tensorrt怎么实现nn.Linear()
时间: 2023-11-05 16:07:24 浏览: 53
TensorRT中没有直接的nn.Linear()函数,但可以通过以下步骤实现:
1. 定义一个TensorRT网络,使用相应的层来实现nn.Linear()。例如,可以使用FullyConnected层来实现线性变换。
2. 将PyTorch模型转换为ONNX格式。
3. 使用TensorRT的ONNX解析器将ONNX模型解析为TensorRT网络。
4. 对TensorRT网络进行优化和推理。
以下是一个使用TensorRT实现nn.Linear()的示例代码:
```
import tensorrt as trt
import onnx
import numpy as np
# Define a TensorRT network
def create_engine():
builder = trt.Builder()
network = builder.create_network()
# Input tensor
input_tensor = network.add_input(name="input", dtype=trt.float32, shape=(1, 10))
# Linear layer
weights = np.random.randn(10, 20).astype(np.float32)
bias = np.random.randn(20).astype(np.float32)
fc_layer = network.add_fully_connected(input_tensor, 20, weights, bias)
# Output tensor
output_tensor = fc_layer.get_output(0)
output_tensor.name = "output"
network.mark_output(output_tensor)
# Build the engine
builder.max_batch_size = 1
builder.max_workspace_size = 1 << 30
engine = builder.build_cuda_engine(network)
return engine
# Convert PyTorch model to ONNX format
dummy_input = torch.randn(1, 10)
model = nn.Linear(10, 20)
torch.onnx.export(model, dummy_input, "model.onnx")
# Parse ONNX model with TensorRT
onnx_model = onnx.load("model.onnx")
engine = create_engine()
context = engine.create_execution_context()
inputs, outputs, bindings = trt.common.allocate_buffers(engine)
# Run inference with TensorRT engine
inputs[0].host = dummy_input.numpy()
context.execute_v2(bindings=bindings)
output = outputs[0].host
```