mirror of
https://github.com/azaion/ai-training.git
synced 2026-04-22 06:56:34 +00:00
Alex Bezdieniezhnykh
51 lines
2.0 KiB
Python
51 lines
2.0 KiB
Python
import pycuda.driver as cuda
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import yaml
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import constants
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from api_client import ApiClient, ApiCredentials
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from cdn_manager import CDNManager, CDNCredentials
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from inference.inference import Inference
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from inference.tensorrt_engine import TensorRTEngine
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from security import Security
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from utils import Dotdict
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def get_engine_filename(device_id=0):
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try:
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device = cuda.Device(device_id)
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sm_count = device.multiprocessor_count
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cc_major, cc_minor = device.compute_capability()
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return f"azaion.cc_{cc_major}.{cc_minor}_sm_{sm_count}.engine"
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except Exception:
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return None
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if __name__ == "__main__":
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# Inference(OnnxEngine('azaion-2025-03-10.onnx', batch_size=4),
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# confidence_threshold=0.5, iou_threshold=0.3).process('ForAI_test.mp4')
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# detection for the first 200sec of video:
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# onnxInference: 81 sec, 6.3Gb VRAM
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# tensorrt: 54 sec, 3.7Gb VRAM
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# Inference(TensorRTEngine('azaion-2025-03-10_int8.engine', batch_size=16),
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# confidence_threshold=0.5, iou_threshold=0.3).process('ForAI_test.mp4')
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# INT8 for 200sec: 54 sec 3.7Gb
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# Inference(TensorRTEngine('azaion-2025-03-10_batch8.engine', batch_size=8),
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# confidence_threshold=0.5, iou_threshold=0.3).process('ForAI_test.mp4')
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api_client = ApiClient()
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key = Security.get_model_encryption_key()
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engine_filename = TensorRTEngine.get_engine_filename()
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model_bytes = api_client.load_big_small_resource(engine_filename, 'models', key)
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Inference(TensorRTEngine(model_bytes),
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confidence_threshold=0.5, iou_threshold=0.3).process('tests/ForAI_test.mp4')
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# cdn_manager.download(cdn_c.bucket, constants.AI_TENSOR_MODEL_FILE_BIG)
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# tensor_model_bytes = api_client.load_resource(constants.AI_TENSOR_MODEL_FILE_BIG, constants.AI_TENSOR_MODEL_FILE_SMALL)
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# Inference(OnnxEngine(onxx_model_bytes, batch_size=4),
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# confidence_threshold=0.5, iou_threshold=0.3).process('tests/ForAI_test.mp4')
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