|
import torch |
|
import torch.nn as nn |
|
import numpy as np |
|
from torch.optim import AdamW |
|
import torch.optim as optim |
|
import itertools |
|
from model.warplayer import warp |
|
from torch.nn.parallel import DistributedDataParallel as DDP |
|
from model.IFNet import * |
|
from model.IFNet_m import * |
|
import torch.nn.functional as F |
|
from model.loss import * |
|
from model.laplacian import * |
|
from model.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) |
|
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 |
|
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, |
|
} |
|
|