text: #   Copyright (c) 2019 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 trilinear_interp_np(input,
                          out_d,
                          out_h,
                          out_w,
                          out_size=None,
                          actual_shape=None,
                          align_corners=True,
                          align_mode=0,
                          data_layout='NCDHW'):
      """trilinear interpolation implement in shape [N, C, D, H, W]"""
      if data_layout == "NDHWC":
          input = np.transpose(input, (0, 4, 1, 2, 3))  # NDHWC => NCDHW
      if out_size is not None:
          out_d = out_size[0]
          out_h = out_size[1]
          out_w = out_size[2]
      if actual_shape is not None:
          out_d = actual_shape[0]
          out_h = actual_shape[1]
          out_w = actual_shape[2]
      batch_size, channel, in_d, in_h, in_w = input.shape
  
      ratio_d = ratio_h = ratio_w = 0.0
      if out_d > 1:
          if (align_corners):
              ratio_d = (in_d - 1.0) / (out_d - 1.0)
          else:
              ratio_d = 1.0 * in_d / out_d
      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_d, out_h, out_w))
  
      for i in range(out_d):
          if (align_mode == 0 and not align_corners):
              d = int(ratio_d * (i + 0.5) - 0.5)
          else:
              d = int(ratio_d * i)
  
          d = max(0, d)
          did = 1 if d < in_d - 1 else 0
          if (align_mode == 0 and not align_corners):
              idx_src_d = max(ratio_d * (i + 0.5) - 0.5, 0)
              d1lambda = idx_src_d - d
          else:
              d1lambda = ratio_d * i - d
          d2lambda = 1.0 - d1lambda
  
          for j in range(out_h):
              if (align_mode == 0 and not align_corners):
                  h = int(ratio_h * (j + 0.5) - 0.5)
              else:
                  h = int(ratio_h * j)
  
              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 * (j + 0.5) - 0.5, 0)
                  h1lambda = idx_src_h - h
              else:
                  h1lambda = ratio_h * j - h
              h2lambda = 1.0 - h1lambda
  
              for k in range(out_w):
                  if (align_mode == 0 and not align_corners):
                      w = int(ratio_w * (k + 0.5) - 0.5)
                  else:
                      w = int(ratio_w * k)
                  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 * (k + 0.5) - 0.5, 0)
                      w1lambda = idx_src_w - w
                  else:
                      w1lambda = ratio_w * k - w
                  w2lambda = 1.0 - w1lambda
  
                  out[:, :, i, j, k] = \
                      d2lambda * \
                      (h2lambda * (w2lambda * input[:, :, d, h, w] + \
                                w1lambda * input[:, :, d, h, w+wid]) + \
                      h1lambda * (w2lambda * input[:, :, d, h+hid, w] + \
                                w1lambda * input[:, :, d, h+hid, w+wid])) + \
                      d1lambda * \
                      (h2lambda * (w2lambda * input[:, :, d+did, h, w] + \
                                w1lambda * input[:, :, d+did, h, w+wid]) + \
                      h1lambda * (w2lambda * input[:, :, d+did, h+hid, w] + \
                                w1lambda * input[:, :, d+did, h+hid, w+wid]))
      if data_layout == "NDHWC":
          out = np.transpose(out, (0, 2, 3, 4, 1))  # NCDHW => NDHWC
  
      return out.astype(input.dtype)
  
  
  class TestTrilinearInterpOp(OpTest):
      def setUp(self):
          self.out_size = None
          self.actual_shape = None
          self.data_layout = 'NCDHW'
          self.init_test_case()
          self.op_type = "trilinear_interp"
          input_np = np.random.random(self.input_shape).astype("float32")
  
          if self.data_layout == "NCDHW":
              in_d = self.input_shape[2]
              in_h = self.input_shape[3]
              in_w = self.input_shape[4]
          else:
              in_d = self.input_shape[1]
              in_h = self.input_shape[2]
              in_w = self.input_shape[3]
  
          if self.scale > 0:
              out_d = int(in_d * self.scale)
              out_h = int(in_h * self.scale)
              out_w = int(in_w * self.scale)
          else:
              out_d = self.out_d
              out_h = self.out_h
              out_w = self.out_w
  
          output_np = trilinear_interp_np(
              input_np, out_d, 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
          # c++ end treat NCDHW the same way as NCHW
          if self.data_layout == 'NCDHW':
              data_layout = 'NCHW'
          else:
              data_layout = 'NHWC'
          self.attrs = {
              'out_d': self.out_d,
              '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': 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 = 'trilinear'
          self.input_shape = [2, 3, 4, 4, 4]
          self.out_d = 2
          self.out_h = 2
          self.out_w = 2
          self.scale = 0.
          self.out_size = np.array([3, 3, 3]).astype("int32")
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestTrilinearInterpCase1(TestTrilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [2, 1, 7, 8, 9]
          self.out_d = 1
          self.out_h = 1
          self.out_w = 1
          self.scale = 0.
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestTrilinearInterpCase2(TestTrilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [2, 3, 9, 6, 8]
          self.out_d = 12
          self.out_h = 12
          self.out_w = 12
          self.scale = 0.
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestTrilinearInterpCase3(TestTrilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [3, 2, 16, 8, 4]
          self.out_d = 32
          self.out_h = 16
          self.out_w = 8
          self.scale = 0.
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestTrilinearInterpCase4(TestTrilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [4, 1, 7, 8, 9]
          self.out_d = 1
          self.out_h = 1
          self.out_w = 1
          self.scale = 0.
          self.out_size = np.array([2, 2, 2]).astype("int32")
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestTrilinearInterpCase5(TestTrilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [3, 3, 9, 6, 8]
          self.out_d = 12
          self.out_h = 12
          self.out_w = 12
          self.scale = 0.
          self.out_size = np.array([11, 11, 11]).astype("int32")
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestTrilinearInterpCase6(TestTrilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [1, 1, 16, 8, 4]
          self.out_d = 8
          self.out_h = 32
          self.out_w = 16
          self.scale = 0.
          self.out_size = np.array([17, 9, 5]).astype("int32")
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestTrilinearInterpSame(TestTrilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [1, 1, 16, 8, 4]
          self.out_d = 16
          self.out_h = 8
          self.out_w = 4
          self.scale = 0.
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestTrilinearInterpSameHW(TestTrilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [1, 1, 16, 8, 4]
          self.out_d = 8
          self.out_h = 8
          self.out_w = 4
          self.scale = 0.
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestTrilinearInterpActualShape(TestTrilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [3, 2, 16, 8, 4]
          self.out_d = 64
          self.out_h = 32
          self.out_w = 16
          self.scale = 0.
          self.out_size = np.array([33, 19, 7]).astype("int32")
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestTrilinearInterpDatalayout(TestTrilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [2, 4, 4, 4, 3]
          self.out_d = 2
          self.out_h = 2
          self.out_w = 2
          self.scale = 0.
          self.out_size = np.array([3, 3, 3]).astype("int32")
          self.align_corners = True
          self.align_mode = 1
          self.data_layout = "NDHWC"
  
  
  class TestTrilinearInterpOpUint8(OpTest):
      def setUp(self):
          self.out_size = None
          self.actual_shape = None
          self.init_test_case()
          self.op_type = "trilinear_interp"
          input_np = np.random.randint(
              low=0, high=256, size=self.input_shape).astype("uint8")
  
          if self.scale > 0:
              out_d = int(self.input_shape[2] * self.scale)
              out_h = int(self.input_shape[3] * self.scale)
              out_w = int(self.input_shape[4] * self.scale)
          else:
              out_d = self.out_d
              out_h = self.out_h
              out_w = self.out_w
  
          output_np = trilinear_interp_np(input_np, out_d, 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_d': self.out_d,
              '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 = 'trilinear'
          self.input_shape = [1, 3, 9, 6, 8]
          self.out_d = 13
          self.out_h = 10
          self.out_w = 9
          self.scale = 0.
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestTrilinearInterpCase1Uint8(TestTrilinearInterpOpUint8):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [2, 3, 16, 8, 4]
          self.out_d = 13
          self.out_h = 7
          self.out_w = 2
          self.scale = 0.
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestTrilinearInterpCase2Uint8(TestTrilinearInterpOpUint8):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [4, 1, 7, 8, 9]
          self.out_d = 3
          self.out_h = 5
          self.out_w = 13
          self.scale = 0.
          self.out_size = np.array([6, 15, 21]).astype("int32")
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestTrilinearInterpOtherMethod1(TestTrilinearInterpOp):
      def set_align_mode(self):
          self.align_corners = False
          self.align_mode = 1
  
  
  class TestTrilinearInterpWithMethod2(TestTrilinearInterpOp):
      def set_align_mode(self):
          self.align_corners = False
          self.align_mode = 0
  
  
  class TestTrilinearInterpWithMethod3(TestTrilinearInterpOp):
      def set_align_mode(self):
          self.align_corners = True
          self.align_mode = 0
  
  
  class TestTrilinearInterpScale1(TestTrilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [2, 3, 5, 7, 9]
          self.out_d = 82
          self.out_h = 60
          self.out_w = 25
          self.scale = 2.
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestTrilinearInterpScale2(TestTrilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [2, 3, 5, 7, 9]
          self.out_d = 60
          self.out_h = 40
          self.out_w = 25
          self.scale = 1.
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestTrilinearInterpScale3(TestTrilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [2, 3, 5, 7, 9]
          self.out_d = 60
          self.out_h = 40
          self.out_w = 25
          self.scale = 1.5
          self.align_corners = True
          self.align_mode = 1
  
  
  class TestTrilinearInterpZero(TestTrilinearInterpOp):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [2, 3, 5, 7, 11]
          self.out_d = 60
          self.out_h = 40
          self.out_w = 25
          self.scale = 0.2
          self.align_corners = False
          self.align_mode = 0
  
  
  class TestTrilinearInterpOp_attr_tensor(OpTest):
      def setUp(self):
          self.out_size = None
          self.actual_shape = None
          self.init_test_case()
          self.op_type = "trilinear_interp"
          self.shape_by_1Dtensor = False
          self.scale_by_1Dtensor = False
          self.attrs = {
              'interp_method': self.interp_method,
              'align_corners': self.align_corners,
              'align_mode': self.align_mode
          }
  
          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_d = int(self.input_shape[2] * self.scale)
              out_h = int(self.input_shape[3] * self.scale)
              out_w = int(self.input_shape[4] * self.scale)
              self.attrs['scale'] = self.scale
          else:
              out_d = self.out_d
              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_d'] = self.out_d
          self.attrs['out_h'] = self.out_h
          self.attrs['out_w'] = self.out_w
          output_np = trilinear_interp_np(input_np, out_d, out_h, out_w,
                                          self.out_size, self.actual_shape,
                                          self.align_corners, self.align_mode)
          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 = 'trilinear'
          self.input_shape = [2, 3, 4, 4, 4]
          self.out_d = 2
          self.out_h = 3
          self.out_w = 3
          self.scale = 0.
          self.out_size = [2, 3, 3]
          self.align_corners = True
          self.align_mode = 1
  
  
  # out_size is a 1-D tensor
  class TestTrilinearInterp_attr_tensor_Case1(TestTrilinearInterpOp_attr_tensor):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [3, 2, 9, 6, 8]
          self.out_d = 32
          self.out_h = 16
          self.out_w = 8
          self.scale = 0.3
          self.out_size = [12, 4, 4]
          self.align_corners = True
          self.align_mode = 1
  
  
  # scale is a 1-D tensor
  class TestTrilinearInterp_attr_tensor_Case2(TestTrilinearInterpOp_attr_tensor):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [2, 3, 8, 8, 4]
          self.out_d = 16
          self.out_h = 12
          self.out_w = 4
          self.scale = 0.
          self.out_size = [16, 4, 10]
          self.align_corners = True
          self.align_mode = 1
          self.shape_by_1Dtensor = True
  
  
  # scale is a 1-D tensor
  class TestTrilinearInterp_attr_tensor_Case3(TestTrilinearInterpOp_attr_tensor):
      def init_test_case(self):
          self.interp_method = 'trilinear'
          self.input_shape = [2, 3, 8, 8, 4]
          self.out_d = 16
          self.out_h = 16
          self.out_w = 8
          self.scale = 2.0
          self.out_size = None
          self.align_corners = True
          self.align_mode = 1
          self.scale_by_1Dtensor = True
  
  
  class TestTrilinearInterpAPI(OpTest):
      def test_case(self):
          x = fluid.data(name="x", shape=[2, 3, 6, 9, 4], dtype="float32")
          y = fluid.data(name="y", shape=[2, 6, 9, 4, 3], dtype="float32")
  
          dim = fluid.data(name="dim", shape=[1], dtype="int32")
          shape_tensor = fluid.data(name="shape_tensor", shape=[3], dtype="int32")
          actual_size = fluid.data(name="actual_size", shape=[3], dtype="int32")
          scale_tensor = fluid.data(
              name="scale_tensor", shape=[1], dtype="float32")
  
          out1 = fluid.layers.resize_trilinear(
              y, out_shape=[12, 18, 8], data_format='NDHWC')
          out2 = fluid.layers.resize_trilinear(x, out_shape=[12, dim, 8])
          out3 = fluid.layers.resize_trilinear(x, out_shape=shape_tensor)
          out4 = fluid.layers.resize_trilinear(
              x, out_shape=[4, 4, 8], actual_shape=actual_size)
          out5 = fluid.layers.resize_trilinear(x, scale=scale_tensor)
  
          x_data = np.random.random((2, 3, 6, 9, 4)).astype("float32")
          dim_data = np.array([18]).astype("int32")
          shape_data = np.array([12, 18, 8]).astype("int32")
          actual_size_data = np.array([12, 18, 8]).astype("int32")
          scale_data = np.array([2.0]).astype("float32")
  
          if core.is_compiled_with_cuda():
              place = core.CUDAPlace(0)
          else:
              place = core.CPUPlace()
          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, 4, 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 = trilinear_interp_np(
              x_data, out_d=12, out_h=18, out_w=8, align_mode=1)
          self.assertTrue(
              np.allclose(results[0], np.transpose(expect_res, (0, 2, 3, 4, 1))))
          for i in range(len(results) - 1):
              self.assertTrue(np.allclose(results[i + 1], expect_res))
  
  
  class TestTrilinearInterpOpException(OpTest):
      def test_exception(self):
          input = fluid.data(name="input", shape=[2, 3, 6, 9, 4], dtype="float32")
  
          def attr_data_format():
              # for 5-D input, data_format only can be NCDHW or NDHWC
              out = fluid.layers.resize_trilinear(
                  input, out_shape=[4, 8, 4], data_format='NHWC')
  
          self.assertRaises(ValueError, attr_data_format)
  
  
  if __name__ == "__main__":
      unittest.main()
  