python - 在 python 上使用 TensorRT .engine 文件进行推理

Question

我使用 Nvidia 的 Transfer Learning Toolkit (TLT) 进行训练，然后使用 tlt-converter 将 .etlt 模型转换为 .engine 文件。

我想在 python 中使用这个 .engine 文件进行推理。但由于我使用 TLT 进行训练，因此我没有任何卡住图或 pb 文件，而这些都是 TensorRT 推理教程所需要的。

我想知道在 .engine 文件上是否可以进行 python 推理。
如果没有，支持哪些转换(UFF，ONNX)来实现这一点？

Answer 1

Python 推理可以通过 .engine 文件实现。下面的示例从磁盘加载 .trt 文件(与 .engine 文件完全相同)并执行单一推理。
在这个项目中，我使用 onnx2trt 将 ONNX 模型转换为 TRT 模型。使用前可执行。您甚至可以使用 ONNX 作为中间件将 PyTorch 模型转换为 TRT。

import tensorrt as trt
import numpy as np
import os

import pycuda.driver as cuda
import pycuda.autoinit



class HostDeviceMem(object):
    def __init__(self, host_mem, device_mem):
        self.host = host_mem
        self.device = device_mem

    def __str__(self):
        return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device)

    def __repr__(self):
        return self.__str__()

class TrtModel:
    
    def __init__(self,engine_path,max_batch_size=1,dtype=np.float32):
        
        self.engine_path = engine_path
        self.dtype = dtype
        self.logger = trt.Logger(trt.Logger.WARNING)
        self.runtime = trt.Runtime(self.logger)
        self.engine = self.load_engine(self.runtime, self.engine_path)
        self.max_batch_size = max_batch_size
        self.inputs, self.outputs, self.bindings, self.stream = self.allocate_buffers()
        self.context = self.engine.create_execution_context()

                
                
    @staticmethod
    def load_engine(trt_runtime, engine_path):
        trt.init_libnvinfer_plugins(None, "")             
        with open(engine_path, 'rb') as f:
            engine_data = f.read()
        engine = trt_runtime.deserialize_cuda_engine(engine_data)
        return engine
    
    def allocate_buffers(self):
        
        inputs = []
        outputs = []
        bindings = []
        stream = cuda.Stream()
        
        for binding in self.engine:
            size = trt.volume(self.engine.get_binding_shape(binding)) * self.max_batch_size
            host_mem = cuda.pagelocked_empty(size, self.dtype)
            device_mem = cuda.mem_alloc(host_mem.nbytes)
            
            bindings.append(int(device_mem))

            if self.engine.binding_is_input(binding):
                inputs.append(HostDeviceMem(host_mem, device_mem))
            else:
                outputs.append(HostDeviceMem(host_mem, device_mem))
        
        return inputs, outputs, bindings, stream
       
            
    def __call__(self,x:np.ndarray,batch_size=2):
        
        x = x.astype(self.dtype)
        
        np.copyto(self.inputs[0].host,x.ravel())
        
        for inp in self.inputs:
            cuda.memcpy_htod_async(inp.device, inp.host, self.stream)
        
        self.context.execute_async(batch_size=batch_size, bindings=self.bindings, stream_handle=self.stream.handle)
        for out in self.outputs:
            cuda.memcpy_dtoh_async(out.host, out.device, self.stream) 
            
        
        self.stream.synchronize()
        return [out.host.reshape(batch_size,-1) for out in self.outputs]


        
        
if __name__ == "__main__":
 
    batch_size = 1
    trt_engine_path = os.path.join("..","models","main.trt")
    model = TrtModel(trt_engine_path)
    shape = model.engine.get_binding_shape(0)

    
    data = np.random.randint(0,255,(batch_size,*shape[1:]))/255
    result = model(data,batch_size)
    

大家注意安全!