video dense caption code
时间: 2023-10-29 18:05:14 浏览: 53
Sure, here is an example of code for video dense captioning using Python and the PyTorch framework:
```
import torch
import torchvision
from torchvision import transforms
from torch.nn.utils.rnn import pack_padded_sequence
# Define the model architecture
class VideoCaptioningModel(torch.nn.Module):
def __init__(self, ...):
super(VideoCaptioningModel, self).__init__()
# Define and initialize your model layers
def forward(self, ...):
# Implement forward pass logic here
return captions
# Set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Instantiate the model
model = VideoCaptioningModel(...).to(device)
# Preprocess video frames
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# Load and preprocess video frames
video_frames = torchvision.io.read_video('path/to/video.mp4', start_pts=0, end_pts=None)
preprocessed_frames = torch.stack([transform(frame) for frame in video_frames[0]])
# Encode video frames using a CNN
cnn = torchvision.models.resnet50(pretrained=True)
cnn = torch.nn.Sequential(*list(cnn.children())[:-1])
cnn.to(device)
with torch.no_grad():
encoded_frames = cnn(preprocessed_frames.unsqueeze(0).to(device)).squeeze()
# Generate captions
with torch.no_grad():
outputs = model(encoded_frames)
# Decode the captions
captions = []
for output in outputs:
tokens = []
for token_id in output:
token = vocabulary.get_word(token_id.item())
if token == '<end>':
break
tokens.append(token)
caption = ' '.join(tokens)
captions.append(caption)
# Print the captions
print(captions)
```
This is just a basic example to give you an idea of how video dense captioning can be implemented using PyTorch. You would need to fill in the details specific to your model architecture and any other customizations required for your task.
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