# Copyright (c) 2020 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 os import numpy as np import paddle from paddle.vision.models import vgg16 from paddle.utils.download import get_path_from_url from .base_model import BaseModel from .builder import MODELS from .generators.builder import build_generator from .discriminators.builder import build_discriminator from .criterions import build_criterion from ..modules.init import init_weights from ..utils.image_pool import ImagePool from ..utils.preprocess import * VGGFACE_WEIGHT_URL = 'https://paddlegan.bj.bcebos.com/vggface.pdparams' @MODELS.register() class MakeupModel(BaseModel): """ PSGAN paper: https://arxiv.org/pdf/1909.06956.pdf """ def __init__(self, generator, discriminator=None, cycle_criterion=None, idt_criterion=None, gan_criterion=None, l1_criterion=None, l2_criterion=None, pool_size=50, direction='a2b', lambda_a=10., lambda_b=10., is_train=True): """Initialize the PSGAN class. Parameters: cfg (dict)-- config of model. """ super(MakeupModel, self).__init__() self.lambda_a = lambda_a self.lambda_b = lambda_b self.is_train = is_train # define networks (both Generators and discriminators) # The naming is different from those used in the paper. # Code (vs. paper): G_A (G), G_B (F), D_A (D_Y), D_B (D_X) self.nets['netG'] = build_generator(generator) init_weights(self.nets['netG'], init_type='xavier', init_gain=1.0) if self.is_train: # define discriminators vgg = vgg16(pretrained=False) self.vgg = vgg.features cur_path = os.path.abspath(os.path.dirname(__file__)) vgg_weight_path = get_path_from_url(VGGFACE_WEIGHT_URL, cur_path) param = paddle.load(vgg_weight_path) vgg.load_dict(param) self.nets['netD_A'] = build_discriminator(discriminator) self.nets['netD_B'] = build_discriminator(discriminator) init_weights(self.nets['netD_A'], init_type='xavier', init_gain=1.0) init_weights(self.nets['netD_B'], init_type='xavier', init_gain=1.0) # create image buffer to store previously generated images self.fake_A_pool = ImagePool(pool_size) self.fake_B_pool = ImagePool(pool_size) # define loss functions if gan_criterion: self.gan_criterion = build_criterion(gan_criterion) if cycle_criterion: self.cycle_criterion = build_criterion(cycle_criterion) if idt_criterion: self.idt_criterion = build_criterion(idt_criterion) if l1_criterion: self.l1_criterion = build_criterion(l1_criterion) if l2_criterion: self.l2_criterion = build_criterion(l2_criterion) def setup_input(self, input): """Unpack input data from the dataloader and perform necessary pre-processing steps. Args: input (dict): include the data itself and its metadata information. """ self.real_A = paddle.to_tensor(input['image_A']) self.real_B = paddle.to_tensor(input['image_B']) self.c_m = paddle.to_tensor(input['consis_mask']) self.P_A = paddle.to_tensor(input['P_A']) self.P_B = paddle.to_tensor(input['P_B']) self.mask_A_aug = paddle.to_tensor(input['mask_A_aug']) self.mask_B_aug = paddle.to_tensor(input['mask_B_aug']) self.c_m_t = paddle.transpose(self.c_m, perm=[0, 2, 1]) if self.is_train: self.mask_A = paddle.to_tensor(input['mask_A']) self.mask_B = paddle.to_tensor(input['mask_B']) self.c_m_idt_a = paddle.to_tensor(input['consis_mask_idt_A']) self.c_m_idt_b = paddle.to_tensor(input['consis_mask_idt_B']) def forward(self): """Run forward pass; called by both functions and .""" self.fake_A, amm = self.nets['netG'](self.real_A, self.real_B, self.P_A, self.P_B, self.c_m, self.mask_A_aug, self.mask_B_aug) # G_A(A) self.fake_B, _ = self.nets['netG'](self.real_B, self.real_A, self.P_B, self.P_A, self.c_m_t, self.mask_A_aug, self.mask_B_aug) # G_A(A) self.rec_A, _ = self.nets['netG'](self.fake_A, self.real_A, self.P_A, self.P_A, self.c_m_idt_a, self.mask_A_aug, self.mask_B_aug) # G_A(A) self.rec_B, _ = self.nets['netG'](self.fake_B, self.real_B, self.P_B, self.P_B, self.c_m_idt_b, self.mask_A_aug, self.mask_B_aug) # G_A(A) # visual self.visual_items['real_A'] = self.real_A self.visual_items['fake_B'] = self.fake_B self.visual_items['rec_A'] = self.rec_A self.visual_items['real_B'] = self.real_B self.visual_items['fake_A'] = self.fake_A self.visual_items['rec_B'] = self.rec_B def backward_D_basic(self, netD, real, fake): """Calculate GAN loss for the discriminator Parameters: netD (network) -- the discriminator D real (tensor array) -- real images fake (tensor array) -- images generated by a generator Return the discriminator loss. We also call loss_D.backward() to calculate the gradients. """ # Real pred_real = netD(real) loss_D_real = self.gan_criterion(pred_real, True) # Fake pred_fake = netD(fake.detach()) loss_D_fake = self.gan_criterion(pred_fake, False) # Combined loss and calculate gradients loss_D = (loss_D_real + loss_D_fake) * 0.5 loss_D.backward() return loss_D def backward_D_A(self): """Calculate GAN loss for discriminator D_A""" fake_B = self.fake_B_pool.query(self.fake_B) self.loss_D_A = self.backward_D_basic(self.nets['netD_A'], self.real_B, fake_B) self.losses['D_A_loss'] = self.loss_D_A def backward_D_B(self): """Calculate GAN loss for discriminator D_B""" fake_A = self.fake_A_pool.query(self.fake_A) self.loss_D_B = self.backward_D_basic(self.nets['netD_B'], self.real_A, fake_A) self.losses['D_B_loss'] = self.loss_D_B def backward_G(self): """Calculate the loss for generators G_A and G_B""" lambda_A = self.lambda_a lambda_B = self.lambda_b lambda_vgg = 5e-3 # Identity loss if self.idt_criterion: self.idt_A, _ = self.nets['netG'](self.real_A, self.real_A, self.P_A, self.P_A, self.c_m_idt_a, self.mask_A_aug, self.mask_B_aug) # G_A(A) self.loss_idt_A = self.idt_criterion(self.idt_A, self.real_A) * lambda_A self.idt_B, _ = self.nets['netG'](self.real_B, self.real_B, self.P_B, self.P_B, self.c_m_idt_b, self.mask_A_aug, self.mask_B_aug) # G_A(A) self.loss_idt_B = self.idt_criterion(self.idt_B, self.real_B) * lambda_B # visual self.visual_items['idt_A'] = self.idt_A self.visual_items['idt_B'] = self.idt_B else: self.loss_idt_A = 0 self.loss_idt_B = 0 # GAN loss D_A(G_A(A)) self.loss_G_A = self.gan_criterion(self.nets['netD_A'](self.fake_A), True) # GAN loss D_B(G_B(B)) self.loss_G_B = self.gan_criterion(self.nets['netD_B'](self.fake_B), True) # Forward cycle loss || G_B(G_A(A)) - A|| self.loss_cycle_A = self.cycle_criterion(self.rec_A, self.real_A) * lambda_A # Backward cycle loss || G_A(G_B(B)) - B|| self.loss_cycle_B = self.cycle_criterion(self.rec_B, self.real_B) * lambda_B self.losses['G_A_adv_loss'] = self.loss_G_A self.losses['G_B_adv_loss'] = self.loss_G_B mask_A_lip = self.mask_A_aug[:, 0].unsqueeze(1) mask_B_lip = self.mask_B_aug[:, 0].unsqueeze(1) mask_A_lip_np = mask_A_lip.numpy().squeeze() mask_B_lip_np = mask_B_lip.numpy().squeeze() mask_A_lip_np, mask_B_lip_np, index_A_lip, index_B_lip = mask_preprocess( mask_A_lip_np, mask_B_lip_np) real_A = paddle.nn.clip((self.real_A + 1.0) / 2.0, 0.0, 1.0) * 255.0 real_A_np = real_A.numpy().squeeze() real_B = paddle.nn.clip((self.real_B + 1.0) / 2.0, 0.0, 1.0) * 255.0 real_B_np = real_B.numpy().squeeze() fake_A = paddle.nn.clip((self.fake_A + 1.0) / 2.0, 0.0, 1.0) * 255.0 fake_A_np = fake_A.numpy().squeeze() fake_B = paddle.nn.clip((self.fake_B + 1.0) / 2.0, 0.0, 1.0) * 255.0 fake_B_np = fake_B.numpy().squeeze() fake_match_lip_A = hisMatch(fake_A_np, real_B_np, mask_A_lip_np, mask_B_lip_np, index_A_lip) fake_match_lip_B = hisMatch(fake_B_np, real_A_np, mask_B_lip_np, mask_A_lip_np, index_B_lip) fake_match_lip_A = paddle.to_tensor(fake_match_lip_A) fake_match_lip_A.stop_gradient = True fake_match_lip_A = fake_match_lip_A.unsqueeze(0) fake_match_lip_B = paddle.to_tensor(fake_match_lip_B) fake_match_lip_B.stop_gradient = True fake_match_lip_B = fake_match_lip_B.unsqueeze(0) fake_A_lip_masked = fake_A * mask_A_lip fake_B_lip_masked = fake_B * mask_B_lip g_A_lip_loss_his = self.l1_criterion(fake_A_lip_masked, fake_match_lip_A) g_B_lip_loss_his = self.l1_criterion(fake_B_lip_masked, fake_match_lip_B) #skin mask_A_skin = self.mask_A_aug[:, 1].unsqueeze(1) mask_B_skin = self.mask_B_aug[:, 1].unsqueeze(1) mask_A_skin_np = mask_A_skin.numpy().squeeze() mask_B_skin_np = mask_B_skin.numpy().squeeze() mask_A_skin_np, mask_B_skin_np, index_A_skin, index_B_skin = mask_preprocess( mask_A_skin_np, mask_B_skin_np) fake_match_skin_A = hisMatch(fake_A_np, real_B_np, mask_A_skin_np, mask_B_skin_np, index_A_skin) fake_match_skin_B = hisMatch(fake_B_np, real_A_np, mask_B_skin_np, mask_A_skin_np, index_B_skin) fake_match_skin_A = paddle.to_tensor(fake_match_skin_A) fake_match_skin_A.stop_gradient = True fake_match_skin_A = fake_match_skin_A.unsqueeze(0) fake_match_skin_B = paddle.to_tensor(fake_match_skin_B) fake_match_skin_B.stop_gradient = True fake_match_skin_B = fake_match_skin_B.unsqueeze(0) fake_A_skin_masked = fake_A * mask_A_skin fake_B_skin_masked = fake_B * mask_B_skin g_A_skin_loss_his = self.l1_criterion(fake_A_skin_masked, fake_match_skin_A) g_B_skin_loss_his = self.l1_criterion(fake_B_skin_masked, fake_match_skin_B) #eye mask_A_eye = self.mask_A_aug[:, 2].unsqueeze(1) mask_B_eye = self.mask_B_aug[:, 2].unsqueeze(1) mask_A_eye_np = mask_A_eye.numpy().squeeze() mask_B_eye_np = mask_B_eye.numpy().squeeze() mask_A_eye_np, mask_B_eye_np, index_A_eye, index_B_eye = mask_preprocess( mask_A_eye_np, mask_B_eye_np) fake_match_eye_A = hisMatch(fake_A_np, real_B_np, mask_A_eye_np, mask_B_eye_np, index_A_eye) fake_match_eye_B = hisMatch(fake_B_np, real_A_np, mask_B_eye_np, mask_A_eye_np, index_B_eye) fake_match_eye_A = paddle.to_tensor(fake_match_eye_A) fake_match_eye_A.stop_gradient = True fake_match_eye_A = fake_match_eye_A.unsqueeze(0) fake_match_eye_B = paddle.to_tensor(fake_match_eye_B) fake_match_eye_B.stop_gradient = True fake_match_eye_B = fake_match_eye_B.unsqueeze(0) fake_A_eye_masked = fake_A * mask_A_eye fake_B_eye_masked = fake_B * mask_B_eye g_A_eye_loss_his = self.l1_criterion(fake_A_eye_masked, fake_match_eye_A) g_B_eye_loss_his = self.l1_criterion(fake_B_eye_masked, fake_match_eye_B) self.loss_G_A_his = (g_A_eye_loss_his + g_A_lip_loss_his + g_A_skin_loss_his * 0.1) * 0.1 self.loss_G_B_his = (g_B_eye_loss_his + g_B_lip_loss_his + g_B_skin_loss_his * 0.1) * 0.1 self.losses['G_A_his_loss'] = self.loss_G_A_his self.losses['G_B_his_loss'] = self.loss_G_B_his #vgg loss vgg_s = self.vgg(self.real_A) vgg_s.stop_gradient = True vgg_fake_A = self.vgg(self.fake_A) self.loss_A_vgg = self.l2_criterion(vgg_fake_A, vgg_s) * lambda_A * lambda_vgg vgg_r = self.vgg(self.real_B) vgg_r.stop_gradient = True vgg_fake_B = self.vgg(self.fake_B) self.loss_B_vgg = self.l2_criterion(vgg_fake_B, vgg_r) * lambda_B * lambda_vgg self.loss_rec = (self.loss_cycle_A * 0.2 + self.loss_cycle_B * 0.2 + self.loss_A_vgg + self.loss_B_vgg) * 0.5 self.loss_idt = (self.loss_idt_A + self.loss_idt_B) * 0.1 self.losses['G_A_vgg_loss'] = self.loss_A_vgg self.losses['G_B_vgg_loss'] = self.loss_B_vgg self.losses['G_rec_loss'] = self.loss_rec self.losses['G_idt_loss'] = self.loss_idt # bg consistency loss mask_A_consis = paddle.cast( (self.mask_A == 0), dtype='float32') + paddle.cast( (self.mask_A == 10), dtype='float32') + paddle.cast( (self.mask_A == 8), dtype='float32') mask_A_consis = paddle.unsqueeze(paddle.clip(mask_A_consis, 0, 1), 1) self.loss_G_bg_consis = self.l1_criterion( self.real_A * mask_A_consis, self.fake_A * mask_A_consis) * 0.1 # combined loss and calculate gradients self.loss_G = self.loss_G_A + self.loss_G_B + self.loss_rec + self.loss_idt + self.loss_G_A_his + self.loss_G_B_his + self.loss_G_bg_consis self.loss_G.backward() def train_iter(self, optimizers=None): """Calculate losses, gradients, and update network weights; called in every training iteration""" # forward self.forward() # compute fake images and reconstruction images. # G_A and G_B self.set_requires_grad( [self.nets['netD_A'], self.nets['netD_B']], False) # Ds require no gradients when optimizing Gs # self.optimizer_G.clear_gradients() #zero_grad() # set G_A and G_B's gradients to zero self.backward_G() # calculate gradients for G_A and G_B self.optimizers['optimizer_G'].minimize( self.loss_G) #step() # update G_A and G_B's weights self.optimizers['optimizer_G'].clear_gradients() # D_A and D_B self.set_requires_grad(self.nets['netD_A'], True) # self.optimizer_D.clear_gradients() #zero_grad() # set D_A and D_B's gradients to zero self.backward_D_A() # calculate gradients for D_A self.optimizers['optimizer_DA'].minimize( self.loss_D_A) #step() # update D_A and D_B's weights self.optimizers['optimizer_DA'].clear_gradients() #zero_g self.set_requires_grad(self.nets['netD_B'], True) self.backward_D_B() # calculate graidents for D_B self.optimizers['optimizer_DB'].minimize( self.loss_D_B) #step() # update D_A and D_B's weights self.optimizers['optimizer_DB'].clear_gradients()