tensorrt加载trt
时间: 2023-09-13 12:07:33 浏览: 59
文件的代码示例:
```python
import tensorrt as trt
import numpy as np
# Load serialized TensorRT engine from file
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
with open('model.trt', 'rb') as f:
engine_data = f.read()
engine = trt.Runtime(TRT_LOGGER).deserialize_cuda_engine(engine_data)
# Create execution context for inference
context = engine.create_execution_context()
# Allocate input and output buffers for inference
input_shape = (3, 224, 224)
input_size = np.prod(input_shape) * 4
input_buf = cuda.mem_alloc(input_size)
output_shape = (1000,)
output_size = np.prod(output_shape) * 4
output_buf = cuda.mem_alloc(output_size)
# Set input data
input_data = np.ones(input_shape, dtype=np.float32)
cuda.memcpy_htod(input_buf, input_data.reshape(-1))
# Run inference
context.execute_v2(bindings=[int(input_buf), int(output_buf)])
output_data = np.zeros(output_shape, dtype=np.float32)
cuda.memcpy_dtoh(output_data, output_buf)
# Print output
print(output_data)
```
这个示例代码展示了如何使用TensorRT加载一个TRT文件,并对其进行推理。首先,我们使用TensorRT运行时反序列化引擎数据并创建执行上下文。然后,我们分配输入和输出缓冲区,并将输入数据复制到输入缓冲区中。最后,我们运行推理并从输出缓冲区中读取输出数据。