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from options.train_options import TrainOptions
from models.networks import ResUnetGenerator, VGGLoss, save_checkpoint, load_checkpoint_part_parallel, \
load_checkpoint_parallel
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
os.makedirs(path, exist_ok=True)
def CreateDataset(opt):
from data.aligned_dataset import AlignedDataset
dataset = AlignedDataset()
print("dataset [%s] was created" % (dataset.name()))
dataset.initialize(opt)
return dataset
os.makedirs('sample', exist_ok=True)
opt = TrainOptions().parse()
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',
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)
PF_warp_model = AFWM(opt, 3)
print(PF_warp_model)
PF_warp_model.train()
PF_warp_model.cuda()
load_checkpoint_part_parallel(PF_warp_model, opt.PBAFN_warp_checkpoint)
PB_warp_model = AFWM(opt, 45)
print(PB_warp_model)
PB_warp_model.eval()
PB_warp_model.cuda()
load_checkpoint_parallel(PB_warp_model, opt.PBAFN_warp_checkpoint)
PB_gen_model = ResUnetGenerator(8, 4, 5, ngf=64, norm_layer=nn.BatchNorm2d)
print(PB_gen_model)
PB_gen_model.eval()
PB_gen_model.cuda()
load_checkpoint_parallel(PB_gen_model, opt.PBAFN_gen_checkpoint)
PF_warp_model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(PF_warp_model).to(device)
if opt.isTrain and len(opt.gpu_ids):
PF_warp_model = torch.nn.parallel.DistributedDataParallel(PF_warp_model, device_ids=[opt.local_rank])
PB_warp_model = torch.nn.parallel.DistributedDataParallel(PB_warp_model, device_ids=[opt.local_rank])
PB_gen_model = torch.nn.parallel.DistributedDataParallel(PB_gen_model, device_ids=[opt.local_rank])
criterionL1 = nn.L1Loss()
criterionVGG = VGGLoss()
criterionL2 = nn.MSELoss('sum')
# optimizer
params = [p for p in PF_warp_model.parameters()]
optimizer = torch.optim.Adam(params, lr=opt.lr, betas=(opt.beta1, 0.999))
params_part = []
for name, param in PF_warp_model.named_parameters():
if 'cond_' in name or 'aflow_net.netRefine' in name:
params_part.append(param)
optimizer_part = torch.optim.Adam(params_part, lr=opt.lr, betas=(opt.beta1, 0.999))
total_steps = (start_epoch - 1) * dataset_size + epoch_iter
if opt.local_rank == 0:
writer = SummaryWriter(path)
step = 0
step_per_batch = dataset_size
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
edge_un = data['edge_un']
pre_clothes_edge_un = torch.FloatTensor((edge_un.detach().numpy() > 0.5).astype(np.int))
clothes_un = data['color_un']
clothes_un = clothes_un * pre_clothes_edge_un
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
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))
face_img = face_mask * real_image
other_clothes_img = other_clothes_mask * real_image
preserve_mask = torch.cat([face_mask, other_clothes_mask], 1)
concat_un = torch.cat([preserve_mask.cuda(), densepose, pose.cuda()], 1)
flow_out_un = PB_warp_model(concat_un.cuda(), clothes_un.cuda(), pre_clothes_edge_un.cuda())
warped_cloth_un, last_flow_un, cond_un_all, flow_un_all, delta_list_un, x_all_un, x_edge_all_un, delta_x_all_un, delta_y_all_un = flow_out_un
warped_prod_edge_un = F.grid_sample(pre_clothes_edge_un.cuda(), last_flow_un.permute(0, 2, 3, 1),
mode='bilinear', padding_mode='zeros')
flow_out_sup = PB_warp_model(concat_un.cuda(), clothes.cuda(), pre_clothes_edge.cuda())
warped_cloth_sup, last_flow_sup, cond_sup_all, flow_sup_all, delta_list_sup, x_all_sup, x_edge_all_sup, delta_x_all_sup, delta_y_all_sup = flow_out_sup
arm_mask = torch.FloatTensor((data['label'].cpu().numpy() == 11).astype(np.float)) + torch.FloatTensor((data['label'].cpu().numpy() == 13).astype(np.float))
hand_mask = torch.FloatTensor((data['densepose'].cpu().numpy() == 3).astype(np.int)) + torch.FloatTensor((data['densepose'].cpu().numpy() == 4).astype(np.int))
dense_preserve_mask = torch.FloatTensor((data['densepose'].cpu().numpy() == 15).astype(np.int)) + torch.FloatTensor((data['densepose'].cpu().numpy() == 16).astype(np.int)) \
+ torch.FloatTensor((data['densepose'].cpu().numpy() == 17).astype(np.int)) + torch.FloatTensor((data['densepose'].cpu().numpy() == 18).astype(np.int)) \
+ torch.FloatTensor((data['densepose'].cpu().numpy() == 19).astype(np.int)) + torch.FloatTensor((data['densepose'].cpu().numpy() == 20).astype(np.int)) \
+ torch.FloatTensor((data['densepose'].cpu().numpy() == 21).astype(np.int)) + torch.FloatTensor((data['densepose'].cpu().numpy() == 22))
hand_img = (arm_mask * hand_mask) * real_image
dense_preserve_mask = dense_preserve_mask.cuda() * (1 - warped_prod_edge_un)
preserve_region = face_img + other_clothes_img + hand_img
gen_inputs_un = torch.cat([preserve_region.cuda(), warped_cloth_un, warped_prod_edge_un, dense_preserve_mask], 1)
gen_outputs_un = PB_gen_model(gen_inputs_un)
p_rendered_un, m_composite_un = torch.split(gen_outputs_un, [3, 1], 1)
p_rendered_un = torch.tanh(p_rendered_un)
m_composite_un = torch.sigmoid(m_composite_un)
m_composite_un = m_composite_un * warped_prod_edge_un
p_tryon_un = warped_cloth_un * m_composite_un + p_rendered_un * (1 - m_composite_un)
flow_out = PF_warp_model(p_tryon_un.detach(), clothes.cuda(), pre_clothes_edge.cuda())
warped_cloth, last_flow, cond_all, flow_all, 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
loss_fea_sup_all = 0
loss_flow_sup_all = 0
l1_loss_batch = torch.abs(warped_cloth_sup.detach() - person_clothes.cuda())
l1_loss_batch = l1_loss_batch.reshape(opt.batchSize, 3 * 256 * 192)
l1_loss_batch = l1_loss_batch.sum(dim=1) / (3 * 256 * 192)
l1_loss_batch_pred = torch.abs(warped_cloth.detach() - person_clothes.cuda())
l1_loss_batch_pred = l1_loss_batch_pred.reshape(opt.batchSize, 3 * 256 * 192)
l1_loss_batch_pred = l1_loss_batch_pred.sum(dim=1) / (3 * 256 * 192)
weight = (l1_loss_batch < l1_loss_batch_pred).float()
num_all = len(np.where(weight.cpu().numpy() > 0)[0])
if num_all == 0:
num_all = 1
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
b1, c1, h1, w1 = cond_all[num].shape
weight_all = weight.reshape(-1, 1, 1, 1).repeat(1, 256, h1, w1)
cond_sup_loss = ((cond_sup_all[num].detach() - cond_all[num]) ** 2 * weight_all).sum() / (256 * h1 * w1 * num_all)
loss_fea_sup_all = loss_fea_sup_all + (5 - num) * 0.04 * cond_sup_loss
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 + (5 - num) * 0.04 * cond_sup_loss
if num >= 2:
b1, c1, h1, w1 = flow_all[num].shape
weight_all = weight.reshape(-1, 1, 1).repeat(1, h1, w1)
flow_sup_loss = (torch.norm(flow_sup_all[num].detach() - flow_all[num], p=2, dim=1) * weight_all).sum() / (h1 * w1 * num_all)
loss_flow_sup_all = loss_flow_sup_all + (num + 1) * 1 * flow_sup_loss
loss_all = loss_all + (num + 1) * 1 * flow_sup_loss
loss_all = 0.01 * loss_smooth + loss_all
# sum per device losses
if opt.local_rank == 0:
writer.add_scalar('loss_all', loss_all, step)
writer.add_scalar('loss_fea_sup_all', loss_fea_sup_all, step)
writer.add_scalar('loss_flow_sup_all', loss_flow_sup_all, step)
if epoch < opt.niter:
optimizer_part.zero_grad()
loss_all.backward()
optimizer_part.step()
else:
optimizer.zero_grad()
loss_all.backward()
optimizer.step()
############## Display results and errors ##########
path = 'sample/' + opt.name
os.makedirs(path, exist_ok=True)
### display output images
if step % 1000 == 0:
if opt.local_rank == 0:
a = real_image.float().cuda()
b = p_tryon_un.detach()
c = clothes.cuda()
d = person_clothes.cuda()
e = torch.cat([person_clothes_edge.cuda(), person_clothes_edge.cuda(), person_clothes_edge.cuda()], 1)
f = torch.cat([densepose_fore.cuda(), densepose_fore.cuda(), densepose_fore.cuda()], 1)
g = warped_cloth
h = 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], g[0], h[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}]--[loss-{:.6f}]--[loss-{:.6f}]--[ETA-{}]'.format(now, epoch_iter, step, loss_all, loss_fea_sup_all, loss_flow_sup_all, eta))
if epoch_iter >= dataset_size:
break
# end of epoch
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))
### save model for this epoch
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(PF_warp_model.module,
os.path.join(opt.checkpoints_dir, opt.name, 'PFAFN_warp_epoch_%03d.pth' % (epoch + 1)))
if epoch > opt.niter:
PF_warp_model.module.update_learning_rate(optimizer)
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