text: #   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.fluid.core as core
  import paddle.fluid as fluid
  from op_test import OpTest
  from paddle.fluid import Program, program_guard
  
  
  def conv2d_forward_naive(input,
                           filter,
                           group,
                           conv_param,
                           padding_algorithm='EXPLICIT',
                           data_format='NCHW'):
      if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]:
          raise ValueError("Unknown Attr(padding_algorithm): '%s'. "
                           "It can only be 'SAME' or 'VALID'." %
                           str(padding_algorithm))
  
      if data_format not in ["NCHW", "NHWC"]:
          raise ValueError("Unknown Attr(data_format): '%s' ."
                           "It can only be 'NCHW' or 'NHWC'." % str(data_format))
  
      channel_last = (data_format == "NHWC")
      if channel_last:
          input = np.transpose(input, [0, 3, 1, 2])
  
      in_n, in_c, in_h, in_w = input.shape
      f_n, f_c, f_h, f_w = filter.shape
      out_n = in_n
      out_c = f_n
      assert f_c * group == in_c
      assert np.mod(out_c, group) == 0
      sub_out_c = out_c // group
      sub_f_n = f_n // group
  
      stride, pad, dilation = conv_param['stride'], conv_param['pad'], conv_param[
          'dilation']
  
      # update pad and dilation
      def _get_padding_with_SAME(input_shape, pool_size, pool_stride):
          padding = []
          for input_size, filter_size, stride_size in zip(input_shape, pool_size,
                                                          pool_stride):
              out_size = int((input_size + stride_size - 1) / stride_size)
              pad_sum = np.max((
                  (out_size - 1) * stride_size + filter_size - input_size, 0))
              pad_0 = int(pad_sum / 2)
              pad_1 = int(pad_sum - pad_0)
              padding.append(pad_0)
              padding.append(pad_1)
          return padding
  
      ksize = filter.shape[2:4]
      if padding_algorithm == "VALID":
          pad = [0, 0, 0, 0]
      elif padding_algorithm == "SAME":
          dilation = [1, 1]
          input_data_shape = input.shape[2:4]
          pad = _get_padding_with_SAME(input_data_shape, ksize, stride)
  
      pad_h_0, pad_h_1 = pad[0], pad[0]
      pad_w_0, pad_w_1 = pad[1], pad[1]
      if len(pad) == 4:
          pad_h_0, pad_h_1 = pad[0], pad[1]
          pad_w_0, pad_w_1 = pad[2], pad[3]
      out_h = 1 + (in_h + pad_h_0 + pad_h_1 - (dilation[0] *
                                               (f_h - 1) + 1)) // stride[0]
      out_w = 1 + (in_w + pad_w_0 + pad_w_1 - (dilation[1] *
                                               (f_w - 1) + 1)) // stride[1]
      out = np.zeros((out_n, out_c, out_h, out_w))
  
      d_bolck_h = (dilation[0] * (f_h - 1) + 1)
      d_bolck_w = (dilation[1] * (f_w - 1) + 1)
  
      input_pad = np.pad(input, ((0, 0), (0, 0), (pad_h_0, pad_h_1),
                                 (pad_w_0, pad_w_1)),
                         mode='constant',
                         constant_values=0)
  
      filter_dilation = np.zeros((f_n, f_c, d_bolck_h, d_bolck_w))
      filter_dilation[:, :, 0:d_bolck_h:dilation[0], 0:d_bolck_w:dilation[
          1]] = filter
  
      for i in range(out_h):
          for j in range(out_w):
              for g in range(group):
                  input_pad_masked = \
                      input_pad[:, g * f_c:(g + 1) * f_c,
                      i * stride[0]:i * stride[0] + d_bolck_h,
                      j * stride[1]:j * stride[1] + d_bolck_w]
  
                  f_sub = filter_dilation[g * sub_f_n:(g + 1) * sub_f_n, :, :, :]
                  # sub_f_n == sub_out_c
                  for k in range(sub_out_c):
                      # Multiplication of Corresponding Elements, then sum all
                      out[:, g * sub_out_c + k, i, j] = \
                          np.sum(input_pad_masked * f_sub[k, :, :, :],
                                 axis=(1, 2, 3))
  
      if channel_last:
          out = np.transpose(out, [0, 2, 3, 1])
  
      return out, in_n, out_h, out_w, out_c
  
  
  def create_test_cudnn_class(parent):
      @unittest.skipIf(not core.is_compiled_with_cuda(),
                       "core is not compiled with CUDA")
      class TestCUDNNCase(parent):
          def init_kernel_type(self):
              self.use_cudnn = True
  
      cls_name = "{0}_{1}".format(parent.__name__, "CUDNN")
      TestCUDNNCase.__name__ = cls_name
      globals()[cls_name] = TestCUDNNCase
  
  
  def create_test_cudnn_fp16_class(parent, grad_check=True):
      @unittest.skipIf(not core.is_compiled_with_cuda(),
                       "core is not compiled with CUDA")
      class TestConv2DCUDNNFp16(parent):
          def init_kernel_type(self):
              self.use_cudnn = True
              self.dtype = np.float16
  
          def test_check_output(self):
              if core.is_compiled_with_cuda():
                  place = core.CUDAPlace(0)
                  if core.is_float16_supported(place):
                      self.check_output_with_place(place, atol=2e-2)
  
          def test_check_grad_no_filter(self):
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place) and grad_check:
                  self.check_grad_with_place(
                      place, ['Input'], 'Output', no_grad_set=set(['Filter']))
  
          def test_check_grad_no_input(self):
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place) and grad_check:
                  self.check_grad_with_place(
                      place, ['Filter'], 'Output', no_grad_set=set(['Input']))
  
      cls_name = "{0}_{1}".format(parent.__name__, "CUDNNFp16")
      TestConv2DCUDNNFp16.__name__ = cls_name
      globals()[cls_name] = TestConv2DCUDNNFp16
  
  
  def create_test_channel_last_class(parent):
      class TestChannelLastCase(parent):
          def init_data_format(self):
              self.data_format = "NHWC"
  
          def init_test_case_2(self):
              N, C, H, W = self.input_size
              self.input_size = [N, H, W, C]
  
      cls_name = "{0}_{1}".format(parent.__name__, "ChannelLast")
      TestChannelLastCase.__name__ = cls_name
      globals()[cls_name] = TestChannelLastCase
  
  
  def create_test_cudnn_channel_last_class(parent):
      @unittest.skipIf(not core.is_compiled_with_cuda(),
                       "core is not compiled with CUDA")
      class TestCudnnChannelLastCase(parent):
          def init_kernel_type(self):
              self.use_cudnn = True
  
          def init_data_format(self):
              self.data_format = "NHWC"
  
          def init_test_case_2(self):
              N, C, H, W = self.input_size
              self.input_size = [N, H, W, C]
  
      cls_name = "{0}_{1}".format(parent.__name__, "CudnnChannelLast")
      TestCudnnChannelLastCase.__name__ = cls_name
      globals()[cls_name] = TestCudnnChannelLastCase
  
  
  def create_test_cudnn_channel_last_fp16_class(parent, grad_check=True):
      @unittest.skipIf(not core.is_compiled_with_cuda(),
                       "core is not compiled with CUDA")
      class TestCudnnChannelLastFp16(parent):
          def init_kernel_type(self):
              self.use_cudnn = True
              self.dtype = np.float16
  
          def test_check_output(self):
              if core.is_compiled_with_cuda():
                  place = core.CUDAPlace(0)
                  if core.is_float16_supported(place):
                      self.check_output_with_place(place, atol=2e-2)
  
          def test_check_grad_no_filter(self):
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place) and grad_check:
                  self.check_grad_with_place(
                      place, ['Input'], 'Output', no_grad_set=set(['Filter']))
  
          def test_check_grad_no_input(self):
              place = core.CUDAPlace(0)
              if core.is_float16_supported(place) and grad_check:
                  self.check_grad_with_place(
                      place, ['Filter'], 'Output', no_grad_set=set(['Input']))
  
          def init_data_format(self):
              self.data_format = "NHWC"
  
          def init_test_case_2(self):
              N, C, H, W = self.input_size
              self.input_size = [N, H, W, C]
  
      cls_name = "{0}_{1}".format(parent.__name__, "CudnnChannelLastFp16")
      TestCudnnChannelLastFp16.__name__ = cls_name
      globals()[cls_name] = TestCudnnChannelLastFp16
  
  
  def create_test_padding_SAME_class(parent):
      class TestPaddingSMAECase(parent):
          def init_paddings(self):
              self.pad = [0, 0]
              self.padding_algorithm = "SAME"
  
      cls_name = "{0}_{1}".format(parent.__name__, "PaddingSAMEOp")
      TestPaddingSMAECase.__name__ = cls_name
      globals()[cls_name] = TestPaddingSMAECase
  
  
  def create_test_padding_VALID_class(parent):
      class TestPaddingVALIDCase(parent):
          def init_paddings(self):
              self.pad = [1, 1]
              self.padding_algorithm = "VALID"
  
      cls_name = "{0}_{1}".format(parent.__name__, "PaddingVALIDOp")
      TestPaddingVALIDCase.__name__ = cls_name
      globals()[cls_name] = TestPaddingVALIDCase
  
  
  def create_test_cudnn_padding_SAME_class(parent):
      @unittest.skipIf(not core.is_compiled_with_cuda(),
                       "core is not compiled with CUDA")
      class TestCUDNNPaddingSMAECase(parent):
          def init_kernel_type(self):
              self.use_cudnn = True
  
          def init_paddings(self):
              self.pad = [1, 1]
              self.padding_algorithm = "SAME"
  
      cls_name = "{0}_{1}".format(parent.__name__, "CudnnPaddingSAMEOp")
      TestCUDNNPaddingSMAECase.__name__ = cls_name
      globals()[cls_name] = TestCUDNNPaddingSMAECase
  
  
  def create_test_cudnn_padding_VALID_class(parent):
      @unittest.skipIf(not core.is_compiled_with_cuda(),
                       "core is not compiled with CUDA")
      class TestCUDNNPaddingVALIDCase(parent):
          def init_kernel_type(self):
              self.use_cudnn = True
  
          def init_paddings(self):
              self.pad = [1, 1]
              self.padding_algorithm = "VALID"
  
      cls_name = "{0}_{1}".format(parent.__name__, "CudnnPaddingVALIDOp")
      TestCUDNNPaddingVALIDCase.__name__ = cls_name
      globals()[cls_name] = TestCUDNNPaddingVALIDCase
  
  
  class TestConv2DOp(OpTest):
      def setUp(self):
          self.op_type = "conv2d"
          self.use_cudnn = False
          self.exhaustive_search = False
          self.use_cuda = False
          self.use_mkldnn = False
          self.fuse_relu_before_depthwise_conv = False
          self.data_format = "AnyLayout"
          self.dtype = np.float64
          self.init_kernel_type()
          self.init_group()
          self.init_dilation()
          self.init_test_case()
  
          conv2d_param = {
              'stride': self.stride,
              'pad': self.pad,
              'dilation': self.dilations
          }
  
          input = np.random.random(self.input_size).astype(self.dtype)
          if not self.has_cuda():
              self.fuse_relu_before_depthwise_conv = False
          if self.fuse_relu_before_depthwise_conv:
              input = input - 0.5
              input -= (input < 0) * 0.1
              input += (input >= 0) * 0.1
              input2 = np.maximum(input, 0.0)
          else:
              input2 = input
          filter = np.random.uniform(-1, 1, self.filter_size).astype(self.dtype)
  
          output, _, _, _, _ = conv2d_forward_naive(input2, filter, self.groups,
                                                    conv2d_param)
          output = output.astype(self.dtype)
  
          self.inputs = {
              'Input': OpTest.np_dtype_to_fluid_dtype(input),
              'Filter': OpTest.np_dtype_to_fluid_dtype(filter)
          }
          self.attrs = {
              'strides': self.stride,
              'paddings': self.pad,
              'groups': self.groups,
              'dilations': self.dilations,
              'use_cudnn': self.use_cudnn,
              'use_mkldnn': self.use_mkldnn,
              'data_format': self.data_format,
              'fuse_relu_before_depthwise_conv':
              self.fuse_relu_before_depthwise_conv,
              'exhaustive_search': self.exhaustive_search
          }
          self.outputs = {'Output': output}
  
      def has_cuda(self):
          return core.is_compiled_with_cuda() and (self.use_cudnn or
                                                   self.use_cuda)
  
      def test_check_output(self):
          place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace()
          # TODO(wangzhongpu): support mkldnn op in dygraph mode
          self.check_output_with_place(
              place, atol=1e-5, check_dygraph=(self.use_mkldnn == False))
  
      def test_check_grad(self):
          if self.dtype == np.float16 or (hasattr(self, "no_need_check_grad") and
                                          self.no_need_check_grad == True):
              return
          place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace()
          # TODO(wangzhongpu): support mkldnn op in dygraph mode
          self.check_grad_with_place(
              place, {'Input', 'Filter'},
              'Output',
              max_relative_error=0.02,
              check_dygraph=(self.use_mkldnn == False))
  
      def test_check_grad_no_filter(self):
          if self.dtype == np.float16 or (hasattr(self, "no_need_check_grad") and
                                          self.no_need_check_grad == True):
              return
          place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace()
          # TODO(wangzhongpu): support mkldnn op in dygraph mode
          self.check_grad_with_place(
              place, ['Input'],
              'Output',
              max_relative_error=0.02,
              no_grad_set=set(['Filter']),
              check_dygraph=(self.use_mkldnn == False))
  
      def test_check_grad_no_input(self):
          if self.dtype == np.float16 or (hasattr(self, "no_need_check_grad") and
                                          self.no_need_check_grad == True):
              return
          place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace()
          # TODO(wangzhongpu): support mkldnn op in dygraph mode
          self.check_grad_with_place(
              place, ['Filter'],
              'Output',
              no_grad_set=set(['Input']),
              check_dygraph=(self.use_mkldnn == False))
  
      def init_test_case(self):
          self.pad = [0, 0]
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [6, f_c, 3, 3]
  
      def init_test_case_2(self):
          pass
  
      def init_dilation(self):
          self.dilations = [1, 1]
  
      def init_group(self):
          self.groups = 1
  
      def init_kernel_type(self):
          pass
  
  
  class TestWithPad(TestConv2DOp):
      def init_test_case(self):
          self.pad = [1, 1]
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [6, f_c, 3, 3]
  
  
  class TestWithStride(TestConv2DOp):
      def init_test_case(self):
          self.pad = [1, 1]
          self.stride = [2, 2]
          self.input_size = [2, 3, 6, 6]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [6, f_c, 3, 3]
  
  
  class TestWithGroup(TestConv2DOp):
      def init_test_case(self):
          self.pad = [0, 0]
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.group = 3
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [18, f_c, 3, 3]
  
  
  class TestWith1x1(TestConv2DOp):
      def init_test_case(self):
          self.pad = [0, 0]
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [120, f_c, 1, 1]
  
      def init_group(self):
          self.groups = 3
  
  
  class TestWithDepthWise3x3(TestConv2DOp):
      def init_test_case(self):
          self.pad = [1, 1]
          self.stride = [1, 1]
          self.input_size = [3, 4, 10, 10]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [12, f_c, 3, 3]
  
      def init_dilation(self):
          self.dilations = [2, 2]
  
      def init_group(self):
          self.groups = 4
  
  
  class TestWithDepthWise5x5(TestConv2DOp):
      def init_test_case(self):
          self.pad = [0, 0]
          self.stride = [1, 1]
          self.input_size = [2, 4, 10, 10]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [8, f_c, 5, 5]
  
      def init_group(self):
          self.groups = 4
  
  
  class TestWithDepthWise7x7(TestConv2DOp):
      def init_test_case(self):
          self.pad = [1, 1]
          self.stride = [2, 2]
          self.input_size = [2, 8, 10, 10]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [16, f_c, 7, 7]
  
      def init_group(self):
          self.groups = 8
  
  
  class TestWithDilation(TestConv2DOp):
      def init_test_case(self):
          self.pad = [0, 0]
          self.stride = [1, 1]
          self.input_size = [2, 3, 10, 10]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [12, f_c, 3, 3]
  
      def init_dilation(self):
          self.dilations = [2, 2]
  
      def init_group(self):
          self.groups = 3
  
  
  class TestWithInput1x1Filter1x1(TestConv2DOp):
      def init_test_case(self):
          self.pad = [0, 0]
          self.stride = [1, 1]
          self.input_size = [100, 3, 1, 1]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [120, f_c, 1, 1]
  
      def init_group(self):
          self.groups = 3
  
  
  #----------------Conv2DCUDNN----------------
  
  create_test_cudnn_class(TestConv2DOp)
  create_test_cudnn_class(TestWithPad)
  create_test_cudnn_class(TestWithStride)
  create_test_cudnn_class(TestWithGroup)
  create_test_cudnn_class(TestWith1x1)
  create_test_cudnn_class(TestWithInput1x1Filter1x1)
  
  #----------------Conv2DCUDNN fp16----------------
  
  create_test_cudnn_fp16_class(TestConv2DOp, grad_check=False)
  create_test_cudnn_fp16_class(TestWithPad, grad_check=False)
  create_test_cudnn_fp16_class(TestWithStride, grad_check=False)
  create_test_cudnn_fp16_class(TestWithGroup, grad_check=False)
  create_test_cudnn_fp16_class(TestWith1x1, grad_check=False)
  create_test_cudnn_fp16_class(TestWithInput1x1Filter1x1, grad_check=False)
  
  #----------------TestDepthwiseConv -----
  
  
  class TestDepthwiseConv(TestConv2DOp):
      def init_test_case(self):
          self.use_cuda = True
          self.pad = [1, 1]
          self.stride = [2, 2]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [12, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
  
  class TestDepthwiseConv2(TestConv2DOp):
      def init_test_case(self):
          self.use_cuda = True
          self.pad = [1, 1]
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [12, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
  
  class TestDepthwiseConv3(TestConv2DOp):
      def init_test_case(self):
          self.use_cuda = True
          self.pad = [1, 1]
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [24, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
  
  class TestDepthwiseConvWithDilation(TestConv2DOp):
      def init_test_case(self):
          self.use_cuda = True
          self.pad = [1, 1]
          self.stride = [2, 2]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          self.dilations = [2, 2]
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [24, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
  
  class TestDepthwiseConvWithDilation2(TestConv2DOp):
      def init_test_case(self):
          self.use_cuda = True
          self.pad = [1, 1]
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          self.dilations = [2, 2]
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [24, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
  
  class TestDepthwiseConvandFuse(TestConv2DOp):
      def init_test_case(self):
          self.fuse_relu_before_depthwise_conv = True
          self.use_cuda = True
          self.pad = [1, 1]
          self.stride = [2, 2]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [12, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
  
  class TestDepthwiseConv2andFuse(TestConv2DOp):
      def init_test_case(self):
          self.fuse_relu_before_depthwise_conv = True
          self.use_cuda = True
          self.pad = [1, 1]
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [12, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
  
  class TestDepthwiseConv3andFuse(TestConv2DOp):
      def init_test_case(self):
          self.fuse_relu_before_depthwise_conv = True
          self.use_cuda = True
          self.pad = [1, 1]
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [24, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
  
  class TestDepthwiseConvWithDilationandFuse(TestConv2DOp):
      def init_test_case(self):
          self.fuse_relu_before_depthwise_conv = True
          self.use_cuda = True
          self.pad = [1, 1]
          self.stride = [2, 2]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          self.dilations = [2, 2]
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [24, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
  
  class TestDepthwiseConvWithDilation2andFuse(TestConv2DOp):
      def init_test_case(self):
          self.fuse_relu_before_depthwise_conv = True
          self.use_cuda = True
          self.pad = [1, 1]
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          self.dilations = [2, 2]
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [24, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
  
  class TestCUDNNExhaustiveSearch(TestConv2DOp):
      def init_kernel_type(self):
          self.use_cudnn = True
          self.exhaustive_search = True
  
  
  class TestConv2DOpError(unittest.TestCase):
      def test_errors(self):
          with program_guard(Program(), Program()):
  
              def test_Variable():
                  # the input of conv2d must be Variable.
                  x1 = fluid.create_lod_tensor(
                      np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace())
                  fluid.layers.conv2d(x1, 1, 1)
  
              self.assertRaises(TypeError, test_Variable)
  
              def test_dtype():
                  # the input dtype of conv2d must be float16 or float32 or float64
                  # float16 only can be set on GPU place
                  x2 = fluid.layers.data(
                      name='x2', shape=[3, 4, 5, 6], dtype="int32")
                  fluid.layers.conv2d(x2, 1, 1)
  
              self.assertRaises(TypeError, test_dtype)
  
  
  # Please Don't remove the following code.
  # Currently, CI use cudnn V5.0 which not support dilation conv.
  # class TestCUDNNWithDilation(TestWithDilation):
  #     def init_op_type(self):
  #         self.op_type = "conv_cudnn"
  
  # ---- test asymmetric padding ----
  
  
  class TestConv2DOp_v2(OpTest):
      def setUp(self):
          self.op_type = "conv2d"
          self.use_cudnn = False
          self.exhaustive_search = False
          self.use_cuda = False
          self.use_mkldnn = False
          self.fuse_relu_before_depthwise_conv = False
          self.dtype = np.float64
          self.init_kernel_type()
          self.init_group()
          self.init_dilation()
          self.init_data_format()
          self.init_test_case()
          self.init_paddings()
          self.init_test_case_2()
  
          conv2d_param = {
              'stride': self.stride,
              'pad': self.pad,
              'dilation': self.dilations
          }
  
          input = np.random.random(self.input_size).astype(self.dtype)
          if not self.has_cuda():
              self.fuse_relu_before_depthwise_conv = False
          if self.fuse_relu_before_depthwise_conv:
              input = input - 0.5
              input -= (input < 0) * 0.1
              input += (input >= 0) * 0.1
              input2 = np.maximum(input, 0.0)
          else:
              input2 = input
          filter = np.random.uniform(-1, 1, self.filter_size).astype(self.dtype)
          output, _, _, _, _ = conv2d_forward_naive(
              input2, filter, self.groups, conv2d_param, self.padding_algorithm,
              self.data_format)
          output = output.astype(self.dtype)
  
          self.inputs = {
              'Input': OpTest.np_dtype_to_fluid_dtype(input),
              'Filter': OpTest.np_dtype_to_fluid_dtype(filter)
          }
          self.attrs = {
              'strides': self.stride,
              'paddings': self.pad,
              'padding_algorithm': self.padding_algorithm,
              'groups': self.groups,
              'dilations': self.dilations,
              'use_cudnn': self.use_cudnn,
              'use_mkldnn': self.use_mkldnn,
              'data_format': self.data_format,
              'fuse_relu_before_depthwise_conv':
              self.fuse_relu_before_depthwise_conv,
              'exhaustive_search': self.exhaustive_search
          }
          self.outputs = {'Output': output}
  
      def has_cuda(self):
          return core.is_compiled_with_cuda() and (self.use_cudnn or
                                                   self.use_cuda)
  
      def test_check_output(self):
          # TODO(wangzhongpu): support mkldnn op in dygraph mode
          place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace()
          self.check_output_with_place(
              place, atol=1e-5, check_dygraph=(self.use_mkldnn == False))
  
      def test_check_grad(self):
          # TODO(wangzhongpu): support mkldnn op in dygraph mode
          if self.dtype == np.float16:
              return
          place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace()
          self.check_grad_with_place(
              place, {'Input', 'Filter'},
              'Output',
              max_relative_error=0.02,
              check_dygraph=(self.use_mkldnn == False))
  
      def test_check_grad_no_filter(self):
          # TODO(wangzhongpu): support mkldnn op in dygraph mode
          if self.dtype == np.float16:
              return
          place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace()
          self.check_grad_with_place(
              place, ['Input'],
              'Output',
              max_relative_error=0.02,
              no_grad_set=set(['Filter']),
              check_dygraph=(self.use_mkldnn == False))
  
      def test_check_grad_no_input(self):
          # TODO(wangzhongpu): support mkldnn op in dygraph mode
          if self.dtype == np.float16:
              return
          place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace()
          self.check_grad_with_place(
              place, ['Filter'],
              'Output',
              no_grad_set=set(['Input']),
              check_dygraph=(self.use_mkldnn == False))
  
      def init_test_case(self):
          self.pad = [0, 0]
          self.stride = [1, 2]
          self.input_size = [2, 3, 5, 5]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [6, f_c, 4, 3]
  
      def init_dilation(self):
          self.dilations = [1, 1]
  
      def init_group(self):
          self.groups = 1
  
      def init_kernel_type(self):
          pass
  
      def init_paddings(self):
          self.pad = [0, 0]
          self.padding_algorithm = "EXPLICIT"
  
      def init_data_format(self):
          self.data_format = "NCHW"
  
      def init_test_case_2(self):
          pass
  
  
  class TestConv2DOp_AsyPadding(TestConv2DOp_v2):
      def init_paddings(self):
          self.pad = [0, 0, 1, 2]
          self.padding_algorithm = "EXPLICIT"
  
  
  class TestWithPad_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [6, f_c, 3, 3]
  
      def init_paddings(self):
          self.pad = [2, 1, 3, 2]
          self.padding_algorithm = "EXPLICIT"
  
  
  class TestWithStride_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.stride = [2, 2]
          self.input_size = [2, 3, 6, 6]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [6, f_c, 3, 3]
  
      def init_paddings(self):
          self.pad = [2, 1, 3, 2]
          self.padding_algorithm = "EXPLICIT"
  
  
  class TestWithGroup_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.pad = [0, 0]
          self.stride = [1, 2]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.group = 3
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [24, f_c, 4, 3]
  
  
  class TestWith1x1_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [120, f_c, 1, 1]
  
      def init_group(self):
          self.groups = 3
  
      def init_paddings(self):
          self.pad = [2, 2, 4, 0]
          self.padding_algorithm = "EXPLICIT"
  
  
  class TestWithDepthWise3x3_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.stride = [1, 1]
          self.input_size = [3, 4, 10, 10]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [16, f_c, 3, 3]
  
      def init_dilation(self):
          self.dilations = [2, 2]
  
      def init_group(self):
          self.groups = 4
  
      def init_paddings(self):
          self.pad = [1, 3, 2, 1]
          self.padding_algorithm = "EXPLICIT"
  
  
  class TestWithDepthWise5x5_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.stride = [1, 1]
          self.input_size = [2, 4, 10, 10]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [8, f_c, 5, 5]
  
      def init_group(self):
          self.groups = 4
  
      def init_paddings(self):
          self.pad = [0, 1, 1, 0]
          self.padding_algorithm = "EXPLICIT"
  
  
  class TestWithDepthWise7x7_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.stride = [2, 2]
          self.input_size = [2, 8, 10, 10]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [16, f_c, 7, 7]
  
      def init_group(self):
          self.groups = 8
  
      def init_paddings(self):
          self.pad = [1, 3, 4, 1]
          self.padding_algorithm = "EXPLICIT"
  
  
  class TestWithDilation_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.stride = [1, 1]
          self.input_size = [2, 3, 10, 10]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [24, f_c, 3, 3]
  
      def init_dilation(self):
          self.dilations = [2, 2]
  
      def init_group(self):
          self.groups = 3
  
      def init_paddings(self):
          self.pad = [0, 1, 3, 0]
          self.padding_algorithm = "EXPLICIT"
  
  
  class TestWithInput1x1Filter1x1_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.stride = [1, 1]
          self.input_size = [40, 3, 1, 1]  # NCHW
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [120, f_c, 1, 1]
  
      def init_group(self):
          self.groups = 3
  
      def init_paddings(self):
          self.pad = [0, 3, 4, 0]
          self.padding_algorithm = "EXPLICIT"
  
  
  create_test_cudnn_class(TestConv2DOp_AsyPadding)
  create_test_cudnn_class(TestWithPad_AsyPadding)
  create_test_cudnn_class(TestWithStride_AsyPadding)
  create_test_cudnn_class(TestWithGroup_AsyPadding)
  create_test_cudnn_class(TestWith1x1_AsyPadding)
  create_test_cudnn_class(TestWithInput1x1Filter1x1_AsyPadding)
  
  
  class TestDepthwiseConv_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.use_cuda = True
          self.stride = [2, 2]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [12, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
      def init_paddings(self):
          self.pad = [1, 1, 0, 1]
          self.padding_algorithm = "EXPLICIT"
  
  
  class TestDepthwiseConv2_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.use_cuda = True
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [12, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
      def init_paddings(self):
          self.pad = [0, 1, 0, 2]
          self.padding_algorithm = "EXPLICIT"
  
  
  class TestDepthwiseConv3_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.use_cuda = True
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [24, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
      def init_paddings(self):
          self.pad = [1, 1, 0, 0]
          self.padding_algorithm = "EXPLICIT"
  
  
  class TestDepthwiseConvWithDilation_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.use_cuda = True
          self.pad = [1, 1]
          self.stride = [2, 2]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          self.dilations = [2, 2]
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [24, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
      def init_paddings(self):
          self.pad = [1, 1, 2, 1]
          self.padding_algorithm = "EXPLICIT"
  
  
  class TestDepthwiseConvWithDilation2_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.use_cuda = True
          self.pad = [1, 1]
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          self.dilations = [2, 2]
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [24, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
      def init_paddings(self):
          self.pad = [0, 1, 1, 0]
          self.padding_algorithm = "EXPLICIT"
  
  
  class TestDepthwiseConvandFuse_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.fuse_relu_before_depthwise_conv = True
          self.use_cuda = True
          self.pad = [1, 1]
          self.stride = [2, 2]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [12, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
      def init_paddings(self):
          self.pad = [2, 1, 2, 3]
          self.padding_algorithm = "EXPLICIT"
  
  
  class TestDepthwiseConv2andFuse_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.fuse_relu_before_depthwise_conv = True
          self.use_cuda = True
          self.pad = [1, 1]
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [12, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
      def init_paddings(self):
          self.pad = [1, 1, 1, 2]
          self.padding_algorithm = "EXPLICIT"
  
  
  class TestDepthwiseConv3andFuse_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.fuse_relu_before_depthwise_conv = True
          self.use_cuda = True
          self.pad = [1, 1]
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [24, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
      def init_paddings(self):
          self.pad = [1, 2, 0, 2]
          self.padding_algorithm = "EXPLICIT"
  
  
  class TestDepthwiseConvWithDilationandFuse_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.fuse_relu_before_depthwise_conv = True
          self.use_cuda = True
          self.pad = [1, 1]
          self.stride = [2, 2]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          self.dilations = [2, 2]
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [24, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
      def init_paddings(self):
          self.pad = [2, 1, 1, 0]
          self.padding_algorithm = "EXPLICIT"
  
  
  class TestDepthwiseConvWithDilation2andFuse_AsyPadding(TestConv2DOp_v2):
      def init_test_case(self):
          self.fuse_relu_before_depthwise_conv = True
          self.use_cuda = True
          self.pad = [1, 1]
          self.stride = [1, 1]
          self.input_size = [2, 3, 5, 5]  # NCHW
          self.groups = 3
          self.dilations = [2, 2]
          assert np.mod(self.input_size[1], self.groups) == 0
          f_c = self.input_size[1] // self.groups
          self.filter_size = [24, f_c, 3, 3]
          self.op_type = "depthwise_conv2d"
  
      def init_paddings(self):
          self.pad = [1, 3, 1, 3]
          self.padding_algorithm = "EXPLICIT"
  
  
  #---------- test SAME VALID -----------
  create_test_padding_SAME_class(TestConv2DOp_AsyPadding)
  create_test_padding_SAME_class(TestWithPad_AsyPadding)
  create_test_padding_SAME_class(TestWithStride_AsyPadding)
  create_test_padding_SAME_class(TestWithGroup_AsyPadding)
  create_test_padding_SAME_class(TestWithInput1x1Filter1x1_AsyPadding)
  
  create_test_padding_VALID_class(TestConv2DOp_AsyPadding)
  create_test_padding_VALID_class(TestWithPad_AsyPadding)
  create_test_padding_VALID_class(TestWithStride_AsyPadding)
  create_test_padding_VALID_class(TestWithGroup_AsyPadding)
  create_test_padding_VALID_class(TestWithInput1x1Filter1x1_AsyPadding)
  
  create_test_cudnn_padding_SAME_class(TestConv2DOp_AsyPadding)
  create_test_cudnn_padding_SAME_class(TestWithPad_AsyPadding)
  create_test_cudnn_padding_SAME_class(TestWithStride_AsyPadding)
  create_test_cudnn_padding_SAME_class(TestWithGroup_AsyPadding)
  create_test_cudnn_padding_SAME_class(TestWithInput1x1Filter1x1_AsyPadding)
  
  create_test_cudnn_padding_VALID_class(TestConv2DOp_AsyPadding)
  create_test_cudnn_padding_VALID_class(TestWithPad_AsyPadding)
  create_test_cudnn_padding_VALID_class(TestWithStride_AsyPadding)
  create_test_cudnn_padding_VALID_class(TestWithGroup_AsyPadding)
  create_test_cudnn_padding_VALID_class(TestWithInput1x1Filter1x1_AsyPadding)
  
  # depthwise conv2d
  
  create_test_padding_SAME_class(TestDepthwiseConv_AsyPadding)
  create_test_padding_SAME_class(TestDepthwiseConvWithDilation_AsyPadding)
  create_test_padding_SAME_class(TestDepthwiseConvandFuse_AsyPadding)
  create_test_padding_SAME_class(TestDepthwiseConvWithDilationandFuse_AsyPadding)
  
  create_test_padding_VALID_class(TestDepthwiseConv_AsyPadding)
  create_test_padding_VALID_class(TestDepthwiseConvWithDilation_AsyPadding)
  create_test_padding_VALID_class(TestDepthwiseConvandFuse_AsyPadding)
  create_test_padding_VALID_class(TestDepthwiseConvWithDilationandFuse_AsyPadding)
  
  # ------------ test channel last ---------
  create_test_channel_last_class(TestConv2DOp_AsyPadding)
  create_test_channel_last_class(TestWithPad_AsyPadding)
  create_test_channel_last_class(TestWithGroup_AsyPadding)
  create_test_channel_last_class(TestWith1x1_AsyPadding)
  create_test_channel_last_class(TestWithInput1x1Filter1x1_AsyPadding)
  
  create_test_channel_last_class(TestDepthwiseConv_AsyPadding)
  create_test_channel_last_class(TestDepthwiseConvWithDilation2_AsyPadding)
  create_test_channel_last_class(TestDepthwiseConvandFuse_AsyPadding)
  create_test_channel_last_class(TestDepthwiseConvWithDilationandFuse_AsyPadding)
  
  create_test_cudnn_channel_last_class(TestConv2DOp_AsyPadding)
  create_test_cudnn_channel_last_class(TestWithPad_AsyPadding)
  create_test_cudnn_channel_last_class(TestWithStride_AsyPadding)
  create_test_cudnn_channel_last_class(TestWithGroup_AsyPadding)
  create_test_cudnn_channel_last_class(TestWithDilation_AsyPadding)
  
  create_test_cudnn_channel_last_fp16_class(
      TestConv2DOp_AsyPadding, grad_check=False)
  create_test_cudnn_channel_last_fp16_class(
      TestWithPad_AsyPadding, grad_check=False)
  create_test_cudnn_channel_last_fp16_class(
      TestWithStride_AsyPadding, grad_check=False)
  create_test_cudnn_channel_last_fp16_class(
      TestWithGroup_AsyPadding, grad_check=False)
  create_test_cudnn_channel_last_fp16_class(
      TestWithDilation_AsyPadding, grad_check=False)
  
  
  # --------- test python API ---------------
  class TestConv2DAPI(unittest.TestCase):
      def test_api(self):
  
          input_NHWC = fluid.layers.data(
              name="input_NHWC",
              shape=[2, 5, 5, 3],
              append_batch_size=False,
              dtype="float32")
  
          input_NCHW = fluid.layers.data(
              name="input_NCHW",
              shape=[2, 3, 5, 5],
              append_batch_size=False,
              dtype="float32")
  
          fluid.layers.conv2d(
              input=input_NHWC,
              num_filters=3,
              filter_size=[3, 3],
              stride=[1, 1],
              padding=0,
              dilation=[1, 1],
              groups=1,
              data_format="NCHW")
  
          fluid.layers.conv2d(
              input=input_NCHW,
              num_filters=3,
              filter_size=[3, 3],
              stride=[1, 1],
              padding=[1, 2, 1, 0],
              dilation=[1, 1],
              groups=1,
              data_format="NCHW")
  
          fluid.layers.conv2d(
              input=input_NCHW,
              num_filters=3,
              filter_size=[3, 3],
              stride=[1, 1],
              padding=[[0, 0], [0, 0], [1, 1], [1, 1]],
              dilation=[1, 1],
              groups=1,
              data_format="NCHW")
  
          fluid.layers.conv2d(
              input=input_NHWC,
              num_filters=3,
              filter_size=[3, 3],
              stride=[1, 1],
              padding=[[0, 0], [1, 1], [1, 1], [0, 0]],
              dilation=[1, 1],
              groups=1,
              data_format="NHWC")
  
          fluid.layers.conv2d(
              input=input_NCHW,
              num_filters=3,
              filter_size=[3, 3],
              stride=[1, 1],
              padding="SAME",
              dilation=[1, 1],
              groups=1,
              data_format="NCHW")
  
          fluid.layers.conv2d(
              input=input_NCHW,
              num_filters=3,
              filter_size=[3, 3],
              stride=[1, 1],
              padding="VALID",
              dilation=[1, 1],
              groups=1,
              data_format="NCHW")
  
  
  class TestConv2DAPI_Error(unittest.TestCase):
      def test_api(self):
          input = fluid.layers.data(
              name="input",
              shape=[2, 5, 5, 5],
              append_batch_size=False,
              dtype="float32")
  
          # ValueError: cudnn
          def run_1():
              fluid.layers.conv2d(
                  input=input,
                  num_filters=3,
                  filter_size=[3, 3],
                  stride=[1, 1],
                  padding=0,
                  dilation=[1, 1],
                  groups=1,
                  use_cudnn=[0],
                  data_format="NCHW")
  
          self.assertRaises(ValueError, run_1)
  
          # ValueError: data_format
          def run_2():
              fluid.layers.conv2d(
                  input=input,
                  num_filters=3,
                  filter_size=[3, 3],
                  stride=[1, 1],
                  padding=0,
                  dilation=[1, 1],
                  groups=1,
                  use_cudnn=False,
                  data_format="NCHWC")
  
          self.assertRaises(ValueError, run_2)
  
          # ValueError: padding
          def run_3():
              fluid.layers.conv2d(
                  input=input,
                  num_filters=3,
                  filter_size=[3, 3],
                  stride=[1, 1],
                  padding="SAMEE",
                  dilation=[1, 1],
                  groups=1,
                  use_cudnn=False,
                  data_format="NCHW")
  
          self.assertRaises(ValueError, run_3)
  
          def run_4():
              fluid.layers.conv2d(
                  input=input,
                  num_filters=3,
                  filter_size=[3, 3],
                  stride=[1, 1],
                  padding=[[0, 1], [0, 1], [0, 1], [0, 1]],
                  dilation=[1, 1],
                  groups=1,
                  use_cudnn=False,
                  data_format="NCHW")
  
          self.assertRaises(ValueError, run_4)
  
          def run_5():
              fluid.layers.conv2d(
                  input=input,
                  num_filters=3,
                  filter_size=[3, 3],
                  stride=[1, 1],
                  padding=[[0, 1], [0, 1], [0, 1], [0, 1]],
                  dilation=[1, 1],
                  groups=1,
                  use_cudnn=False,
                  data_format="NHWC")
  
          self.assertRaises(ValueError, run_5)
  
          # ValueError: channel dimmention
          x = fluid.layers.data(
              name="x",
              shape=[2, 5, 5, -1],
              append_batch_size=False,
              dtype="float32")
  
          def run_6():
              fluid.layers.conv2d(
                  input=x,
                  num_filters=3,
                  filter_size=[3, 3],
                  stride=[1, 1],
                  padding=0,
                  dilation=[1, 1],
                  groups=1,
                  use_cudnn=False,
                  data_format="NHWC")
  
          self.assertRaises(ValueError, run_6)
  
          # ValueError: groups
          def run_7():
              fluid.layers.conv2d(
                  input=input,
                  num_filters=3,
                  filter_size=[3, 3],
                  stride=[1, 1],
                  padding=0,
                  dilation=[1, 1],
                  groups=3,
                  use_cudnn=False,
                  data_format="NHWC")
  
          self.assertRaises(ValueError, run_7)
  
  
  if __name__ == '__main__':
      unittest.main()
  