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fix waring to master

This commit is contained in:
shenwei41 2021-07-30 11:47:27 +08:00
parent 65ec61a480
commit e3385e2564
19 changed files with 88 additions and 23 deletions
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2
mindspore/ccsrc/minddata/dataset/api/datasets.cc
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@ -155,8 +155,6 @@ std::shared_ptr<Iterator> Dataset::CreateIteratorCharIF(std::vector<std::vector<
MS_LOG(ERROR) << "CreateIterator failed." << rc;
return nullptr;
}
return iter;
} catch (const std::exception &err) {
MS_LOG(ERROR) << "CreateIterator: Iterator exception caught: " << err.what();
return nullptr;

3
mindspore/ccsrc/minddata/dataset/callback/ds_callback.h
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@ -33,6 +33,9 @@ class DSCallback {
/// \param step_size number of steps to call DSNStepBegin()
explicit DSCallback(int32_t step_size = 1) : step_size_(step_size) {}
/// \brief Destructor
~DSCallback() = default;
/// \brief actual callback function for begin, needs to be overridden in the derived class
/// \param cb_param, callback parameter passed in from DatasetOp when calling the callback
/// \return Status

3
mindspore/ccsrc/minddata/dataset/engine/consumers/tree_consumer.h
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@ -36,6 +36,9 @@ class TreeConsumer {
public:
/// Constructor that prepares an empty tree_adapter
TreeConsumer();
/// \brief Destructor
~TreeConsumer() = default;
/// Initializes the consumer, this involves constructing and preparing the tree.
/// \param d The dataset node that represent the root of the IR tree.
/// \return Status error code.

2
mindspore/ccsrc/minddata/dataset/include/dataset/iterator.h
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@ -92,7 +92,7 @@ class Iterator {
// Destructor
~_Iterator() {
if (cur_row_) {
if (cur_row_ != nullptr) {
delete cur_row_;
cur_row_ = nullptr;
}

25
mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/image_process.cc
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@ -992,14 +992,22 @@ bool Pad(const LiteMat &src, LiteMat &dst, int top, int bottom, int left, int ri
}
std::vector<std::vector<float>> GetDefaultBoxes(BoxesConfig config) {
size_t size = config.num_default.size();
if (size <= 1 || config.feature_size.size() != size || config.steps.size() != size ||
config.aspect_rations.size() != size) {
return {};
}
if (config.max_scale < config.min_scale) {
return {};
}
std::vector<float> fk;
float num = static_cast<float>(config.img_shape[0]);
for (int i = 0; i < config.steps.size(); i++) {
if (config.steps[i] == 0) {
return {};
}
fk.push_back(num / config.steps[i]);
}
if (config.num_default.size() < 2) {
return {};
}
float scale_rate = (config.max_scale - config.min_scale) / (config.num_default.size() - 1);
std::vector<float> scales(config.num_default.size());
for (int i = 0; i < scales.size(); i++) {
@ -1043,7 +1051,15 @@ std::vector<std::vector<float>> GetDefaultBoxes(BoxesConfig config) {
void ConvertBoxes(std::vector<std::vector<float>> &boxes, const std::vector<std::vector<float>> &default_boxes,
const BoxesConfig config) {
if (boxes.size() != default_boxes.size() || config.prior_scaling.size() != 2) {
boxes = {};
return;
}
for (int i = 0; i < default_boxes.size(); i++) {
if (boxes[i].size() != 4 || default_boxes[i].size() != 4) {
boxes = {};
return;
}
boxes[i][0] = boxes[i][0] * config.prior_scaling[0] * default_boxes[i][2] + default_boxes[i][0];
boxes[i][1] = boxes[i][1] * config.prior_scaling[0] * default_boxes[i][3] + default_boxes[i][1];
boxes[i][2] = exp(boxes[i][2] * config.prior_scaling[1]) * default_boxes[i][2];
@ -1057,6 +1073,9 @@ std::vector<int> ApplyNms(const std::vector<std::vector<float>> &all_boxes, std:
std::vector<float> areas(boxes_num);
std::vector<int> order(boxes_num);
for (int i = 0; i < boxes_num; i++) {
if (all_boxes[i].size() < 4) {
return {};
}
areas[i] = (all_boxes[i][3] - all_boxes[i][1] + 1) * (all_boxes[i][2] - all_boxes[i][0] + 1);
order[i] = i;
}

2
mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/image_process.h
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@ -215,7 +215,7 @@ bool GetAffineTransform(std::vector<Point> src_point, std::vector<Point> dst_poi
/// \brief Matrix transpose.
/// \param[in] src Input matrix.
/// \param[in] dst Output matrix.
bool Transpose(LiteMat &src, LiteMat &dst);
bool Transpose(const LiteMat &src, LiteMat &dst);
/// \brief Filter the image by a Gaussian kernel
/// \param[in] src LiteMat image to be processed. Only LiteMat of type UINT8 is supported now.

38
mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/lite_mat.cc
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@ -177,6 +177,10 @@ void LiteMat::Init(int width, LDataType data_type) {
dims_ = 1;
height_ = 1;
channel_ = 1;
if (!CheckLiteMat()) {
Release();
return;
}
c_step_ = width;
size_ = c_step_ * elem_size_;
data_ptr_ = AlignMalloc(size_);
@ -193,6 +197,10 @@ void LiteMat::Init(int width, int height, LDataType data_type) {
height_ = height;
dims_ = 2;
channel_ = 1;
if (!CheckLiteMat()) {
Release();
return;
}
c_step_ = width_ * height_;
size_ = c_step_ * elem_size_;
data_ptr_ = AlignMalloc(size_);
@ -209,6 +217,10 @@ void LiteMat::Init(int width, int height, void *p_data, LDataType data_type) {
height_ = height;
dims_ = 2;
channel_ = 1;
if (!CheckLiteMat()) {
Release();
return;
}
c_step_ = height_ * width_;
size_ = c_step_ * channel_ * elem_size_;
data_ptr_ = p_data;
@ -225,6 +237,10 @@ void LiteMat::Init(int width, int height, int channel, LDataType data_type, bool
height_ = height;
dims_ = 3;
channel_ = channel;
if (!CheckLiteMat()) {
Release();
return;
}
if (align_memory) {
c_step_ = ((height_ * width_ * elem_size_ + ALIGN - 1) & (-ALIGN)) / elem_size_;
} else {
@ -247,6 +263,10 @@ void LiteMat::Init(int width, int height, int channel, void *p_data, LDataType d
height_ = height;
dims_ = 3;
channel_ = channel;
if (!CheckLiteMat()) {
Release();
return;
}
c_step_ = height_ * width_;
size_ = c_step_ * channel_ * elem_size_;
data_ptr_ = p_data;
@ -299,6 +319,24 @@ void LiteMat::AlignFree(void *ptr) {
inline void LiteMat::InitElemSize(LDataType data_type) { elem_size_ = data_type.SizeInBytes(); }
bool LiteMat::CheckLiteMat() {
if (width_ <= 0 || height_ <= 0 || channel_ <= 0 || elem_size_ <= 0) {
return false;
}
if (height_ != 1 && height_ > std::numeric_limits<int>::max() / width_) {
return false;
}
int area = height_ * width_;
if (channel_ != 1 && channel_ > std::numeric_limits<int>::max() / area) {
return false;
}
int size = area * channel_;
if (elem_size_ > std::numeric_limits<int>::max() / size) {
return false;
}
return true;
}
bool LiteMat::GetROI(int x, int y, int w, int h, LiteMat &m) {
if (x < 0 || y < 0 || x > width_ - w || h > height_ - y || w <= 0 || h <= 0) {
return false;

2
mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/lite_mat.h
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@ -290,6 +290,8 @@ class LiteMat {
/// \param[in] c2 The number used to set teh value of step[2].
void setSteps(int c0, int c1, int c2);
bool CheckLiteMat();
public:
void *data_ptr_ = nullptr;
int elem_size_;

2
mindspore/ccsrc/minddata/dataset/util/arena.h
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@ -110,7 +110,7 @@ class Arena : public MemoryPool {
~Arena() override {
#ifdef ENABLE_GPUQUE
if (is_cuda_malloc_) {
if (ptr_) {
if (ptr_ != nullptr) {
(void)cudaFreeHost(ptr_);
}
}

2
mindspore/ccsrc/minddata/dataset/util/auto_index.h
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@ -57,7 +57,7 @@ class AutoIndexObj : public BPlusTree<int64_t, V, A, std::less<int64_t>, T> {
Status insert(std::unique_ptr<value_type> &&val, key_type *key = nullptr) {
key_type my_inx = inx_.fetch_add(1);
if (key) {
if (key != nullptr) {
*key = my_inx;
}
return my_tree::DoInsert(my_inx, std::move(val));

16
mindspore/ccsrc/minddata/dataset/util/treap.h
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@ -77,7 +77,7 @@ class Treap {
// @return
// Return the root of the heap. It has the highest priority. But not necessarily the first key.
std::pair<NodeValue, bool> Top() const {
if (root_) {
if (root_ != nullptr) {
return std::make_pair(root_->nv, true);
} else {
return std::make_pair(NodeValue{key_type(), priority_type()}, false);
@ -86,7 +86,7 @@ class Treap {
// Remove the root of the heap.
void Pop() {
if (root_) {
if (root_ != nullptr) {
DeleteKey(root_->nv.key);
}
}
@ -116,9 +116,9 @@ class Treap {
class iterator : public std::iterator<std::forward_iterator_tag, TreapNode> {
public:
explicit iterator(Treap *tr) : tr_(tr), cur_(nullptr) {
if (tr_) {
if (tr_ != nullptr) {
cur_ = tr_->root_;
while (cur_) {
while (cur_ != nullptr) {
stack_.push(cur_);
cur_ = cur_->left;
}
@ -196,9 +196,9 @@ class Treap {
class const_iterator : public std::iterator<std::forward_iterator_tag, TreapNode> {
public:
explicit const_iterator(const Treap *tr) : tr_(tr), cur_(nullptr) {
if (tr_) {
if (tr_ != nullptr) {
cur_ = tr_->root_;
while (cur_) {
while (cur_ != nullptr) {
stack_.push(cur_);
cur_ = cur_->left;
}
@ -226,7 +226,7 @@ class Treap {
const_iterator &operator++() {
if (cur_) {
stack_.pop();
if (cur_->right) {
if (cur_->right != nullptr) {
TreapNode *n = cur_->right;
while (n) {
stack_.push(n);
@ -247,7 +247,7 @@ class Treap {
iterator tmp(*this);
if (cur_) {
stack_.pop();
if (cur_->right) {
if ((cur_->right) != nullptr) {
TreapNode *n = cur_->right;
while (n) {
stack_.push(n);

2
mindspore/ccsrc/minddata/mindrecord/io/shard_index_generator.cc
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@ -357,7 +357,7 @@ MSRStatus ShardIndexGenerator::BindParameterExecuteSQL(
const std::vector<std::vector<std::tuple<std::string, std::string, std::string>>> &data) {
sqlite3_stmt *stmt = nullptr;
if (sqlite3_prepare_v2(db, common::SafeCStr(sql), -1, &stmt, 0) != SQLITE_OK) {
if (stmt) {
if (stmt != nullptr) {
(void)sqlite3_finalize(stmt);
}
MS_LOG(ERROR) << "SQL error: could not prepare statement, sql: " << sql;

1
mindspore/ccsrc/minddata/mindrecord/io/shard_writer.cc
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@ -558,6 +558,7 @@ int ShardWriter::LockWriter(bool parallel_writer) {
flock(fd, LOCK_EX);
} else {
MS_LOG(ERROR) << "Shard writer failed when locking file";
close(fd);
return -1;
}
#endif

1
mindspore/ccsrc/minddata/mindrecord/meta/shard_column.cc
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@ -424,6 +424,7 @@ MSRStatus ShardColumn::UncompressInt(const uint64_t &column_id, std::unique_ptr<
int ret_code = memcpy_s(data_ptr->get(), *num_bytes, data, *num_bytes);
if (ret_code != 0) {
MS_LOG(ERROR) << "Failed to copy data!";
return FAILED;
}
return SUCCESS;

2
mindspore/dataset/datapreprocess/preprocess_imagenet_validate_dataset.py
View File

@ -34,7 +34,7 @@ def preprocess_imagenet_validation_dataset(train_dataset_path, validation_datase
validate_sub_dir = os.path.join(validation_dataset_path, sub_dir_name)
validate_sub_dir = os.path.realpath(validate_sub_dir)
if not os.path.exists(validate_sub_dir):
os.makedirs(validate_sub_dir)
os.makedirs(validate_sub_dir, mode=0o750)
real_file_path = os.path.realpath(image_label_mapping_file)
mappings = [mapping.strip() for mapping in open(real_file_path).readlines()]

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

@ -284,7 +284,7 @@ def check_save(method):
nreq_param_str = ['file_name', 'file_type']
validate_dataset_param_value(nreq_param_int, param_dict, int)
if (param_dict.get('num_files') <= 0 or param_dict.get('num_files') > 1000):
raise ValueError("num_files should between {} and {}.".format(1, 1000))
raise ValueError("num_files should between 0 and 1000.")
validate_dataset_param_value(nreq_param_str, param_dict, str)
if param_dict.get('file_type') != 'mindrecord':
raise ValueError("{} dataset format is not supported.".format(param_dict.get('file_type')))

2
mindspore/dataset/text/validators.py
View File

@ -318,7 +318,7 @@ def check_from_dataset(method):
type_check(freq_range, (tuple,), "freq_range")
if len(freq_range) != 2:
raise ValueError("freq_range needs to be a tuple of 2 integers or an int and a None.")
raise ValueError("freq_range needs to be a tuple of 2 element.")
for num in freq_range:
if num is not None and (not isinstance(num, int)):

2
tests/ut/python/dataset/test_from_dataset.py
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@ -131,7 +131,7 @@ def test_from_dataset_exceptions():
except (TypeError, ValueError) as e:
assert s in str(e), str(e)
test_config("text", (), 1, "freq_range needs to be a tuple of 2 integers or an int and a None.")
test_config("text", (), 1, "freq_range needs to be a tuple of 2 element.")
test_config("text", (2, 3), 1.2345,
"Argument top_k with value 1.2345 is not of type [<class 'int'>, <class 'NoneType'>]")
test_config(23, (2, 3), 1.2345, "Argument col[0] with value 23 is not of type [<class 'str'>]")

2
tests/ut/python/dataset/test_save_op.py
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@ -381,7 +381,7 @@ def test_case_05(add_remove_file):
d1 = ds.GeneratorDataset(generator_1d, ["data"], shuffle=False)
with pytest.raises(Exception, match="num_files should between 1 and 1000."):
with pytest.raises(Exception, match="num_files should between 0 and 1000."):
d1.save(AUTO_FILE, 0)