# Reshape for model video_tensor = video_tensor.unsqueeze(0) # Add batch dimension
import torch import torch.nn as nn import torchvision import torchvision.transforms as transforms import cv2 anal friend request.mp4
# Extract features with torch.no_grad(): features = model(video_tensor) # Reshape for model video_tensor = video_tensor
# Load a pre-trained model model = torchvision.models.video.i3d_resnet50(pretrained=True) anal friend request.mp4
print(features.shape) The extracted features can be used for various downstream tasks such as video clustering, similarity search, classification, etc.
# Prepare a transform for preprocessing frames transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ])