# Copyright (c) 2020 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 absolute_import from __future__ import division from __future__ import print_function import numpy as np from layers import ConvBN, DeConvBN import paddle.fluid as fluid from model import Model, Loss class ResnetBlock(fluid.dygraph.Layer): def __init__(self, dim, dropout=False): super(ResnetBlock, self).__init__() self.dropout = dropout self.conv0 = ConvBN(dim, dim, 3, 1) self.conv1 = ConvBN(dim, dim, 3, 1, act=None) def forward(self, inputs): out_res = fluid.layers.pad2d(inputs, [1, 1, 1, 1], mode="reflect") out_res = self.conv0(out_res) if self.dropout: out_res = fluid.layers.dropout(out_res, dropout_prob=0.5) out_res = fluid.layers.pad2d(out_res, [1, 1, 1, 1], mode="reflect") out_res = self.conv1(out_res) return out_res + inputs class ResnetGenerator(fluid.dygraph.Layer): def __init__(self, input_channel, n_blocks=9, dropout=False): super(ResnetGenerator, self).__init__() self.conv0 = ConvBN(input_channel, 32, 7, 1) self.conv1 = ConvBN(32, 64, 3, 2, padding=1) self.conv2 = ConvBN(64, 128, 3, 2, padding=1) dim = 128 self.resnet_blocks = [] for i in range(n_blocks): block = self.add_sublayer("generator_%d" % (i + 1), ResnetBlock(dim, dropout)) self.resnet_blocks.append(block) self.deconv0 = DeConvBN( dim, 32 * 2, 3, 2, padding=[1, 1], outpadding=[0, 1, 0, 1]) self.deconv1 = DeConvBN( 32 * 2, 32, 3, 2, padding=[1, 1], outpadding=[0, 1, 0, 1]) self.conv3 = ConvBN( 32, input_channel, 7, 1, norm=False, act=False, use_bias=True) def forward(self, inputs): pad_input = fluid.layers.pad2d(inputs, [3, 3, 3, 3], mode="reflect") y = self.conv0(pad_input) y = self.conv1(y) y = self.conv2(y) for resnet_block in self.resnet_blocks: y = resnet_block(y) y = self.deconv0(y) y = self.deconv1(y) y = fluid.layers.pad2d(y, [3, 3, 3, 3], mode="reflect") y = self.conv3(y) y = fluid.layers.tanh(y) return y class NLayerDiscriminator(fluid.dygraph.Layer): def __init__(self, input_channel, d_dims=64, d_nlayers=3): super(NLayerDiscriminator, self).__init__() self.conv0 = ConvBN( input_channel, d_dims, 4, 2, 1, norm=False, use_bias=True, relufactor=0.2) nf_mult, nf_mult_prev = 1, 1 self.conv_layers = [] for n in range(1, d_nlayers): nf_mult_prev = nf_mult nf_mult = min(2**n, 8) conv = self.add_sublayer( 'discriminator_%d' % (n), ConvBN( d_dims * nf_mult_prev, d_dims * nf_mult, 4, 2, 1, relufactor=0.2)) self.conv_layers.append(conv) nf_mult_prev = nf_mult nf_mult = min(2**d_nlayers, 8) self.conv4 = ConvBN( d_dims * nf_mult_prev, d_dims * nf_mult, 4, 1, 1, relufactor=0.2) self.conv5 = ConvBN( d_dims * nf_mult, 1, 4, 1, 1, norm=False, act=None, use_bias=True, relufactor=0.2) def forward(self, inputs): y = self.conv0(inputs) for conv in self.conv_layers: y = conv(y) y = self.conv4(y) y = self.conv5(y) return y class Generator(Model): def __init__(self, input_channel=3): super(Generator, self).__init__() self.g = ResnetGenerator(input_channel) def forward(self, input): fake = self.g(input) return fake class GeneratorCombine(Model): def __init__(self, g_AB=None, g_BA=None, d_A=None, d_B=None, is_train=True): super(GeneratorCombine, self).__init__() self.g_AB = g_AB self.g_BA = g_BA self.is_train = is_train if self.is_train: self.d_A = d_A self.d_B = d_B def forward(self, input_A, input_B): # Translate images to the other domain fake_B = self.g_AB(input_A) fake_A = self.g_BA(input_B) # Translate images back to original domain cyc_A = self.g_BA(fake_B) cyc_B = self.g_AB(fake_A) if not self.is_train: return fake_A, fake_B, cyc_A, cyc_B # Identity mapping of images idt_A = self.g_AB(input_B) idt_B = self.g_BA(input_A) # Discriminators determines validity of translated images # d_A(g_AB(A)) valid_A = self.d_A.d(fake_B) # d_B(g_BA(A)) valid_B = self.d_B.d(fake_A) return input_A, input_B, fake_A, fake_B, cyc_A, cyc_B, idt_A, idt_B, valid_A, valid_B class GLoss(Loss): def __init__(self, lambda_A=10., lambda_B=10., lambda_identity=0.5): super(GLoss, self).__init__() self.lambda_A = lambda_A self.lambda_B = lambda_B self.lambda_identity = lambda_identity def forward(self, outputs, labels=None): input_A, input_B, fake_A, fake_B, cyc_A, cyc_B, idt_A, idt_B, valid_A, valid_B = outputs def mse(a, b): return fluid.layers.reduce_mean(fluid.layers.square(a - b)) def mae(a, b): # L1Loss return fluid.layers.reduce_mean(fluid.layers.abs(a - b)) g_A_loss = mse(valid_A, 1.) g_B_loss = mse(valid_B, 1.) g_loss = g_A_loss + g_B_loss cyc_A_loss = mae(input_A, cyc_A) * self.lambda_A cyc_B_loss = mae(input_B, cyc_B) * self.lambda_B cyc_loss = cyc_A_loss + cyc_B_loss idt_loss_A = mae(input_B, idt_A) * (self.lambda_B * self.lambda_identity) idt_loss_B = mae(input_A, idt_B) * (self.lambda_A * self.lambda_identity) idt_loss = idt_loss_A + idt_loss_B loss = cyc_loss + g_loss + idt_loss return loss class Discriminator(Model): def __init__(self, input_channel=3): super(Discriminator, self).__init__() self.d = NLayerDiscriminator(input_channel) def forward(self, real, fake): pred_real = self.d(real) pred_fake = self.d(fake) return pred_real, pred_fake class DLoss(Loss): def __init__(self): super(DLoss, self).__init__() def forward(self, inputs, labels=None): pred_real, pred_fake = inputs loss = fluid.layers.square(pred_fake) + fluid.layers.square(pred_real - 1.) loss = fluid.layers.reduce_mean(loss / 2.0) return loss