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import torch
from torch.optim import AdamW
from torch.nn.parallel import DistributedDataParallel as DDP
from modules.rife.model_ifnet import IFNet
from modules.rife.loss import EPE, SOBEL
from modules import devices
class RifeModel:
def __init__(self, local_rank=-1):
self.flownet = IFNet()
self.device()
self.optimG = AdamW(self.flownet.parameters(), lr=1e-6, weight_decay=1e-4)
self.epe = EPE()
self.version = 3.9
# self.vgg = VGGPerceptualLoss().to(device)
self.sobel = SOBEL()
if local_rank != -1:
self.flownet = DDP(self.flownet, device_ids=[local_rank], output_device=local_rank)
def train(self):
self.flownet.train()
def eval(self):
self.flownet.eval()
def device(self):
self.flownet.to(devices.device)
self.flownet.to(devices.dtype)
def load_model(self, model_file, rank=0):
def convert(param):
if rank == -1:
return { k.replace("module.", ""): v for k, v in param.items() if "module." in k }
else:
return param
if rank <= 0:
if torch.cuda.is_available():
self.flownet.load_state_dict(convert(torch.load(model_file)), False)
else:
self.flownet.load_state_dict(convert(torch.load(model_file, map_location='cpu')), False)
def save_model(self, model_file, rank=0):
if rank == 0:
torch.save(self.flownet.state_dict(), model_file)
def inference(self, img0, img1, timestep=0.5, scale=1.0):
imgs = torch.cat((img0, img1), 1)
scale_list = [8/scale, 4/scale, 2/scale, 1/scale]
_flow, _mask, merged = self.flownet(imgs, timestep, scale_list)
return merged[3]
def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None): # pylint: disable=unused-argument
for param_group in self.optimG.param_groups:
param_group['lr'] = learning_rate
# img0 = imgs[:, :3]
# img1 = imgs[:, 3:]
if training:
self.train()
else:
self.eval()
scale = [8, 4, 2, 1]
flow, mask, merged = self.flownet(torch.cat((imgs, gt), 1), scale=scale, training=training)
loss_l1 = (merged[3] - gt).abs().mean()
loss_smooth = self.sobel(flow[3], flow[3]*0).mean()
# loss_vgg = self.vgg(merged[2], gt)
if training:
self.optimG.zero_grad()
loss_G = loss_l1 + loss_smooth * 0.1
loss_G.backward()
self.optimG.step()
# else:
# flow_teacher = flow[2]
return merged[3], {
'mask': mask,
'flow': flow[3][:, :2],
'loss_l1': loss_l1,
'loss_smooth': loss_smooth,
}
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