Polish error messages of pool_2d/3d and add Raises in English document. test=develop (#21017)

paddle/fluid/operators/pool_op.cc

@@ -38,10 +38,10 @@ int PoolOutputSize(int input_size, int filter_size, int padding_1,
   }
   PADDLE_ENFORCE_GT(
       output_size, 0,
-      "Due to the settings of padding(%d,%d), filter_size(%d) and "
-      "stride(%d), the output size is less than 0, please check "
-      "again. Input_size:%d",
-      padding_1, padding_2, filter_size, stride, input_size);
+      "ShapeError: the output size must be greater than 0. But received: "
+      "output_size = %d due to the settings of input_size(%d), padding(%d,%d), "
+      "k_size(%d) and stride(%d). Please check again!",
+      output_size, input_size, padding_1, padding_2, filter_size, stride);
   return output_size;
 }
 
@@ -63,13 +63,30 @@ void PoolOp::InferShape(framework::InferShapeContext* ctx) const {
       ctx->Attrs().Get<std::string>("padding_algorithm");
 
   auto in_x_dims = ctx->GetInputDim("X");
-  PADDLE_ENFORCE_EQ(in_x_dims.size() == 4 || in_x_dims.size() == 5, true,
-                    "Pooling intput should be 4-D or 5-D tensor.");
+  PADDLE_ENFORCE_EQ(
+      in_x_dims.size() == 4 || in_x_dims.size() == 5, true,
+      "ShapeError: the input of Op(pool) should be 4-D or 5-D Tensor. But "
+      "received: %u-D Tensor and it's shape is [%s].",
+      in_x_dims.size(), in_x_dims);
+
+  PADDLE_ENFORCE_EQ(
+      in_x_dims.size() - ksize.size(), 2U,
+      "ShapeError: the dimension of input minus the size of "
+      "Attr(ksize) must be euqal to 2 in Op(pool). "
+      "But received: the dimension of input minus the size "
+      "of Attr(ksize) is %d, the "
+      "input's dimension is %d, the shape of input "
+      "is [%s], the Attr(ksize)'s size is %d, the Attr(ksize) is [%s].",
+      in_x_dims.size() - ksize.size(), in_x_dims.size(), in_x_dims,
+      ksize.size(), framework::make_ddim(ksize));
 
-  PADDLE_ENFORCE_EQ(in_x_dims.size() - ksize.size(), 2U,
-                    "Input size and pooling size should be consistent.");
   PADDLE_ENFORCE_EQ(ksize.size(), strides.size(),
-                    "Strides size and pooling size should be the same.");
+                    "ShapeError: the size of Attr(ksize) and Attr(strides) in "
+                    "Op(pool) must be equal. "
+                    "But received: Attr(ksize)'s size is %d, Attr(strides)'s "
+                    "size is %d, Attr(ksize) is [%s], Attr(strides)is [%s].",
+                    ksize.size(), strides.size(), framework::make_ddim(ksize),
+                    framework::make_ddim(strides));
 
   const bool channel_last = (data_format == "NHWC" || data_format == "NDHWC");
 
@@ -91,7 +108,7 @@ void PoolOp::InferShape(framework::InferShapeContext* ctx) const {
   if (adaptive) {
     output_shape.insert(output_shape.end(), ksize.begin(), ksize.end());
   } else {
-    for (size_t i = 0; i < data_dims.size(); ++i) {
+    for (int i = 0; i < data_dims.size(); ++i) {
       if ((!ctx->IsRuntime()) && (data_dims[i] < 0)) {
         output_shape.push_back(data_dims[i]);
       } else {

paddle/fluid/operators/pool_op.h

@@ -66,7 +66,7 @@ inline void UpdatePadding(std::vector<int>* paddings, const bool global_pooling,
   // set padding size == data_dims.size() * 2
   auto data_shape = framework::vectorize<int>(data_dims);
   if (paddings->size() == data_dims.size()) {
-    for (size_t i = 0; i < data_dims.size(); ++i) {
+    for (int i = 0; i < data_dims.size(); ++i) {
       int copy_pad = *(paddings->begin() + 2 * i);
       paddings->insert(paddings->begin() + 2 * i + 1, copy_pad);
     }
@@ -78,7 +78,7 @@ inline void UpdatePadding(std::vector<int>* paddings, const bool global_pooling,
 
   // when padding_desc is "VALID" or "SAME"
   if (padding_algorithm == "SAME") {
-    for (size_t i = 0; i < data_dims.size(); ++i) {
+    for (int i = 0; i < data_dims.size(); ++i) {
       int out_size = (data_dims[i] + strides[i] - 1) / strides[0];
       int pad_sum =
           std::max((out_size - 1) * strides[i] + ksize[i] - data_shape[i], 0);

python/paddle/fluid/layers/nn.py

@@ -3458,9 +3458,18 @@ def pool2d(input,
         Variable: The output tensor of pooling result. The data type is same as input tensor.
 
     Raises:
-        ValueError: If `pool_type` is not "max" nor "avg"
-        ValueError: If `global_pooling` is False and `pool_size` is -1
-        ValueError: If `use_cudnn` is not a bool value.
+        ValueError: If `pool_type` is not "max" nor "avg".
+        ValueError: If `global_pooling` is False and `pool_size` is -1.
+        TypeError: If `use_cudnn` is not a bool value.
+        ValueError: If `data_format` is not "NCHW" or "NHWC".
+        ValueError: If `pool_padding` is a string, but not "SAME" or "VALID".
+        ValueError: If `pool_padding` is "VALID", but `ceil_mode` is True.
+        ValueError: If `pool_padding` is a list or tuple, but the elements in the batch or channel dimensions are non-zero.
+        ShapeError: If the input is not a 4-D or 5-D Tensor.
+        ShapeError: If the dimension of input minus the size of `pool_stride` is not 2.
+        ShapeError: If the size of `pool_size` and `pool_stride` is not equal.
+        ShapeError: If the output's shape calculated is not greater than 0.
+
 
     Examples:
 
@@ -3523,7 +3532,7 @@ def pool2d(input,
             "and be a valid value. Received pool_size: %s." % str(pool_size))
 
     if not isinstance(use_cudnn, bool):
-        raise ValueError("Attr(use_cudnn) should be True or False. Received "
+        raise TypeError("Attr(use_cudnn) should be True or False. Received "
                         "Attr(use_cudnn): %s." % str(use_cudnn))
 
     if data_format not in ["NCHW", "NHWC"]:
@@ -3663,6 +3672,19 @@ def pool3d(input,
     Returns:
         Variable: The output tensor of pooling result. The data type is same as input tensor.
 
+    Raises:
+        ValueError: If `pool_type` is not "max" nor "avg".
+        ValueError: If `global_pooling` is False and `pool_size` is -1.
+        TypeError: If `use_cudnn` is not a bool value.
+        ValueError: If `data_format` is not "NCDHW" or "NDHWC".
+        ValueError: If `pool_padding` is a string, but not "SAME" or "VALID".
+        ValueError: If `pool_padding` is "VALID", but `ceil_mode` is True.
+        ValueError: If `pool_padding` is a list or tuple, but the elements in the batch or channel dimensions are non-zero.
+        ShapeError: If the input is not a 4-D or 5-D Tensor.
+        ShapeError: If the dimension of input minus the size of `pool_stride` is not 2.
+        ShapeError: If the size of `pool_size` and `pool_stride` is not equal.
+        ShapeError: If the output's shape calculated is not greater than 0.
+
     Examples:
 
         .. code-block:: python
@@ -3730,7 +3752,7 @@ def pool3d(input,
             str(pool_size))
 
     if not isinstance(use_cudnn, bool):
-        raise ValueError("Attr(use_cudnn) should be True or False. Received "
+        raise TypeError("Attr(use_cudnn) should be True or False. Received "
                         "Attr(use_cudnn): %s. " % str(use_cudnn))
 
     if data_format not in ["NCDHW", "NDHWC"]:

python/paddle/fluid/tests/unittests/test_pool2d_op.py

@@ -1179,7 +1179,7 @@ class TestPool2dAPI_Error(OpTest):
             dtype="float32")
         ksize = [3, 3]
 
-        # cudnn value error
+        # cudnn type error
         def run_1():
             out_1 = fluid.layers.pool2d(
                 input=input_NHWC,
@@ -1189,7 +1189,7 @@ class TestPool2dAPI_Error(OpTest):
                 use_cudnn=[0],
                 data_format="NHWC")
 
-        self.assertRaises(ValueError, run_1)
+        self.assertRaises(TypeError, run_1)
 
         # data_format value error
         def run_2():

python/paddle/fluid/tests/unittests/test_pool3d_op.py

@@ -1086,7 +1086,7 @@ class TestPool3dAPI_Error(OpTest):
             dtype="float32")
         ksize = [3, 3, 3]
 
-        # cudnn value error
+        # cudnn type error
         def run_1():
             out_1 = fluid.layers.pool3d(
                 input=input_NDHWC,
@@ -1096,7 +1096,7 @@ class TestPool3dAPI_Error(OpTest):
                 use_cudnn=[0],
                 data_format="NDHWC")
 
-        self.assertRaises(ValueError, run_1)
+        self.assertRaises(TypeError, run_1)
 
         # data_format value error
         def run_2():