# 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 import paddle.fluid as fluid from op_test import OpTest def conv2dtranspose_forward_naive(input_, filter_, attrs): padding_algorithm = attrs['padding_algorithm'] if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]: raise ValueError("Unknown Attr(padding_algorithm): '%s'. " "It can only be 'SAME' or 'VALID'." % str(padding_algorithm)) if attrs['data_format'] == 'NHWC': input_ = np.transpose(input_, [0, 3, 1, 2]) 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'] # update pad and dilation def _get_padding_with_SAME(input_shape, kernel_size, kernel_stride): padding = [] for input_size, filter_size, stride_size in zip( input_shape, kernel_size, kernel_stride): out_size = int((input_size + stride_size - 1) / stride_size) pad_sum = np.max(( (out_size - 1) * stride_size + filter_size - input_size, 0)) pad_0 = int(pad_sum / 2) pad_1 = int(pad_sum - pad_0) padding.append(pad_0) padding.append(pad_1) return padding ksize = filter_.shape[2:4] if padding_algorithm == "VALID": pad = [0, 0, 0, 0] elif padding_algorithm == "SAME": dilations = [1, 1] input_data_shape = input_.shape[2:4] pad = _get_padding_with_SAME(input_data_shape, ksize, stride) pad_h_0, pad_h_1 = pad[0], pad[0] pad_w_0, pad_w_1 = pad[1], pad[1] if len(pad) == 4: pad_h_0, pad_h_1 = pad[0], pad[1] pad_w_0, pad_w_1 = pad[2], pad[3] 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 if 'output_size' in attrs: output_size = attrs['output_size'] out_h = output_size[0] + pad_h_0 + pad_h_1 out_w = output_size[1] + pad_w_0 + pad_w_1 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[1], j * stride[1] + d_bolck_w out[n, g * f_out_c + k, i1:i2:dilations[0], j1:j2: dilations[1]] += tmp_out out = out[:, :, pad_h_0:out_h - pad_h_1, pad_w_0:out_w - pad_w_1] if attrs['data_format'] == 'NHWC': out = np.transpose(out, [0, 2, 3, 1]) return out class TestConv2dTransposeOp(OpTest): def setUp(self): # init as conv transpose self.is_test = False self.use_cudnn = False self.use_mkldnn = False self.output_size = None self.data_format = "NCHW" self.pad = [0, 0] self.padding_algorithm = "EXPLICIT" 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, 'padding_algorithm': self.padding_algorithm, 'groups': self.groups, 'dilations': self.dilations, 'use_cudnn': self.use_cudnn, 'is_test': self.is_test, 'use_mkldnn': self.use_mkldnn, 'data_format': self.data_format } if self.output_size is not None: self.attrs['output_size'] = self.output_size 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 TestWithSymmetricPad(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 TestWithAsymmetricPad(TestConv2dTransposeOp): def init_test_case(self): self.pad = [1, 0, 1, 2] 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 TestWithSAMEPad(TestConv2dTransposeOp): def init_test_case(self): self.stride = [2, 1] self.dilations = [1, 2] self.groups = 1 self.input_size = [2, 3, 6, 5] # NCHW f_c = self.input_size[1] self.filter_size = [f_c, 6, 4, 3] self.padding_algorithm = 'SAME' class TestWithVALIDPad(TestConv2dTransposeOp): def init_test_case(self): 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] self.padding_algorithm = 'VALID' 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] class TestWithEvenUpsample(TestConv2dTransposeOp): def init_test_case(self): self.pad = [2, 2] self.stride = [2, 2] self.groups = 1 self.dilations = [1, 1] self.output_size = [14, 14] self.input_size = [2, 3, 7, 7] # NCHW f_c = self.input_size[1] self.filter_size = [f_c, 6, 5, 5] class Test_NHWC(TestConv2dTransposeOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] self.dilations = [1, 1] self.groups = 1 self.input_size = [2, 5, 5, 3] # NHWC f_c = self.input_size[-1] self.filter_size = [f_c, 6, 3, 3] self.data_format = 'NHWC' class TestWithSymmetricPad_NHWC(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, 5, 5, 3] # NHWC f_c = self.input_size[-1] self.filter_size = [f_c, 6, 3, 3] self.data_format = 'NHWC' class TestWithAsymmetricPad_NHWC(TestConv2dTransposeOp): def init_test_case(self): self.pad = [1, 0, 1, 2] self.stride = [1, 1] self.dilations = [1, 1] self.groups = 1 self.input_size = [2, 5, 5, 3] # NHWC f_c = self.input_size[-1] self.filter_size = [f_c, 6, 3, 3] self.data_format = 'NHWC' class TestWithGroups_NHWC(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, 5, 5, 4] # NHWC f_c = self.input_size[-1] self.filter_size = [f_c, 3, 3, 3] self.data_format = 'NHWC' class TestWithStride_NHWC(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, 5, 5, 3] # NCHW f_c = self.input_size[-1] self.filter_size = [f_c, 6, 3, 3] self.data_format = 'NHWC' class TestWithDilation_NHWC(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, 5, 5, 3] # NHWC f_c = self.input_size[-1] self.filter_size = [f_c, 6, 3, 3] self.data_format = 'NHWC' class TestWithEvenUpsample_NHWC(TestConv2dTransposeOp): def init_test_case(self): self.pad = [2, 2] self.stride = [2, 2] self.groups = 1 self.dilations = [1, 1] self.output_size = [14, 14] self.input_size = [2, 7, 7, 3] # NHWC f_c = self.input_size[-1] self.filter_size = [f_c, 6, 5, 5] self.data_format = 'NHWC' # ------------ 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 TestCUDNNWithSymmetricPad(TestWithSymmetricPad): 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 TestCUDNNWithAsymmetricPad(TestWithAsymmetricPad): def init_test_case(self): self.pad = [1, 0, 1, 2] 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 TestCUDNNWithSAMEPad(TestWithSAMEPad): def init_test_case(self): self.pad = [1, 0, 1, 2] self.stride = [1, 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 TestCUDNNWithVALIDPad(TestWithVALIDPad): def init_test_case(self): self.pad = [1, 0, 1, 2] 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" # ------------ test_cudnn ------------ @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNNWithEvenUpsample(TestWithEvenUpsample): def init_op_type(self): self.use_cudnn = True self.op_type = "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" @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNN_NHWC(TestConv2dTransposeOp): def init_test_case(self): self.pad = [0, 0] self.stride = [1, 1] self.dilations = [1, 1] self.groups = 1 self.input_size = [2, 5, 5, 3] # NHWC f_c = self.input_size[-1] self.filter_size = [f_c, 6, 3, 3] self.data_format = 'NHWC' 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 TestCUDNNWithSymmetricPad_NHWC(TestWithSymmetricPad): def init_test_case(self): self.pad = [1, 1] self.stride = [1, 1] self.groups = 1 self.dilations = [1, 1] self.input_size = [2, 5, 5, 3] # NHWC f_c = self.input_size[-1] self.filter_size = [f_c, 6, 3, 3] self.data_format = 'NHWC' 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 TestCUDNNWithAsymmetricPad_NHWC(TestWithSymmetricPad): def init_test_case(self): self.pad = [1, 0, 2, 3] self.stride = [2, 2] self.groups = 1 self.dilations = [1, 1] self.input_size = [2, 5, 5, 3] # NHWC f_c = self.input_size[-1] self.filter_size = [f_c, 6, 3, 3] self.data_format = 'NHWC' 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_NHWC(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, 5, 5, 3] # NHWC f_c = self.input_size[-1] self.filter_size = [f_c, 6, 3, 3] self.data_format = 'NHWC' 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_NHWC(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, 5, 5, 4] # NCHW f_c = self.input_size[-1] self.filter_size = [f_c, 3, 3, 3] self.data_format = 'NHWC' 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 TestCUDNNWithEvenUpsample_NHWC(TestWithEvenUpsample): def init_test_case(self): self.pad = [2, 2] self.stride = [2, 2] self.groups = 1 self.dilations = [1, 1] self.output_size = [14, 14] self.input_size = [2, 7, 7, 3] # NHWC f_c = self.input_size[-1] self.filter_size = [f_c, 6, 5, 5] self.data_format = 'NHWC' 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" class TestDepthwiseConvTransposeAsymmetricPad(TestConv2dTransposeOp): def init_test_case(self): self.pad = [1, 0, 1, 2] 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, 3, 3] self.op_type = "depthwise_conv2d_transpose" self.data_format = 'NCHW' class TestDepthwiseConvTransposeSAMEPad(TestConv2dTransposeOp): def init_test_case(self): self.stride = [2, 2] self.dilations = [1, 1] self.input_size = [2, 8, 16, 16] # NHWC 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, 3, 3] self.op_type = "depthwise_conv2d_transpose" self.padding_algorithm = 'SAME' class TestDepthwiseConvTransposeVALIDPad(TestConv2dTransposeOp): def init_test_case(self): self.stride = [2, 2] self.dilations = [1, 1] self.input_size = [2, 8, 16, 16] # NHWC 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, 3, 3] self.op_type = "depthwise_conv2d_transpose" self.padding_algorithm = 'VALID' class TestDepthwiseConvTranspose_NHWC_4x4kernel(TestConv2dTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [2, 2] self.dilations = [1, 1] self.input_size = [2, 16, 16, 8] # NHWC self.groups = 8 assert np.mod(self.input_size[3], self.groups) == 0 f_c = self.input_size[3] // self.groups self.filter_size = [self.input_size[3], f_c, 4, 4] self.op_type = "depthwise_conv2d_transpose" self.data_format = 'NHWC' class TestDepthwiseConvTranspose_NHWC_3x3kernel(TestConv2dTransposeOp): def init_test_case(self): self.pad = [1, 1] self.stride = [2, 2] self.dilations = [1, 1] self.input_size = [2, 16, 16, 8] # NHWC self.groups = 8 assert np.mod(self.input_size[3], self.groups) == 0 f_c = self.input_size[3] // self.groups self.filter_size = [self.input_size[3], f_c, 3, 3] self.op_type = "depthwise_conv2d_transpose" self.data_format = 'NHWC' class TestDepthwiseConvTransposeAsymmetricPad_NHWC(TestConv2dTransposeOp): def init_test_case(self): self.pad = [1, 0, 1, 2] self.stride = [2, 2] self.dilations = [1, 1] self.input_size = [2, 16, 16, 8] # NHWC self.groups = 8 assert np.mod(self.input_size[3], self.groups) == 0 f_c = self.input_size[3] // self.groups self.filter_size = [self.input_size[3], f_c, 3, 3] self.op_type = "depthwise_conv2d_transpose" self.data_format = 'NHWC' class TestConv2dTransposeAPI(OpTest): def test_case1(self): data1 = fluid.layers.data( name='data1', shape=[3, 5, 5], dtype='float32') data2 = fluid.layers.data( name='data2', shape=[5, 5, 3], dtype='float32') out1 = fluid.layers.conv2d_transpose( input=data1, groups=1, num_filters=6, filter_size=3, data_format='NCHW') out2 = fluid.layers.conv2d_transpose( input=data2, groups=1, num_filters=6, filter_size=3, data_format='NHWC') out3 = fluid.layers.conv2d_transpose( input=data1, groups=1, num_filters=6, filter_size=3, padding=[[0, 0], [1, 1], [1, 1], [0, 0]], data_format='NHWC') out4 = fluid.layers.conv2d_transpose( input=data1, groups=3, num_filters=6, filter_size=3, padding=[[0, 0], [0, 0], [2, 1], [0, 0]], data_format='NCHW') out5 = fluid.layers.conv2d_transpose( input=data2, groups=1, num_filters=6, filter_size=3, padding='SAME', data_format='NCHW') out6 = fluid.layers.conv2d_transpose( input=data1, groups=1, num_filters=6, filter_size=3, padding='VALID', data_format='NHWC') out7 = fluid.layers.conv2d_transpose( input=data1, groups=1, num_filters=6, output_size=[7, 7], padding=[0, 0], data_format='NHWC') data1_np = np.random.random((2, 3, 5, 5)).astype("float32") data2_np = np.random.random((2, 5, 5, 3)).astype("float32") if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) else: place = core.CPUPlace() exe = fluid.Executor(place) exe.run(fluid.default_startup_program()) results = exe.run( fluid.default_main_program(), feed={"data1": data1_np, "data2": data2_np}, fetch_list=[out1, out2, out3, out4, out5, out6, out7], return_numpy=True) self.assertIsNotNone(results[0]) self.assertIsNotNone(results[1]) self.assertIsNotNone(results[2]) self.assertIsNotNone(results[3]) self.assertIsNotNone(results[4]) self.assertIsNotNone(results[5]) self.assertIsNotNone(results[6]) class TestConv2dTransposeOpException(OpTest): def test_exception(self): data = fluid.layers.data(name='data', shape=[3, 5, 5], dtype="float32") def attr_data_format(): out = fluid.layers.conv2d_transpose( input=data, groups=1, num_filters=6, filter_size=3, data_format="NCDHW") self.assertRaises(ValueError, attr_data_format) def attr_padding_str(): out = fluid.layers.conv2d_transpose( input=data, groups=1, num_filters=6, filter_size=3, padding='Vald') self.assertRaises(ValueError, attr_padding_str) def attr_padding_list(): out = fluid.layers.conv2d_transpose( input=data, groups=1, num_filters=6, filter_size=3, padding=[[1, 1], [1, 1], [0, 0], [0, 0]]) self.assertRaises(ValueError, attr_padding_list) def attr_padding_with_data_format(): out = fluid.layers.conv2d_transpose( input=data, groups=1, num_filters=6, filter_size=3, padding=[[1, 1], [0, 0], [0, 0], [1, 1]], data_format='NHWC') self.assertRaises(ValueError, attr_padding_with_data_format) if __name__ == '__main__': unittest.main()