mirror of
https://github.com/THUDM/CogVideo.git
synced 2025-12-02 10:32:09 +08:00
957a210a72
- Integrated progress tracking with upscale model loading. - Implemented conditional latent upscaling using the upscale model. - Processed batch video frames using PyTorch and converted them to PIL images. - load_torch_file for params_ema convert weights
95 lines
3.6 KiB
Python
95 lines
3.6 KiB
Python
from torch.optim import AdamW
|
|
from torch.nn.parallel import DistributedDataParallel as DDP
|
|
from rife.IFNet import *
|
|
from rife.IFNet_m import *
|
|
from rife.loss import *
|
|
from rife.laplacian import *
|
|
from rife.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,
|
|
}
|