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fix pclint

This commit is contained in:
liyong 2021-09-18 17:40:49 +08:00
parent 1fb68a2612
commit cf49d8bf04
7 changed files with 30 additions and 27 deletions
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42
mindspore/ccsrc/minddata/dataset/audio/kernels/audio_utils.cc
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@ -43,7 +43,7 @@ Status Linspace(std::shared_ptr<Tensor> *output, T start, T end, int n) {
TensorShape out_shape({n});
std::vector<T> linear_vect(n);
T interval = (n == 1) ? 0 : ((end - start) / (n - 1));
for (int i = 0; i < linear_vect.size(); ++i) {
for (auto i = 0; i < linear_vect.size(); ++i) {
linear_vect[i] = start + i * interval;
}
std::shared_ptr<Tensor> out_t;
@ -158,13 +158,14 @@ template <typename T>
Status PadComplexTensor(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, int length, int dim) {
TensorShape input_shape = input->shape();
std::vector<int64_t> pad_shape_vec = {input_shape[0], input_shape[1], input_shape[2], input_shape[3]};
pad_shape_vec[dim] += length;
pad_shape_vec[dim] += static_cast<int64_t>(length);
TensorShape input_shape_with_pad(pad_shape_vec);
std::vector<T> in_vect(input_shape_with_pad[0] * input_shape_with_pad[1] * input_shape_with_pad[2] *
input_shape_with_pad[3]);
auto itr_input = input->begin<T>();
int input_cnt = 0;
for (int ind = 0; ind < in_vect.size(); ind++) {
int64_t input_cnt = 0;
/*lint -e{446} ind is modified in the body of the for loop */
for (int ind = 0; ind < static_cast<int>(in_vect.size()); ind++) {
in_vect[ind] = (*itr_input);
input_cnt = (input_cnt + 1) % (input_shape[2] * input_shape[3]);
itr_input++;
@ -210,18 +211,18 @@ Status Phase(const std::shared_ptr<Tensor> &angle_0, const std::shared_ptr<Tenso
}
// concat phase time 0
int ind = 0;
int64_t ind = 0;
auto itr_p0 = phase_time0->begin<T>();
phase.insert(phase.begin(), (*itr_p0));
(void)phase.insert(phase.begin(), (*itr_p0));
while (itr_p0 != phase_time0->end<T>()) {
itr_p0++;
ind += phase_shape[2];
phase[ind] = (*itr_p0);
}
phase.erase(phase.begin() + static_cast<int>(angle_0->Size()), phase.end());
(void)phase.erase(phase.begin() + static_cast<int>(angle_0->Size()), phase.end());
// cal phase accum
for (ind = 0; ind < phase.size(); ind++) {
for (ind = 0; ind < static_cast<int64_t>(phase.size()); ind++) {
if (ind % phase_shape[2] != 0) {
phase[ind] = phase[ind] + phase[ind - 1];
}
@ -267,12 +268,13 @@ Status TimeStretch(std::shared_ptr<Tensor> input, std::shared_ptr<Tensor> *outpu
return Status::OK();
}
// calculate time step and alphas
int ind = 0;
std::vector<dsize_t> time_steps_0, time_steps_1;
std::vector<T> alphas;
for (T val = 0;; ind++) {
val = ind * rate;
if (val >= input_shape[-2]) break;
for (int ind = 0;; ind++) {
auto val = ind * rate;
if (val >= input_shape[-2]) {
break;
}
int val_int = static_cast<int>(val);
time_steps_0.push_back(val_int);
time_steps_1.push_back(val_int + 1);
@ -327,7 +329,7 @@ Status TimeStretch(std::shared_ptr<Tensor> input, std::shared_ptr<Tensor> *outpu
return Status::OK();
}
Status TimeStretch(std::shared_ptr<Tensor> input, std::shared_ptr<Tensor> *output, float rate, float hop_length,
Status TimeStretch(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, float rate, float hop_length,
float n_freq) {
std::shared_ptr<Tensor> phase_advance;
switch (input->type().value()) {
@ -366,7 +368,7 @@ Status MaskAlongAxis(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tenso
TensorShape input_shape = input->shape();
// squeeze input
TensorShape squeeze_shape = TensorShape({-1, input_shape[-2], input_shape[-1]});
input->Reshape(squeeze_shape);
(void)input->Reshape(squeeze_shape);
int check_dim_ind = (axis == 1) ? -2 : -1;
CHECK_FAIL_RETURN_UNEXPECTED(0 <= mask_start && mask_start <= input_shape[check_dim_ind],
@ -413,7 +415,7 @@ Status MaskAlongAxis(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tenso
}
}
// unsqueeze input
input->Reshape(input_shape);
(void)input->Reshape(input_shape);
*output = input;
return Status::OK();
}
@ -422,7 +424,7 @@ template <typename T>
Status Norm(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, float power) {
// calculate the output dimension
auto input_size = input->shape().AsVector();
int32_t dim_back = input_size.back();
int32_t dim_back = static_cast<int32_t>(input_size.back());
CHECK_FAIL_RETURN_UNEXPECTED(
dim_back == 2, "ComplexNorm: expect complex input of shape <..., 2>, but got: " + std::to_string(dim_back));
input_size.pop_back();
@ -452,11 +454,11 @@ Status ComplexNorm(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor>
std::shared_ptr<Tensor> input_tensor;
RETURN_IF_NOT_OK(TypeCast(input, &input_tensor, DataType(DataType::DE_FLOAT32)));
Norm<float>(input_tensor, output, power);
RETURN_IF_NOT_OK(Norm<float>(input_tensor, output, power));
} else if (input->type().value() == DataType::DE_FLOAT32) {
Norm<float>(input, output, power);
RETURN_IF_NOT_OK(Norm<float>(input, output, power));
} else if (input->type().value() == DataType::DE_FLOAT64) {
Norm<double>(input, output, power);
RETURN_IF_NOT_OK(Norm<double>(input, output, power));
} else {
RETURN_STATUS_UNEXPECTED("ComplexNorm: input tensor type should be int, float or double, but got: " +
input->type().ToString());
@ -469,7 +471,7 @@ Status ComplexNorm(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor>
template <typename T>
float sgn(T val) {
return (static_cast<T>(0) < val) - (val < static_cast<T>(0));
return static_cast<float>(static_cast<T>(0) < val) - static_cast<float>(val < static_cast<T>(0));
}
template <typename T>

2
mindspore/ccsrc/minddata/dataset/audio/kernels/audio_utils.h
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@ -277,7 +277,7 @@ Status LFilter(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *ou
/// \param phase_advance: Expected phase advance in each bin.
/// \param output: Tensor after stretch in time domain.
/// \return Status code.
Status TimeStretch(std::shared_ptr<Tensor> input, std::shared_ptr<Tensor> *output, float rate, float hop_length,
Status TimeStretch(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output, float rate, float hop_length,
float n_freq);
/// \brief Apply a mask along axis.

4
mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/div2k_node.cc
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@ -29,7 +29,7 @@ namespace dataset {
// Constructor for DIV2KNode
DIV2KNode::DIV2KNode(const std::string &dataset_dir, const std::string &usage, const std::string &downgrade,
int32_t scale, bool decode, std::shared_ptr<SamplerObj> sampler,
int32_t scale, bool decode, const std::shared_ptr<SamplerObj> &sampler,
std::shared_ptr<DatasetCache> cache)
: MappableSourceNode(std::move(cache)),
dataset_dir_(dataset_dir),
@ -109,7 +109,7 @@ Status DIV2KNode::Build(std::vector<std::shared_ptr<DatasetOp>> *const node_ops)
// Get the shard id of node
Status DIV2KNode::GetShardId(int32_t *shard_id) {
*shard_id = sampler_->ShardId();
*shard_id = static_cast<int32_t>(sampler_->ShardId());
return Status::OK();
}

4
mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/source/div2k_node.h
View File

@ -30,7 +30,7 @@ class DIV2KNode : public MappableSourceNode {
public:
/// \brief Constructor.
DIV2KNode(const std::string &dataset_dir, const std::string &usage, const std::string &downgrade, int32_t scale,
bool decode, std::shared_ptr<SamplerObj> sampler, std::shared_ptr<DatasetCache> cache);
bool decode, const std::shared_ptr<SamplerObj> &sampler, std::shared_ptr<DatasetCache> cache);
/// \brief Destructor.
~DIV2KNode() = default;
@ -99,8 +99,8 @@ class DIV2KNode : public MappableSourceNode {
private:
std::string dataset_dir_;
std::string usage_;
int32_t scale_;
std::string downgrade_;
int32_t scale_;
bool decode_;
std::shared_ptr<SamplerObj> sampler_;
};

2
mindspore/ccsrc/minddata/dataset/kernels/image/dvpp/utils/ResourceManager.cc
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@ -91,7 +91,7 @@ APP_ERROR ResourceManager::InitResource(ResourceInfo &resourceInfo) {
MS_LOG(ERROR) << "Failed to init acl.";
return APP_ERR_COMM_FAILURE;
}
std::copy(resourceInfo.deviceIds.begin(), resourceInfo.deviceIds.end(), std::back_inserter(deviceIds_));
(void)std::copy(resourceInfo.deviceIds.begin(), resourceInfo.deviceIds.end(), std::back_inserter(deviceIds_));
MS_LOG(INFO) << "Initialized acl successfully.";
// Open device and create context for each chip, note: it create one context for each chip
for (size_t i = 0; i < deviceIds_.size(); i++) {

1
mindspore/ccsrc/minddata/dataset/kernels/ir/data/transforms_ir.cc
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@ -16,6 +16,7 @@
#include <algorithm>
#include <typeinfo>
#include <utility>
#include "minddata/dataset/kernels/ir/data/transforms_ir.h"

2
mindspore/ccsrc/minddata/mindrecord/meta/shard_sample.cc
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@ -143,7 +143,7 @@ Status ShardSample::Execute(ShardTaskList &tasks) {
no_of_samples_ = std::min(no_of_samples_, total_no);
taking = no_of_samples_ - no_of_samples_ % no_of_categories;
} else if (sampler_type_ == kSubsetRandomSampler || sampler_type_ == kSubsetSampler) {
CHECK_FAIL_RETURN_UNEXPECTED(indices_.size() <= static_cast<size_t>(total_no),
CHECK_FAIL_RETURN_UNEXPECTED(static_cast<int>(indices_.size()) <= total_no,
"Invalid input, indices size: " + std::to_string(indices_.size()) +
" need to be less than dataset size: " + std::to_string(total_no) + ".");
} else { // constructor TopPercent