Author, do you have a complete Python version that reads the engine model of Tensorrt to infer strength segmentation code, which is a simple version of the official inference code. It can be run in just one file without calling too many Python files or libraries

[yxl23]

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Author, do you have a complete Python version that reads the engine model of Tensorrt to infer strength segmentation code, which is a simple version of the official inference code. It can be run in just one file without calling too many Python files or libraries

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[glenn-jocher]

Hello,

Thank you for reaching out with your query. Currently, we don't have a single-file Python script specifically for running inference with a TensorRT .engine model that minimizes external dependencies. However, you can achieve this by using the tensorrt library in Python to load and run inference with the .engine file.

Here’s a basic outline of what the code could look like:

import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit
import numpy as np

TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
engine_file_path = 'path_to_your_engine_file.engine'

def load_engine(engine_file_path):
    with open(engine_file_path, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime:
        return runtime.deserialize_cuda_engine(f.read())

def main():
    engine = load_engine(engine_file_path)
    context = engine.create_execution_context()

    # Allocate buffers and create a stream.
    inputs, outputs, bindings, stream = [], [], [], cuda.Stream()
    for binding in engine:
        size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
        dtype = trt.nptype(engine.get_binding_dtype(binding))
        # Allocate host and device buffers
        host_mem = cuda.pagelocked_empty(size, dtype)
        device_mem = cuda.mem_alloc(host_mem.nbytes)
        # Append the device buffer to device bindings.
        bindings.append(int(device_mem))
        # Append to the appropriate list.
        if engine.binding_is_input(binding):
            inputs.append({'host': host_mem, 'device': device_mem})
        else:
            outputs.append({'host': host_mem, 'device': device_mem})

    # Assuming input data is in numpy array `input_data`
    np.copyto(inputs[0]['host'], input_data.ravel())
    cuda.memcpy_htod_async(inputs[0]['device'], inputs[0]['host'], stream)

    # Run inference
    context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
    cuda.memcpy_dtoh_async(outputs[0]['host'], outputs[0]['device'], stream)
    stream.synchronize()

    # Output data will be in outputs[0]['host']
    print("Inference output:", outputs[0]['host'])

if __name__ == '__main__':
    main()

This script is a simplified example and assumes you have the necessary setup for TensorRT and PyCUDA. You might need to adjust the data handling and buffer management based on your specific model inputs and outputs.

For more comprehensive guidance on exporting and running YOLOv5 models with TensorRT, please refer to our documentation on model export and inference.

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