import os import torch import torch.nn as nn from torch import autograd from model.networks import Generator, LocalDis, GlobalDis from utils.tools import get_model_list, local_patch, spatial_discounting_mask from utils.logger import get_logger logger = get_logger() class Trainer(nn.Module): def __init__(self, config): super(Trainer, self).__init__() self.config = config self.use_cuda = self.config['cuda'] self.device_ids = self.config['gpu_ids'] self.netG = Generator(self.config['netG'], self.use_cuda, self.device_ids) self.localD = LocalDis(self.config['netD'], self.use_cuda, self.device_ids) self.globalD = GlobalDis(self.config['netD'], self.use_cuda, self.device_ids) self.optimizer_g = torch.optim.Adam(self.netG.parameters(), lr=self.config['lr'], betas=(self.config['beta1'], self.config['beta2'])) d_params = list(self.localD.parameters()) + list(self.globalD.parameters()) self.optimizer_d = torch.optim.Adam(d_params, lr=config['lr'], betas=(self.config['beta1'], self.config['beta2'])) if self.use_cuda: self.netG.to(self.device_ids[0]) self.localD.to(self.device_ids[0]) self.globalD.to(self.device_ids[0]) def forward(self, x, bboxes, masks, ground_truth, compute_loss_g=False): self.train() l1_loss = nn.L1Loss() losses = {} x1, x2, offset_flow = self.netG(x, masks) local_patch_gt = local_patch(ground_truth, bboxes) x1_inpaint = x1 * masks + x * (1. - masks) x2_inpaint = x2 * masks + x * (1. - masks) local_patch_x1_inpaint = local_patch(x1_inpaint, bboxes) local_patch_x2_inpaint = local_patch(x2_inpaint, bboxes) # D part # wgan d loss local_patch_real_pred, local_patch_fake_pred = self.dis_forward( self.localD, local_patch_gt, local_patch_x2_inpaint.detach()) global_real_pred, global_fake_pred = self.dis_forward( self.globalD, ground_truth, x2_inpaint.detach()) losses['wgan_d'] = torch.mean(local_patch_fake_pred - local_patch_real_pred) + \ torch.mean(global_fake_pred - global_real_pred) * self.config['global_wgan_loss_alpha'] # gradients penalty loss local_penalty = self.calc_gradient_penalty( self.localD, local_patch_gt, local_patch_x2_inpaint.detach()) global_penalty = self.calc_gradient_penalty(self.globalD, ground_truth, x2_inpaint.detach()) losses['wgan_gp'] = local_penalty + global_penalty # G part if compute_loss_g: sd_mask = spatial_discounting_mask(self.config) losses['l1'] = l1_loss(local_patch_x1_inpaint * sd_mask, local_patch_gt * sd_mask) * \ self.config['coarse_l1_alpha'] + \ l1_loss(local_patch_x2_inpaint * sd_mask, local_patch_gt * sd_mask) losses['ae'] = l1_loss(x1 * (1. - masks), ground_truth * (1. - masks)) * \ self.config['coarse_l1_alpha'] + \ l1_loss(x2 * (1. - masks), ground_truth * (1. - masks)) # wgan g loss local_patch_real_pred, local_patch_fake_pred = self.dis_forward( self.localD, local_patch_gt, local_patch_x2_inpaint) global_real_pred, global_fake_pred = self.dis_forward( self.globalD, ground_truth, x2_inpaint) losses['wgan_g'] = - torch.mean(local_patch_fake_pred) - \ torch.mean(global_fake_pred) * self.config['global_wgan_loss_alpha'] return losses, x2_inpaint, offset_flow def dis_forward(self, netD, ground_truth, x_inpaint): assert ground_truth.size() == x_inpaint.size() batch_size = ground_truth.size(0) batch_data = torch.cat([ground_truth, x_inpaint], dim=0) batch_output = netD(batch_data) real_pred, fake_pred = torch.split(batch_output, batch_size, dim=0) return real_pred, fake_pred # Calculate gradient penalty def calc_gradient_penalty(self, netD, real_data, fake_data): batch_size = real_data.size(0) alpha = torch.rand(batch_size, 1, 1, 1) alpha = alpha.expand_as(real_data) if self.use_cuda: alpha = alpha.cuda() interpolates = alpha * real_data + (1 - alpha) * fake_data interpolates = interpolates.requires_grad_().clone() disc_interpolates = netD(interpolates) grad_outputs = torch.ones(disc_interpolates.size()) if self.use_cuda: grad_outputs = grad_outputs.cuda() gradients = autograd.grad(outputs=disc_interpolates, inputs=interpolates, grad_outputs=grad_outputs, create_graph=True, retain_graph=True, only_inputs=True)[0] gradients = gradients.view(batch_size, -1) gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean() return gradient_penalty def inference(self, x, masks): self.eval() x1, x2, offset_flow = self.netG(x, masks) # x1_inpaint = x1 * masks + x * (1. - masks) x2_inpaint = x2 * masks + x * (1. - masks) return x2_inpaint, offset_flow def save_model(self, checkpoint_dir, iteration): # Save generators, discriminators, and optimizers gen_name = os.path.join(checkpoint_dir, 'gen_%08d.pt' % iteration) dis_name = os.path.join(checkpoint_dir, 'dis_%08d.pt' % iteration) opt_name = os.path.join(checkpoint_dir, 'optimizer.pt') torch.save(self.netG.state_dict(), gen_name) torch.save({'localD': self.localD.state_dict(), 'globalD': self.globalD.state_dict()}, dis_name) torch.save({'gen': self.optimizer_g.state_dict(), 'dis': self.optimizer_d.state_dict()}, opt_name) def resume(self, checkpoint_dir, iteration=0, test=False): # Load generators last_model_name = get_model_list(checkpoint_dir, "gen", iteration=iteration) self.netG.load_state_dict(torch.load(last_model_name)) iteration = int(last_model_name[-11:-3]) if not test: # Load discriminators last_model_name = get_model_list(checkpoint_dir, "dis", iteration=iteration) state_dict = torch.load(last_model_name) self.localD.load_state_dict(state_dict['localD']) self.globalD.load_state_dict(state_dict['globalD']) # Load optimizers state_dict = torch.load(os.path.join(checkpoint_dir, 'optimizer.pt')) self.optimizer_d.load_state_dict(state_dict['dis']) self.optimizer_g.load_state_dict(state_dict['gen']) print("Resume from {} at iteration {}".format(checkpoint_dir, iteration)) logger.info("Resume from {} at iteration {}".format(checkpoint_dir, iteration)) return iteration