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| 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, | |
| } | |