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| 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 | |