Flask应用在预测时一直处于加载状态(TensorRT) [英] Flask app is keep on loading at the time of prediction(TensorRT)
本文介绍了Flask应用在预测时一直处于加载状态(TensorRT)的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
这是问题的续篇
面对问题,而在Jetson nano上运行带有TensorRt模型的Flask应用
上面是解决方法,但是当我运行flask'app'时,它将继续加载并且不显示视频.
Above is resolve but when I am running flask 'app' it keep loading and not showing video.
代码:
def callback():
cuda.init()
device = cuda.Device(0)
ctx = device.make_context()
onnx_model_path = './some.onnx'
fp16_mode = False
int8_mode = False
trt_engine_path = './model_fp16_{}_int8_{}.trt'.format(fp16_mode, int8_mode)
max_batch_size = 1
engine = get_engine(max_batch_size, onnx_model_path, trt_engine_path, fp16_mode, int8_mode)
context = engine.create_execution_context()
inputs, outputs, bindings, stream = allocate_buffers(engine)
ctx.pop()
##callback function ends
worker_thread = threading.Thread(target=callback())
worker_thread.start()
trt_outputs = do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
def do_inference(context, bindings, inputs, outputs, stream, batch_size=1):
print("start in do_inferece")
# Transfer data from CPU to the GPU.
[cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
# Run inference.
print("before run infernce in do_inferece")
context.execute_async(batch_size=batch_size, bindings=bindings, stream_handle=stream.handle)
# Transfer predictions back from the GPU.
print("before output in do_inferece")
[cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
print("before stream synchronize in do_inferece")
# Synchronize the stream
stream.synchronize()
# Return only the host outputs.
print("before return in do_inferece")
return [out.host for out in outputs]
推荐答案
您的worker_thread
创建do_inference
所需的context
.您应该在callback()
Your worker_thread
creates the context
required for do_inference
. You should call the do_inference
method inside the callback()
def callback():
cuda.init()
device = cuda.Device(0)
ctx = device.make_context()
onnx_model_path = './some.onnx'
fp16_mode = False
int8_mode = False
trt_engine_path = './model_fp16_{}_int8_{}.trt'.format(fp16_mode, int8_mode)
max_batch_size = 1
engine = get_engine(max_batch_size, onnx_model_path, trt_engine_path, fp16_mode, int8_mode)
context = engine.create_execution_context()
inputs, outputs, bindings, stream = allocate_buffers(engine)
trt_outputs = do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
# post-process the trt_outputs
ctx.pop()
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