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378 lines
11 KiB
378 lines
11 KiB
# Copyright (c) 2018 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.fluid.core as core
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from op_test import OpTest
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def conv2d_forward_naive(input, filter, group, conv_param):
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in_n, in_c, in_h, in_w = input.shape
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out_c, f_c, f_h, f_w = filter.shape
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assert f_c * group == in_c
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assert np.mod(out_c, group) == 0
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sub_out_c = out_c / group
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stride, pad, dilation = conv_param['stride'], conv_param['pad'], conv_param[
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'dilation']
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out_h = 1 + (in_h + 2 * pad[0] - (dilation[0] * (f_h - 1) + 1)) / stride[0]
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out_w = 1 + (in_w + 2 * pad[1] - (dilation[1] * (f_w - 1) + 1)) / stride[1]
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out = np.zeros((in_n, out_c, out_h, out_w))
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d_bolck_h = (dilation[0] * (f_h - 1) + 1)
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d_bolck_w = (dilation[1] * (f_w - 1) + 1)
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input_pad = np.pad(input, ((0, ), (0, ), (pad[0], ), (pad[1], )),
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mode='constant',
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constant_values=0)
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filter_dilation = np.zeros((out_c, f_c, d_bolck_h, d_bolck_w))
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filter_dilation[:, :, 0:d_bolck_h:dilation[0], 0:d_bolck_w:dilation[
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1]] = filter
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for i in range(out_h):
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for j in range(out_w):
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for g in range(group):
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input_pad_masked = \
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input_pad[:, g * f_c:(g + 1) * f_c,
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i * stride[0]:i * stride[0] + d_bolck_h,
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j * stride[1]:j * stride[1] + d_bolck_w]
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f_sub = filter_dilation[g * sub_out_c:(g + 1) *
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sub_out_c, :, :, :]
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for k in range(sub_out_c):
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out[:, g * sub_out_c + k, i, j] = \
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np.sum(input_pad_masked * f_sub[k, :, :, :],
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axis=(1, 2, 3))
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return out
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class TestConv2dOp(OpTest):
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def setUp(self):
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self.op_type = "conv2d"
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self.use_cudnn = False
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self.use_mkldnn = False
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self.dtype = np.float32
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self.init_kernel_type()
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self.init_group()
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self.init_dilation()
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self.init_test_case()
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conv2d_param = {
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'stride': self.stride,
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'pad': self.pad,
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'dilation': self.dilations
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}
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input = np.random.random(self.input_size).astype(self.dtype)
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filter = np.random.random(self.filter_size).astype(self.dtype)
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output = conv2d_forward_naive(input, filter, self.groups,
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conv2d_param).astype(self.dtype)
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self.inputs = {
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'Input': OpTest.np_dtype_to_fluid_dtype(input),
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'Filter': OpTest.np_dtype_to_fluid_dtype(filter)
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}
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self.attrs = {
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'strides': self.stride,
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'paddings': self.pad,
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'groups': self.groups,
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'dilations': self.dilations,
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'use_cudnn': self.use_cudnn,
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'use_mkldnn': self.use_mkldnn
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}
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self.outputs = {'Output': output}
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def testcudnn(self):
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return core.is_compiled_with_cuda() and self.use_cudnn
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def test_check_output(self):
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if self.testcudnn():
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place = core.CUDAPlace(0)
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self.check_output_with_place(place, atol=1e-5)
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else:
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self.check_output()
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def test_check_grad(self):
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if self.dtype == np.float16:
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return
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if self.testcudnn():
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place = core.CUDAPlace(0)
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self.check_grad_with_place(
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place,
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set(['Input', 'Filter']),
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'Output',
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max_relative_error=0.02)
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else:
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self.check_grad(
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set(['Input', 'Filter']), 'Output', max_relative_error=0.02)
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def test_check_grad_no_filter(self):
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if self.dtype == np.float16:
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return
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if self.testcudnn():
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place = core.CUDAPlace(0)
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self.check_grad_with_place(
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place, ['Input'],
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'Output',
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max_relative_error=0.02,
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no_grad_set=set(['Filter']))
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else:
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self.check_grad(
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['Input'],
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'Output',
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max_relative_error=0.02,
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no_grad_set=set(['Filter']))
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def test_check_grad_no_input(self):
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if self.dtype == np.float16:
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return
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if self.testcudnn():
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place = core.CUDAPlace(0)
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self.check_grad_with_place(
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place, ['Filter'],
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'Output',
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max_relative_error=0.02,
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no_grad_set=set(['Input']))
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else:
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self.check_grad(
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['Filter'],
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'Output',
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max_relative_error=0.02,
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no_grad_set=set(['Input']))
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def init_test_case(self):
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self.pad = [0, 0]
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self.stride = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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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 = [6, f_c, 3, 3]
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def init_dilation(self):
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self.dilations = [1, 1]
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def init_group(self):
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self.groups = 1
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def init_kernel_type(self):
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pass
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class TestWithPad(TestConv2dOp):
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def init_test_case(self):
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self.pad = [1, 1]
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self.stride = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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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 = [6, f_c, 3, 3]
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class TestWithStride(TestConv2dOp):
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def init_test_case(self):
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self.pad = [1, 1]
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self.stride = [2, 2]
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self.input_size = [2, 3, 6, 6] # NCHW
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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 = [6, f_c, 3, 3]
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class TestWithGroup(TestConv2dOp):
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def init_group(self):
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self.groups = 3
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class TestWith1x1(TestConv2dOp):
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def init_test_case(self):
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self.pad = [0, 0]
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self.stride = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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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 = [6, f_c, 1, 1]
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def init_group(self):
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self.groups = 3
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class TestWithDilation(TestConv2dOp):
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def init_test_case(self):
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self.pad = [0, 0]
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self.stride = [1, 1]
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self.input_size = [2, 3, 10, 10] # NCHW
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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 = [6, f_c, 3, 3]
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def init_dilation(self):
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self.dilations = [2, 2]
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def init_group(self):
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self.groups = 3
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class TestWithInput1x1Filter1x1(TestConv2dOp):
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def init_test_case(self):
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self.pad = [0, 0]
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self.stride = [1, 1]
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self.input_size = [2, 3, 1, 1] # NCHW
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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 = [6, f_c, 1, 1]
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def init_group(self):
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self.groups = 3
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#----------------Conv2dCUDNN----------------
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class TestCUDNN(TestConv2dOp):
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def init_kernel_type(self):
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self.use_cudnn = True
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class TestFP16CUDNN(TestConv2dOp):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=2e-2)
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class TestCUDNNWithPad(TestWithPad):
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def init_kernel_type(self):
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self.use_cudnn = True
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class TestFP16CUDNNWithPad(TestWithPad):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=2e-2)
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class TestCUDNNWithStride(TestWithStride):
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def init_kernel_type(self):
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self.use_cudnn = True
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class TestFP16CUDNNWithStride(TestWithStride):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=2e-2)
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class TestCUDNNWithGroup(TestWithGroup):
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def init_kernel_type(self):
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self.use_cudnn = True
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class TestFP16CUDNNWithGroup(TestWithGroup):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=2e-2)
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class TestCUDNNWith1x1(TestWith1x1):
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def init_kernel_type(self):
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self.use_cudnn = True
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class TestFP16CUDNNWith1x1(TestWith1x1):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=2e-2)
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class TestCUDNNWithInput1x1Filter1x1(TestWithInput1x1Filter1x1):
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def init_kernel_type(self):
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self.use_cudnn = True
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class TestFP16CUDNNWithInput1x1Filter1x1(TestWithInput1x1Filter1x1):
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def init_kernel_type(self):
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self.use_cudnn = True
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self.dtype = np.float16
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def test_check_output(self):
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if core.is_compiled_with_cuda():
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place):
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self.check_output_with_place(place, atol=2e-2)
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class TestDepthwiseConv(TestConv2dOp):
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def init_test_case(self):
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self.pad = [1, 1]
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self.stride = [2, 2]
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self.input_size = [2, 3, 5, 5] # NCHW
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self.groups = 3
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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 = [6, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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class TestDepthwiseConv2(TestConv2dOp):
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def init_test_case(self):
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self.pad = [1, 1]
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self.stride = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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self.groups = 3
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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 = [6, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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# Please Don't remove the following code.
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# Currently, CI use cudnn V5.0 which not support dilation conv.
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# class TestCUDNNWithDilation(TestWithDilation):
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# def init_op_type(self):
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# self.op_type = "conv_cudnn"
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if __name__ == '__main__':
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unittest.main()
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