add CPU ops: Greater/GreaterEqual/Range/GatherNd for center net

mindspore/ccsrc/backend/kernel_compiler/cpu/arithmetic_cpu_kernel.cc

@@ -167,6 +167,24 @@ void ArithmeticCPUKernel::SquaredDifference(const T *input1, const T *input2, T
   }
 }
 
+template <typename T>
+void ArithmeticCPUKernel::Greater(const T *input1, const T *input2, bool *out, size_t start, size_t end) {
+  for (size_t i = start; i < end; i++) {
+    std::vector<size_t> idx;
+    GenIndex(i, &idx);
+    out[i] = input1[idx[0]] > input2[idx[1]];
+  }
+}
+
+template <typename T>
+void ArithmeticCPUKernel::GreaterEqual(const T *input1, const T *input2, bool *out, size_t start, size_t end) {
+  for (size_t i = start; i < end; i++) {
+    std::vector<size_t> idx;
+    GenIndex(i, &idx);
+    out[i] = input1[idx[0]] >= input2[idx[1]];
+  }
+}
+
 void ArithmeticCPUKernel::InitKernel(const CNodePtr &kernel_node) {
   MS_EXCEPTION_IF_NULL(kernel_node);
   std::string kernel_name = AnfAlgo::GetCNodeName(kernel_node);
@@ -190,6 +208,10 @@ void ArithmeticCPUKernel::InitKernel(const CNodePtr &kernel_node) {
     operate_type_ = EQUAL;
   } else if (kernel_name == prim::kPrimNotEqual->name()) {
     operate_type_ = NOTEQUAL;
+  } else if (kernel_name == prim::kPrimGreater->name()) {
+    operate_type_ = GREATER;
+  } else if (kernel_name == prim::kPrimGreaterEqual->name()) {
+    operate_type_ = GREATEREQUAL;
   } else if (kernel_name == prim::kPrimAssignAdd->name()) {
     operate_type_ = ASSIGNADD;
   } else if (kernel_name == prim::kPrimSquaredDifference->name()) {
@@ -301,6 +323,11 @@ void ArithmeticCPUKernel::LaunchKernelLogic(const std::vector<AddressPtr> &input
       threads.emplace_back(std::thread(&ArithmeticCPUKernel::Equal<T>, this, input1, input2, output, start, end));
     } else if (operate_type_ == NOTEQUAL) {
       threads.emplace_back(std::thread(&ArithmeticCPUKernel::NotEqual<T>, this, input1, input2, output, start, end));
+    } else if (operate_type_ == GREATER) {
+      threads.emplace_back(std::thread(&ArithmeticCPUKernel::Greater<T>, this, input1, input2, output, start, end));
+    } else if (operate_type_ == GREATEREQUAL) {
+      threads.emplace_back(
+        std::thread(&ArithmeticCPUKernel::GreaterEqual<T>, this, input1, input2, output, start, end));
     } else {
       MS_LOG(EXCEPTION) << "Not support " << operate_type_;
     }

mindspore/ccsrc/backend/kernel_compiler/cpu/arithmetic_cpu_kernel.h

@@ -63,6 +63,10 @@ class ArithmeticCPUKernel : public CPUKernel {
   void NotEqual(const T *input1, const T *input2, bool *out, size_t start, size_t end);
   template <typename T>
   void SquaredDifference(const T *input1, const T *input2, T *out, size_t start, size_t end);
+  template <typename T>
+  void Greater(const T *input1, const T *input2, bool *out, size_t start, size_t end);
+  template <typename T>
+  void GreaterEqual(const T *input1, const T *input2, bool *out, size_t start, size_t end);
   std::vector<size_t> input_shape0_;
   std::vector<size_t> input_shape1_;
   std::vector<size_t> input_element_num0_;
@@ -213,6 +217,28 @@ MS_REG_CPU_KERNEL(
   SquaredDifference,
   KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
   ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  Greater, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  Greater,
+  KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  Greater, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  GreaterEqual,
+  KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  GreaterEqual,
+  KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
+MS_REG_CPU_KERNEL(
+  GreaterEqual,
+  KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeBool),
+  ArithmeticCPUKernel);
 }  // namespace kernel
 }  // namespace mindspore
 

mindspore/ccsrc/backend/kernel_compiler/cpu/cpu_kernel.h

@@ -53,6 +53,9 @@ const char END[] = "end";
 const char SIZE[] = "size";
 const char USE_NESTEROV[] = "use_nesterov";
 const char GROUP[] = "group";
+const char START[] = "start";
+const char LIMIT[] = "limit";
+const char DELTA[] = "delta";
 
 enum OperateType {
   ADD = 0,
@@ -79,7 +82,9 @@ enum OperateType {
   EQUAL,
   NOTEQUAL,
   FLOOR,
-  SQUAREDDIFFERENCE
+  SQUAREDDIFFERENCE,
+  GREATER,
+  GREATEREQUAL,
 };
 
 class CPUKernel : public kernel::KernelMod {

mindspore/ccsrc/backend/kernel_compiler/cpu/gathernd_cpu_kernel.cc

@@ -0,0 +1,104 @@
+/**
+ * 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.
+ */
+#include "backend/kernel_compiler/cpu/gathernd_cpu_kernel.h"
+#include "runtime/device/cpu/cpu_device_address.h"
+
+namespace mindspore {
+namespace kernel {
+
+void GatherNdCPUKernel::InitKernel(const CNodePtr &kernel_node) {
+  input_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 0);
+  indices_shapes_ = AnfAlgo::GetPrevNodeOutputInferShape(kernel_node, 1);
+  output_shapes_ = AnfAlgo::GetOutputInferShape(kernel_node, 0);
+
+  dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0);
+
+  // ReShape()
+  size_t dim_of_indices = 1;
+  for (size_t i = 0; i < indices_shapes_.size() - IntToSize(1); ++i) {
+    dim_of_indices *= indices_shapes_[i];
+  }
+
+  size_t dim_after_indices = 1;
+  size_t dim_indices_last = indices_shapes_[indices_shapes_.size() - IntToSize(1)];
+  for (size_t i = dim_indices_last; i < input_shapes_.size(); i++) {
+    dim_after_indices *= input_shapes_[i];
+  }
+
+  dims_.emplace_back(dim_of_indices);
+  dims_.emplace_back(dim_after_indices);
+  dims_.emplace_back(dim_indices_last);
+
+  batch_strides_.resize(dim_indices_last, 0);
+  batch_indices_.resize(dim_indices_last, 0);
+
+  if (dim_indices_last > 0) {
+    batch_strides_[dim_indices_last - 1] = input_shapes_[dim_indices_last - 1];
+    batch_indices_[dim_indices_last - 1] = dims_[1];
+  }
+
+  for (size_t i = dim_indices_last - 1; i > 0; --i) {
+    batch_strides_[i - 1] = input_shapes_[i - 1];
+    batch_indices_[i - 1] = batch_indices_[i] * input_shapes_[i];
+  }
+}
+
+bool GatherNdCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
+                               const std::vector<kernel::AddressPtr> & /*workspace*/,
+                               const std::vector<kernel::AddressPtr> &outputs) {
+  if (dtype_ == kNumberTypeInt32) {
+    return LaunchKernel<int32_t>(inputs, outputs);
+  } else if (dtype_ == kNumberTypeInt64) {
+    return LaunchKernel<int64_t>(inputs, outputs);
+  } else if (dtype_ == kNumberTypeFloat32) {
+    return LaunchKernel<float>(inputs, outputs);
+  } else if (dtype_ == kNumberTypeFloat64) {
+    return LaunchKernel<double>(inputs, outputs);
+  } else {
+    MS_LOG(EXCEPTION) << "Only support int, float, but actual data type is " << TypeIdLabel(dtype_);
+  }
+}
+
+template <typename T>
+bool GatherNdCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs) {
+  auto input_addr = reinterpret_cast<T *>(inputs[0]->addr);
+  auto indices_addr = reinterpret_cast<int *>(inputs[1]->addr);
+  auto output_addr = reinterpret_cast<T *>(outputs[0]->addr);
+
+  //
+  size_t output_dim0 = dims_[0];
+  size_t output_dim1 = dims_[1];
+  size_t indices_dim1 = dims_[2];
+
+  int num = output_dim0 * output_dim1;
+
+  for (int write_index = 0; write_index < num; write_index++) {
+    int i = write_index / output_dim1 % output_dim0;
+    int j = write_index % output_dim1;
+
+    int read_index = 0;
+    for (size_t k = 0; k < indices_dim1; k++) {
+      size_t ind = indices_dim1 * i + k;
+      int indices_i = indices_addr[ind];
+      read_index += indices_i * batch_indices_[k];
+    }
+    read_index += j;
+    output_addr[write_index] = input_addr[read_index];
+  }
+  return true;
+}
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/cpu/gathernd_cpu_kernel.h

@@ -0,0 +1,67 @@
+/**
+ * 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 MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_GATHERND_CPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_GATHERND_CPU_KERNEL_H_
+#include <vector>
+#include <memory>
+#include "backend/kernel_compiler/cpu/cpu_kernel.h"
+#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+class GatherNdCPUKernel : public CPUKernel {
+ public:
+  GatherNdCPUKernel() = default;
+  ~GatherNdCPUKernel() override = default;
+
+  void InitKernel(const CNodePtr &kernel_node) override;
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs) override;
+
+  template <typename T>
+  bool LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs);
+
+ private:
+  std::vector<size_t> input_shapes_;
+  std::vector<size_t> indices_shapes_;
+  std::vector<size_t> output_shapes_;
+
+  std::vector<size_t> dims_;
+  std::vector<int> batch_indices_;
+  std::vector<int> batch_strides_;
+
+  TypeId dtype_{kTypeUnknown};
+};
+
+MS_REG_CPU_KERNEL(
+  GatherNd, KernelAttr().AddInputAttr(kNumberTypeInt32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32),
+  GatherNdCPUKernel);
+MS_REG_CPU_KERNEL(
+  GatherNd, KernelAttr().AddInputAttr(kNumberTypeInt64).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt64),
+  GatherNdCPUKernel);
+MS_REG_CPU_KERNEL(
+  GatherNd,
+  KernelAttr().AddInputAttr(kNumberTypeFloat32).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat32),
+  GatherNdCPUKernel);
+MS_REG_CPU_KERNEL(
+  GatherNd,
+  KernelAttr().AddInputAttr(kNumberTypeFloat64).AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeFloat64),
+  GatherNdCPUKernel);
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_GATHERND_CPU_KERNEL_H_

mindspore/ccsrc/backend/kernel_compiler/cpu/range_cpu_kernel.cc

@@ -0,0 +1,56 @@
+/**
+ * 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 "backend/kernel_compiler/cpu/range_cpu_kernel.h"
+#include "runtime/device/cpu/cpu_device_address.h"
+
+namespace mindspore {
+namespace kernel {
+void RangeCPUKernel::InitKernel(const CNodePtr &kernel_node) {
+  MS_EXCEPTION_IF_NULL(kernel_node);
+  dtype_ = AnfAlgo::GetInputDeviceDataType(kernel_node, 0);
+
+  start_ = AnfAlgo::GetNodeAttr<float>(kernel_node, START);
+  limit_ = AnfAlgo::GetNodeAttr<float>(kernel_node, LIMIT);
+  delta_ = AnfAlgo::GetNodeAttr<float>(kernel_node, DELTA);
+}
+
+bool RangeCPUKernel::Launch(const std::vector<kernel::AddressPtr> &inputs,
+                            const std::vector<kernel::AddressPtr> & /*workspace*/,
+                            const std::vector<kernel::AddressPtr> &outputs) {
+  if (dtype_ == kNumberTypeInt32) {
+    return LaunchKernel<int32_t>(inputs, outputs);
+  } else if (dtype_ == kNumberTypeInt64) {
+    return LaunchKernel<int64_t>(inputs, outputs);
+  } else if (dtype_ == kNumberTypeFloat32) {
+    return LaunchKernel<float>(inputs, outputs);
+  } else if (dtype_ == kNumberTypeFloat64) {
+    return LaunchKernel<double>(inputs, outputs);
+  } else {
+    MS_LOG(EXCEPTION) << "Only support int, float, but actual data type is " << TypeIdLabel(dtype_);
+  }
+}
+
+template <typename T>
+bool RangeCPUKernel::LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs) {
+  auto output_addr = reinterpret_cast<T *>(outputs[0]->addr);
+  size_t elem_num = outputs[0]->size / sizeof(T);
+  for (size_t i = 0; i < elem_num; i++) {
+    output_addr[i] = start_ + i * delta_;
+  }
+  return true;
+}
+}  // namespace kernel
+}  // namespace mindspore

mindspore/ccsrc/backend/kernel_compiler/cpu/range_cpu_kernel.h

@@ -0,0 +1,54 @@
+/**
+ * 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.
+ */
+#ifndef MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_RANGE_CPU_KERNEL_H_
+#define MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_RANGE_CPU_KERNEL_H_
+#include <vector>
+#include <memory>
+#include "backend/kernel_compiler/cpu/cpu_kernel.h"
+#include "backend/kernel_compiler/cpu/cpu_kernel_factory.h"
+
+namespace mindspore {
+namespace kernel {
+class RangeCPUKernel : public CPUKernel {
+ public:
+  RangeCPUKernel() = default;
+  ~RangeCPUKernel() override = default;
+
+  void InitKernel(const CNodePtr &kernel_node) override;
+
+  bool Launch(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &workspace,
+              const std::vector<AddressPtr> &outputs) override;
+  template <typename T>
+  bool LaunchKernel(const std::vector<AddressPtr> &inputs, const std::vector<AddressPtr> &outputs);
+
+ private:
+  TypeId dtype_{kTypeUnknown};
+  int64_t start_;
+  int64_t limit_;
+  int64_t delta_;
+};
+
+MS_REG_CPU_KERNEL(Range, KernelAttr().AddInputAttr(kNumberTypeInt32).AddOutputAttr(kNumberTypeInt32), RangeCPUKernel);
+MS_REG_CPU_KERNEL(Range, KernelAttr().AddInputAttr(kNumberTypeInt64).AddOutputAttr(kNumberTypeInt64), RangeCPUKernel);
+MS_REG_CPU_KERNEL(Range, KernelAttr().AddInputAttr(kNumberTypeFloat32).AddOutputAttr(kNumberTypeFloat32),
+                  RangeCPUKernel);
+MS_REG_CPU_KERNEL(Range, KernelAttr().AddInputAttr(kNumberTypeFloat64).AddOutputAttr(kNumberTypeFloat64),
+                  RangeCPUKernel);
+
+}  // namespace kernel
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_BACKEND_KERNEL_COMPILER_CPU_RANGE_CPU_KERNEL_H_

mindspore/nn/layer/math.py

@@ -116,7 +116,7 @@ class Range(Cell):
         Tensor, the dtype is int if the dtype of `start`, `limit` and `delta` all are int. Otherwise, dtype is float.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``CPU``
 
     Examples:
         >>> net = nn.Range(1, 8, 2)

mindspore/ops/operations/array_ops.py

@@ -3078,7 +3078,7 @@ class GatherNd(PrimitiveWithInfer):
         Tensor, has the same type as `input_x` and the shape is indices_shape[:-1] + x_shape[indices_shape[-1]:].
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)

mindspore/ops/operations/math_ops.py

@@ -2698,7 +2698,7 @@ class Greater(_LogicBinaryOp):
         Tensor, the shape is the same as the one after broadcasting,and the data type is bool.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)
@@ -2739,7 +2739,7 @@ class GreaterEqual(_LogicBinaryOp):
         Tensor, the shape is the same as the one after broadcasting,and the data type is bool.
 
     Supported Platforms:
-        ``Ascend`` ``GPU``
+        ``Ascend`` ``GPU`` ``CPU``
 
     Examples:
         >>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int32)

tests/st/ops/cpu/test_gathernd_op.py

@@ -0,0 +1,188 @@
+# 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 numpy as np
+import pytest
+
+import mindspore
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
+
+
+class OpNetWrapper(nn.Cell):
+    def __init__(self, op):
+        super(OpNetWrapper, self).__init__()
+        self.op = op
+
+    def construct(self, *inputs):
+        return self.op(*inputs)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_case1_basic_func():
+    op = P.GatherNd()
+    op_wrapper = OpNetWrapper(op)
+
+    indices = Tensor(np.array([[0, 0], [1, 1]]), mindspore.int32)
+    params = Tensor(np.array([[0, 1], [2, 3]]), mindspore.float32)
+    outputs = op_wrapper(params, indices)
+    print(outputs)
+    expected = [0, 3]
+    assert np.allclose(outputs.asnumpy(), np.array(expected))
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_case2_indices_to_matrix():
+    op = P.GatherNd()
+    op_wrapper = OpNetWrapper(op)
+
+    indices = Tensor(np.array([[1], [0]]), mindspore.int32)
+    params = Tensor(np.array([[0, 1], [2, 3]]), mindspore.float32)
+    outputs = op_wrapper(params, indices)
+    print(outputs)
+    expected = [[2, 3], [0, 1]]
+    assert np.allclose(outputs.asnumpy(), np.array(expected))
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_case3_indices_to_3d_tensor():
+    op = P.GatherNd()
+    op_wrapper = OpNetWrapper(op)
+
+    indices = Tensor(np.array([[1]]), mindspore.int32)  # (1, 1)
+    params = Tensor(np.array([[[0, 1], [2, 3]],
+                              [[4, 5], [6, 7]]]), mindspore.float32)  # (2, 2, 2)
+    outputs = op_wrapper(params, indices)
+    print(outputs)
+    expected = [[[4, 5], [6, 7]]]  # (1, 2, 2)
+    assert np.allclose(outputs.asnumpy(), np.array(expected))
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_case4():
+    op = P.GatherNd()
+    op_wrapper = OpNetWrapper(op)
+
+    indices = Tensor(np.array([[0, 1], [1, 0]]), mindspore.int32)  # (2, 2)
+    params = Tensor(np.array([[[0, 1], [2, 3]],
+                              [[4, 5], [6, 7]]]), mindspore.float32)  # (2, 2, 2)
+    outputs = op_wrapper(params, indices)
+    print(outputs)
+    expected = [[2, 3], [4, 5]]  # (2, 2)
+    assert np.allclose(outputs.asnumpy(), np.array(expected))
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_case5():
+    op = P.GatherNd()
+    op_wrapper = OpNetWrapper(op)
+
+    indices = Tensor(np.array([[0, 0, 1], [1, 0, 1]]), mindspore.int32)  # (2, 3)
+    params = Tensor(np.array([[[0, 1], [2, 3]],
+                              [[4, 5], [6, 7]]]), mindspore.float32)  # (2, 2, 2)
+    outputs = op_wrapper(params, indices)
+    print(outputs)
+    expected = [1, 5]  # (2,)
+    assert np.allclose(outputs.asnumpy(), np.array(expected))
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_case6():
+    op = P.GatherNd()
+    op_wrapper = OpNetWrapper(op)
+
+    indices = Tensor(np.array([[[0, 0]], [[0, 1]]]), mindspore.int32)  # (2, 1, 2)
+    params = Tensor(np.array([[[0, 1], [2, 3]],
+                              [[4, 5], [6, 7]]]), mindspore.float32)  # (2, 2, 2)
+    outputs = op_wrapper(params, indices)
+    print(outputs)
+    expected = [[[0, 1]], [[2, 3]]]  # (2, 1, 2)
+    assert np.allclose(outputs.asnumpy(), np.array(expected))
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_case7():
+    op = P.GatherNd()
+    op_wrapper = OpNetWrapper(op)
+
+    indices = Tensor(np.array([[[1]], [[0]]]), mindspore.int32)  # (2, 1, 1)
+    params = Tensor(np.array([[[0, 1], [2, 3]],
+                              [[4, 5], [6, 7]]]), mindspore.float32)  # (2, 2, 2)
+    outputs = op_wrapper(params, indices)
+    print(outputs)
+    expected = [[[[4, 5], [6, 7]]], [[[0, 1], [2, 3]]]]  # (2, 1, 2, 2)
+    assert np.allclose(outputs.asnumpy(), np.array(expected))
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_case8():
+    op = P.GatherNd()
+    op_wrapper = OpNetWrapper(op)
+
+    indices = Tensor(np.array([[[0, 1], [1, 0]], [[0, 0], [1, 1]]]), mindspore.int32)  # (2, 2, 2)
+    params = Tensor(np.array([[[0, 1], [2, 3]],
+                              [[4, 5], [6, 7]]]), mindspore.float32)  # (2, 2, 2)
+    outputs = op_wrapper(params, indices)
+    print(outputs)
+    expected = [[[2, 3], [4, 5]], [[0, 1], [6, 7]]]  # (2, 2, 2)
+    assert np.allclose(outputs.asnumpy(), np.array(expected))
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_case9():
+    op = P.GatherNd()
+    op_wrapper = OpNetWrapper(op)
+
+    indices = Tensor(np.array([[[0, 0, 1], [1, 0, 1]], [[0, 1, 1], [1, 1, 0]]]), mindspore.int32)  # (2, 2, 3)
+    params = Tensor(np.array([[[0, 1], [2, 3]],
+                              [[4, 5], [6, 7]]]), mindspore.int64)  # (2, 2, 2)
+    outputs = op_wrapper(params, indices)
+    print(outputs)
+    expected = [[1, 5], [3, 6]]  # (2, 2, 2)
+    assert np.allclose(outputs.asnumpy(), np.array(expected))
+
+
+if __name__ == '__main__':
+    test_case1_basic_func()
+    test_case2_indices_to_matrix()
+    test_case3_indices_to_3d_tensor()
+    test_case4()
+    test_case5()
+    test_case6()
+    test_case7()
+    test_case8()
+    test_case9()

tests/st/ops/cpu/test_greater_equal_op.py

@@ -0,0 +1,70 @@
+# 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 numpy as np
+import pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
+
+
+class OpNetWrapper(nn.Cell):
+    def __init__(self, op):
+        super(OpNetWrapper, self).__init__()
+        self.op = op
+
+    def construct(self, *inputs):
+        return self.op(*inputs)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_int32():
+    op = P.GreaterEqual()
+    op_wrapper = OpNetWrapper(op)
+
+    input_x = Tensor(np.array([1, 2, 3]).astype(np.int32))
+    input_y = Tensor(np.array([3, 2, 1]).astype(np.int32))
+    outputs = op_wrapper(input_x, input_y)
+
+    print(outputs)
+    assert outputs.shape == (3,)
+    assert np.allclose(outputs.asnumpy(), [False, True, True])
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_float32():
+    op = P.GreaterEqual()
+    op_wrapper = OpNetWrapper(op)
+
+    input_x = Tensor(np.array([1, 2, -1]).astype(np.float32))
+    input_y = Tensor(np.array([-3, 2, -1]).astype(np.float32))
+    outputs = op_wrapper(input_x, input_y)
+
+    print(outputs)
+    assert outputs.shape == (3,)
+    assert np.allclose(outputs.asnumpy(), [True, True, True])
+
+
+if __name__ == '__main__':
+    test_int32()
+    test_float32()

tests/st/ops/cpu/test_greater_op.py

@@ -0,0 +1,70 @@
+# 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 numpy as np
+import pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
+
+
+class OpNetWrapper(nn.Cell):
+    def __init__(self, op):
+        super(OpNetWrapper, self).__init__()
+        self.op = op
+
+    def construct(self, *inputs):
+        return self.op(*inputs)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_int32():
+    op = P.Greater()
+    op_wrapper = OpNetWrapper(op)
+
+    input_x = Tensor(np.array([1, 2, 3]).astype(np.int32))
+    input_y = Tensor(np.array([3, 2, 1]).astype(np.int32))
+    outputs = op_wrapper(input_x, input_y)
+
+    print(outputs)
+    assert outputs.shape == (3,)
+    assert np.allclose(outputs.asnumpy(), [False, False, True])
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_float32():
+    op = P.Greater()
+    op_wrapper = OpNetWrapper(op)
+
+    input_x = Tensor(np.array([1, 2, -1]).astype(np.float32))
+    input_y = Tensor(np.array([-3, 2, -1]).astype(np.float32))
+    outputs = op_wrapper(input_x, input_y)
+
+    print(outputs)
+    assert outputs.shape == (3,)
+    assert np.allclose(outputs.asnumpy(), [True, False, False])
+
+
+if __name__ == '__main__':
+    test_int32()
+    test_float32()

tests/st/ops/cpu/test_range_op.py

@@ -0,0 +1,62 @@
+# 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 numpy as np
+import pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+
+context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
+
+
+class OpNetWrapper(nn.Cell):
+    def __init__(self, op):
+        super(OpNetWrapper, self).__init__()
+        self.op = op
+
+    def construct(self, *inputs):
+        return self.op(*inputs)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_int():
+    op = nn.Range(0, 100, 10)
+    op_wrapper = OpNetWrapper(op)
+
+    outputs = op_wrapper()
+    print(outputs)
+    assert outputs.shape == (10,)
+    assert np.allclose(outputs.asnumpy(), range(0, 100, 10))
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+def test_float():
+    op = nn.Range(10., 100., 20.)
+    op_wrapper = OpNetWrapper(op)
+
+    outputs = op_wrapper()
+    print(outputs)
+    assert outputs.shape == (5,)
+    assert np.allclose(outputs.asnumpy(), [10., 30., 50., 70., 90.])
+
+
+if __name__ == '__main__':
+    test_int()
+    test_float()