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Fix FDASSERT in tensorrt backend

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This commit is contained in:
jiangjiajun
2022-08-17 05:57:21 +00:00
parent 13a3d6f9f5
commit e623a7b6de
2 changed files with 10 additions and 21 deletions
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26
csrc/fastdeploy/backends/tensorrt/trt_backend.cc
View File

@@ -351,8 +351,7 @@ void TrtBackend::AllocateBufferInDynamicShape(
// find the original index of output
auto iter = outputs_order_.find(outputs_desc_[i].name);
FDASSERT(iter != outputs_order_.end(),
"Cannot find output:" + outputs_desc_[i].name +
" of tensorrt network from the original model.");
"Cannot find output: %s of tensorrt network from the original model.", outputs_desc_[i].name.c_str());
auto ori_idx = iter->second;
(*outputs)[ori_idx].dtype = GetFDDataType(outputs_desc_[i].dtype);
(*outputs)[ori_idx].shape.assign(output_dims.d,
@@ -431,29 +430,24 @@ bool TrtBackend::CreateTrtEngine(const std::string& onnx_model,
FDASSERT(profile->setDimensions(item.first.c_str(),
nvinfer1::OptProfileSelector::kMIN,
sample::toDims(item.second)),
"[TrtBackend] Failed to set min_shape for input: " + item.first +
" in TrtBackend.");
"[TrtBackend] Failed to set min_shape for input: %s in TrtBackend.", item.first.c_str());
// set optimization shape
auto iter = option.opt_shape.find(item.first);
FDASSERT(iter != option.opt_shape.end(),
"[TrtBackend] Cannot find input name: " + item.first +
" in TrtBackendOption::opt_shape.");
"[TrtBackend] Cannot find input name: %s in TrtBackendOption::opt_shape.", item.first.c_str());
FDASSERT(profile->setDimensions(item.first.c_str(),
nvinfer1::OptProfileSelector::kOPT,
sample::toDims(iter->second)),
"[TrtBackend] Failed to set opt_shape for input: " + item.first +
" in TrtBackend.");
"[TrtBackend] Failed to set opt_shape for input: %s in TrtBackend.", item.first.c_str());
// set max shape
iter = option.max_shape.find(item.first);
FDASSERT(iter != option.max_shape.end(),
"[TrtBackend] Cannot find input name: " + item.first +
" in TrtBackendOption::max_shape.");
"[TrtBackend] Cannot find input name: %s in TrtBackendOption::max_shape.", item.first);
FDASSERT(profile->setDimensions(item.first.c_str(),
nvinfer1::OptProfileSelector::kMAX,
sample::toDims(iter->second)),
"[TrtBackend] Failed to set max_shape for input: " + item.first +
" in TrtBackend.");
"[TrtBackend] Failed to set max_shape for input: %s in TrtBackend.", item.first);
}
config->addOptimizationProfile(profile);
}
@@ -502,9 +496,7 @@ bool TrtBackend::CreateTrtEngine(const std::string& onnx_model,
}
TensorInfo TrtBackend::GetInputInfo(int index) {
FDASSERT(index < NumInputs(), "The index:" + std::to_string(index) +
" should less than the number of inputs:" +
std::to_string(NumInputs()) + ".");
FDASSERT(index < NumInputs(), "The index: %d should less than the number of inputs: %d.", index, NumInputs());
TensorInfo info;
info.name = inputs_desc_[index].name;
info.shape.assign(inputs_desc_[index].shape.begin(),
@@ -515,9 +507,7 @@ TensorInfo TrtBackend::GetInputInfo(int index) {
TensorInfo TrtBackend::GetOutputInfo(int index) {
FDASSERT(index < NumOutputs(),
"The index:" + std::to_string(index) +
" should less than the number of outputs:" +
std::to_string(NumOutputs()) + ".");
"The index: %d should less than the number of outputs: %d.", index, NumOutputs());
TensorInfo info;
info.name = outputs_desc_[index].name;
info.shape.assign(outputs_desc_[index].shape.begin(),

5
csrc/fastdeploy/pybind/main.cc
View File

@@ -34,7 +34,7 @@ pybind11::dtype FDDataTypeToNumpyDataType(const FDDataType& fd_dtype) {
dt = pybind11::dtype::of<uint8_t>();
} else {
FDASSERT(false, "The function doesn't support data type of %s.",
Str(fd_dtype).c_str());
Str(fd_dtype).c_str());
}
return dt;
}
@@ -73,8 +73,7 @@ void PyArrayToTensor(pybind11::array& pyarray, FDTensor* tensor,
pybind11::array TensorToPyArray(const FDTensor& tensor) {
auto numpy_dtype = FDDataTypeToNumpyDataType(tensor.dtype);
auto out = pybind11::array(numpy_dtype, tensor.shape);
memcpy(out.mutable_data(), tensor.Data(),
tensor.Numel() * FDDataTypeSize(tensor.dtype));
memcpy(out.mutable_data(), tensor.Data(), tensor.Numel() * FDDataTypeSize(tensor.dtype));
return out;
}