# Copyright (c) 2022 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 sys sys.path.append("..") import unittest import numpy as np import paddle.fluid.core as core import paddle.fluid as fluid from op_test_xpu import XPUOpTest import paddle import paddle.nn as nn from paddle.fluid import Program, program_guard 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(XPUOpTest): def setUp(self): # init as conv transpose self.dtype = np.float32 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() self.__class__.op_type = "conv2d_transpose" 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): if core.is_compiled_with_xpu(): paddle.enable_static() place = paddle.XPUPlace(0) self.check_output_with_place(place) def test_check_grad_no_input(self): if self.need_check_grad: if core.is_compiled_with_xpu(): paddle.enable_static() place = paddle.XPUPlace(0) self.check_grad_with_place( place, ['Filter'], 'Output', no_grad_set=set(['Input'])) def test_check_grad_no_filter(self): if self.need_check_grad: if core.is_compiled_with_xpu(): paddle.enable_static() place = paddle.XPUPlace(0) self.check_grad_with_place( place, ['Input'], 'Output', no_grad_set=set(['Filter'])) def test_check_grad(self): if self.need_check_grad: if core.is_compiled_with_xpu(): paddle.enable_static() place = paddle.XPUPlace(0) self.check_grad_with_place(place, set(['Input', 'Filter']), 'Output') 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] if __name__ == '__main__': unittest.main()