligned_val.data();
}
#endif
Mat(sizes, CV_64FC1, val).convertTo(blob, CV_32FC1);
}
else if (datatype == opencv_onnx::TensorProto_DataType_INT32)
{

5
modules/dnn/test/test_onnx_importer.cpp
View File

@ -2098,6 +2098,11 @@ TEST_P(Test_ONNX_nets, MobileNet_v2)
testONNXModels("mobilenetv2", pb, default_l1, default_lInf, true);
}
TEST_P(Test_ONNX_nets, MobileNet_v2_FP16)
{
testONNXModels("mobilenetv2_fp16", npy, default_l1, default_lInf, true);
}
TEST_P(Test_ONNX_nets, LResNet100E_IR)
{
applyTestTag(