p60975123
/
mindspore
forked from mindspore-Ecosystem/mindspore
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

fix code review

This commit is contained in:
YangLuo 2021-09-18 17:12:28 +08:00
parent 9d4f4da8d0
commit 7554a7eb0c
14 changed files with 53 additions and 17 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
mindspore/ccsrc/minddata/dataset/audio/kernels/amplitude_to_db_op.cc
View File

@ -34,7 +34,7 @@ Status AmplitudeToDBOp::Compute(const std::shared_ptr<Tensor> &input, std::share
float top_db = top_db_;
float multiplier = stype_ == ScaleType::kPower ? 10.0 : 20.0;
float amin = 1e-10;
const float amin = 1e-10;
float db_multiplier = std::log10(std::max(amin_, ref_value_));
// typecast

1
mindspore/ccsrc/minddata/dataset/audio/kernels/audio_utils.cc
View File

@ -481,6 +481,7 @@ Status Decoding(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *o
while (itr != end) {
auto x_mu = *itr;
CHECK_FAIL_RETURN_SYNTAX_ERROR(mu != 0, "mu can not be zero.");
x_mu = ((x_mu) / mu) * 2 - 1.0;
x_mu = sgn(x_mu) * expm1(fabs(x_mu) * log1p(mu)) / mu;
*itr_out = x_mu;

1
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/voc_op.cc
View File

@ -50,6 +50,7 @@ VOCOp::VOCOp(const TaskType &task_type, const std::string &task_mode, const std:
std::unique_ptr<DataSchema> data_schema, std::shared_ptr<SamplerRT> sampler, bool extra_metadata)
: MappableLeafOp(num_workers, queue_size, std::move(sampler)),
decode_(decode),
row_cnt_(0),
task_type_(task_type),
usage_(task_mode),
folder_path_(folder_path),

1
mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/div2k_node.cc
View File

@ -126,7 +126,6 @@ Status DIV2KNode::GetDatasetSize(const std::shared_ptr<DatasetSizeGetter> &size_
std::shared_ptr<SamplerRT> sampler_rt = nullptr;
RETURN_IF_NOT_OK(sampler_->SamplerBuild(&sampler_rt));
sample_size = sampler_rt->CalculateNumSamples(num_rows);
if (sample_size == -1) {
RETURN_IF_NOT_OK(size_getter->DryRun(shared_from_this(), &sample_size));
}

2
mindspore/ccsrc/minddata/dataset/kernels/image/center_crop_op.cc
View File

@ -41,7 +41,7 @@ Status CenterCropOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_p
", crop width: " + std::to_string(crop_wid_) + "\t"
: "";
if (err_msg.length() != 0) RETURN_STATUS_UNEXPECTED(err_head + err_msg);
CHECK_FAIL_RETURN_SYNTAX_ERROR(err_msg.length() == 0, err_head + err_msg);
int32_t top = crop_het_ - input->shape()[0]; // number of pixels to pad (top and bottom)
int32_t left = crop_wid_ - input->shape()[1];

7
mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/lite_mat.cc
View File

@ -37,6 +37,7 @@ LiteMat::LiteMat() {
data_type_ = LDataType::UINT8;
ref_count_ = nullptr;
setSteps(0, 0, 0);
release_flag = false;
}
LiteMat::LiteMat(int width, LDataType data_type) {
@ -51,6 +52,7 @@ LiteMat::LiteMat(int width, LDataType data_type) {
ref_count_ = nullptr;
size_ = 0;
setSteps(0, 0, 0);
release_flag = false;
Init(width, data_type);
}
@ -66,6 +68,7 @@ LiteMat::LiteMat(int width, int height, LDataType data_type) {
ref_count_ = nullptr;
size_ = 0;
setSteps(0, 0, 0);
release_flag = false;
Init(width, height, data_type);
}
@ -81,6 +84,7 @@ LiteMat::LiteMat(int width, int height, void *p_data, LDataType data_type) {
ref_count_ = nullptr;
size_ = 0;
setSteps(0, 0, 0);
release_flag = false;
Init(width, height, p_data, data_type);
}
@ -96,6 +100,7 @@ LiteMat::LiteMat(int width, int height, int channel, LDataType data_type) {
ref_count_ = nullptr;
size_ = 0;
setSteps(0, 0, 0);
release_flag = false;
Init(width, height, channel, data_type);
}
@ -111,6 +116,7 @@ LiteMat::LiteMat(int width, int height, int channel, void *p_data, LDataType dat
ref_count_ = nullptr;
size_ = 0;
setSteps(0, 0, 0);
release_flag = false;
Init(width, height, channel, p_data, data_type);
}
@ -133,6 +139,7 @@ LiteMat::LiteMat(const LiteMat &m) {
data_type_ = m.data_type_;
ref_count_ = m.ref_count_;
size_ = m.size_;
release_flag = m.release_flag;
setSteps(m.steps_[0], m.steps_[1], m.steps_[2]);
if (ref_count_) {
addRef(ref_count_, 1);

16
mindspore/ccsrc/minddata/dataset/kernels/image/resize_cubic_op.cc
View File

@ -34,7 +34,7 @@ static const std::vector<uint8_t> _clip8_table = []() {
static const uint8_t *clip8_table = &_clip8_table[640];
static inline uint8_t clip8(int input) { return clip8_table[input >> PrecisionBits]; }
static inline uint8_t clip8(unsigned int input) { return clip8_table[input >> PrecisionBits]; }
static inline double cubic_interp(double x) {
double a = -0.5;
@ -60,6 +60,10 @@ int calc_coeff(int input_size, int out_size, int input0, int input1, struct inte
double threshold, scale, interp_scale;
int kernel_size;
if (out_size == 0) {
MS_LOG(ERROR) << "out_size can not be zero.";
return 0;
}
scale = static_cast<double>((input1 - input0)) / out_size;
if (scale < 1.0) {
interp_scale = 1.0;
@ -133,7 +137,7 @@ void normalize_coeff(int out_size, int kernel_size, const std::vector<double> &p
Status ImagingHorizontalInterp(LiteMat &output, LiteMat input, int offset, int kernel_size,
const std::vector<int> &regions, const std::vector<double> &prekk) {
int ss0, ss1, ss2;
int32_t *k;
int32_t *k = nullptr;
// normalize previous calculated coefficients
std::vector<int> kk(prekk.begin(), prekk.end());
@ -202,7 +206,7 @@ Status ImagingVerticalInterp(LiteMat &output, LiteMat input, int offset, int ker
}
bool ImageInterpolation(LiteMat input, LiteMat &output, int x_size, int y_size, struct interpolation *interp,
int rect[4]) {
const int rect[4]) {
int horizontal_interp, vertical_interp, horiz_kernel, vert_kernel, rect_y0, rect_y1;
std::vector<int> horiz_region, vert_region;
std::vector<double> horiz_coeff, vert_coeff;
@ -269,7 +273,11 @@ bool ResizeCubic(const LiteMat &input, LiteMat &dst, int dst_w, int dst_h) {
struct interpolation interp = {cubic_interp, 2.0};
bool res = ImageInterpolation(input, output, x_size, y_size, &interp, rect);
memcpy_s(dst.data_ptr_, output.size_, output.data_ptr_, output.size_);
auto ret_code = memcpy_s(dst.data_ptr_, output.size_, output.data_ptr_, output.size_);
if (ret_code != 0) {
MS_LOG(ERROR) << "memcpy_s failed when copying tensor.";
return false;
}
return res;
}
} // namespace dataset

2
mindspore/ccsrc/minddata/dataset/kernels/image/resize_cubic_op.h
View File

@ -47,7 +47,7 @@ Status ImagingVerticalInterp(LiteMat &output, LiteMat input, int offset, int ker
/// \brief Mainly logic of Cubic interpolation
bool ImageInterpolation(LiteMat input, LiteMat &output, int x_size, int y_size, struct interpolation *interp,
int rect[4]);
const int rect[4]);
/// \brief Apply cubic interpolation on input image and obtain the output image
/// \param[in] input Input image

13
mindspore/ccsrc/minddata/dataset/kernels/image/rotate_op.cc
View File

@ -31,11 +31,20 @@ const uint8_t RotateOp::kDefFillR = 0;
const uint8_t RotateOp::kDefFillG = 0;
const uint8_t RotateOp::kDefFillB = 0;
RotateOp::RotateOp(int angle_id) : angle_id_(angle_id) {}
RotateOp::RotateOp(int angle_id)
: angle_id_(angle_id),
degrees_(0),
center_({}),
interpolation_(InterpolationMode::kLinear),
expand_(false),
fill_r_(0),
fill_g_(0),
fill_b_(0) {}
RotateOp::RotateOp(float degrees, InterpolationMode resample, bool expand, std::vector<float> center, uint8_t fill_r,
uint8_t fill_g, uint8_t fill_b)
: degrees_(degrees),
: angle_id_(0),
degrees_(degrees),
center_(center),
interpolation_(resample),
expand_(expand),

15
mindspore/ccsrc/minddata/dataset/kernels/ir/vision/rotate_ir.cc
View File

@ -22,11 +22,22 @@ namespace mindspore {
namespace dataset {
namespace vision {
// RotateOperation
RotateOperation::RotateOperation(FixRotationAngle angle) : angle_id_(static_cast<uint64_t>(angle)) {}
RotateOperation::RotateOperation(FixRotationAngle angle)
: angle_id_(static_cast<uint64_t>(angle)),
degrees_(0),
interpolation_mode_(InterpolationMode::kLinear),
expand_(false),
center_({}),
fill_value_({}) {}
RotateOperation::RotateOperation(float degrees, InterpolationMode resample, bool expand, std::vector<float> center,
std::vector<uint8_t> fill_value)
: degrees_(degrees), interpolation_mode_(resample), expand_(expand), center_(center), fill_value_(fill_value) {}
: angle_id_(0),
degrees_(degrees),
interpolation_mode_(resample),
expand_(expand),
center_(center),
fill_value_(fill_value) {}
RotateOperation::~RotateOperation() = default;

4
mindspore/ccsrc/minddata/dataset/kernels/plugin_op.cc
View File

@ -45,8 +45,8 @@ Status PluginOp::TensorRowToPlugin(const TensorRow &in_row, std::vector<plugin::
int ret_code = memcpy_s(tensor.buffer_.data(), tensor.buffer_.size(), in_row[ind]->GetBuffer(), buffer_size);
CHECK_FAIL_RETURN_UNEXPECTED(ret_code == 0, "Failed to copy data into plugin tensor.");
} else {
auto ret_code = std::memcpy(tensor.buffer_.data(), in_row[ind]->GetBuffer(), buffer_size);
CHECK_FAIL_RETURN_UNEXPECTED(ret_code == tensor.buffer_.data(), "Failed to copy data into plugin tensor.");
int ret_code = memcpy_s(tensor.buffer_.data(), buffer_size, in_row[ind]->GetBuffer(), buffer_size);
CHECK_FAIL_RETURN_UNEXPECTED(ret_code == 0, "Failed to copy data into plugin tensor.");
}
} else { // string tensor, for now, only tensor with 1 string is supported!
CHECK_FAIL_RETURN_UNEXPECTED(in_row[ind]->shape().NumOfElements() == 1,

2
mindspore/ccsrc/minddata/dataset/util/btree.h
View File

@ -316,7 +316,7 @@ class BPlusTree {
void Init() {
typename LeafNode::alloc_type alloc(alloc_);
LeafNode *p;
LeafNode *p = nullptr;
try {
p = alloc.allocate(1);
} catch (std::bad_alloc &e) {

2
mindspore/dataset/engine/datasets.py
View File

@ -3693,7 +3693,7 @@ class SamplerFn:
if multi_process is True:
try:
self.eof = multiprocessing.Event()
except:
except Exception:
raise RuntimeError("Init multiprocessing.Event() failed, This might be caused by insufficient shm,"
+ " and the recommended shm size is at least 5 GB.")
else:

2
mindspore/dataset/vision/py_transforms_util.py
View File

@ -581,7 +581,7 @@ def to_type(img, output_type):
try:
return img.astype(output_type)
except:
except Exception:
raise RuntimeError("output_type: " + str(output_type) + " is not a valid datatype.")