Add pad_constant_like_op (#12943)

* Add pad_constant_batch_size_like

* refine pad_op

* optimize memory

paddle/fluid/operators/math/padding.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+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. */
+
+#pragma once
+#include <utility>
+#include <vector>
+#include "paddle/fluid/framework/tensor.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template <typename T, size_t D, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
+
+template <typename DeviceContext, typename T, size_t D>
+void PadFunction(const framework::ExecutionContext& context,
+                 const std::vector<int>& pads, const framework::Tensor& src,
+                 T pad_value, framework::Tensor* out) {
+  Eigen::array<std::pair<int, int>, D> paddings;
+
+  for (size_t i = 0; i < paddings.size(); ++i) {
+    paddings[i].first = pads[i * 2];
+    paddings[i].second = pads[i * 2 + 1];
+  }
+
+  auto src_tensor = EigenTensor<T, D>::From(src);
+  auto out_tensor = EigenTensor<T, D>::From(*out);
+
+  auto& place =
+      *context.template device_context<DeviceContext>().eigen_device();
+  out_tensor.device(place) = src_tensor.pad(paddings, pad_value);
+}
+
+template <typename DeviceContext, typename T, size_t D>
+void PadGradFunction(const framework::ExecutionContext& context,
+                     const std::vector<int>& pads, const framework::Tensor& src,
+                     framework::Tensor* d_out) {
+  Eigen::array<std::pair<int, int>, D> paddings;
+  for (size_t i = 0; i < paddings.size(); ++i) {
+    paddings[i].first = -pads[i * 2];
+    paddings[i].second = -pads[i * 2 + 1];
+  }
+
+  auto d_out_tensor = EigenTensor<T, D>::From(*d_out);
+  auto src_tensor = EigenTensor<T, D>::From(src);
+  auto& place =
+      *context.template device_context<DeviceContext>().eigen_device();
+  d_out_tensor.device(place) = src_tensor.pad(paddings, 0);
+}
+
+template <typename DeviceContext, typename T>
+void PaddingFunctor(int rank, const framework::ExecutionContext& context,
+                    const std::vector<int>& pads, T pad_value,
+                    const framework::Tensor& src, framework::Tensor* out) {
+  switch (rank) {
+    case 1:
+      PadFunction<DeviceContext, T, 1>(context, pads, src, pad_value, out);
+      break;
+    case 2:
+      PadFunction<DeviceContext, T, 2>(context, pads, src, pad_value, out);
+      break;
+    case 3:
+      PadFunction<DeviceContext, T, 3>(context, pads, src, pad_value, out);
+      break;
+    case 4:
+      PadFunction<DeviceContext, T, 4>(context, pads, src, pad_value, out);
+      break;
+    case 5:
+      PadFunction<DeviceContext, T, 5>(context, pads, src, pad_value, out);
+      break;
+    case 6:
+      PadFunction<DeviceContext, T, 6>(context, pads, src, pad_value, out);
+      break;
+    default:
+      PADDLE_THROW(
+          "PadOp only support tensors with no more than 6 dimensions.");
+  }
+}
+
+template <typename DeviceContext, typename T>
+void PaddingGradFunctor(int rank, const framework::ExecutionContext& context,
+                        const std::vector<int>& pads,
+                        const framework::Tensor& src, framework::Tensor* out) {
+  switch (rank) {
+    case 1:
+      PadGradFunction<DeviceContext, T, 1>(context, pads, src, out);
+      break;
+    case 2:
+      PadGradFunction<DeviceContext, T, 2>(context, pads, src, out);
+      break;
+    case 3:
+      PadGradFunction<DeviceContext, T, 3>(context, pads, src, out);
+      break;
+    case 4:
+      PadGradFunction<DeviceContext, T, 4>(context, pads, src, out);
+      break;
+    case 5:
+      PadGradFunction<DeviceContext, T, 5>(context, pads, src, out);
+      break;
+    case 6:
+      PadGradFunction<DeviceContext, T, 6>(context, pads, src, out);
+      break;
+    default:
+      PADDLE_THROW(
+          "PadOp only support tensors with no more than 6 dimensions.");
+  }
+}
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/pad_constant_like_op.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+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 "paddle/fluid/operators/pad_constant_like_op.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class PadConstantLikeOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of PadConstantLikeOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Y"),
+                   "Input(Y) of PadConstantLikeOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output(Out) of PadConstantLikeOp should not be null.");
+
+    auto x_dim = ctx->GetInputDim("X");
+    auto y_dim = ctx->GetInputDim("Y");
+
+    PADDLE_ENFORCE_EQ(x_dim.size(), y_dim.size(),
+                      "The dimention of X and Y should be the same.");
+
+    for (int i = 0; i < x_dim.size(); ++i) {
+      PADDLE_ENFORCE_GE(x_dim[i], y_dim[i]);
+    }
+    ctx->SetOutputDim("Out", x_dim);
+    ctx->ShareLoD("X", /*->*/ "Out");
+  }
+};
+
+class PadConstantLikeOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "The input of pad_constant_like op. "
+             "The input should be a k-D tensor(k > 0 and k < 7)");
+    AddInput("Y",
+             "The input of pad_constant_like op. "
+             "The input should be a k-D tensor(k > 0 and k < 7)");
+    AddOutput("Out",
+              "The output of pad_constant_like op. "
+              "A tensor with the same shape as X.");
+    AddAttr<float>("pad_value",
+                   "(float, default 0.0) "
+                   "The value to fill the padded areas.")
+        .SetDefault(0.0f);
+    AddComment(R"DOC(
+PadConstantLikeOp Operator.
+
+Pad input(Y) with a pad_value, the number of values padded to the edges of each
+axis is specified by the difference of the shape of X and Y.
+((0, shape_x_0 - shape_y_0), … (0, shape_x_n - shape_y_n)) unique pad widths for
+each axis.
+The input should be a k-D tensor(k > 0 and k < 7). As an example:
+
+case1:
+    Given:
+        X = [[1, 2],
+             [3, 4],
+             [1, 2],
+             [3, 4]]],
+        X.shape = (4, 2)
+
+        Y = [[5, 6],
+            [7, 8]],
+        Y.shape = (2, 2)
+
+    And
+        pad_value = 0,
+
+    Return:
+        Out = [[5, 6],
+               [7, 8],
+               [0, 0],
+               [0, 0]]
+        Out.shape = (4, 2)
+
+case2:
+    Given:
+        X = [[[[ 0,  1,  2],
+               [ 3,  4,  5]],
+              [[ 6,  7,  8],
+               [ 9, 10, 11]],
+              [[12, 13, 14],
+               [15, 16, 17]]],
+             [[[18, 19, 20],
+               [21, 22, 23]],
+              [[24, 25, 26],
+               [27, 28, 29]],
+              [[30, 31, 32],
+               [33, 34, 35]]]]
+        X.shape = (2, 3, 2, 3)
+
+        Y = [[[[35, 36, 37]],
+              [[38, 39, 40]],
+              [[41, 42, 43]]]]
+        Y.shape = (1, 3, 1, 3)
+
+    And
+        pad_value = -1,
+
+    Return:
+
+        Out = [[[[35, 36, 37],
+                 [-1, -1, -1]],
+                [[38, 39, 40],
+                 [-1, -1, -1]],
+                [[41, 42, 43],
+                 [-1, -1, -1]]],
+               [[[-1, -1, -1],
+                 [-1, -1, -1]],
+                [[-1, -1, -1],
+                 [-1, -1, -1]],
+                [[-1, -1, -1],
+                 [-1, -1, -1]]]]
+        Out.shape = (2, 3, 2, 3)
+)DOC");
+  }
+};
+
+class PadConstantLikeOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null");
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
+                   "Input(Out@GRAD) should not be null");
+    auto y_dim = ctx->GetInputDim("Y");
+    auto dout_dim = ctx->GetInputDim(framework::GradVarName("Out"));
+
+    PADDLE_ENFORCE_EQ(dout_dim.size(), y_dim.size(),
+                      "The dimention of X and Y should be the same.");
+
+    auto y_grad_name = framework::GradVarName("Y");
+    if (ctx->HasOutput(y_grad_name)) {
+      ctx->SetOutputDim(y_grad_name, y_dim);
+      ctx->ShareLoD("Y", /*->*/ y_grad_name);
+
+      for (int i = 0; i < y_dim.size(); ++i) {
+        PADDLE_ENFORCE_GE(dout_dim[i], y_dim[i]);
+      }
+    }
+  }
+};
+
+class PadConstantLikeOpGradMaker : public framework::SingleGradOpDescMaker {
+ public:
+  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
+
+ protected:
+  std::unique_ptr<framework::OpDesc> Apply() const override {
+    auto *bind = new framework::OpDesc();
+    bind->SetType("pad_constant_like_grad");
+    bind->SetInput("Y", Input("Y"));
+    bind->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
+    bind->SetOutput(framework::GradVarName("Y"), InputGrad("Y"));
+    bind->SetAttrMap(Attrs());
+    return std::unique_ptr<framework::OpDesc>(bind);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(pad_constant_like, ops::PadConstantLikeOp,
+                  ops::PadConstantLikeOpMaker, ops::PadConstantLikeOpGradMaker);
+REGISTER_OPERATOR(pad_constant_like_grad, ops::PadConstantLikeOpGrad);
+
+REGISTER_OP_CPU_KERNEL(
+    pad_constant_like,
+    ops::PadConstantLikeKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::PadConstantLikeKernel<paddle::platform::CPUDeviceContext, double>);
+REGISTER_OP_CPU_KERNEL(
+    pad_constant_like_grad,
+    ops::PadConstantLikeGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::PadConstantLikeGradKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/pad_constant_like_op.cu

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+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. */
+
+#define EIGEN_USE_GPU
+#include "paddle/fluid/operators/pad_constant_like_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    pad_constant_like,
+    ops::PadConstantLikeKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::PadConstantLikeKernel<paddle::platform::CUDADeviceContext, double>);
+REGISTER_OP_CUDA_KERNEL(
+    pad_constant_like_grad,
+    ops::PadConstantLikeGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::PadConstantLikeGradKernel<paddle::platform::CUDADeviceContext,
+                                   double>);

paddle/fluid/operators/pad_constant_like_op.h

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+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. */
+
+#pragma once
+
+#include <utility>
+#include <vector>
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/tensor.h"
+#include "paddle/fluid/framework/tensor_util.h"
+#include "paddle/fluid/operators/math/padding.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class PadConstantLikeKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto in_x = context.Input<framework::Tensor>("X");
+    auto in_y = context.Input<framework::Tensor>("Y");
+    auto* out = context.Output<framework::Tensor>("Out");
+
+    if (in_x->dims() == in_y->dims()) {
+      // TensorCopy(in_y, context.GetPlace(), context, out);
+      out->ShareDataWith(*in_y);
+      return;
+    }
+
+    T pad_value = context.Attr<T>("pad_value");
+    out->mutable_data<T>(context.GetPlace());
+
+    int rank = context.Input<framework::Tensor>("X")->dims().size();
+
+    std::vector<int> pads(rank * 2, 0);
+
+    for (int j = 0; j < rank; ++j) {
+      pads[j * 2] = 0;
+      pads[j * 2 + 1] = static_cast<int>(in_x->dims()[j] - in_y->dims()[j]);
+    }
+
+    math::PaddingFunctor<DeviceContext, T>(rank, context, pads, pad_value,
+                                           *in_y, out);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class PadConstantLikeGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto in_y = context.Input<framework::Tensor>("Y");
+    auto in_dout =
+        context.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto* d_y = context.Output<framework::Tensor>(framework::GradVarName("Y"));
+
+    if (d_y == nullptr) {
+      return;
+    }
+
+    if (in_dout->dims() == in_y->dims()) {
+      // TensorCopy(in_dout, context.GetPlace(), context, d_y);
+      d_y->ShareDataWith(*in_dout);
+      return;
+    }
+
+    d_y->mutable_data<T>(context.GetPlace());
+    int rank = in_dout->dims().size();
+
+    std::vector<int> pads(static_cast<size_t>(rank) * 2, 0);
+    for (int j = 0; j < rank; ++j) {
+      pads[j * 2] = 0;
+      pads[j * 2 + 1] = static_cast<int>(in_dout->dims()[j] - in_y->dims()[j]);
+    }
+
+    math::PaddingGradFunctor<DeviceContext, T>(rank, context, pads, *in_dout,
+                                               d_y);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/operators/pad_op.h

@@ -18,117 +18,44 @@ limitations under the License. */
 #include <vector>
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/math/padding.h"
 
 namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
 
-template <typename T, size_t D, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
-
-template <typename DeviceContext, typename T, size_t D>
-void PadFunction(const framework::ExecutionContext& context) {
-  auto pads = context.Attr<std::vector<int>>("paddings");
-  Eigen::array<std::pair<int, int>, D> paddings;
-  for (size_t i = 0; i < paddings.size(); ++i) {
-    paddings[i].first = pads[i * 2];
-    paddings[i].second = pads[i * 2 + 1];
-  }
-  T pad_value = context.Attr<T>("pad_value");
-
-  auto* x = context.Input<Tensor>("X");
-  auto* out = context.Output<Tensor>("Out");
-  out->mutable_data<T>(context.GetPlace());
-
-  auto x_tensor = EigenTensor<T, D>::From(*x);
-  auto out_tensor = EigenTensor<T, D>::From(*out);
-  auto& place =
-      *context.template device_context<DeviceContext>().eigen_device();
-  out_tensor.device(place) = x_tensor.pad(paddings, pad_value);
-}
-
 template <typename DeviceContext, typename T>
 class PadKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    int rank = context.Input<Tensor>("X")->dims().size();
-    switch (rank) {
-      case 1:
-        PadFunction<DeviceContext, T, 1>(context);
-        break;
-      case 2:
-        PadFunction<DeviceContext, T, 2>(context);
-        break;
-      case 3:
-        PadFunction<DeviceContext, T, 3>(context);
-        break;
-      case 4:
-        PadFunction<DeviceContext, T, 4>(context);
-        break;
-      case 5:
-        PadFunction<DeviceContext, T, 5>(context);
-        break;
-      case 6:
-        PadFunction<DeviceContext, T, 6>(context);
-        break;
-      default:
-        PADDLE_THROW(
-            "PadOp only support tensors with no more than 6 dimensions.");
-    }
+    auto pads = context.Attr<std::vector<int>>("paddings");
+    T pad_value = context.Attr<T>("pad_value");
+    auto* x = context.Input<Tensor>("X");
+    auto* out = context.Output<Tensor>("Out");
+    out->mutable_data<T>(context.GetPlace());
+
+    int rank = x->dims().size();
+    math::PaddingFunctor<DeviceContext, T>(rank, context, pads, pad_value, *x,
+                                           out);
   }
 };
 
-template <typename DeviceContext, typename T, size_t D>
-void PadGradFunction(const framework::ExecutionContext& context) {
-  auto pads = context.Attr<std::vector<int>>("paddings");
-  Eigen::array<std::pair<int, int>, D> paddings;
-  for (size_t i = 0; i < paddings.size(); ++i) {
-    paddings[i].first = -pads[i * 2];
-    paddings[i].second = -pads[i * 2 + 1];
-  }
-  auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
-  auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
-  if (d_x != nullptr) {
-    d_x->mutable_data<T>(context.GetPlace());
-    auto d_x_tensor = EigenTensor<T, D>::From(*d_x);
-    auto d_out_tensor = EigenTensor<T, D>::From(*d_out);
-    auto& place =
-        *context.template device_context<DeviceContext>().eigen_device();
-    d_x_tensor.device(place) = d_out_tensor.pad(paddings, 0);
-  }
-}
-
 template <typename DeviceContext, typename T>
 class PadGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    size_t rank =
-        context.Input<Tensor>(framework::GradVarName("Out"))->dims().size();
-    switch (rank) {
-      case 1:
-        PadGradFunction<DeviceContext, T, 1>(context);
-        break;
-      case 2:
-        PadGradFunction<DeviceContext, T, 2>(context);
-        break;
-      case 3:
-        PadGradFunction<DeviceContext, T, 3>(context);
-        break;
-      case 4:
-        PadGradFunction<DeviceContext, T, 4>(context);
-        break;
-      case 5:
-        PadGradFunction<DeviceContext, T, 5>(context);
-        break;
-      case 6:
-        PadGradFunction<DeviceContext, T, 6>(context);
-        break;
-      default:
-        PADDLE_THROW(
-            "PadOp only support tensors with no more than 6 dimensions.");
+    auto pads = context.Attr<std::vector<int>>("paddings");
+    auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
+    auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
+    if (d_x == nullptr) {
+      return;
     }
+
+    d_x->mutable_data<T>(context.GetPlace());
+    int rank = d_out->dims().size();
+    math::PaddingGradFunctor<DeviceContext, T>(rank, context, pads, *d_out,
+                                               d_x);
   }
 };
 

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

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestPadOp(OpTest):
+    def setUp(self):
+        self.initTestCase()
+        self.op_type = "pad_constant_like"
+        self.inputs = {
+            'X': np.random.random(self.x_shape).astype("float32"),
+            'Y': np.random.random(self.y_shape).astype("float32")
+        }
+        self.attrs = {}
+        self.attrs['pad_value'] = self.pad_value
+        self.outputs = {
+            'Out': np.pad(self.inputs['Y'],
+                          self.paddings,
+                          mode='constant',
+                          constant_values=self.pad_value)
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['Y'], 'Out', max_relative_error=0.006)
+
+    def initTestCase(self):
+        self.x_shape = (16, 16)
+        self.y_shape = (3, 16)
+        self.pad_value = 0.1
+        self.paddings = [(0, 13), (0, 0)]
+
+
+class TestCase1(TestPadOp):
+    def initTestCase(self):
+        self.x_shape = (4, 3, 4, 4)
+        self.y_shape = (2, 3, 4, 4)
+        self.paddings = [(0, 2), (0, 0), (0, 0), (0, 0)]
+        self.pad_value = 0.5
+
+
+class TestCase2(TestPadOp):
+    def initTestCase(self):
+        self.x_shape = (4, 3, 4, 4)
+        self.y_shape = (2, 3, 2, 4)
+        self.paddings = [(0, 2), (0, 0), (0, 2), (0, 0)]
+        self.pad_value = 0.5
+
+
+if __name__ == '__main__':
+    unittest.main()