Change Name convention of operator attributes (#4807)

* Change dataType to data_type

Follow PEP8

* Change name_convention to fit PEP8

paddle/framework/backward.cc

@@ -433,7 +433,7 @@ ParamGradInfoMap AppendBackward(
       new OpDescBind("fill_constant", {}, {{"Out", {fill_one_op_out}}},
                      {{"shape", std::vector<int>{1}},
                       {"value", static_cast<float>(1.0)},
-                      {"dataType", framework::DataType::FP32}}));
+                      {"data_type", framework::DataType::FP32}}));
   all_ops.push_back(std::move(fill_one_op));
   size_t forward_op_num = all_ops.size();
   size_t forward_block_num = program_desc.Size();

paddle/operators/cross_entropy_op.cc

@@ -34,13 +34,13 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(x_dims[0], label_dims[0],
                       "The 1st dimension of Input(X) and Input(Label) should "
                       "be equal.");
-    if (ctx->Attrs().Get<bool>("softLabel")) {
+    if (ctx->Attrs().Get<bool>("soft_label")) {
       PADDLE_ENFORCE_EQ(x_dims[1], label_dims[1],
-                        "If Attr(softLabel) == true, the 2nd dimension of "
+                        "If Attr(soft_label) == true, the 2nd dimension of "
                         "Input(X) and Input(Label) should be equal.");
     } else {
       PADDLE_ENFORCE_EQ(label_dims[1], 1,
-                        "If Attr(softLabel) == false, the 2nd dimension of "
+                        "If Attr(soft_label) == false, the 2nd dimension of "
                         "Input(Label) should be 1.");
     }
 
@@ -82,13 +82,13 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
                       "be equal.");
     PADDLE_ENFORCE_EQ(dy_dims[1], 1,
                       "The 2nd dimension of Input(Y@Grad) should be 1.");
-    if (ctx->Attrs().Get<bool>("softLabel")) {
+    if (ctx->Attrs().Get<bool>("soft_label")) {
       PADDLE_ENFORCE_EQ(x_dims[1], label_dims[1],
-                        "When Attr(softLabel) == true, the 2nd dimension of "
+                        "When Attr(soft_label) == true, the 2nd dimension of "
                         "Input(X) and Input(Label) should be equal.");
     } else {
       PADDLE_ENFORCE_EQ(label_dims[1], 1,
-                        "When Attr(softLabel) == false, the 2nd dimension of "
+                        "When Attr(soft_label) == false, the 2nd dimension of "
                         "Input(Label) should be 1.");
     }
     ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
@@ -115,15 +115,15 @@ class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
         "Label",
         "(Tensor, default Tensor<int>), the ground truth which is "
         "a 2-D tensor. "
-        "When softLabel is set to false, `Label` is a Tensor<int> with shape "
+        "When soft_label is set to false, `Label` is a Tensor<int> with shape "
         "[N x 1]. "
-        "When softLabel is set to true, `Label` is a Tensor<float/double> "
+        "When soft_label is set to true, `Label` is a Tensor<float/double> "
         "with shape [N x K].");
     AddOutput("Y",
               "(Tensor, default Tensor<float>), a 2-D tensor "
               "with shape [N x 1]. The cross entropy loss.");
     AddAttr<bool>(
-        "softLabel",
+        "soft_label",
         "(bool, default false), a flag to indicate whether to interpretate "
         "the given labels as soft labels.")
         .SetDefault(false);
@@ -133,12 +133,12 @@ CrossEntropy Operator.
 It supports both standard cross-entropy and soft-label cross-entropy loss
 computation.
 1) One-hot cross-entropy:
-    softLabel = false, Label[i, 0] indicates the class index for sample i:
+    soft_label = false, Label[i, 0] indicates the class index for sample i:
 
                 Y[i] = -log(X[i, Label[i]])
 
 2) Soft-label cross-entropy:
-    softLabel = true, Label[i, j] indicates the soft label of class j
+    soft_label = true, Label[i, j] indicates the soft label of class j
     for sample i:
 
                 Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}

paddle/operators/cross_entropy_op.cu

@@ -56,7 +56,7 @@ class CrossEntropyOpCUDAKernel : public framework::OpKernel<T> {
     y->mutable_data<T>(ctx.GetPlace());
 
     math::CrossEntropyFunctor<platform::GPUPlace, T>()(
-        ctx.device_context(), y, x, label, ctx.Attr<bool>("softLabel"));
+        ctx.device_context(), y, x, label, ctx.Attr<bool>("soft_label"));
   }
 };
 
@@ -83,7 +83,7 @@ class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel<T> {
     int block = 512;
     int grid = (batch_size * class_num + block - 1) / block;
 
-    if (ctx.Attr<bool>("softLabel")) {
+    if (ctx.Attr<bool>("soft_label")) {
       auto* label_data = label->data<T>();
       SoftCrossEntropyGradientKernel<T><<<
           grid, block, 0, reinterpret_cast<const platform::CUDADeviceContext&>(

paddle/operators/cross_entropy_op.h

@@ -38,7 +38,7 @@ class CrossEntropyOpKernel : public framework::OpKernel<T> {
     y->mutable_data<T>(ctx.GetPlace());
 
     math::CrossEntropyFunctor<platform::CPUPlace, T>()(
-        ctx.device_context(), y, x, labels, ctx.Attr<bool>("softLabel"));
+        ctx.device_context(), y, x, labels, ctx.Attr<bool>("soft_label"));
   }
 };
 
@@ -55,7 +55,7 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel<T> {
     T* dx_data = dx->mutable_data<T>(ctx.GetPlace());
 
     int class_num = x->dims()[1];
-    if (ctx.Attr<bool>("softLabel")) {
+    if (ctx.Attr<bool>("soft_label")) {
       auto x_mat = EigenMatrix<T>::From(*x);
       auto dy_mat = EigenMatrix<T>::From(*dy);
       auto lbl_mat = EigenMatrix<T>::From(*label);

paddle/operators/fill_constant_op.cc

@@ -35,7 +35,7 @@ class FillConstantOp : public framework::OperatorWithKernel {
 
   framework::DataType IndicateDataType(
       const framework::ExecutionContext &ctx) const override {
-    return static_cast<framework::DataType>(ctx.Attr<int>("dataType"));
+    return static_cast<framework::DataType>(ctx.Attr<int>("data_type"));
   }
 };
 
@@ -44,7 +44,7 @@ class FillConstantOpMaker : public framework::OpProtoAndCheckerMaker {
   FillConstantOpMaker(framework::OpProto *proto,
                       framework::OpAttrChecker *op_checker)
       : framework::OpProtoAndCheckerMaker(proto, op_checker) {
-    AddAttr<int>("dataType",
+    AddAttr<int>("data_type",
                  "(int, default 5 (FP32)) "
                  "Output data type")
         .SetDefault(framework::DataType::FP32);

paddle/operators/name_convention.md

@@ -11,7 +11,7 @@ When defining an operator in Paddle, a corresponding [OpProtoMaker](https://gith
   - If an operator's Input/Output are tensors in math, not match to any meaningful words, input name should starts from `X`. e.g. `X`, `Y`, and output name should starts from `Out`. e.g. `Out`. This rule intends making operators which have few inputs/outputs unified.
 
 - Attribute.
-  - Attribute name follows the **camelCase**. e.g. `x`, `y`, `axis`, `rowwiseMatrix`. Also, attribute name prefers to meaningful English words.
+  - Attribute name follows the **snake_case**. e.g. `x`, `y`, `axis`, `rowwise_matrix`. Also, attribute name prefers to meaningful English words.
 
 - Comments.
   - Input/Output/Attr comment follow the format of **(type,default value) usage**, corresponding to which type it can be and how it will be used in the operator. e.g.  Attribute in Accumulator`"gamma" `,`(float, default 1.0) Accumulation multiplier`.

paddle/operators/pool_op.cc

@@ -29,7 +29,7 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
 
   auto in_x_dims = ctx->GetInputDim("X");
 
-  std::string pooling_type = ctx->Attrs().Get<std::string>("poolingType");
+  std::string pooling_type = ctx->Attrs().Get<std::string>("pooling_type");
   std::vector<int> ksize = ctx->Attrs().Get<std::vector<int>>("ksize");
   std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
   std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
@@ -37,7 +37,7 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
   PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
                  "Pooling intput should be 4-D or 5-D tensor.");
 
-  if (ctx->Attrs().Get<bool>("globalPooling")) {
+  if (ctx->Attrs().Get<bool>("global_pooling")) {
     ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
     for (size_t i = 0; i < ksize.size(); ++i)
       ksize[i] = static_cast<int>(in_x_dims[i + 2]);
@@ -80,23 +80,23 @@ Pool2dOpMaker::Pool2dOpMaker(framework::OpProto *proto,
             "the number of channels, H and W is the height and "
             "width of feature.");
 
-  AddAttr<std::string>("poolingType",
-                       "PoolingType of pooling operator."
+  AddAttr<std::string>("pooling_type",
+                       "Pooling_type of pooling operator."
                        "Str constant equal to 'max' or 'avg'.")
       .InEnum({"max", "avg"});
 
   AddAttr<std::vector<int>>(
       "ksize",
       "The pooling window size(height, width) of pooling operator."
-      "If globalPooling = true, ksize is ignored and need not be "
+      "If global_pooling = true, ksize is ignored and need not be "
       "specified.");  // TODO(Chengduo): Add checker. (Currently,
                       // TypedAttrChecker don't support vector type.)
   AddAttr<bool>(
-      "globalPooling",
-      "Whether to use the globalPooling."
+      "global_pooling",
+      "Whether to use the global_pooling."
       "Bool constant equal to false or true."
       "Default false."
-      "If globalPooling = true, ksize is ignored and need not be specified.")
+      "If global_pooling = true, ksize is ignored and need not be specified.")
       .SetDefault(false);
   AddAttr<std::vector<int>>("strides",
                             "The strides(height, width) of pooling window."
@@ -146,7 +146,7 @@ Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto,
             "the number of channels, D, H and W is the depth, height and "
             "width of feature.");
 
-  AddAttr<std::string>("poolingType",
+  AddAttr<std::string>("pooling_type",
                        "PoolingType of pooling operator."
                        "Str constant equal to 'max' or 'avg'.")
       .InEnum({"max", "avg"});
@@ -154,15 +154,15 @@ Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto,
   AddAttr<std::vector<int>>(
       "ksize",
       "The pooling window size(depth, height, width) of pooling operator."
-      "If globalPooling = true, ksize is ignored and need not be "
+      "If global_pooling = true, ksize is ignored and need not be "
       "specified.");  // TODO(Chengduo): Add checker. (Currently,
                       // TypedAttrChecker don't support vector type.)
   AddAttr<bool>(
-      "globalPooling",
-      "Whether to use the globalPooling."
+      "global_pooling",
+      "Whether to use the global_pooling."
       "Bool constant equal to false or true."
       "Default false."
-      "If globalPooling = true, ksize is ignored and need not be specified.")
+      "If global_pooling = true, ksize is ignored and need not be specified.")
       .SetDefault(false);
   AddAttr<std::vector<int>>("strides",
                             "Strides(depth, height, width) of pooling operator."

paddle/operators/pool_op.h

@@ -59,11 +59,11 @@ class PoolKernel : public framework::OpKernel<T> {
     const Tensor* in_x = context.Input<Tensor>("X");
     Tensor* out = context.Output<Tensor>("Out");
 
-    std::string pooling_type = context.Attr<std::string>("poolingType");
+    std::string pooling_type = context.Attr<std::string>("pooling_type");
     std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
-    if (context.Attr<bool>("globalPooling")) {
+    if (context.Attr<bool>("global_pooling")) {
       for (size_t i = 0; i < ksize.size(); ++i) {
         ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
       }
@@ -119,12 +119,12 @@ class PoolGradKernel : public framework::OpKernel<T> {
         context.Input<Tensor>(framework::GradVarName("Out"));
     Tensor* in_x_grad = context.Output<Tensor>(framework::GradVarName("X"));
 
-    std::string pooling_type = context.Attr<std::string>("poolingType");
+    std::string pooling_type = context.Attr<std::string>("pooling_type");
     std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
 
-    if (context.Attr<bool>("globalPooling")) {
+    if (context.Attr<bool>("global_pooling")) {
       for (size_t i = 0; i < ksize.size(); ++i)
         ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
     }

paddle/operators/pool_with_index_op.cc

@@ -45,7 +45,7 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5,
                    "Pooling intput should be 4-D or 5-D tensor.");
 
-    if (ctx->Attrs().Get<bool>("globalPooling")) {
+    if (ctx->Attrs().Get<bool>("global_pooling")) {
       ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
       for (size_t i = 0; i < ksize.size(); ++i)
         ksize[i] = static_cast<int>(in_x_dims[i + 2]);
@@ -108,15 +108,15 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<std::vector<int>>(
         "ksize",
         "The pooling window size(height, width) of pooling operator."
-        "If globalPooling = true, ksize is ignored and need not be "
+        "If global_pooling = true, ksize is ignored and need not be "
         "specified.");  // TODO(Chengduo): Add checker. (Currently,
                         // TypedAttrChecker don't support vector type.)
     AddAttr<bool>(
-        "globalPooling",
-        "Whether to use the globalPooling."
+        "global_pooling",
+        "Whether to use the global_pooling."
         "Bool constant equal to false or true."
         "Default false."
-        "If globalPooling = true, ksize is ignored and need not be specified.")
+        "If global_pooling = true, ksize is ignored and need not be specified.")
         .SetDefault(false);
     AddAttr<std::vector<int>>("strides",
                               "The strides(height, width) of pooling window."
@@ -179,15 +179,15 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
     AddAttr<std::vector<int>>(
         "ksize",
         "The pooling window size(depth, height, width) of pooling operator."
-        "If globalPooling = true, ksize is ignored and need not be "
+        "If global_pooling = true, ksize is ignored and need not be "
         "specified.");  // TODO(Chengduo): Add checker. (Currently,
                         // TypedAttrChecker don't support vector type.)
     AddAttr<bool>(
-        "globalPooling",
-        "Whether to use the globalPooling."
+        "global_pooling",
+        "Whether to use the global_pooling."
         "Bool constant equal to false or true."
         "Default false."
-        "If globalPooling = true, ksize is ignored and need not be specified.")
+        "If global_pooling = true, ksize is ignored and need not be specified.")
         .SetDefault(false);
     AddAttr<std::vector<int>>(
         "strides",

paddle/operators/pool_with_index_op.h

@@ -35,7 +35,7 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T> {
     std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
-    if (context.Attr<bool>("globalPooling")) {
+    if (context.Attr<bool>("global_pooling")) {
       for (size_t i = 0; i < ksize.size(); ++i) {
         ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
       }
@@ -70,7 +70,7 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T> {
     std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
     std::vector<int> strides = context.Attr<std::vector<int>>("strides");
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
-    if (context.Attr<bool>("globalPooling")) {
+    if (context.Attr<bool>("global_pooling")) {
       for (size_t i = 0; i < ksize.size(); ++i) {
         ksize[i] = static_cast<int>(in_x_grad->dims()[i + 2]);
       }

paddle/operators/softmax_with_cross_entropy_op.cc

@@ -46,7 +46,7 @@ class SoftmaxWithCrossEntropyOpMaker
               "(Tensor, default: Tensor<float>), A 2-D tensor. The cross "
               "entropy loss with shape [N x 1].");
     AddAttr<bool>(
-        "softLabel",
+        "soft_label",
         "(bool, default: false), A flag to indicate whether to interpretate "
         "the given labels as soft labels.")
         .SetDefault(false);
@@ -100,13 +100,13 @@ class SoftmaxWithCrossEntropyOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(labels_dims.size(), 2UL,
                       "The labels should be a 2-D tensor.");
 
-    if (ctx->Attrs().Get<bool>("softLabel")) {
+    if (ctx->Attrs().Get<bool>("soft_label")) {
       PADDLE_ENFORCE_EQ(logits_dims[1], labels_dims[1],
-                        "If Attr(softLabel) == true, the 2nd dimension of "
+                        "If Attr(soft_label) == true, the 2nd dimension of "
                         "Input(X) and Input(Label) should be equal.");
     } else {
       PADDLE_ENFORCE_EQ(labels_dims[1], 1UL,
-                        "If Attr(softLabel) == false, the 2nd dimension of "
+                        "If Attr(soft_label) == false, the 2nd dimension of "
                         "Input(Label) should be 1.");
     }
 
@@ -142,13 +142,13 @@ class SoftmaxWithCrossEntropyOpGrad : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(labels_dims.size(), 2UL,
                       "The labels should be a 2-D tensor.");
 
-    if (ctx->Attrs().Get<bool>("softLabel")) {
+    if (ctx->Attrs().Get<bool>("soft_label")) {
       PADDLE_ENFORCE_EQ(softmax_dims[1], labels_dims[1],
-                        "When Attr(softLabel) == true, the 2nd dimension of "
+                        "When Attr(soft_label) == true, the 2nd dimension of "
                         "Input(X) and Input(Label) should be equal.");
     } else {
       PADDLE_ENFORCE_EQ(labels_dims[1], 1UL,
-                        "When Attr(softLabel) == false, the 2nd dimension of "
+                        "When Attr(soft_label) == false, the 2nd dimension of "
                         "Input(Label) should be 1.");
     }
 

paddle/operators/softmax_with_cross_entropy_op.cu

@@ -70,7 +70,7 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel<T> {
                                                   logits, softmax);
     math::CrossEntropyFunctor<platform::GPUPlace, T>()(
         context.device_context(), loss, softmax, labels,
-        context.Attr<bool>("softLabel"));
+        context.Attr<bool>("soft_label"));
   }
 };
 
@@ -93,7 +93,7 @@ class SoftmaxWithCrossEntropyGradCUDAKernel : public framework::OpKernel<T> {
     int block = 512;
     int grid = (batch_size * class_num + block - 1) / block;
 
-    if (context.Attr<bool>("softLabel")) {
+    if (context.Attr<bool>("soft_label")) {
       const T* label_data = labels->data<T>();
       SoftCrossEntropyGradientKernel<T><<<
           grid, block, 0, reinterpret_cast<const platform::CUDADeviceContext&>(

paddle/operators/softmax_with_cross_entropy_op.h

@@ -44,7 +44,7 @@ class SoftmaxWithCrossEntropyKernel : public framework::OpKernel<T> {
                                                   logits, softmax);
     math::CrossEntropyFunctor<platform::CPUPlace, T>()(
         context.device_context(), loss, softmax, labels,
-        context.Attr<bool>("softLabel"));
+        context.Attr<bool>("soft_label"));
   }
 };
 
@@ -60,7 +60,7 @@ class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel<T> {
     logit_grad->ShareDataWith<T>(*context.Input<Tensor>("Softmax"));
 
     const int class_num = logit_grad->dims()[1];
-    if (context.Attr<bool>("softLabel")) {
+    if (context.Attr<bool>("soft_label")) {
       auto out_grad_mat = EigenMatrix<T>::From(*out_grad);
       auto logit_grad_mat = EigenMatrix<T>::From(*logit_grad);
       auto lbl_mat = EigenMatrix<T>::From(*labels);

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

@@ -49,7 +49,7 @@ class TestCrossEntropyOp2(OpTest):
 
         self.inputs = {"X": X, "Label": label}
         self.outputs = {"Y": cross_entropy}
-        self.attrs = {"softLabel": True}
+        self.attrs = {"soft_label": True}
 
     def test_check_output(self):
         self.check_output()
@@ -82,7 +82,7 @@ class TestCrossEntropyOp3(OpTest):
 
         self.inputs = {"X": X, "Label": label.astype(np.float32)}
         self.outputs = {"Y": cross_entropy}
-        self.attrs = {"softLabel": True}
+        self.attrs = {"soft_label": True}
 
     def test_check_output(self):
         self.check_output()

python/paddle/v2/framework/tests/test_pool2d_op.py

@@ -56,8 +56,8 @@ class TestPool2d_Op(OpTest):
             'strides': self.strides,
             'paddings': self.paddings,
             'ksize': self.ksize,
-            'poolingType': self.pool_type,
-            'globalPooling': self.global_pool,
+            'pooling_type': self.pool_type,
+            'global_pooling': self.global_pool,
         }
 
         self.outputs = {'Out': output}

python/paddle/v2/framework/tests/test_pool3d_op.py

@@ -64,8 +64,8 @@ class TestPool3d_Op(OpTest):
             'strides': self.strides,
             'paddings': self.paddings,
             'ksize': self.ksize,
-            'poolingType': self.pool_type,
-            'globalPooling': self.global_pool,
+            'pooling_type': self.pool_type,
+            'global_pooling': self.global_pool,
         }
 
         self.outputs = {'Out': output}

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

@@ -86,7 +86,7 @@ class TestMaxPoolWithIndex_Op(OpTest):
             'strides': self.strides,
             'paddings': self.paddings,
             'ksize': self.ksize,
-            'globalPooling': self.global_pool,
+            'global_pooling': self.global_pool,
         }
 
         self.inputs = {'X': input}

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

@@ -57,7 +57,7 @@ class TestSoftmaxWithCrossEntropyOp2(OpTest):
 
         self.inputs = {"Logits": logits, "Label": labels}
         self.outputs = {"Softmax": softmax, "Loss": cross_entropy}
-        self.attrs = {"softLabel": True}
+        self.attrs = {"soft_label": True}
 
     def test_check_output(self):
         self.check_output()