util/rife/RIFE.py

from torch.optim import AdamW
from torch.nn.parallel import DistributedDataParallel as DDP
from .IFNet import *
from .IFNet_m import *
from .loss import *
from .laplacian import *
from .refine import *

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


class Model:
    def __init__(self, local_rank=-1, arbitrary=False):
        if arbitrary == True:
            self.flownet = IFNet_m()
        else:
            self.flownet = IFNet()
        self.device()
        self.optimG = AdamW(
            self.flownet.parameters(), lr=1e-6, weight_decay=1e-3
        )  # use large weight decay may avoid NaN loss
        self.epe = EPE()
        self.lap = LapLoss()
        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(device)

    def load_model(self, path, rank=0):
        def convert(param):
            return {k.replace("module.", ""): v for k, v in param.items() if "module." in k}

        if rank <= 0:
            self.flownet.load_state_dict(convert(torch.load("{}/flownet.pkl".format(path))))

    def save_model(self, path, rank=0):
        if rank == 0:
            torch.save(self.flownet.state_dict(), "{}/flownet.pkl".format(path))

    def inference(self, img0, img1, scale=1, scale_list=[4, 2, 1], TTA=False, timestep=0.5):
        for i in range(3):
            scale_list[i] = scale_list[i] * 1.0 / scale
        imgs = torch.cat((img0, img1), 1)
        flow, mask, merged, flow_teacher, merged_teacher, loss_distill = self.flownet(
            imgs, scale_list, timestep=timestep
        )
        if TTA == False:
            return merged[2]
        else:
            flow2, mask2, merged2, flow_teacher2, merged_teacher2, loss_distill2 = self.flownet(
                imgs.flip(2).flip(3), scale_list, timestep=timestep
            )
            return (merged[2] + merged2[2].flip(2).flip(3)) / 2

    def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
        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()
        flow, mask, merged, flow_teacher, merged_teacher, loss_distill = self.flownet(
            torch.cat((imgs, gt), 1), scale=[4, 2, 1]
        )
        loss_l1 = (self.lap(merged[2], gt)).mean()
        loss_tea = (self.lap(merged_teacher, gt)).mean()
        if training:
            self.optimG.zero_grad()
            loss_G = (
                loss_l1 + loss_tea + loss_distill * 0.01
            )  # when training RIFEm, the weight of loss_distill should be 0.005 or 0.002
            loss_G.backward()
            self.optimG.step()
        else:
            flow_teacher = flow[2]
        return merged[2], {
            "merged_tea": merged_teacher,
            "mask": mask,
            "mask_tea": mask,
            "flow": flow[2][:, :2],
            "flow_tea": flow_teacher,
            "loss_l1": loss_l1,
            "loss_tea": loss_tea,
            "loss_distill": loss_distill,
        }