Mirrors/ECCV2022-RIFE
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Move HD model

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hzwer
2021-09-02 17:38:01 +08:00
parent ea632dd3fb
commit 335bff589a
5 changed files with 5 additions and 5 deletions
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6
inference_video.py
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@@ -85,17 +85,17 @@ if torch.cuda.is_available():
try: try:
try: try:
from model.RIFE_HDv2 import Model from model.oldmodel.RIFE_HDv2 import Model
model = Model() model = Model()
model.load_model(args.modelDir, -1) model.load_model(args.modelDir, -1)
print("Loaded v2.x HD model.") print("Loaded v2.x HD model.")
except: except:
from train_log.RIFE_HDv3 import Model from train_log.oldmodel.RIFE_HDv3 import Model
model = Model() model = Model()
model.load_model(args.modelDir, -1) model.load_model(args.modelDir, -1)
print("Loaded v3.x HD model.") print("Loaded v3.x HD model.")
except: except:
from model.RIFE_HD import Model from model.oldmodel.RIFE_HD import Model
model = Model() model = Model()
model.load_model(args.modelDir, -1) model.load_model(args.modelDir, -1)
print("Loaded v1.x HD model") print("Loaded v1.x HD model")

0
model/IFNet_HD.py → model/old_model/IFNet_HD.py
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0
model/IFNet_HDv2.py → model/old_model/IFNet_HDv2.py
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2
model/RIFE_HD.py → model/old_model/RIFE_HD.py
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@@ -6,7 +6,7 @@ import torch.optim as optim
import itertools import itertools
from model.warplayer import warp from model.warplayer import warp
from torch.nn.parallel import DistributedDataParallel as DDP from torch.nn.parallel import DistributedDataParallel as DDP
from model.IFNet_HD import * from model.oldmodel.IFNet_HD import *
import torch.nn.functional as F import torch.nn.functional as F
from model.loss import * from model.loss import *

2
model/RIFE_HDv2.py → model/old_model/RIFE_HDv2.py
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@@ -6,7 +6,7 @@ import torch.optim as optim
import itertools import itertools
from model.warplayer import warp from model.warplayer import warp
from torch.nn.parallel import DistributedDataParallel as DDP from torch.nn.parallel import DistributedDataParallel as DDP
from model.IFNet_HDv2 import * from model.oldmodel.IFNet_HDv2 import *
import torch.nn.functional as F import torch.nn.functional as F
from model.loss import * from model.loss import *