如何结局INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
时间: 2024-05-23 18:10:28 浏览: 30
这个INFO是TensorFlow Lite XNNPACK delegate在CPU上的创建信息。它意味着TensorFlow Lite正在使用XNNPACK代理来优化在CPU上的计算性能。因此,这个INFO的结局取决于你的TensorFlow Lite应用程序的实际情况。如果你的应用程序成功地使用了XNNPACK代理,并获得了更好的性能,则这个INFO的结局应该是积极的。否则,你可能需要重新评估你的应用程序和代理设置,以获得更好的性能。
相关问题
Created TensorFlow Lite XNNPACK delegate for CPU
As an AI language model, I can provide you with information on the TensorFlow Lite XNNPACK delegate for CPU.
The TensorFlow Lite XNNPACK delegate is a delegate that allows TensorFlow Lite models to run on CPU using the XNNPACK library. The XNNPACK library is a highly optimized library for implementing neural network operations on CPUs. This delegate provides faster and more efficient execution of TensorFlow Lite models on devices that support the XNNPACK library.
The XNNPACK delegate is available in TensorFlow Lite for ARM CPUs, and it supports various types of operators such as convolution, depthwise convolution, fully connected, and pooling. The delegate also supports quantized and float models.
To use the XNNPACK delegate in your TensorFlow Lite model, you need to build TensorFlow Lite with the XNNPACK library and enable the delegate during runtime. You can do this using the following code:
```python
import tensorflow as tf
interpreter = tf.lite.Interpreter(model_path="model.tflite")
interpreter.allocate_tensors()
# Enable XNNPACK delegate
interpreter.set_tensor(
interpreter.get_input_details()[0]["index"],
np.zeros(interpreter.get_input_details()[0]["shape"], dtype=np.float32))
interpreter.invoke()
interpreter.set_delegate(tf.lite.experimental.load_delegate('libedgetpu.so.1'))
```
With the XNNPACK delegate, you can expect faster inference times and better performance on CPU devices.
如何解决INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
这是 TensorFlow Lite 使用 XNNPACK 委托来加速 CPU 推理的信息提示。如果您没有遇到任何错误或警告,那么您不需要解决这个问题。然而,如果您希望禁用 XNNPACK 委托,可以尝试以下方法:
在创建 TFLite 解释器之前设置环境变量 `TF_LITE_DISABLE_XNNPACK=1`,例如:
```
import os
os.environ['TF_LITE_DISABLE_XNNPACK'] = '1'
```
或者,在创建 TFLite 解释器时,使用 `Interpreter.Options` 对象禁用 XNNPACK 委托,例如:
```
interpreter = tf.lite.Interpreter(model_path=model_path)
interpreter_options = tf.lite.Interpreter.Options()
interpreter_options.experimental_delegate = tf.lite.experimental.load_delegate('libedgetpu.so.1', options=delegate_options) # 加载其他的委托
interpreter_options.disable_xnnpack = True # 禁用 XNNPACK 委托
interpreter.set_tensor_details_allocator(tensor_details_allocator)
interpreter.allocate_tensors()
```
相关推荐
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![-](https://csdnimg.cn/download_wenku/file_type_column_c1.png)
![-](https://csdnimg.cn/download_wenku/file_type_column_c1.png)
![-](https://csdnimg.cn/download_wenku/file_type_column_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)