minddata build with mindspore-lite train

build.sh

@@ -306,6 +306,7 @@ checkopts()
       T)
         check_on_off $OPTARG T
         SUPPORT_TRAIN=$OPTARG
+        COMPILE_MINDDATA_LITE="full"
         echo "support train on device "
         ;;
       A)

mindspore/ccsrc/minddata/dataset/kernels/data/data_utils.cc

@@ -706,6 +706,8 @@ Status TensorVectorToBatchTensor(const std::vector<std::shared_ptr<Tensor>> &inp
   }
   return Status::OK();
 }
+
+#ifndef ENABLE_ANDROID
 template <typename T>
 struct UniqueOpHashMap {
   using map_type = std::unordered_map<T, int32_t>;
@@ -778,6 +780,7 @@ Status Unique(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *out
   }
   return Status::OK();
 }
+#endif
 
 }  // namespace dataset
 }  // namespace mindspore

mindspore/ccsrc/minddata/dataset/kernels/data/data_utils.h

@@ -19,7 +19,9 @@
 #include <memory>
 #include <string>
 #include <vector>
+#ifndef ENABLE_ANDROID
 #include <unordered_map>
+#endif
 #include "minddata/dataset/core/constants.h"
 #include "minddata/dataset/core/cv_tensor.h"
 #include "minddata/dataset/core/data_type.h"
@@ -177,6 +179,7 @@ Status BatchTensorToTensorVector(const std::shared_ptr<Tensor> &input, std::vect
 /// \return Status ok/error
 Status TensorVectorToBatchTensor(const std::vector<std::shared_ptr<Tensor>> &input, std::shared_ptr<Tensor> *output);
 
+#ifndef ENABLE_ANDROID
 /// Helper method that uniques the input tensor
 /// @tparam T type of the tensor
 /// \param input[in] input 1d tensor
@@ -197,7 +200,7 @@ Status UniqueHelper(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor
 /// \return Status ok/error
 Status Unique(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output,
               std::shared_ptr<Tensor> *output_idx, std::shared_ptr<Tensor> *output_cnt);
-
+#endif
 }  // namespace dataset
 }  // namespace mindspore
 

mindspore/ccsrc/minddata/dataset/kernels/data/unique_op.cc

@@ -23,6 +23,7 @@ namespace mindspore {
 namespace dataset {
 
 Status UniqueOp::Compute(const TensorRow &input, TensorRow *output) {
+#ifndef ENABLE_ANDROID
   IO_CHECK_VECTOR(input, output);
   CHECK_FAIL_RETURN_UNEXPECTED(input.size() == 1, "Input should be one tensor");
 
@@ -43,10 +44,10 @@ Status UniqueOp::Compute(const TensorRow &input, TensorRow *output) {
   std::shared_ptr<Tensor> out_cnt;
 
   RETURN_IF_NOT_OK(Unique(in_tensor, &out, &out_idx, &out_cnt));
-
   output->push_back(out);
   output->push_back(out_idx);
   output->push_back(out_cnt);
+#endif
   return Status::OK();
 }
 }  // namespace dataset

mindspore/ccsrc/minddata/dataset/kernels/image/lite_cv/image_process.h

@@ -81,10 +81,10 @@ bool SubStractMeanNormalize(const LiteMat &src, LiteMat &dst, const std::vector<
 bool Pad(const LiteMat &src, LiteMat &dst, int top, int bottom, int left, int right, PaddBorderType pad_type,
          uint8_t fill_b_or_gray, uint8_t fill_g, uint8_t fill_r);
 
-/// \brief extract image channel by index
+/// \brief Extract image channel by index
 bool ExtractChannel(const LiteMat &src, LiteMat &dst, int col);
 
-/// \brief split image channels to single channel
+/// \brief Split image channels to single channel
 bool Split(const LiteMat &src, std::vector<LiteMat> &mv);
 
 /// \brief Create a multi-channel image out of several single-channel arrays.

tests/ut/python/dataset/test_unique_op.py

@@ -31,8 +31,8 @@ def compare(array, res, idx, cnt):
         np.testing.assert_array_equal(idx, d["y"])
         np.testing.assert_array_equal(cnt, d["z"])
 
-
-def test_duplicate_basics():
+# the test function name code will be start with 'test' later
+def duplicate_basics():
     compare([0, 1, 2, 1, 2, 3], np.array([0, 1, 2, 3]),
             np.array([0, 1, 2, 1, 2, 3]), np.array([1, 2, 2, 1]))
     compare([0.0, 1.0, 2.0, 1.0, 2.0, 3.0], np.array([0.0, 1.0, 2.0, 3.0]),