mirror of https://github.com/n00mkrad/flowframes
89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
import torch
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import torch.nn as nn
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import numpy as np
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from torch.optim import AdamW
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import torch.optim as optim
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import itertools
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from model.warplayer import warp
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from torch.nn.parallel import DistributedDataParallel as DDP
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from model.IFNet_HDv3 import *
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import torch.nn.functional as F
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from model.loss import *
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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class Model:
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def __init__(self, local_rank=-1):
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self.flownet = IFNet()
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self.device()
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self.optimG = AdamW(self.flownet.parameters(), lr=1e-6, weight_decay=1e-4)
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self.epe = EPE()
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# self.vgg = VGGPerceptualLoss().to(device)
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self.sobel = SOBEL()
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if local_rank != -1:
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self.flownet = DDP(self.flownet, device_ids=[local_rank], output_device=local_rank)
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def train(self):
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self.flownet.train()
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def eval(self):
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self.flownet.eval()
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def device(self):
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self.flownet.to(device)
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def load_model(self, path, rank=0):
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def convert(param):
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if rank == -1:
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return {
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k.replace("module.", ""): v
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for k, v in param.items()
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if "module." in k
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}
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else:
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return param
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if rank <= 0:
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if torch.cuda.is_available():
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self.flownet.load_state_dict(convert(torch.load('{}/flownet.pkl'.format(path))))
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else:
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self.flownet.load_state_dict(convert(torch.load('{}/flownet.pkl'.format(path), map_location ='cpu')))
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def save_model(self, path, rank=0):
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if rank == 0:
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torch.save(self.flownet.state_dict(),'{}/flownet.pkl'.format(path))
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def inference(self, img0, img1, scale=1.0):
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imgs = torch.cat((img0, img1), 1)
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scale_list = [4, 2, 1]
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flow, mask, merged = self.flownet(imgs, scale_list, scale=scale)
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return merged[2]
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def update(self, imgs, gt, learning_rate=0, mul=1, training=True, flow_gt=None):
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for param_group in self.optimG.param_groups:
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param_group['lr'] = learning_rate
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img0 = imgs[:, :3]
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img1 = imgs[:, 3:]
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if training:
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self.train()
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else:
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self.eval()
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scale = [4, 2, 1]
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flow, mask, merged = self.flownet(torch.cat((imgs, gt), 1), scale=scale, training=training)
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loss_l1 = (merged[2] - gt).abs().mean()
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loss_smooth = self.sobel(flow[2], flow[2]*0).mean()
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# loss_vgg = self.vgg(merged[2], gt)
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if training:
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self.optimG.zero_grad()
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loss_G = loss_cons + loss_smooth * 0.1
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loss_G.backward()
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self.optimG.step()
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else:
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flow_teacher = flow[2]
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return merged[2], {
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'mask': mask,
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'flow': flow[2][:, :2],
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'loss_l1': loss_l1,
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'loss_cons': loss_cons,
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'loss_smooth': loss_smooth,
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}
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