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!28979 fix waring 

Merge pull request !28979 from shenwei41/fix_waring
This commit is contained in:
i-robot 2022-01-13 07:02:14 +00:00 committed by Gitee
parent b6fa77e7a7 01b476201a
commit 8539de0e01
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GPG Key ID: 173E9B9CA92EEF8F
12 changed files with 15 additions and 13 deletions
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8
mindspore/ccsrc/minddata/dataset/audio/kernels/audio_utils.cc
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@ -1175,9 +1175,9 @@ Status Blackman(std::shared_ptr<Tensor> *output, int len) {
RETURN_IF_NOT_OK(Tensor::CreateEmpty(TensorShape({len}), DataType(DataType::DE_FLOAT32), output));
// Blackman window function.
auto iter = (*output)->begin<float>();
float alpha = 0.42;
float half = 0.5;
float delta = 0.08;
const float alpha = 0.42;
const float half = 0.5;
const float delta = 0.08;
for (ptrdiff_t i = 0; i < len; ++i) {
*(iter + i) = alpha - half * std::cos(TWO * PI * i / len) + delta * std::cos(TWO * TWO * PI * i / len);
}
@ -1200,7 +1200,7 @@ Status Hann(std::shared_ptr<Tensor> *output, int len) {
RETURN_IF_NOT_OK(Tensor::CreateEmpty(TensorShape({len}), DataType(DataType::DE_FLOAT32), output));
// Hann window function.
auto iter = (*output)->begin<float>();
float half = 0.5;
const float half = 0.5;
for (ptrdiff_t i = 0; i < len; ++i) {
*(iter + i) = half - half * std::cos(TWO * PI * i / len);
}

4
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/iwslt_op.cc
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@ -306,10 +306,10 @@ Status IWSLTOp::CleanXmlFile(const std::string &src_file_path, const std::string
std::ofstream new_file(new_file_path);
CHECK_FAIL_RETURN_UNEXPECTED(new_file.is_open(), "Invalid file, failed to open file: " + new_file_path);
while (src_doc && target_doc) {
while (src_doc != nullptr && target_doc != nullptr) {
XMLElement *src_seg = src_doc->FirstChildElement("seg");
XMLElement *target_seg = target_doc->FirstChildElement("seg");
while (src_seg && target_seg) {
while (src_seg != nullptr && target_seg != nullptr) {
src_content = src_seg->GetText();
target_content = target_seg->GetText();
RETURN_IF_NOT_OK(Trim(&src_content, " "));

2
mindspore/ccsrc/minddata/dataset/engine/datasetops/source/tedlium_op.cc
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@ -211,7 +211,7 @@ Status TedliumOp::ReadSph(const Path &file_sph_path, double start_time, double e
for (int32_t i = 0; i < size; ++i) {
char bh = temp[2 * i];
char bl = temp[2 * i + 1];
// SPH aduio files is big-endian, so we should convert the two bytes of data into int16_t based
// SPH audio files is big-endian, so we should convert the two bytes of data into int16_t based
// on the high 8 bits and the low 8 bits.
int16_t s = static_cast<int16_t>(((bh & 0x00FF) << 8) | (bl & 0x00FF));
// Data normalization: Convert the data from the interval [-32768,32767] to the interval [-1,1].

1
mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/ag_news_node.h
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@ -113,7 +113,6 @@ class AGNewsNode : public NonMappableSourceNode {
private:
std::string dataset_dir_;
std::string usage_;
char field_delim_;
std::vector<std::shared_ptr<CsvBase>> column_defaults_;
std::vector<std::string> column_names_;
int64_t num_samples_;

1
mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/amazon_review_node.h
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@ -107,7 +107,6 @@ class AmazonReviewNode : public NonMappableSourceNode {
private:
std::string dataset_dir_;
std::string usage_;
char field_delim_;
std::vector<std::shared_ptr<CsvBase>> column_defaults_;
std::vector<std::string> column_names_;
int64_t num_samples_;

1
mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/sogou_news_node.h
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@ -122,7 +122,6 @@ class SogouNewsNode : public NonMappableSourceNode {
private:
std::string dataset_dir_;
std::string usage_;
char field_delim_;
std::vector<std::shared_ptr<CsvBase>> column_defaults_;
std::vector<std::string> column_names_;
int64_t num_samples_;

1
mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/yelp_review_node.h
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@ -122,7 +122,6 @@ class YelpReviewNode : public NonMappableSourceNode {
private:
std::string dataset_dir_;
std::string usage_;
char field_delim_;
std::vector<std::shared_ptr<CsvBase>> column_defaults_;
std::vector<std::string> column_names_;
int64_t num_samples_;

1
mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/transfer_node.h
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@ -95,7 +95,6 @@ class TransferNode : public DatasetNode {
std::string queue_name_;
int32_t device_id_;
std::string device_type_;
int32_t prefetch_size_;
bool send_epoch_end_;
int32_t total_batch_;
bool create_data_info_queue_;

1
mindspore/ccsrc/minddata/dataset/engine/perf/auto_tune.cc
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@ -33,6 +33,7 @@ AutoTune::AutoTune(TreeAdapter *tree_adap, ProfilingManager *profiling_mgr)
profiling_manager_(profiling_mgr),
leaf_op_id_(-1),
cur_epoch_(1),
mode_(0),
skip_bool_(true),
last_step_profiled_(0) {
tree_modifier_ = std::make_unique<TreeModifier>(tree_adapter_);

4
mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/image_process.cc
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@ -101,9 +101,11 @@ static bool ResizeBilinear3C(const unsigned char *src, int src_width, int src_he
int16_t *y_weight = reinterpret_cast<int16_t *>(x_weight + dst_width);
if (!InitBilinearWeight(x_offset, x_weight, scale_width, dst_width, src_width, 3)) {
delete[] data_buf;
return false;
}
if (!InitBilinearWeight(y_offset, y_weight, scale_height, dst_height, src_height, 1)) {
delete[] data_buf;
return false;
}
@ -192,9 +194,11 @@ static bool ResizeBilinear1C(const unsigned char *src, int src_width, int src_he
int16_t *y_weight = reinterpret_cast<int16_t *>(x_weight + dst_width);
if (!InitBilinearWeight(x_offset, x_weight, scale_width, dst_width, src_width, 1)) {
delete[] data_buf;
return false;
}
if (!InitBilinearWeight(y_offset, y_weight, scale_height, dst_height, src_height, 1)) {
delete[] data_buf;
return false;
}

1
mindspore/python/mindspore/dataset/engine/datasets_text.py
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@ -647,6 +647,7 @@ class EnWik9Dataset(SourceDataset):
return cde.EnWik9Node(self.dataset_dir, self.num_samples, self.shuffle_flag, self.num_shards,
self.shard_id)
class IMDBDataset(MappableDataset):
"""
A source dataset for reading and parsing Internet Movie Database (IMDb).

3
mindspore/python/mindspore/dataset/engine/datasets_vision.py
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@ -2214,7 +2214,8 @@ class ImageFolderDataset(MappableDataset):
>>> dataset = ds.ImageFolderDataset(dataset_dir=image_folder_dataset_dir,
... class_indexing={"cat":0, "dog":1})
>>>
>>> # 3) Read all samples (image files) in image_folder_dataset_dir with extensions .JPEG and .png (case sensitive)
>>> # 3) Read all samples (image files) in image_folder_dataset_dir with extensions .JPEG
>>> # and .png (case sensitive)
>>> dataset = ds.ImageFolderDataset(dataset_dir=image_folder_dataset_dir,
... extensions=[".JPEG", ".png"])