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507 lines
16 KiB
507 lines
16 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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from __future__ import print_function
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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, in_n, out_h, out_w, out_c
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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.exhaustive_search = False
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self.use_cuda = False
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self.use_mkldnn = False
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self.fuse_relu_before_depthwise_conv = False
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self.data_format = "AnyLayout"
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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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if not self.has_cuda():
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self.fuse_relu_before_depthwise_conv = False
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if self.fuse_relu_before_depthwise_conv:
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input = input - 0.5
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input -= (input < 0) * 0.1
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input += (input >= 0) * 0.1
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input2 = np.maximum(input, 0.0)
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else:
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input2 = input
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filter = np.random.uniform(-1, 1, self.filter_size).astype(self.dtype)
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output, _, _, _, _ = conv2d_forward_naive(input2, filter, self.groups,
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conv2d_param)
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output = output.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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'data_format': self.data_format,
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'fuse_relu_before_depthwise_conv':
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self.fuse_relu_before_depthwise_conv,
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'exhaustive_search': self.exhaustive_search
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}
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self.outputs = {'Output': output}
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def has_cuda(self):
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return core.is_compiled_with_cuda() and (self.use_cudnn or
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self.use_cuda)
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def test_check_output(self):
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place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace()
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self.check_output_with_place(place, atol=1e-5)
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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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place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace()
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self.check_grad_with_place(
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place, {'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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place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace()
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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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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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place = core.CUDAPlace(0) if self.has_cuda() else core.CPUPlace()
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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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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 TestWithDepthWise3x3(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 = [3, 4, 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 = [8, 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 = 4
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class TestWithDepthWise5x5(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, 4, 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 = [8, f_c, 5, 5]
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def init_group(self):
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self.groups = 4
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class TestWithDepthWise7x7(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, 8, 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 = [16, f_c, 7, 7]
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def init_group(self):
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self.groups = 8
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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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def create_test_cudnn_class(parent):
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestCUDNNCase(parent):
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def init_kernel_type(self):
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self.use_cudnn = True
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cls_name = "{0}_{1}".format(parent.__name__, "CUDNN")
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TestCUDNNCase.__name__ = cls_name
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globals()[cls_name] = TestCUDNNCase
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create_test_cudnn_class(TestConv2dOp)
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create_test_cudnn_class(TestWithPad)
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create_test_cudnn_class(TestWithStride)
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create_test_cudnn_class(TestWithGroup)
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create_test_cudnn_class(TestWith1x1)
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create_test_cudnn_class(TestWithInput1x1Filter1x1)
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#----------------Conv2dCUDNN----------------
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def create_test_cudnn_fp16_class(parent, grad_check=True):
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@unittest.skipIf(not core.is_compiled_with_cuda(),
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"core is not compiled with CUDA")
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class TestConv2DCUDNNFp16(parent):
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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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def test_check_grad_no_filter(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place) and grad_check:
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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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def test_check_grad_no_input(self):
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place = core.CUDAPlace(0)
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if core.is_float16_supported(place) and grad_check:
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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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cls_name = "{0}_{1}".format(parent.__name__, "CUDNNFp16")
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TestConv2DCUDNNFp16.__name__ = cls_name
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globals()[cls_name] = TestConv2DCUDNNFp16
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create_test_cudnn_fp16_class(TestConv2dOp, grad_check=False)
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create_test_cudnn_fp16_class(TestWithPad, grad_check=False)
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create_test_cudnn_fp16_class(TestWithStride, grad_check=False)
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create_test_cudnn_fp16_class(TestWithGroup, grad_check=False)
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create_test_cudnn_fp16_class(TestWith1x1, grad_check=False)
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create_test_cudnn_fp16_class(TestWithInput1x1Filter1x1, grad_check=False)
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# -------TestDepthwiseConv
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class TestDepthwiseConv(TestConv2dOp):
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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 = [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 = [3, 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.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, 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 = [3, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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class TestDepthwiseConv3(TestConv2dOp):
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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, 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 TestDepthwiseConvWithDilation(TestConv2dOp):
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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 = [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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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 = [6, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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class TestDepthwiseConvWithDilation2(TestConv2dOp):
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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, 3, 5, 5] # NCHW
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self.groups = 3
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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 = [6, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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class TestDepthwiseConvandFuse(TestConv2dOp):
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def init_test_case(self):
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self.fuse_relu_before_depthwise_conv = True
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self.use_cuda = True
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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 = [3, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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class TestDepthwiseConv2andFuse(TestConv2dOp):
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def init_test_case(self):
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self.fuse_relu_before_depthwise_conv = True
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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, 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 = [3, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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class TestDepthwiseConv3andFuse(TestConv2dOp):
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def init_test_case(self):
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self.fuse_relu_before_depthwise_conv = True
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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, 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 TestDepthwiseConvWithDilationandFuse(TestConv2dOp):
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def init_test_case(self):
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self.fuse_relu_before_depthwise_conv = True
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self.use_cuda = True
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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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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 = [6, f_c, 3, 3]
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self.op_type = "depthwise_conv2d"
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|
|
|
|
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class TestDepthwiseConvWithDilation2andFuse(TestConv2dOp):
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|
def init_test_case(self):
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|
self.fuse_relu_before_depthwise_conv = True
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|
self.use_cuda = True
|
|
self.pad = [1, 1]
|
|
self.stride = [1, 1]
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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 = [6, f_c, 3, 3]
|
|
self.op_type = "depthwise_conv2d"
|
|
|
|
|
|
class TestCUDNNExhaustiveSearch(TestConv2dOp):
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|
def init_kernel_type(self):
|
|
self.use_cudnn = True
|
|
self.exhaustive_search = True
|
|
|
|
|
|
# Please Don't remove the following code.
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|
# Currently, CI use cudnn V5.0 which not support dilation conv.
|
|
# class TestCUDNNWithDilation(TestWithDilation):
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|
# def init_op_type(self):
|
|
# self.op_type = "conv_cudnn"
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|