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"""
Example usage of VINE HuggingFace interface with pretrained VINE weights
This script demonstrates how to use the VINE model with your pretrained weights
from the ensemble format or from video-fm/vine_v0.
"""
import os
import sys
import torch
from transformers import pipeline
from transformers.pipelines import PIPELINE_REGISTRY
# Set your OpenAI API key here or via environment variable
#os.environ['OPENAI_API_KEY'] = "dummy-key"
# Add the parent directory to the path to import vine_hf
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from vine_hf import VineConfig, VineModel, VinePipeline
def example_with_local_pretrained_weights():
print("=== Using Local Pretrained VINE Weights ===")
# Download https://huggingface.co/video-fm/vine_v0/tree/main/laser_model_v1.pt
pretrained_vine_file = "/path/to/your/local/laser_model_v1.pt" # Replace with your local path
# Create configuration with your pretrained path (local file)
config = VineConfig(
model_name="openai/clip-vit-base-patch32",
segmentation_method="grounding_dino_sam2",
target_fps=1,
visualize=True,
visualization_dir="path/to/visualization/dir",
debug_visualizations=True,
use_hf_repo=False,
local_dir=os.path.dirname(pretrained_vine_file),
local_filename=os.path.basename(pretrained_vine_file),
)
# Method 1: Initialize model directly
print("Method 1: Direct model initialization")
vine_model = VineModel(config)
print(f"✓ Model initialized with pretrained weights from: {pretrained_vine_file}")
# Method 2: Use the from_pretrained_vine class method
print("\nMethod 2: Using from_pretrained_vine class method")
vine_model_2 = VineModel.from_pretrained_vine(
model_path=pretrained_vine_file,
config=config,
epoch=0 # Specify epoch number
)
print("✓ Model loaded using from_pretrained_vine method")
return vine_model
def example_with_huggingface_hub():
"""Example using VINE weights from HuggingFace Hub."""
print("\n=== Using HuggingFace Hub Weights ===")
# Create configuration to use HuggingFace Hub weights
config = VineConfig(
model_name="openai/clip-vit-base-patch32",
use_hf_repo=True,
model_repo="video-fm/vine_v0", # Your HF Hub model
segmentation_method="grounding_dino_sam2",
visualize=True,
visualization_dir="path/to/visualization/dir",
debug_visualizations=True,
)
try:
# Initialize model (will try to load from HF Hub)
vine_model = VineModel(config)
print("✓ Model loaded from HuggingFace Hub: video-fm/vine_v0")
return vine_model
except Exception as e:
print(f"✗ Could not load from HuggingFace Hub: {e}")
print("Make sure your model is pushed to video-fm/vine_v0")
return None
def example_pipeline_with_pretrained():
"""Example using pipeline with pretrained VINE weights."""
print("\n=== Pipeline with Pretrained VINE ===")
# Register the pipeline
PIPELINE_REGISTRY.register_pipeline(
"vine-video-understanding",
pipeline_class=VinePipeline,
pt_model=VineModel,
type="multimodal",
)
# Create configuration with your weights
pretrained_vine_file = "/path/to/your/local/laser_model_v1.pt" # Replace with your local path
config = VineConfig(
model_name="openai/clip-vit-base-patch32",
segmentation_method="grounding_dino_sam2",
visualize=True,
visualization_dir="path/to/visualization/dir",
debug_visualizations=True,
use_hf_repo=False,
local_dir=os.path.dirname(pretrained_vine_file),
local_filename=os.path.basename(pretrained_vine_file),
)
# Create model with pretrained weights
vine_model = VineModel(config)
# Create pipeline with segmentation model paths
vine_pipeline = VinePipeline(
model=vine_model,
tokenizer=None,
sam_config_path="path/to/sam2/configs/sam2.1_hiera_b+.yaml",
sam_checkpoint_path="path/to/sam2/checkpoints/sam2.1_hiera_base_plus.pt",
gd_config_path="path/to/GroundingDINO/config/GroundingDINO_SwinT_OGC.py",
gd_checkpoint_path="path/to/GroundingDINO/checkpoints/groundingdino_swint_ogc.pth",
device=0
)
print("✓ Pipeline created with pretrained VINE weights")
# Example usage (would require actual video file)
demo_video = os.path.join(os.path.dirname(__file__), "../demo/videos/v1.mp4")
if os.path.exists(demo_video):
print(f"Found demo video: {demo_video}")
print("Example pipeline call:")
print(f"results = vine_pipeline(")
print(f" '{demo_video}',")
print(f" categorical_keywords=['human', 'dog', 'frisbee'],")
print(f" unary_keywords=['running', 'jumping', 'sitting'],")
print(f" binary_keywords=['behind', 'chasing', 'next to']")
print(f" debug_visualizations=True")
print(f")")
# Uncomment to actually run (requires segmentation models)
# results = vine_pipeline(
# demo_video,
# categorical_keywords=['human', 'dog', 'frisbee'],
# unary_keywords=['running', 'jumping', 'sitting'],
# binary_keywords=['behind', 'chasing', 'next to'],
# debug_visualizations=True,
# )
# print("Results:", results['summary'])
return vine_pipeline
def example_manual_weight_loading():
"""Example of manually loading weights after model creation."""
print("\n=== Manual Weight Loading ===")
# Create model with base CLIP weights
# No pretrained path: create base config (no HF repo or local file configured)
config = VineConfig()
vine_model = VineModel(config)
print("✓ Model created with base CLIP weights")
model_dir = "/path/to/your/local/ensemble/model_dir.pt" # Replace with your model directory
if os.path.exists(model_dir):
success = vine_model.load_pretrained_vine_weights(model_dir, epoch=0)
if success:
print("✓ Successfully loaded pretrained VINE weights manually")
else:
print("✗ Failed to load pretrained weights")
else:
print(f"✗ Model directory not found: {model_dir}")
return vine_model
def compare_model_outputs():
"""Compare outputs between base CLIP and pretrained VINE."""
print("\n=== Comparing Model Outputs ===")
# Create dummy data for testing
video_frames = torch.randn(3, 224, 224, 3) * 255 # 3 frames
video_frames = video_frames.clamp(0, 255).byte()
masks = {
0: {1: torch.ones(224, 224, 1)},
1: {1: torch.ones(224, 224, 1)},
2: {1: torch.ones(224, 224, 1)}
}
bboxes = {
0: {1: [50, 50, 150, 150]},
1: {1: [52, 52, 152, 152]},
2: {1: [54, 54, 154, 154]}
}
keywords = ['human', 'dog', 'frisbee']
# Model 1: Base CLIP
print("Creating model with base CLIP weights...")
config_base = VineConfig()
model_base = VineModel(config_base)
# Model 2: Pretrained VINE (if available)
data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../data"))
model_dir = os.path.join(data_dir, "LLaVA-Video-178K-v2/models/ensemble-02-10")
if os.path.exists(model_dir):
print("Creating model with pretrained VINE weights...")
config_vine = VineConfig(
use_hf_repo=False,
local_dir=model_dir,
local_filename=None,
)
model_vine = VineModel(config_vine)
print("\nComparing predictions...")
# Get predictions from both models
with torch.no_grad():
results_base = model_base.predict(
video_frames=video_frames,
masks=masks,
bboxes=bboxes,
categorical_keywords=keywords,
return_top_k=3
)
results_vine = model_vine.predict(
video_frames=video_frames,
masks=masks,
bboxes=bboxes,
categorical_keywords=keywords,
return_top_k=3
)
print("Base CLIP confidence scores:", results_base['confidence_scores'])
print("Pretrained VINE confidence scores:", results_vine['confidence_scores'])
print("✓ Successfully compared both models")
else:
print(f"Pretrained model not found at: {model_dir}")
print("Skipping comparison")
if __name__ == "__main__":
print("VINE HuggingFace Interface - Pretrained Weights Examples")
print("=" * 60)
try:
# Test local pretrained weights
model1 = example_with_local_pretrained_weights()
except Exception as e:
print(f"Local weights example failed: {e}")
try:
# Test HuggingFace Hub weights
model2 = example_with_huggingface_hub()
except Exception as e:
print(f"HuggingFace Hub example failed: {e}")
try:
# Test pipeline with pretrained weights
pipeline = example_pipeline_with_pretrained()
except Exception as e:
print(f"Pipeline example failed: {e}")
# try:
# # Test manual weight loading
# #model3 = example_manual_weight_loading()
# except Exception as e:
# print(f"Manual loading example failed: {e}")
# try:
# # Compare model outputs
# #compare_model_outputs()
# except Exception as e:
# print(f"Comparison example failed: {e}")
print("\n" + "=" * 60)
print("Examples completed!")
print("\nUsage Summary:")
print("1. Configure VineConfig with `use_hf_repo` + `model_repo` for Hub models, or `use_hf_repo=False` + `local_dir`/`local_filename` for local weights")
print("2. Use VineModel.from_pretrained_vine() for direct loading")
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