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model.py
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model.py
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import torch
import torch.optim as optim
from utils.io import load_ckpt
from utils.io import save_ckpt
from torchvision.utils import make_grid
from torchvision.utils import save_image
from modules.RFRNet import RFRNet, VGG16FeatureExtractor
import os
import time
class RFRNetModel():
def __init__(self):
self.G = None
self.lossNet = None
self.iter = None
self.optm_G = None
self.device = None
self.real_A = None
self.real_B = None
self.fake_B = None
self.comp_B = None
self.l1_loss_val = 0.0
def initialize_model(self, path=None, train=True):
self.G = RFRNet()
self.optm_G = optim.Adam(self.G.parameters(), lr = 2e-4)
if train:
self.lossNet = VGG16FeatureExtractor()
try:
start_iter = load_ckpt(path, [('generator', self.G)], [('optimizer_G', self.optm_G)])
if train:
self.optm_G = optim.Adam(self.G.parameters(), lr = 2e-4)
print('Model Initialized, iter: ', start_iter)
self.iter = start_iter
except:
print('No trained model, from start')
self.iter = 0
def cuda(self):
if torch.cuda.is_available():
self.device = torch.device("cuda")
print("Model moved to cuda")
self.G.cuda()
if self.lossNet is not None:
self.lossNet.cuda()
else:
self.device = torch.device("cpu")
def train(self, train_loader, save_path, finetune = False, iters=450000):
# writer = SummaryWriter(log_dir="log_info")
self.G.train(finetune = finetune)
if finetune:
self.optm_G = optim.Adam(filter(lambda p:p.requires_grad, self.G.parameters()), lr = 5e-5)
print("Starting training from iteration:{:d}".format(self.iter))
s_time = time.time()
while self.iter<iters:
for items in train_loader:
gt_images, masks = self.__cuda__(*items)
masked_images = gt_images * masks
self.forward(masked_images, masks, gt_images)
self.update_parameters()
self.iter += 1
if self.iter % 50 == 0:
e_time = time.time()
int_time = e_time - s_time
print("Iteration:%d, l1_loss:%.4f, time_taken:%.2f" %(self.iter, self.l1_loss_val/50, int_time))
s_time = time.time()
self.l1_loss_val = 0.0
if self.iter % 40000 == 0:
if not os.path.exists('{:s}'.format(save_path)):
os.makedirs('{:s}'.format(save_path))
save_ckpt('{:s}/g_{:d}.pth'.format(save_path, self.iter ), [('generator', self.G)], [('optimizer_G', self.optm_G)], self.iter)
if not os.path.exists('{:s}'.format(save_path)):
os.makedirs('{:s}'.format(save_path))
save_ckpt('{:s}/g_{:s}.pth'.format(save_path, "final"), [('generator', self.G)], [('optimizer_G', self.optm_G)], self.iter)
def test(self, test_loader, result_save_path):
self.G.eval()
for para in self.G.parameters():
para.requires_grad = False
count = 0
for items in test_loader:
gt_images, masks = self.__cuda__(*items)
masked_images = gt_images * masks
masks = torch.cat([masks]*3, dim = 1)
fake_B, mask = self.G(masked_images, masks)
comp_B = fake_B * (1 - masks) + gt_images * masks
if not os.path.exists('{:s}/results'.format(result_save_path)):
os.makedirs('{:s}/results'.format(result_save_path))
for k in range(comp_B.size(0)):
count += 1
grid = make_grid(comp_B[k:k+1])
file_path = '{:s}/results/img_{:d}.png'.format(result_save_path, count)
save_image(grid, file_path)
grid = make_grid(masked_images[k:k+1] +1 - masks[k:k+1] )
file_path = '{:s}/results/masked_img_{:d}.png'.format(result_save_path, count)
save_image(grid, file_path)
def forward(self, masked_image, mask, gt_image):
self.real_A = masked_image
self.real_B = gt_image
self.mask = mask
fake_B, _ = self.G(masked_image, mask)
self.fake_B = fake_B
self.comp_B = self.fake_B * (1 - mask) + self.real_B * mask
def update_parameters(self):
self.update_G()
self.update_D()
def update_G(self):
self.optm_G.zero_grad()
loss_G = self.get_g_loss()
loss_G.backward()
self.optm_G.step()
def update_D(self):
return
def get_g_loss(self):
real_B = self.real_B
fake_B = self.fake_B
comp_B = self.comp_B
real_B_feats = self.lossNet(real_B)
fake_B_feats = self.lossNet(fake_B)
comp_B_feats = self.lossNet(comp_B)
tv_loss = self.TV_loss(comp_B * (1 - self.mask))
style_loss = self.style_loss(real_B_feats, fake_B_feats) + self.style_loss(real_B_feats, comp_B_feats)
preceptual_loss = self.preceptual_loss(real_B_feats, fake_B_feats) + self.preceptual_loss(real_B_feats, comp_B_feats)
valid_loss = self.l1_loss(real_B, fake_B, self.mask)
hole_loss = self.l1_loss(real_B, fake_B, (1 - self.mask))
loss_G = ( tv_loss * 0.1
+ style_loss * 120
+ preceptual_loss * 0.05
+ valid_loss * 1
+ hole_loss * 6)
self.l1_loss_val += valid_loss.detach() + hole_loss.detach()
return loss_G
def l1_loss(self, f1, f2, mask = 1):
return torch.mean(torch.abs(f1 - f2)*mask)
def style_loss(self, A_feats, B_feats):
assert len(A_feats) == len(B_feats), "the length of two input feature maps lists should be the same"
loss_value = 0.0
for i in range(len(A_feats)):
A_feat = A_feats[i]
B_feat = B_feats[i]
_, c, w, h = A_feat.size()
A_feat = A_feat.view(A_feat.size(0), A_feat.size(1), A_feat.size(2) * A_feat.size(3))
B_feat = B_feat.view(B_feat.size(0), B_feat.size(1), B_feat.size(2) * B_feat.size(3))
A_style = torch.matmul(A_feat, A_feat.transpose(2, 1))
B_style = torch.matmul(B_feat, B_feat.transpose(2, 1))
loss_value += torch.mean(torch.abs(A_style - B_style)/(c * w * h))
return loss_value
def TV_loss(self, x):
h_x = x.size(2)
w_x = x.size(3)
h_tv = torch.mean(torch.abs(x[:,:,1:,:]-x[:,:,:h_x-1,:]))
w_tv = torch.mean(torch.abs(x[:,:,:,1:]-x[:,:,:,:w_x-1]))
return h_tv + w_tv
def preceptual_loss(self, A_feats, B_feats):
assert len(A_feats) == len(B_feats), "the length of two input feature maps lists should be the same"
loss_value = 0.0
for i in range(len(A_feats)):
A_feat = A_feats[i]
B_feat = B_feats[i]
loss_value += torch.mean(torch.abs(A_feat - B_feat))
return loss_value
def __cuda__(self, *args):
return (item.to(self.device) for item in args)