[TOPI][IMAGE][RESIZE] Bilinear interpolation for resize and upsampling. (#1181)

docs/api/python/topi.rst

@@ -51,6 +51,7 @@ List of operators
    topi.broadcast_div
    topi.broadcast_maximum
    topi.broadcast_minimum
+   topi.image.resize
 
 
 List of schedules
@@ -114,6 +115,10 @@ topi.nn
 .. autofunction:: topi.nn.depthwise_conv2d_nchw
 .. autofunction:: topi.nn.depthwise_conv2d_nhwc
 
+topi.image
+~~~~~~~~~~
+.. autofunction:: topi.image.resize
+
 
 topi.generic
 ~~~~~~~~~~~~

nnvm/include/nnvm/top/nn.h

@@ -288,16 +288,22 @@ struct GlobalPool2DParam : public dmlc::Parameter<GlobalPool2DParam> {
 struct UpSamplingParam : public dmlc::Parameter<UpSamplingParam> {
   int scale;
   std::string layout;
+  std::string method;
 
   DMLC_DECLARE_PARAMETER(UpSamplingParam) {
     DMLC_DECLARE_FIELD(scale)
       .describe("upsampling scaling factor");
     DMLC_DECLARE_FIELD(layout)
       .set_default("NCHW")
-      .describe("Dimension ordering of data and weight. Can be 'NCHW', 'NHWC', etc."
+      .describe("Dimension ordering of data. Can be 'NCHW', 'NHWC', etc."
                 "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
-                "dimensions respectively. Convolution is applied on the 'H' and"
+                "dimensions respectively. Upsampling is applied on the 'H' and"
                 "'W' dimensions.");
+    DMLC_DECLARE_FIELD(method)
+      .set_default("NEAREST_NEIGHBOR")
+      .describe("Specify the mode to use for scaling."
+                "NEAREST_NEIGHBOR -  Nearest Neighbor"
+                "BILINEAR - Bilinear Interpolation");
   }
 };
 

nnvm/python/nnvm/top/__init__.py

@@ -8,6 +8,7 @@ from . import nn
 from . import transform
 from . import reduction
 from . import vision
+from . import image
 
 from .registry import OpPattern
 from .registry import register_compute, register_schedule, register_pattern

nnvm/python/nnvm/top/image.py

+# pylint: disable=invalid-name, unused-argument
+"""Definition of image ops"""
+from __future__ import absolute_import
+
+import topi
+import tvm
+from . import registry as reg
+from .registry import OpPattern
+
+# resize
+@reg.register_schedule("resize")
+def schedule_resize(_, outs, target):
+    """Schedule definition of resize"""
+    with tvm.target.create(target):
+        return topi.generic.schedule_injective(outs)
+
+reg.register_pattern("resize", OpPattern.INJECTIVE)

nnvm/python/nnvm/top/nn.py

@@ -235,20 +235,10 @@ def schedule_global_avg_pool2d(_, outs, target):
 
 reg.register_pattern("global_avg_pool2d", OpPattern.OUT_ELEMWISE_FUSABLE)
 
-
-@reg.register_compute("upsampling")
-def compute_upsampling(attrs, inputs, _):
-    """Compute definition of upsampling"""
-    scale = attrs.get_int("scale")
-    layout = attrs["layout"]
-    if layout:
-        assert layout == "NCHW" or layout == "NHWC"
-        return topi.nn.upsampling(inputs[0], scale, layout)
-    return topi.nn.upsampling(inputs[0], scale)
-
+# upsampling
 @reg.register_schedule("upsampling")
 def schedule_upsampling(_, outs, target):
-    """Compute definition of upsampling"""
+    """Schedule definition of upsampling"""
     with tvm.target.create(target):
         return topi.generic.schedule_injective(outs)
 

nnvm/src/top/image/resize.cc

+/*!
+ *  Copyright (c) 2017 by Contributors
+ * \file resize.cc
+ * \brief Property def of resize operators.
+ */
+#include <tvm/tvm.h>
+#include <tvm/expr.h>
+#include <tvm/packed_func_ext.h>
+#include <nnvm/layout.h>
+#include <nnvm/compiler/op_attr_types.h>
+#include <nnvm/op.h>
+#include <nnvm/node.h>
+#include <nnvm/op_attr_types.h>
+#include "../nn/nn_common.h"
+#include "../op_common.h"
+#include "../elemwise_op_common.h"
+#include "topi/elemwise.h"
+#include "topi/transform.h"
+#include "topi/image/resize.h"
+#include "resize.h"
+
+namespace nnvm {
+namespace top {
+using tvm::Expr;
+using tvm::Array;
+using tvm::Tensor;
+using nnvm::compiler::FTVMCompute;
+
+DMLC_REGISTER_PARAMETER(ResizeParam);
+
+inline bool ResizeInferShape(const nnvm::NodeAttrs& attrs,
+                             std::vector<TShape>* in_shape,
+                             std::vector<TShape>* out_shape) {
+  static const Layout kNCHW("NCHW");
+  const ResizeParam& param = nnvm::get<ResizeParam>(attrs.parsed);
+  CHECK_EQ(in_shape->size(), 1U);
+  CHECK_EQ(out_shape->size(), 1U);
+  TShape dshape = (*in_shape)[0];
+  if (dshape.ndim() ==  0) return false;
+  dshape = ConvertLayout(dshape, param.layout, kNCHW);
+
+  TShape oshape = dshape;
+  if (param.layout == "NCHW") {
+    oshape[2] = param.size[0];
+    oshape[3] = param.size[1];
+  } else {
+    oshape[1] = param.size[0];
+    oshape[2] = param.size[1];
+  }
+  oshape = ConvertLayout(oshape, kNCHW, param.layout);
+  NNVM_ASSIGN_OUTPUT_SHAPE(attrs, *out_shape, 0, oshape);
+
+  return true;
+}
+
+inline bool ResizeLayout(const NodeAttrs& attrs,
+                         std::vector<Layout> *in_layouts,
+                         const std::vector<Layout> *last_in_layouts,
+                         std::vector<Layout> *out_layouts) {
+  const ResizeParam& param = nnvm::get<ResizeParam>(attrs.parsed);
+  CHECK_EQ(in_layouts->size(), 1U);
+  CHECK_EQ(out_layouts->size(), 1U);
+  const Layout layout(param.layout);
+  NNVM_ASSIGN_LAYOUT(*in_layouts, 0, layout);
+  NNVM_ASSIGN_LAYOUT(*out_layouts, 0, layout);
+  return true;
+}
+
+NNVM_REGISTER_OP(resize)
+.describe(R"(Perform resize to input array with nearest neighbour or bilinear interpolation.
+
+- **data**: data is 4D array of shape
+            (batch_size, channels, in_height, in_width) for NCHW
+            (batch_size, in_height, in_width, channels) for NHWC
+
+- **out**: Output is 4D array of shape
+           for layout NCHW
+           (batch_size, channels, size[0], size[1])
+
+           for layout NHWC
+           (batch_size, size[0], size[1], channels)
+
+)" NNVM_ADD_FILELINE)
+.add_argument("data", "4D Tensor", "Input data.")
+.add_arguments(ResizeParam::__FIELDS__())
+.set_attr_parser(ParamParser<ResizeParam>)
+.set_attr<FGetAttrDict>("FGetAttrDict", ParamGetAttrDict<ResizeParam>)
+.set_attr<FInferShape>("FInferShape", ResizeInferShape)
+.set_attr<FInferType>("FInferType", ElemwiseType<1, 1>)
+.set_attr<FCorrectLayout>("FCorrectLayout", ResizeLayout)
+.set_num_outputs(1)
+.set_num_inputs(1)
+.set_attr<FTVMCompute>(
+  "FTVMCompute", [](const NodeAttrs& attrs,
+                    const Array<Tensor>& inputs,
+                    const Array<Tensor>& out_info) {
+  const ResizeParam& param = nnvm::get<ResizeParam>(attrs.parsed);
+  Array<Expr> oshape;
+  if (param.layout == "NCHW") {
+    oshape.push_back(out_info[0]->shape[2]);
+    oshape.push_back(out_info[0]->shape[3]);
+  } else {
+    oshape.push_back(out_info[0]->shape[1]);
+    oshape.push_back(out_info[0]->shape[2]);
+  }
+
+  return Array<Tensor>{ topi::image::resize(inputs[0], oshape, param.layout,
+                                             param.align_corners, param.method)};
+})
+.set_support_level(2);
+
+}  // namespace top
+}  // namespace nnvm

nnvm/src/top/image/resize.h

+/*!
+ *  Copyright (c) 2018 by Contributors
+ * \file resize.h
+ */
+#ifndef NNVM_TOP_IMAGE_RESIZE_H_
+#define NNVM_TOP_IMAGE_RESIZE_H_
+
+#include <string>
+#include <vector>
+#include <utility>
+#include <iostream>
+#include <sstream>
+
+namespace nnvm {
+namespace top {
+
+struct ResizeParam : public dmlc::Parameter<ResizeParam> {
+  TShape size;
+  std::string layout;
+  std::string method;
+  bool align_corners;
+
+  DMLC_DECLARE_PARAMETER(ResizeParam) {
+    DMLC_DECLARE_FIELD(size)
+      .describe("Output size");
+    DMLC_DECLARE_FIELD(layout)
+      .set_default("NCHW")
+      .describe("Dimension ordering of data. Can be 'NCHW', 'NHWC', etc."
+                "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+                "dimensions respectively. Resize is applied on the 'H' and"
+                "'W' dimensions.");
+    DMLC_DECLARE_FIELD(method)
+      .set_default("BILINEAR")
+      .describe("Specify the mode to use for scaling."
+                "NEAREST_NEIGHBOR -  Nearest Neighbor"
+                "BILINEAR - Bilinear Interpolation");
+    DMLC_DECLARE_FIELD(align_corners)
+      .set_default(false)
+      .describe("Should be true to preserve the values at the corner pixels");
+  }
+};
+
+}  // namespace top
+}  // namespace nnvm
+#endif  // NNVM_TOP_IMAGE_RESIZE_H_

nnvm/src/top/nn/upsampling.cc

 /*!
  *  Copyright (c) 2017 by Contributors
- * \file pooling.cc
- * \brief Property def of pooling operators.
+ * \file upsampling.cc
+ * \brief Property def of upsampling operators.
  */
+#include <tvm/tvm.h>
+#include <tvm/expr.h>
+#include <nnvm/layout.h>
+#include <nnvm/compiler/op_attr_types.h>
 #include <nnvm/op.h>
 #include <nnvm/node.h>
 #include <nnvm/op_attr_types.h>
@@ -10,9 +14,16 @@
 #include "./nn_common.h"
 #include "../op_common.h"
 #include "../elemwise_op_common.h"
+#include "topi/elemwise.h"
+#include "topi/transform.h"
+#include "topi/nn/upsampling.h"
 
 namespace nnvm {
 namespace top {
+using tvm::Expr;
+using tvm::Array;
+using tvm::Tensor;
+using nnvm::compiler::FTVMCompute;
 
 DMLC_REGISTER_PARAMETER(UpSamplingParam);
 
@@ -25,12 +36,14 @@ inline bool UpSamplingInferShape(const nnvm::NodeAttrs& attrs,
   CHECK_EQ(out_shape->size(), 1U);
   TShape dshape = (*in_shape)[0];
   if (dshape.ndim() ==  0) return false;
+
   dshape = ConvertLayout(dshape, param.layout, kNCHW);
   TShape oshape = dshape;
   oshape[2] = oshape[2] * param.scale;
   oshape[3] = oshape[3] * param.scale;
   oshape = ConvertLayout(oshape, kNCHW, param.layout);
   NNVM_ASSIGN_OUTPUT_SHAPE(attrs, *out_shape, 0, oshape);
+
   return true;
 }
 
@@ -48,10 +61,18 @@ inline bool UpsamplingLayout(const NodeAttrs& attrs,
 }
 
 NNVM_REGISTER_OP(upsampling)
-.describe(R"(Perform nearest neighbor upsampling to input array.
+.describe(R"(Perform upsampling to input array with nearest neighbour or bilinear interpolation.
+
+- **data**: data is 4D array of shape
+            (batch_size, channels, in_height, in_width) for NCHW
+            (batch_size, in_height, in_width, channels) for NHWC
 
-- **data**: Input is 4D array of shape (batch_size, channels, in_height, in_width).
-- **out**: Output is 4D array of shape (batch_size, channels, in_height*scale, in_width*scale).
+- **out**: Output is 4D array of shape
+           for layout NCHW
+           (batch_size, channels, in_height*scale, in_width*scale)
+
+           for layout NHWC
+           (batch_size, in_height*scale, in_width*scale, channels)
 
 )" NNVM_ADD_FILELINE)
 .add_argument("data", "4D Tensor", "Input data.")
@@ -63,6 +84,22 @@ NNVM_REGISTER_OP(upsampling)
 .set_attr<FCorrectLayout>("FCorrectLayout", UpsamplingLayout)
 .set_num_outputs(1)
 .set_num_inputs(1)
+.set_attr<FTVMCompute>(
+  "FTVMCompute", [](const NodeAttrs& attrs,
+                    const Array<Tensor>& inputs,
+                    const Array<Tensor>& out_info) {
+  const UpSamplingParam& param = nnvm::get<UpSamplingParam>(attrs.parsed);
+  Array<Expr> oshape;
+  if (param.layout == "NCHW") {
+    oshape.push_back(out_info[0]->shape[2]);
+    oshape.push_back(out_info[0]->shape[3]);
+  } else {
+    oshape.push_back(out_info[0]->shape[1]);
+    oshape.push_back(out_info[0]->shape[2]);
+  }
+
+  return Array<Tensor>{ topi::nn::upsampling(inputs[0], oshape, param.layout, param.method)};
+})
 .set_support_level(2);
 
 }  // namespace top

nnvm/tests/python/compiler/test_top_level2.py

@@ -210,7 +210,7 @@ def test_global_avg_pool2d():
         np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5)
 
 
-def test_upsampling():
+def test_upsampling_nearest_neighbor():
     x = sym.Variable("x")
     scale = 2
     y = sym.upsampling(x, scale=scale, name="y")
@@ -225,9 +225,46 @@ def test_upsampling():
         data = tvm.nd.array(a_np)
         m.run(x=data)
         out = m.get_output(0, tvm.nd.empty(oshape, dtype))
-        b_np = topi.testing.upsampling_python(a_np, scale)
+        b_np = topi.testing.upsampling_python(a_np, scale, "NCHW")
         np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5)
 
+def test_upsampling_bilinear():
+    x = sym.Variable("x")
+    scale = 2
+    y = sym.upsampling(x, scale=scale, method="BILINEAR", name="y", layout="NCHW")
+    dtype = "float32"
+    dshape = (1, 4, 32, 32)
+    oshape = (1, 4, 32*scale, 32*scale)
+    shape_dict = {"x": dshape}
+    dtype_dict = {"x": dtype}
+    for target, ctx in ctx_list():
+        graph, lib, _ = nnvm.compiler.build(y, target, shape_dict, dtype_dict)
+        m = graph_runtime.create(graph, lib, ctx)
+        a_np = np.random.uniform(size=dshape).astype(dtype)
+        data = tvm.nd.array(a_np)
+        m.run(x=data)
+        out = m.get_output(0, tvm.nd.empty(oshape, dtype))
+        b_np = topi.testing.bilinear_resize_python(a_np, (32*scale, 32*scale), "NCHW")
+        np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5, atol=1e-5)
+
+def test_resize_bilinear():
+    x = sym.Variable("x")
+    scale = 2
+    y = sym.upsampling(x, scale=scale, method="BILINEAR", name="y", layout="NHWC")
+    dtype = "float32"
+    dshape = (1, 32, 32, 4)
+    oshape = (1, 32*scale, 32*scale, 4)
+    shape_dict = {"x": dshape}
+    dtype_dict = {"x": dtype}
+    for target, ctx in ctx_list():
+        graph, lib, _ = nnvm.compiler.build(y, target, shape_dict, dtype_dict)
+        m = graph_runtime.create(graph, lib, ctx)
+        a_np = np.random.uniform(size=dshape).astype(dtype)
+        data = tvm.nd.array(a_np)
+        m.run(x=data)
+        out = m.get_output(0, tvm.nd.empty(oshape, dtype))
+        b_np = topi.testing.bilinear_resize_python(a_np, (32*scale, 32*scale), "NHWC")
+        np.testing.assert_allclose(out.asnumpy(), b_np, rtol=1e-5, atol=1e-5)
 
 if __name__ == "__main__":
     test_conv2d()
@@ -239,4 +276,6 @@ if __name__ == "__main__":
     test_avg_pool2d_no_count_pad()
     test_global_max_pool2d()
     test_global_avg_pool2d()
-    test_upsampling()
+    test_upsampling_nearest_neighbor()
+    test_upsampling_bilinear()
+    test_resize_bilinear()

topi/include/topi/nn/upsampling.h

+/*!
+ *  Copyright (c) 2017 by Contributors
+ * \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 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 Angorithm 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<Expr> 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_

topi/python/topi/__init__.py

@@ -30,6 +30,7 @@ from . import opengl
 from . import util
 from . import rocm
 from . import vision
+from . import image
 # not import testing by default
 # because testing can have extra deps that are not necessary
 # we can import them from test cases explicitly

topi/python/topi/cpp.py

@@ -50,6 +50,8 @@ vision = _create_module("vision")
 _init_api_prefix("topi.cpp.vision", "topi.vision")
 yolo2 = _create_module("vision.yolo2")
 _init_api_prefix("topi.cpp.vision.yolo2", "topi.vision.yolo2")
+image = _create_module("image")
+_init_api_prefix("topi.cpp.image", "topi.image")
 
 class IntVector(object):
     """Handle to std::vector<int> instance """

topi/python/topi/image/__init__.py

+# pylint: disable=wildcard-import
+"""IMAGE network operators"""
+from __future__ import absolute_import as _abs
+
+from .resize import *

topi/python/topi/image/resize.py

+"""TVM operator input resize compute."""
+from __future__ import absolute_import
+import topi
+
+def resize(data, size, layout="NCHW", align_corners=False, method="BILINEAR"):
+    """Perform resize operation on the data.
+
+    Parameters
+    ----------
+    inputs : tvm.Tensor
+        inputs is a 4-D tensor with shape
+        [batch, channel, in_height, in_width]
+        or  [batch, in_height, in_width, channel]
+
+    size: Tuple
+        Output resolution scale to
+
+    layout: string, optional
+        either "NCHW" or "NHWC"
+
+    align_corners: Boolean, optional
+        To preserve the values at the corner pixels
+
+    method: {"BILINEAR", "NEAREST_NEIGHBOR"}
+        Method to be used for resizing.
+
+    Returns
+    -------
+    output : tvm.Tensor
+        4-D with shape [batch, channel, in_height*scale, in_width*scale]
+        or [batch, in_height*scale, in_width*scale, channel]
+    """
+    return topi.cpp.image.resize(data, size, layout, align_corners, method)

topi/python/topi/nn/upsampling.py

 """TVM operator upsampling compute."""
 from __future__ import absolute_import
-import tvm
-from .. import util
+import topi
 
 
-def upsampling(data, scale, layout="NCHW"):
-    """Perform nearest neighbor upsampling on the data.
-       Bilinear upsampling is not supported.
+def upsampling(data, scale, layout="NCHW", method='NEAREST_NEIGHBOR'):
+    """Perform upsampling on the data.
+       Nearest neighbor and bilinear upsampling are supported.
 
     Parameters
     ----------
-    data : tvm.Tensor
-        4-D with shape [batch, channel, in_height, in_width]
+    inputs : tvm.Tensor
+        inputs is a 4-D tensor with shape
+        [batch, channel, in_height, in_width]
         or  [batch, in_height, in_width, channel]
 
-    scale: int
-        upsampling scaling factor
+    scale : int
+        Scaling factor
 
-    layout: string
+    layout : string, optional
         either "NCHW" or "NHWC"
 
+    method : {"BILINEAR", "NEAREST_NEIGHBOR"}
+        Method to be used for upsampling.
+
     Returns
     -------
     output : tvm.Tensor
@@ -28,53 +31,10 @@ def upsampling(data, scale, layout="NCHW"):
     """
 
     if layout == "NCHW":
-        return upsampling_nchw(data, scale)
+        out_shape = (data.shape[2] * scale, data.shape[3] * scale)
     elif layout == "NHWC":
-        return upsampling_nhwc(data, scale)
+        out_shape = (data.shape[1] * scale, data.shape[2] * scale)
     else:
         raise ValueError("not support this layout {} yet".format(layout))
 
-
-def upsampling_nchw(data, scale):
-    """Perform nearest neighor upsampling on NCHW layout input.
-
-    Parameters
-    ----------
-    data : tvm.Tensor
-        4-D with shape [batch, channel, in_height, in_width]
-
-    scale: int
-        upsampling scaling factor
-
-    Returns
-    -------
-    output : tvm.Tensor
-        4-D with shape [batch, channel, in_height*scale, in_width*scale]
-    """
-    batch, channel, height, width = data.shape
-    out_height = util.simplify(height * scale)
-    out_width = util.simplify(width * scale)
-
-    return tvm.compute((batch, channel, out_height, out_width), \
-                        lambda n, c, h, w: data[n, c, h/scale, w/scale])
-
-
-def upsampling_nhwc(data, scale):
-    """Perform nearest neighor upsampling on NHWC layout input.
-
-    Parameters
-    ----------
-    data : tvm.Tensor
-        4-D with shape [batch, in_height, in_width, channel]
-
-    scale: int
-        upsampling scaling factor
-
-    """
-
-    batch, height, width, channel = data.shape
-    out_height = util.simplify(height * scale)
-    out_width = util.simplify(width * scale)
-
-    return tvm.compute((batch, out_height, out_width, channel), \
-                        lambda n, h, w, c: data[n, h/scale, w/scale, c])
+    return topi.cpp.nn.upsampling(data, out_shape, layout, method)

topi/python/topi/testing/__init__.py

@@ -12,6 +12,7 @@ from .depthwise_conv2d_python import depthwise_conv2d_python_nchw, depthwise_con
 from .dilate_python import dilate_python
 from .softmax_python import softmax_python, log_softmax_python
 from .upsampling_python import upsampling_python
+from .bilinear_resize_python import bilinear_resize_python
 from .reorg_python import reorg_python
 from .region_python import region_python
 from .shortcut_python import shortcut_python

topi/python/topi/testing/bilinear_resize_python.py

+# pylint: disable=invalid-name, line-too-long, unused-variable, too-many-locals
+"""Bilinear Scale in python"""
+import math
+import numpy as np
+
+def bilinear_weights(height, width, new_h, new_w, align_corners=False):
+    """ Helper function to generate weights for bilinear scaling """
+
+    if align_corners:
+        x_ratio = np.float32(np.float32(width)/np.float32(new_w))
+        y_ratio = np.float32(np.float32(height)/np.float32(new_h))
+    else:
+        x_ratio = np.float32(np.float32(width-1)/np.float32(new_w-1))
+        y_ratio = np.float32(np.float32(height-1)/np.float32(new_h-1))
+
+    def _bilinear_interpolation(y, x):
+        x_coord = math.floor(x_ratio * x)
+        y_coord = math.floor(y_ratio * y)
+        x_diff = np.float32((x_ratio * x) - x_coord)
+        y_diff = np.float32((y_ratio * y) - y_coord)
+
+        return [y_coord, x_coord, y_diff, x_diff]
+
+    # weights to hold (srcx, srcy, x_diff, y_diff) for each out value.
+    weights = np.empty([new_h, new_w, 4], dtype='float32')
+
+    for i in range(new_h):
+        for j in range(new_w):
+            weights[i][j] = _bilinear_interpolation(i, j)
+    return weights
+
+def bilinear_resize_python(image, out_size, layout, align_corners=False):
+    """ Bilinear scaling using python"""
+    (new_h, new_w) = out_size
+
+    if layout == 'NHWC':
+        (batch, h, w, channel) = image.shape
+        scaled_image = np.ones((batch, new_h, new_w, channel))
+    else:
+        (batch, channel, h, w) = image.shape
+        scaled_image = np.ones((batch, channel, new_h, new_w))
+
+    weights = bilinear_weights(h, w, new_h, new_w, align_corners)
+
+    for b in range(batch):
+        for i in range(channel):
+            for j in range(new_h):
+                for k in range(new_w):
+                    y0 = int(weights[j][k][0])
+                    x0 = int(weights[j][k][1])
+
+                    x1 = min((x0+1), (w-1))
+                    y1 = min((y0+1), (h-1))
+
+                    y_diff = weights[j][k][2]
+                    x_diff = weights[j][k][3]
+
+                    if layout == 'NHWC':
+                        A = image[b][y0][x0][i]
+                        B = image[b][y0][x1][i]
+                        C = image[b][y1][x0][i]
+                        D = image[b][y1][x1][i]
+                    else:
+                        A = image[b][i][y0][x0]
+                        B = image[b][i][y0][x1]
+                        C = image[b][i][y1][x0]
+                        D = image[b][i][y1][x1]
+
+                    pixel = np.float32((A*(1-x_diff)*(1-y_diff) +
+                                        B*(x_diff)*(1-y_diff) +
+                                        C*(y_diff)*(1-x_diff) +
+                                        D*(x_diff*y_diff)))
+
+                    if layout == 'NHWC':
+                        scaled_image[b][j][k][i] = pixel
+                    else:
+                        scaled_image[b][i][j][k] = pixel
+
+    return scaled_image

topi/python/topi/testing/upsampling_python.py

@@ -3,13 +3,26 @@
 import numpy as np
 
 def upsample_nearest(arr, scale):
+    """ Populate the array by scale factor"""
     return arr.repeat(scale, axis=0).repeat(scale, axis=1)
 
-def upsampling_python(data, scale):
+def upsampling_python(data, scale, layout='NCHW'):
+    """ Python version of scaling using nearest neighbour """
+
     ishape = data.shape
+    if layout == 'NCHW':
         oshape = (ishape[0], ishape[1], ishape[2]*scale, ishape[3]*scale)
         output_np = np.zeros(oshape, dtype=data.dtype)
         for b in range(oshape[0]):
             for c in range(oshape[1]):
                 output_np[b, c, :, :] = upsample_nearest(data[b, c, :, :], scale)
         return output_np
+    elif layout == 'NHWC':
+        oshape = (ishape[0], ishape[1]*scale, ishape[1]*scale, ishape[3])
+        output_np = np.zeros(oshape, dtype=data.dtype)
+        for b in range(oshape[0]):
+            for c in range(oshape[3]):
+                output_np[b, :, :, c] = upsample_nearest(data[b, :, :, c], scale)
+        return output_np
+    else:
+        raise ValueError("not support this layout {} yet".format(layout))

topi/src/topi.cc

@@ -23,8 +23,10 @@
 #include <topi/nn/mapping.h>
 #include <topi/nn/pooling.h>
 #include <topi/nn/softmax.h>
+#include <topi/nn/upsampling.h>
 
 #include <topi/vision/reorg.h>
+#include <topi/image/resize.h>
 #include <topi/vision/yolo2/region.h>
 #include <topi/generic/default.h>
 #include <topi/generic/extern.h>
@@ -285,6 +287,12 @@ TVM_REGISTER_GLOBAL("topi.strided_slice")
   *rv = strided_slice(args[0], args[1], args[2], args[3]);
   });
 
+/* 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/batch_norm.h */
 TVM_REGISTER_GLOBAL("topi.nn.batch_norm_inference")
 .set_body([](TVMArgs args, TVMRetValue *rv) {
@@ -366,10 +374,18 @@ TVM_REGISTER_GLOBAL("topi.vision.reorg")
 .set_body([](TVMArgs args, TVMRetValue *rv) {
   *rv = vision::reorg(args[0], args[1]);
   });
+
 TVM_REGISTER_GLOBAL("topi.vision.yolo2.region")
 .set_body([](TVMArgs args, TVMRetValue *rv) {
   *rv = vision::yolo2::region(args[0], args[1], args[2], args[3], args[4], args[5]);
   });
+
+/* Ops from image/resize.h */
+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) {

topi/tests/python/test_topi_resize.py

+"""Test code for bilinear scale """
+import numpy as np
+import tvm
+import topi
+import topi.testing
+import math
+
+def verify_bilinear_scale(batch, in_channel, in_height, in_width, out_height, out_width, layout='NCHW', align_corners=False):
+
+    if layout == 'NCHW':
+        A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A', dtype='float32')
+        dtype = A.dtype
+        out_shape = (batch, in_channel, out_height, out_width)
+        a_np = np.random.uniform(size=(batch, in_channel, in_height, in_width)).astype(dtype)
+    elif layout == 'NHWC':
+        A = tvm.placeholder((batch, in_height, in_width, in_channel), name='A', dtype='float32')
+        dtype = A.dtype
+        out_shape = (batch, out_height, out_width, in_channel)
+        a_np = np.random.uniform(size=(batch, in_height, in_width, in_channel)).astype(dtype)
+    else:
+        raise NotImplementedError(
+            'Layout not supported {} '.format(layout))
+
+    B = topi.image.resize(A, (out_height, out_width), layout=layout, align_corners=align_corners)
+
+    b_np = topi.testing.bilinear_resize_python(a_np, (out_height, out_width), layout, align_corners)
+
+    def check_device(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
+            print("Skip because %s is not enabled" % device)
+            return
+        print("Running on target: %s" % device)
+        with tvm.target.create(device):
+            s = topi.generic.schedule_injective(B)
+        a = tvm.nd.array(a_np, ctx)
+        b = tvm.nd.array(np.zeros(out_shape, dtype=dtype), ctx)
+        f = tvm.build(s, [A, B], device)
+        f(a, b)
+
+        np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-3, atol=1e-3)
+
+    for device in ['llvm', 'cuda', 'vulkan']:
+        check_device(device)
+
+def test_resize():
+    # Scale NCHW
+    verify_bilinear_scale(4, 16, 32, 32, 50, 50, 'NCHW')
+    # Scale NCHW + Align Corners
+    verify_bilinear_scale(6, 32, 64, 64, 20, 20, 'NCHW', True)
+    # Scale NHWC
+    verify_bilinear_scale(4, 16, 32, 32, 50, 50, "NHWC")
+    # Scale NHWC + Align Corners
+    verify_bilinear_scale(6, 32, 64, 64, 20, 20, "NHWC", True)
+
+if __name__ == "__main__":
+    test_resize()

topi/tests/python/test_topi_upsampling.py

@@ -5,14 +5,26 @@ import topi
 import topi.testing
 import math
 
-def verify_upsampling(batch, in_channel, in_height, in_width, scale):
+def verify_upsampling(batch, in_channel, in_height, in_width, scale, layout='NCHW'):
+
+
+    if layout == 'NCHW':
         A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A')
-    B = topi.nn.upsampling(A, scale)
+        dtype = A.dtype
         out_shape = (batch, in_channel, in_height*scale, in_width*scale)
+        a_np = np.random.uniform(size=(batch, in_channel, in_height, in_width)).astype(dtype)
+    elif layout == 'NHWC':
+        A = tvm.placeholder((batch, in_height, in_width, in_channel), name='A')
         dtype = A.dtype
+        out_shape = (batch, in_height*scale, in_width*scale, in_channel)
+        a_np = np.random.uniform(size=(batch, in_height, in_width, in_channel)).astype(dtype)
+    else:
+        raise NotImplementedError(
+            'Layout not supported {} '.format(layout))
 
-    a_np = np.random.uniform(size=(batch, in_channel, in_height, in_width)).astype(dtype)
-    b_np = topi.testing.upsampling_python(a_np, scale)
+    B = topi.nn.upsampling(A, scale, layout=layout)
+
+    b_np = topi.testing.upsampling_python(a_np, scale, layout)
 
     def check_device(device):
         ctx = tvm.context(device, 0)
@@ -33,8 +45,12 @@ def verify_upsampling(batch, in_channel, in_height, in_width, scale):
         check_device(device)
 
 def test_upsampling():
+    # NCHW
     verify_upsampling(8, 16, 32, 32, 2)
     verify_upsampling(12, 32, 64, 64, 3)
+    # NHWC
+    verify_upsampling(8, 16, 32, 32, 2, "NHWC")
+    verify_upsampling(12, 32, 64, 64, 3, "NHWC")
 
 if __name__ == "__main__":
     test_upsampling()