input只允许输入日期_tensorrt动态输入（Dynamic shapes）

记录此贴的原因有两个：1.肯定也有很多人需要 。2.就我搜索的帖子没一个讲的明明白白的，官方文档也不利索，需要连蒙带猜。话不多少，直接上代码。

以pytorch转onnx转tensorrt为例，动态shape是图像的长宽。

pytorch转onnx：

def export_onnx(model,image_shape,onnx_path, batch_size=1):
    x,y=image_shape
    img = torch.zeros((batch_size, 3, x, y))
    dynamic_onnx=True
    if dynamic_onnx:
        dynamic_ax = {'input_1' : {2 : 'image_height',3:'image_wdith'},   
                                'output_1' : {2 : 'image_height',3:'image_wdith'}}
        torch.onnx.export(model, (img), onnx_path, 
           input_names=["input_1"], output_names=["output_1"], verbose=False, opset_version=11,dynamic_axes=dynamic_ax)
    else:
        torch.onnx.export(model, (img), onnx_path, 
           input_names=["input_1"], output_names=["output_1"], verbose=False, opset_version=11
    )

onnx转tensorrt：

按照nvidia官方文档对dynamic shape的定义，所谓动态，无非是定义engine的时候不指定，用-1代替，在推理的时候再确定，因此建立engine 和推理部分的代码都需要修改。

建立engine时，从onnx读取的network，本身的输入输出就是dynamic shapes，只需要增加optimization_profile来确定一下输入的尺寸范围。

def build_engine(onnx_path, using_half,engine_file,dynamic_input=True):
    trt.init_libnvinfer_plugins(None, '')
    with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(network, TRT_LOGGER) as parser:
        builder.max_batch_size = 1 # always 1 for explicit batch
        config = builder.create_builder_config()
        config.max_workspace_size = GiB(1)
        if using_half:
            config.set_flag(trt.BuilderFlag.FP16)
        # Load the Onnx model and parse it in order to populate the TensorRT network.
        with open(onnx_path, 'rb') as model:
            if not parser.parse(model.read()):
                print ('ERROR: Failed to parse the ONNX file.')
                for error in range(parser.num_errors):
                    print (parser.get_error(error))
                return None
        ##增加部分
        if dynamic_input:
            profile = builder.create_optimization_profile();
            profile.set_shape("input_1", (1,3,512,512), (1,3,1024,1024), (1,3,1600,1600)) 
            config.add_optimization_profile(profile)
        #加上一个sigmoid层
        previous_output = network.get_output(0)
        network.unmark_output(previous_output)
        sigmoid_layer=network.add_activation(previous_output,trt.ActivationType.SIGMOID)
        network.mark_output(sigmoid_layer.get_output(0))
        return builder.build_engine(network, config) 

进行推理时，有个不小的暗坑，按照我之前的理解，既然动态输入，我只需要在给输入分配合适的缓存，然后不管什么尺寸直接推理就行了呗，事实证明还是年轻了。按照官方文档的提示，在推理的时候一定要增加这么一行，context.active_optimization_profile = 0，来选择对应的optimization_profile，ok，我加了，但是还是报错了，原因是我们既然在定义engine的时候没有定义输入尺寸，那么在推理的时候就需要根据实际的输入定义好输入尺寸。

def profile_trt(engine, imagepath,batch_size):
    assert(engine is not None)  
    
    input_image,input_shape=preprocess_image(imagepath)

    segment_inputs, segment_outputs, segment_bindings = allocate_buffers(engine, True,input_shape)
    
    stream = cuda.Stream()    
    with engine.create_execution_context() as context:
        context.active_optimization_profile = 0#增加部分
        origin_inputshape=context.get_binding_shape(0)
        #增加部分
        if (origin_inputshape[-1]==-1):
            origin_inputshape[-2],origin_inputshape[-1]=(input_shape)
            context.set_binding_shape(0,(origin_inputshape))
        input_img_array = np.array([input_image] * batch_size)
        img = torch.from_numpy(input_img_array).float().numpy()
        segment_inputs[0].host = img
        [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in segment_inputs]#Copy from the Python buffer src to the device pointer dest (an int or a DeviceAllocation) asynchronously,
        stream.synchronize()#Wait for all activity on this stream to cease, then return.
       
        context.execute_async(bindings=segment_bindings, stream_handle=stream.handle)#Asynchronously execute inference on a batch. 
        stream.synchronize(）
        [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in segment_outputs]#Copy from the device pointer src (an int or a DeviceAllocation) to the Python buffer dest asynchronously
        stream.synchronize()
        results = np.array(segment_outputs[0].host).reshape(batch_size, input_shape[0],input_shape[1])    
    return results.transpose(1,2,0)

只是短短几行代码，结果折腾了一整天，不过好在解决了动态输入的问题，不需要再写一堆乱七八糟的代码，希望让有缘人少走一点弯路。