add tensor(tensor and tensor in list) support for argument starts and ends in slice op; (#19208)

add support parameter inference when arguments starts or ends is a list containing integer and tensor variable;
test=develop,test=document_preview

improve slice op according to review(from hongyu). test=develop

fix slice op according to review: infer_flags, test=develop

fix slice op: improve overload operator __getitem__ to support attrs(starts and ends) are Variable.
test=develop,test=document_preview

fix test_slice_op: add TestSliceOp_decs_dim_6 to resolve conflict with test_slice_ngraph_op. test=develop

add stop_gradient=True when attr(starts) or attr(ends) is tensor Variable.
test=develop,test=document_preview

paddle/fluid/API.spec

@@ -243,7 +243,7 @@ paddle.fluid.layers.gaussian_random (ArgSpec(args=['shape', 'mean', 'std', 'seed
 paddle.fluid.layers.sampling_id (ArgSpec(args=['x', 'min', 'max', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0, 'float32')), ('document', 'c39b647b6cf08e058d96ee503d5284fe'))
 paddle.fluid.layers.gaussian_random_batch_size_like (ArgSpec(args=['input', 'shape', 'input_dim_idx', 'output_dim_idx', 'mean', 'std', 'seed', 'dtype'], varargs=None, keywords=None, defaults=(0, 0, 0.0, 1.0, 0, 'float32')), ('document', 'b24d0b21361c4bb8ef2cec8c26fb12b2'))
 paddle.fluid.layers.sum (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', 'f4b60847cb0f1ae00823ba6fb1b11310'))
-paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '3ca6a761570d86e303e473afba99bb49'))
+paddle.fluid.layers.slice (ArgSpec(args=['input', 'axes', 'starts', 'ends'], varargs=None, keywords=None, defaults=None), ('document', '315b4870f294e33a27ecbdf440bed3ff'))
 paddle.fluid.layers.shape (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', 'bf61c8f79d795a8371bdb3b5468aa82b'))
 paddle.fluid.layers.rank (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', '096df0e0273145ab80ed119a4c294db3'))
 paddle.fluid.layers.size (ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None), ('document', 'cf2e156beae36378722666c4c33bebfe'))

paddle/fluid/operators/slice_op.cc

@@ -15,6 +15,7 @@ limitations under the License. */
 #include "paddle/fluid/operators/slice_op.h"
 #include <algorithm>
 #include <memory>
+#include <string>
 #include <vector>
 
 namespace paddle {
@@ -26,44 +27,81 @@ class SliceOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Input"),
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE_EQ(ctx->HasInput("Input"), true,
                       "Input (Input) of slice op should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+
+    PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
                       "Output (Out) of slice op should not be null.");
 
     auto in_dims = ctx->GetInputDim("Input");
-    PADDLE_ENFORCE(in_dims.size() < 7,
+    PADDLE_ENFORCE_LT(in_dims.size(), 7,
                       "The rank of input should be less than 7.");
     framework::DDim out_dims(in_dims);
+
     auto axes = ctx->Attrs().Get<std::vector<int>>("axes");
     auto starts = ctx->Attrs().Get<std::vector<int>>("starts");
     auto ends = ctx->Attrs().Get<std::vector<int>>("ends");
+    auto infer_flags = ctx->Attrs().Get<std::vector<int>>("infer_flags");
     auto decrease_axis = ctx->Attrs().Get<std::vector<int>>("decrease_axis");
 
-    PADDLE_ENFORCE_EQ(starts.size(), ends.size());
-    PADDLE_ENFORCE_EQ(starts.size(), axes.size());
+    auto starts_size = starts.size();
+    auto ends_size = ends.size();
+    if (infer_flags.empty()) {
+      // Initialize infer_flags with 1.
+      // To be compatible with other op tests in which infer_flags is not set.
+      infer_flags = std::vector<int>(axes.size(), 1);
+    }
+
+    if (ctx->HasInputs("StartsTensorList")) {
+      auto StartsTensorList = ctx->Inputs("StartsTensorList");
+      PADDLE_ENFORCE_GT(StartsTensorList.size(), 0,
+                        "StartsTensorList size can't be zero");
+      starts_size = StartsTensorList.size();
+    }
+    if (ctx->HasInputs("EndsTensorList")) {
+      auto EndsTensorList = ctx->Inputs("EndsTensorList");
+      PADDLE_ENFORCE_GT(EndsTensorList.size(), 0,
+                        "EndsTensorList size can't be zero");
+      ends_size = EndsTensorList.size();
+    }
+
+    if (ctx->HasInput("StartsTensor") == false) {
+      PADDLE_ENFORCE_EQ(
+          starts_size, axes.size(),
+          "The size of starts must be equal to the size of axes.");
+    }
+    if (ctx->HasInput("EndsTensor") == false) {
+      PADDLE_ENFORCE_EQ(ends_size, axes.size(),
+                        "The size of ends must be equal to the size of axes.");
+    }
+
     int dim_value, start, end;
     for (size_t i = 0; i < axes.size(); ++i) {
+      PADDLE_ENFORCE_LT(static_cast<int>(axes[i]), in_dims.size(),
+                        "The index of dimension in axes must be less "
+                        "than the size of input shape.");
+      if (infer_flags[i] == -1) {
+        out_dims[axes[i]] = -1;
+      } else {
+        // infer out_dim shape
         dim_value = out_dims[axes[i]];
         if (dim_value > 0) {
           start = starts[i] < 0 ? (starts[i] + dim_value) : starts[i];
           end = ends[i] < 0 ? (ends[i] + dim_value) : ends[i];
           start = std::max(start, 0);
           end = std::max(end, 0);
-        // start = std::min(start, dim_value);
           end = std::min(end, dim_value);
-        // start = std::min(start, end);
           PADDLE_ENFORCE_GT(end, start, "end should greater than start");
           out_dims[axes[i]] = end - start;
         }
       }
-
+    }
     // generate new shape
     if (decrease_axis.size() > 0) {
       std::vector<int> new_out_shape;
       for (size_t i = 0; i < decrease_axis.size(); ++i) {
-        if (ctx->IsRuntime()) {
+        if (ctx->IsRuntime() && infer_flags[i] != -1) {
           PADDLE_ENFORCE_EQ(out_dims[decrease_axis[i]], 1,
                             "decrease dim should be 1");
         }
@@ -81,7 +119,6 @@ class SliceOp : public framework::OperatorWithKernel {
 
       out_dims = framework::make_ddim(new_out_shape);
     }
-
     ctx->SetOutputDim("Out", out_dims);
     if (axes[0] != 0) {
       ctx->ShareLoD("Input", /*->*/ "Out");
@@ -90,28 +127,67 @@ class SliceOp : public framework::OperatorWithKernel {
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
+      const framework::ExecutionContext &ctx) const override {
     return framework::OpKernelType(ctx.Input<Tensor>("Input")->type(),
-                                   ctx.Input<Tensor>("Input")->place());
+                                   ctx.device_context());
+  }
+  framework::OpKernelType GetKernelTypeForVar(
+      const std::string &var_name, const Tensor &tensor,
+      const framework::OpKernelType &expected_kernel_type) const override {
+    if (var_name == "StartsTensor" || var_name == "EndsTensor") {
+      return expected_kernel_type;
+    }
+    if (var_name == "StartsTensorList" || var_name == "EndsTensorList") {
+      return expected_kernel_type;
+    }
+    return framework::OpKernelType(expected_kernel_type.data_type_,
+                                   tensor.place(), tensor.layout());
   }
 };
 
 class SliceOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
-    AddInput("Input", "Tensor of data to extract slices from.");
+    AddInput("Input", "(Tensor) Tensor of data to extract slices from.");
+    AddInput("StartsTensor",
+             "(Tensor<int32>, optional) If provided, slice will use this."
+             "It has the highest priority of StartsTensor, StartsTensorList "
+             "and attr(starts).")
+        .AsDispensable();
+    AddInput("EndsTensor",
+             "(Tensor<int32>, optional) If provided, slice will use this."
+             "It has the highest priority of EndsTensor, EndsTensorList and "
+             "attr(ends).")
+        .AsDispensable();
+    AddInput(
+        "StartsTensorList",
+        "(vector<Tensor<int32>>, optional) If provided, slice will use this."
+        "The shape of the tensor in vector MUST BE [1]."
+        "It has higher priority compare with attr(starts).")
+        .AsDuplicable()
+        .AsDispensable();
+    AddInput(
+        "EndsTensorList",
+        "(vector<Tensor<int32>>, optional) If provided, slice will use this."
+        "The shape of the tensor in vector MUST BE [1]."
+        "It has higher priority compare with attr(ends).")
+        .AsDuplicable()
+        .AsDispensable();
     AddOutput("Out", "Sliced data tensor.");
-
     AddAttr<std::vector<int>>(
         "axes",
         "(list<int>) Axes that `starts` and `ends` apply to. It's optional."
         "If not present, will be treated as [0, 1, ..., len(`starts`) - 1].");
     AddAttr<std::vector<int>>(
         "starts",
-        "(list<int>) Starting indices of corresponding axis in `axes`");
+        "(list<int>) Starting indices of corresponding axis in `axes`")
+        .SetDefault({});
+    AddAttr<std::vector<int>>(
+        "ends", "(list<int>) Ending indices of corresponding axis in `axes`.")
+        .SetDefault({});
     AddAttr<std::vector<int>>(
-        "ends",
-        "(list<int>) Starting indices of corresponding axis in `axes`.");
+        "infer_flags", "(list<int>) Flags of inferring dims in attributes.")
+        .SetDefault({});
     AddAttr<std::vector<int>>("decrease_axis", "(list<int>) decrease_axis")
         .SetDefault({});
     AddComment(R"DOC(
@@ -155,9 +231,9 @@ class SliceOpGrad : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Input"), "Input should not be null");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE_EQ(ctx->HasInput("Input"), true, "Input should not be null");
+    PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Out")), true,
                       "Input(Out@GRAD) should not be null");
     auto x_dims = ctx->GetInputDim("Input");
     auto x_grad_name = framework::GradVarName("Input");
@@ -165,12 +241,23 @@ class SliceOpGrad : public framework::OperatorWithKernel {
       ctx->SetOutputDim(x_grad_name, x_dims);
     }
   }
-
   framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
+      const framework::ExecutionContext &ctx) const override {
     return framework::OpKernelType(
         ctx.Input<framework::Tensor>(framework::GradVarName("Out"))->type(),
-        ctx.GetPlace());
+        ctx.device_context());
+  }
+  framework::OpKernelType GetKernelTypeForVar(
+      const std::string &var_name, const Tensor &tensor,
+      const framework::OpKernelType &expected_kernel_type) const override {
+    if (var_name == "StartsTensor" || var_name == "EndsTensor") {
+      return expected_kernel_type;
+    }
+    if (var_name == "StartsTensorList" || var_name == "EndsTensorList") {
+      return expected_kernel_type;
+    }
+    return framework::OpKernelType(expected_kernel_type.data_type_,
+                                   tensor.place(), tensor.layout());
   }
 };
 
@@ -180,8 +267,12 @@ class SliceOpGradMaker : public framework::SingleGradOpDescMaker {
 
  protected:
   std::unique_ptr<framework::OpDesc> Apply() const override {
-    auto* bind = new framework::OpDesc();
+    auto *bind = new framework::OpDesc();
     bind->SetInput("Input", Input("Input"));
+    bind->SetInput("StartsTensor", Input("StartsTensor"));
+    bind->SetInput("EndsTensor", Input("EndsTensor"));
+    bind->SetInput("StartsTensorList", Input("StartsTensorList"));
+    bind->SetInput("EndsTensorList", Input("EndsTensorList"));
     bind->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
     bind->SetOutput(framework::GradVarName("Input"), InputGrad("Input"));
     bind->SetAttrMap(Attrs());

paddle/fluid/operators/slice_op.cu

@@ -65,6 +65,16 @@ class SliceGradKernel<paddle::platform::CUDADeviceContext,
     auto axes = ctx.Attr<std::vector<int>>("axes");
     auto starts = ctx.Attr<std::vector<int>>("starts");
 
+    auto list_new_starts_tensor =
+        ctx.MultiInput<framework::Tensor>("StartsTensorList");
+
+    if (list_new_starts_tensor.size() > 0) {
+      starts = get_new_data_from_tensorlist(list_new_starts_tensor);
+    } else if (ctx.HasInput("StartsTensor")) {
+      auto* starts_tensor = ctx.Input<framework::Tensor>("StartsTensor");
+      starts = get_new_data_from_tensor(starts_tensor);
+    }
+
     for (size_t i = 0; i < starts.size(); ++i) {
       if (starts[i] < 0) {
         starts[i] += in_dims[axes[i]];

paddle/fluid/operators/slice_op.h

@@ -20,6 +20,39 @@ limitations under the License. */
 
 namespace paddle {
 namespace operators {
+using Tensor = framework::Tensor;
+
+inline std::vector<int> get_new_data_from_tensorlist(
+    const std::vector<const Tensor*>& list_new_data_tensor) {
+  // get tensor from
+  std::vector<int> vec_new_data;
+  for (size_t i = 0; i < list_new_data_tensor.size(); ++i) {
+    auto tensor = list_new_data_tensor[i];
+    PADDLE_ENFORCE_EQ(tensor->dims(), framework::make_ddim({1}),
+                      "shape of dim tensor should be [1]");
+    if (platform::is_gpu_place(tensor->place())) {
+      framework::Tensor temp;
+      TensorCopySync(*tensor, platform::CPUPlace(), &temp);
+      vec_new_data.push_back(static_cast<int32_t>(*temp.data<int32_t>()));
+    } else {
+      vec_new_data.push_back(static_cast<int32_t>(*tensor->data<int32_t>()));
+    }
+  }
+  return vec_new_data;
+}
+inline std::vector<int> get_new_data_from_tensor(
+    const Tensor* new_data_tensor) {
+  std::vector<int> vec_new_data;
+  auto* new_data = new_data_tensor->data<int>();
+  framework::Tensor cpu_starts_tensor;
+  if (platform::is_gpu_place(new_data_tensor->place())) {
+    TensorCopySync(*new_data_tensor, platform::CPUPlace(), &cpu_starts_tensor);
+    new_data = cpu_starts_tensor.data<int>();
+  }
+  vec_new_data =
+      std::vector<int>(new_data, new_data + new_data_tensor->numel());
+  return vec_new_data;
+}
 
 template <typename DeviceContext, typename T>
 class SliceKernel : public framework::OpKernel<T> {
@@ -58,8 +91,90 @@ class SliceKernel : public framework::OpKernel<T> {
     auto out_dims = out->dims();
     auto in_dims = in->dims();
 
-    // resize out_dims
+    auto axes = context.Attr<std::vector<int>>("axes");
+    auto starts = context.Attr<std::vector<int>>("starts");
+    auto ends = context.Attr<std::vector<int>>("ends");
     auto decrease_axis = context.Attr<std::vector<int>>("decrease_axis");
+    auto infer_flags = context.Attr<std::vector<int>>("infer_flags");
+
+    auto list_new_ends_tensor =
+        context.MultiInput<framework::Tensor>("EndsTensorList");
+    auto list_new_starts_tensor =
+        context.MultiInput<framework::Tensor>("StartsTensorList");
+
+    bool need_infer = false;
+    if (context.HasInput("StartsTensor") || context.HasInput("EndsTensor")) {
+      need_infer = true;
+    }
+    if (list_new_starts_tensor.size() > 0 || list_new_ends_tensor.size() > 0) {
+      need_infer = true;
+    }
+
+    if (need_infer) {
+      if (context.HasInput("StartsTensor")) {
+        auto* starts_tensor = context.Input<framework::Tensor>("StartsTensor");
+        starts = get_new_data_from_tensor(starts_tensor);
+      } else if (list_new_starts_tensor.size() > 0) {
+        starts = get_new_data_from_tensorlist(list_new_starts_tensor);
+      }
+      PADDLE_ENFORCE_EQ(
+          starts.size(), axes.size(),
+          "The size of starts must be equal to the size of axes.");
+      if (context.HasInput("EndsTensor")) {
+        auto* ends_tensor = context.Input<framework::Tensor>("EndsTensor");
+        ends = get_new_data_from_tensor(ends_tensor);
+      } else if (list_new_ends_tensor.size() > 0) {
+        ends = get_new_data_from_tensorlist(list_new_ends_tensor);
+      }
+      PADDLE_ENFORCE_EQ(ends.size(), axes.size(),
+                        "The size of ends must be equal to the size of axes.");
+      out_dims = in_dims;
+      int dim_value, start, end;
+      for (size_t i = 0; i < axes.size(); ++i) {
+        dim_value = out_dims[axes[i]];
+        if (dim_value > 0) {
+          // when end = start+1 and start == -1
+          if (starts[i] == -1 && ends[i] == 0 && infer_flags[i] == -1) {
+            auto ret =
+                std::find(decrease_axis.begin(), decrease_axis.end(), axes[i]);
+            if (ret != decrease_axis.end()) {
+              ends[i] = 10000000;
+            }
+          }
+
+          start = starts[i] < 0 ? (starts[i] + dim_value) : starts[i];
+          end = ends[i] < 0 ? (ends[i] + dim_value) : ends[i];
+          start = std::max(start, 0);
+          end = std::max(end, 0);
+          end = std::min(end, dim_value);
+          PADDLE_ENFORCE_GT(end, start, "end should greater than start");
+          out_dims[axes[i]] = end - start;
+        }
+      }
+      out->Resize(out_dims);
+      // generate new shape
+      if (decrease_axis.size() > 0) {
+        std::vector<int> new_out_shape;
+        for (size_t i = 0; i < decrease_axis.size(); ++i) {
+          PADDLE_ENFORCE_EQ(out_dims[decrease_axis[i]], 1,
+                            "decrease dim should be 1");
+          out_dims[decrease_axis[i]] = 0;
+        }
+
+        for (int i = 0; i < out_dims.size(); ++i) {
+          if (out_dims[i] != 0) {
+            new_out_shape.push_back(out_dims[i]);
+          }
+        }
+        if (new_out_shape.size() == 0) {
+          new_out_shape.push_back(1);
+        }
+
+        out_dims = framework::make_ddim(new_out_shape);
+      }
+    }
+
+    // resize out_dims
     if (decrease_axis.size() > 0) {
       if (decrease_axis.size() == (size_t)in_dims.size()) {
         std::vector<int> vec_origin_out_shape(decrease_axis.size(), 1);
@@ -85,8 +200,6 @@ class SliceKernel : public framework::OpKernel<T> {
     }
 
     out->mutable_data<T>(context.GetPlace());
-    auto axes = context.Attr<std::vector<int>>("axes");
-    auto starts = context.Attr<std::vector<int>>("starts");
 
     auto new_out_dims = out->dims();
     auto offsets = Eigen::array<int, D>();
@@ -157,6 +270,26 @@ class SliceGradKernel : public framework::OpKernel<T> {
     auto in_dims = d_input->dims();
     auto axes = context.Attr<std::vector<int>>("axes");
     auto starts = context.Attr<std::vector<int>>("starts");
+    auto ends = context.Attr<std::vector<int>>("ends");
+
+    auto list_new_ends_tensor =
+        context.MultiInput<framework::Tensor>("EndsTensorList");
+    auto list_new_starts_tensor =
+        context.MultiInput<framework::Tensor>("StartsTensorList");
+
+    if (list_new_starts_tensor.size() > 0) {
+      starts = get_new_data_from_tensorlist(list_new_starts_tensor);
+    } else if (context.HasInput("StartsTensor")) {
+      auto* starts_tensor = context.Input<framework::Tensor>("StartsTensor");
+      starts = get_new_data_from_tensor(starts_tensor);
+    }
+
+    if (list_new_ends_tensor.size() > 0) {
+      ends = get_new_data_from_tensorlist(list_new_ends_tensor);
+    } else if (context.HasInput("EndsTensor")) {
+      auto* ends_tensor = context.Input<framework::Tensor>("EndsTensor");
+      ends = get_new_data_from_tensor(ends_tensor);
+    }
 
     auto decrease_axis = context.Attr<std::vector<int>>("decrease_axis");
     if (decrease_axis.size() > 0) {

python/paddle/fluid/framework.py

@@ -902,6 +902,21 @@ class Variable(object):
         slice_end = []
         reverse_axis = []
 
+        def fill_constant(shape, dtype, value, force_cpu=False, out=None):
+            self.block.append_op(
+                type='fill_constant',
+                inputs={},
+                outputs={'Out': [out]},
+                attrs={
+                    'shape': shape,
+                    'dtype': out.dtype,
+                    'value': float(value),
+                    'force_cpu': force_cpu or force_init_on_cpu()
+                },
+                stop_gradient=True)
+            out.stop_gradient = True
+            return out
+
         for dim, slice_item in enumerate(item):
             if isinstance(slice_item, slice):
                 start = slice_item.start
@@ -927,17 +942,81 @@ class Variable(object):
                 slice_start.append(start)
                 slice_end.append(end)
             else:
-                # int
                 decrease_axis.append(dim)
                 slice_axis.append(dim)
                 slice_start.append(slice_item)
+                if isinstance(slice_item, Variable):
+                    temp_1 = self.block.create_var(dtype='int32')
+                    fill_constant([1], 'int32', 1, force_cpu=True, out=temp_1)
+                    temp_end = self.block.create_var(dtype='int32')
+                    self.block.append_op(
+                        type='elementwise_add',
+                        inputs={'X': slice_item,
+                                'Y': temp_1},
+                        outputs={'Out': temp_end},
+                        attrs={'axis': -1})
+                    slice_end.append(temp_end)
+                else:
                     slice_end.append(slice_item + 1
                                      if slice_item != -1 else 10000000)
 
+        def contain_var(one_list):
+            for ele in one_list:
+                if isinstance(ele, Variable):
+                    return True
+            return False
+
+        def get_new_list_tensor(old_list):
+            new_list_tensor = []
+            for dim in old_list:
+                if isinstance(dim, Variable):
+                    dim.stop_gradient = True
+                    new_list_tensor.append(dim)
+                else:
+                    assert (isinstance(dim, int))
+                    temp_out = self.block.create_var(dtype='int32')
+                    fill_constant(
+                        [1], 'int32', dim, force_cpu=True, out=temp_out)
+                    new_list_tensor.append(temp_out)
+            return new_list_tensor
+
+        inputs = {'Input': [self]}
+        attrs = {
+            'axes': slice_axis,
+            'starts': [],
+            'ends': [],
+            'decrease_axis': decrease_axis
+        }
+        infer_flags = list(1 for i in range(len(slice_axis)))
+
+        # starts
+        if not contain_var(slice_start):
+            attrs['starts'] = slice_start
+        else:
+            inputs['StartsTensorList'] = get_new_list_tensor(slice_start)
+            for i, dim in enumerate(slice_start):
+                if isinstance(dim, Variable):
+                    attrs['starts'].append(-1)
+                    infer_flags[i] = -1
+                else:
+                    attrs['starts'].append(dim)
+        # ends
+        if not contain_var(slice_end):
+            attrs['ends'] = slice_end
+        else:
+            inputs['EndsTensorList'] = get_new_list_tensor(slice_end)
+            for i, dim in enumerate(slice_end):
+                if isinstance(dim, Variable):
+                    attrs['ends'].append(-1)
+                    infer_flags[i] = -1
+                else:
+                    attrs['ends'].append(dim)
+        # infer_flags
+        attrs['infer_flags'] = infer_flags
+
         out = self
         if len(slice_axis) > 0:
             # append slice_op here
-
             slice_out_var = self.block.create_var(
                 name=unique_name.generate_with_ignorable_key(self.name +
                                                              "_slice"),
@@ -945,14 +1024,9 @@ class Variable(object):
 
             self.block.append_op(
                 type="slice",
-                inputs={'Input': [out]},
+                inputs=inputs,
                 outputs={'Out': [slice_out_var]},
-                attrs={
-                    'axes': slice_axis,
-                    'starts': slice_start,
-                    'ends': slice_end,
-                    'decrease_axis': decrease_axis
-                })
+                attrs=attrs)
 
             out = slice_out_var
 

python/paddle/fluid/layers/nn.py

@@ -10635,8 +10635,8 @@ def slice(input, axes, starts, ends):
     Args:
         input (Variable): ${input_comment}.
         axes (List): ${axes_comment}
-        starts (List): ${starts_comment}
-        ends (List): ${ends_comment}
+        starts (List|Variable): ${starts_comment}
+        ends (List|Variable): ${ends_comment}
 
     Returns:
         out (Variable): ${out_comment}
@@ -10646,26 +10646,104 @@ def slice(input, axes, starts, ends):
 
             import paddle.fluid as fluid
 
-            starts = [1, 0, 2]
-            ends = [3, 3, 4]
-            axes = [0, 1, 2]
-
             input = fluid.layers.data(
                 name="input", shape=[3, 4, 5, 6], dtype='float32')
 
-            out = fluid.layers.slice(input, axes=axes, starts=starts, ends=ends)
+            # example 1:
+            # attr starts is a list which doesn't contain tensor Variable.
+            axes = [0, 1, 2]
+            starts = [-3, 0, 2]
+            ends = [3, 2, 4]
+            sliced_1 = fluid.layers.slice(input, axes=axes, starts=starts, ends=ends)
+
+            # example 2:
+            # attr starts is a list which contain tensor Variable.
+            minus_3 = fluid.layers.fill_constant([1], "int32", -3)
+            sliced_2 = fluid.layers.slice(input, axes=axes, starts=[minus_3, 0, 2], ends=ends)
     """
 
+    if not isinstance(starts, (list, tuple, Variable)):
+        raise ValueError(
+            "Input starts must be an Variable, python list or tuple.")
+    if not isinstance(ends, (list, tuple, Variable)):
+        raise ValueError(
+            "Input ends must be an Variable, python list or tuple.")
+
     helper = LayerHelper('slice', **locals())
+
+    def contain_var(one_list):
+        for ele in one_list:
+            if isinstance(ele, Variable):
+                return True
+        return False
+
+    def get_new_list_tensor(old_list):
+        new_list_tensor = []
+        for dim in old_list:
+            if isinstance(dim, Variable):
+                dim.stop_gradient = True
+                new_list_tensor.append(dim)
+            else:
+                assert (isinstance(dim, int))
+                temp_out = helper.create_variable_for_type_inference('int32')
+                fill_constant([1], 'int32', dim, force_cpu=True, out=temp_out)
+                new_list_tensor.append(temp_out)
+        return new_list_tensor
+
+    inputs = {'Input': input}
+    attrs = {'axes': axes}
+    infer_flags = list(1 for i in range(len(axes)))
+
+    if in_dygraph_mode():
+        inputs = {'Input': input}
+        attrs = {
+            'axes': axes,
+            'starts': starts,
+            'ends': ends,
+            'infer_flags': infer_flags
+        }
+    else:
+        # starts
+        if isinstance(starts, Variable):
+            starts.stop_gradient = True
+            inputs['StartsTensor'] = starts
+            infer_flags = list(-1 for i in range(len(axes)))
+        elif isinstance(starts, (list, tuple)):
+            attrs['starts'] = []
+            if not contain_var(starts):
+                attrs['starts'] = starts
+            else:
+                inputs['StartsTensorList'] = get_new_list_tensor(starts)
+                for i, dim in enumerate(starts):
+                    if isinstance(dim, Variable):
+                        attrs['starts'].append(-1)
+                        infer_flags[i] = -1
+                    else:
+                        attrs['starts'].append(dim)
+
+        # ends
+        if isinstance(ends, Variable):
+            ends.stop_gradient = True
+            inputs['EndsTensor'] = ends
+            infer_flags = list(-1 for i in range(len(axes)))
+        elif isinstance(ends, (list, tuple)):
+            attrs['ends'] = []
+            if not contain_var(ends):
+                attrs['ends'] = ends
+            else:
+                inputs['EndsTensorList'] = get_new_list_tensor(ends)
+                for i, dim in enumerate(ends):
+                    if isinstance(dim, Variable):
+                        attrs['ends'].append(-1)
+                        infer_flags[i] = -1
+                    else:
+                        attrs['ends'].append(dim)
+        # infer_flags
+        attrs['infer_flags'] = infer_flags
     out = helper.create_variable_for_type_inference(
         dtype=helper.input_dtype('input'))
     helper.append_op(
-        type='slice',
-        inputs={'Input': input},
-        outputs={'Out': out},
-        attrs={'axes': axes,
-               'starts': starts,
-               'ends': ends})
+        type='slice', inputs=inputs, attrs=attrs, outputs={'Out': out})
 
     return out
 

python/paddle/fluid/tests/unittests/test_slice_op.py

@@ -18,8 +18,11 @@ import unittest
 import numpy as np
 import paddle.fluid.core as core
 from op_test import OpTest
+import paddle.fluid as fluid
 
 
+# Situation 1: starts(list, no tensor), ends(list, no tensor)
+# 1.1 without attr(decrease)
 class TestSliceOp(OpTest):
     def setUp(self):
         self.op_type = "slice"
@@ -29,7 +32,8 @@ class TestSliceOp(OpTest):
         self.attrs = {
             'axes': self.axes,
             'starts': self.starts,
-            'ends': self.ends
+            'ends': self.ends,
+            'infer_flags': self.infer_flags
         }
 
     def config(self):
@@ -37,6 +41,7 @@ class TestSliceOp(OpTest):
         self.starts = [1, 0, 2]
         self.ends = [3, 3, 4]
         self.axes = [0, 1, 2]
+        self.infer_flags = [1, 1, 1]
         self.out = self.input[1:3, 0:3, 2:4, :]
 
     def test_check_output(self):
@@ -46,6 +51,27 @@ class TestSliceOp(OpTest):
         self.check_grad(['Input'], 'Out', max_relative_error=0.006)
 
 
+class TestCase1(TestSliceOp):
+    def config(self):
+        self.input = np.random.random([3, 4, 5, 6]).astype("float32")
+        self.starts = [-3, 0, 2]
+        self.ends = [3, 100, -1]
+        self.axes = [0, 1, 2]
+        self.infer_flags = [1, 1, 1]
+        self.out = self.input[-3:3, 0:100, 2:-1, :]
+
+
+class TestCase2(TestSliceOp):
+    def config(self):
+        self.input = np.random.random([3, 4, 5, 6]).astype("float32")
+        self.starts = [-3, 0, 2]
+        self.ends = [3, 100, -1]
+        self.axes = [0, 1, 3]
+        self.infer_flags = [1, 1, 1]
+        self.out = self.input[-3:3, 0:100, :, 2:-1]
+
+
+# 1.2 with attr(decrease)
 class TestSliceOp_decs_dim(OpTest):
     def setUp(self):
         self.op_type = "slice"
@@ -56,6 +82,7 @@ class TestSliceOp_decs_dim(OpTest):
             'axes': self.axes,
             'starts': self.starts,
             'ends': self.ends,
+            'infer_flags': self.infer_flags,
             'decrease_axis': self.decrease_axis,
         }
 
@@ -65,6 +92,7 @@ class TestSliceOp_decs_dim(OpTest):
         self.ends = [2, 3, 4]
         self.axes = [0, 1, 2]
         self.decrease_axis = [0]
+        self.infer_flags = [1, 1, 1]
         self.out = self.input[1, 0:3, 2:4, :]
 
     def test_check_output(self):
@@ -74,26 +102,91 @@ class TestSliceOp_decs_dim(OpTest):
         self.check_grad(['Input'], 'Out', max_relative_error=0.006)
 
 
-class TestSliceOp_decs_dim_2(OpTest):
+class TestSliceOp_decs_dim_2(TestSliceOp_decs_dim):
+    def config(self):
+        self.input = np.random.random([3, 4, 5, 6]).astype("float32")
+        self.starts = [1, 0, 2]
+        self.ends = [2, 1, 4]
+        self.axes = [0, 1, 2]
+        self.decrease_axis = [0, 1]
+        self.infer_flags = [1, 1, 1]
+        self.out = self.input[1, 0, 2:4, :]
+
+
+class TestSliceOp_decs_dim_3(TestSliceOp_decs_dim):
+    def config(self):
+        self.input = np.random.random([3, 4, 5, 6]).astype("float32")
+        self.starts = [-1, 0, 2]
+        self.ends = [1000000, 1, 4]
+        self.axes = [0, 1, 2]
+        self.decrease_axis = [0, 1]
+        self.infer_flags = [1, 1, 1]
+        self.out = self.input[-1, 0, 2:4, :]
+
+
+class TestSliceOp_decs_dim_4(TestSliceOp_decs_dim):
+    def config(self):
+        self.input = np.random.random([3, 4, 5, 7]).astype("float32")
+        self.starts = [0, 1, 2, 3]
+        self.ends = [1, 2, 3, 4]
+        self.axes = [0, 1, 2, 3]
+        self.decrease_axis = [0, 1, 2, 3]
+        self.infer_flags = [1, 1, 1]
+        self.out = self.input[0, 1, 2, 3:4]
+
+
+class TestSliceOp_decs_dim_5(TestSliceOp_decs_dim):
+    def config(self):
+        self.input = np.random.random([3, 4, 5, 6]).astype("float32")
+        self.starts = [-1]
+        self.ends = [1000000]
+        self.axes = [3]
+        self.decrease_axis = [3]
+        self.infer_flags = [1, 1, 1]
+        self.out = self.input[:, :, :, -1]
+
+
+class TestSliceOp_decs_dim_6(TestSliceOp_decs_dim):
+    def config(self):
+        self.input = np.random.random([3, 4, 5, 6]).astype("float32")
+        self.starts = [0, 1, 2, 3]
+        self.ends = [1, 2, 3, 4]
+        self.axes = [0, 1, 2, 3]
+        self.decrease_axis = [0, 1, 2, 3]
+        self.infer_flags = [1, 1, 1]
+        self.out = self.input[0, 1, 2, 3:4]
+
+
+# Situation 2: starts(list, have tensor), ends(list, no tensor)
+# without attr(decrease)
+class TestSliceOp_starts_ListTensor(OpTest):
     def setUp(self):
         self.op_type = "slice"
         self.config()
-        self.inputs = {'Input': self.input}
+
+        starts_tensor = []
+        for index, ele in enumerate(self.starts):
+            starts_tensor.append(("x" + str(index), np.ones(
+                (1)).astype('int32') * ele))
+
+        self.inputs = {'Input': self.input, 'StartsTensorList': starts_tensor}
         self.outputs = {'Out': self.out}
         self.attrs = {
             'axes': self.axes,
-            'starts': self.starts,
+            'starts': self.starts_infer,
             'ends': self.ends,
-            'decrease_axis': self.decrease_axis,
+            'infer_flags': self.infer_flags
         }
 
     def config(self):
         self.input = np.random.random([3, 4, 5, 6]).astype("float32")
         self.starts = [1, 0, 2]
-        self.ends = [2, 1, 4]
+        self.ends = [3, 3, 4]
         self.axes = [0, 1, 2]
-        self.decrease_axis = [0, 1]
-        self.out = self.input[1, 0, 2:4, :]
+        self.infer_flags = [-1, 1, -1]
+        self.out = self.input[1:3, 0:3, 2:4, :]
+
+        self.starts_infer = [-1, 0, -1]
 
     def test_check_output(self):
         self.check_output()
@@ -102,26 +195,39 @@ class TestSliceOp_decs_dim_2(OpTest):
         self.check_grad(['Input'], 'Out', max_relative_error=0.006)
 
 
-class TestSliceOp_decs_dim_3(OpTest):
+# Situation 2: starts(list, have tensor), ends(list, no tensor)
+#  with attr(decrease)
+class TestSliceOp_decs_dim_starts_ListTensor(OpTest):
     def setUp(self):
         self.op_type = "slice"
         self.config()
-        self.inputs = {'Input': self.input}
+
+        starts_tensor = []
+        for index, ele in enumerate(self.starts):
+            starts_tensor.append(("x" + str(index), np.ones(
+                (1)).astype('int32') * ele))
+
+        self.inputs = {'Input': self.input, 'StartsTensorList': starts_tensor}
+
         self.outputs = {'Out': self.out}
         self.attrs = {
             'axes': self.axes,
-            'starts': self.starts,
+            'starts': self.starts_infer,
             'ends': self.ends,
+            'infer_flags': self.infer_flags,
             'decrease_axis': self.decrease_axis,
         }
 
     def config(self):
         self.input = np.random.random([3, 4, 5, 6]).astype("float32")
-        self.starts = [-1, 0, 2]
-        self.ends = [1000000, 1, 4]
+        self.starts = [1, 0, 2]
+        self.ends = [2, 3, 4]
         self.axes = [0, 1, 2]
-        self.decrease_axis = [0, 1]
-        self.out = self.input[-1, 0, 2:4, :]
+        self.decrease_axis = [0]
+        self.infer_flags = [1, -1, 1]
+        self.out = self.input[1, 0:3, 2:4, :]
+
+        self.starts_infer = [1, -1, 2]
 
     def test_check_output(self):
         self.check_output()
@@ -130,26 +236,48 @@ class TestSliceOp_decs_dim_3(OpTest):
         self.check_grad(['Input'], 'Out', max_relative_error=0.006)
 
 
-class TestSliceOp_decs_dim_5(OpTest):
+class TestSliceOp_decs_dim_5_starts_ListTensor(
+        TestSliceOp_decs_dim_starts_ListTensor):
+    def config(self):
+        self.input = np.random.random([3, 4, 5, 6]).astype("float32")
+        self.starts = [-1]
+        self.ends = [1000000]
+        self.axes = [3]
+        self.decrease_axis = [3]
+        self.infer_flags = [-1]
+        self.out = self.input[:, :, :, -1]
+
+        self.starts_infer = [-1]
+
+
+# Situation 3: starts(tensor), ends(list, no tensor)
+# with attr(decrease)
+class TestSliceOp_decs_dim_starts_OneTensor(OpTest):
     def setUp(self):
         self.op_type = "slice"
         self.config()
-        self.inputs = {'Input': self.input}
+        self.inputs = {
+            'Input': self.input,
+            "StartsTensor": np.array(
+                self.starts, dtype="int32")
+        }
         self.outputs = {'Out': self.out}
         self.attrs = {
             'axes': self.axes,
-            'starts': self.starts,
+            #'starts': self.starts,
             'ends': self.ends,
+            'infer_flags': self.infer_flags,
             'decrease_axis': self.decrease_axis,
         }
 
     def config(self):
         self.input = np.random.random([3, 4, 5, 6]).astype("float32")
-        self.starts = [-1]
-        self.ends = [1000000]
-        self.axes = [3]
-        self.decrease_axis = [3]
-        self.out = self.input[:, :, :, -1]
+        self.starts = [1, 0, 2]
+        self.ends = [2, 3, 4]
+        self.axes = [0, 1, 2]
+        self.decrease_axis = [0]
+        self.infer_flags = [-1, -1, -1]
+        self.out = self.input[1, 0:3, 2:4, :]
 
     def test_check_output(self):
         self.check_output()
@@ -158,26 +286,35 @@ class TestSliceOp_decs_dim_5(OpTest):
         self.check_grad(['Input'], 'Out', max_relative_error=0.006)
 
 
-class TestSliceOp_decs_dim_6(OpTest):
+# Situation 4: starts(tensor), ends(tensor)
+#  without attr(decrease)
+class TestSliceOp_starts_OneTensor_ends_OneTensor(OpTest):
     def setUp(self):
         self.op_type = "slice"
         self.config()
-        self.inputs = {'Input': self.input}
+
+        self.inputs = {
+            'Input': self.input,
+            "StartsTensor": np.array(
+                self.starts, dtype="int32"),
+            "EndsTensor": np.array(
+                self.ends, dtype="int32")
+        }
         self.outputs = {'Out': self.out}
         self.attrs = {
             'axes': self.axes,
-            'starts': self.starts,
-            'ends': self.ends,
-            'decrease_axis': self.decrease_axis,
+            #'starts': self.starts,
+            #'ends': self.ends_infer,
+            'infer_flags': self.infer_flags
         }
 
     def config(self):
         self.input = np.random.random([3, 4, 5, 6]).astype("float32")
-        self.starts = [0, 1, 2, 3]
-        self.ends = [1, 2, 3, 4]
-        self.axes = [0, 1, 2, 3]
-        self.decrease_axis = [0, 1, 2, 3]
-        self.out = self.input[0, 1, 2, 3:4]
+        self.starts = [1, 0, 2]
+        self.ends = [3, 3, 4]
+        self.axes = [0, 1, 2]
+        self.infer_flags = [-1, -1, -1]
+        self.out = self.input[1:3, 0:3, 2:4, :]
 
     def test_check_output(self):
         self.check_output()
@@ -186,27 +323,103 @@ class TestSliceOp_decs_dim_6(OpTest):
         self.check_grad(['Input'], 'Out', max_relative_error=0.006)
 
 
-class TestCase1(TestSliceOp):
+# Situation 5: starts(tensor), ends(tensor)
+#  with attr(decrease)
+class TestSliceOp_decs_dim_starts_and_ends_OneTensor(OpTest):
+    def setUp(self):
+        self.op_type = "slice"
+        self.config()
+        self.inputs = {
+            'Input': self.input,
+            "StartsTensor": np.array(
+                self.starts, dtype="int32"),
+            "EndsTensor": np.array(
+                self.ends, dtype="int32")
+        }
+        self.outputs = {'Out': self.out}
+        self.attrs = {
+            'axes': self.axes,
+            #'starts': self.starts,
+            #'ends': self.ends,
+            'infer_flags': self.infer_flags,
+            'decrease_axis': self.decrease_axis,
+        }
+
     def config(self):
         self.input = np.random.random([3, 4, 5, 6]).astype("float32")
-        self.starts = [-3, 0, 2]
-        self.ends = [3, 100, -1]
+        self.starts = [1, 0, 2]
+        self.ends = [2, 1, 4]
         self.axes = [0, 1, 2]
-        self.out = self.input[-3:3, 0:100, 2:-1, :]
+        self.decrease_axis = [0, 1]
+        self.infer_flags = [-1, -1, -1]
+        self.out = self.input[1, 0, 2:4, :]
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['Input'], 'Out', max_relative_error=0.006)
 
 
-class TestCase2(TestSliceOp):
+# Situation 6: starts(tensor), ends(list, have tensor)
+# without attr(decrease)
+class TestSliceOp_starts_OneTensor_ends_ListTensor(OpTest):
+    def setUp(self):
+        self.op_type = "slice"
+        self.config()
+
+        ends_tensor = []
+        for index, ele in enumerate(self.ends):
+            ends_tensor.append(("y" + str(index), np.ones(
+                (1)).astype('int32') * ele))
+
+        self.inputs = {
+            'Input': self.input,
+            "StartsTensor": np.array(
+                self.starts, dtype="int32"),
+            'EndsTensorList': ends_tensor
+        }
+        self.outputs = {'Out': self.out}
+        self.attrs = {
+            'axes': self.axes,
+            #'starts': self.starts,
+            'ends': self.ends_infer,
+            'infer_flags': self.infer_flags
+        }
+
     def config(self):
         self.input = np.random.random([3, 4, 5, 6]).astype("float32")
-        self.starts = [-3, 0, 2]
-        self.ends = [3, 100, -1]
-        self.axes = [0, 1, 3]
-        self.out = self.input[-3:3, 0:100, :, 2:-1]
+        self.starts = [1, 0, 2]
+        self.ends = [3, 3, 4]
+        self.axes = [0, 1, 2]
+        self.infer_flags = [-1, -1, -1]
+        self.out = self.input[1:3, 0:3, 2:4, :]
+
+        self.ends_infer = [-1, 3, 4]
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['Input'], 'Out', max_relative_error=0.006)
 
 
+# Test CUDA float16
 @unittest.skipIf(not core.is_compiled_with_cuda(),
                  "core is not compiled with CUDA")
-class TestFP16(TestSliceOp):
+class TestFP16(OpTest):
+    def setUp(self):
+        self.op_type = "slice"
+        self.config()
+        self.inputs = {'Input': self.input}
+        self.outputs = {'Out': self.out}
+        self.attrs = {
+            'axes': self.axes,
+            'starts': self.starts,
+            'ends': self.ends,
+            'infer_flags': self.infer_flags
+        }
+
     def config(self):
         self.dtype = "float16"
         self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
@@ -214,6 +427,7 @@ class TestFP16(TestSliceOp):
         self.ends = [3, 100, -1]
         self.axes = [0, 1, 3]
         self.out = self.input[-3:3, 0:100, :, 2:-1]
+        self.infer_flags = [1, 1, 1]
 
     def test_check_output(self):
         place = core.CUDAPlace(0)
@@ -229,7 +443,19 @@ class TestFP16(TestSliceOp):
 
 @unittest.skipIf(not core.is_compiled_with_cuda(),
                  "core is not compiled with CUDA")
-class TestFP16_2(TestSliceOp):
+class TestFP16_2(OpTest):
+    def setUp(self):
+        self.op_type = "slice"
+        self.config()
+        self.inputs = {'Input': self.input}
+        self.outputs = {'Out': self.out}
+        self.attrs = {
+            'axes': self.axes,
+            'starts': self.starts,
+            'ends': self.ends,
+            'infer_flags': self.infer_flags
+        }
+
     def config(self):
         self.dtype = "float16"
         self.input = np.random.random([3, 4, 5]).astype(self.dtype)
@@ -237,6 +463,7 @@ class TestFP16_2(TestSliceOp):
         self.ends = [1]
         self.axes = [1]
         self.out = self.input[:, 0:1, :]
+        self.infer_flags = [1]
 
     def test_check_output(self):
         place = core.CUDAPlace(0)
@@ -253,5 +480,53 @@ class TestFP16_2(TestSliceOp):
                 numeric_grad_delta=0.5)
 
 
+# Test python API
+class TestSliceAPI(OpTest):
+    def test_1(self):
+        input = np.random.random([3, 4, 5, 6]).astype("float32")
+        minus_1 = fluid.layers.fill_constant([1], "int32", -1)
+        minus_3 = fluid.layers.fill_constant([1], "int32", -3)
+        starts = fluid.layers.data(
+            name='starts', shape=[1, 3], append_batch_size=False)
+        ends = fluid.layers.data(
+            name='ends', shape=[3], append_batch_size=False)
+
+        x = fluid.layers.data(
+            name="x",
+            shape=[3, 4, 5, 6],
+            append_batch_size=False,
+            dtype="float32")
+
+        out_1 = fluid.layers.slice(
+            x, axes=[0, 1, 2], starts=[-3, 0, 2], ends=[3, 100, -1])
+        out_2 = fluid.layers.slice(
+            x, axes=[0, 1, 3], starts=[minus_3, 0, 2], ends=[3, 100, -1])
+        out_3 = fluid.layers.slice(
+            x, axes=[0, 1, 3], starts=[minus_3, 0, 2], ends=[3, 100, minus_1])
+        out_4 = fluid.layers.slice(x, axes=[0, 1, 2], starts=starts, ends=ends)
+
+        out_5 = x[-3:3, 0:100, 2:-1]
+        out_6 = x[minus_3:3, 0:100, :, 2:-1]
+        out_7 = x[minus_1, 0:100, :, 2:minus_1]
+
+        exe = fluid.Executor(place=fluid.CPUPlace())
+        res_1, res_2, res_3, res_4, res_5, res_6, res_7 = exe.run(
+            fluid.default_main_program(),
+            feed={
+                "x": input,
+                'starts': np.array([-3, 0, 2]).astype("int32"),
+                'ends': np.array([3, 100, -1]).astype("int32")
+            },
+            fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6, out_7])
+
+        assert np.array_equal(res_1, input[-3:3, 0:100, 2:-1, :])
+        assert np.array_equal(res_2, input[-3:3, 0:100, :, 2:-1])
+        assert np.array_equal(res_3, input[-3:3, 0:100, :, 2:-1])
+        assert np.array_equal(res_4, input[-3:3, 0:100, 2:-1, :])
+        assert np.array_equal(res_5, input[-3:3, 0:100, 2:-1, :])
+        assert np.array_equal(res_6, input[-3:3, 0:100, :, 2:-1])
+        assert np.array_equal(res_7, input[-1, 0:100, :, 2:-1])
+
+
 if __name__ == '__main__':
     unittest.main()