tensorrt动态输入分辨率尺寸

网友投稿 1705 2022-05-30

本文只有 tensorrt python部分涉动态分辨率设置,没有c++的。

目录

pytorch转onnx:

onnx转tensorrt:

python tensorrt推理:

知乎博客也可以参考:

tensorrt动态输入(Dynamic shapes) - 知乎

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):

tensorrt动态输入分辨率尺寸

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)

python tensorrt推理:

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)

pytorch

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