Fix eigen error.

paddle/operators/pad_op.cc

@@ -26,18 +26,18 @@ class PadOp : public framework::OperatorWithKernel {
  protected:
   void InferShape(const framework::InferShapeContext &ctx) const override {
     auto dim0 = ctx.Input<Tensor>("X")->dims();
-    auto dim1 = ctx.Output<Tensor>("Out")->dims();
-    auto paddings = GetAttr<std::vector<std::pair<int32, int32>>>("paddings");
+    auto paddings = GetAttr<std::vector<std::pair<int, int>>>("paddings");
+    std::vector<int> dim1(dim0.size());
     for (int i = 0; i < dim0.size(); ++i) {
-      dim1[i] = dim0[i] + paddings[i][0] + paddings[i][1];
+      dim1[i] = dim0[i] + paddings[i].first + paddings[i].second;
     }
-    ctx.Output<Tensor>("Out")->Resize(dim1);
+    ctx.Output<Tensor>("Out")->Resize(paddle::framework::make_ddim(dim1));
   }
 };
 
-class MulOpMaker : public framework::OpProtoAndCheckerMaker {
+class PadOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
-  MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+  PadOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "The input of pad op");
     AddOutput("Out", "The output of pad op");

paddle/operators/pad_op.h

@@ -28,52 +28,102 @@ template <typename T, size_t D, int MajorType = Eigen::RowMajor,
           typename IndexType = Eigen::DenseIndex>
 using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
 
+template <typename Place, typename T, size_t D>
+void PadFunction(const framework::ExecutionContext& context) {
+  auto pads = context.op_.GetAttr<std::vector<std::pair<int, int>>>("paddings");
+  Eigen::array<std::pair<int, int>, D> paddings;
+  for (int i = 0; i < pads.size(); ++i) {
+    paddings[i] = pads[i];
+  }
+  T pad_value = context.op_.GetAttr<T>("pad_value");
+
+  auto* X = context.Input<Tensor>("X");
+  auto* Out = context.Output<Tensor>("Out");
+  Out->mutable_data<T>(context.GetPlace());
+  auto dims = X->dims();
+
+  auto X_tensor = EigenTensor<T, D>::From(*X);
+  auto Out_tensor = EigenTensor<T, D>::From(*Out);
+  auto place = context.GetEigenDevice<Place>();
+  Out_tensor.device(place) = X_tensor.pad(paddings, pad_value);
+}
+
 template <typename Place, typename T>
 class PadKernel : public framework::OpKernel {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
-    auto paddings =
-        context.op_.GetAttr<std::vector<std::pair<int, int>>>("paddings");
-    T pad_value = context.op_.GetAttr<T>("pad_value");
-
-    auto* X = context.Input<Tensor>("X");
-    auto* Out = context.Output<Tensor>("Out");
-    Out->mutable_data<T>(context.GetPlace());
-    auto dims = X->dims();
-
-    // Eigen::TensorMap<Eigen::Tensor<const T, 2, Eigen::RowMajor,
-    // Eigen::DenseIndex>> X_tensor = EigenTensor<T, 2>::From(*X);
-    // Eigen::TensorMap<Eigen::Tensor<T, 2, Eigen::RowMajor, Eigen::DenseIndex>>
-    // Out_tensor = EigenTensor<T, 2>::From(*Out);
-    EigenTensor<T, dims.size()>::ConstType X_tensor =
-        EigenTensor<T, dims.size()>::From(*X);
-    EigenTensor<T, dims.size()>::Type Out_tensor =
-        EigenTensor<T, dims.size()>::From(*Out);
-    Out_tensor = X_tensor.pad(paddings, pad_value);
+    int dim = context.Input<Tensor>("X")->dims().size();
+    switch (dim) {
+      case 1:
+        PadFunction<Place, T, 1>(context);
+        break;
+      case 2:
+        PadFunction<Place, T, 2>(context);
+        break;
+      case 3:
+        PadFunction<Place, T, 3>(context);
+        break;
+      case 4:
+        PadFunction<Place, T, 4>(context);
+        break;
+      case 5:
+        PadFunction<Place, T, 5>(context);
+        break;
+      case 6:
+        PadFunction<Place, T, 6>(context);
+        break;
+      default:
+        LOG(ERROR) << "Only ranks up to 6 supported.";
+    }
   }
 };
 
+template <typename Place, typename T, size_t D>
+void PadGradFunction(const framework::ExecutionContext& context) {
+  auto pads = context.op_.GetAttr<std::vector<std::pair<int, int>>>("paddings");
+  Eigen::array<std::pair<int, int>, D> paddings;
+  for (int i = 0; i < pads.size(); ++i) {
+    paddings[0].first = -paddings[0].first;
+    paddings[1].second = -paddings[1].second;
+  }
+  auto* dOut = context.Input<Tensor>(framework::GradVarName("Out"));
+  auto* dX = context.Output<Tensor>(framework::GradVarName("X"));
+  dX->mutable_data<T>(context.GetPlace());
+
+  auto dX_tensor = EigenTensor<T, D>::From(*dX);
+  auto dOut_tensor = EigenTensor<T, D>::From(*dOut);
+  auto place = context.GetEigenDevice<Place>();
+  dX_tensor.device(place) = dOut_tensor.pad(paddings, 0);
+}
+
 template <typename Place, typename T>
 class PadGradKernel : public framework::OpKernel {
  public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    std::vector<std::pair<int, int>> paddings =
-        context.op_.GetAttr<std::vector<std::pair<int, int>>>("paddings");
-    for (int i = 0; i < paddings.size(); ++i) {
-      paddings[0].first = -paddings[0].first;
-      paddings[1].second = -paddings[1].second;
+  void Compute(const framework::ExecutionContext& context) const override {
+    size_t dim =
+        context.Input<Tensor>(framework::GradVarName("Out"))->dims().size();
+    switch (dim) {
+      case 1:
+        PadGradFunction<Place, T, 1>(context);
+        break;
+      case 2:
+        PadGradFunction<Place, T, 2>(context);
+        break;
+      case 3:
+        PadGradFunction<Place, T, 3>(context);
+        break;
+      case 4:
+        PadGradFunction<Place, T, 4>(context);
+        break;
+      case 5:
+        PadGradFunction<Place, T, 5>(context);
+        break;
+      case 6:
+        PadGradFunction<Place, T, 6>(context);
+        break;
+      default:
+        LOG(ERROR) << "Only ranks up to 6 supported.";
     }
-    auto* dOut = ctx.Input<Tensor>(framework::GradVarName("Out"));
-    auto dims = dOut->dims();
-
-    auto* dX = ctx.Output<Tensor>(framework::GradVarName("X"));
-    dX->mutable_data<T>(ctx.GetPlace());
-
-    EigenTensor<T, dims.size()>::Type dX_tensor =
-        EigenTensor<T, dims.size()>::From(*dX);
-    EigenTensor<T, dims.size()>::ConstType dOut_tensor =
-        EigenTensor<T, dims.size()>::From(*dOut);
-    dX_tensor = dOut_tensor.pad(paddings, 0);
   }
 };
 

python/paddle/v2/framework/tests/test_pad_op.py

@@ -1,5 +1,6 @@
 import unittest
 import numpy as np
+from paddle.v2.framework.op import Operator
 from gradient_checker import GradientChecker, create_op
 from op_test_util import OpTestMeta
 
@@ -10,19 +11,25 @@ class TestPadOp(unittest.TestCase):
     def setUp(self):
         self.type = "pad"
         self.inputs = {'X': np.random.random((16, 16)).astype("float32"), }
-        self.attrs['paddings'] = ((0, 1), (2, 3))
+        self.attrs = {}
+        self.attrs['paddings'] = [(0, 1), (2, 3)]
         self.attrs['pad_value'] = 0
         self.outputs = {
             'Out': np.pad(self.inputs['X'],
                           self.attrs['paddings'],
                           mode='constant',
-                          constant_value=0)
+                          constant_values=0)
         }
 
 
 class PadGradOpTest(GradientChecker):
     def test_pad(self):
-        op = Operator("pad", paddings=((0, 1), (2, 3)), pad_value=0)
+        op = Operator(
+            type="pad",
+            X="X",
+            Out="Out",
+            paddings=[(0, 1), (2, 3)],
+            pad_value=0)
         inputs = {'X': np.random.random((16, 16)).astype("float32"), }
 
         self.check_grad(op, inputs, set(["X"]), "Out", max_relative_error=0.5)