ОШИБКА: engine.cpp (370) - ошибка Cuda в ~ ExecutionContext: 77 - PullRequest
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ОШИБКА: engine.cpp (370) - ошибка Cuda в ~ ExecutionContext: 77

0 голосов
/ 19 июня 2019

Я выполняю калибровку Int8 с использованием TensorRT.

После завершения калибровки и проверки вывода.У меня ошибка в stream.synchronize() в следующей функции.

Нет проблем, работающих на двигателях FP32 и FP16.Ошибка только на двигателе Int8.Что может быть не так? 

def infer(engine, x, batch_size, context):
  inputs = []
  outputs = []
  bindings = []
  stream = cuda.Stream()
  for binding in engine:
    size = trt.volume(engine.get_binding_shape(binding)) * batch_size
    dtype = trt.nptype(engine.get_binding_dtype(binding))
    # Allocate host and device buffers
    host_mem = cuda.pagelocked_empty(size, dtype)
    device_mem = cuda.mem_alloc(host_mem.nbytes)
    # Append the device buffer to device bindings.
    bindings.append(int(device_mem))
    # Append to the appropriate list.
    if engine.binding_is_input(binding):
      inputs.append(HostDeviceMem(host_mem, device_mem))
    else:
      outputs.append(HostDeviceMem(host_mem, device_mem))
    #img = np.array(x).ravel()
    im = np.array(x, dtype=np.float32, order='C')
    im = im[:,:,::-1]
    #im = im.transpose((2,0,1))
    #np.copyto(inputs[0].host, x.flatten())  #1.0 - img / 255.0
    np.copyto(inputs[0].host, im.flatten())
    [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
    context.execute_async(batch_size=batch_size, bindings=bindings, stream_handle=stream.handle)
    # Transfer predictions back from the GPU.
    [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
    # Synchronize the stream
    stream.synchronize()
    # Return only the host outputs.

1 Ответ

0 голосов
/ 19 июня 2019

Следующий код не содержит ошибок.Только engine.max_batch_size и batch_size отличаются.

def allocate_buffers(engine):
    inputs = []
    outputs = []
    bindings = []
    stream = cuda.Stream()
    for binding in engine:
        size = trt.volume(engine.get_binding_shape(binding)) * engine.max_batch_size
        dtype = trt.nptype(engine.get_binding_dtype(binding))
        # Allocate host and device buffers
        host_mem = cuda.pagelocked_empty(size, dtype)
        device_mem = cuda.mem_alloc(host_mem.nbytes)
        # Append the device buffer to device bindings.
        bindings.append(int(device_mem))
        # Append to the appropriate list.
        if 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

# This function is generalized for multiple inputs/outputs.
# inputs and outputs are expected to be lists of HostDeviceMem objects.
def do_inference(context, bindings, inputs, outputs, stream, batch_size=1):
    # Transfer input data to the GPU.
    [cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
    # Run inference.
    context.execute_async(batch_size=batch_size, bindings=bindings, stream_handle=stream.handle)
    # Transfer predictions back from the GPU.
    [cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
    # Synchronize the stream
    stream.synchronize()
    # Return only the host outputs.
    return [out.host for out in outputs]
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