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Unify the data type of tensor shape (#74)

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int32_t->int64_t
This commit is contained in:
Jack Zhou
2022-08-08 14:02:56 +08:00
committed by GitHub
parent 0587c4bbac
commit 32c2154bbe
3 changed files with 10 additions and 16 deletions
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4
csrcs/fastdeploy/core/fd_tensor.cc
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@@ -57,14 +57,14 @@ const void* FDTensor::Data() const {
return data.data(); return data.data();
} }
void FDTensor::SetExternalData(const std::vector<int>& new_shape, void FDTensor::SetExternalData(const std::vector<int64_t>& new_shape,
const FDDataType& data_type, void* data_buffer) { const FDDataType& data_type, void* data_buffer) {
dtype = data_type; dtype = data_type;
shape.assign(new_shape.begin(), new_shape.end()); shape.assign(new_shape.begin(), new_shape.end());
external_data_ptr = data_buffer; external_data_ptr = data_buffer;
} }
void FDTensor::Allocate(const std::vector<int>& new_shape, void FDTensor::Allocate(const std::vector<int64_t>& new_shape,
const FDDataType& data_type, const FDDataType& data_type,
const std::string& tensor_name) { const std::string& tensor_name) {
dtype = data_type; dtype = data_type;

5
csrcs/fastdeploy/core/fd_tensor.h
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@@ -59,13 +59,14 @@ struct FASTDEPLOY_DECL FDTensor {
// Set user memory buffer for Tensor, the memory is managed by // Set user memory buffer for Tensor, the memory is managed by
// the user it self, but the Tensor will share the memory with user // the user it self, but the Tensor will share the memory with user
// So take care with the user buffer // So take care with the user buffer
void SetExternalData(const std::vector<int>& new_shape, void SetExternalData(const std::vector<int64_t>& new_shape,
const FDDataType& data_type, void* data_buffer); const FDDataType& data_type, void* data_buffer);
// Initialize Tensor // Initialize Tensor
// Include setting attribute for tensor // Include setting attribute for tensor
// and allocate cpu memory buffer // and allocate cpu memory buffer
void Allocate(const std::vector<int>& new_shape, const FDDataType& data_type, void Allocate(const std::vector<int64_t>& new_shape,
const FDDataType& data_type,
const std::string& tensor_name = ""); const std::string& tensor_name = "");
// Total size of tensor memory buffer in bytes // Total size of tensor memory buffer in bytes

15
examples/text/ernie_tokencls.cc
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@@ -54,12 +54,8 @@ void Softmax(const fastdeploy::FDTensor& input, fastdeploy::FDTensor* output) {
} }
}; };
std::vector<int32_t> output_shape; output->Allocate(input.shape, input.dtype);
for (int i = 0; i < input.shape.size(); ++i) { int label_num = output->shape.back();
output_shape.push_back(input.shape[i]);
}
output->Allocate(output_shape, input.dtype);
int label_num = output_shape.back();
int batch_size = input.Numel() / label_num; int batch_size = input.Numel() / label_num;
int offset = 0; int offset = 0;
const T* input_ptr = reinterpret_cast<const T*>(input.Data()); const T* input_ptr = reinterpret_cast<const T*>(input.Data());
@@ -73,11 +69,8 @@ void Softmax(const fastdeploy::FDTensor& input, fastdeploy::FDTensor* output) {
// Only useful for axis = -1 // Only useful for axis = -1
template <typename T> template <typename T>
void Max(const fastdeploy::FDTensor& input, fastdeploy::FDTensor* output) { void Max(const fastdeploy::FDTensor& input, fastdeploy::FDTensor* output) {
std::vector<int32_t> output_shape; auto output_shape = input.shape;
for (int i = 0; i < input.shape.size() - 1; ++i) { output_shape.back() = 1;
output_shape.push_back(input.shape[i]);
}
output_shape.push_back(1);
output->Allocate(output_shape, input.dtype); output->Allocate(output_shape, input.dtype);
int batch_size = output->Numel(); int batch_size = output->Numel();
int label_num = input.shape.back(); int label_num = input.shape.back();