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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torch.optim as optim | |
| from torch.optim import AdamW | |
| import numpy as np | |
| import itertools | |
| from .warplayer import warp | |
| from torch.nn.parallel import DistributedDataParallel as DDP | |
| from .IFNet_HDv3 import * | |
| from .loss import * | |
| import devicetorch | |
| device = devicetorch.get(torch) | |
| class Model: | |
| 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.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(device) | |
| def load_model(self, path, 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: | |
| model_path = "{}/flownet.pkl".format(path) | |
| # Check PyTorch version to safely use weights_only | |
| from packaging import version | |
| use_weights_only = version.parse(torch.__version__) >= version.parse("1.13") | |
| load_kwargs = {} | |
| if not torch.cuda.is_available(): | |
| load_kwargs['map_location'] = "cpu" | |
| if use_weights_only: | |
| # For modern PyTorch, be explicit and safe | |
| load_kwargs['weights_only'] = True | |
| # print(f"PyTorch >= 1.13 detected. Loading RIFE model with weights_only=True.") | |
| state_dict = torch.load(model_path, **load_kwargs) | |
| else: | |
| # For older PyTorch, load the old way | |
| print(f"PyTorch < 1.13 detected. Loading RIFE model using legacy method.") | |
| state_dict = torch.load(model_path, **load_kwargs) | |
| self.flownet.load_state_dict(convert(state_dict)) | |
| def inference(self, img0, img1, scale=1.0): | |
| imgs = torch.cat((img0, img1), 1) | |
| scale_list = [4 / scale, 2 / scale, 1 / scale] | |
| flow, mask, merged = self.flownet(imgs, scale_list) | |
| return merged[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() | |
| scale = [4, 2, 1] | |
| flow, mask, merged = self.flownet(torch.cat((imgs, gt), 1), scale=scale, training=training) | |
| loss_l1 = (merged[2] - gt).abs().mean() | |
| loss_smooth = self.sobel(flow[2], flow[2] * 0).mean() | |
| # loss_vgg = self.vgg(merged[2], gt) | |
| if training: | |
| self.optimG.zero_grad() | |
| loss_G = loss_cons + loss_smooth * 0.1 | |
| loss_G.backward() | |
| self.optimG.step() | |
| else: | |
| flow_teacher = flow[2] | |
| return merged[2], { | |
| "mask": mask, | |
| "flow": flow[2][:, :2], | |
| "loss_l1": loss_l1, | |
| "loss_cons": loss_cons, | |
| "loss_smooth": loss_smooth, | |
| } | |