# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. # # 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. import unittest import numpy as np import paddle.v2.fluid.core as core from op_test import OpTest def conv3d_forward_naive(input, filter, group, conv_param): in_n, in_c, in_d, in_h, in_w = input.shape out_c, f_c, f_d, f_h, f_w = filter.shape assert f_c * group == in_c assert np.mod(out_c, group) == 0 sub_out_c = out_c / group stride, pad, dilation = conv_param['stride'], conv_param['pad'], conv_param[ 'dilations'] out_d = 1 + (in_d + 2 * pad[0] - (dilation[0] * (f_d - 1) + 1)) / stride[0] out_h = 1 + (in_h + 2 * pad[1] - (dilation[1] * (f_h - 1) + 1)) / stride[1] out_w = 1 + (in_w + 2 * pad[2] - (dilation[2] * (f_w - 1) + 1)) / stride[2] out = np.zeros((in_n, out_c, out_d, out_h, out_w)) d_bolck_d = (dilation[0] * (f_d - 1) + 1) d_bolck_h = (dilation[1] * (f_h - 1) + 1) d_bolck_w = (dilation[2] * (f_w - 1) + 1) input_pad = np.pad(input, ((0, ), (0, ), (pad[0], ), (pad[1], ), (pad[2], )), mode='constant', constant_values=0) filter_dilation = np.zeros((out_c, f_c, d_bolck_d, d_bolck_h, d_bolck_w)) filter_dilation[:, :, 0:d_bolck_d:dilation[0], 0:d_bolck_h:dilation[1], 0: d_bolck_w:dilation[2]] = filter for d in range(out_d): for i in range(out_h): for j in range(out_w): for g in range(group): input_pad_masked = \ input_pad[:, g * f_c:(g + 1) * f_c, d * stride[0]:d * stride[0] + d_bolck_d, i * stride[1]:i * stride[1] + d_bolck_h, j * stride[2]:j * stride[2] + d_bolck_w] f_sub = filter_dilation[g * sub_out_c:(g + 1) * sub_out_c, :, :, :, :] for k in range(sub_out_c): out[:, g * sub_out_c + k, d, i, j] = \ np.sum(input_pad_masked * f_sub[k, :, :, :, :], axis=(1, 2, 3, 4)) return out class TestConv3dOp(OpTest): def setUp(self): self.use_cudnn = False self.init_group() self.init_op_type() self.init_dilation() self.init_test_case() conv3d_param = { 'stride': self.stride, 'pad': self.pad, 'dilations': self.dilations, 'use_cudnn': self.use_cudnn, 'data_format': 'AnyLayout' # TODO(dzhwinter) : should be fix latter } input = np.random.random(self.input_size).astype("float32") filter = np.random.random(self.filter_size).astype("float32") output = conv3d_forward_naive(input, filter, self.groups, conv3d_param).astype("float32") self.inputs = {'Input': input, 'Filter': filter} self.attrs = { 'strides': self.stride, 'paddings': self.pad, 'groups': self.groups, 'dilations': self.dilations } 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(self): if self.use_cudnn: place = core.CUDAPlace(0) self.check_grad_with_place( place, set(['Input', 'Filter']), 'Output', max_relative_error=0.03) else: self.check_grad( set(['Input', 'Filter']), 'Output', max_relative_error=0.03) 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=0.03, no_grad_set=set(['Filter'])) else: self.check_grad( ['Input'], 'Output', max_relative_error=0.03, no_grad_set=set(['Filter'])) 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=0.03, no_grad_set=set(['Input'])) else: self.check_grad( ['Filter'], 'Output', max_relative_error=0.03, no_grad_set=set(['Input'])) def init_test_case(self): self.pad = [0, 0, 0] self.stride = [1, 1, 1] self.input_size = [2, 3, 4, 4, 4] # NCDHW 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, 3] def init_dilation(self): self.dilations = [1, 1, 1] def init_group(self): self.groups = 1 def init_op_type(self): self.op_type = "conv3d" class TestCase1(TestConv3dOp): def init_test_case(self): self.pad = [1, 1, 1] self.stride = [1, 1, 1] self.input_size = [2, 3, 4, 4, 4] # NCDHW 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, 3] class TestWithGroup1(TestConv3dOp): def init_group(self): self.groups = 3 class TestWithGroup2(TestCase1): def init_group(self): self.groups = 3 class TestWith1x1(TestConv3dOp): def init_test_case(self): self.pad = [0, 0, 0] self.stride = [1, 1, 1] self.input_size = [2, 3, 4, 4, 4] # NCHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] / self.groups self.filter_size = [6, f_c, 1, 1, 1] def init_dilation(self): self.dilations = [1, 1, 1] def init_group(self): self.groups = 3 class TestWithDilation(TestConv3dOp): def init_test_case(self): self.pad = [0, 0, 0] self.stride = [1, 1, 1] self.input_size = [2, 3, 6, 6, 6] # NCDHW assert np.mod(self.input_size[1], self.groups) == 0 f_c = self.input_size[1] / self.groups self.filter_size = [6, f_c, 2, 2, 2] def init_dilation(self): self.dilations = [2, 2, 2] def init_group(self): self.groups = 3 class TestCUDNN(TestConv3dOp): def init_op_type(self): self.use_cudnn = True self.op_type = "conv3d" class TestWithGroup1CUDNN(TestWithGroup1): def init_op_type(self): self.use_cudnn = True self.op_type = "conv3d" class TestWithGroup2CUDNN(TestWithGroup2): def init_op_type(self): self.use_cudnn = True self.op_type = "conv3d" class TestWith1x1CUDNN(TestWith1x1): def init_op_type(self): self.use_cudnn = True self.op_type = "conv3d" # FIXME(typhoonzero): find a way to determine if # using cudnn > 6 in python # class TestWithDilationCUDNN(TestWithDilation): # def init_op_type(self): # self.op_type = "conv3d" if __name__ == '__main__': unittest.main()