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!18094 [MD][LITE] CodeCheck fix 

Merge pull request !18094 from danishfarid/master_code_check
This commit is contained in:
i-robot 2021-06-11 04:09:43 +08:00 committed by Gitee
parent 1cdaa12cfd 32178c6fdd
commit 6b949d8821
9 changed files with 55 additions and 43 deletions
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11
mindspore/ccsrc/minddata/dataset/engine/tree_adapter_lite.cc
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@ -30,11 +30,14 @@ Status TreeAdapterLite::BuildExecutionTreeRecur(std::shared_ptr<DatasetNode> ir,
(*op) = ops.front(); // return the first op to be added as child by the caller of this function
RETURN_IF_NOT_OK(tree_->AssociateNode(*op));
for (size_t i = 1; i < ops.size(); i++) {
if (op == NULL) {
return StatusCode::kLiteNullptr;
}
for (size_t i = 0; i < ops.size(); i++) {
RETURN_IF_NOT_OK(tree_->AssociateNode(ops[i]));
RETURN_IF_NOT_OK(ops[i - 1]->AddChild(ops[i]));
if (i > 0) {
RETURN_IF_NOT_OK(ops[i - 1]->AddChild(ops[i]));
}
}
// Build the children of IR, once they return, add the return value to *op

27
mindspore/ccsrc/minddata/dataset/kernels/image/lite_image_utils.cc
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@ -104,17 +104,21 @@ static Status JpegReadScanlines(jpeg_decompress_struct *const cinfo, int max_sca
const int k = scanline_ptr[cmyk_pixel + 3];
int r, g, b;
if (cinfo->saw_Adobe_marker) {
r = (k * c) / 255;
g = (k * m) / 255;
b = (k * y) / 255;
r = (k * c) / MAX_PIXEL_VALUE;
g = (k * m) / MAX_PIXEL_VALUE;
b = (k * y) / MAX_PIXEL_VALUE;
} else {
r = (255 - c) * (255 - k) / 255;
g = (255 - m) * (255 - k) / 255;
b = (255 - y) * (255 - k) / 255;
r = (MAX_PIXEL_VALUE - c) * (MAX_PIXEL_VALUE - k) / MAX_PIXEL_VALUE;
g = (MAX_PIXEL_VALUE - m) * (MAX_PIXEL_VALUE - k) / MAX_PIXEL_VALUE;
b = (MAX_PIXEL_VALUE - y) * (MAX_PIXEL_VALUE - k) / MAX_PIXEL_VALUE;
}
buffer[3 * i + 0] = r;
buffer[3 * i + 1] = g;
buffer[3 * i + 2] = b;
constexpr int buffer_rgb_val_size = 3;
constexpr int channel_red = 0;
constexpr int channel_green = 1;
constexpr int channel_blue = 2;
buffer[buffer_rgb_val_size * i + channel_red] = r;
buffer[buffer_rgb_val_size * i + channel_green] = g;
buffer[buffer_rgb_val_size * i + channel_blue] = b;
}
} else if (num_lines_read > 0) {
auto copy_status = memcpy_s(buffer, buffer_size, scanline_ptr + offset, stride);
@ -361,8 +365,9 @@ Status Resize(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *out
RETURN_STATUS_UNEXPECTED("Resize: image datatype is not uint8.");
}
// resize image too large or too small
if (output_height == 0 || output_height > input->shape()[0] * 1000 || output_width == 0 ||
output_width > input->shape()[1] * 1000) {
const int height_width_scale_limit = 1000;
if (output_height == 0 || output_height > input->shape()[0] * height_width_scale_limit || output_width == 0 ||
output_width > input->shape()[1] * height_width_scale_limit) {
std::string err_msg =
"Resize: the resizing width or height 1) is too big, it's up to "
"1000 times the original image; 2) can not be 0.";

2
mindspore/ccsrc/minddata/dataset/kernels/image/lite_image_utils.h
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@ -33,6 +33,8 @@
#include "minddata/dataset/kernels/image/lite_cv/image_process.h"
#include "minddata/dataset/util/status.h"
#define MAX_PIXEL_VALUE 255
namespace mindspore {
namespace dataset {
void JpegErrorExitCustom(j_common_ptr cinfo);

4
mindspore/ccsrc/minddata/dataset/kernels/image/random_crop_and_resize_with_bbox_op.cc
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@ -30,8 +30,8 @@ Status RandomCropAndResizeWithBBoxOp::Compute(const TensorRow &input, TensorRow
RETURN_IF_NOT_OK(BoundingBox::ValidateBoundingBoxes(input));
CHECK_FAIL_RETURN_UNEXPECTED(input[0]->shape().Size() >= 2,
"RandomCropAndResizeWithBBox: image shape is not <H,W,C> or <H,W>.");
output->resize(2);
const int output_count = 2;
output->resize(output_count);
(*output)[1] = std::move(input[1]); // move boxes over to output
size_t bboxCount = input[1]->shape()[0]; // number of rows in bbox tensor

4
mindspore/ccsrc/minddata/dataset/kernels/image/random_crop_with_bbox_op.cc
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@ -34,8 +34,8 @@ Status RandomCropWithBBoxOp::Compute(const TensorRow &input, TensorRow *output)
int32_t padded_image_h;
int32_t padded_image_w;
output->resize(2);
const int output_count = 2;
output->resize(output_count);
(*output)[1] = std::move(input[1]); // since some boxes may be removed
bool crop_further = true; // Whether further cropping will be required or not, true unless required size matches

4
mindspore/ccsrc/minddata/dataset/kernels/image/random_vertical_flip_with_bbox_op.cc
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@ -43,8 +43,8 @@ Status RandomVerticalFlipWithBBoxOp::Compute(const TensorRow &input, TensorRow *
bbox->SetY(newBoxCorner_y);
RETURN_IF_NOT_OK(bbox->WriteToTensor(input[1], i));
}
output->resize(2);
const int output_count = 2;
output->resize(output_count);
(*output)[1] = std::move(input[1]);
return VerticalFlip(input[0], &(*output)[0]);

19
mindspore/lite/minddata/wrapper/MDToDApi.cc
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@ -76,11 +76,11 @@ class MDToDApi {
std::vector<std::string> MDToDBuffToVector(MDToDBuff_t StrBuff) {
std::vector<std::string> strVector;
if (StrBuff.DataSize > 0) {
const char *p = reinterpret_cast<char *>(StrBuff.Buff);
const char *p = static_cast<char *>(StrBuff.Buff);
do {
strVector.push_back(std::string(p));
p += strVector.back().size() + 1;
} while (p < reinterpret_cast<char *>(StrBuff.Buff) + StrBuff.DataSize);
} while (p < static_cast<char *>(StrBuff.Buff) + StrBuff.DataSize);
}
return strVector;
}
@ -94,11 +94,12 @@ int MDToDApi_pathTest(const char* path) {
auto dir_it = Path::DirIterator::OpenDirectory(&f);
MS_LOG(INFO) << dir_it.get();
int i = 0;
const int path_len_limit = 5;
while (dir_it->hasNext()) {
Path v = dir_it->next();
MS_LOG(INFO) << v.toString() << "\n";
i++;
if (i > 5) break;
if (i > path_len_limit) break;
}
return 0;
}
@ -182,7 +183,7 @@ void MDBuffToVector(const MDToDBuff_t &MDBuff, std::vector<T> *vec) {
template <typename T>
void GetValue(std::unordered_map<std::string, std::shared_ptr<Tensor>> row, std::string columnName, T *o) {
auto column = row[columnName];
if (NULL != column) {
if (column != NULL) {
MS_LOG(INFO) << "Tensor " << columnName << " shape: " << column->shape() << " type: " << column->type()
<< " bytes: " << column->SizeInBytes();
column->GetItemAt<T>(o, {});
@ -200,7 +201,7 @@ void GetTensorToBuff(std::unordered_map<std::string, std::shared_ptr<Tensor>> ro
resBuff->TensorSize[0] = resBuff->TensorSize[1] = resBuff->TensorSize[2] = resBuff->TensorSize[3] =
0; // Mark all dims do not exist in tensor
int firstDim = (hasBatch) ? 1 : 0;
if (NULL != column) {
if (column != NULL) {
MS_LOG(INFO) << "Tensor " << columnName << " shape: " << column->shape() << " type: " << column->type()
<< " bytes: " << column->SizeInBytes() << "nof elements: " << column->shape()[firstDim];
auto tesoreShape = column->shape().AsVector();
@ -242,7 +243,7 @@ void GetTensorToBuff(std::unordered_map<std::string, std::shared_ptr<Tensor>> ro
}
MS_LOG(INFO) << columnName << " " << resBuff->DataSize
<< " bytesCopyed to buff (MaxBuffSize: " << resBuff->MaxBuffSize << ") ";
if (0 == resBuff->DataSize) {
if (resBuff->DataSize == 0) {
MS_LOG(ERROR) << "COPY FAIL!!!! " << columnName << " Too large"
<< "."; // memcpy failed
}
@ -373,7 +374,7 @@ extern "C" int MDToDApi_UpdateEmbeding(MDToDApi *pMDToDApi, const char *column,
MS_LOG(INFO) << "Saved file " << embedding_file_path;
std::string file_path;
if (0 != GetJsonFullFileName(pMDToDApi, &file_path)) {
if (GetJsonFullFileName(pMDToDApi, &file_path) != 0) {
MS_LOG(ERROR) << "Failed to update " << columnName;
return -1;
}
@ -390,7 +391,7 @@ extern "C" int MDToDApi_UpdateEmbeding(MDToDApi *pMDToDApi, const char *column,
extern "C" int MDToDApi_UpdateStringArray(MDToDApi *pMDToDApi, const char *column, MDToDBuff_t MDbuff) {
auto columnName = std::string(column);
std::string file_path;
if (0 != GetJsonFullFileName(pMDToDApi, &file_path)) {
if (GetJsonFullFileName(pMDToDApi, &file_path) != 0) {
MS_LOG(ERROR) << "Failed to update " << columnName;
return -1;
}
@ -415,7 +416,7 @@ extern "C" int MDToDApi_UpdateStringArray(MDToDApi *pMDToDApi, const char *colum
extern "C" int MDToDApi_UpdateFloatArray(MDToDApi *pMDToDApi, const char *column, MDToDBuff_t MDBuff) {
auto columnName = std::string(column);
std::string file_path;
if (0 != GetJsonFullFileName(pMDToDApi, &file_path)) {
if (GetJsonFullFileName(pMDToDApi, &file_path) != 0) {
MS_LOG(ERROR) << "Failed to updaet " << columnName;
return -1;
}

24
mindspore/lite/minddata/wrapper/MDToDApi.h
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@ -58,17 +58,17 @@ typedef struct MDToDResult {
MDToDBuff_t faceEmbeddingsBuff;
} MDToDResult_t;
typedef int (*MDToDApi_pathTest_t)(const char *path);
typedef int (*MDToDApi_testAlbum_t)();
typedef MDToDApi *(*MDToDApi_createPipeLine_t)(MDToDConf_t MDConf);
typedef int (*MDToDApi_GetNext_t)(MDToDApi *pMDToDApi, MDToDResult_t *results);
typedef int (*MDToDApi_UpdateEmbeding_t)(MDToDApi *pMDToDApi, const char *column, float *emmbeddings,
size_t emmbeddingsSize);
typedef int (*MDToDApi_UpdateStringArray_t)(MDToDApi *pMDToDApi, const char *column, MDToDBuff_t MDbuff);
typedef int (*MDToDApi_UpdateFloatArray_t)(MDToDApi *pMDToDApi, const char *column, MDToDBuff_t MDbuff);
typedef int (*MDToDApi_UpdateIsForTrain_t)(MDToDApi *pMDToDApi, uint8_t isForTrain);
typedef int (*MDToDApi_UpdateNoOfFaces_t)(MDToDApi *pMDToDApi, int32_t noOfFaces);
typedef int (*MDToDApi_Stop_t)(MDToDApi *pMDToDApi);
typedef int (*MDToDApi_Destroy_t)(MDToDApi *pMDToDApi);
using (*MDToDApi_pathTest_t)(const char *path) = int;
using (*MDToDApi_testAlbum_t)() = int;
using *(*MDToDApi_createPipeLine_t)(MDToDConf_t MDConf) = MDToDApi;
using (*MDToDApi_GetNext_t)(MDToDApi *pMDToDApi, MDToDResult_t *results) = int;
using (*MDToDApi_UpdateEmbeding_t)(MDToDApi *pMDToDApi, const char *column, float *emmbeddings,
size_t emmbeddingsSize) = int;
using (*MDToDApi_UpdateStringArray_t)(MDToDApi *pMDToDApi, const char *column, MDToDBuff_t MDbuff) = int;
using (*MDToDApi_UpdateFloatArray_t)(MDToDApi *pMDToDApi, const char *column, MDToDBuff_t MDbuff) = int;
using (*MDToDApi_UpdateIsForTrain_t)(MDToDApi *pMDToDApi, uint8_t isForTrain) = int;
using (*MDToDApi_UpdateNoOfFaces_t)(MDToDApi *pMDToDApi, int32_t noOfFaces) = int;
using (*MDToDApi_Stop_t)(MDToDApi *pMDToDApi) = int;
using (*MDToDApi_Destroy_t)(MDToDApi *pMDToDApi) = int;
#endif

3
mindspore/lite/minddata/wrapper/x86-example.cc
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@ -28,8 +28,9 @@ int main() {
MS_LOG(INFO) << "Doing MindDataTestPipeline-TestCifar10Dataset.";
// Create a Cifar10 Dataset
const int64_t num_samples = 10;
std::string folder_path = "./testCifar10Data/";
std::shared_ptr<Dataset> ds = Cifar10(folder_path, std::string(), RandomSampler(false, 10));
std::shared_ptr<Dataset> ds = Cifar10(folder_path, std::string(), RandomSampler(false, num_samples));
// Create an iterator over the result of the above dataset
// This will trigger the creation of the Execution Tree and launch it.