[NPU] Support NPU kernel for nearest_interp and nearest_interp_grad op (#34881)

* Add NPU kernel for nearest_interp op

* Add grad op

* Modify codes according to the review comments

* Modify codes according to the review comments

paddle/fluid/operators/interpolate_op_npu.cc

+/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed 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. */
+
+#include "paddle/fluid/operators/interpolate_op.h"
+#include <string>
+#include <vector>
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+using Tensor = framework::Tensor;
+using DataLayout = framework::DataLayout;
+
+inline static void CheckArgument(const framework::ExecutionContext& ctx) {
+  const std::string interp_method = ctx.Attr<std::string>("interp_method");
+  bool align_corners = ctx.Attr<bool>("align_corners");
+  PADDLE_ENFORCE_EQ(
+      align_corners, false,
+      platform::errors::InvalidArgument(
+          "NPU Interpolate Kernel has diff when align_corners is true."));
+  PADDLE_ENFORCE_EQ(
+      interp_method, "nearest",
+      platform::errors::InvalidArgument(
+          "NPU Interpolate Kernel only support nearest interpolotion."));
+}
+
+inline static void ExtractNCHW(const framework::DDim& dims,
+                               const DataLayout& data_layout, int32_t* n,
+                               int32_t* c, int32_t* h, int32_t* w) {
+  *n = dims[0];
+  if (data_layout == DataLayout::kNCHW) {
+    *c = dims[1];
+    *h = dims[2];
+    *w = dims[3];
+  } else {  // kNHWC
+    *h = dims[1];
+    *w = dims[2];
+    *c = dims[3];
+  }
+}
+
+static void CalcOutSize(const framework::ExecutionContext& ctx, int32_t in_h,
+                        int32_t in_w, int32_t* out_h, int32_t* out_w) {
+  // Priority: SizeTensor > OutSize > Scale > scale > out_h & out_w
+  *out_h = ctx.Attr<int>("out_h");
+  *out_w = ctx.Attr<int>("out_w");
+
+  auto dev_ctx = platform::DeviceContextPool::Instance().Get(ctx.GetPlace());
+  auto list_new_size_tensor = ctx.MultiInput<Tensor>("SizeTensor");
+
+  if (list_new_size_tensor.size() > 0) {
+    std::vector<int32_t> new_size_h(1);
+    std::vector<int32_t> new_size_w(1);
+    framework::TensorToVector(*list_new_size_tensor[0], *dev_ctx, &new_size_h);
+    framework::TensorToVector(*list_new_size_tensor[1], *dev_ctx, &new_size_w);
+    *out_h = new_size_h[0];
+    *out_w = new_size_w[0];
+  } else {
+    float scale;
+    auto scale_tensor = ctx.Input<Tensor>("Scale");
+    if (scale_tensor != nullptr) {
+      std::vector<float> scale_data;
+      framework::TensorToVector(*scale_tensor, *dev_ctx, &scale_data);
+      scale = scale_data[0];
+    } else {
+      scale = ctx.Attr<float>("scale");
+    }
+
+    if (scale > 0) {
+      *out_h = static_cast<int32_t>(in_h * scale);
+      *out_w = static_cast<int32_t>(in_w * scale);
+    }
+
+    auto out_size = ctx.Input<Tensor>("OutSize");
+    if (out_size != nullptr) {
+      std::vector<int> out_size_data;
+      framework::TensorToVector(*out_size, *dev_ctx, &out_size_data);
+      *out_h = out_size_data[0];
+      *out_w = out_size_data[1];
+    }
+  }
+
+  PADDLE_ENFORCE_GT(*out_h, 0,
+                    platform::errors::InvalidArgument(
+                        "out_h in Attr(out_shape) of Op(interpolate) "
+                        "should be greater than 0."));
+  PADDLE_ENFORCE_GT(*out_w, 0,
+                    platform::errors::InvalidArgument(
+                        "out_w in Attr(out_shape) of Op(interpolate) "
+                        "should be greater than 0."));
+}
+
+template <typename T>
+class InterpolateNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    // NOTE(Ruibiao):
+    // this kernel only support nearest interpolotion for 2D images
+    // the Ascend 'ResizeNearestNeighborV2' used in this kernle has diff
+    // when 'align_corners' is 'true' or data type is 'double'
+    CheckArgument(ctx);
+
+    auto* input = ctx.Input<Tensor>("X");
+    framework::DDim input_dims = input->dims();
+
+    const std::string data_layout_str =
+        ctx.Attr<std::string>("data_layout");  // kNCHW or kNHWC
+    const DataLayout data_layout =
+        framework::StringToDataLayout(data_layout_str);
+
+    int32_t n, c, h, w, out_h, out_w;
+    ExtractNCHW(input_dims, data_layout, &n, &c, &h, &w);
+    CalcOutSize(ctx, h, w, &out_h, &out_w);
+
+    // the 'input' tensor may has no set (or wrong set) of the layout
+    Tensor input_x(input->type());
+    input_x.ShareDataWith(*input);
+    input_x.set_layout(data_layout);
+
+    auto* output = ctx.Output<Tensor>("Out");
+    framework::DDim output_dims;
+    if (data_layout == DataLayout::kNCHW) {
+      output_dims = {n, c, out_h, out_w};
+    } else {
+      output_dims = {n, out_h, out_w, c};
+    }
+    output->set_layout(data_layout);
+    output->mutable_data<T>(output_dims, ctx.GetPlace());
+
+    NpuOpRunner npu_op_runner;
+    auto npu_stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+    npu_op_runner.SetType("ResizeNearestNeighborV2")
+        .AddInput(input_x)
+        .AddInput(std::vector<int32_t>{out_h, out_w})
+        .AddOutput(*output)
+        .AddAttr("align_corners", false)
+        .AddAttr("half_pixel_centers", false)
+        .Run(npu_stream);
+  }
+};
+
+template <typename T>
+class InterpolateGradNPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    // NOTE(Ruibiao):
+    // this kernel only support nearest interpolotion for 2D images
+    // the Ascend 'ResizeNearestNeighborV2' used in this kernle has diff
+    // when 'align_corners' is 'true' or data type is 'double'
+    CheckArgument(ctx);
+
+    auto* input = ctx.Input<Tensor>("X");
+    framework::DDim input_dims = input->dims();
+
+    const std::string data_layout_str =
+        ctx.Attr<std::string>("data_layout");  // kNCHW or kNHWC
+    const DataLayout data_layout =
+        framework::StringToDataLayout(data_layout_str);
+
+    int32_t n, c, h, w, out_h, out_w;
+    ExtractNCHW(input_dims, data_layout, &n, &c, &h, &w);
+    CalcOutSize(ctx, h, w, &out_h, &out_w);
+
+    // the 'output_grad' tensor may has no set (or wrong set) of the layout
+    auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    Tensor output_grad_tmp(output_grad->type());
+    output_grad_tmp.ShareDataWith(*output_grad);
+    output_grad_tmp.set_layout(data_layout);
+
+    auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    input_grad->set_layout(data_layout);
+    framework::DDim input_grad_dims;
+    if (data_layout == DataLayout::kNCHW) {
+      input_grad_dims = {n, c, h, w};
+    } else {
+      input_grad_dims = {n, h, w, c};
+    }
+    input_grad->mutable_data<T>(input_grad_dims, ctx.GetPlace());
+
+    NpuOpRunner npu_op_runner;
+    auto npu_stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+    npu_op_runner.SetType("ResizeNearestNeighborV2Grad")
+        .AddInput(output_grad_tmp)
+        .AddInput(std::vector<int32_t>{h, w})
+        .AddOutput(*input_grad)
+        .AddAttr("align_corners", false)
+        .AddAttr("half_pixel_centers", false)
+        .Run(npu_stream);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_NPU_KERNEL(nearest_interp, ops::InterpolateNPUKernel<float>,
+                       ops::InterpolateNPUKernel<uint8_t>);
+REGISTER_OP_NPU_KERNEL(nearest_interp_grad,
+                       ops::InterpolateGradNPUKernel<float>);

python/paddle/fluid/tests/unittests/npu/test_nearest_interp_op_npu.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import sys
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+from test_nearest_interp_op import nearest_neighbor_interp_np
+
+paddle.enable_static()
+
+
+class TestNearestInterpOp(OpTest):
+    def setUp(self):
+        self.__class__.use_npu = True
+        self.place = paddle.NPUPlace(0)
+
+        self.out_size = None
+        self.actual_shape = None
+        self.data_layout = 'NCHW'
+        self.init_test_case()
+        self.op_type = "nearest_interp"
+        input_np = np.random.random(self.input_shape).astype("float32")
+        if self.data_layout == "NCHW":
+            in_h = self.input_shape[2]
+            in_w = self.input_shape[3]
+        else:
+            in_h = self.input_shape[1]
+            in_w = self.input_shape[2]
+
+        if self.scale > 0:
+            out_h = int(in_h * self.scale)
+            out_w = int(in_w * self.scale)
+        else:
+            out_h = self.out_h
+            out_w = self.out_w
+
+        output_np = nearest_neighbor_interp_np(
+            input_np, out_h, out_w, self.out_size, self.actual_shape,
+            self.align_corners, self.data_layout)
+        self.inputs = {'X': input_np}
+        if self.out_size is not None:
+            self.inputs['OutSize'] = self.out_size
+        if self.actual_shape is not None:
+            self.inputs['OutSize'] = self.actual_shape
+        self.attrs = {
+            'out_h': self.out_h,
+            'out_w': self.out_w,
+            'scale': self.scale,
+            'interp_method': self.interp_method,
+            'align_corners': self.align_corners,
+            'data_layout': self.data_layout
+        }
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        self.__class__.no_need_check_grad = True
+        self.check_output_with_place(self.place)
+
+    def test_check_grad(self):
+        self.check_grad_with_place(self.place, ['X'], 'Out', in_place=True)
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [2, 3, 4, 5]
+        self.out_h = 2
+        self.out_w = 2
+        self.scale = 0.
+        self.out_size = np.array([3, 3]).astype("int32")
+        self.align_corners = False
+
+
+class TestNearestNeighborInterpCase1(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 1
+        self.out_w = 1
+        self.scale = 0.
+        self.align_corners = False
+
+
+class TestNearestNeighborInterpCase2(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+        self.scale = 0.
+        self.align_corners = False
+
+    def test_check_grad(self):
+        self.check_grad_with_place(
+            self.place, ['X'], 'Out', in_place=True, max_relative_error=0.006)
+
+
+class TestNearestNeighborInterpCase3(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [1, 1, 32, 64]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 0.
+        self.align_corners = False
+
+
+class TestNearestNeighborInterpCase4(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 1
+        self.out_w = 1
+        self.scale = 0.
+        self.out_size = np.array([2, 2]).astype("int32")
+        self.align_corners = False
+
+
+class TestNearestNeighborInterpCase5(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+        self.scale = 0.
+        self.out_size = np.array([11, 11]).astype("int32")
+        self.align_corners = False
+
+
+class TestNearestNeighborInterpCase6(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [1, 1, 32, 64]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 0.
+        self.out_size = np.array([65, 129]).astype("int32")
+        self.align_corners = False
+
+
+class TestNearestNeighborInterpSame(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [2, 3, 32, 64]
+        self.out_h = 32
+        self.out_w = 64
+        self.scale = 0.
+        self.align_corners = False
+
+
+class TestNearestNeighborInterpActualShape(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 0.
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = False
+
+
+class TestNearestNeighborInterpDataLayout(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [2, 4, 4, 5]
+        self.out_h = 2
+        self.out_w = 2
+        self.scale = 0.
+        self.out_size = np.array([3, 8]).astype("int32")
+        self.align_corners = False
+        self.data_layout = "NHWC"
+
+
+class TestNearestInterpOpUint8(OpTest):
+    def setUp(self):
+        self.__class__.use_npu = True
+        self.place = paddle.NPUPlace(0)
+
+        self.out_size = None
+        self.actual_shape = None
+        self.init_test_case()
+        self.op_type = "nearest_interp"
+        input_np = np.random.randint(
+            low=0, high=256, size=self.input_shape).astype("uint8")
+
+        if self.scale > 0:
+            out_h = int(self.input_shape[2] * self.scale)
+            out_w = int(self.input_shape[3] * self.scale)
+        else:
+            out_h = self.out_h
+            out_w = self.out_w
+
+        output_np = nearest_neighbor_interp_np(input_np, out_h, out_w,
+                                               self.out_size, self.actual_shape,
+                                               self.align_corners)
+        self.inputs = {'X': input_np}
+        if self.out_size is not None:
+            self.inputs['OutSize'] = self.out_size
+        self.attrs = {
+            'out_h': self.out_h,
+            'out_w': self.out_w,
+            'scale': self.scale,
+            'interp_method': self.interp_method,
+            'align_corners': self.align_corners
+        }
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [1, 3, 9, 6]
+        self.out_h = 10
+        self.out_w = 9
+        self.scale = 0.
+        self.align_corners = False
+
+
+class TestNearestNeighborInterpCase1Uint8(TestNearestInterpOpUint8):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [2, 3, 32, 64]
+        self.out_h = 80
+        self.out_w = 40
+        self.scale = 0.
+        self.align_corners = False
+
+
+class TestNearestNeighborInterpCase2Uint8(TestNearestInterpOpUint8):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [4, 1, 7, 8]
+        self.out_h = 5
+        self.out_w = 13
+        self.scale = 0.
+        self.out_size = np.array([6, 15]).astype("int32")
+        self.align_corners = False
+
+
+class TestNearestNeighborInterpScale1(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 2, 7, 5]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 2.
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = False
+
+
+class TestNearestNeighborInterpScale2(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 2, 5, 7]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 1.5
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = False
+
+
+class TestNearestNeighborInterpScale3(TestNearestInterpOp):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 2, 7, 5]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 1.
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = False
+
+
+class TestNearestInterpOp_attr_tensor(OpTest):
+    def setUp(self):
+        self.__class__.use_npu = True
+        self.place = paddle.NPUPlace(0)
+
+        self.out_size = None
+        self.actual_shape = None
+        self.init_test_case()
+        self.op_type = "nearest_interp"
+        self.shape_by_1Dtensor = False
+        self.scale_by_1Dtensor = False
+        self.attrs = {
+            'interp_method': self.interp_method,
+            'align_corners': self.align_corners,
+        }
+
+        input_np = np.random.random(self.input_shape).astype("float32")
+        self.inputs = {'X': input_np}
+
+        if self.scale_by_1Dtensor:
+            self.inputs['Scale'] = np.array([self.scale]).astype("float64")
+        elif self.scale > 0:
+            out_h = int(self.input_shape[2] * self.scale)
+            out_w = int(self.input_shape[3] * self.scale)
+            self.attrs['scale'] = self.scale
+        else:
+            out_h = self.out_h
+            out_w = self.out_w
+
+        if self.shape_by_1Dtensor:
+            self.inputs['OutSize'] = self.out_size
+        elif self.out_size is not None:
+            size_tensor = []
+            for index, ele in enumerate(self.out_size):
+                size_tensor.append(("x" + str(index), np.ones(
+                    (1)).astype('int32') * ele))
+            self.inputs['SizeTensor'] = size_tensor
+
+        self.attrs['out_h'] = self.out_h
+        self.attrs['out_w'] = self.out_w
+        output_np = nearest_neighbor_interp_np(input_np, out_h, out_w,
+                                               self.out_size, self.actual_shape,
+                                               self.align_corners)
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+    def test_check_grad(self):
+        self.check_grad_with_place(self.place, ['X'], 'Out', in_place=True)
+
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [2, 5, 4, 4]
+        self.out_h = 3
+        self.out_w = 3
+        self.scale = 0.
+        self.out_size = [3, 3]
+        self.align_corners = False
+
+
+# out_size is a tensor list
+class TestNearestInterp_attr_tensor_Case1(TestNearestInterpOp_attr_tensor):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 3, 9, 6]
+        self.out_h = 12
+        self.out_w = 12
+        self.scale = 0.
+        self.out_size = [8, 12]
+        self.align_corners = False
+
+
+# out_size is a 1-D tensor
+class TestNearestInterp_attr_tensor_Case2(TestNearestInterpOp_attr_tensor):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 0.
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = False
+        self.shape_by_1Dtensor = True
+
+
+# scale is a 1-D tensor
+class TestNearestInterp_attr_tensor_Case3(TestNearestInterpOp_attr_tensor):
+    def init_test_case(self):
+        self.interp_method = 'nearest'
+        self.input_shape = [3, 2, 32, 16]
+        self.out_h = 64
+        self.out_w = 32
+        self.scale = 2.0
+        self.out_size = None
+        self.align_corners = False
+        self.scale_by_1Dtensor = True
+
+
+class TestNearestAPI(unittest.TestCase):
+    def test_case(self):
+        x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
+        y = fluid.data(name="y", shape=[2, 6, 6, 3], dtype="float32")
+
+        dim = fluid.data(name="dim", shape=[1], dtype="int32")
+        shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
+        actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
+        scale_tensor = fluid.data(
+            name="scale_tensor", shape=[1], dtype="float32")
+
+        out1 = fluid.layers.resize_nearest(
+            y, out_shape=[12, 12], data_format='NHWC', align_corners=False)
+        out2 = fluid.layers.resize_nearest(
+            x, out_shape=[12, dim], align_corners=False)
+        out3 = fluid.layers.resize_nearest(
+            x, out_shape=shape_tensor, align_corners=False)
+        out4 = fluid.layers.resize_nearest(
+            x, out_shape=[4, 4], actual_shape=actual_size, align_corners=False)
+        out5 = fluid.layers.resize_nearest(
+            x, scale=scale_tensor, align_corners=False)
+
+        x_data = np.random.random((2, 3, 6, 6)).astype("float32")
+        dim_data = np.array([12]).astype("int32")
+        shape_data = np.array([12, 12]).astype("int32")
+        actual_size_data = np.array([12, 12]).astype("int32")
+        scale_data = np.array([2.0]).astype("float32")
+
+        place = paddle.NPUPlace(0)
+        exe = fluid.Executor(place)
+        exe.run(fluid.default_startup_program())
+        results = exe.run(fluid.default_main_program(),
+                          feed={
+                              "x": x_data,
+                              "y": np.transpose(x_data, (0, 2, 3, 1)),
+                              "dim": dim_data,
+                              "shape_tensor": shape_data,
+                              "actual_size": actual_size_data,
+                              "scale_tensor": scale_data
+                          },
+                          fetch_list=[out1, out2, out3, out4, out5],
+                          return_numpy=True)
+
+        expect_res = nearest_neighbor_interp_np(
+            x_data, out_h=12, out_w=12, align_corners=False)
+        self.assertTrue(
+            np.allclose(results[0], np.transpose(expect_res, (0, 2, 3, 1))))
+        for i in range(len(results) - 1):
+            self.assertTrue(np.allclose(results[i + 1], expect_res))
+
+
+class TestNearestInterpException(unittest.TestCase):
+    def test_exception(self):
+        input = fluid.data(name="input", shape=[1, 3, 6, 6], dtype="float32")
+
+        def attr_data_format():
+            # for 4-D input, data_format can only be NCHW or NHWC
+            out = fluid.layers.resize_nearest(
+                input, out_shape=[4, 8], data_format='NDHWC')
+
+        def attr_scale_type():
+            out = fluid.layers.resize_nearest(input, scale='scale')
+
+        def attr_scale_value():
+            out = fluid.layers.resize_nearest(input, scale=-0.3)
+
+        self.assertRaises(ValueError, attr_data_format)
+        self.assertRaises(TypeError, attr_scale_type)
+        self.assertRaises(ValueError, attr_scale_value)
+
+
+if __name__ == "__main__":
+    unittest.main()