!2162 SliceOp

Merge pull request !2162 from h.farahat/slice_op

mindspore/ccsrc/dataset/api/python_bindings.cc

@@ -37,8 +37,9 @@
 #include "dataset/kernels/image/resize_bilinear_op.h"
 #include "dataset/kernels/image/resize_op.h"
 #include "dataset/kernels/image/uniform_aug_op.h"
-#include "dataset/kernels/data/type_cast_op.h"
 #include "dataset/kernels/data/fill_op.h"
+#include "dataset/kernels/data/slice_op.h"
+#include "dataset/kernels/data/type_cast_op.h"
 #include "dataset/engine/datasetops/source/cifar_op.h"
 #include "dataset/engine/datasetops/source/image_folder_op.h"
 #include "dataset/engine/datasetops/source/io_block.h"
@@ -369,6 +370,37 @@ void bindTensorOps2(py::module *m) {
     *m, "FillOp", "Tensor operation to return tensor filled with same value as input fill value.")
     .def(py::init<std::shared_ptr<Tensor>>());
 
+  (void)py::class_<SliceOp, TensorOp, std::shared_ptr<SliceOp>>(*m, "SliceOp", "")
+    .def(py::init<bool>())
+    .def(py::init([](const py::list &py_list) {
+      std::vector<dsize_t> c_list;
+      for (auto l : py_list) {
+        if (!l.is_none()) {
+          c_list.push_back(py::reinterpret_borrow<py::int_>(l));
+        }
+      }
+      return std::make_shared<SliceOp>(c_list);
+    }))
+    .def(py::init([](const py::tuple &py_slice) {
+      if (py_slice.size() != 3) {
+        THROW_IF_ERROR(Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Wrong slice object"));
+      }
+      Slice c_slice;
+      if (!py_slice[0].is_none() && !py_slice[1].is_none() && !py_slice[2].is_none()) {
+        c_slice = Slice(py::reinterpret_borrow<py::int_>(py_slice[0]), py::reinterpret_borrow<py::int_>(py_slice[1]),
+                        py::reinterpret_borrow<py::int_>(py_slice[2]));
+      } else if (py_slice[0].is_none() && py_slice[2].is_none()) {
+        c_slice = Slice(py::reinterpret_borrow<py::int_>(py_slice[1]));
+      } else if (!py_slice[0].is_none() && !py_slice[1].is_none()) {
+        c_slice = Slice(py::reinterpret_borrow<py::int_>(py_slice[0]), py::reinterpret_borrow<py::int_>(py_slice[1]));
+      }
+
+      if (!c_slice.valid()) {
+        THROW_IF_ERROR(Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Wrong slice object"));
+      }
+      return std::make_shared<SliceOp>(c_slice);
+    }));
+
   (void)py::class_<RandomRotationOp, TensorOp, std::shared_ptr<RandomRotationOp>>(
     *m, "RandomRotationOp",
     "Tensor operation to apply RandomRotation."

mindspore/ccsrc/dataset/core/tensor.cc

@@ -916,6 +916,61 @@ Status Tensor::CopyLastDimAt(const std::shared_ptr<Tensor> &src, const std::vect
   CHECK_FAIL_RETURN_UNEXPECTED(memcpy_s(dst_addr, len, src_addr, len) == 0, "memcpy error");
   return Status::OK();
 }
+Status Tensor::Slice(std::shared_ptr<Tensor> *out, const std::vector<dsize_t> &indices) {
+  CHECK_FAIL_RETURN_UNEXPECTED(shape_.Rank() == 1, "Currently Slice work with rank 1 tensors only.");
+  CHECK_FAIL_RETURN_UNEXPECTED(!indices.empty(), "Indices are empty, generated tensor would be empty.");
+  if (type_.IsNumeric()) {
+    return SliceNumeric(out, indices);
+  } else {
+    return SliceString(out, indices);
+  }
+}
+Status Tensor::SliceNumeric(std::shared_ptr<Tensor> *out, const std::vector<dsize_t> &indices) {
+  RETURN_IF_NOT_OK(
+    CreateTensor(out, TensorImpl::kFlexible, TensorShape({static_cast<dsize_t>(indices.size())}), type_));
+  (*out)->GetMutableBuffer();
+  dsize_t out_index = 0;
+  dsize_t dim_length = shape_[0];
+  dsize_t type_size = type_.SizeInBytes();
+  dsize_t src_start = handleNeg(indices[0], dim_length);
+  uchar *dst_addr = (*out)->data_;
+  dsize_t count = 1;
+
+  for (dsize_t i = 0; i < indices.size(); i++) {
+    dsize_t cur_index = handleNeg(indices[i], dim_length);
+    CHECK_FAIL_RETURN_UNEXPECTED(
+      cur_index >= 0 && cur_index < dim_length,
+      "Index " + std::to_string(indices[i]) + " is out of bounds [0," + std::to_string(dim_length) + ")");
+    if (i < indices.size() - 1) {
+      dsize_t next_index = handleNeg(indices[i + 1], dim_length);
+      if (next_index == cur_index + 1) {
+        count++;
+        continue;
+      }
+    }
+    memcpy_s(dst_addr + out_index * type_size, (*out)->SizeInBytes(), data_ + src_start * type_size, count * type_size);
+    out_index += count;
+    if (i < indices.size() - 1) {
+      src_start = handleNeg(indices[i + 1], dim_length);  // next index
+    }
+    count = 1;
+  }
+  return Status::OK();
+}
+Status Tensor::SliceString(std::shared_ptr<Tensor> *out, const std::vector<dsize_t> &indices) {
+  dsize_t dim_length = shape_[0];
+  std::vector<std::string> strings;
+  for (dsize_t index : indices) {
+    dsize_t cur_index = handleNeg(index, dim_length);
+    CHECK_FAIL_RETURN_UNEXPECTED(
+      cur_index >= 0 && cur_index < dim_length,
+      "Index " + std::to_string(index) + " is out of bounds [0," + std::to_string(dim_length) + ")");
+    std::string_view sv;
+    GetItemAt(&sv, {cur_index});
+    strings.emplace_back(sv);
+  }
+  return CreateTensor(out, strings);
+}
 
 }  // namespace dataset
 }  // namespace mindspore

mindspore/ccsrc/dataset/core/tensor.h

@@ -347,6 +347,22 @@ class Tensor {
     return ss.str();
   }
 
+  // Handle negative indices.
+  static inline dsize_t handleNeg(dsize_t index, dsize_t length) { return (index < 0) ? (index + length) : index; }
+
+  // Slice tensor bases on the given indicies. Copy the sliced data into out tensor. Only rank1 tensors are supported.
+  // Based on the type of tensor, SliceNumeric or SliceString will be called
+  // @param out Tensor
+  // @param indices vector of indices
+  // @return Status error code
+  Status Slice(std::shared_ptr<Tensor> *out, const std::vector<dsize_t> &indices);
+
+  // Slice numeric tensors.
+  Status SliceNumeric(std::shared_ptr<Tensor> *out, const std::vector<dsize_t> &indices);
+
+  // Slice string tensors
+  Status SliceString(std::shared_ptr<Tensor> *out, const std::vector<dsize_t> &indices);
+
   // Constructs numpy array from input tensor
   // @param data this data is the location of python data
   // @return Status code

mindspore/ccsrc/dataset/kernels/data/CMakeLists.txt

@@ -5,4 +5,5 @@ add_library(kernels-data OBJECT
     one_hot_op.cc
     type_cast_op.cc
     to_float16_op.cc
-    fill_op.cc)
+    fill_op.cc
+    slice_op.cc)

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

+/**
+ * Copyright 2019 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.
+ */
+#include "dataset/kernels/data/slice_op.h"
+
+#include "dataset/core/tensor.h"
+#include "dataset/kernels/data/data_utils.h"
+#include "dataset/kernels/tensor_op.h"
+
+namespace mindspore {
+namespace dataset {
+Status SliceOp::Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) {
+  IO_CHECK(input, output);
+  CHECK_FAIL_RETURN_UNEXPECTED(input->shape().Rank() == 1, "SliceOp supports 1D Tensors only for now.");
+
+  // if `all` flag is true, output is just the input.
+  if (all_) {
+    *output = input;
+    return Status::OK();
+  }
+
+  // if slice object was provided, indices should be empty. Generate indices from the slice object.
+  if (slice_.valid() && indices_.empty()) {
+    dsize_t len = input->shape()[0];
+    indices_ = slice_.Indices(len);
+    return input->Slice(output, indices_);
+  }
+
+  // if indices are not empty, slices should be invalid, use indices_ to slice
+  if (!indices_.empty() && !slice_.valid()) {
+    return input->Slice(output, indices_);
+  }
+  RETURN_STATUS_UNEXPECTED("The indexing parameters are invalid");
+}
+}  // namespace dataset
+}  // namespace mindspore

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

+/**
+ * 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.
+ */
+#ifndef DATASET_KERNELS_DATA_SLICE_OP_H_
+#define DATASET_KERNELS_DATA_SLICE_OP_H_
+
+#include <algorithm>
+#include <memory>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "dataset/core/tensor.h"
+#include "dataset/kernels/tensor_op.h"
+
+namespace mindspore {
+namespace dataset {
+class Slice {
+ public:
+  Slice() : start_(0), stop_(0), step_(0) {}
+  Slice(dsize_t start, dsize_t stop, dsize_t step) : start_(start), stop_(stop), step_(step) {}
+  Slice(dsize_t start, dsize_t stop) : start_(start), stop_(stop), step_(1) {}
+  explicit Slice(dsize_t stop) : start_(0), stop_(stop), step_(1) {}
+
+  std::vector<dsize_t> Indices(dsize_t length) {
+    std::vector<dsize_t> indices;
+    dsize_t index = std::min(Tensor::handleNeg(start_, length), length);
+    dsize_t end_index = std::min(Tensor::handleNeg(stop_, length), length);
+    if (step_ > 0) {
+      for (; index < end_index; index += step_) {
+        indices.push_back(index);
+      }
+    } else {
+      for (; index > end_index; index += step_) {
+        indices.push_back(index);
+      }
+    }
+    return indices;
+  }
+
+  bool valid() { return !(start_ == 0 && stop_ == 0 && step_ == 0); }
+
+  dsize_t start_;
+  dsize_t stop_;
+  dsize_t step_;
+};
+
+class SliceOp : public TensorOp {
+ public:
+  explicit SliceOp(std::vector<dsize_t> indices) : indices_(std::move(indices)) {}
+  explicit SliceOp(Slice slice) : slice_(slice) {}
+  explicit SliceOp(bool all) : all_(all) {}
+
+  ~SliceOp() override = default;
+
+  void Print(std::ostream &out) const override { out << "SliceOp"; }
+
+  Status Compute(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *output) override;
+
+ private:
+  // only on of the following will be valid
+  // given indices to slice the Tensor. Empty vector if invalid.
+  std::vector<dsize_t> indices_;
+  // Slice object. All start, stop and step are 0 if invalid.
+  Slice slice_;
+  // Flag to read all indcies in the dim.
+  bool all_ = false;
+};
+}  // namespace dataset
+}  // namespace mindspore
+#endif  // DATASET_KERNELS_DATA_ONE_HOT_OP_H_

mindspore/dataset/transforms/c_transforms.py

@@ -17,7 +17,8 @@ This module c_transforms provides common operations, including OneHotOp and Type
 """
 import numpy as np
 import mindspore._c_dataengine as cde
-from .validators import check_num_classes, check_de_type, check_fill_value
+
+from .validators import check_num_classes, check_de_type, check_fill_value, check_slice_op
 from ..core.datatypes import mstype_to_detype
 
 
@@ -64,3 +65,46 @@ class TypeCast(cde.TypeCastOp):
         data_type = mstype_to_detype(data_type)
         self.data_type = str(data_type)
         super().__init__(data_type)
+
+
+class Slice(cde.SliceOp):
+    """
+    Slice operation to extract a tensor out using the given n slices.
+
+    The functionality of Slice is similar to NumPy indexing feature.
+
+    (Currently only rank 1 Tensors are supported)
+
+    Args:
+     *slices: Maximum n number of objects to slice a tensor of rank n.
+         One object in slices can be one of:
+             1.  int: slice this index only. Negative index is supported.
+             2.  slice object: slice the generated indices from the slice object. Similar to `start:stop:step`.
+             3.  None: slice the whole dimension. Similar to `:` in python indexing.
+             4.  Ellipses ...: slice all dimensions between the two slices.
+    Examples:
+     >>> # Data before
+     >>> # |   col   |
+     >>> # +---------+
+     >>> # | [1,2,3] |
+     >>> # +---------|
+     >>> data = data.map(operations=Slice(slice(1,3))) # slice indices 1 and 2 only
+     >>> # Data after
+     >>> # |    col     |
+     >>> # +------------+
+     >>> # |    [1,2]   |
+     >>> # +------------|
+    """
+
+    @check_slice_op
+    def __init__(self, *slices):
+        dim0 = slices[0]
+        if isinstance(dim0, int):
+            dim0 = [dim0]
+        elif dim0 is None:
+            dim0 = True
+        elif isinstance(dim0, slice):
+            dim0 = (dim0.start, dim0.stop, dim0.step)
+        elif dim0 is Ellipsis:
+            dim0 = True
+        super().__init__(dim0)

mindspore/dataset/transforms/validators.py

@@ -15,6 +15,7 @@
 """Validators for TensorOps.
 """
 from functools import wraps
+
 from mindspore._c_expression import typing
 
 # POS_INT_MIN is used to limit values from starting from 0
@@ -195,3 +196,20 @@ def check_de_type(method):
         return method(self, **kwargs)
 
     return new_method
+
+
+def check_slice_op(method):
+    """Wrapper method to check the parameters of slice."""
+
+    @wraps(method)
+    def new_method(self, *args):
+        for i, arg in enumerate(args):
+            if arg is not None and arg is not Ellipsis and not isinstance(arg, (int, slice, list)):
+                raise TypeError("Indexing of dim " + str(i) + "is not of valid type")
+            if isinstance(arg, list):
+                for a in arg:
+                    if not isinstance(a, int):
+                        raise TypeError("Index " + a + " is not an int")
+        return method(self, *args)
+
+    return new_method

tests/ut/cpp/dataset/tensor_test.cc

@@ -28,17 +28,13 @@ using namespace mindspore::dataset;
 
 namespace py = pybind11;
 
-
 class MindDataTestTensorDE : public UT::Common {
  public:
   MindDataTestTensorDE() {}
 
-    void SetUp() {
-      GlobalInit();
-    }
+  void SetUp() { GlobalInit(); }
 };
 
-
 TEST_F(MindDataTestTensorDE, Basics) {
   std::shared_ptr<Tensor> t = std::make_shared<Tensor>(TensorShape({2, 3}), DataType(DataType::DE_UINT64));
   ASSERT_TRUE((t->AllocateBuffer(t->SizeInBytes())).IsOk());
@@ -167,8 +163,7 @@ TEST_F(MindDataTestTensorDE, InsertTensor) {
 
 // Test the bug of Tensor::ToString will exec failed for Tensor which store bool values
 TEST_F(MindDataTestTensorDE, BoolTensor) {
-  std::shared_ptr<Tensor> t = std::make_shared<Tensor>(TensorShape({2}),
-                                                       DataType(DataType::DE_BOOL));
+  std::shared_ptr<Tensor> t = std::make_shared<Tensor>(TensorShape({2}), DataType(DataType::DE_BOOL));
   t->SetItemAt<bool>({0}, true);
   t->SetItemAt<bool>({1}, true);
   std::string out = t->ToString();
@@ -255,14 +250,19 @@ void checkCvMat(TensorShape shape, DataType type) {
     } else {
       ASSERT_EQ(m.size[0], shape[0]);
     }
-    if (shape.Rank() == 3) { ASSERT_EQ(m.channels(), shape[2]); }
+    if (shape.Rank() == 3) {
+      ASSERT_EQ(m.channels(), shape[2]);
+    }
     ASSERT_EQ(m.dims, 2);
     ASSERT_EQ(m.size.dims(), 2);
-    if (shape.Rank() > 0) { ASSERT_EQ(m.rows, shape[0]); }
-    if (shape.Rank() > 1) { ASSERT_EQ(m.cols, shape[1]); }
+    if (shape.Rank() > 0) {
+      ASSERT_EQ(m.rows, shape[0]);
+    }
+    if (shape.Rank() > 1) {
+      ASSERT_EQ(m.cols, shape[1]);
+    }
   } else {
-    for (dsize_t i = 0; i < shape.Rank(); i++)
-      ASSERT_EQ(m.size[static_cast<int>(i)], shape[i]);
+    for (dsize_t i = 0; i < shape.Rank(); i++) ASSERT_EQ(m.size[static_cast<int>(i)], shape[i]);
     ASSERT_EQ(m.dims, shape.Rank());
     ASSERT_EQ(m.size.dims(), shape.Rank());
     ASSERT_EQ(m.rows, -1);
@@ -394,3 +394,16 @@ TEST_F(MindDataTestTensorDE, TensorIterator) {
   }
   ASSERT_TRUE(ctr == 6);
 }
+
+TEST_F(MindDataTestTensorDE, TensorSlice) {
+  std::shared_ptr<Tensor> t;
+  Tensor::CreateTensor(&t, std::vector<dsize_t>{0, 1, 2, 3, 4});
+  std::shared_ptr<Tensor> t2;
+  auto x = std::vector<dsize_t>{0, 3, 4};
+  std::shared_ptr<Tensor> expected;
+  Tensor::CreateTensor(&expected, x);
+  t->Slice(&t2, x);
+  ASSERT_EQ(*t2, *expected);
+  t->Slice(&t2, std::vector<dsize_t>{0, 1, 2, 3, 4});
+  ASSERT_EQ(*t2, *t);
+}

tests/ut/python/dataset/test_slice_op.py

+# Copyright 2019 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.
+# ==============================================================================
+"""
+Testing TypeCast op in DE
+"""
+import numpy as np
+import pytest
+
+import mindspore.dataset as ds
+import mindspore.dataset.transforms.c_transforms as ops
+
+
+def slice_compare(array, indexing):
+    data = ds.NumpySlicesDataset([array])
+    array = np.array(array)
+    data = data.map(operations=ops.Slice(indexing))
+    for d in data:
+        if indexing is None:
+            array = array[:]
+        else:
+            array = array[indexing]
+        np.testing.assert_array_equal(array, d[0])
+
+
+def test_slice_all():
+    slice_compare([1, 2, 3, 4, 5], None)
+    slice_compare([1, 2, 3, 4, 5], ...)
+
+
+def test_slice_single_index():
+    slice_compare([1, 2, 3, 4, 5], 0)
+    slice_compare([1, 2, 3, 4, 5], 4)
+    slice_compare([1, 2, 3, 4, 5], 2)
+    slice_compare([1, 2, 3, 4, 5], -1)
+    slice_compare([1, 2, 3, 4, 5], -5)
+    slice_compare([1, 2, 3, 4, 5], -3)
+
+
+def test_slice_list_index():
+    slice_compare([1, 2, 3, 4, 5], [0, 1, 4])
+    slice_compare([1, 2, 3, 4, 5], [4, 1, 0])
+    slice_compare([1, 2, 3, 4, 5], [-1, 1, 0])
+    slice_compare([1, 2, 3, 4, 5], [-1, -4, -2])
+    slice_compare([1, 2, 3, 4, 5], [3, 3, 3])
+    slice_compare([1, 2, 3, 4, 5], [1, 1, 1, 1, 1])
+
+
+def test_slice_slice_obj_2s():
+    slice_compare([1, 2, 3, 4, 5], slice(0, 2))
+    slice_compare([1, 2, 3, 4, 5], slice(2, 4))
+    slice_compare([1, 2, 3, 4, 5], slice(4, 10))
+
+
+def test_slice_slice_obj_1s():
+    slice_compare([1, 2, 3, 4, 5], slice(1))
+    slice_compare([1, 2, 3, 4, 5], slice(4))
+    slice_compare([1, 2, 3, 4, 5], slice(10))
+
+
+def test_slice_slice_obj_3s():
+    slice_compare([1, 2, 3, 4, 5], slice(0, 2, 1))
+    slice_compare([1, 2, 3, 4, 5], slice(0, 4, 1))
+    slice_compare([1, 2, 3, 4, 5], slice(0, 10, 1))
+    slice_compare([1, 2, 3, 4, 5], slice(0, 5, 2))
+    slice_compare([1, 2, 3, 4, 5], slice(0, 2, 2))
+    slice_compare([1, 2, 3, 4, 5], slice(0, 1, 2))
+    slice_compare([1, 2, 3, 4, 5], slice(4, 5, 1))
+    slice_compare([1, 2, 3, 4, 5], slice(2, 5, 3))
+
+
+def test_slice_slice_obj_3s_double():
+    slice_compare([1., 2., 3., 4., 5.], slice(0, 2, 1))
+    slice_compare([1., 2., 3., 4., 5.], slice(0, 4, 1))
+    slice_compare([1., 2., 3., 4., 5.], slice(0, 10, 1))
+    slice_compare([1., 2., 3., 4., 5.], slice(0, 5, 2))
+    slice_compare([1., 2., 3., 4., 5.], slice(0, 2, 2))
+    slice_compare([1., 2., 3., 4., 5.], slice(0, 1, 2))
+    slice_compare([1., 2., 3., 4., 5.], slice(4, 5, 1))
+    slice_compare([1., 2., 3., 4., 5.], slice(2, 5, 3))
+
+
+def test_slice_slice_obj_neg():
+    slice_compare([1, 2, 3, 4, 5], slice(-1, -5, -1))
+    slice_compare([1, 2, 3, 4, 5], slice(-1))
+    slice_compare([1, 2, 3, 4, 5], slice(-2))
+    slice_compare([1, 2, 3, 4, 5], slice(-1, -5, -2))
+    slice_compare([1, 2, 3, 4, 5], slice(-5, -1, 2))
+    slice_compare([1, 2, 3, 4, 5], slice(-5, -1))
+
+
+def test_slice_exceptions():
+    with pytest.raises(RuntimeError) as info:
+        slice_compare([1, 2, 3, 4, 5], 5)
+    assert "Index 5 is out of bounds [0,5)" in str(info.value)
+
+    with pytest.raises(RuntimeError) as info:
+        slice_compare([1, 2, 3, 4, 5], slice(0))
+    assert "Indices are empty, generated tensor would be empty." in str(info.value)
+
+    with pytest.raises(RuntimeError) as info:
+        slice_compare([1, 2, 3, 4, 5], slice(5, 10, 1))
+    assert "Indices are empty, generated tensor would be empty." in str(info.value)
+
+    with pytest.raises(RuntimeError) as info:
+        slice_compare([1, 2, 3, 4, 5], slice(-1, -5, 1))
+    assert "Indices are empty, generated tensor would be empty." in str(info.value)
+
+
+def test_slice_all_str():
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], None)
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], ...)
+
+
+def test_slice_single_index_str():
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], 0)
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], 4)
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], 2)
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], -1)
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], -5)
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], -3)
+
+
+def test_slice_list_index_str():
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], [0, 1, 4])
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], [4, 1, 0])
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], [-1, 1, 0])
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], [-1, -4, -2])
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], [3, 3, 3])
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], [1, 1, 1, 1, 1])
+
+
+def test_slice_slice_obj_2s_str():
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(0, 2))
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(2, 4))
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(4, 10))
+
+
+def test_slice_slice_obj_1s_str():
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(1))
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(4))
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(10))
+
+
+def test_slice_slice_obj_3s_str():
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(0, 2, 1))
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(0, 4, 1))
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(0, 10, 1))
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(0, 5, 2))
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(0, 2, 2))
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(0, 1, 2))
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(4, 5, 1))
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(2, 5, 3))
+
+
+def test_slice_slice_obj_neg_str():
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(-1, -5, -1))
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(-1))
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(-2))
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(-1, -5, -2))
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(-5, -1, 2))
+    slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(-5, -1))
+
+
+def test_slice_exceptions_str():
+    with pytest.raises(RuntimeError) as info:
+        slice_compare([b"1", b"2", b"3", b"4", b"5"], 5)
+    assert "Index 5 is out of bounds [0,5)" in str(info.value)
+
+    with pytest.raises(RuntimeError) as info:
+        slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(0))
+    assert "Indices are empty, generated tensor would be empty." in str(info.value)
+
+    with pytest.raises(RuntimeError) as info:
+        slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(5, 10, 1))
+    assert "Indices are empty, generated tensor would be empty." in str(info.value)
+
+    with pytest.raises(RuntimeError) as info:
+        slice_compare([b"1", b"2", b"3", b"4", b"5"], slice(-1, -5, 1))
+    assert "Indices are empty, generated tensor would be empty." in str(info.value)
+
+
+if __name__ == "__main__":
+    test_slice_all()
+    test_slice_single_index()
+    test_slice_list_index()
+    test_slice_slice_obj_3s()
+    test_slice_slice_obj_2s()
+    test_slice_slice_obj_1s()
+    test_slice_slice_obj_neg()
+    test_slice_exceptions()
+    test_slice_slice_obj_3s_double()
+    test_slice_all_str()
+    test_slice_single_index_str()
+    test_slice_list_index_str()
+    test_slice_slice_obj_3s_str()
+    test_slice_slice_obj_2s_str()
+    test_slice_slice_obj_1s_str()
+    test_slice_slice_obj_neg_str()
+    test_slice_exceptions_str()