text: #   Copyright (c) 2022 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 sys
  
  sys.path.append("..")
  import unittest
  import numpy as np
  
  import paddle
  
  paddle.enable_static()
  import paddle.fluid.core as core
  import paddle.fluid as fluid
  from op_test_xpu import XPUOpTest
  from paddle.fluid import Program, program_guard
  from test_conv2d_op_xpu import XPUTestConv2DOp, XPUTestConv2DOp_v2
  from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper
  
  
  class XPUTestDepthwiseConv2DOp(XPUOpTestWrapper):
  
      def __init__(self):
          self.op_name = 'depthwise_conv2d'
          self.use_dynamic_create_class = False
  
      class TestDepthwiseConv(XPUTestConv2DOp.TestConv2DOp):
  
          def init_test_case(self):
              self.use_cuda = False
              self.pad = [1, 1]
              self.stride = [2, 2]
              self.input_size = [2, 12, 5, 5]  # NCHW
              self.groups = 12
              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(XPUTestConv2DOp.TestConv2DOp):
  
          def init_test_case(self):
              self.use_cuda = False
              self.pad = [1, 1]
              self.stride = [1, 1]
              self.input_size = [2, 12, 5, 5]  # NCHW
              self.groups = 12
              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(XPUTestConv2DOp.TestConv2DOp):
  
          def init_test_case(self):
              self.use_cuda = False
              self.pad = [1, 1]
              self.stride = [1, 1]
              self.input_size = [2, 24, 5, 5]  # NCHW
              self.groups = 24
              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(XPUTestConv2DOp.TestConv2DOp):
  
          def init_test_case(self):
              self.use_cuda = False
              self.pad = [1, 1]
              self.stride = [2, 2]
              self.input_size = [2, 24, 5, 5]  # NCHW
              self.groups = 24
              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(XPUTestConv2DOp.TestConv2DOp):
  
          def init_test_case(self):
              self.use_cuda = False
              self.pad = [1, 1]
              self.stride = [1, 1]
              self.input_size = [2, 24, 5, 5]  # NCHW
              self.groups = 24
              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 XPUTestDepthwiseConv2DOp_v2(XPUOpTestWrapper):
  
      def __init__(self):
          self.op_name = 'depthwise_conv2d'
          self.use_dynamic_create_class = False
  
      class TestDepthwiseConv_AsyPadding(XPUTestConv2DOp_v2.TestConv2DOp_v2):
  
          def init_test_case(self):
              self.use_cuda = False
              self.stride = [2, 2]
              self.input_size = [2, 12, 5, 5]  # NCHW
              self.groups = 12
              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(XPUTestConv2DOp_v2.TestConv2DOp_v2):
  
          def init_test_case(self):
              self.use_cuda = False
              self.stride = [1, 1]
              self.input_size = [2, 12, 5, 5]  # NCHW
              self.groups = 12
              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(XPUTestConv2DOp_v2.TestConv2DOp_v2):
  
          def init_test_case(self):
              self.use_cuda = False
              self.stride = [1, 1]
              self.input_size = [2, 24, 5, 5]  # NCHW
              self.groups = 24
              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(
              XPUTestConv2DOp_v2.TestConv2DOp_v2):
  
          def init_test_case(self):
              self.use_cuda = False
              self.pad = [1, 1]
              self.stride = [2, 2]
              self.input_size = [2, 24, 5, 5]  # NCHW
              self.groups = 24
              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(
              XPUTestConv2DOp_v2.TestConv2DOp_v2):
  
          def init_test_case(self):
              self.use_cuda = True
              self.pad = [1, 1]
              self.stride = [1, 1]
              self.input_size = [2, 24, 5, 5]  # NCHW
              self.groups = 24
              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"
  
  
  support_types = get_xpu_op_support_types('depthwise_conv2d')
  for stype in support_types:
      create_test_class(globals(), XPUTestDepthwiseConv2DOp, stype)
      create_test_class(globals(), XPUTestDepthwiseConv2DOp_v2, stype)
  
  #### 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)
  
  #### channel last
  
  # 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)
  
  if __name__ == '__main__':
      unittest.main()
  