wehub-resource-sync
195 lines
6.9 KiB
Python
195 lines
6.9 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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import paddle
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paddle.enable_static()
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from get_test_cover_info import (
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XPUOpTestWrapper,
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create_test_class,
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get_xpu_op_support_types,
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)
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from test_conv2d_op_xpu import XPUTestConv2DOp, XPUTestConv2DOp_v2
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class XPUTestDepthwiseConv2DOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'depthwise_conv2d'
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self.use_dynamic_create_class = False
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class TestDepthwiseConv(XPUTestConv2DOp.TestConv2DOp):
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def init_test_case(self):
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self.use_cuda = False
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self.pad = [1, 1]
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self.stride = [2, 2]
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self.input_size = [2, 12, 5, 5] # NCHW
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self.groups = 12
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [12, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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class TestDepthwiseConv2(XPUTestConv2DOp.TestConv2DOp):
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def init_test_case(self):
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self.use_cuda = False
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self.pad = [1, 1]
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self.stride = [1, 1]
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self.input_size = [2, 12, 5, 5] # NCHW
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self.groups = 12
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [12, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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class TestDepthwiseConv3(XPUTestConv2DOp.TestConv2DOp):
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def init_test_case(self):
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self.use_cuda = False
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self.pad = [1, 1]
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self.stride = [1, 1]
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self.input_size = [2, 24, 5, 5] # NCHW
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self.groups = 24
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [24, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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class TestDepthwiseConvWithDilation(XPUTestConv2DOp.TestConv2DOp):
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def init_test_case(self):
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self.use_cuda = False
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self.pad = [1, 1]
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self.stride = [2, 2]
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self.input_size = [2, 24, 5, 5] # NCHW
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self.groups = 24
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self.dilations = [2, 2]
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [24, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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class TestDepthwiseConvWithDilation2(XPUTestConv2DOp.TestConv2DOp):
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def init_test_case(self):
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self.use_cuda = False
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self.pad = [1, 1]
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self.stride = [1, 1]
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self.input_size = [2, 24, 5, 5] # NCHW
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self.groups = 24
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self.dilations = [2, 2]
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [24, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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class XPUTestDepthwiseConv2DOp_v2(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'depthwise_conv2d'
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self.use_dynamic_create_class = False
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class TestDepthwiseConv_AsyPadding(XPUTestConv2DOp_v2.TestConv2DOp_v2):
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def init_test_case(self):
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self.use_cuda = False
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self.stride = [2, 2]
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self.input_size = [2, 12, 5, 5] # NCHW
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self.groups = 12
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [12, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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def init_paddings(self):
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self.pad = [1, 1, 0, 1]
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self.padding_algorithm = "EXPLICIT"
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class TestDepthwiseConv2_AsyPadding(XPUTestConv2DOp_v2.TestConv2DOp_v2):
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def init_test_case(self):
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self.use_cuda = False
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self.stride = [1, 1]
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self.input_size = [2, 12, 5, 5] # NCHW
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self.groups = 12
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [12, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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def init_paddings(self):
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self.pad = [0, 1, 0, 2]
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self.padding_algorithm = "EXPLICIT"
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class TestDepthwiseConv3_AsyPadding(XPUTestConv2DOp_v2.TestConv2DOp_v2):
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def init_test_case(self):
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self.use_cuda = False
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self.stride = [1, 1]
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self.input_size = [2, 24, 5, 5] # NCHW
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self.groups = 24
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [24, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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def init_paddings(self):
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self.pad = [1, 1, 0, 0]
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self.padding_algorithm = "EXPLICIT"
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class TestDepthwiseConvWithDilation_AsyPadding(
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XPUTestConv2DOp_v2.TestConv2DOp_v2
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):
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def init_test_case(self):
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self.use_cuda = False
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self.pad = [1, 1]
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self.stride = [2, 2]
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self.input_size = [2, 24, 5, 5] # NCHW
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self.groups = 24
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self.dilations = [2, 2]
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [24, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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def init_paddings(self):
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self.pad = [1, 1, 2, 1]
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self.padding_algorithm = "EXPLICIT"
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class TestDepthwiseConvWithDilation2_AsyPadding(
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XPUTestConv2DOp_v2.TestConv2DOp_v2
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):
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def init_test_case(self):
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self.use_cuda = True
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self.pad = [1, 1]
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self.stride = [1, 1]
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self.input_size = [2, 24, 5, 5] # NCHW
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self.groups = 24
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self.dilations = [2, 2]
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assert np.mod(self.input_size[1], self.groups) == 0
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f_c = self.input_size[1] // self.groups
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self.filter_size = [24, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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def init_paddings(self):
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self.pad = [0, 1, 1, 0]
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self.padding_algorithm = "EXPLICIT"
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support_types = get_xpu_op_support_types('depthwise_conv2d')
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for stype in support_types:
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create_test_class(globals(), XPUTestDepthwiseConv2DOp, stype)
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create_test_class(globals(), XPUTestDepthwiseConv2DOp_v2, stype)
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if __name__ == '__main__':
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unittest.main()
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