| import time
|
| from options.train_options import TrainOptions
|
| from models.networks import VGGLoss,save_checkpoint
|
| from models.afwm import TVLoss,AFWM
|
| import torch.nn as nn
|
| import torch.nn.functional as F
|
| import os
|
| import numpy as np
|
| import torch
|
| from torch.utils.data import DataLoader
|
| from torch.utils.data.distributed import DistributedSampler
|
| from tensorboardX import SummaryWriter
|
| import cv2
|
| import datetime
|
|
|
| opt = TrainOptions().parse()
|
| path = 'runs/'+opt.name
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| os.makedirs(path,exist_ok=True)
|
|
|
| def CreateDataset(opt):
|
| from data.aligned_dataset import AlignedDataset
|
| dataset = AlignedDataset()
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| print("dataset [%s] was created" % (dataset.name()))
|
| dataset.initialize(opt)
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| return dataset
|
|
|
| os.makedirs('sample',exist_ok=True)
|
| iter_path = os.path.join(opt.checkpoints_dir, opt.name, 'iter.txt')
|
|
|
| torch.cuda.set_device(opt.local_rank)
|
| torch.distributed.init_process_group(
|
| 'nccl',
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| init_method='env://'
|
| )
|
| device = torch.device(f'cuda:{opt.local_rank}')
|
|
|
| start_epoch, epoch_iter = 1, 0
|
|
|
| train_data = CreateDataset(opt)
|
| train_sampler = DistributedSampler(train_data)
|
| train_loader = DataLoader(train_data, batch_size=opt.batchSize, shuffle=False,
|
| num_workers=4, pin_memory=True, sampler=train_sampler)
|
| dataset_size = len(train_loader)
|
| print('#training images = %d' % dataset_size)
|
|
|
| warp_model = AFWM(opt, 45)
|
| print(warp_model)
|
| warp_model.train()
|
| warp_model.cuda()
|
| warp_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(warp_model).to(device)
|
|
|
| if opt.isTrain and len(opt.gpu_ids):
|
| model = torch.nn.parallel.DistributedDataParallel(warp_model, device_ids=[opt.local_rank])
|
|
|
| criterionL1 = nn.L1Loss()
|
| criterionVGG = VGGLoss()
|
|
|
| params_warp = [p for p in model.parameters()]
|
| optimizer_warp = torch.optim.Adam(params_warp, lr=opt.lr, betas=(opt.beta1, 0.999))
|
|
|
| total_steps = (start_epoch-1) * dataset_size + epoch_iter
|
| step = 0
|
| step_per_batch = dataset_size
|
|
|
| if opt.local_rank == 0:
|
| writer = SummaryWriter(path)
|
|
|
| for epoch in range(start_epoch, opt.niter + opt.niter_decay + 1):
|
| epoch_start_time = time.time()
|
| if epoch != start_epoch:
|
| epoch_iter = epoch_iter % dataset_size
|
|
|
| train_sampler.set_epoch(epoch)
|
|
|
| for i, data in enumerate(train_loader):
|
| iter_start_time = time.time()
|
|
|
| total_steps += 1
|
| epoch_iter += 1
|
| save_fake = True
|
|
|
| t_mask = torch.FloatTensor((data['label'].cpu().numpy()==7).astype(np.float))
|
| data['label'] = data['label']*(1-t_mask)+t_mask*4
|
| edge = data['edge']
|
| pre_clothes_edge = torch.FloatTensor((edge.detach().numpy() > 0.5).astype(np.int))
|
| clothes = data['color']
|
| clothes = clothes * pre_clothes_edge
|
| person_clothes_edge = torch.FloatTensor((data['label'].cpu().numpy()==4).astype(np.int))
|
| real_image = data['image']
|
| person_clothes = real_image * person_clothes_edge
|
| pose = data['pose']
|
| size = data['label'].size()
|
| oneHot_size1 = (size[0], 25, size[2], size[3])
|
| densepose = torch.cuda.FloatTensor(torch.Size(oneHot_size1)).zero_()
|
| densepose = densepose.scatter_(1,data['densepose'].data.long().cuda(),1.0)
|
| densepose_fore = data['densepose']/24.0
|
| face_mask = torch.FloatTensor((data['label'].cpu().numpy()==1).astype(np.int)) + torch.FloatTensor((data['label'].cpu().numpy()==12).astype(np.int))
|
| other_clothes_mask = torch.FloatTensor((data['label'].cpu().numpy()==5).astype(np.int)) + torch.FloatTensor((data['label'].cpu().numpy()==6).astype(np.int)) + \
|
| torch.FloatTensor((data['label'].cpu().numpy()==8).astype(np.int)) + torch.FloatTensor((data['label'].cpu().numpy()==9).astype(np.int)) + \
|
| torch.FloatTensor((data['label'].cpu().numpy()==10).astype(np.int))
|
| preserve_mask = torch.cat([face_mask,other_clothes_mask],1)
|
| concat = torch.cat([preserve_mask.cuda(),densepose,pose.cuda()],1)
|
|
|
| flow_out = model(concat.cuda(), clothes.cuda(), pre_clothes_edge.cuda())
|
| warped_cloth, last_flow, _1, _2, delta_list, x_all, x_edge_all, delta_x_all, delta_y_all = flow_out
|
| warped_prod_edge = x_edge_all[4]
|
|
|
| epsilon = 0.001
|
| loss_smooth = sum([TVLoss(x) for x in delta_list])
|
| loss_all = 0
|
|
|
| for num in range(5):
|
| cur_person_clothes = F.interpolate(person_clothes, scale_factor=0.5**(4-num), mode='bilinear')
|
| cur_person_clothes_edge = F.interpolate(person_clothes_edge, scale_factor=0.5**(4-num), mode='bilinear')
|
| loss_l1 = criterionL1(x_all[num], cur_person_clothes.cuda())
|
| loss_vgg = criterionVGG(x_all[num], cur_person_clothes.cuda())
|
| loss_edge = criterionL1(x_edge_all[num], cur_person_clothes_edge.cuda())
|
| b,c,h,w = delta_x_all[num].shape
|
| loss_flow_x = (delta_x_all[num].pow(2)+ epsilon*epsilon).pow(0.45)
|
| loss_flow_x = torch.sum(loss_flow_x)/(b*c*h*w)
|
| loss_flow_y = (delta_y_all[num].pow(2)+ epsilon*epsilon).pow(0.45)
|
| loss_flow_y = torch.sum(loss_flow_y)/(b*c*h*w)
|
| loss_second_smooth = loss_flow_x + loss_flow_y
|
| loss_all = loss_all + (num+1) * loss_l1 + (num + 1) * 0.2 * loss_vgg + (num+1) * 2 * loss_edge + (num + 1) * 6 * loss_second_smooth
|
|
|
| loss_all = 0.01 * loss_smooth + loss_all
|
|
|
| if opt.local_rank == 0:
|
| writer.add_scalar('loss_all', loss_all, step)
|
|
|
| optimizer_warp.zero_grad()
|
| loss_all.backward()
|
| optimizer_warp.step()
|
|
|
|
|
| path = 'sample/'+opt.name
|
| os.makedirs(path,exist_ok=True)
|
| if step % 1000 == 0:
|
| if opt.local_rank == 0:
|
| a = real_image.float().cuda()
|
| b = person_clothes.cuda()
|
| c = clothes.cuda()
|
| d = torch.cat([densepose_fore.cuda(),densepose_fore.cuda(),densepose_fore.cuda()],1)
|
| e = warped_cloth
|
| f = torch.cat([warped_prod_edge,warped_prod_edge,warped_prod_edge],1)
|
| combine = torch.cat([a[0],b[0],c[0],d[0],e[0],f[0]], 2).squeeze()
|
| cv_img=(combine.permute(1,2,0).detach().cpu().numpy()+1)/2
|
| writer.add_image('combine', (combine.data + 1) / 2.0, step)
|
| rgb=(cv_img*255).astype(np.uint8)
|
| bgr=cv2.cvtColor(rgb,cv2.COLOR_RGB2BGR)
|
| cv2.imwrite('sample/'+opt.name+'/'+str(step)+'.jpg',bgr)
|
|
|
| step += 1
|
| iter_end_time = time.time()
|
| iter_delta_time = iter_end_time - iter_start_time
|
| step_delta = (step_per_batch-step%step_per_batch) + step_per_batch*(opt.niter + opt.niter_decay-epoch)
|
| eta = iter_delta_time*step_delta
|
| eta = str(datetime.timedelta(seconds=int(eta)))
|
| time_stamp = datetime.datetime.now()
|
| now = time_stamp.strftime('%Y.%m.%d-%H:%M:%S')
|
| if step % 100 == 0:
|
| if opt.local_rank == 0:
|
| print('{}:{}:[step-{}]--[loss-{:.6f}]--[ETA-{}]'.format(now, epoch_iter,step, loss_all,eta))
|
|
|
| if epoch_iter >= dataset_size:
|
| break
|
|
|
|
|
| iter_end_time = time.time()
|
| if opt.local_rank == 0:
|
| print('End of epoch %d / %d \t Time Taken: %d sec' %
|
| (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
|
|
|
|
|
| if epoch % opt.save_epoch_freq == 0:
|
| if opt.local_rank == 0:
|
| print('saving the model at the end of epoch %d, iters %d' % (epoch, total_steps))
|
| save_checkpoint(model.module, os.path.join(opt.checkpoints_dir, opt.name, 'PBAFN_warp_epoch_%03d.pth' % (epoch+1)))
|
|
|
| if epoch > opt.niter:
|
| model.module.update_learning_rate(optimizer_warp)
|
|
|
