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train.py
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train.py
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import os
from config import Config
opt = Config('training.yml')
gpus = ','.join([str(i) for i in opt.GPU])
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = gpus
import torch
torch.backends.cudnn.benchmark = True
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from natsort import natsorted
import random
import time
import numpy as np
import utils
from dataloaders.data_rgb import get_training_data, get_validation_data
from pdb import set_trace as stx
from networks.LLCaps import LLCaps
from utils.losses import CharbonnierLoss
from tqdm import tqdm
from warmup_scheduler import GradualWarmupScheduler
from networks.Discriminator import Discriminator
######### Set Seeds ###########
random.seed(1234)
np.random.seed(1234)
torch.manual_seed(1234)
torch.cuda.manual_seed_all(1234)
start_epoch = 1
mode = opt.MODEL.MODE
session = opt.MODEL.SESSION
result_dir = os.path.join(opt.TRAINING.SAVE_DIR, mode, 'results', session)
model_dir = os.path.join(opt.TRAINING.SAVE_DIR, mode, 'models', session)
utils.mkdir(result_dir)
utils.mkdir(model_dir)
train_dir = opt.TRAINING.TRAIN_DIR
val_dir = opt.TRAINING.VAL_DIR
save_images = opt.TRAINING.SAVE_IMAGES
######### Model ###########
model_restoration = LLCaps(device = 'cuda:1')
model_restoration.cuda()
model_discriminator = Discriminator()
model_discriminator.cuda()
device_ids = [i for i in range(torch.cuda.device_count())]
if torch.cuda.device_count() > 1:
print("\n\nLet's use", torch.cuda.device_count(), "GPUs!\n\n")
new_lr = opt.OPTIM.LR_INITIAL
optimizer = optim.Adam(model_restoration.parameters(), lr=new_lr, betas=(0.9, 0.999),eps=1e-8, weight_decay=1e-8)
optimizer_d = optim.Adam(model_discriminator.parameters(), lr=new_lr, betas=(0.9, 0.999),eps=1e-8, weight_decay=1e-8)
######### Scheduler ###########
warmup_epochs = 3
scheduler_cosine = optim.lr_scheduler.CosineAnnealingLR(optimizer, opt.OPTIM.NUM_EPOCHS-warmup_epochs, eta_min=1e-6)
scheduler = GradualWarmupScheduler(optimizer, multiplier=1, total_epoch=warmup_epochs, after_scheduler=scheduler_cosine)
scheduler.step()
######### Resume ###########
if opt.TRAINING.RESUME:
path_chk_rest = utils.get_last_path(model_dir, '_latest.pth')
utils.load_checkpoint(model_restoration,path_chk_rest)
start_epoch = utils.load_start_epoch(path_chk_rest) + 1
utils.load_optim(optimizer, path_chk_rest)
for i in range(1, start_epoch):
scheduler.step()
new_lr = scheduler.get_lr()[0]
print('------------------------------------------------------------------------------')
print("==> Resuming Training with learning rate:", new_lr)
print('------------------------------------------------------------------------------')
if len(device_ids)>1:
model_restoration = nn.DataParallel(model_restoration, device_ids = device_ids)
if len(device_ids)>1:
model_discriminator = nn.DataParallel(model_discriminator, device_ids = device_ids)
######### Loss ###########
criterion = CharbonnierLoss().cuda()
######### DataLoaders ###########
img_options_train = {'patch_size':opt.TRAINING.TRAIN_PS}
train_dataset = get_training_data(train_dir, img_options_train)
train_loader = DataLoader(dataset=train_dataset, batch_size=opt.OPTIM.BATCH_SIZE, shuffle=True, num_workers=16, drop_last=False)
val_dataset = get_validation_data(val_dir, img_options_train)
val_loader = DataLoader(dataset=val_dataset, batch_size=16, shuffle=False, num_workers=8, drop_last=False)
print('===> Start Epoch {} End Epoch {}'.format(start_epoch,opt.OPTIM.NUM_EPOCHS + 1))
print('===> Loading datasets')
mixup = utils.MixUp_AUG()
best_psnr = 0
best_epoch = 0
best_iter = 0
eval_now = len(train_loader)//4 - 1
print(f"\nEvaluation after every {eval_now} Iterations !!!\n")
for epoch in range(start_epoch, opt.OPTIM.NUM_EPOCHS + 1):
epoch_start_time = time.time()
epoch_loss = 0
train_id = 1
for i, data in enumerate(tqdm(train_loader), 0):
# zero_grad
for param in model_restoration.parameters():
param.grad = None
target = data[0].cuda()
input_ = data[1].cuda()
if epoch>5:
target, input_ = mixup.aug(target, input_)
restored = model_restoration(input_)
restored = torch.clamp(restored,0,1)
loss = criterion(restored, target)
loss.backward()
optimizer.step()
epoch_loss +=loss.item()
#### Evaluation ####
if i%eval_now==0 and i>0:
if save_images:
utils.mkdir(result_dir + '%d/%d'%(epoch,i))
model_restoration.eval()
with torch.no_grad():
psnr_val_rgb = []
for ii, data_val in enumerate((val_loader), 0):
target = data_val[0].cuda()
input_ = data_val[1].cuda()
filenames = data_val[2]
restored = model_restoration(input_)
restored = torch.clamp(restored,0,1)
psnr_val_rgb.append(utils.batch_PSNR(restored, target, 1.))
if save_images:
target = target.permute(0, 2, 3, 1).cpu().detach().numpy()
input_ = input_.permute(0, 2, 3, 1).cpu().detach().numpy()
restored = restored.permute(0, 2, 3, 1).cpu().detach().numpy()
for batch in range(input_.shape[0]):
temp = np.concatenate((input_[batch]*255, restored[batch]*255, target[batch]*255),axis=1)
utils.save_img(os.path.join(result_dir, str(epoch), str(i), filenames[batch][:-4] +'.jpg'),temp.astype(np.uint8))
psnr_val_rgb = sum(psnr_val_rgb)/len(psnr_val_rgb)
if psnr_val_rgb > best_psnr:
best_psnr = psnr_val_rgb
best_epoch = epoch
best_iter = i
torch.save({'epoch': epoch,
'state_dict': model_restoration.state_dict(),
'optimizer' : optimizer.state_dict()
}, os.path.join(model_dir,"model_best.pth"))
print("[Ep %d it %d\t PSNR SIDD: %.4f\t] ---- [best_Ep_SIDD %d best_it_SIDD %d Best_PSNR_SIDD %.4f] " % (epoch, i, psnr_val_rgb,best_epoch,best_iter,best_psnr))
model_restoration.train()
scheduler.step()
print("------------------------------------------------------------------")
print("Epoch: {}\tTime: {:.4f}\tLoss: {:.4f}\tLearningRate {:.6f}".format(epoch, time.time()-epoch_start_time,epoch_loss, scheduler.get_lr()[0]))
print("------------------------------------------------------------------")
torch.save({'epoch': epoch,
'state_dict': model_restoration.state_dict(),
'optimizer' : optimizer.state_dict()
}, os.path.join(model_dir,"model_latest.pth"))
torch.save({'epoch': epoch,
'state_dict': model_restoration.state_dict(),
'optimizer' : optimizer.state_dict()
}, os.path.join(model_dir,f"model_epoch_{epoch}.pth"))