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import os
import numpy as np
import torch
import matplotlib.pyplot as plt
import torchvision.transforms.functional as F
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.enabled=False
torch.backends.cudnn.deterministic = True
from torchvision.models.optical_flow import Raft_Large_Weights
weights = Raft_Large_Weights.DEFAULT
transforms = weights.transforms()
def preprocess(source_batch, target_batch):
source_batch = F.resize(source_batch, size=[480, 832], antialias=False)
target_batch = F.resize(target_batch, size=[480, 832], antialias=False)
return transforms(source_batch, target_batch)
from torchvision.models.optical_flow import raft_large
# If you can, run this example on a GPU, it will be a lot faster.
device = "cuda" if torch.cuda.is_available() else "cpu"
model = raft_large(weights=Raft_Large_Weights.DEFAULT, progress=False).to(device)
model = model.eval()
def calculate_epe(img1_batch, img2_batch):
# img [N, C, H, W]
# first calculate the op of img1 and img2
img1_source, img1_target = preprocess(img1_batch[:-1], img1_batch[1:])
img2_source, img2_target = preprocess(img2_batch[:-1], img2_batch[1:])
# op
img1_flows = model(img1_source.to(device).contiguous(), img1_target.to(device).contiguous())[-1] # [N, 2, H, W]
img2_flows = model(img2_source.to(device).contiguous(), img2_target.to(device).contiguous())[-1]
# epe
diff = img1_flows - img2_flows
epe = torch.norm(diff, p=2, dim=1)
mean_epe = epe.mean()
return mean_epe.cpu().numpy()
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