stm32中的TRACED
时间: 2024-05-17 18:16:21 浏览: 10
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相关问题
Convert model to Traced-model
To convert a PyTorch model to a traced model, you can use the `torch.jit.trace` function. Here's an example:
```
import torch
# Define your PyTorch model
class MyModel(torch.nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.linear = torch.nn.Linear(1, 1)
def forward(self, x):
return self.linear(x)
# Create an instance of your model
model = MyModel()
# Create some example input data
x = torch.randn(1, 1)
# Trace the model with the input data
traced_model = torch.jit.trace(model, x)
# Save the traced model to a file
traced_model.save("traced_model.pt")
```
In this example, we define a simple `MyModel` class and create an instance of it. We then create some example input data (`x`) and use the `torch.jit.trace` function to trace the model with that input data. Finally, we save the traced model to a file called `traced_model.pt`.
ChitGPT如何在Android开发中应用
ChitGPT可以在Android应用中使用,方法如下:
1.首先,安装以下库:
PyTorch for Android
PyTorch for Android依赖于Android NDK(Native Development Kit)和CMake。请确保安装了最新版本的NDK和CMake。
2.使用Python将ChitGPT模型转换为Torch Script文件
使用以下代码将ChitGPT模型转换为Torch Script文件:
```
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
model.eval()
input_text = "My input text"
input_ids = tokenizer.encode(input_text, return_tensors='pt')
with torch.no_grad():
outputs = model(input_ids=input_ids)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
model = model.to('cpu')
traced_model = torch.jit.trace(model, input_ids)
traced_model.save("chitgpt.pt")
```
3.将Torch Script文件添加到Android应用程序中。
4.在Android应用程序中使用Torch API从Torch Script文件加载模型并进行预测。
使用以下代码从Torch Script文件中加载模型:
```
import org.pytorch.IValue;
import org.pytorch.Module;
import org.pytorch.Tensor;
Module module = Module.load(assetFilePath(this, "chitgpt.pt"));
// ...
Tensor inputTensor = Tensor.fromBlob(inputData, new long[]{1, 20, 20}).to(device);
IValue inputs = IValue.from(inputTensor);
Tensor outputTensor = module.forward(inputs).toTensor();
float[] scores = outputTensor.getDataAsFloatArray();
```
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