add nearest_interp_v2 on kunlun (#29725)

* add nearest_interp_v2 on kunlun

* add nearest_interp_v2 on kunlun

paddle/fluid/operators/interpolate_v2_op_xpu.cc

+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
+   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 <memory>
+#include <string>
+#include <vector>
+
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/interpolate_op.h"
+
+#ifdef PADDLE_WITH_XPU
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+using DataLayout = framework::DataLayout;
+
+inline std::vector<int> get_new_shape_xpu(
+    const std::vector<const Tensor*>& list_new_shape_tensor) {
+  // get tensor from
+  std::vector<int> vec_new_shape;
+  for (size_t i = 0; i < list_new_shape_tensor.size(); ++i) {
+    auto tensor = list_new_shape_tensor[i];
+    PADDLE_ENFORCE_EQ(
+        tensor->dims(), framework::make_ddim({1}),
+        platform::errors::InvalidArgument("shape of dim tensor should be [1]"));
+    framework::Tensor temp;
+    TensorCopySync(*tensor, platform::CPUPlace(), &temp);
+    vec_new_shape.push_back(static_cast<int32_t>(*temp.data<int32_t>()));
+  }
+
+  return vec_new_shape;
+}
+
+template <typename T>
+inline std::vector<T> get_new_data_from_tensor_xpu(
+    const Tensor* new_data_tensor) {
+  std::vector<T> vec_new_data;
+  framework::Tensor cpu_starts_tensor;
+  TensorCopySync(*new_data_tensor, platform::CPUPlace(), &cpu_starts_tensor);
+  auto* new_data = cpu_starts_tensor.data<T>();
+  vec_new_data = std::vector<T>(new_data, new_data + new_data_tensor->numel());
+  return vec_new_data;
+}
+
+template <typename T>
+class InterpolateV2XPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<Tensor>("X");
+    auto* output = ctx.Output<Tensor>("Out");
+
+    auto input_dims = input->dims();
+    PADDLE_ENFORCE_EQ(
+        input_dims.size(), 4,
+        platform::errors::External("XPU Interpolate kernel only support 2d"));
+
+    const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
+    const DataLayout data_layout =
+        framework::StringToDataLayout(data_layout_str);
+    int n, c, in_d, in_h, in_w;
+    ExtractNCDWH(input_dims, data_layout, &n, &c, &in_d, &in_h, &in_w);
+
+    auto interp_method = ctx.Attr<std::string>("interp_method");
+    bool align_corners = ctx.Attr<bool>("align_corners");
+    int align_mode = ctx.Attr<int>("align_mode");
+
+    int out_h = ctx.Attr<int>("out_h");
+    int out_w = ctx.Attr<int>("out_w");
+    float scale_h = -1;
+    float scale_w = -1;
+
+    auto list_new_size_tensor = ctx.MultiInput<framework::Tensor>("SizeTensor");
+    if (list_new_size_tensor.size() > 0) {
+      // have size tensor
+      auto new_size = get_new_shape_xpu(list_new_size_tensor);
+      out_h = new_size[0];
+      out_w = new_size[1];
+    } else {
+      auto scale_tensor = ctx.Input<Tensor>("Scale");
+      auto scale = ctx.Attr<std::vector<float>>("scale");
+      if (scale_tensor != nullptr) {
+        auto scale_data = get_new_data_from_tensor_xpu<float>(scale_tensor);
+        if (scale_data.size() > 1) {
+          scale_h = scale_data[0];
+          scale_w = scale_data[1];
+        } else {
+          scale_h = scale_data[0];
+          scale_w = scale_data[0];
+        }
+        PADDLE_ENFORCE_EQ(
+            scale_w > 0 && scale_h > 0, true,
+            platform::errors::InvalidArgument("scale  of Op(interpolate) "
+                                              "should be greater than 0."));
+      } else {
+        if (scale.size() > 1) {
+          scale_h = scale[0];
+          scale_w = scale[1];
+
+          PADDLE_ENFORCE_EQ(
+              scale_w > 0 && scale_h > 0, true,
+              platform::errors::InvalidArgument("scale  of Op(interpolate) "
+                                                "should be greater than 0."));
+        }
+      }
+      if (scale_h > 0. && scale_w > 0.) {
+        out_h = static_cast<int>(in_h * scale_h);
+        out_w = static_cast<int>(in_w * scale_w);
+      }
+      auto out_size = ctx.Input<Tensor>("OutSize");
+      if (out_size != nullptr) {
+        auto out_size_data = get_new_data_from_tensor<int>(out_size);
+        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."));
+    framework::DDim dim_out;
+    if (data_layout == DataLayout::kNCHW) {
+      dim_out = {n, c, out_h, out_w};
+    } else {
+      dim_out = {n, out_h, out_w, c};
+    }
+    output->mutable_data<T>(dim_out, ctx.GetPlace());
+
+    if (in_h == out_h && in_w == out_w) {
+      framework::TensorCopy(*input, ctx.GetPlace(), output);
+      return;
+    }
+    bool nearest = "nearest" == interp_method;
+    int trans_mode = (align_corners) ? (0) : ((align_mode == 0) ? (1) : (2));
+    auto& dev_ctx = ctx.template device_context<platform::XPUDeviceContext>();
+    if (nearest) {
+      PADDLE_ENFORCE_EQ((data_layout == DataLayout::kNCHW), true,
+                        platform::errors::InvalidArgument(
+                            "XPU nearest is only support NCHW"));
+    }
+    int r = xpu::interpolate2d<T>(dev_ctx.x_context(), input->data<T>(),
+                                  output->data<T>(), n, c, in_h, in_w, out_h,
+                                  out_w, nearest, trans_mode,
+                                  (data_layout == DataLayout::kNCHW));
+    PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
+                      platform::errors::External("XPU interpolate2d kernel "
+                                                 "return wrong value[%d %s]",
+                                                 r, XPUAPIErrorMsg[r]));
+  }
+};
+
+template <typename T>
+class InterpolateV2GradXPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
+
+    auto output_grad_dims = output_grad->dims();
+
+    PADDLE_ENFORCE_EQ(output_grad_dims.size(), 4,
+                      platform::errors::External(
+                          "XPU Interpolategrad kernel only support 2d"));
+
+    auto* input = ctx.Input<Tensor>("X");
+    const std::string data_layout_str = ctx.Attr<std::string>("data_layout");
+    const DataLayout data_layout =
+        framework::StringToDataLayout(data_layout_str);
+    int n, c, in_d, in_h, in_w;
+    ExtractNCDWH(input->dims(), data_layout, &n, &c, &in_d, &in_h, &in_w);
+
+    auto interp_method = ctx.Attr<std::string>("interp_method");
+    bool align_corners = ctx.Attr<bool>("align_corners");
+    int align_mode = ctx.Attr<int>("align_mode");
+
+    int out_h = ctx.Attr<int>("out_h");
+    int out_w = ctx.Attr<int>("out_w");
+    float scale_h = -1;
+    float scale_w = -1;
+
+    auto list_new_size_tensor = ctx.MultiInput<framework::Tensor>("SizeTensor");
+    if (list_new_size_tensor.size() > 0) {
+      // have size tensor
+      auto new_size = get_new_shape_xpu(list_new_size_tensor);
+      out_h = new_size[0];
+      out_w = new_size[1];
+    } else {
+      auto scale_tensor = ctx.Input<Tensor>("Scale");
+      auto scale = ctx.Attr<std::vector<float>>("scale");
+      if (scale_tensor != nullptr) {
+        auto scale_data = get_new_data_from_tensor_xpu<float>(scale_tensor);
+        if (scale_data.size() > 1) {
+          scale_h = scale_data[0];
+          scale_w = scale_data[1];
+        } else {
+          scale_h = scale_data[0];
+          scale_w = scale_data[0];
+        }
+        PADDLE_ENFORCE_EQ(
+            scale_w > 0 && scale_h > 0, true,
+            platform::errors::InvalidArgument("scale  of Op(interpolate) "
+                                              "should be greater than 0."));
+      } else {
+        if (scale.size() > 1) {
+          scale_h = scale[0];
+          scale_w = scale[1];
+
+          PADDLE_ENFORCE_EQ(
+              scale_w > 0 && scale_h > 0, true,
+              platform::errors::InvalidArgument("scale  of Op(interpolate) "
+                                                "should be greater than 0."));
+        }
+      }
+      if (scale_h > 0. && scale_w > 0.) {
+        out_h = static_cast<int>(in_h * scale_h);
+        out_w = static_cast<int>(in_w * scale_w);
+      }
+      auto out_size = ctx.Input<Tensor>("OutSize");
+      if (out_size != nullptr) {
+        auto out_size_data = get_new_data_from_tensor<int>(out_size);
+        out_h = out_size_data[0];
+        out_w = out_size_data[1];
+      }
+    }
+
+    framework::DDim dim_grad;
+    if (data_layout == DataLayout::kNCHW) {
+      dim_grad = {n, c, in_h, in_w};
+    } else {
+      dim_grad = {n, in_h, in_w, c};
+    }
+    input_grad->mutable_data<T>(dim_grad, ctx.GetPlace());
+
+    auto& dev_ctx = ctx.template device_context<platform::XPUDeviceContext>();
+
+    int r = XPU_SUCCESS;
+    r = xpu::constant<T>(dev_ctx.x_context(), input_grad->data<T>(),
+                         input_grad->numel(), static_cast<T>(0.0));
+    PADDLE_ENFORCE_EQ(r, XPU_SUCCESS,
+                      platform::errors::External(
+                          "XPU constant in interpolate2d_grad kernel return "
+                          "wrong value[%d %s]",
+                          r, XPUAPIErrorMsg[r]));
+
+    if (in_h == out_h && in_w == out_w) {
+      framework::TensorCopy(*output_grad, ctx.GetPlace(), input_grad);
+      return;
+    }
+
+    bool nearest = "nearest" == interp_method;
+    int trans_mode = (align_corners) ? (0) : ((align_mode == 0) ? (1) : (2));
+
+    if (nearest) {
+      trans_mode = (align_corners) ? (0) : (2);
+    }
+
+    r = xpu::interpolate2d_grad<T>(dev_ctx.x_context(), output_grad->data<T>(),
+                                   input_grad->data<T>(), n, c, in_h, in_w,
+                                   out_h, out_w, nearest, trans_mode,
+                                   (data_layout == DataLayout::kNCHW));
+    PADDLE_ENFORCE_EQ(
+        r, XPU_SUCCESS,
+        platform::errors::External("XPU interpolate2d_grad kernel return "
+                                   "wrong value[%d %s]",
+                                   r, XPUAPIErrorMsg[r]));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_XPU_KERNEL(bilinear_interp_v2, ops::InterpolateV2XPUKernel<float>);
+REGISTER_OP_XPU_KERNEL(nearest_interp_v2, ops::InterpolateV2XPUKernel<float>);
+
+REGISTER_OP_XPU_KERNEL(bilinear_interp_v2_grad,
+                       ops::InterpolateV2GradXPUKernel<float>);
+REGISTER_OP_XPU_KERNEL(nearest_interp_v2_grad,
+                       ops::InterpolateV2GradXPUKernel<float>);
+#endif

python/paddle/fluid/tests/unittests/xpu/test_nearest_interp_v2_op_xpu.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 paddle
+import paddle.fluid.core as core
+import sys
+sys.path.append("..")
+from op_test_xpu import XPUOpTest
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
+
+paddle.enable_static()
+
+
+def nearest_neighbor_interp_np(X,
+                               out_h,
+                               out_w,
+                               scale_h=0,
+                               scale_w=0,
+                               out_size=None,
+                               actual_shape=None,
+                               align_corners=True,
+                               data_layout='NCHW'):
+    """nearest neighbor interpolation implement in shape [N, C, H, W]"""
+    if data_layout == "NHWC":
+        X = np.transpose(X, (0, 3, 1, 2))  # NHWC => NCHW
+    if out_size is not None:
+        out_h = out_size[0]
+        out_w = out_size[1]
+    if actual_shape is not None:
+        out_h = actual_shape[0]
+        out_w = actual_shape[1]
+    n, c, in_h, in_w = X.shape
+
+    ratio_h = ratio_w = 0.0
+    if (out_h > 1):
+        if (align_corners):
+            ratio_h = (in_h - 1.0) / (out_h - 1.0)
+        else:
+            if scale_h > 0:
+                ratio_h = 1.0 / scale_h
+            else:
+                ratio_h = 1.0 * in_h / out_h
+    if (out_w > 1):
+        if (align_corners):
+            ratio_w = (in_w - 1.0) / (out_w - 1.0)
+        else:
+            if scale_w > 0:
+                ratio_w = 1.0 / scale_w
+            else:
+                ratio_w = 1.0 * in_w / out_w
+    out = np.zeros((n, c, out_h, out_w))
+
+    if align_corners:
+        for i in range(out_h):
+            in_i = int(ratio_h * i + 0.5)
+            for j in range(out_w):
+                in_j = int(ratio_w * j + 0.5)
+                out[:, :, i, j] = X[:, :, in_i, in_j]
+    else:
+        for i in range(out_h):
+            in_i = int(ratio_h * i)
+            for j in range(out_w):
+                in_j = int(ratio_w * j)
+                out[:, :, i, j] = X[:, :, in_i, in_j]
+
+    if data_layout == "NHWC":
+        out = np.transpose(out, (0, 2, 3, 1))  # NCHW => NHWC
+
+    return out.astype(X.dtype)
+
+
+class TestNearestInterpOp(XPUOpTest):
+    def setUp(self):
+        self.use_xpu = True
+        self.out_size = None
+        self.actual_shape = None
+        self.init_test_case()
+        self.op_type = "nearest_interp_v2"
+        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("float32")
+        elif self.scale:
+            if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if self.scale > 0:
+                    scale_h = scale_w = float(self.scale)
+            if isinstance(self.scale, list) and len(self.scale) == 1:
+                scale_w = scale_h = self.scale[0]
+            elif isinstance(self.scale, list) and len(self.scale) > 1:
+                scale_w = self.scale[1]
+                scale_h = self.scale[0]
+            out_h = int(self.input_shape[2] * scale_h)
+            out_w = int(self.input_shape[3] * scale_w)
+        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
+        if self.scale:
+            if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if self.scale > 0:
+                    self.scale = [self.scale]
+            if isinstance(self.scale, list) and len(self.scale) == 1:
+                self.scale = [self.scale[0], self.scale[0]]
+            self.attrs['scale'] = self.scale
+        output_np = nearest_neighbor_interp_np(input_np, out_h, out_w, 0, 0,
+                                               self.out_size, self.actual_shape,
+                                               self.align_corners)
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_output_with_place(place)
+
+    def test_check_grad(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_grad_with_place(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 = True
+
+
+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 = True
+
+
+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 = True
+
+
+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 = True
+
+
+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 = True
+
+
+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 = True
+
+
+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 = True
+
+
+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 = True
+
+
+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 = True
+
+
+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 = True
+        self.data_layout = "NHWC"
+
+
+class TestNearestInterpWithoutCorners(TestNearestInterpOp):
+    def set_align_corners(self):
+        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 = True
+
+
+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 = True
+
+
+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 = [2.0, 3.0]
+        self.out_size = np.array([66, 40]).astype("int32")
+        self.align_corners = True
+
+
+class TestNearestInterpOp_attr_tensor(XPUOpTest):
+    def setUp(self):
+        self.use_xpu = True
+        self.out_size = None
+        self.actual_shape = None
+        self.init_test_case()
+        self.op_type = "nearest_interp_v2"
+        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("float32")
+        elif self.scale:
+            if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if self.scale > 0:
+                    scale_h = scale_w = float(self.scale)
+            if isinstance(self.scale, list) and len(self.scale) == 1:
+                scale_w = scale_h = self.scale[0]
+            elif isinstance(self.scale, list) and len(self.scale) > 1:
+                scale_w = self.scale[1]
+                scale_h = self.scale[0]
+            out_h = int(self.input_shape[2] * scale_h)
+            out_w = int(self.input_shape[3] * scale_w)
+        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
+        if self.scale:
+            if isinstance(self.scale, float) or isinstance(self.scale, int):
+                if self.scale > 0:
+                    self.scale = [self.scale]
+            if isinstance(self.scale, list) and len(self.scale) == 1:
+                self.scale = [self.scale[0], self.scale[0]]
+            self.attrs['scale'] = self.scale
+        output_np = nearest_neighbor_interp_np(input_np, out_h, out_w, 0, 0,
+                                               self.out_size, self.actual_shape,
+                                               self.align_corners)
+        self.outputs = {'Out': output_np}
+
+    def test_check_output(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_output_with_place(place)
+
+    def test_check_grad(self):
+        if paddle.is_compiled_with_xpu():
+            place = paddle.XPUPlace(0)
+            self.check_grad_with_place(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 = True
+
+
+# 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 = True
+
+
+# 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 = True
+        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 = True
+        self.scale_by_1Dtensor = True
+
+
+if __name__ == "__main__":
+    unittest.main()