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train_ablation_ff.py
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train_ablation_ff.py
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from torch.autograd import Variable
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
import argparse
from utils import *
from utils2 import *
# import basicsrwsl
from model_simple_ff import *
import cv2
from skimage.metrics import structural_similarity as compare_ssim
from math import log10, sqrt
import math
def PSNR(original, compressed):
mse = np.mean((original - compressed) ** 2)
if(mse == 0): # MSE is zero means no noise is present in the signal .
# Therefore PSNR have no importance.
return 100
max_pixel = 1.0
try:
psnr = 20 * log10(max_pixel / sqrt(mse))
except:
psnr = 38
return psnr
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--scale_factor", type=int, default=4)
parser.add_argument('--device', type=str, default='cuda:0')
parser.add_argument('--batch_size', type=int, default=24)
parser.add_argument('--lr', type=float, default=1e-3, help='initial learning rate')
parser.add_argument('--gamma', type=float, default=0.5, help='')
parser.add_argument('--start_epoch', type=int, default=0, help='start epoch')
parser.add_argument('--n_epochs', type=int, default=30, help='number of epochs to train')
parser.add_argument('--n_steps', type=int, default=20, help='number of epochs to update learning rate')
parser.add_argument('--trainset_dir', type=str, default='./data/train/')
parser.add_argument('--model_name', type=str, default='TransSNSR')
parser.add_argument('--load_pretrain', type=bool, default=False)
parser.add_argument('--model_path', type=str, default='log/TransSNSR.pth.tar')
return parser.parse_args()
def train(train_loader, cfg):
spatial_dim = (128, 128)
net = Net(cfg.scale_factor, spatial_dim, cfg).to(cfg.device)
print("n_param:", sum(p.numel() for p in net.parameters() if p.requires_grad))
print("n_param_all:",sum(p.numel() for p in net.parameters()))
cudnn.benchmark = True
scale = cfg.scale_factor
if cfg.load_pretrain:
if os.path.isfile(cfg.model_path):
model = torch.load(cfg.model_path, map_location={'cuda:0': cfg.device})
net.load_state_dict(model['state_dict'])
cfg.start_epoch = model["epoch"]
else:
print("=> no model found at '{}'".format(cfg.load_model))
criterion_L1 = torch.nn.L1Loss().to(cfg.device)
optimizer = torch.optim.Adam([paras for paras in net.parameters() if paras.requires_grad == True], lr=cfg.lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=cfg.n_steps, gamma=cfg.gamma)
torch.autograd.set_detect_anomaly(True)
loss_epoch = []
loss_list = []
losses = AverageMeter()
net.train()
for idx_epoch in range(cfg.start_epoch, cfg.n_epochs):
for idx_iter, (HR_left, HR_right, LR_left, LR_right) in enumerate(train_loader):
b, p, c, h, w = LR_left.shape
HR_left, HR_right, LR_left, LR_right = Variable(HR_left).to(cfg.device), Variable(HR_right).to(cfg.device),\
Variable(LR_left).to(cfg.device), Variable(LR_right).to(cfg.device)
SR_left, SR_right = net(LR_left, LR_right, is_training=1)
''' SR Loss '''
loss_SR = criterion_L1(SR_left, HR_left) + criterion_L1(SR_right, HR_right)
''' Photometric Loss '''
# Res_left = torch.abs(HR_left - F.interpolate(LR_left, scale_factor=scale, mode='bicubic', align_corners=False))
# Res_left = F.interpolate(Res_left, scale_factor=1 / scale, mode='bicubic', align_corners=False)
# Res_right = torch.abs(HR_right - F.interpolate(LR_right, scale_factor=scale, mode='bicubic', align_corners=False))
# Res_right = F.interpolate(Res_right, scale_factor=1 / scale, mode='bicubic', align_corners=False)
# Res_leftT = torch.bmm(M_right_to_left.contiguous().view(b * h, w, w), Res_right.permute(0, 2, 3, 1).contiguous().view(b * h, w, c)
# ).view(b, h, w, c).contiguous().permute(0, 3, 1, 2)
# Res_rightT = torch.bmm(M_left_to_right.contiguous().view(b * h, w, w), Res_left.permute(0, 2, 3, 1).contiguous().view(b * h, w, c)
# ).view(b, h, w, c).contiguous().permute(0, 3, 1, 2)
# loss_photo = criterion_L1(Res_left * V_left.repeat(1, 3, 1, 1), Res_leftT * V_left.repeat(1, 3, 1, 1)) + \
# criterion_L1(Res_right * V_right.repeat(1, 3, 1, 1), Res_rightT * V_right.repeat(1, 3, 1, 1))
''' Smoothness Loss '''
# loss_h = criterion_L1(M_right_to_left[:, :-1, :, :], M_right_to_left[:, 1:, :, :]) + \
# criterion_L1(M_left_to_right[:, :-1, :, :], M_left_to_right[:, 1:, :, :])
# loss_w = criterion_L1(M_right_to_left[:, :, :-1, :-1], M_right_to_left[:, :, 1:, 1:]) + \
# criterion_L1(M_left_to_right[:, :, :-1, :-1], M_left_to_right[:, :, 1:, 1:])
# loss_smooth = loss_w + loss_h
''' Cycle Loss '''
# Res_left_cycle = torch.bmm(M_right_to_left.contiguous().view(b * h, w, w), Res_rightT.permute(0, 2, 3, 1).contiguous().view(b * h, w, c)
# ).view(b, h, w, c).contiguous().permute(0, 3, 1, 2)
# Res_right_cycle = torch.bmm(M_left_to_right.contiguous().view(b * h, w, w), Res_leftT.permute(0, 2, 3, 1).contiguous().view(b * h, w, c)
# ).view(b, h, w, c).contiguous().permute(0, 3, 1, 2)
# loss_cycle = criterion_L1(Res_left * V_left.repeat(1, 3, 1, 1), Res_left_cycle * V_left.repeat(1, 3, 1, 1)) + \
# criterion_L1(Res_right * V_right.repeat(1, 3, 1, 1), Res_right_cycle * V_right.repeat(1, 3, 1, 1))
''' Consistency Loss '''
# SR_left_res = F.interpolate(torch.abs(HR_left - SR_left), scale_factor=1 / scale, mode='bicubic', align_corners=False)
# SR_right_res = F.interpolate(torch.abs(HR_right - SR_right), scale_factor=1 / scale, mode='bicubic', align_corners=False)
# SR_left_resT = torch.bmm(M_right_to_left.detach().contiguous().view(b * h, w, w), SR_right_res.permute(0, 2, 3, 1).contiguous().view(b * h, w, c)
# ).view(b, h, w, c).contiguous().permute(0, 3, 1, 2)
# SR_right_resT = torch.bmm(M_left_to_right.detach().contiguous().view(b * h, w, w), SR_left_res.permute(0, 2, 3, 1).contiguous().view(b * h, w, c)
# ).view(b, h, w, c).contiguous().permute(0, 3, 1, 2)
# loss_cons = criterion_L1(SR_left_res * V_left.repeat(1, 3, 1, 1), SR_left_resT * V_left.repeat(1, 3, 1, 1)) + \
# criterion_L1(SR_right_res * V_right.repeat(1, 3, 1, 1), SR_right_resT * V_right.repeat(1, 3, 1, 1))
''' Total Loss '''
loss = loss_SR
optimizer.zero_grad()
# if math.isnan(loss):
# continue
loss.backward()
optimizer.step()
psnr_value = 0
psnr_value2 = 0
for ii in range(HR_left.shape[0]):
psnr_value = psnr_value + PSNR(HR_left[ii].cpu().detach().numpy(), SR_left[ii].cpu().detach().numpy())
psnr_value2 = psnr_value2 + PSNR(HR_right[ii].cpu().detach().numpy(), SR_right[ii].cpu().detach().numpy())
psnr_value = psnr_value / HR_left.shape[0]
psnr_value2 = psnr_value2 / HR_left.shape[0]
losses.update(psnr_value)
loss_epoch.append(loss.data.cpu())
print("idx_epoch: ",idx_epoch,"idx_iter: ", idx_iter,"(",len(train_loader),") " 'Loss: ',float(np.array(loss_SR.data.cpu()).mean()), "psnr: ", psnr_value, "pm",losses.avg)
scheduler.step()
if idx_epoch % 1 == 0:
loss_list.append(float(np.array(loss_epoch).mean()))
print('Epoch--%4d, loss--%f, loss_SR--%f' %
(idx_epoch + 1, float(np.array(loss_SR.data.cpu()).mean()), float(np.array(loss_SR.data.cpu()).mean())))
torch.save({'epoch': idx_epoch + 1, 'state_dict': net.state_dict()},
'log/' + cfg.model_name + '_' + str(cfg.scale_factor) + 'xSR_epoch' + str(idx_epoch + 1) + '.pth.tar')
loss_epoch = []
def main(cfg):
train_set = TrainSetLoader(cfg)
train_loader = DataLoader(dataset=train_set, num_workers=0,pin_memory=True, batch_size=cfg.batch_size, shuffle=True)
train(train_loader, cfg)
if __name__ == '__main__':
cfg = parse_args()
main(cfg)