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mindspore2022/mindspore/ccsrc/minddata/dataset/api/execute.cc

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/**
* Copyright 2020-2021 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <algorithm>
#include <fstream>
#include "minddata/dataset/include/execute.h"
#include "minddata/dataset/core/de_tensor.h"
#include "minddata/dataset/core/device_resource.h"
#include "minddata/dataset/core/device_tensor.h"
#include "minddata/dataset/core/tensor_row.h"
#include "minddata/dataset/core/tensor.h"
#include "minddata/dataset/core/type_id.h"
#include "minddata/dataset/kernels/ir/tensor_operation.h"
#include "minddata/dataset/kernels/tensor_op.h"
#ifndef ENABLE_ANDROID
#include "utils/log_adapter.h"
#else
#include "mindspore/lite/src/common/log_adapter.h"
#endif
#ifdef ENABLE_ACL
#include "minddata/dataset/core/ascend_resource.h"
#include "minddata/dataset/kernels/image/dvpp/utils/CommonDataType.h"
#include "minddata/dataset/kernels/ir/vision/ascend_vision_ir.h"
#endif
namespace mindspore {
namespace dataset {
using json = nlohmann::json;
struct Execute::ExtraInfo {
std::multimap<std::string, std::vector<uint32_t>> aipp_cfg_;
bool init_with_shared_ptr_ = true; // Initial execute object with shared_ptr as default
#ifdef ENABLE_ACL
std::multimap<std::string, std::string> op2para_map_ = {{vision::kDvppCropJpegOperation, "size"},
{vision::kDvppDecodeResizeOperation, "size"},
{vision::kDvppDecodeResizeCropOperation, "crop_size"},
{vision::kDvppDecodeResizeCropOperation, "resize_size"},
{vision::kDvppNormalizeOperation, "mean"},
{vision::kDvppNormalizeOperation, "std"},
{vision::kDvppResizeJpegOperation, "size"}};
#endif
};
// FIXME - Temporarily overload Execute to support both TensorOperation and TensorTransform
Execute::Execute(std::shared_ptr<TensorOperation> op, MapTargetDevice deviceType, uint32_t device_id) {
ops_.emplace_back(std::move(op));
device_type_ = deviceType;
info_ = std::make_shared<ExtraInfo>();
#ifdef ENABLE_ACL
if (device_type_ == MapTargetDevice::kAscend310) {
device_resource_ = std::make_shared<AscendResource>();
Status rc = device_resource_->InitResource(device_id);
if (!rc.IsOk()) {
device_resource_ = nullptr;
MS_LOG(ERROR) << "Initialize Ascend310 resource fail";
}
}
#endif
}
Execute::Execute(std::shared_ptr<TensorTransform> op, MapTargetDevice deviceType, uint32_t device_id) {
// Initialize the op and other context
transforms_.emplace_back(op);
info_ = std::make_shared<ExtraInfo>();
device_type_ = deviceType;
#ifdef ENABLE_ACL
if (device_type_ == MapTargetDevice::kAscend310) {
device_resource_ = std::make_shared<AscendResource>();
Status rc = device_resource_->InitResource(device_id);
if (!rc.IsOk()) {
device_resource_ = nullptr;
MS_LOG(ERROR) << "Initialize Ascend310 resource fail";
}
}
#endif
}
Execute::Execute(std::reference_wrapper<TensorTransform> op, MapTargetDevice deviceType, uint32_t device_id) {
// Initialize the transforms_ and other context
std::shared_ptr<TensorOperation> operation = op.get().Parse();
ops_.emplace_back(std::move(operation));
info_ = std::make_shared<ExtraInfo>();
info_->init_with_shared_ptr_ = false;
device_type_ = deviceType;
#ifdef ENABLE_ACL
if (device_type_ == MapTargetDevice::kAscend310) {
device_resource_ = std::make_shared<AscendResource>();
Status rc = device_resource_->InitResource(device_id);
if (!rc.IsOk()) {
device_resource_ = nullptr;
MS_LOG(ERROR) << "Initialize Ascend310 resource fail";
}
}
#endif
}
// Execute function for the example case: auto decode(new vision::Decode());
Execute::Execute(TensorTransform *op, MapTargetDevice deviceType, uint32_t device_id) {
// Initialize the transforms_ and other context
std::shared_ptr<TensorTransform> smart_ptr_op(op);
transforms_.emplace_back(smart_ptr_op);
info_ = std::make_shared<ExtraInfo>();
device_type_ = deviceType;
#ifdef ENABLE_ACL
if (device_type_ == MapTargetDevice::kAscend310) {
device_resource_ = std::make_shared<AscendResource>();
Status rc = device_resource_->InitResource(device_id);
if (!rc.IsOk()) {
device_resource_ = nullptr;
MS_LOG(ERROR) << "Initialize Ascend310 resource fail";
}
}
#endif
}
Execute::Execute(std::vector<std::shared_ptr<TensorOperation>> ops, MapTargetDevice deviceType, uint32_t device_id)
: ops_(std::move(ops)), device_type_(deviceType) {
info_ = std::make_shared<ExtraInfo>();
#ifdef ENABLE_ACL
if (device_type_ == MapTargetDevice::kAscend310) {
device_resource_ = std::make_shared<AscendResource>();
Status rc = device_resource_->InitResource(device_id);
if (!rc.IsOk()) {
device_resource_ = nullptr;
MS_LOG(ERROR) << "Initialize Ascend310 resource fail";
}
}
#endif
}
Execute::Execute(std::vector<std::shared_ptr<TensorTransform>> ops, MapTargetDevice deviceType, uint32_t device_id) {
// Initialize the transforms_ and other context
transforms_ = ops;
info_ = std::make_shared<ExtraInfo>();
device_type_ = deviceType;
#ifdef ENABLE_ACL
if (device_type_ == MapTargetDevice::kAscend310) {
device_resource_ = std::make_shared<AscendResource>();
Status rc = device_resource_->InitResource(device_id);
if (!rc.IsOk()) {
device_resource_ = nullptr;
MS_LOG(ERROR) << "Initialize Ascend310 resource fail";
}
}
#endif
}
Execute::Execute(const std::vector<std::reference_wrapper<TensorTransform>> ops, MapTargetDevice deviceType,
uint32_t device_id) {
// Initialize the transforms_ and other context
if (deviceType == MapTargetDevice::kCpu) {
(void)std::transform(
ops.begin(), ops.end(), std::back_inserter(ops_),
[](TensorTransform &operation) -> std::shared_ptr<TensorOperation> { return operation.Parse(); });
} else {
for (auto &op : ops) {
ops_.emplace_back(op.get().Parse(deviceType));
}
}
info_ = std::make_shared<ExtraInfo>();
info_->init_with_shared_ptr_ = false;
device_type_ = deviceType;
#ifdef ENABLE_ACL
if (device_type_ == MapTargetDevice::kAscend310) {
device_resource_ = std::make_shared<AscendResource>();
Status rc = device_resource_->InitResource(device_id);
if (!rc.IsOk()) {
device_resource_ = nullptr;
MS_LOG(ERROR) << "Initialize Ascend310 resource fail";
}
}
#endif
}
// Execute function for the example vector case: auto decode(new vision::Decode());
Execute::Execute(std::vector<TensorTransform *> ops, MapTargetDevice deviceType, uint32_t device_id) {
// Initialize the transforms_ and other context
for (auto &op : ops) {
std::shared_ptr<TensorTransform> smart_ptr_op(op);
transforms_.emplace_back(smart_ptr_op);
}
info_ = std::make_shared<ExtraInfo>();
device_type_ = deviceType;
#ifdef ENABLE_ACL
if (device_type_ == MapTargetDevice::kAscend310) {
device_resource_ = std::make_shared<AscendResource>();
Status rc = device_resource_->InitResource(device_id);
if (!rc.IsOk()) {
device_resource_ = nullptr;
MS_LOG(ERROR) << "Initialize Ascend310 resource fail";
}
}
#endif
}
Execute::~Execute() {
#ifdef ENABLE_ACL
if (device_type_ == MapTargetDevice::kAscend310) {
if (device_resource_) {
device_resource_->FinalizeResource();
} else {
MS_LOG(ERROR) << "Device resource is nullptr which is illegal under case Ascend310";
}
}
#endif
}
Status Execute::operator()(const mindspore::MSTensor &input, mindspore::MSTensor *output) {
// Validate input tensor
CHECK_FAIL_RETURN_UNEXPECTED(input.DataSize() > 0, "Input Tensor has no data");
CHECK_FAIL_RETURN_UNEXPECTED(validate_device_(), "Device Type should be 'Ascend310' or 'CPU'");
// Parse TensorTransform transforms_ into TensorOperation ops_
if (info_->init_with_shared_ptr_) {
RETURN_IF_NOT_OK(ParseTransforms_());
info_->init_with_shared_ptr_ = false;
}
CHECK_FAIL_RETURN_UNEXPECTED(!ops_.empty(), "Input TensorOperation should be provided");
// Validate and build runtime ops
std::vector<std::shared_ptr<TensorOp>> transforms; // record the transformations
std::map<MapTargetDevice, std::string> env_list = {
{MapTargetDevice::kCpu, "kCpu"}, {MapTargetDevice::kGpu, "kGpu"}, {MapTargetDevice::kAscend310, "kAscend310"}};
for (int32_t i = 0; i < ops_.size(); i++) {
if (ops_[i] == nullptr) {
MS_LOG(ERROR) << "Input TensorOperation["
<< std::to_string(i) + "] is unsupported on your input device:" << env_list.at(device_type_);
}
CHECK_FAIL_RETURN_UNEXPECTED(ops_[i] != nullptr, "Input TensorOperation[" + std::to_string(i) + "] is null");
RETURN_IF_NOT_OK(ops_[i]->ValidateParams());
transforms.emplace_back(ops_[i]->Build());
}
if (device_type_ == MapTargetDevice::kCpu) {
// Convert mindspore::Tensor to dataset::Tensor
std::shared_ptr<dataset::Tensor> de_tensor;
Status rc = dataset::Tensor::CreateFromMemory(dataset::TensorShape(input.Shape()),
MSTypeToDEType(static_cast<TypeId>(input.DataType())),
(const uchar *)(input.Data().get()), input.DataSize(), &de_tensor);
if (rc.IsError()) {
MS_LOG(ERROR) << rc;
return rc;
}
// Apply transforms on tensor
for (auto &t : transforms) {
std::shared_ptr<dataset::Tensor> de_output;
Status rc_ = t->Compute(de_tensor, &de_output);
if (rc_.IsError()) {
MS_LOG(ERROR) << rc_;
return rc_;
}
// For next transform
de_tensor = std::move(de_output);
}
// Convert dataset::Tensor to mindspore::Tensor
CHECK_FAIL_RETURN_UNEXPECTED(de_tensor->HasData(), "Apply transform failed, output tensor has no data");
*output = mindspore::MSTensor(std::make_shared<DETensor>(de_tensor));
} else { // Ascend310 case, where we must set Ascend resource on each operators
#ifdef ENABLE_ACL
CHECK_FAIL_RETURN_UNEXPECTED(device_resource_, "Device resource is nullptr which is illegal under case Ascend310");
// Sink data from host into device
std::shared_ptr<mindspore::dataset::DeviceTensor> device_input;
RETURN_IF_NOT_OK(device_resource_->Sink(input, &device_input));
for (auto &t : transforms) {
// Initialize AscendResource for each operators
std::shared_ptr<DeviceTensor> device_output;
RETURN_IF_NOT_OK(t->SetAscendResource(device_resource_));
RETURN_IF_NOT_OK(t->Compute(device_input, &device_output));
// For next transform
device_input = std::move(device_output);
}
CHECK_FAIL_RETURN_UNEXPECTED(device_input->HasDeviceData(), "Apply transform failed, output tensor has no data");
*output = mindspore::MSTensor(std::make_shared<DETensor>(device_input, true));
#endif
}
return Status::OK();
}
Status Execute::operator()(const std::vector<MSTensor> &input_tensor_list, std::vector<MSTensor> *output_tensor_list) {
// Validate input tensor
CHECK_FAIL_RETURN_UNEXPECTED(!input_tensor_list.empty(), "Input Tensor is not valid");
for (auto &tensor : input_tensor_list) {
CHECK_FAIL_RETURN_UNEXPECTED(tensor.DataSize() > 0, "Input Tensor has no data");
}
CHECK_FAIL_RETURN_UNEXPECTED(validate_device_(), "Device Type should be 'Ascend310' or 'CPU'");
// Parse TensorTransform transforms_ into TensorOperation ops_
if (info_->init_with_shared_ptr_) {
RETURN_IF_NOT_OK(ParseTransforms_());
info_->init_with_shared_ptr_ = false;
}
CHECK_FAIL_RETURN_UNEXPECTED(!ops_.empty(), "Input TensorOperation should be provided");
std::map<MapTargetDevice, std::string> env_list = {
{MapTargetDevice::kCpu, "kCpu"}, {MapTargetDevice::kGpu, "kGpu"}, {MapTargetDevice::kAscend310, "kAscend310"}};
// Validate and build runtime ops
std::vector<std::shared_ptr<TensorOp>> transforms;
for (int32_t i = 0; i < ops_.size(); i++) {
if (ops_[i] == nullptr) {
MS_LOG(ERROR) << "Input TensorOperation["
<< std::to_string(i) + "] is unsupported on your input device:" << env_list.at(device_type_);
}
CHECK_FAIL_RETURN_UNEXPECTED(ops_[i] != nullptr, "Input TensorOperation[" + std::to_string(i) + "] is null");
RETURN_IF_NOT_OK(ops_[i]->ValidateParams());
transforms.emplace_back(ops_[i]->Build());
}
if (device_type_ == MapTargetDevice::kCpu) { // Case CPU
TensorRow de_tensor_list;
for (auto &tensor : input_tensor_list) {
std::shared_ptr<dataset::Tensor> de_tensor;
Status rc = dataset::Tensor::CreateFromMemory(
dataset::TensorShape(tensor.Shape()), MSTypeToDEType(static_cast<TypeId>(tensor.DataType())),
(const uchar *)(tensor.Data().get()), tensor.DataSize(), &de_tensor);
if (rc.IsError()) {
MS_LOG(ERROR) << rc;
RETURN_IF_NOT_OK(rc);
}
de_tensor_list.emplace_back(std::move(de_tensor));
}
// Apply transforms on tensor
for (auto &t : transforms) {
TensorRow de_output_list;
RETURN_IF_NOT_OK(t->Compute(de_tensor_list, &de_output_list));
// For next transform
de_tensor_list = std::move(de_output_list);
}
for (auto &tensor : de_tensor_list) {
CHECK_FAIL_RETURN_UNEXPECTED(tensor->HasData(), "Apply transform failed, output tensor has no data");
auto ms_tensor = mindspore::MSTensor(std::make_shared<DETensor>(tensor));
output_tensor_list->emplace_back(ms_tensor);
}
CHECK_FAIL_RETURN_UNEXPECTED(!output_tensor_list->empty(), "Output Tensor is not valid");
} else { // Case Ascend310
#ifdef ENABLE_ACL
CHECK_FAIL_RETURN_UNEXPECTED(device_resource_, "Device resource is nullptr which is illegal under case Ascend310");
for (auto &input_tensor : input_tensor_list) {
// Sink each data from host into device
std::shared_ptr<dataset::DeviceTensor> device_input;
RETURN_IF_NOT_OK(device_resource_->Sink(input_tensor, &device_input));
for (auto &t : transforms) {
std::shared_ptr<DeviceTensor> device_output;
RETURN_IF_NOT_OK(t->SetAscendResource(device_resource_));
RETURN_IF_NOT_OK(t->Compute(device_input, &device_output));
// For next transform
device_input = std::move(device_output);
}
CHECK_FAIL_RETURN_UNEXPECTED(device_input->HasDeviceData(), "Apply transform failed, output tensor has no data");
// Due to the limitation of Ascend310 memory, we have to pop every data onto host memory
// So the speed of this batch method is slower than solo mode
std::shared_ptr<mindspore::dataset::Tensor> host_output;
RETURN_IF_NOT_OK(device_resource_->Pop(device_input, &host_output));
auto ms_tensor = mindspore::MSTensor(std::make_shared<DETensor>(host_output));
output_tensor_list->emplace_back(ms_tensor);
// Release the data on the device because we have copied one piece onto host
RETURN_IF_NOT_OK(device_resource_->DeviceDataRelease());
}
CHECK_FAIL_RETURN_UNEXPECTED(!output_tensor_list->empty(), "Output Tensor vector is empty");
#endif
}
return Status::OK();
}
std::vector<uint32_t> AippSizeFilter(const std::vector<uint32_t> &resize_para, const std::vector<uint32_t> &crop_para) {
std::vector<uint32_t> aipp_size;
// Special condition where (no Crop and no Resize) or (no Crop and resize with fixed ratio) will lead to dynamic input
if ((resize_para.size() == 0 || resize_para.size() == 1) && crop_para.size() == 0) {
aipp_size = {0, 0};
MS_LOG(WARNING) << "Dynamic input shape is not supported, incomplete aipp config file will be generated. Please "
"checkout your TensorTransform input, both src_image_size_h and src_image_size will be 0";
return aipp_size;
}
if (resize_para.size() == 0) { // If only Crop operator exists
aipp_size = crop_para;
} else if (crop_para.size() == 0) { // If only Resize operator with 2 parameters exists
aipp_size = resize_para;
} else { // If both of them exist
if (resize_para.size() == 1) {
aipp_size = crop_para;
} else {
aipp_size =
*min_element(resize_para.begin(), resize_para.end()) < *min_element(crop_para.begin(), crop_para.end())
? resize_para
: crop_para;
}
}
#ifdef ENABLE_ACL
aipp_size[0] = DVPP_ALIGN_UP(aipp_size[0], VPC_HEIGHT_ALIGN); // H
aipp_size[1] = DVPP_ALIGN_UP(aipp_size[1], VPC_WIDTH_ALIGN); // W
#endif
return aipp_size;
}
std::vector<uint32_t> AippMeanFilter(const std::vector<uint32_t> &normalize_para) {
std::vector<uint32_t> aipp_mean;
if (normalize_para.size() == 6) { // If Normalize operator exist
std::transform(normalize_para.begin(), normalize_para.begin() + 3, std::back_inserter(aipp_mean),
[](uint32_t i) { return static_cast<uint32_t>(i / 10000); });
} else {
aipp_mean = {0, 0, 0};
}
return aipp_mean;
}
std::vector<float> AippStdFilter(const std::vector<uint32_t> &normalize_para) {
std::vector<float> aipp_std;
if (normalize_para.size() == 6) { // If Normalize operator exist
auto zeros = std::find(std::begin(normalize_para), std::end(normalize_para), 0);
if (zeros == std::end(normalize_para)) {
std::transform(normalize_para.begin() + 3, normalize_para.end(), std::back_inserter(aipp_std),
[](uint32_t i) { return 10000 / static_cast<float>(i); });
} else { // If 0 occurs in std vector
MS_LOG(WARNING) << "Detect 0 in std vector, please verify your input";
aipp_std = {1.0, 1.0, 1.0};
}
} else {
aipp_std = {1.0, 1.0, 1.0};
}
return aipp_std;
}
Status AippInfoCollection(std::map<std::string, std::string> *aipp_options, const std::vector<uint32_t> &aipp_size,
const std::vector<uint32_t> &aipp_mean, const std::vector<float> &aipp_std) {
// Several aipp config parameters
aipp_options->insert(std::make_pair("related_input_rank", "0"));
aipp_options->insert(std::make_pair("src_image_size_w", std::to_string(aipp_size[1])));
aipp_options->insert(std::make_pair("src_image_size_h", std::to_string(aipp_size[0])));
aipp_options->insert(std::make_pair("crop", "false"));
aipp_options->insert(std::make_pair("input_format", "YUV420SP_U8"));
aipp_options->insert(std::make_pair("aipp_mode", "static"));
aipp_options->insert(std::make_pair("csc_switch", "true"));
aipp_options->insert(std::make_pair("rbuv_swap_switch", "false"));
// Y = AX + b, this part is A
std::vector<int32_t> color_space_matrix = {256, 0, 359, 256, -88, -183, 256, 454, 0};
int count = 0;
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 3; j++) {
std::string key_word = "matrix_r" + std::to_string(i) + "c" + std::to_string(j);
aipp_options->insert(std::make_pair(key_word, std::to_string(color_space_matrix[count])));
++count;
}
}
// This part is b
std::vector<uint32_t> color_space_bias = {0, 128, 128};
for (int i = 0; i < 3; i++) {
std::string key_word = "input_bias_" + std::to_string(i);
aipp_options->insert(std::make_pair(key_word, std::to_string(color_space_bias[i])));
}
// Y = (X - mean - min) * [std^(-1)], this part is mean
for (int i = 0; i < aipp_mean.size(); i++) {
std::string key_word = "mean_chn_" + std::to_string(i);
aipp_options->insert(std::make_pair(key_word, std::to_string(aipp_mean[i])));
}
// This part is min
for (int i = 0; i < aipp_mean.size(); i++) {
std::string key_word = "min_chn_" + std::to_string(i);
aipp_options->insert(std::make_pair(key_word, "0.0"));
}
// This part is std^(-1)
for (int i = 0; i < aipp_std.size(); i++) {
std::string key_word = "var_reci_chn_" + std::to_string(i);
aipp_options->insert(std::make_pair(key_word, std::to_string(aipp_std[i])));
}
return Status::OK();
}
std::string Execute::AippCfgGenerator() {
std::string config_location = "./aipp.cfg";
#ifdef ENABLE_ACL
if (info_->init_with_shared_ptr_) {
ParseTransforms_();
info_->init_with_shared_ptr_ = false;
}
std::vector<uint32_t> paras; // Record the parameters value of each Ascend operators
for (int32_t i = 0; i < ops_.size(); i++) {
// Validate operator ir
json ir_info;
if (ops_[i] == nullptr) {
MS_LOG(ERROR) << "Input TensorOperation[" + std::to_string(i) + "] is null";
return "";
}
// Define map between operator name and parameter name
ops_[i]->to_json(&ir_info);
// Collect the information of operators
for (auto pos = info_->op2para_map_.equal_range(ops_[i]->Name()); pos.first != pos.second; ++pos.first) {
auto paras_key_word = pos.first->second;
paras = ir_info[paras_key_word].get<std::vector<uint32_t>>();
info_->aipp_cfg_.insert(std::make_pair(ops_[i]->Name(), paras));
}
}
std::ofstream outfile;
outfile.open(config_location, std::ofstream::out);
if (!outfile.is_open()) {
MS_LOG(ERROR) << "Fail to open Aipp config file, please verify your system config(including authority)"
<< "We will return empty string which represent the location of Aipp config file in this case";
std::string except = "";
return except;
}
if (device_type_ == MapTargetDevice::kAscend310) {
// Process resize parameters and crop parameters to find out the final size of input data
std::vector<uint32_t> resize_paras;
std::vector<uint32_t> crop_paras;
// Find resize parameters
std::map<std::string, std::vector<uint32_t>>::iterator iter;
if (info_->aipp_cfg_.find(vision::kDvppResizeJpegOperation) != info_->aipp_cfg_.end()) {
iter = info_->aipp_cfg_.find(vision::kDvppResizeJpegOperation);
resize_paras = iter->second;
} else if (info_->aipp_cfg_.find(vision::kDvppDecodeResizeOperation) != info_->aipp_cfg_.end()) {
iter = info_->aipp_cfg_.find(vision::kDvppDecodeResizeOperation);
resize_paras = iter->second;
}
// Find crop parameters
if (info_->aipp_cfg_.find(vision::kDvppCropJpegOperation) != info_->aipp_cfg_.end()) {
iter = info_->aipp_cfg_.find(vision::kDvppCropJpegOperation);
crop_paras = iter->second;
} else if (info_->aipp_cfg_.find(vision::kDvppDecodeResizeCropOperation) != info_->aipp_cfg_.end()) {
iter = info_->aipp_cfg_.find(vision::kDvppDecodeResizeCropOperation);
crop_paras = iter->second;
}
if (crop_paras.size() == 1) {
crop_paras.emplace_back(crop_paras[0]);
}
std::vector<uint32_t> aipp_size = AippSizeFilter(resize_paras, crop_paras);
// Process normalization parameters to find out the final normalization parameters for Aipp module
std::vector<uint32_t> normalize_paras;
if (info_->aipp_cfg_.find(vision::kDvppNormalizeOperation) != info_->aipp_cfg_.end()) {
for (auto pos = info_->aipp_cfg_.equal_range(vision::kDvppNormalizeOperation); pos.first != pos.second;
++pos.first) {
auto mean_or_std = pos.first->second;
normalize_paras.insert(normalize_paras.end(), mean_or_std.begin(), mean_or_std.end());
}
}
std::vector<uint32_t> aipp_mean = AippMeanFilter(normalize_paras);
std::vector<float> aipp_std = AippStdFilter(normalize_paras);
std::map<std::string, std::string> aipp_options;
AippInfoCollection(&aipp_options, aipp_size, aipp_mean, aipp_std);
std::string tab_char(4, ' ');
outfile << "aipp_op {" << std::endl;
for (auto &option : aipp_options) {
outfile << tab_char << option.first << " : " << option.second << std::endl;
}
outfile << "}";
outfile.close();
} else { // For case GPU or CPU
outfile << "aipp_op {" << std::endl << "}";
outfile.close();
MS_LOG(WARNING) << "Your runtime environment is not Ascend310, this config file will lead to undefined behavior on "
"computing result. Please check that.";
}
#endif
return config_location;
}
bool IsEmptyPtr(std::shared_ptr<TensorTransform> api_ptr) { return api_ptr == nullptr; }
Status Execute::ParseTransforms_() {
auto iter = std::find_if(transforms_.begin(), transforms_.end(), IsEmptyPtr);
if (iter != transforms_.end()) {
std::string err_msg = "Your input TensorTransforms contain at least one nullptr, please check your input";
MS_LOG(ERROR) << err_msg;
RETURN_STATUS_UNEXPECTED(err_msg);
}
if (device_type_ == MapTargetDevice::kCpu) {
(void)std::transform(transforms_.begin(), transforms_.end(), std::back_inserter(ops_),
[](std::shared_ptr<TensorTransform> operation) -> std::shared_ptr<TensorOperation> {
return operation->Parse();
});
} else {
for (auto &transform_ : transforms_) {
ops_.emplace_back(transform_->Parse(device_type_));
}
}
return Status::OK();
}
Status Execute::validate_device_() {
if (device_type_ != MapTargetDevice::kCpu && device_type_ != MapTargetDevice::kAscend310 &&
device_type_ != MapTargetDevice::kGpu) {
std::string err_msg = "Your input device is not supported. (Option: CPU or GPU or Ascend310)";
MS_LOG(ERROR) << err_msg;
RETURN_STATUS_UNEXPECTED(err_msg);
}
return Status::OK();
}
Status Execute::DeviceMemoryRelease() {
CHECK_FAIL_RETURN_UNEXPECTED(device_resource_, "Device resource is nullptr which is illegal under case Ascend310");
Status rc = device_resource_->DeviceDataRelease();
if (rc.IsError()) {
std::string err_msg = "Error in device data release";
MS_LOG(ERROR) << err_msg;
RETURN_STATUS_UNEXPECTED(err_msg);
}
return Status::OK();
}
} // namespace dataset
} // namespace mindspore
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