# 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 os import unittest import numpy as np import paddle import paddle.nn as nn paddle.enable_static() import paddle.fluid.core as core import paddle.fluid as fluid from paddle.fluid import Program, program_guard from test_attribute_var import UnittestBase 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_pad_h = 0 out_pad_w = 0 if 'output_padding' in attrs: out_pad_h = attrs['output_padding'][0] out_pad_w = attrs['output_padding'][1] out = np.zeros((in_n, out_c, out_h + out_pad_h, out_w + out_pad_w), dtype=input_.dtype) 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 + out_pad_h, pad_w_0:out_w - pad_w_1 + out_pad_w] 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.dtype = np.float32 if core.is_compiled_with_rocm() else np.float64 self.need_check_grad = True self.is_test = False self.use_cudnn = False self.use_mkldnn = False self.output_size = None self.output_padding = [] 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(self.dtype) filter_ = np.random.random(self.filter_size).astype(self.dtype) 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 if len(self.output_padding) > 0: self.attrs['output_padding'] = self.output_padding output = conv2dtranspose_forward_naive(input_, filter_, self.attrs).astype(self.dtype) self.outputs = {'Output': output} def test_check_output(self): # TODO(wangzhongpu): support mkldnn op in dygraph mode if self.use_cudnn: place = core.CUDAPlace(0) self.check_output_with_place( place, atol=1e-5, check_dygraph=(self.use_mkldnn == False)) else: self.check_output(check_dygraph=(self.use_mkldnn == False)) def test_check_grad_no_input(self): if self.need_check_grad: 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', no_grad_set=set(['Input'])) def test_check_grad_no_filter(self): if self.need_check_grad: if self.use_cudnn: place = core.CUDAPlace(0) self.check_grad_with_place(place, ['Input'], 'Output', no_grad_set=set(['Filter'])) else: self.check_grad(['Input'], 'Output', no_grad_set=set(['Filter'])) def test_check_grad(self): if self.need_check_grad: 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 TestWithEvenUpsampleOutputPadding(TestConv2DTransposeOp): def init_test_case(self): self.pad = [2, 2] self.stride = [2, 2] self.groups = 1 self.dilations = [1, 1] self.output_padding = [1, 1] 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' class TestWithEvenUpsample_NHWC_output_padding(TestConv2DTransposeOp): def init_test_case(self): self.pad = [2, 2] self.stride = [2, 2] self.groups = 1 self.dilations = [1, 1] self.output_padding = [1, 1] 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" @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNN_FP16(TestConv2DTransposeOp): def init_test_case(self): self.dtype = np.float16 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.need_check_grad = False self.use_cudnn = True self.op_type = "conv2d_transpose" def test_check_output(self): if self.use_cudnn: place = core.CUDAPlace(0) self.check_output_with_place( place, atol=0.02, check_dygraph=(self.use_mkldnn == False)) else: self.check_output(check_dygraph=(self.use_mkldnn == False)) @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNN_NHWC_FP16(TestCUDNN_FP16): def init_test_case(self): self.dtype = np.float16 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' @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNNWithSymmetricPad_NHWC_FP16(TestCUDNN_FP16): def init_test_case(self): self.dtype = np.float16 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' @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNNWithAsymmetricPad_NHWC_FP16(TestCUDNN_FP16): def init_test_case(self): self.dtype = np.float16 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' @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNNWithStride_NHWC_FP16(TestCUDNN_FP16): def init_test_case(self): self.dtype = np.float16 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' @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNNWithGroups_NHWC_FP16(TestCUDNN_FP16): def init_test_case(self): self.dtype = np.float16 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' @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestCUDNNWithEvenUpsample_NHWC_FP16(TestCUDNN_FP16): def init_test_case(self): self.dtype = np.float16 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' class TestConv2DTransposeAPI(unittest.TestCase): 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(unittest.TestCase): 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) error_input = fluid.layers.data(name='error_data', shape=[1], dtype="float32") def error_input_size(): out = fluid.layers.conv2d_transpose(input=error_input, groups=1, num_filters=6, filter_size=3) self.assertRaises(ValueError, error_input_size) def error_groups(): out = fluid.layers.conv2d_transpose(input=data, groups=0, num_filters=6, filter_size=3, data_format='NHWC') self.assertRaises(ValueError, error_groups) class TestConv2DTransposeRepr(unittest.TestCase): def test_case(self): paddle.disable_static() x_var = paddle.uniform((2, 4, 8, 8), dtype='float32', min=-1., max=1.) conv = nn.Conv2DTranspose(4, 6, (3, 3), output_padding=1, stride=2) print(conv) y_var = conv(x_var) y_np = y_var.numpy() self.assertIsNotNone(y_np) paddle.enable_static() class TestTensorOutputSize1(UnittestBase): def init_info(self): self.shapes = [[2, 3, 8, 8]] self.save_path = os.path.join(self.temp_dir.name, self.path_prefix()) def path_prefix(self): return 'conv2d_transpose_tensor_output_size1' def var_prefix(self): return "Vars[" def call_func(self, x): w_var = paddle.randn((3, 6, 3, 3), dtype='float32') output_size = paddle.assign([17]) out = paddle.paddle.nn.functional.conv2d_transpose( x, w_var, stride=2, output_size=output_size) return out def test_static(self): main_prog = Program() starup_prog = Program() with program_guard(main_prog, starup_prog): fc = paddle.nn.Linear(8, 8) x = paddle.randn([2, 3, 8, 8]) x.stop_gradient = False feat = fc(x) out = self.call_func(feat) sgd = paddle.optimizer.SGD() sgd.minimize(paddle.mean(out)) self.assertTrue(self.var_prefix() in str(main_prog)) exe = paddle.static.Executor() exe.run(starup_prog) res = exe.run(fetch_list=[feat, out]) np.testing.assert_allclose(res[1].shape, (2, 6, 17, 17)) paddle.static.save_inference_model(self.save_path, [x], [feat, out], exe) # Test for Inference Predictor infer_outs = self.infer_prog() np.testing.assert_allclose(infer_outs[1].shape, (2, 6, 17, 17)) class TestTensorOutputSize2(TestTensorOutputSize1): def path_prefix(self): return 'conv2d_transpose_tensor_output_size2' def call_func(self, x): w_var = paddle.randn((3, 6, 3, 3), dtype='float32') output_size = [17, paddle.assign([17])] out = paddle.paddle.nn.functional.conv2d_transpose( x, w_var, stride=2, output_size=output_size) return out class TestTensorOutputSize3(TestTensorOutputSize1): def path_prefix(self): return 'conv2d_transpose_tensor_output_size3' def call_func(self, x): w_var = paddle.randn((3, 6, 3, 3), dtype='float32') output_size = paddle.assign([17]) out = paddle.fluid.layers.conv2d_transpose(x, num_filters=6, output_size=output_size, filter_size=3, stride=2) return out class TestTensorOutputSize4(TestTensorOutputSize1): def path_prefix(self): return 'conv2d_transpose_tensor_output_size4' def call_func(self, x): output_size = [17, paddle.assign([17])] out = paddle.fluid.layers.conv2d_transpose(x, num_filters=6, output_size=output_size, filter_size=3, stride=2) return out class TestTensorOutputSize5(TestTensorOutputSize1): def path_prefix(self): return 'conv2d_transpose_tensor_output_size5' def call_func(self, x): w_var = paddle.randn((3, 6, 3, 3), dtype='float32') output_size = [17, paddle.assign([17])] conv2d_trans = paddle.fluid.dygraph.Conv2DTranspose( num_channels=3, num_filters=6, filter_size=3, output_size=output_size, stride=2) out = conv2d_trans(x) return out class TestTensorOutputSize6(TestTensorOutputSize1): def path_prefix(self): return 'conv2d_transpose_tensor_output_size6' def var_prefix(self): return "Var[" def call_func(self, x): w_var = paddle.randn((3, 6, 3, 3), dtype='float32') output_size = paddle.assign([17, 17]) conv2d_trans = paddle.fluid.dygraph.Conv2DTranspose( num_channels=3, num_filters=6, filter_size=3, output_size=output_size, stride=2) out = conv2d_trans(x) return out class TestTensorOutputSize7(TestTensorOutputSize1): def path_prefix(self): return 'conv2d_transpose_tensor_output_size7' def var_prefix(self): return "" def call_func(self, x): w_var = paddle.randn((3, 6, 3, 3), dtype='float32') output_size = 17 conv2d_trans = paddle.fluid.dygraph.Conv2DTranspose( num_channels=3, num_filters=6, filter_size=3, output_size=output_size, stride=2) out = conv2d_trans(x) return out class TestTensorOutputSize8(TestTensorOutputSize1): def path_prefix(self): return 'conv2d_transpose_tensor_output_size8' def var_prefix(self): return "" def call_func(self, x): w_var = paddle.randn((3, 6, 3, 3), dtype='float32') output_size = [17, 17] conv2d_trans = paddle.fluid.dygraph.Conv2DTranspose( num_channels=3, num_filters=6, filter_size=3, output_size=output_size, stride=2) out = conv2d_trans(x) return out if __name__ == '__main__': unittest.main()