Tensorrt 可以将PyTorch中的神经网络模型转换为onnx 格式吗

是的，TensorRT可以将PyTorch中的神经网络模型转换为ONNX格式。TensorRT提供了一个Python API，您可以使用它来将PyTorch模型转换为ONNX格式，然后使用TensorRT将其优化为适用于GPU加速推理的序列化引擎。具体步骤如下：

1. 将PyTorch模型转换为ONNX格式：

import torch
import onnx

# Load the PyTorch model
model = torch.load('model.pt')

# Convert the PyTorch model to ONNX
dummy_input = torch.randn(1, 3, 224, 224)
input_names = ['input']
output_names = ['output']
onnx_path = 'model.onnx'
torch.onnx.export(model, dummy_input, onnx_path, verbose=False, input_names=input_names, output_names=output_names)

2. 使用TensorRT将ONNX模型优化为序列化引擎：

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

# Load the ONNX model
onnx_path = 'model.onnx'
onnx_model = onnx.load(onnx_path)

# Create a TensorRT builder and network
trt_logger = trt.Logger(trt.Logger.WARNING)
trt_builder = trt.Builder(trt_logger)
trt_network = trt_builder.create_network()

# Create an ONNX parser to parse the ONNX model into the TensorRT network
onnx_parser = trt.OnnxParser(trt_network, trt_logger)
onnx_parser.parse(onnx_model.SerializeToString())

# Set the maximum batch size and maximum workspace size
trt_builder.max_batch_size = 1
trt_builder.max_workspace_size = 1 << 30

# Build the TensorRT engine from the TensorRT network
trt_engine = trt_builder.build_cuda_engine(trt_network)

# Serialize the TensorRT engine to a file
trt_engine_path = 'model.engine'
with open(trt_engine_path, 'wb') as f:
    f.write(trt_engine.serialize())

3. 使用TensorRT引擎进行推理：

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

# Load the serialized TensorRT engine
trt_engine_path = 'model.engine'
with open(trt_engine_path, 'rb') as f:
    trt_engine_data = f.read()

# Create a TensorRT runtime and deserialize the TensorRT engine
trt_logger = trt.Logger(trt.Logger.WARNING)
trt_runtime = trt.Runtime(trt_logger)
trt_engine = trt_runtime.deserialize_cuda_engine(trt_engine_data)

# Create a TensorRT execution context
trt_context = trt_engine.create_execution_context()

# Allocate GPU memory for the input and output tensors
input_shape = (1, 3, 224, 224)
output_shape = (1, 1000)
input_dtype = np.float32
output_dtype = np.float32
input_size = np.product(input_shape) * np.dtype(input_dtype).itemsize
output_size = np.product(output_shape) * np.dtype(output_dtype).itemsize
input_gpu = cuda.mem_alloc(input_size)
output_gpu = cuda.mem_alloc(output_size)

# Create a CUDA stream
stream = cuda.Stream()

# Initialize the input tensor with random data
input_cpu = np.random.rand(*input_shape).astype(input_dtype)
cuda.memcpy_htod_async(input_gpu, input_cpu, stream)

# Run inference on the TensorRT engine
trt_context.execute_async(1, [int(input_gpu), int(output_gpu)], stream.handle, None)

# Copy the output tensor back to the CPU
output_cpu = np.empty(output_shape, dtype=output_dtype)
cuda.memcpy_dtoh_async(output_cpu, output_gpu, stream)

# Synchronize the CUDA stream
stream.synchronize()

# Print the output tensor
print(output_cpu)