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267 lines
8.6 KiB
267 lines
8.6 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 conv3dtranspose_forward_naive(input_, filter_, attrs):
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in_n, in_c, in_d, in_h, in_w = input_.shape
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f_c, f_out_c, f_d, f_h, f_w = filter_.shape
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groups = attrs['groups']
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assert in_c == f_c
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out_c = f_out_c * groups
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sub_in_c = in_c / groups
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stride, pad, dilations = attrs['strides'], attrs['paddings'], attrs[
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'dilations']
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d_bolck_d = dilations[0] * (f_d - 1) + 1
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d_bolck_h = dilations[1] * (f_h - 1) + 1
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d_bolck_w = dilations[2] * (f_w - 1) + 1
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out_d = (in_d - 1) * stride[0] + d_bolck_d
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out_h = (in_h - 1) * stride[1] + d_bolck_h
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out_w = (in_w - 1) * stride[2] + d_bolck_w
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out = np.zeros((in_n, out_c, out_d, out_h, out_w))
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for n in range(in_n):
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for d in range(in_d):
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for i in range(in_h):
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for j in range(in_w):
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for g in range(groups):
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input_masked = input_[n, g * sub_in_c:(g + 1
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) * sub_in_c, d,
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i, j] # (c)
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input_masked = np.reshape(input_masked,
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(sub_in_c, 1, 1, 1))
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input_masked = np.tile(input_masked, (1, f_d, f_h, f_w))
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for k in range(f_out_c):
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tmp_out = np.sum(input_masked * filter_[
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g * sub_in_c:(g + 1) * sub_in_c, k, :, :, :],
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axis=0)
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d1, d2 = d * stride[0], d * stride[0] + d_bolck_d
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i1, i2 = i * stride[1], i * stride[1] + d_bolck_h
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j1, j2 = j * stride[2], j * stride[2] + d_bolck_w
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out[n, g * f_out_c + k, d1:d2:dilations[0], i1:i2:
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dilations[1], j1:j2:dilations[2]] += tmp_out
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out = out[:, :, pad[0]:out_d - pad[0], pad[1]:out_h - pad[1], pad[2]:out_w -
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pad[2]]
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return out
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class TestConv3dTransposeOp(OpTest):
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def setUp(self):
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# init as conv transpose
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self.use_cudnn = False
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self.init_op_type()
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self.init_test_case()
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input_ = np.random.random(self.input_size).astype("float32")
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filter_ = np.random.random(self.filter_size).astype("float32")
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self.inputs = {'Input': input_, 'Filter': filter_}
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self.attrs = {
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'strides': self.stride,
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'paddings': self.pad,
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'dilations': self.dilations,
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'groups': self.groups,
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'use_cudnn': self.use_cudnn,
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'data_format': 'AnyLayout' # TODO(dzhwinter) : should be fix latter
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}
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output = conv3dtranspose_forward_naive(input_, filter_,
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self.attrs).astype("float32")
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self.outputs = {'Output': output}
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def test_check_output(self):
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if self.use_cudnn:
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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.use_cudnn:
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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=0.03)
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else:
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self.check_grad(
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set(['Input', 'Filter']), 'Output', max_relative_error=0.03)
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def test_check_grad_no_filter(self):
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if self.use_cudnn:
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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=0.03,
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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=0.03,
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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.use_cudnn:
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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=0.03,
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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=0.03,
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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, 0]
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self.stride = [1, 1, 1]
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self.dilations = [1, 1, 1]
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self.groups = 1
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self.input_size = [2, 3, 5, 5, 5] # NCDHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3, 3]
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def init_op_type(self):
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self.op_type = "conv3d_transpose"
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class TestWithPad(TestConv3dTransposeOp):
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def init_test_case(self):
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self.pad = [1, 1, 1]
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self.stride = [1, 1, 1]
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self.dilations = [1, 1, 1]
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self.groups = 1
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self.input_size = [2, 3, 5, 5, 5] # NCDHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3, 3]
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class TestWithGroups(TestConv3dTransposeOp):
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def init_test_case(self):
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self.pad = [1, 1, 1]
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self.stride = [1, 1, 1]
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self.dilations = [1, 1, 1]
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self.groups = 2
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self.input_size = [2, 4, 5, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 3, 3, 3, 3]
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class TestWithStride(TestConv3dTransposeOp):
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def init_test_case(self):
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self.pad = [1, 1, 1]
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self.stride = [2, 2, 2]
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self.dilations = [1, 1, 1]
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self.groups = 1
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self.input_size = [2, 3, 5, 5, 5] # NCDHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3, 3]
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class TestWithDilation(TestConv3dTransposeOp):
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def init_test_case(self):
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self.pad = [1, 1, 1]
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self.stride = [1, 1, 1]
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self.dilations = [2, 2, 2]
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self.groups = 1
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self.input_size = [2, 3, 5, 5, 5] # NCDHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3, 3]
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# ------------ test_cudnn ------------
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class TestCUDNN(TestConv3dTransposeOp):
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def init_op_type(self):
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self.use_cudnn = True
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self.op_type = "conv3d_transpose"
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class TestCUDNNWithPad(TestWithPad):
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def init_test_case(self):
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self.pad = [1, 1, 1]
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self.stride = [1, 1, 1]
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self.dilations = [1, 1, 1]
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self.groups = 1
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self.input_size = [2, 3, 5, 5, 5] # NCDHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3, 3]
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def init_op_type(self):
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self.use_cudnn = True
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self.op_type = "conv3d_transpose"
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class TestCUDNNWithStride(TestWithStride):
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def init_test_case(self):
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self.pad = [1, 1, 1]
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self.stride = [2, 2, 2]
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self.dilations = [1, 1, 1]
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self.groups = 1
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self.input_size = [2, 3, 5, 5, 5] # NCDHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3, 3]
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def init_op_type(self):
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self.use_cudnn = True
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self.op_type = "conv3d_transpose"
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class TestCUDNNWithGroups(TestWithGroups):
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def init_test_case(self):
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self.pad = [1, 1, 1]
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self.stride = [1, 1, 1]
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self.dilations = [1, 1, 1]
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self.groups = 2
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self.input_size = [2, 4, 5, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 3, 3, 3, 3]
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def init_op_type(self):
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self.use_cudnn = True
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self.op_type = "conv3d_transpose"
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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_test_case(self):
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# self.pad = [1, 1, 1]
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# self.stride = [2, 2, 2]
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# self.dilations = [2, 2, 2]
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# self.input_size = [2, 3, 5, 5, 5] # NCDHW
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# f_c = self.input_size[1]
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# self.filter_size = [f_c, 6, 3, 3, 3]
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#
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# def init_op_type(self):
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# self.op_type = "conv3d_transpose"
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if __name__ == '__main__':
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unittest.main()
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