trainer.py
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import os
import cv2
import time
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.utils.data as data
from torch.optim.lr_scheduler import StepLR
from torchvision.utils import save_image
from models import *
from losses import *
from datasets import Places365Dataset, FacemaskDataset
def adjust_learning_rate(optimizer, gamma, num_steps=1):
for i in range(num_steps):
for param_group in optimizer.param_groups:
param_group['lr'] *= gamma
def get_epoch_iters(path):
path = os.path.basename(path)
tokens = path[:-4].split('_')
try:
if tokens[-1] == 'interrupted':
epoch_idx = int(tokens[-3])
iter_idx = int(tokens[-2])
else:
epoch_idx = int(tokens[-2])
iter_idx = int(tokens[-1])
except:
return 0, 0
return epoch_idx, iter_idx
def load_checkpoint(model_G, model_D, path):
state = torch.load(path,map_location='cpu')
model_G.load_state_dict(state['G'])
model_D.load_state_dict(state['D'])
print('Loaded checkpoint successfully')
class Trainer():
def __init__(self, args, cfg):
if args.resume is not None:
epoch, iters = get_epoch_iters(args.resume)
else:
epoch = 0
iters = 0
if not os.path.exists(cfg.checkpoint_path):
os.makedirs(cfg.checkpoint_path)
if not os.path.exists(cfg.sample_folder):
os.makedirs(cfg.sample_folder)
self.cfg = cfg
self.step_iters = cfg.step_iters
self.gamma = cfg.gamma
self.visualize_per_iter = cfg.visualize_per_iter
self.print_per_iter = cfg.print_per_iter
self.save_per_iter = cfg.save_per_iter
self.start_iter = iters
self.iters = 0
self.num_epochs = cfg.num_epochs
self.device = torch.device('cuda' if cfg.cuda else 'cpu')
trainset = FacemaskDataset(cfg) # Places365Dataset(cfg) #
self.trainloader = data.DataLoader(
trainset,
batch_size=cfg.batch_size,
num_workers = cfg.num_workers,
pin_memory = True,
shuffle=True,
collate_fn = trainset.collate_fn)
self.epoch = int(self.start_iter / len(self.trainloader))
self.iters = self.start_iter
self.num_iters = (self.num_epochs+1) * len(self.trainloader)
self.model_G = GatedGenerator().to(self.device)
self.model_D = NLayerDiscriminator(cfg.d_num_layers, use_sigmoid=False).to(self.device)
self.model_P = PerceptualNet(name = "vgg16", resize=False).to(self.device)
if args.resume is not None:
load_checkpoint(self.model_G, self.model_D, args.resume)
self.criterion_adv = GANLoss(target_real_label=0.9, target_fake_label=0.1)
self.criterion_rec = nn.SmoothL1Loss()
self.criterion_ssim = SSIM(window_size = 11)
self.criterion_per = nn.SmoothL1Loss()
self.optimizer_D = torch.optim.Adam(self.model_D.parameters(), lr=cfg.lr)
self.optimizer_G = torch.optim.Adam(self.model_G.parameters(), lr=cfg.lr)
def validate(self, sample_folder, sample_name, img_list):
save_img_path = os.path.join(sample_folder, sample_name+'.png')
img_list = [i.clone().cpu() for i in img_list]
imgs = torch.stack(img_list, dim=1)
# imgs shape: Bx5xCxWxH
imgs = imgs.view(-1, *list(imgs.size())[2:])
save_image(imgs, save_img_path, nrow= 5)
print(f"Save image to {save_img_path}")
def fit(self):
self.model_G.train()
self.model_D.train()
running_loss = {
'D': 0,
'G': 0,
'P': 0,
'R_1': 0,
'R_2': 0,
'T': 0,
}
running_time = 0
step = 0
try:
for epoch in range(self.epoch, self.num_epochs):
self.epoch = epoch
for i, batch in enumerate(self.trainloader):
start_time = time.time()
imgs = batch['imgs'].to(self.device)
masks = batch['masks'].to(self.device)
# Train discriminator
self.optimizer_D.zero_grad()
self.optimizer_G.zero_grad()
first_out, second_out = self.model_G(imgs, masks)
first_out_wholeimg = imgs * (1 - masks) + first_out * masks
second_out_wholeimg = imgs * (1 - masks) + second_out * masks
masks = masks.cpu()
fake_D = self.model_D(second_out_wholeimg.detach())
real_D = self.model_D(imgs)
loss_fake_D = self.criterion_adv(fake_D, target_is_real=False)
loss_real_D = self.criterion_adv(real_D, target_is_real=True)
loss_D = (loss_fake_D + loss_real_D) * 0.5
loss_D.backward()
self.optimizer_D.step()
real_D = None
# Train Generator
self.optimizer_D.zero_grad()
self.optimizer_G.zero_grad()
fake_D = self.model_D(second_out_wholeimg)
loss_G = self.criterion_adv(fake_D, target_is_real=True)
fake_D = None
# Reconstruction loss
loss_l1_1 = self.criterion_rec(first_out_wholeimg, imgs)
loss_l1_2 = self.criterion_rec(second_out_wholeimg, imgs)
loss_ssim_1 = self.criterion_ssim(first_out_wholeimg, imgs)
loss_ssim_2 = self.criterion_ssim(second_out_wholeimg, imgs)
loss_rec_1 = 0.5 * loss_l1_1 + 0.5 * (1 - loss_ssim_1)
loss_rec_2 = 0.5 * loss_l1_2 + 0.5 * (1 - loss_ssim_2)
# Perceptual loss
loss_P = self.model_P(second_out_wholeimg, imgs)
loss = self.cfg.lambda_G * loss_G + self.cfg.lambda_rec_1 * loss_rec_1 + self.cfg.lambda_rec_2 * loss_rec_2 + self.cfg.lambda_per * loss_P
loss.backward()
self.optimizer_G.step()
end_time = time.time()
imgs = imgs.cpu()
# Visualize number
running_time += (end_time - start_time)
running_loss['D'] += loss_D.item()
running_loss['G'] += (self.cfg.lambda_G * loss_G.item())
running_loss['P'] += (self.cfg.lambda_per * loss_P.item())
running_loss['R_1'] += (self.cfg.lambda_rec_1 * loss_rec_1.item())
running_loss['R_2'] += (self.cfg.lambda_rec_2 * loss_rec_2.item())
running_loss['T'] += loss.item()
if self.iters % self.print_per_iter == 0:
for key in running_loss.keys():
running_loss[key] /= self.print_per_iter
running_loss[key] = np.round(running_loss[key], 5)
loss_string = '{}'.format(running_loss)[1:-1].replace("'",'').replace(",",' ||')
print("[{}|{}] [{}|{}] || {} || Time: {:10.4f}s".format(self.epoch, self.num_epochs, self.iters, self.num_iters, loss_string, running_time))
running_loss = {
'D': 0,
'G': 0,
'P': 0,
'R_1': 0,
'R_2': 0,
'T': 0,
}
running_time = 0
if self.iters % self.save_per_iter == 0:
torch.save({
'D': self.model_D.state_dict(),
'G': self.model_G.state_dict(),
}, os.path.join(self.cfg.checkpoint_path, f"model_{self.epoch}_{self.iters}.pth"))
# Step learning rate
if self.iters == self.step_iters[step]:
adjust_learning_rate(self.optimizer_D, self.gamma)
adjust_learning_rate(self.optimizer_G, self.gamma)
step+=1
# Visualize sample
if self.iters % self.visualize_per_iter == 0:
masked_imgs = imgs * (1 - masks) + masks
img_list = [imgs, masked_imgs, first_out, second_out, second_out_wholeimg]
#name_list = ['gt', 'mask', 'masked_img', 'first_out', 'second_out']
filename = f"{self.epoch}_{str(self.iters)}"
self.validate(self.cfg.sample_folder, filename , img_list)
self.iters += 1
except KeyboardInterrupt:
torch.save({
'D': self.model_D.state_dict(),
'G': self.model_G.state_dict(),
}, os.path.join(self.cfg.checkpoint_path, f"model_{self.epoch}_{self.iters}.pth"))