!1993 [Dataset] Fix codedex.

mindspore/ccsrc/dataset/core/tensor_row.h

@@ -35,13 +35,13 @@ class TensorRow {
   static constexpr row_id_type kDefaultRowId = -1;  // Default row id
 
   // Type definitions
-  typedef dsize_t size_type;
-  typedef std::shared_ptr<Tensor> value_type;
-  typedef std::shared_ptr<Tensor> &reference;
-  typedef const std::shared_ptr<Tensor> &const_reference;
-  typedef std::vector<std::shared_ptr<Tensor>> vector_type;
-  typedef std::vector<std::shared_ptr<Tensor>>::iterator iterator;
-  typedef std::vector<std::shared_ptr<Tensor>>::const_iterator const_iterator;
+  using size_type = dsize_t;
+  using value_type = std::shared_ptr<Tensor>;
+  using reference = std::shared_ptr<Tensor> &;
+  using const_reference = const std::shared_ptr<Tensor> &;
+  using vector_type = std::vector<std::shared_ptr<Tensor>>;
+  using iterator = std::vector<std::shared_ptr<Tensor>>::iterator;
+  using const_iterator = std::vector<std::shared_ptr<Tensor>>::const_iterator;
 
   TensorRow() noexcept;
 

mindspore/ccsrc/dataset/engine/dataset_iterator.cc

@@ -84,7 +84,12 @@ Status IteratorBase::FetchNextTensorRow(TensorRow *out_row) {
 
 // Constructor of the DatasetIterator
 DatasetIterator::DatasetIterator(std::shared_ptr<ExecutionTree> exe_tree)
-    : IteratorBase(), root_(exe_tree->root()), tracing_(nullptr), cur_batch_num_(0), cur_connector_size_(0) {
+    : IteratorBase(),
+      root_(exe_tree->root()),
+      tracing_(nullptr),
+      cur_batch_num_(0),
+      cur_connector_size_(0),
+      cur_connector_capacity_(0) {
   std::shared_ptr<Tracing> node;
   Status s = exe_tree->GetProfilingManager()->GetTracingNode(kDatasetIteratorTracingName, &node);
   if (s.IsOk()) {

mindspore/ccsrc/dataset/engine/datasetops/bucket_batch_by_length_op.cc

@@ -237,6 +237,5 @@ Status BucketBatchByLengthOp::Reset() {
 
   return Status::OK();
 }
-
 }  // namespace dataset
 }  // namespace mindspore

mindspore/ccsrc/dataset/engine/datasetops/bucket_batch_by_length_op.h

@@ -146,7 +146,6 @@ class BucketBatchByLengthOp : public PipelineOp {
   std::unique_ptr<ChildIterator> child_iterator_;
   std::vector<std::unique_ptr<TensorQTable>> buckets_;
 };
-
 }  // namespace dataset
 }  // namespace mindspore
 

mindspore/ccsrc/dataset/engine/datasetops/build_vocab_op.h

@@ -112,6 +112,8 @@ class BuildVocabOp : public ParallelOp {
   BuildVocabOp(std::shared_ptr<Vocab> vocab, std::vector<std::string> col_names, std::pair<int64_t, int64_t> freq_range,
                int64_t top_k, int32_t num_workers, int32_t op_connector_size);
 
+  ~BuildVocabOp() = default;
+
   Status WorkerEntry(int32_t worker_id) override;
 
   // collect the work product from each worker

mindspore/ccsrc/dataset/engine/datasetops/source/clue_op.cc

@@ -30,7 +30,6 @@
 
 namespace mindspore {
 namespace dataset {
-
 ClueOp::Builder::Builder()
     : builder_device_id_(0), builder_num_devices_(1), builder_num_samples_(0), builder_shuffle_files_(false) {
   std::shared_ptr<ConfigManager> config_manager = GlobalContext::config_manager();
@@ -545,6 +544,5 @@ Status ClueOp::CountAllFileRows(const std::vector<std::string> &files, int64_t *
   }
   return Status::OK();
 }
-
 }  // namespace dataset
 }  // namespace mindspore

mindspore/ccsrc/dataset/engine/datasetops/source/clue_op.h

@@ -264,7 +264,6 @@ class ClueOp : public ParallelOp {
   bool load_jagged_connector_;
   ColKeyMap cols_to_keyword_;
 };
-
 }  // namespace dataset
 }  // namespace mindspore
 #endif  // DATASET_ENGINE_DATASETOPS_SOURCE_CLUE_OP_H_

mindspore/ccsrc/dataset/engine/datasetops/source/coco_op.cc

@@ -59,8 +59,8 @@ CocoOp::Builder::Builder() : builder_decode_(false), builder_sampler_(nullptr) {
 Status CocoOp::Builder::Build(std::shared_ptr<CocoOp> *ptr) {
   RETURN_IF_NOT_OK(SanityCheck());
   if (builder_sampler_ == nullptr) {
-    int64_t num_samples = 0;
-    int64_t start_index = 0;
+    const int64_t num_samples = 0;
+    const int64_t start_index = 0;
     builder_sampler_ = std::make_shared<SequentialSampler>(start_index, num_samples);
   }
   builder_schema_ = std::make_unique<DataSchema>();

mindspore/ccsrc/dataset/engine/perf/connector_size.h

@@ -44,6 +44,8 @@ class ConnectorSize : public Sampling {
  public:
   explicit ConnectorSize(ExecutionTree *tree) : tree_(tree) {}
 
+  ~ConnectorSize() = default;
+
   // Driver function for connector size sampling.
   // This function samples the connector size of every nodes within the ExecutionTree
   Status Sample() override;
@@ -54,7 +56,7 @@ class ConnectorSize : public Sampling {
   // @return Status - The error code return
   Status SaveToFile() override;
 
-  Status Init(const std::string &dir_path, const std::string &device_id);
+  Status Init(const std::string &dir_path, const std::string &device_id) override;
 
   // Parse op infomation and transform to json format
   json ParseOpInfo(const DatasetOp &node, const std::vector<int32_t> &size);

mindspore/ccsrc/dataset/engine/perf/dataset_iterator_tracing.h

@@ -28,7 +28,7 @@ class DatasetIteratorTracing : public Tracing {
   DatasetIteratorTracing() = default;
 
   // Destructor
-  ~DatasetIteratorTracing() = default;
+  ~DatasetIteratorTracing() override = default;
 
   // Record tracing data
   // @return Status - The error code return
@@ -40,7 +40,7 @@ class DatasetIteratorTracing : public Tracing {
   // @return Status - The error code return
   Status SaveToFile() override;
 
-  Status Init(const std::string &dir_path, const std::string &device_id);
+  Status Init(const std::string &dir_path, const std::string &device_id) override;
 
  private:
   std::vector<std::string> value_;

mindspore/ccsrc/dataset/engine/perf/device_queue_tracing.h

@@ -29,7 +29,7 @@ class DeviceQueueTracing : public Tracing {
   DeviceQueueTracing() = default;
 
   // Destructor
-  ~DeviceQueueTracing() = default;
+  ~DeviceQueueTracing() override = default;
 
   // Record tracing data
   // @return Status - The error code return
@@ -41,7 +41,7 @@ class DeviceQueueTracing : public Tracing {
   // @return Status - The error code return
   Status SaveToFile() override;
 
-  Status Init(const std::string &dir_path, const std::string &device_id);
+  Status Init(const std::string &dir_path, const std::string &device_id) override;
 
  private:
   std::vector<std::string> value_;

mindspore/ccsrc/dataset/engine/perf/monitor.cc

@@ -25,6 +25,7 @@ namespace dataset {
 Monitor::Monitor(ExecutionTree *tree) : tree_(tree) {
   std::shared_ptr<ConfigManager> cfg = GlobalContext::config_manager();
   sampling_interval_ = cfg->monitor_sampling_interval();
+  max_samples_ = 0;
 }
 
 Status Monitor::operator()() {

mindspore/ccsrc/dataset/engine/perf/monitor.h

@@ -33,6 +33,8 @@ class Monitor {
 
   Monitor() = default;
 
+  ~Monitor() = default;
+
   // Functor for Perf Monitor main loop.
   // This function will be the entry point of Mindspore::Dataset::Task
   Status operator()();

mindspore/ccsrc/dataset/engine/perf/profiling.h

@@ -99,7 +99,7 @@ class ProfilingManager {
   // If profiling is enabled.
   bool IsProfilingEnable() const;
 
-  std::unordered_map<std::string, std::shared_ptr<Sampling>> &GetSamplingNodes() { return sampling_nodes_; }
+  const std::unordered_map<std::string, std::shared_ptr<Sampling>> &GetSamplingNodes() { return sampling_nodes_; }
 
  private:
   std::unordered_map<std::string, std::shared_ptr<Tracing>> tracing_nodes_;

mindspore/ccsrc/dataset/engine/tdt/tdt_plugin.cc

@@ -119,7 +119,8 @@ TdtStatus TdtPlugin::translate(const TensorRow &ts_row, std::vector<DataItem> &i
     data_item.tensorShape_ = dataShapes;
     data_item.tensorType_ = datatype;
     data_item.dataLen_ = ts->SizeInBytes();
-    data_item.dataPtr_ = std::shared_ptr<void>(reinterpret_cast<uchar *>(&(*ts->begin<uint8_t>())), [](void *elem) {});
+    data_item.dataPtr_ =
+      std::shared_ptr<void>(reinterpret_cast<uchar *>(&(*ts->begin<uint8_t>())), [](const void *elem) {});
     items.emplace_back(data_item);
     MS_LOG(INFO) << "TDT data type is " << datatype << ", data shape is " << dataShapes << ", data length is "
                  << ts->Size() << ".";

mindspore/ccsrc/dataset/kernels/data/fill_op.cc

@@ -21,7 +21,6 @@
 
 namespace mindspore {
 namespace dataset {
-
 Status FillOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
   IO_CHECK(input, output);
   Status s = Fill(input, output, fill_value_);

mindspore/ccsrc/dataset/kernels/data/fill_op.h

@@ -26,7 +26,6 @@
 
 namespace mindspore {
 namespace dataset {
-
 class FillOp : public TensorOp {
  public:
   explicit FillOp(std::shared_ptr<Tensor> value) : fill_value_(value) {}
@@ -39,9 +38,7 @@ class FillOp : public TensorOp {
  private:
   std::shared_ptr<Tensor> fill_value_;
 };
-
 }  // namespace dataset
-
 }  // namespace mindspore
 
 #endif  // MINDSPORE_FILL_OP_H

mindspore/ccsrc/mindrecord/meta/shard_column.cc

@@ -351,7 +351,7 @@ vector<uint8_t> ShardColumn::CompressInt(const vector<uint8_t> &src_bytes, const
     // Write this int to destination blob
     uint64_t u_n = *reinterpret_cast<uint64_t *>(&i_n);
     auto temp_bytes = UIntToBytesLittle(u_n, dst_int_type);
-    for (uint64_t j = 0; j < (kUnsignedOne << dst_int_type); j++) {
+    for (uint64_t j = 0; j < (kUnsignedOne << static_cast<uint8_t>(dst_int_type)); j++) {
       dst_bytes[i_dst++] = temp_bytes[j];
     }
 
@@ -406,7 +406,10 @@ MSRStatus ShardColumn::UncompressInt(const uint64_t &column_id, std::unique_ptr<
 
   auto data = reinterpret_cast<const unsigned char *>(array_data.get());
   *data_ptr = std::make_unique<unsigned char[]>(*num_bytes);
-  memcpy_s(data_ptr->get(), *num_bytes, data, *num_bytes);
+  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 SUCCESS;
 }
@@ -444,7 +447,8 @@ int64_t ShardColumn::BytesLittleToMinIntType(const std::vector<uint8_t> &bytes_a
                                              const IntegerType &src_i_type, IntegerType *dst_i_type) {
   uint64_t u_temp = 0;
   for (uint64_t i = 0; i < (kUnsignedOne << static_cast<uint8_t>(src_i_type)); i++) {
-    u_temp = (u_temp << kBitsOfByte) + bytes_array[pos + (kUnsignedOne << src_i_type) - kUnsignedOne - i];
+    u_temp = (u_temp << kBitsOfByte) +
+             bytes_array[pos + (kUnsignedOne << static_cast<uint8_t>(src_i_type)) - kUnsignedOne - i];
   }
 
   int64_t i_out;

mindspore/dataset/transforms/vision/py_transforms_util.py

@@ -554,26 +554,28 @@ def adjust_hue(img, hue_factor):
     Returns:
         img (PIL Image), Hue adjusted image.
     """
-    if not -0.5 <= hue_factor <= 0.5:
-        raise ValueError('hue_factor {} is not in [-0.5, 0.5].'.format(hue_factor))
+    image = img
+    image_hue_factor = hue_factor
+    if not -0.5 <= image_hue_factor <= 0.5:
+        raise ValueError('image_hue_factor {} is not in [-0.5, 0.5].'.format(image_hue_factor))
 
-    if not is_pil(img):
-        raise TypeError(augment_error_message.format(type(img)))
+    if not is_pil(image):
+        raise TypeError(augment_error_message.format(type(image)))
 
-    input_mode = img.mode
-    if input_mode in {'L', '1', 'I', 'F'}:
-        return img
+    mode = image.mode
+    if mode in {'L', '1', 'I', 'F'}:
+        return image
 
-    h, s, v = img.convert('HSV').split()
+    hue, saturation, value = img.convert('HSV').split()
 
-    np_h = np.array(h, dtype=np.uint8)
+    np_hue = np.array(hue, dtype=np.uint8)
 
     with np.errstate(over='ignore'):
-        np_h += np.uint8(hue_factor * 255)
-    h = Image.fromarray(np_h, 'L')
+        np_hue += np.uint8(image_hue_factor * 255)
+    hue = Image.fromarray(np_hue, 'L')
 
-    img = Image.merge('HSV', (h, s, v)).convert(input_mode)
-    return img
+    image = Image.merge('HSV', (hue, saturation, value)).convert(mode)
+    return image
 
 
 def to_type(img, output_type):

tests/ut/python/dataset/prep_data.py

@@ -1,124 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# import jsbeautifier
-
-import os
-import urllib
-import urllib.request
-
-
-def create_data_cache_dir():
-    cwd = os.getcwd()
-    target_directory = os.path.join(cwd, "data_cache")
-    try:
-        if not os.path.exists(target_directory):
-            os.mkdir(target_directory)
-    except OSError:
-        print("Creation of the directory %s failed" % target_directory)
-    return target_directory
-
-
-def download_and_uncompress(files, source_url, target_directory, is_tar=False):
-    for f in files:
-        url = source_url + f
-        target_file = os.path.join(target_directory, f)
-
-        ##check if file already downloaded
-        if not (os.path.exists(target_file) or os.path.exists(target_file[:-3])):
-            urllib.request.urlretrieve(url, target_file)
-            if is_tar:
-                print("extracting from local tar file " + target_file)
-                rc = os.system("tar -C " + target_directory + " -xvf " + target_file)
-            else:
-                print("unzipping " + target_file)
-                rc = os.system("gunzip -f " + target_file)
-            if rc != 0:
-                print("Failed to uncompress ", target_file, " removing")
-                os.system("rm " + target_file)
-                ##exit with error so that build script will fail
-                raise SystemError
-        else:
-            print("Using cached dataset at ", target_file)
-
-
-def download_mnist(target_directory=None):
-    if target_directory is None:
-        target_directory = create_data_cache_dir()
-
-        ##create mnst directory
-        target_directory = os.path.join(target_directory, "mnist")
-        try:
-            if not os.path.exists(target_directory):
-                os.mkdir(target_directory)
-        except OSError:
-            print("Creation of the directory %s failed" % target_directory)
-
-    MNIST_URL = "http://yann.lecun.com/exdb/mnist/"
-    files = ['train-images-idx3-ubyte.gz',
-             'train-labels-idx1-ubyte.gz',
-             't10k-images-idx3-ubyte.gz',
-             't10k-labels-idx1-ubyte.gz']
-    download_and_uncompress(files, MNIST_URL, target_directory, is_tar=False)
-
-    return target_directory, os.path.join(target_directory, "datasetSchema.json")
-
-
-CIFAR_URL = "https://www.cs.toronto.edu/~kriz/"
-
-
-def download_cifar(target_directory, files, directory_from_tar):
-    if target_directory is None:
-        target_directory = create_data_cache_dir()
-
-    download_and_uncompress([files], CIFAR_URL, target_directory, is_tar=True)
-
-    ##if target dir was specify move data from directory created by tar
-    ##and put data into target dir
-    if target_directory is not None:
-        tar_dir_full_path = os.path.join(target_directory, directory_from_tar)
-        all_files = os.path.join(tar_dir_full_path, "*")
-        cmd = "mv " + all_files + " " + target_directory
-        if os.path.exists(tar_dir_full_path):
-            print("copy files back to target_directory")
-            print("Executing: ", cmd)
-            rc1 = os.system(cmd)
-            rc2 = os.system("rm -r " + tar_dir_full_path)
-            if rc1 != 0 or rc2 != 0:
-                print("error when running command: ", cmd)
-                download_file = os.path.join(target_directory, files)
-                print("removing " + download_file)
-                os.system("rm " + download_file)
-
-                ##exit with error so that build script will fail
-                raise SystemError
-
-    ##change target directory to directory after tar
-    return os.path.join(target_directory, directory_from_tar)
-
-
-def download_cifar10(target_directory=None):
-    return download_cifar(target_directory, "cifar-10-binary.tar.gz", "cifar-10-batches-bin")
-
-
-def download_cifar100(target_directory=None):
-    return download_cifar(target_directory, "cifar-100-binary.tar.gz", "cifar-100-binary")
-
-
-def download_all_for_test(cwd):
-    download_mnist(os.path.join(cwd, "testMnistData"))
-
-
-##Download all datasets to existing test directories
-if __name__ == "__main__":
-    download_all_for_test(os.getcwd())