Add count_include_pad support to AvgPool (apache#1163)

* Add count_include_pad support to AvgPool

* Fix python_cpp/test_topi_pooling.py

* Change auto to explicitly type, and fix format.

CONTRIBUTORS.md

@@ -62,3 +62,4 @@ List of Contributors
 - [Haolong Zhang](https://github.com/haolongzhangm)
 - [Cody Hao Yu](https://github.com/comaniac)
 - [Chris Nuernberger](https://github.com/cnuernber)
+- [Tatsuya Nishiyama](https://github.com/nishi-t)

topi/include/topi/nn/pooling.h

@@ -36,6 +36,7 @@ enum PoolType : int {
 * \param ceil_mode Whether to use ceil when calculating the output size
 * \param height_axis index of the height dimension
 * \param width_axis index of the width dimension
+* \param count_include_pad Whether include padding in the calculation
 *
 * \return The output tensor in same layout order
 */
@@ -46,7 +47,8 @@ inline Tensor pool_impl(const Tensor& x,
                         PoolType pool_type,
                         bool ceil_mode,
                         const size_t height_axis,
-                        const size_t width_axis) {
+                        const size_t width_axis,
+                        bool count_include_pad) {
   CHECK(x->shape.size() >= 2) << "Pooling input must >= 2-D (H, W)";
   CHECK_EQ(kernel_size.size(), 2) << "Pooling kernel_size must have 2 elements";
   CHECK_EQ(stride_size.size(), 2) << "Pooling stride_size must have 2 elements";
@@ -120,7 +122,19 @@ inline Tensor pool_impl(const Tensor& x,
 
     return tvm::compute(out_shape,
     [&](const Array<Var>& output) {
-      return tsum(output) / (kernel_height * kernel_width);
+      if (count_include_pad) {
+        return tsum(output) / (kernel_height * kernel_width);
+      } else {
+        Expr h_start = output[height_axis] * stride_height - padding_height;
+        Expr w_start = output[width_axis] * stride_width - padding_width;
+        Expr h_end = ir::Min::make(h_start + kernel_height, height);
+        Expr w_end = ir::Min::make(w_start + kernel_width, width);
+        h_start = ir::Max::make(h_start, make_const(Int(32), 0));
+        w_start = ir::Max::make(w_start, make_const(Int(32), 0));
+        Expr divide_factor = ir::Max::make((h_end - h_start) * (w_end - w_start),
+                                           make_const(Int(32), 1));
+        return tsum(output) / divide_factor;
+      }
     }, "tensor", kElementWise);
   } else {
     LOG(ERROR) << "Unrecognized pool_type: " << pool_type;
@@ -177,6 +191,9 @@ inline bool find_height_width(const std::string& layout,
 *        it can be used to decide the output shape).
 *        Since pooling does not care about the factor size of dimensions
 *        other than `H` and `W`, one can pass `NCHWc` as well.
+* \param  count_include_pad Whether include padding in the calculation when pool_type is 'avg'
+*        
+*
 * \return The output tensor in the same layout
 */
 inline Tensor pool(const Tensor& x,
@@ -185,12 +202,14 @@ inline Tensor pool(const Tensor& x,
                    const Array<Expr>& padding_size,
                    PoolType pool_type,
                    bool ceil_mode,
-                   const std::string& layout = "NCHW") {
+                   const std::string& layout = "NCHW",
+                   bool count_include_pad = true) {
   int height_axis = -1, width_axis = -1;
   CHECK(find_height_width(layout, &height_axis, &width_axis))
     << "Unsupported layout " << layout;
   return pool_impl(x, kernel_size, stride_size, padding_size,
-                   pool_type, ceil_mode, height_axis, width_axis);
+                   pool_type, ceil_mode, height_axis, width_axis,
+                   count_include_pad);
 }
 
 /*!

topi/python/topi/nn/pooling.py

@@ -42,7 +42,14 @@ def global_pool(data, pool_type, layout="NCHW"):
     return cpp.nn.global_pool(data, POOL_TYPE_CODE[pool_type], layout)
 
 
-def pool(data, kernel, stride, padding, pool_type, ceil_mode=False, layout="NCHW"):
+def pool(data,
+         kernel,
+         stride,
+         padding,
+         pool_type,
+         ceil_mode=False,
+         layout="NCHW",
+         count_include_pad=True):
     """Perform pooling on height and width dimension of data.
        It decides the height and width dimension according to the layout string,
        in which 'W' and 'H' means width and height respectively.
@@ -80,10 +87,13 @@ def pool(data, kernel, stride, padding, pool_type, ceil_mode=False, layout="NCHW
         [batch_size, channel, height, width, channel_block],
         in which channel_block=16 is a split of dimension channel.
 
+    count_include_pad: bool
+        Whether include padding in the calculation when pool_type is 'avg'
+
     Returns
     -------
     output : tvm.Tensor
         n-D in the same layout
     """
     return cpp.nn.pool(data, kernel, stride, padding,
-                       POOL_TYPE_CODE[pool_type], ceil_mode, layout)
+                       POOL_TYPE_CODE[pool_type], ceil_mode, layout, count_include_pad)

topi/src/topi.cc

@@ -322,7 +322,7 @@ TVM_REGISTER_GLOBAL("topi.nn.pool")
 .set_body([](TVMArgs args, TVMRetValue *rv) {
   *rv = nn::pool(args[0], args[1], args[2], args[3],
                  static_cast<nn::PoolType>(static_cast<int>(args[4])),
-                 args[5], args[6]);
+                 args[5], args[6], args[7]);
   });
 
 TVM_REGISTER_GLOBAL("topi.nn.global_pool")

topi/tests/python/test_topi_pooling.py

@@ -5,14 +5,14 @@
 import math
 from topi.util import get_const_tuple
 
-def verify_pool(n, ic, ih, kh, sh, padding, pool_type, ceil_mode):
+def verify_pool(n, ic, ih, kh, sh, padding, pool_type, ceil_mode, count_include_pad=True):
     iw = ih
     kw = kh
     sw = sh
     ph, pw = padding
     A = tvm.placeholder((n, ic, ih, iw), name='A')
     B = topi.nn.pool(A, kernel=[kh, kw], stride=[sh, sw], padding=padding,
-                     pool_type=pool_type, ceil_mode=ceil_mode)
+                     pool_type=pool_type, ceil_mode=ceil_mode, count_include_pad=count_include_pad)
     B = topi.nn.relu(B)
     dtype = A.dtype
 
@@ -26,7 +26,7 @@ def verify_pool(n, ic, ih, kh, sh, padding, pool_type, ceil_mode):
         assert bshape[3] == int(math.floor(float(ashape[3] - kw + pw * 2) / sw) + 1)
 
 
-    a_np = np.random.uniform(size=(n, ic, ih, iw)).astype(dtype)
+    a_np = np.random.uniform(low=0.001, size=(n, ic, ih, iw)).astype(dtype)
     pad_np = np.zeros(shape=(n, ic, ih+2*ph, iw+2*pw)).astype(dtype)
     no_zero = (range(n), range(ic), (range(ph, ih+ph)), (range(pw, iw+pw)))
     pad_np[np.ix_(*no_zero)] = a_np
@@ -36,7 +36,12 @@ def verify_pool(n, ic, ih, kh, sh, padding, pool_type, ceil_mode):
     if pool_type == 'avg':
         for i in range(oh):
             for j in range(ow):
-                b_np[:,:,i,j] = np.mean(pad_np[:, :, i*sh:i*sh+kh, j*sw:j*sw+kw], axis=(2,3))
+                if count_include_pad:
+                    b_np[:,:,i,j] = np.mean(pad_np[:, :, i*sh:i*sh+kh, j*sw:j*sw+kw], axis=(2,3))
+                else:
+                    pad_count = np.sum(pad_np[:, :, i*sh:i*sh+kh, j*sw:j*sw+kw] > 0, axis=(2,3))
+                    b_np[:,:,i,j] = np.sum(pad_np[:, :, i*sh:i*sh+kh, j*sw:j*sw+kw], axis=(2,3)) / np.maximum(pad_count, 1)
+
     elif pool_type =='max':
         for i in range(oh):
             for j in range(ow):
@@ -62,8 +67,11 @@ def check_device(device):
         check_device(device)
 
 def test_pool():
-    verify_pool(1, 256, 32, 2, 2, [0, 0], 'avg', False)
-    verify_pool(1, 256, 31, 3, 3, [1, 2], 'avg', False)
+    verify_pool(1, 256, 32, 2, 2, [0, 0], 'avg', False, True)
+    verify_pool(1, 256, 31, 3, 3, [1, 2], 'avg', False, True)
+    verify_pool(1, 256, 32, 2, 2, [1, 2], 'avg', False, False)
+    verify_pool(1, 256, 31, 4, 4, [3, 3], 'avg', False, False)
+    verify_pool(1, 256, 31, 4, 4, [0, 0], 'avg', False, False)
     verify_pool(1, 256, 32, 2, 2, [0, 0], 'max', False)
     verify_pool(1, 256, 31, 3, 3, [2, 1], 'max', False)
     verify_pool(1, 256, 31, 3, 3, [2, 1], 'max', True)

topi/tests/python_cpp/test_topi_pooling.py

@@ -9,14 +9,14 @@
     "avg": 0,
     "max": 1
 }
-def verify_pool(n, ic, ih, kh, sh, padding, pool_type, ceil_mode):
+def verify_pool(n, ic, ih, kh, sh, padding, pool_type, ceil_mode, count_include_pad=True):
     iw = ih
     kw = kh
     sw = sh
     ph, pw = padding
     A = tvm.placeholder((n, ic, ih, iw), name='A')
     B = topi.cpp.nn.pool(A, [kh, kw], [sh, sw], padding,
-                         pool_code[pool_type], ceil_mode, "NCHW")
+                         pool_code[pool_type], ceil_mode, "NCHW", count_include_pad)
     B = topi.cpp.nn.relu(B)
     dtype = A.dtype
 
@@ -40,7 +40,12 @@ def verify_pool(n, ic, ih, kh, sh, padding, pool_type, ceil_mode):
     if pool_type == 'avg':
         for i in range(oh):
             for j in range(ow):
-                b_np[:,:,i,j] = np.mean(pad_np[:, :, i*sh:i*sh+kh, j*sw:j*sw+kw], axis=(2,3))
+                if count_include_pad:
+                    b_np[:,:,i,j] = np.mean(pad_np[:, :, i*sh:i*sh+kh, j*sw:j*sw+kw], axis=(2,3))
+                else:
+                    pad_count = np.sum(pad_np[:, :, i*sh:i*sh+kh, j*sw:j*sw+kw] > 0, axis=(2,3))
+                    b_np[:,:,i,j] = np.sum(pad_np[:, :, i*sh:i*sh+kh, j*sw:j*sw+kw], axis=(2,3)) / np.maximum(pad_count, 1)
+
     elif pool_type =='max':
         for i in range(oh):
             for j in range(ow):
@@ -68,8 +73,11 @@ def check_device(device):
         check_device(device)
 
 def test_pool():
-    verify_pool(1, 256, 32, 2, 2, [0, 0], 'avg', False)
-    verify_pool(1, 256, 31, 3, 3, [1, 2], 'avg', False)
+    verify_pool(1, 256, 32, 2, 2, [0, 0], 'avg', False, True)
+    verify_pool(1, 256, 31, 3, 3, [1, 2], 'avg', False, True)
+    verify_pool(1, 256, 32, 2, 2, [1, 2], 'avg', False, False)
+    verify_pool(1, 256, 31, 4, 4, [3, 3], 'avg', False, False)
+    verify_pool(1, 256, 31, 4, 4, [0, 0], 'avg', False, False)
     verify_pool(1, 256, 32, 2, 2, [0, 0], 'max', False)
     verify_pool(1, 256, 31, 3, 3, [2, 1], 'max', False)
     verify_pool(1, 256, 31, 3, 3, [2, 1], 'max', True)