tensorrt 多输入输出
时间: 2023-09-13 10:03:32 浏览: 126
TensorRT 支持多输入多输出,可以通过以下步骤实现:
1. 创建输入和输出张量:
```python
input1 = trt_(builder_.add_input_tensor(input_shape1))
input2 = trt_(builder_.add_input_tensor(input_shape2))
output1 = trt_(builder_.add_output_tensor(output_shape1))
output2 = trt_(builder_.add_output_tensor(output_shape2))
```
2. 将输入和输出张量添加到网络:
```python
network = builder_.create_network()
input1 = network.add_input(name1, dtype1, input_shape1)
input2 = network.add_input(name2, dtype2, input_shape2)
output1 = network.add_output(name3, dtype3, output_shape1)
output2 = network.add_output(name4, dtype4, output_shape2)
```
3. 添加操作到网络:
```python
conv1 = network.add_convolution(input=input1, ...)
relu1 = network.add_activation(input=conv1.get_output(0), ...)
pool1 = network.add_pooling(input=relu1.get_output(0), ...)
conv2 = network.add_convolution(input=input2, ...)
relu2 = network.add_activation(input=conv2.get_output(0), ...)
pool2 = network.add_pooling(input=relu2.get_output(0), ...)
```
4. 将操作设置为输出:
```python
output1.set_tensor_name(name1)
output1.set_precission(precision1)
output1.set_network_output(True)
output2.set_tensor_name(name2)
output2.set_precission(precision2)
output2.set_network_output(True)
```
5. 编译并执行 TensorRT 引擎:
```python
engine = builder_.build_cuda_engine(network)
context = engine.create_execution_context()
inputs = [input1, input2]
outputs = [output1, output2]
bindings = [None] * (len(inputs) + len(outputs))
stream = cuda.Stream()
for i in range(len(inputs)):
bindings[i] = cuda.mem_alloc(inputs[i].host.shape[0] * inputs[i].host.dtype.itemsize)
cuda.memcpy_htod_async(bindings[i], inputs[i].host, stream)
for i in range(len(outputs)):
bindings[i + len(inputs)] = cuda.mem_alloc(outputs[i].host.shape[0] * outputs[i].host.dtype.itemsize)
context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
for i in range(len(outputs)):
cuda.memcpy_dtoh_async(outputs[i].host, bindings[i + len(inputs)], stream)
stream.synchronize()
```
注意事项:
- 多输入多输出时,需要设置每个输出的名称和精度;
- 在执行 TensorRT 引擎时,需要将输入和输出张量绑定到 CUDA 内存,并使用异步 CUDA 流进行传输和同步。
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