如何将自己训练好的pytroch模型转换成c++ 的tensorRT推理模型

要将PyTorch模型转换为TensorRT推理模型，需要执行以下步骤：

1. 安装TensorRT：在NVIDIA的官方网站上下载适用于您的系统的TensorRT，然后按照说明进行安装。

2. 将PyTorch模型转换为ONNX格式：使用PyTorch的“torch.onnx.export”函数将PyTorch模型转换为ONNX格式。确保指定输入和输出张量的形状和数据类型，以便在TensorRT中正确解释模型。

3. 使用TensorRT的“uff.from_onnx”函数将ONNX模型转换为UFF格式。 UFF是一种高效的模型格式，可在TensorRT中实现更快的推理性能。

4. 使用TensorRT的“create_inference_engine”函数将UFF模型转换为TensorRT推理引擎。此函数将创建TensorRT的推理引擎对象，该对象将用于推理过程。

5. 加载数据和运行推理：加载测试数据并将其输入到推理引擎中，然后将输出与预期输出进行比较以验证模型的正确性。

以下是一个示例代码片段，演示了如何将PyTorch模型转换为TensorRT推理模型：

import torch
import tensorrt as trt
import numpy as np

# Step 1: Install TensorRT and verify the installation

# Step 2: Convert PyTorch model to ONNX format
pytorch_model = ... # your PyTorch model
dummy_input = torch.randn(...) # create a dummy input tensor
input_names = ... # list of input tensor names
output_names = ... # list of output tensor names
onnx_model_path = "model.onnx" # path to save the ONNX model
torch.onnx.export(pytorch_model, dummy_input, onnx_model_path, input_names=input_names, output_names=output_names)

# Step 3: Convert ONNX model to UFF format
uff_model_path = "model.uff" # path to save the UFF model
uff_model = trt.UffParser().parse(onnx_model_path, output_names)
uff_model.write_to_file(uff_model_path)

# Step 4: Create TensorRT inference engine
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
trt_runtime = trt.Runtime(TRT_LOGGER)
engine_path = "model.engine" # path to save the TensorRT engine
with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network, trt.UffParser() as parser:
    builder.max_workspace_size = 1 << 30 # set maximum workspace size
    builder.max_batch_size = 1 # set maximum batch size
    builder.fp16_mode = True # enable FP16 precision
    input_shape = (..., ...) # set input shape
    input_tensor = network.add_input(input_names[0], trt.float32, input_shape)
    output_tensor = parser.parse_buffer(output_names[0], uff_model)
    network.mark_output(output_tensor)
    engine = builder.build_cuda_engine(network)
    with open(engine_path, "wb") as f:
        f.write(engine.serialize())

# Step 5: Load data and run inference
input_data = np.random.random(input_shape).astype(np.float32)
with engine.create_execution_context() as context:
    output_data = np.empty_like(output_tensor.host_buffer)
    context.execute(batch_size=1, bindings=[int(input_tensor.device_buffer), int(output_tensor.device_buffer)])
    np.copyto(output_data, output_tensor.host_buffer)
    expected_output_data = ... # expected output data
    assert np.allclose(output_data, expected_output_data, rtol=1e-3, atol=1e-3) # verify output