[TOPI] Remove cpp upsampling and resize op (#4769)

* remove cpp upsampling

* remove cpp resize

diff --git a/topi/include/topi/detail/tensor_utils.h b/topi/include/topi/detail/tensor_utils.h
index dfa7943..6ac3982 100644 (file)
--- a/topi/include/topi/detail/tensor_utils.h
+++ b/topi/include/topi/detail/tensor_utils.h
@@ -25,6 +25,8 @@
 #define TOPI_DETAIL_TENSOR_UTILS_H_
 
 
+#include <tvm/te/operation.h>
+
 namespace topi {
 namespace detail {
 using namespace tvm;
@@ -50,7 +52,45 @@ inline bool is_empty_shape(const Array<PrimExpr>& x) {
   return is_empty;
 }
 
+/*!
+ * \brief Sample a point in a tensor using bilinear interpolation.
+ *
+ * \param input The input tensor.
+ * \param indices The index of the target point, which can be fractional
+ * \param max_y The maximum of y dimension
+ * \param max_x The maximum of x dimension
+ *
+ * \return The interpolated value in the given index.
+ */
+inline PrimExpr bilinear_sample_nchw(const Tensor& input, const Array<PrimExpr>& indices,
+                                 const PrimExpr max_y, const PrimExpr max_x) {
+  auto in_y = indices[2];
+  auto yf = tvm::floor(in_y);
+  auto yc = tvm::cast(DataType::Int(32), tvm::ceil(in_y));
+
+  auto y0 = tvm::cast(DataType::Int(32), tvm::floor(in_y));
+  auto y1 = tvm::if_then_else((yc > max_y), max_y, yc);
+  auto y_lerp = in_y - yf;
+
+  auto in_x = indices[3];
+  auto xf = tvm::floor(in_x);
+  auto xc = tvm::cast(DataType::Int(32), tvm::ceil(in_x));
+
+  auto x0 = tvm::cast(DataType::Int(32), tvm::floor(in_x));
+  auto x1 = tvm::if_then_else((xc > max_x), max_x, xc);
+  auto x_lerp = in_x - xf;
+
+  auto A = input(indices[0], indices[1], y0, x0);
+  auto B = input(indices[0], indices[1], y0, x1);
+  auto C = input(indices[0], indices[1], y1, x0);
+  auto D = input(indices[0], indices[1], y1, x1);
+
+  return A * ( 1 - x_lerp) * ( 1 - y_lerp) +
+         B * x_lerp * (1 - y_lerp) +
+         C * (1 - x_lerp) * y_lerp +
+         D * x_lerp * y_lerp;
+}
+
 }  // namespace detail
 }  // namespace topi
 #endif  // TOPI_DETAIL_TENSOR_UTILS_H_
-

diff --git a/topi/include/topi/image/resize.h b/topi/include/topi/image/resize.h
deleted file mode 100644 (file)
index 8f1d02a..0000000
--- a/topi/include/topi/image/resize.h
+++ /dev/null
@@ -1,408 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*!
- * \file topi/image/resize.h
- * \brief image resize constructors
- */
-#ifndef TOPI_IMAGE_RESIZE_H_
-#define TOPI_IMAGE_RESIZE_H_
-
-#include <tvm/te/operation.h>
-#include <topi/tags.h>
-#include <topi/elemwise.h>
-#include <topi/detail/ravel_unravel.h>
-#include <topi/detail/constant_utils.h>
-
-#include <string>
-#include <vector>
-#include <iterator>
-#include <algorithm>
-
-namespace topi {
-namespace image {
-using namespace tvm;
-using namespace tvm::te;
-
-/*!
- * \brief Sample a point in a tensor using bilinear interpolation.
- *
- * \param input The input tensor.
- * \param indices The index of the target point, which can be fractional
- * \param max_y The maximum of y dimension
- * \param max_x The maximum of x dimension
- *
- * \return The interpolated value in the given index.
- */
-inline PrimExpr bilinear_sample_nchw(const Tensor& input, const Array<PrimExpr>& indices,
-                                 const PrimExpr max_y, const PrimExpr max_x) {
-  auto in_y = indices[2];
-  auto yf = tvm::floor(in_y);
-  auto yc = tvm::cast(DataType::Int(32), tvm::ceil(in_y));
-
-  auto y0 = tvm::cast(DataType::Int(32), tvm::floor(in_y));
-  auto y1 = tvm::if_then_else((yc > max_y), max_y, yc);
-  auto y_lerp = in_y - yf;
-
-  auto in_x = indices[3];
-  auto xf = tvm::floor(in_x);
-  auto xc = tvm::cast(DataType::Int(32), tvm::ceil(in_x));
-
-  auto x0 = tvm::cast(DataType::Int(32), tvm::floor(in_x));
-  auto x1 = tvm::if_then_else((xc > max_x), max_x, xc);
-  auto x_lerp = in_x - xf;
-
-  auto A = input(indices[0], indices[1], y0, x0);
-  auto B = input(indices[0], indices[1], y0, x1);
-  auto C = input(indices[0], indices[1], y1, x0);
-  auto D = input(indices[0], indices[1], y1, x1);
-
-  return A * ( 1 - x_lerp) * ( 1 - y_lerp) +
-         B * x_lerp * (1 - y_lerp) +
-         C * (1 - x_lerp) * y_lerp +
-         D * x_lerp * y_lerp;
-}
-
-/*!
-* \brief Resize given tensor to given shape using nearest neighbour for NHWC
-*
-* \param input The input tensor.
-* \param shape Output shape to resize to.
-* \param align_corners To preserve centers of 4 corner pixels
-* \param name Name of the operation
-* \param tag The tag to mark the operation
-*
-* \return A Tensor resized to given shape
-*/
-inline Tensor resize_nearest_neighbor_nhwc(const Tensor& input,
-                                           const Array<PrimExpr>& shape,
-                                           bool align_corners = false,
-                                           std::string name = "tensor",
-                                           std::string tag = kInjective) {
-  Array<PrimExpr> out_shape;
-  out_shape.push_back(input->shape[0]);
-  out_shape.push_back(cast(DataType::Int(32), shape[0]));
-  out_shape.push_back(cast(DataType::Int(32), shape[1]));
-  out_shape.push_back(input->shape[3]);
-
-  return compute(
-    out_shape, [&](const Array<Var>& indices) {
-    Array<PrimExpr> idx;
-    idx.push_back(indices[0]);
-    idx.push_back(indices[1] * input->shape[1] / shape[0]);
-    idx.push_back(indices[2] * input->shape[2] / shape[1]);
-    idx.push_back(indices[3]);
-
-    return input(idx);
-    }, name, tag);
-}
-
-/*!
-* \brief Resize given tensor to given shape using nearest neighbour for NCHW
-*
-* \param input The input tensor.
-* \param shape Output shape to resize to.
-* \param align_corners To preserve centers of 4 corner pixels
-* \param name Name of the operation
-* \param tag The tag to mark the operation
-*
-* \return A Tensor resized to given shape
-*/
-inline Tensor resize_nearest_neighbor_nchw(const Tensor& input,
-                                           const Array<PrimExpr>& shape,
-                                           bool align_corners = false,
-                                           std::string name = "tensor",
-                                           std::string tag = kInjective) {
-  Array<PrimExpr> out_shape;
-  out_shape.push_back(input->shape[0]);
-  out_shape.push_back(input->shape[1]);
-  out_shape.push_back(cast(DataType::Int(32), shape[0]));
-  out_shape.push_back(cast(DataType::Int(32), shape[1]));
-
-  return compute(
-    out_shape, [&](const Array<Var>& indices) {
-    Array<PrimExpr> idx;
-    idx.push_back(indices[0]);
-    idx.push_back(indices[1]);
-    idx.push_back(indices[2] * input->shape[2] / shape[0]);
-    idx.push_back(indices[3] * input->shape[3] / shape[1]);
-
-    return input(idx);
-    }, name, tag);
-}
-
-/*!
-* \brief Resize given tensor to given shape using nearest neighbour for NCHWc
-*
-* \param input The input tensor.
-* \param shape Output shape to resize to.
-* \param align_corners To preserve centers of 4 corner pixels
-* \param name Name of the operation
-* \param tag The tag to mark the operation
-*
-* \return A Tensor resized to given shape
-*/
-inline Tensor resize_nearest_neighbor_nchwc(const Tensor& input,
-                                            const Array<PrimExpr>& shape,
-                                            bool align_corners = false,
-                                            std::string name = "tensor",
-                                            std::string tag = kInjective) {
-  Array<PrimExpr> out_shape;
-  out_shape.push_back(input->shape[0]);
-  out_shape.push_back(input->shape[1]);
-  out_shape.push_back(cast(DataType::Int(32), shape[0]));
-  out_shape.push_back(cast(DataType::Int(32), shape[1]));
-  out_shape.push_back(input->shape[4]);
-
-  return compute(
-    out_shape, [&](const Array<Var>& indices) {
-    Array<PrimExpr> idx;
-    idx.push_back(indices[0]);
-    idx.push_back(indices[1]);
-    idx.push_back(indices[2] * input->shape[2] / shape[0]);
-    idx.push_back(indices[3] * input->shape[3] / shape[1]);
-    idx.push_back(indices[4]);
-
-     return input(idx);
-    }, name, tag);
-}
-
-/*!
-* \brief Resize given tensor to given shape using nearest neighbour
-*
-* \param input The input tensor.
-* \param shape Output shape to resize to.
-* \param layout input layout
-* \param align_corners To preserve centers of 4 corner pixels
-* \param name Name of the operation
-* \param tag The tag to mark the operation
-*
-* \return A Tensor resized to given shape
-*/
-inline Tensor resize_nearest_neighbor(const Tensor& input,
-                                      const Array<PrimExpr>& shape,
-                                      std::string layout = "NCHW",
-                                      bool align_corners = false,
-                                      std::string name = "tensor",
-                                      std::string tag = kInjective) {
-  auto base_layout = layout.substr(0, 4);
-  if (layout == "NHWC") {
-    return resize_nearest_neighbor_nhwc(input, shape, align_corners);
-  } else if (layout == "NCHW") {
-    return resize_nearest_neighbor_nchw(input, shape, align_corners);
-  } else if (base_layout == "NCHW") {
-    // NCHWc
-    return resize_nearest_neighbor_nchwc(input, shape, align_corners);
-  } else {
-    LOG(FATAL) << "Unknown layout: " << layout;
-    return Tensor();
-  }
-}
-
-/*!
-* \brief Resize given tensor to given shape using bilinear interpolation for NHWC
-*
-* \param input The input tensor.
-* \param shape Output shape to resize to.
-* \param align_corners To preserve centers of 4 corner pixels
-* \param name Name of the operation
-* \param tag The tag to mark the operation
-*
-* \return A Tensor resized to given shape
-*/
-inline Tensor resize_bilinear_nhwc(const Tensor& input,
-                                   const Array<PrimExpr>& shape,
-                                   bool align_corners = false,
-                                   std::string name = "tensor",
-                                   std::string tag = kInjective) {
-  Array<PrimExpr> out_shape;
-  out_shape.push_back(input->shape[0]);
-  out_shape.push_back(cast(DataType::Int(32), shape[0]));
-  out_shape.push_back(cast(DataType::Int(32), shape[1]));
-  out_shape.push_back(input->shape[3]);
-
-  PrimExpr cone = make_const(DataType::Int(32), 1);
-
-  auto in_height = as_const_int(input->shape[1]);
-  auto in_width = as_const_int(input->shape[2]);
-  auto out_height = as_const_int(shape[0]);
-  auto out_width = as_const_int(shape[1]);
-
-  PrimExpr y_ratio;
-  PrimExpr x_ratio;
-
-  if (!align_corners) {
-    y_ratio = make_const(DataType::Float(32), (static_cast<float>(*in_height) /
-                                     static_cast<float>(*out_height)));
-    x_ratio = make_const(DataType::Float(32), (static_cast<float>(*in_width) /
-                                     static_cast<float>(*out_width)));
-  } else {
-    y_ratio = make_const(DataType::Float(32), (static_cast<float>(*in_height - 1) /
-                                     static_cast<float>(*out_height - 1)));
-    x_ratio = make_const(DataType::Float(32), (static_cast<float>(*in_width - 1) /
-                                     static_cast<float>(*out_width - 1)));
-  }
-
-  PrimExpr other_y = tvm::tir::Simplify(input->shape[1] - cone);
-  PrimExpr other_x = tvm::tir::Simplify(input->shape[2] - cone);
-
-  return compute(
-    out_shape, [&](const Array<Var>& indices) {
-    auto in_y = indices[1] * y_ratio;
-    auto yf = tvm::floor(in_y);
-    auto yc = tvm::cast(DataType::Int(32), tvm::ceil(in_y));
-
-    auto y0 = tvm::cast(DataType::Int(32), tvm::floor(in_y));
-    auto y1 = tvm::if_then_else((yc > other_y), other_y, yc);
-    auto y_lerp  = in_y - yf;
-
-    auto in_x = indices[2] * x_ratio;
-    auto xf = tvm::floor(in_x);
-    auto xc = tvm::cast(DataType::Int(32), tvm::ceil(in_x));
-
-    auto x0 = tvm::cast(DataType::Int(32), tvm::floor(in_x));
-    auto x1 = tvm::if_then_else((xc > other_x), other_x, xc);
-    auto x_lerp  = in_x - xf;
-
-    auto A = input(indices[0], y0, x0, indices[3]);
-    auto B = input(indices[0], y0, x1, indices[3]);
-    auto C = input(indices[0], y1, x0, indices[3]);
-    auto D = input(indices[0], y1, x1, indices[3]);
-
-    return A * ( 1 - x_lerp) * ( 1 - y_lerp) +
-           B * x_lerp * (1 - y_lerp) +
-           C * (1 - x_lerp) * y_lerp +
-           D * x_lerp * y_lerp;
-    }, name, tag);
-}
-
-/*!
-* \brief Resize given tensor to given shape using bilinear interpolation for NCHW
-*
-* \param input The input tensor.
-* \param shape Output shape to resize to.
-* \param align_corners To preserve centers of 4 corner pixels
-* \param name Name of the operation
-* \param tag The tag to mark the operation
-*
-* \return A Tensor resized to given shape
-*/
-inline Tensor resize_bilinear_nchw(const Tensor& input,
-                                   const Array<PrimExpr>& shape,
-                                   bool align_corners = false,
-                                   std::string name = "tensor",
-                                   std::string tag = kInjective) {
-  Array<PrimExpr> out_shape;
-  out_shape.push_back(input->shape[0]);
-  out_shape.push_back(input->shape[1]);
-  out_shape.push_back(cast(DataType::Int(32), shape[0]));
-  out_shape.push_back(cast(DataType::Int(32), shape[1]));
-
-  PrimExpr cone = make_const(DataType::Int(32), 1);
-
-  auto in_height = as_const_int(input->shape[2]);
-  auto in_width = as_const_int(input->shape[3]);
-  auto out_height = as_const_int(shape[0]);
-  auto out_width = as_const_int(shape[1]);
-
-  PrimExpr y_ratio;
-  PrimExpr x_ratio;
-
-  if (!align_corners) {
-    y_ratio = make_const(DataType::Float(32), (static_cast<float>(*in_height) /
-                                     static_cast<float>(*out_height)));
-    x_ratio = make_const(DataType::Float(32), (static_cast<float>(*in_width) /
-                                     static_cast<float>(*out_width)));
-  } else {
-    y_ratio = make_const(DataType::Float(32), (static_cast<float>(*in_height - 1) /
-                                     static_cast<float>(*out_height - 1)));
-    x_ratio = make_const(DataType::Float(32), (static_cast<float>(*in_width - 1) /
-                                     static_cast<float>(*out_width - 1)));
-  }
-
-  PrimExpr other_y = tvm::tir::Simplify(input->shape[2] - cone);
-  PrimExpr other_x = tvm::tir::Simplify(input->shape[3] - cone);
-
-  return compute(
-    out_shape, [&](const Array<Var>& indices) {
-    auto in_y = indices[2] * y_ratio;
-    auto in_x = indices[3] * x_ratio;
-    return bilinear_sample_nchw(input, {indices[0], indices[1], in_y, in_x}, other_y, other_x);
-    }, name, tag);
-}
-
-/*!
-* \brief Resize given tensor to given shape using bilinear interpolation
-*
-* \param input The input tensor.
-* \param shape Output shape to resize to.
-* \param layout input layout
-* \param align_corners To preserve centers of 4 corner pixels
-* \param name Name of the operation
-* \param tag The tag to mark the operation
-*
-* \return A Tensor resized to given shape
-*/
-inline Tensor resize_bilinear(const Tensor& input,
-                              const Array<tvm::PrimExpr>& shape,
-                              std::string layout = "NCHW",
-                              bool align_corners = false,
-                              std::string name = "tensor",
-                              std::string tag = kInjective) {
-  Tensor ret;
-
-  if (layout == "NHWC") {
-    ret = resize_bilinear_nhwc(input, shape, align_corners);
-  } else {
-    ret = resize_bilinear_nchw(input, shape, align_corners);
-  }
-
-  return cast(ret, input->dtype);
-}
-
-/*!
-* \brief Resize given tensor to given shape
-*
-* \param input The input tensor.
-* \param shape Output shape to resize to.
-* \param layout input layout
-* \param align_corners To preserve centers of 4 corner pixels
-* \param mode Algorithm to use (NEAREST_NEIGHBOR / BILINEAR)
-* \param name Name of the operation
-* \param tag The tag to mark the operation
-*
-* \return A Tensor resized to given shape
-*/
-inline Tensor resize(const Tensor& input,
-                     const Array<PrimExpr>& shape,
-                     std::string layout = "NCHW",
-                     bool align_corners = false,
-                     std::string mode = "BILINEAR",
-                     std::string name = "tensor",
-                     std::string tag = kInjective) {
-  if (mode == "NEAREST_NEIGHBOR") {
-    return resize_nearest_neighbor(input, shape, layout, align_corners);
-  } else {
-    return resize_bilinear(input, shape, layout, align_corners);
-  }
-}
-
-}  // namespace image
-}  // namespace topi
-#endif  // TOPI_IMAGE_RESIZE_H_

diff --git a/topi/include/topi/nn/upsampling.h b/topi/include/topi/nn/upsampling.h
deleted file mode 100644 (file)
index bd46d44..0000000
--- a/topi/include/topi/nn/upsampling.h
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements.  See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership.  The ASF licenses this file
- * to you under the Apache License, Version 2.0 (the
- * "License"); you may not use this file except in compliance
- * with the License.  You may obtain a copy of the License at
- *
- *   http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing,
- * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
- * KIND, either express or implied.  See the License for the
- * specific language governing permissions and limitations
- * under the License.
- */
-
-/*!
- * \file topi/nn/upsampling.h
- * \brief upsampling op constructors
- */
-#ifndef TOPI_NN_UPSAMPLING_H_
-#define TOPI_NN_UPSAMPLING_H_
-
-#include <string>
-#include <vector>
-#include <iterator>
-#include <algorithm>
-
-#include "topi/image/resize.h"
-
-namespace topi {
-namespace nn {
-using namespace tvm;
-using namespace tvm::te;
-using namespace topi::image;
-
-/*!
-* \brief Upsample given tensor to given shape
-*
-* \param input The input tensor.
-* \param shape Output shape to upsample.
-* \param layout input layout
-* \param mode Algorithm to use (NEAREST_NEIGHBOR / BILINEAR)
-* \param name Name of the operation
-* \param tag The tag to mark the operation
-*
-* \return A Tensor upsampled to given shape
-*/
-inline Tensor upsampling(const Tensor& input,
-                         const Array<PrimExpr> shape,
-                         std::string layout = "NCHW",
-                         std::string mode = "NEAREST_NEIGHBOR",
-                         std::string name = "tensor",
-                         std::string tag = kInjective) {
-  return resize(input, shape, layout, false, mode);
-}
-
-}  // namespace nn
-}  // namespace topi
-#endif  // TOPI_NN_UPSAMPLING_H_

diff --git a/topi/python/topi/cpp/__init__.py b/topi/python/topi/cpp/__init__.py
index 9a11149..788b888 100644 (file)
--- a/topi/python/topi/cpp/__init__.py
+++ b/topi/python/topi/cpp/__init__.py
@@ -23,5 +23,4 @@ from . import vision
 from . import x86
 from . import generic
 from . import rocm
-from . import image
 from . import util

diff --git a/topi/python/topi/cpp/image.py b/topi/python/topi/cpp/image.py
deleted file mode 100644 (file)
index c6a8f2c..0000000
--- a/topi/python/topi/cpp/image.py
+++ /dev/null
@@ -1,21 +0,0 @@
-# Licensed to the Apache Software Foundation (ASF) under one
-# or more contributor license agreements.  See the NOTICE file
-# distributed with this work for additional information
-# regarding copyright ownership.  The ASF licenses this file
-# to you under the Apache License, Version 2.0 (the
-# "License"); you may not use this file except in compliance
-# with the License.  You may obtain a copy of the License at
-#
-#   http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing,
-# software distributed under the License is distributed on an
-# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
-# KIND, either express or implied.  See the License for the
-# specific language governing permissions and limitations
-# under the License.
-"""FFI for image TOPI ops and schedules"""
-
-from tvm._ffi.function import _init_api_prefix
-
-_init_api_prefix("topi.cpp.image", "topi.image")

diff --git a/topi/python/topi/nn/deformable_conv2d.py b/topi/python/topi/nn/deformable_conv2d.py
index fb05016..2417411 100644 (file)
--- a/topi/python/topi/nn/deformable_conv2d.py
+++ b/topi/python/topi/nn/deformable_conv2d.py
@@ -20,7 +20,7 @@ import tvm
 
 from .util import get_pad_tuple
 from ..util import get_const_tuple
-from ..cpp.image import bilinear_sample_nchw
+from ..cpp.util import bilinear_sample_nchw
 
 @tvm.target.generic_func
 def deformable_conv2d_nchw(data, offset, kernel, strides, padding, dilation, deformable_groups,

diff --git a/topi/python/topi/vision/rcnn/roi_align.py b/topi/python/topi/vision/rcnn/roi_align.py
index c861ddd..a6540b3 100644 (file)
--- a/topi/python/topi/vision/rcnn/roi_align.py
+++ b/topi/python/topi/vision/rcnn/roi_align.py
@@ -18,7 +18,7 @@
 """Roi align operator"""
 import tvm
 from ...util import get_const_tuple
-from ...cpp.image import bilinear_sample_nchw
+from ...cpp.util import bilinear_sample_nchw
 
 
 @tvm.target.generic_func

diff --git a/topi/src/topi.cc b/topi/src/topi.cc
index f86c2d4..25fbc75 100644 (file)
--- a/topi/src/topi.cc
+++ b/topi/src/topi.cc
@@ -43,13 +43,11 @@
 #include <topi/nn/mapping.h>
 #include <topi/nn/pooling.h>
 #include <topi/nn/softmax.h>
-#include <topi/nn/upsampling.h>
 #include <topi/nn/l2_normalize.h>
 #include <topi/nn/local_response_norm.h>
 #include <topi/nn/batch_matmul.h>
 
 #include <topi/vision/reorg.h>
-#include <topi/image/resize.h>
 #include <topi/generic/default.h>
 #include <topi/generic/extern.h>
 #include <topi/generic/injective.h>
@@ -451,12 +449,6 @@ TVM_REGISTER_GLOBAL("topi.one_hot")
   *rv = one_hot(args[0], args[1], args[2], depth, axis, dtype);
   });
 
-/* Ops from nn/upsampling.h */
-TVM_REGISTER_GLOBAL("topi.nn.upsampling")
-.set_body([](TVMArgs args, TVMRetValue *rv) {
-  *rv = nn::upsampling(args[0], args[1], args[2], args[3]);
-  });
-
 /* Ops from nn/bnn.h */
 TVM_REGISTER_GLOBAL("topi.nn.binarize_pack")
 .set_body([](TVMArgs args, TVMRetValue *rv) {
@@ -581,17 +573,6 @@ TVM_REGISTER_GLOBAL("topi.vision.reorg")
   *rv = vision::reorg(args[0], args[1]);
   });
 
-/* Ops from image/resize.h */
-TVM_REGISTER_GLOBAL("topi.image.bilinear_sample_nchw")
-.set_body([](TVMArgs args, TVMRetValue *rv) {
-  *rv = image::bilinear_sample_nchw(args[0], args[1], args[2], args[3]);
-  });
-
-TVM_REGISTER_GLOBAL("topi.image.resize")
-.set_body([](TVMArgs args, TVMRetValue *rv) {
-  *rv = image::resize(args[0], args[1], args[2], args[3], args[4]);
-  });
-
 /* Generic schedules */
 TVM_REGISTER_GLOBAL("topi.generic.default_schedule")
 .set_body([](TVMArgs args, TVMRetValue *rv) {
@@ -755,6 +736,11 @@ TVM_REGISTER_GLOBAL("topi.util.is_empty_shape")
   *rv = topi::detail::is_empty_shape(args[0]);
   });
 
+TVM_REGISTER_GLOBAL("topi.util.bilinear_sample_nchw")
+.set_body([](TVMArgs args, TVMRetValue *rv) {
+  *rv = detail::bilinear_sample_nchw(args[0], args[1], args[2], args[3]);
+  });
+
 /*! \brief Builder function for instantiating schedules. */
 using FTVMScheduleBuilder = std::function<
   tvm::te::Schedule(const tvm::Target& target, const tvm::Array<tvm::te::Tensor>& outs)>;