[NPU] add nearest_interp_v2 and nearest_interp_v2_grad, test=develop (#34769)

paddle/fluid/operators/interpolate_v2_op.h

@@ -58,6 +58,12 @@ inline std::vector<T> get_new_data_from_tensor(const Tensor* new_data_tensor) {
     TensorCopySync(*new_data_tensor, platform::CPUPlace(), &cpu_starts_tensor);
     new_data = cpu_starts_tensor.data<T>();
   }
+#ifdef PADDLE_WITH_ASCEND_CL
+  if (platform::is_npu_place(new_data_tensor->place())) {
+    TensorCopySync(*new_data_tensor, platform::CPUPlace(), &cpu_starts_tensor);
+    new_data = cpu_starts_tensor.data<T>();
+  }
+#endif
   vec_new_data = std::vector<T>(new_data, new_data + new_data_tensor->numel());
   return vec_new_data;
 }

paddle/fluid/operators/interpolate_v2_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 Licnse. */
+
+#include "paddle/fluid/operators/interpolate_v2_op.h"
+#include "paddle/fluid/operators/npu_op_runner.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using DataLayout = framework::DataLayout;
+
+template <typename DeviceContext, typename T>
+class InterpolateV2NPUKernel : 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(), 4UL,
+                      platform::errors::External(
+                          "NPU Interpolate Kernel only support 4-D Tensor."));
+
+    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);
+
+    PADDLE_ENFORCE_EQ(
+        input->layout(), data_layout,
+        platform::errors::InvalidArgument(
+            "Interpolate OP's input tensor layout should equal to attr "
+            "data_layout, but got tensor layout <%s>, attr layout <%s>",
+            framework::DataLayoutToString(input->layout()), data_layout_str));
+    PADDLE_ENFORCE_EQ(
+        output->layout(), data_layout,
+        platform::errors::InvalidArgument(
+            "Interpolate OP's output tensor layout should equal to attr "
+            "data_layout, but got tensor layout <%s>, attr layout <%s>",
+            framework::DataLayoutToString(output->layout()), data_layout_str));
+
+    auto interp_method = ctx.Attr<std::string>("interp_method");
+    bool align_corners = ctx.Attr<bool>("align_corners");
+
+    // To-do(qili93): need to support align_corners = true case, try ReSizeD
+    PADDLE_ENFORCE_EQ(
+        align_corners, false,
+        platform::errors::InvalidArgument(
+            "NPU Interpolate Kernel has diff when align_corners is true."));
+
+    int out_h = ctx.Attr<int>("out_h");
+    int out_w = ctx.Attr<int>("out_w");
+    float scale_h = -1;
+    float scale_w = -1;
+
+    // Priority: SizeTensor > OutSize > Scale > scale > out_h & out_w
+    auto list_new_shape_tensor =
+        ctx.MultiInput<framework::Tensor>("SizeTensor");
+    if (list_new_shape_tensor.size() > 0) {
+      std::vector<int32_t> output_h(1);
+      std::vector<int32_t> output_w(1);
+      auto dev_ctx =
+          platform::DeviceContextPool::Instance().Get(ctx.GetPlace());
+      framework::TensorToVector(*list_new_shape_tensor[0], *dev_ctx, &output_h);
+      framework::TensorToVector(*list_new_shape_tensor[1], *dev_ctx, &output_w);
+      out_h = output_h[0];
+      out_w = output_w[0];
+    } else if (ctx.HasInput("OutSize")) {
+      auto out_size = ctx.Input<Tensor>("OutSize");
+      auto out_size_data = get_new_data_from_tensor<int>(out_size);
+      out_h = out_size_data[0];
+      out_w = out_size_data[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<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, true,
+            platform::errors::InvalidArgument(
+                "The scale_w in input 'Scale' Tensor of Operator(interpolate) "
+                "should be greater than 0, but received value is %d.",
+                scale_w));
+        PADDLE_ENFORCE_EQ(
+            scale_h > 0, true,
+            platform::errors::InvalidArgument(
+                "The scale_h in input 'Scale' Tensor of Operator(interpolate) "
+                "should be greater than 0, but received value is %d.",
+                scale_h));
+      } else {
+        if (scale.size() > 1) {
+          scale_h = scale[0];
+          scale_w = scale[1];
+
+          PADDLE_ENFORCE_EQ(
+              scale_w > 0, true,
+              platform::errors::InvalidArgument(
+                  "The scale_w in Attr(scale) of Operator(interpolate) "
+                  "should be greater than 0, but received value is %d.",
+                  scale_w));
+          PADDLE_ENFORCE_EQ(
+              scale_h > 0, true,
+              platform::errors::InvalidArgument(
+                  "The scale_h in Attr(scale) of Operator(interpolate) "
+                  "should be greater than 0, but received value is %d.",
+                  scale_h));
+        }
+      }
+      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);
+      }
+    }
+    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;
+    }
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    NpuOpRunner runner;
+    // To-do(qili93): need to support bilineare, try ResizeD
+    if ("nearest" == interp_method) {
+      runner.SetType("ResizeNearestNeighborV2")
+          .AddInput(*input)
+          .AddInput(std::vector<int32_t>{out_h, out_w})
+          .AddOutput(*output)
+          .AddAttr("align_corners", align_corners)
+          .AddAttr("half_pixel_centers", false);
+    }
+    runner.Run(stream);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class InterpolateV2NPUGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<Tensor>("X");
+    auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("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);
+
+    PADDLE_ENFORCE_EQ(
+        input->layout(), data_layout,
+        platform::errors::InvalidArgument(
+            "Interpolate OP's input tensor layout should equal to attr "
+            "data_layout, but got tensor layout <%s>, attr layout <%s>",
+            framework::DataLayoutToString(input->layout()), data_layout_str));
+    PADDLE_ENFORCE_EQ(output_grad->layout(), data_layout,
+                      platform::errors::InvalidArgument(
+                          "Interpolate OP's output_grad tensor layout should "
+                          "equal to attr data_layout, but got tensor layout is "
+                          "<%s>, and attr layout is <%s>",
+                          framework::DataLayoutToString(output_grad->layout()),
+                          data_layout_str));
+    PADDLE_ENFORCE_EQ(input_grad->layout(), data_layout,
+                      platform::errors::InvalidArgument(
+                          "Interpolate OP's input_grad tensor layout should "
+                          "equal to attr data_layout, but got tensor layout is "
+                          "<%s>, and attr layout is <%s>",
+                          framework::DataLayoutToString(input_grad->layout()),
+                          data_layout_str));
+
+    auto interp_method = ctx.Attr<std::string>("interp_method");
+    bool align_corners = ctx.Attr<bool>("align_corners");
+
+    // To-do(qili93): need to support align_corners = true case, try ReSizeD
+    PADDLE_ENFORCE_EQ(
+        align_corners, false,
+        platform::errors::InvalidArgument(
+            "NPU Interpolate Kernel has diff when align_corners is true."));
+
+    int out_h = ctx.Attr<int>("out_h");
+    int out_w = ctx.Attr<int>("out_w");
+    float scale_h = -1;
+    float scale_w = -1;
+
+    // Priority: SizeTensor > OutSize > Scale > scale > out_h & out_w
+    auto list_new_size_tensor = ctx.MultiInput<framework::Tensor>("SizeTensor");
+    if (list_new_size_tensor.size() > 0) {
+      std::vector<int32_t> output_h(1);
+      std::vector<int32_t> output_w(1);
+      auto dev_ctx =
+          platform::DeviceContextPool::Instance().Get(ctx.GetPlace());
+      framework::TensorToVector(*list_new_size_tensor[0], *dev_ctx, &output_h);
+      framework::TensorToVector(*list_new_size_tensor[1], *dev_ctx, &output_w);
+      out_h = output_h[0];
+      out_w = output_w[0];
+    } else if (ctx.HasInput("OutSize")) {
+      auto out_size = ctx.Input<Tensor>("OutSize");
+      auto out_size_data = get_new_data_from_tensor<int>(out_size);
+      out_h = out_size_data[0];
+      out_w = out_size_data[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<float>(scale_tensor);
+        if (scale_data.size() > 1) {
+          scale_h = scale_data[0];
+          scale_w = scale_data[1];
+        } else {
+          scale_w = scale_data[0];
+          scale_h = scale_data[0];
+        }
+        PADDLE_ENFORCE_EQ(
+            scale_w > 0, true,
+            platform::errors::InvalidArgument(
+                "The scale_w in input 'Scale' Tensor of Operator(interpolate) "
+                "should be greater than 0, but received value is %d.",
+                scale_w));
+        PADDLE_ENFORCE_EQ(
+            scale_h > 0, true,
+            platform::errors::InvalidArgument(
+                "The scale_h in input 'Scale' Tensor of Operator(interpolate) "
+                "should be greater than 0, but received value is %d.",
+                scale_h));
+      } else {
+        if (scale.size() > 1) {
+          scale_h = scale[0];
+          scale_w = scale[1];
+          PADDLE_ENFORCE_EQ(
+              scale_w > 0, true,
+              platform::errors::InvalidArgument(
+                  "The scale_w in Attr(scale) of Operator(interpolate) "
+                  "should be greater than 0, but received value is %d.",
+                  scale_w));
+          PADDLE_ENFORCE_EQ(
+              scale_h > 0, true,
+              platform::errors::InvalidArgument(
+                  "The scale_h in Attr(scale) of Operator(interpolate) "
+                  "should be greater than 0, but received value is %d.",
+                  scale_h));
+        }
+      }
+      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);
+      }
+    }
+
+    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());
+
+    if (in_h == out_h && in_w == out_w) {
+      framework::TensorCopy(*output_grad, ctx.GetPlace(), input_grad);
+      return;
+    }
+
+    auto stream =
+        ctx.template device_context<paddle::platform::NPUDeviceContext>()
+            .stream();
+
+    NpuOpRunner runner;
+    // To-do(qili93): need to support bilineare, try ResizeGradD
+    if ("nearest" == interp_method) {
+      runner.SetType("ResizeNearestNeighborV2Grad")
+          .AddInput(*output_grad)
+          .AddInput(std::vector<int32_t>{in_h, in_w})
+          .AddOutput(*input_grad)
+          .AddAttr("align_corners", align_corners)
+          .AddAttr("half_pixel_centers", false);
+    }
+    runner.Run(stream);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+
+REGISTER_OP_NPU_KERNEL(
+    nearest_interp_v2,
+    ops::InterpolateV2NPUKernel<plat::NPUDeviceContext, float>,
+    ops::InterpolateV2NPUKernel<plat::NPUDeviceContext, plat::float16>);
+
+REGISTER_OP_NPU_KERNEL(
+    nearest_interp_v2_grad,
+    ops::InterpolateV2NPUGradKernel<plat::NPUDeviceContext, float>,
+    ops::InterpolateV2NPUGradKernel<plat::NPUDeviceContext, plat::float16>);

python/paddle/fluid/tests/unittests/npu/test_nearest_interp_v2_op_npu.py

+#   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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import sys
+sys.path.append("..")
+from op_test import OpTest
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+import paddle.nn as nn
+import paddle
+from paddle.nn.functional import interpolate
+
+from test_nearest_interp_v2_op import nearest_neighbor_interp_np
+
+paddle.enable_static()
+
+
+class TestNearestInterpOp(OpTest):
+    def set_npu(self):
+        self.__class__.use_npu = True
+        self.place = paddle.NPUPlace(0)
+
+    def setUp(self):
+        self.set_npu()
+        self.out_size = None
+        self.actual_shape = None
+        self.data_layout = 'NCHW'
+        self.init_test_case()
+        self.op_type = "nearest_interp_v2"
+        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]
+        scale_h = 0
+        scale_w = 0
+        if 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]
+            output_h = int(in_h * scale_h)
+            output_w = int(in_w * scale_w)
+        else:
+            output_h = self.out_h
+            output_w = self.out_w
+
+        output_np = nearest_neighbor_interp_np(
+            input_np, output_h, output_w, scale_h, scale_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,
+            'interp_method': self.interp_method,
+            'align_corners': self.align_corners,
+            'data_layout': self.data_layout
+        }
+        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
+        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, max_relative_error=0.006)
+
+    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
+
+
+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 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 = None
+        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 = None
+        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 = [2.0, 3.0]
+        self.out_size = None
+        self.align_corners = False
+
+
+class TestNearestInterpOp_attr_tensor(OpTest):
+    def set_npu(self):
+        self.__class__.use_npu = True
+        self.place = paddle.NPUPlace(0)
+
+    def setUp(self):
+        self.set_npu()
+        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):
+        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 TestNearestInterpOpAPI_dy(unittest.TestCase):
+    def test_case(self):
+        import paddle
+        if core.is_compiled_with_npu():
+            place = core.NPUPlace(0)
+        else:
+            place = core.CPUPlace()
+        with fluid.dygraph.guard(place):
+            input_data = np.random.random((2, 3, 6, 6)).astype("float32")
+            scale_np = np.array([2, 2]).astype("int64")
+            input_x = paddle.to_tensor(input_data)
+            scale = paddle.to_tensor(scale_np)
+            expect_res = nearest_neighbor_interp_np(
+                input_data, out_h=12, out_w=12, align_corners=False)
+            out = interpolate(
+                x=input_x,
+                scale_factor=scale,
+                mode="nearest",
+                align_corners=False)
+            self.assertTrue(np.allclose(out.numpy(), expect_res))
+
+
+if __name__ == "__main__":
+    unittest.main()