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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,
}
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