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317 lines
10 KiB
317 lines
10 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 conv2dtranspose_forward_naive(input_, filter_, attrs):
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in_n, in_c, in_h, in_w = input_.shape
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f_c, f_out_c, 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_h = dilations[0] * (f_h - 1) + 1
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d_bolck_w = dilations[1] * (f_w - 1) + 1
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out_h = (in_h - 1) * stride[0] + d_bolck_h
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out_w = (in_w - 1) * stride[1] + d_bolck_w
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if 'output_size' in attrs:
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output_size = attrs['output_size']
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out_h = output_size[0] + 2 * pad[0]
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out_w = output_size[1] + 2 * pad[1]
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out = np.zeros((in_n, out_c, out_h, out_w))
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for n in range(in_n):
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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) * sub_in_c, i,
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j] # (c)
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input_masked = np.reshape(input_masked, (sub_in_c, 1, 1))
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input_masked = np.tile(input_masked, (1, f_h, f_w))
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for k in range(f_out_c):
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tmp_out = np.sum(
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input_masked *
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filter_[g * sub_in_c:(g + 1) * sub_in_c, k, :, :],
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axis=0)
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i1, i2 = i * stride[0], i * stride[0] + d_bolck_h
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j1, j2 = j * stride[0], j * stride[0] + d_bolck_h
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out[n, g * f_out_c + k, i1:i2:dilations[0], j1:j2:
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dilations[1]] += tmp_out
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out = out[:, :, pad[0]:out_h - pad[0], pad[1]:out_w - pad[1]]
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return out
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class TestConv2dTransposeOp(OpTest):
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def setUp(self):
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# init as conv transpose
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self.is_test = False
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self.use_cudnn = False
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self.use_mkldnn = False
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self.output_size = None
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self.data_format = "AnyLayout"
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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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'groups': self.groups,
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'dilations': self.dilations,
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'use_cudnn': self.use_cudnn,
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'is_test': self.is_test,
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'use_mkldnn': self.use_mkldnn,
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'data_format': self.data_format
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}
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if self.output_size is not None:
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self.attrs['output_size'] = self.output_size
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output = conv2dtranspose_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_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=0.02,
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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 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=0.02,
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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(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=0.02)
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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 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.dilations = [1, 1]
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self.groups = 1
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3]
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def init_op_type(self):
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self.op_type = "conv2d_transpose"
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class TestWithPad(TestConv2dTransposeOp):
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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.dilations = [1, 1]
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self.groups = 1
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3]
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class TestWithGroups(TestConv2dTransposeOp):
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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.dilations = [1, 1]
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self.groups = 2
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self.input_size = [2, 4, 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]
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class TestWithStride(TestConv2dTransposeOp):
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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.dilations = [1, 1]
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self.groups = 1
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3]
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class TestWithDilation(TestConv2dTransposeOp):
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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.groups = 1
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self.dilations = [2, 2]
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 3, 3]
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class TestWithEvenUpsample(TestConv2dTransposeOp):
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def init_test_case(self):
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self.pad = [2, 2]
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self.stride = [2, 2]
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self.groups = 1
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self.dilations = [1, 1]
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self.output_size = [14, 14]
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self.input_size = [2, 3, 7, 7] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 5, 5]
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# ------------ test_cudnn ------------
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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 TestCUDNN(TestConv2dTransposeOp):
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def init_op_type(self):
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self.use_cudnn = True
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self.op_type = "conv2d_transpose"
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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 TestCUDNNWithPad(TestWithPad):
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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.groups = 1
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self.dilations = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 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 = "conv2d_transpose"
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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 TestCUDNNWithStride(TestWithStride):
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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.groups = 1
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self.dilations = [1, 1]
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self.input_size = [2, 3, 5, 5] # NCHW
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f_c = self.input_size[1]
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self.filter_size = [f_c, 6, 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 = "conv2d_transpose"
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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 TestCUDNNWithGroups(TestWithGroups):
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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.dilations = [1, 1]
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self.groups = 2
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self.input_size = [2, 4, 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]
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def init_op_type(self):
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self.use_cudnn = True
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self.op_type = "conv2d_transpose"
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class TestDepthwiseConvTranspose(TestConv2dTransposeOp):
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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.dilations = [1, 1]
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self.input_size = [2, 8, 16, 16] # NCHW
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self.groups = 8
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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 = [self.input_size[1], f_c, 4, 4]
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self.op_type = "depthwise_conv2d_transpose"
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# ------------ test_cudnn ------------
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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 TestCUDNNWithEvenUpsample(TestWithEvenUpsample):
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def init_op_type(self):
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self.use_cudnn = True
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self.op_type = "conv2d_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]
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# self.stride = [2, 2]
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# self.dilations = [2, 2]
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# self.input_size = [2, 3, 5, 5] # NCHW
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# f_c = self.input_size[1]
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# self.filter_size = [f_c, 6, 3, 3]
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#
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# def init_op_type(self):
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# self.op_type = "conv2d_transpose"
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if __name__ == '__main__':
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unittest.main()
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