# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import numpy as np import paddle.fluid.core as core from op_test import OpTest def conv2dtranspose_forward_naive(input_, filter_, attrs): in_n, in_c, in_h, in_w = input_.shape f_c, f_out_c, f_h, f_w = filter_.shape groups = attrs['groups'] assert in_c == f_c out_c = f_out_c * groups sub_in_c = in_c // groups stride, pad, dilations = attrs['strides'], attrs['paddings'], attrs[ 'dilations'] d_bolck_h = dilations[0] * (f_h - 1) + 1 d_bolck_w = dilations[1] * (f_w - 1) + 1 out_h = (in_h - 1) * stride[0] + d_bolck_h out_w = (in_w - 1) * stride[1] + d_bolck_w out = np.zeros((in_n, out_c, out_h, out_w)) for n in range(in_n): for i in range(in_h): for j in range(in_w): for g in range(groups): input_masked = input_[n, g * sub_in_c:(g + 1) * sub_in_c, i, j] # (c) input_masked = np.reshape(input_masked, (sub_in_c, 1, 1)) input_masked = np.tile(input_masked, (1, f_h, f_w)) for k in range(f_out_c): tmp_out = np.sum( input_masked * filter_[g * sub_in_c:(g + 1) * sub_in_c, k, :, :], axis=0) i1, i2 = i * stride[0], i * stride[0] + d_bolck_h j1, j2 = j * stride[0], j * stride[0] + d_bolck_h out[n, g * f_out_c + k, i1:i2:dilations[0], j1:j2: dilations[1]] += tmp_out out = out[:, :, pad[0]:out_h - pad[0], pad[1]:out_w - pad[1]] return out class TestConv2dTransposeOp(OpTest): def setUp(self): # init as conv transpose self.use_cudnn = False self.init_op_type() self.init_test_case() input_ = np.random.random(self.input_size).astype("float32") filter_ = np.random.random(self.filter_size).astype("float32") self.inputs = {'Input': input_, 'Filter': filter_} self.attrs = { 'strides': self.stride, 'paddings': self.pad, 'groups': self.groups, 'dilations': self.dilations, 'use_cudnn': self.use_cudnn, 'data_format': 'AnyLayout' # TODO(dzhwinter) : should be fix latter } output = conv2dtranspose_forward_naive(input_, filter_, self.attrs).astype('float32') self.outputs = {'Output': output} def test_check_output(self): if self.use_cudnn: place = core.CUDAPlace(0) self.check_output_with_place(place, atol=1e-5) else: self.check_output() def test_check_grad_no_input(self): if self.use_cudnn: place = core.CUDAPlace(0) self.check_grad_with_place( place, ['Filter'], 'Output', max_relative_error=0.02, no_grad_set=set(['Input'])) else: self.check_grad( ['Filter'], 'Output', max_relative_error=0.02, no_grad_set=set(['Input'])) def test_check_grad_no_filter(self): if self.use_cudnn: place = core.CUDAPlace(0) self.check_grad_with_place( place, ['Input'], 'Output', max_relative_error=0.02, no_grad_set=set(['Filter'])) else: self.check_grad( ['Input'], 'Output', max_relative_error=0.02, no_grad_set=set(['Filter'])) def test_check_grad(self): if self.use_cudnn: place = core.CUDAPlace(0) self.check_grad_with_place( place, set(['Input', 'Filter']), 'Output', max_relative_error=0.02) else: self.check_grad( set(['Input', 'Filter']), 'Output', max_relative_error=0.02) def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] self.dilations = [1, 1] self.groups = 1 self.input_size = [2, 3, 5, 5] # NCHW f_c = self.input_size[1] self.filter_size = [f_c, 6, 3, 3] def init_op_type(self): self.op_type = "conv2d_transpose" class TestWithPad(TestConv2dTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] self.dilations = [1, 1] self.groups = 1 self.input_size = [2, 3, 5, 5] # NCHW f_c = self.input_size[1] self.filter_size = [f_c, 6, 3, 3] class TestWithGroups(TestConv2dTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] self.dilations = [1, 1] self.groups = 2 self.input_size = [2, 4, 5, 5] # NCHW f_c = self.input_size[1] self.filter_size = [f_c, 3, 3, 3] class TestWithStride(TestConv2dTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [2, 2] self.dilations = [1, 1] self.groups = 1 self.input_size = [2, 3, 5, 5] # NCHW f_c = self.input_size[1] self.filter_size = [f_c, 6, 3, 3] class TestWithDilation(TestConv2dTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] self.groups = 1 self.dilations = [2, 2] self.input_size = [2, 3, 5, 5] # NCHW f_c = self.input_size[1] self.filter_size = [f_c, 6, 3, 3] # ------------ test_cudnn ------------ @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNN(TestConv2dTransposeOp): def init_op_type(self): self.use_cudnn = True self.op_type = "conv2d_transpose" @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNNWithPad(TestWithPad): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] self.groups = 1 self.dilations = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW f_c = self.input_size[1] self.filter_size = [f_c, 6, 3, 3] def init_op_type(self): self.use_cudnn = True self.op_type = "conv2d_transpose" @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNNWithStride(TestWithStride): def init_test_case(self): self.pad = [1, 1] self.stride = [2, 2] self.groups = 1 self.dilations = [1, 1] self.input_size = [2, 3, 5, 5] # NCHW f_c = self.input_size[1] self.filter_size = [f_c, 6, 3, 3] def init_op_type(self): self.use_cudnn = True self.op_type = "conv2d_transpose" @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNNWithGroups(TestWithGroups): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] self.dilations = [1, 1] self.groups = 2 self.input_size = [2, 4, 5, 5] # NCHW f_c = self.input_size[1] self.filter_size = [f_c, 3, 3, 3] def init_op_type(self): self.use_cudnn = True self.op_type = "conv2d_transpose" class TestDepthwiseConvTranspose(TestConv2dTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [2, 2] self.dilations = [1, 1] self.input_size = [2, 8, 16, 16] # NCHW self.groups = 8 assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] // self.groups self.filter_size = [self.input_size[1], f_c, 4, 4] self.op_type = "depthwise_conv2d_transpose" # Please Don't remove the following code. # Currently, CI use cudnn V5.0 which not support dilation conv. # class TestCUDNNWithDilation(TestWithDilation): # def init_test_case(self): # self.pad = [1, 1] # self.stride = [2, 2] # self.dilations = [2, 2] # self.input_size = [2, 3, 5, 5] # NCHW # f_c = self.input_size[1] # self.filter_size = [f_c, 6, 3, 3] # # def init_op_type(self): # self.op_type = "conv2d_transpose" if __name__ == '__main__': unittest.main()