Merge pull request #5187 from chengduoZH/fix_pool_op

fix pool op

paddle/operators/pool_cudnn_op.cu

@@ -43,6 +43,7 @@ class PoolCudnnOpKernel : public framework::OpKernel<T> {
     std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
     if (ctx.Attr<bool>("globalPooling")) {
       for (size_t i = 0; i < ksize.size(); ++i) {
+        paddings[i] = 0;
         ksize[i] = static_cast<int>(input->dims()[i + 2]);
       }
     }
@@ -97,8 +98,10 @@ class PoolCudnnGradOpKernel : public framework::OpKernel<T> {
     std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
 
     if (ctx.Attr<bool>("globalPooling")) {
-      for (size_t i = 0; i < ksize.size(); ++i)
+      for (size_t i = 0; i < ksize.size(); ++i) {
+        paddings[i] = 0;
         ksize[i] = static_cast<int>(input->dims()[i + 2]);
+      }
     }
 
     const T *input_data = input->data<T>();

paddle/operators/pool_op.cc

@@ -39,8 +39,10 @@ void PoolOp::InferShape(framework::InferShapeContext *ctx) const {
 
   if (ctx->Attrs().Get<bool>("globalPooling")) {
     ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
-    for (size_t i = 0; i < ksize.size(); ++i)
+    for (size_t i = 0; i < ksize.size(); ++i) {
+      paddings[i] = 0;
       ksize[i] = static_cast<int>(in_x_dims[i + 2]);
+    }
   }
 
   PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U,
@@ -84,15 +86,16 @@ Pool2dOpMaker::Pool2dOpMaker(framework::OpProto *proto,
                        "(string), pooling type, can be \"max\" for max-pooling "
                        "and \"avg\" for average-pooling.")
       .InEnum({"max", "avg"});
-  AddAttr<std::vector<int>>(
-      "ksize",
-      "(vector ), the pooling window size(height, width) of pooling operator."
-      "If globalPooling = true, ksize is ignored and need not be "
-      "specified.");  // TODO(Chengduo): Add checker. (Currently,
+  AddAttr<std::vector<int>>("ksize",
+                            "(vector ), the pooling window size(height, width) "
+                            "of pooling operator."
+                            "If globalPooling = true, ksize and paddings will "
+                            "be ignored.");  // TODO(Chengduo): Add checker.
+                                             // (Currently,
   // TypedAttrChecker don't support vector type.)
   AddAttr<bool>("globalPooling",
                 "(bool default: false), whether to use the global pooling."
-                "If globalPooling = true, ksize is ignored.")
+                "If globalPooling = true, ksize and paddings will be ignored.")
       .SetDefault(false);
   AddAttr<std::vector<int>>(
       "strides",
@@ -101,7 +104,8 @@ Pool2dOpMaker::Pool2dOpMaker(framework::OpProto *proto,
   // TypedAttrChecker don't support vector type.)
   AddAttr<std::vector<int>>(
       "paddings",
-      "(vector defalut:{0,0}), paddings(height, width) of pooling operator.")
+      "(vector defalut:{0,0}), paddings(height, width) of pooling operator."
+      "If globalPooling = true, paddings and ksize will be ignored.")
       .SetDefault({0, 0});  // TODO(Chengduo): Add checker. (Currently,
   // TypedAttrChecker don't support vector type.)
 
@@ -145,25 +149,28 @@ Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto,
                        "(string), pooling type, can be \"max\" for max-pooling "
                        "and \"avg\" for average-pooling.")
       .InEnum({"max", "avg"});
-  AddAttr<std::vector<int>>(
-      "ksize",
-      "(vector ), the pooling window size(depth, height, width) of pooling "
-      "operator."
-      "If globalPooling = true, ksize is ignored and need not be "
-      "specified.");  // TODO(Chengduo): Add checker. (Currently,
-                      // TypedAttrChecker don't support vector type.)
+  AddAttr<std::vector<int>>("ksize",
+                            "(vector ), the pooling window size(depth, height, "
+                            "width) of pooling "
+                            "operator."
+                            "If globalPooling = true, ksize and paddings wille "
+                            "be ignored.");  // TODO(Chengduo): Add checker.
+                                             // (Currently,
+  // TypedAttrChecker don't support vector type.)
   AddAttr<bool>("globalPooling",
                 "(bool default: false), whether to use the global pooling."
-                "If globalPooling = true, ksize is ignored.")
+                "If globalPooling = true, ksize and paddings wille be ignored.")
       .SetDefault(false);
   AddAttr<std::vector<int>>("strides",
                             "(vector, default:{1,1,1}), strides(depth, height, "
                             "width) of pooling operator.")
       .SetDefault({1, 1, 1});  // TODO(Chengduo): Add checker. (Currently,
                                // TypedAttrChecker don't support vector type.)
-  AddAttr<std::vector<int>>("paddings",
-                            "(vector defalut:{0,0,0}), paddings(depth, height, "
-                            "width) of pooling operator.")
+  AddAttr<std::vector<int>>(
+      "paddings",
+      "(vector defalut:{0,0,0}), paddings(depth, height, "
+      "width) of pooling operator."
+      "If globalPooling = true, ksize and paddings wille be ignored.")
       .SetDefault({0, 0, 0});  // TODO(Chengduo): Add checker. (Currently,
                                // TypedAttrChecker don't support vector type.)
 

paddle/operators/pool_op.h

@@ -63,6 +63,7 @@ class PoolKernel : public framework::OpKernel<T> {
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
     if (context.Attr<bool>("globalPooling")) {
       for (size_t i = 0; i < ksize.size(); ++i) {
+        paddings[i] = 0;
         ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
       }
     }
@@ -103,6 +104,7 @@ class PoolKernel : public framework::OpKernel<T> {
                          paddings, pool_process);
         }
       } break;
+      default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
     }
   }
 };
@@ -123,8 +125,10 @@ class PoolGradKernel : public framework::OpKernel<T> {
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
 
     if (context.Attr<bool>("globalPooling")) {
-      for (size_t i = 0; i < ksize.size(); ++i)
+      for (size_t i = 0; i < ksize.size(); ++i) {
+        paddings[i] = 0;
         ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
+      }
     }
 
     if (in_x_grad) {
@@ -164,6 +168,7 @@ class PoolGradKernel : public framework::OpKernel<T> {
                             *out_grad, ksize, strides, paddings, pool_process);
           }
         } break;
+        default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
       }
     }
   }

paddle/operators/pool_with_index_op.cc

@@ -46,8 +46,10 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
 
     if (ctx->Attrs().Get<bool>("globalPooling")) {
       ksize.resize(static_cast<size_t>(in_x_dims.size()) - 2);
-      for (size_t i = 0; i < ksize.size(); ++i)
+      for (size_t i = 0; i < ksize.size(); ++i) {
+        paddings[i] = 0;
         ksize[i] = static_cast<int>(in_x_dims[i + 2]);
+      }
     }
 
     PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U,
@@ -87,31 +89,33 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput(
         "X",
-        "(Tensor) The input tensor of pooling operator. "
+        "(Tensor), the input tensor of pooling operator. "
         "The format of input tensor is NCHW. Where N is batch size, C is the "
         "number of channels, H and W is the height and width of image.");
     AddOutput("Out",
-              "(Tensor) The output tensor of pooling operator."
+              "(Tensor), the output tensor of pooling operator."
               "The format of output tensor is also NCHW."
               "Where N is batch size, C is "
               "the number of channels, H and W is the height and "
               "width of image.");
     AddOutput("Mask",
-              "(Tensor) The Mask tensor of pooling operator."
+              "(Tensor), the Mask tensor of pooling operator."
               "The format of output tensor is also NCHW."
               "Where N is batch size, C is the number of channels, H and W "
               "is the height and width of image."
               "The value in it is the index in current feature map");
 
-    AddAttr<std::vector<int>>(
-        "ksize",
-        "(vector ), the pooling window size(height, width) of pooling operator."
-        "If globalPooling = true, ksize is ignored and need not be "
-        "specified.");  // TODO(Chengduo): Add checker. (Currently,
+    AddAttr<std::vector<int>>("ksize",
+                              "(vector ), the pooling window size(height, "
+                              "width) of pooling operator."
+                              "If globalPooling = true, ksize and paddings "
+                              "will be ignored.");  // TODO(Chengduo): Add
+                                                    // checker. (Currently,
     // TypedAttrChecker don't support vector type.)
-    AddAttr<bool>("globalPooling",
-                  "(bool default: false), whether to use the global pooling."
-                  "If globalPooling = true, ksize is ignored.")
+    AddAttr<bool>(
+        "globalPooling",
+        "(bool default: false), whether to use the global pooling."
+        "If globalPooling = true, ksize and paddings will be ignored.")
         .SetDefault(false);
     AddAttr<std::vector<int>>(
         "strides",
@@ -120,7 +124,8 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
     // TypedAttrChecker don't support vector type.)
     AddAttr<std::vector<int>>(
         "paddings",
-        "(vector defalut:{0,0}), paddings(height, width) of pooling operator.")
+        "(vector defalut:{0, 0}), paddings(height, width) of pooling operator."
+        "If globalPooling = true, paddings and will be ignored.")
         .SetDefault({0, 0});  // TODO(Chengduo): Add checker. (Currently,
     // TypedAttrChecker don't support vector type.)
 
@@ -153,42 +158,46 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput(
         "X",
-        "(Tensor) The input tensor of pooling operator. "
+        "(Tensor), the input tensor of pooling operator. "
         "The format of input tensor is NCDHW. Where N is batch size, C is "
         "the number of channels, D, H and W is the depth, height and width of "
         "image.");
     AddOutput("Out",
-              "(Tensor) The output tensor of pooling operator."
+              "(Tensor), the output tensor of pooling operator."
               "The format of output tensor is also NCDHW."
               "Where N is batch size, C is "
               "the number of channels, D, H and W is the depth, height and "
               "width of image.");
     AddOutput("Mask",
-              "(Tensor) The Mask tensor of pooling operator."
+              "(Tensor), the Mask tensor of pooling operator."
               "The format of output tensor is also NCDHW."
               "Where N is batch size, C is the number of channels, D, H and W "
               "is the depth, height and width of image."
               "The value in it is the index in current feature map");
 
-    AddAttr<std::vector<int>>(
-        "ksize",
-        "(vector ), the pooling window size(depth, height, width) of pooling "
-        "operator."
-        "If globalPooling = true, ksize is ignored and need not be "
-        "specified.");  // TODO(Chengduo): Add checker. (Currently,
+    AddAttr<std::vector<int>>("ksize",
+                              "(vector), the pooling window size(depth, "
+                              "height, width) of pooling "
+                              "operator."
+                              "If globalPooling = true, ksize and paddings "
+                              "will be ignored.");  // TODO(Chengduo): Add
+                                                    // checker. (Currently,
     // TypedAttrChecker don't support vector type.)
-    AddAttr<bool>("globalPooling",
-                  "(bool default: false), whether to use the global pooling."
-                  "If globalPooling = true, ksize is ignored.")
+    AddAttr<bool>(
+        "globalPooling",
+        "(bool default: false), whether to use the global pooling."
+        "If globalPooling = true, ksize and paddings will be ignored.")
         .SetDefault(false);
     AddAttr<std::vector<int>>("strides",
                               "(vector, default:{1,1,1}), strides(depth, "
                               "height, width) of pooling operator.")
         .SetDefault({1, 1, 1});  // TODO(Chengduo): Add checker. (Currently,
     // TypedAttrChecker don't support vector type.)
-    AddAttr<std::vector<int>>("paddings",
-                              "(vector defalut:{0,0,0}), paddings(depth, "
-                              "height, width) of pooling operator.")
+    AddAttr<std::vector<int>>(
+        "paddings",
+        "(vector defalut:{0,0,0}), paddings(depth, "
+        "height, width) of pooling operator."
+        "If globalPooling = true, paddings and ksize will be ignored.")
         .SetDefault({0, 0, 0});  // TODO(Chengduo): Add checker. (Currently,
     // TypedAttrChecker don't support vector type.)
 

paddle/operators/pool_with_index_op.h

@@ -37,6 +37,7 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T> {
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
     if (context.Attr<bool>("globalPooling")) {
       for (size_t i = 0; i < ksize.size(); ++i) {
+        paddings[i] = 0;
         ksize[i] = static_cast<int>(in_x->dims()[i + 2]);
       }
     }
@@ -54,6 +55,7 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T> {
         pool3d_forward(context.device_context(), *in_x, *out, *mask, ksize,
                        strides, paddings);
       } break;
+      default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
     }
   }
 };
@@ -72,6 +74,7 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T> {
     std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
     if (context.Attr<bool>("globalPooling")) {
       for (size_t i = 0; i < ksize.size(); ++i) {
+        paddings[i] = 0;
         ksize[i] = static_cast<int>(in_x_grad->dims()[i + 2]);
       }
     }
@@ -95,6 +98,7 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T> {
           pool3d_backward(context.device_context(), *in_x_grad, *out_grad,
                           *mask, ksize, strides, paddings);
         } break;
+        default: { PADDLE_THROW("Pool op only supports 2D and 3D input."); }
       }
     }
   }

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

@@ -49,9 +49,12 @@ class TestPool2d_Op(OpTest):
         self.init_test_case()
         self.init_op_type()
         self.init_pool_type()
+        if self.global_pool:
+            self.paddings = [0 for _ in range(len(self.paddings))]
         input = np.random.random(self.shape).astype("float32")
         output = self.pool2D_forward_naive(input, self.ksize, self.strides,
-                                           self.paddings, self.global_pool)
+                                           self.paddings,
+                                           self.global_pool).astype("float32")
         self.inputs = {'X': input}
 
         self.attrs = {

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

@@ -54,10 +54,13 @@ def avg_pool3D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
 
 class TestPool3d_Op(OpTest):
     def setUp(self):
-        self.initTestCase()
+        self.init_test_case()
+        if self.global_pool:
+            self.paddings = [0 for _ in range(len(self.paddings))]
         input = np.random.random(self.shape).astype("float32")
         output = self.pool3D_forward_naive(input, self.ksize, self.strides,
-                                           self.paddings, self.global_pool)
+                                           self.paddings,
+                                           self.global_pool).astype("float32")
         self.inputs = {'X': input}
 
         self.attrs = {
@@ -77,7 +80,7 @@ class TestPool3d_Op(OpTest):
         if self.pool_type != "max":
             self.check_grad(set(['X']), 'Out', max_relative_error=0.07)
 
-    def initTestCase(self):
+    def init_test_case(self):
         self.global_pool = True
         self.op_type = "pool3d"
         self.pool_type = "avg"
@@ -89,7 +92,7 @@ class TestPool3d_Op(OpTest):
 
 
 class TestCase1(TestPool3d_Op):
-    def initTestCase(self):
+    def init_test_case(self):
         self.global_pool = False
         self.op_type = "pool3d"
         self.pool_type = "avg"
@@ -101,7 +104,7 @@ class TestCase1(TestPool3d_Op):
 
 
 class TestCase2(TestPool3d_Op):
-    def initTestCase(self):
+    def init_test_case(self):
         self.global_pool = False
         self.op_type = "pool3d"
         self.pool_type = "avg"
@@ -113,7 +116,7 @@ class TestCase2(TestPool3d_Op):
 
 
 class TestCase3(TestPool3d_Op):
-    def initTestCase(self):
+    def init_test_case(self):
         self.global_pool = True
         self.op_type = "pool3d"
         self.pool_type = "max"
@@ -125,7 +128,7 @@ class TestCase3(TestPool3d_Op):
 
 
 class TestCase4(TestPool3d_Op):
-    def initTestCase(self):
+    def init_test_case(self):
         self.global_pool = False
         self.op_type = "pool3d"
         self.pool_type = "max"
@@ -137,7 +140,7 @@ class TestCase4(TestPool3d_Op):
 
 
 class TestCase5(TestPool3d_Op):
-    def initTestCase(self):
+    def init_test_case(self):
         self.global_pool = False
         self.op_type = "pool3d"
         self.pool_type = "max"

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

@@ -3,11 +3,7 @@ import numpy as np
 from op_test import OpTest
 
 
-def max_pool3D_forward_naive(x,
-                             ksize,
-                             strides,
-                             paddings=[0, 0, 0],
-                             global_pool=0):
+def max_pool3D_forward_naive(x, ksize, strides, paddings, global_pool=0):
 
     N, C, D, H, W = x.shape
     if global_pool == 1:
@@ -44,7 +40,7 @@ def max_pool3D_forward_naive(x,
     return out, mask
 
 
-def max_pool2D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
+def max_pool2D_forward_naive(x, ksize, strides, paddings, global_pool=0):
 
     N, C, H, W = x.shape
     if global_pool == 1:
@@ -77,10 +73,14 @@ def max_pool2D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0):
 
 class TestMaxPoolWithIndex_Op(OpTest):
     def setUp(self):
-        self.initTestCase()
+        self.init_test_case()
+        if self.global_pool:
+            self.paddings = [0 for _ in range(len(self.paddings))]
         input = np.random.random(self.shape).astype("float32")
         output, mask = self.pool_forward_naive(input, self.ksize, self.strides,
                                                self.paddings, self.global_pool)
+        output = output.astype("float32")
+        mask = mask.astype("float32")
 
         self.attrs = {
             'strides': self.strides,
@@ -98,7 +98,7 @@ class TestMaxPoolWithIndex_Op(OpTest):
     # def test_check_grad(self):
     #     self.check_grad(set(['X']), ['Out'], max_relative_error=0.07)
 
-    def initTestCase(self):
+    def init_test_case(self):
         self.global_pool = True
         self.index = "max_pool3d_with_index"
         self.op_type = "%s" % self.index
@@ -110,7 +110,7 @@ class TestMaxPoolWithIndex_Op(OpTest):
 
 
 class TestCase1(TestMaxPoolWithIndex_Op):
-    def initTestCase(self):
+    def init_test_case(self):
         self.global_pool = True
         self.op_type = "max_pool3d_with_index"
         self.pool_forward_naive = max_pool3D_forward_naive
@@ -121,7 +121,7 @@ class TestCase1(TestMaxPoolWithIndex_Op):
 
 
 class TestCase2(TestMaxPoolWithIndex_Op):
-    def initTestCase(self):
+    def init_test_case(self):
         self.global_pool = False
         self.op_type = "max_pool3d_with_index"
         self.pool_forward_naive = max_pool3D_forward_naive
@@ -132,7 +132,7 @@ class TestCase2(TestMaxPoolWithIndex_Op):
 
 
 class TestCase3(TestMaxPoolWithIndex_Op):
-    def initTestCase(self):
+    def init_test_case(self):
         self.global_pool = False
         self.op_type = "max_pool3d_with_index"
         self.pool_forward_naive = max_pool3D_forward_naive
@@ -143,7 +143,7 @@ class TestCase3(TestMaxPoolWithIndex_Op):
 
 
 class TestCase4(TestMaxPoolWithIndex_Op):
-    def initTestCase(self):
+    def init_test_case(self):
         self.global_pool = True
         self.op_type = "max_pool3d_with_index"
         self.pool_forward_naive = max_pool3D_forward_naive
@@ -154,7 +154,7 @@ class TestCase4(TestMaxPoolWithIndex_Op):
 
 
 class TestCase5(TestMaxPoolWithIndex_Op):
-    def initTestCase(self):
+    def init_test_case(self):
         self.global_pool = True
         self.op_type = "max_pool3d_with_index"
         self.pool_forward_naive = max_pool3D_forward_naive
@@ -165,7 +165,7 @@ class TestCase5(TestMaxPoolWithIndex_Op):
 
 
 class TestCase6(TestMaxPoolWithIndex_Op):
-    def initTestCase(self):
+    def init_test_case(self):
         self.global_pool = False
         self.op_type = "max_pool2d_with_index"
         self.pool_forward_naive = max_pool2D_forward_naive
@@ -176,7 +176,7 @@ class TestCase6(TestMaxPoolWithIndex_Op):
 
 
 class TestCase7(TestMaxPoolWithIndex_Op):
-    def initTestCase(self):
+    def init_test_case(self):
         self.global_pool = False
         self.op_type = "max_pool2d_with_index"
         self.pool_forward_naive = max_pool2D_forward_naive
@@ -187,7 +187,7 @@ class TestCase7(TestMaxPoolWithIndex_Op):
 
 
 class TestCase8(TestMaxPoolWithIndex_Op):
-    def initTestCase(self):
+    def init_test_case(self):
         self.global_pool = True
         self.op_type = "max_pool2d_with_index"
         self.pool_forward_naive = max_pool2D_forward_naive
@@ -198,7 +198,7 @@ class TestCase8(TestMaxPoolWithIndex_Op):
 
 
 class TestCase9(TestMaxPoolWithIndex_Op):
-    def initTestCase(self):
+    def init_test_case(self):
         self.global_pool = True
         self.op_type = "max_pool2d_with_index"
         self.pool_forward_naive = max_pool2D_forward_naive