code: #   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
  from op_test import OpTest
  import paddle.fluid.core as core
  import paddle.fluid as fluid
  
  
  def bilinear_interp_np(input,
                         out_h,
                         out_w,
                         out_size=None,
                         actual_shape=None,
                         align_corners=True,
                         align_mode=0,
                         data_layout='NCHW'):
      """bilinear interpolation implement in shape [N, C, H, W]"""
      if data_layout == "NHWC":
          input = np.transpose(input, (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]
      batch_size, channel, in_h, in_w = input.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:
              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:
              ratio_w = 1.0 * in_w / out_w
  
      out = np.zeros((batch_size, channel, out_h, out_w))
  
      for i in range(out_h):
          if (align_mode == 0 and not align_corners):
              h = int(ratio_h * (i + 0.5) - 0.5)
          else:
              h = int(ratio_h * i)
  
          h = max(0, h)
          hid = 1 if h < in_h - 1 else 0
          if (align_mode == 0 and not align_corners):
              idx_src_h = max(ratio_h * (i + 0.5) - 0.5, 0)
              h1lambda = idx_src_h - h
          else:
              h1lambda = ratio_h * i - h
          h2lambda = 1.0 - h1lambda
          for j in range(out_w):
              if (align_mode == 0 and not align_corners):
                  w = int(ratio_w * (j + 0.5) - 0.5)
              else:
                  w = int(ratio_w * j)
              w = max(0, w)
              wid = 1 if w < in_w - 1 else 0
              if (align_mode == 0 and not align_corners):
                  idx_src_w = max(ratio_w * (j + 0.5) - 0.5, 0)
                  w1lambda = idx_src_w - w
              else:
                  w1lambda = ratio_w * j - w
              w2lambda = 1.0 - w1lambda
  
              out[:, :, i, j] = h2lambda*(w2lambda*input[:, :, h, w] +
                                          w1lambda*input[:, :, h, w+wid]) + \
                  h1lambda*(w2lambda*input[:, :, h+hid, w] +
                            w1lambda*input[:, :, h+hid, w+wid])
  
      if data_layout == "NHWC":
          out = np.transpose(out, (0, 2, 3, 1))  # NCHW => NHWC
  
      return out.astype(input.dtype)
  
  
  class TestBilinearInterpOp(OpTest):
      def setUp(self):
          self.out_size = None
          self.actual_shape = None
          self.data_layout = 'NCHW'
          self.init_test_case()
          self.op_type = "bilinear_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 = bilinear_interp_np(input_np, out_h, out_w, self.out_size,
                                         self.actual_shape, self.align_corners,
                                         self.align_mode, 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,
              'align_mode': self.align_mode,
              'data_layout': self.data_layout
          }
          self.outputs = {'Out': output_np}
  
      def test_check_output(self):
          self.check_output()
  
      def test_check_grad(self):
          self.check_grad(['X'], 'Out', in_place=True)
  
      def init_test_case(self):
          self.interp_method = 'bilinear'
          self.input_shape = [2, 3, 4, 4]
          self.out_h = 2
          self.out_w = 2
          self.scale = 0.
          self.out_size = np.array([3, 3]).astype("int32")
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestBilinearInterpCase1(TestBilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          self.input_shape = [4, 1, 7, 8]
          self.out_h = 1
          self.out_w = 1
          self.scale = 0.
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestBilinearInterpCase2(TestBilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          self.input_shape = [3, 3, 9, 6]
          self.out_h = 12
          self.out_w = 12
          self.scale = 0.
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestBilinearInterpCase3(TestBilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          self.input_shape = [1, 1, 128, 64]
          self.out_h = 64
          self.out_w = 128
          self.scale = 0.
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestBilinearInterpCase4(TestBilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          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
          self.align_mode = 1
  
  
  class TestBilinearInterpCase5(TestBilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          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
          self.align_mode = 1
  
  
  class TestBilinearInterpCase6(TestBilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          self.input_shape = [1, 1, 128, 64]
          self.out_h = 64
          self.out_w = 128
          self.scale = 0.
          self.out_size = np.array([65, 129]).astype("int32")
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestBilinearInterpSame(TestBilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          self.input_shape = [2, 3, 128, 64]
          self.out_h = 128
          self.out_w = 64
          self.scale = 0.
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestBilinearInterpActualShape(TestBilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          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.align_mode = 1
  
  
  class TestBilinearInterpDataLayout(TestBilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          self.input_shape = [2, 4, 4, 3]
          self.out_h = 2
          self.out_w = 2
          self.scale = 0.
          self.out_size = np.array([3, 3]).astype("int32")
          self.align_corners = True
          self.align_mode = 1
          self.data_layout = "NHWC"
  
  
  class TestBilinearInterpOpUint8(OpTest):
      def setUp(self):
          self.out_size = None
          self.actual_shape = None
          self.init_test_case()
          self.op_type = "bilinear_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 = bilinear_interp_np(input_np, out_h, out_w, self.out_size,
                                         self.actual_shape, self.align_corners,
                                         self.align_mode)
          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,
              'align_mode': self.align_mode
          }
          self.outputs = {'Out': output_np}
  
      def test_check_output(self):
          self.check_output_with_place(place=core.CPUPlace(), atol=1)
  
      def init_test_case(self):
          self.interp_method = 'bilinear'
          self.input_shape = [1, 3, 9, 6]
          self.out_h = 10
          self.out_w = 9
          self.scale = 0.
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestBilinearInterpCase1Uint8(TestBilinearInterpOpUint8):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          self.input_shape = [2, 3, 128, 64]
          self.out_h = 120
          self.out_w = 50
          self.scale = 0.
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestBilinearInterpCase2Uint8(TestBilinearInterpOpUint8):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          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 = True
          self.align_mode = 1
  
  
  class TestBilinearInterpOtherMethod1(TestBilinearInterpOp):
      def set_align_mode(self):
          self.align_corners = False
          self.align_mode = 1
  
  
  class TestBilinearInterpWithMethod2(TestBilinearInterpOp):
      def set_align_mode(self):
          self.align_corners = False
          self.align_mode = 0
  
  
  class TestBilinearInterpWithMethod3(TestBilinearInterpOp):
      def set_align_mode(self):
          self.align_corners = True
          self.align_mode = 0
  
  
  class TestBilinearInterpScale1(TestBilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          self.input_shape = [2, 3, 5, 7]
          self.out_h = 60
          self.out_w = 25
          self.scale = 2.
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestBilinearInterpScale2(TestBilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          self.input_shape = [2, 3, 5, 7]
          self.out_h = 60
          self.out_w = 25
          self.scale = 1.
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestBilinearInterpScale3(TestBilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          self.input_shape = [2, 3, 5, 7]
          self.out_h = 60
          self.out_w = 25
          self.scale = 1.5
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestBilinearInterpZero(TestBilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          self.input_shape = [2, 3, 5, 7]
          self.out_h = 60
          self.out_w = 25
          self.scale = 0.2
          self.align_corners = False
          self.align_mode = 0
  
  
  class TestBilinearInterpOp_attr_tensor(OpTest):
      def setUp(self):
          self.out_size = None
          self.actual_shape = None
          self.init_test_case()
          self.op_type = "bilinear_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("float32")
          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 = bilinear_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()
  
      def test_check_grad(self):
          self.check_grad(['X'], 'Out', in_place=True)
  
      def init_test_case(self):
          self.interp_method = 'bilinear'
          self.input_shape = [2, 3, 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 1-D tensor
  class TestBilinearInterp_attr_tensor_Case1(TestBilinearInterpOp_attr_tensor):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          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
  
  
  # scale is a 1-D tensor
  class TestBilinearInterp_attr_tensor_Case2(TestBilinearInterpOp_attr_tensor):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          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 TestBilinearInterp_attr_tensor_Case3(TestBilinearInterpOp_attr_tensor):
      def init_test_case(self):
          self.interp_method = 'bilinear'
          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
  
  
  class TestBilinearInterpOpAPI(OpTest):
      def test_case(self):
          x = fluid.layers.data(name="x", shape=[3, 6, 6], dtype="float32")
  
          dim = fluid.layers.data(
              name="dim", shape=[1], dtype="int32", append_batch_size=False)
          shape_tensor = fluid.layers.data(
              name="shape_tensor",
              shape=[2],
              dtype="int32",
              append_batch_size=False)
          actual_size = fluid.layers.data(
              name="actual_size",
              shape=[2],
              dtype="int32",
              append_batch_size=False)
          scale_tensor = fluid.layers.data(
              name="scale_tensor",
              shape=[1],
              dtype="float32",
              append_batch_size=False)
  
          out1 = fluid.layers.resize_bilinear(x, out_shape=[12, 12])
          out2 = fluid.layers.resize_bilinear(x, out_shape=[12, dim])
          out3 = fluid.layers.resize_bilinear(x, out_shape=shape_tensor)
          out4 = fluid.layers.resize_bilinear(
              x, out_shape=[4, 4], actual_shape=actual_size)
          out5 = fluid.layers.resize_bilinear(x, scale=scale_tensor)
  
          x_data = np.random.random((1, 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 = core.CPUPlace()
          exe = fluid.Executor(place)
          results = exe.run(fluid.default_main_program(),
                            feed={
                                "x": x_data,
                                "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 = bilinear_interp_np(
              x_data, out_h=12, out_w=12, align_corners=True)
          for res in results:
              self.assertTrue(np.allclose(res, expect_res))
  
  
  if __name__ == "__main__":
      unittest.main()
  
apis: numpy.allclose numpy.ones numpy.transpose numpy.zeros numpy.random.random numpy.array numpy.random.randint