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Main script for running multi-modal inference using Helium framework.
Example usage for end users.
"""
import os
from inference_runner import InferenceRunner
def run_inference_example():
"""Example showing how to use the inference runner"""
# Path to your model checkpoint
MODEL_PATH = os.path.join(os.path.dirname(__file__), "checkpoints/model")
# Initialize the inference runner
print("Initializing inference runner...")
runner = InferenceRunner(
model_path=MODEL_PATH,
device_id="vgpu0", # Use first virtual GPU
batch_size=4, # Process 4 items at once
cache_dir="cache" # Cache results here
)
try:
# Example 1: Single inference with text only
print("\nRunning text-only inference...")
result = runner(text="Analyze this sentence.")
print("Text features shape:", result["text_features"].shape)
# Example 2: Multi-modal inference
print("\nRunning multi-modal inference...")
result = runner(
text="Describe this image and sound:",
image_path="samples/scene.jpg",
audio_path="samples/audio.wav"
)
print("Available features:", list(result.keys()))
# Example 3: Batch processing
print("\nProcessing batch...")
texts = [f"Sample text {i}" for i in range(3)]
images = ["samples/img1.jpg", "samples/img2.jpg", "samples/img3.jpg"]
for text, img in zip(texts, images):
runner.add_to_batch(text=text, image_path=img)
batch_result = runner.process_batch()
print("Batch processing complete. Output shapes:")
for key, value in batch_result.items():
print(f" {key}: {value.shape}")
# Example 4: Generate from multi-modal context
print("\nGenerating text from multi-modal context...")
generated = runner.generate_from_context(
context_text="What's happening in this scene?",
context_image="samples/scene.jpg",
context_audio="samples/ambient.wav",
max_length=50
)
print("Generated sequence shape:", generated.shape)
finally:
# Always cleanup
print("\nCleaning up resources...")
runner.cleanup()
def main():
"""Main entry point"""
try:
run_inference_example()
print("\nInference completed successfully!")
except FileNotFoundError as e:
print(f"\nError: Required file not found: {e}")
print("Please make sure all model files and samples are in the correct location.")
except ValueError as e:
print(f"\nError: Invalid input: {e}")
print("Please check your input values and formats.")
except Exception as e:
print(f"\nUnexpected error: {e}")
print("If this persists, please check the logs or contact support.")
if __name__ == "__main__":
main()
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