Merge branch 'docs/update_tutorial_docs' into 'master'

Docs/update tutorial docs

See merge request ai/esp-dl!115

Author: BlueSkyB

README.md

@@ -1,6 +1,6 @@
 # ESP-DL [[中文]](./README_cn.md)
 
-[![Documentation Status](./docs/_static/doc_latest.svg)](https://docs.espressif.com/projects/esp-dl/en/latest/index.html)
+[![Documentation Status](./docs/_static/doc_latest.svg)](https://docs.espressif.com/projects/esp-dl/en/latest/index.html)  [![Component Registry](https://components.espressif.com/components/espressif/esp-dl/badge.svg)](https://components.espressif.com/components/espressif/esp-dl)
 
 ESP-DL is a lightweight and efficient neural network inference framework designed specifically for ESP series chips. With ESP-DL, you can easily and quickly develop AI applications using Espressif's System on Chips (SoCs).
 
@@ -40,7 +40,7 @@ pip install git+https://github.com/espressif/esp-ppq.git
 First, please refer to the [ESP-DL Operator Support State](./operator_support_state.md) to ensure that the operators in your model are already supported.  
 
 ESP-PPQ can directly read ONNX models for quantization. Pytorch and TensorFlow need to be converted to ONNX models first, so make sure your model can be converted to ONNX models.  
-We provide the following python script templates. Please select the appropriate template to quantize your models. For more details about quantization, please refer to [tutorial/how_to_quantize_model](./tutorial/how_to_quantize_model_en.md).  
+We provide the following python script templates. Please select the appropriate template to quantize your models. For more details about quantization, please refer to [Using ESP-PPQ for Model Quantization](https://docs.espressif.com/projects/esp-dl/en/latest/tutorials/how_to_quantize_model.html).  
 
 [quantize_onnx_model.py](./tools/quantization/quantize_onnx_model.py): Quantize ONNX models
 
@@ -61,7 +61,7 @@ Model *model = new Model((const char *)espdl_model, fbs::MODEL_LOCATION_IN_FLASH
 model->run(inputs); // inputs is a tensor or a map of tensors
 ```
 
-For more details, please refer to [tutorial/how_to_load_model](./tutorial/how_to_load_model_en.md) and [mobilenet_v2 examples](./examples/mobilenet_v2/)
+For more details, please refer to [Loading Models with ESP-DL](https://docs.espressif.com/projects/esp-dl/en/latest/tutorials/how_to_load_model.html) and [mobilenet_v2 examples](./examples/mobilenet_v2/)
 
 
 ## Support Models
@@ -74,6 +74,6 @@ For more details, please refer to [tutorial/how_to_load_model](./tutorial/how_to
 
 ## Suport Operators
 
-If you encounter unsupported operators, please point them out in the [issues](https://github.com/espressif/esp-dl/issues), and we will support them as soon as possible. Contributions to this ESPDL are also welcomed.
+If you encounter unsupported operators, please point them out in the [issues](https://github.com/espressif/esp-dl/issues), and we will support them as soon as possible. Contributions to this ESP-DL are also welcomed, please refer to [Creating a New Module (Operator)](https://docs.espressif.com/projects/esp-dl/en/latest/tutorials/how_to_add_a_new_module%28operator%29.html) for more details.
 
 [ESP-DL Operator Support State](./operator_support_state.md)

README_cn.md

@@ -1,6 +1,6 @@
 # ESP-DL [[English]](./README.md)
 
-[![Documentation Status](./docs/_static/doc_latest.svg)](https://docs.espressif.com/projects/esp-dl/zh_CN/latest/index.html)
+[![Documentation Status](./docs/_static/doc_latest.svg)](https://docs.espressif.com/projects/esp-dl/zh_CN/latest/index.html)    [![Component Registry](https://components.espressif.com/components/espressif/esp-dl/badge.svg)](https://components.espressif.com/components/espressif/esp-dl)
 
 ESP-DL 是一个专为 ESP 系列芯片设计的轻量级且高效的神经网络推理框架。通过 ESP-DL，您可以轻松快速地使用乐鑫的系统级芯片 (SoC) 开发 AI 应用。
 
@@ -40,7 +40,7 @@ pip install git+https://github.com/espressif/esp-ppq.git
 
 ESP-PPQ 可以直接读取 ONNX 模型进行量化。Pytorch 和 TensorFlow 需要先转换为 ONNX 模型，因此请确保你的模型可以转换为 ONNX 模型。
 
-我们提供了以下 Python 脚本模板。你可以根据你自己的模型选择合适的模板进行修改。更多详细信息请参阅 [tutorial/how_to_quantize_model](./tutorial/how_to_quantize_model_cn.md)。  
+我们提供了以下 Python 脚本模板。你可以根据你自己的模型选择合适的模板进行修改。更多详细信息请参阅 [使用 ESP-PPQ 量化模型](https://docs.espressif.com/projects/esp-dl/zh_CN/latest/tutorials/how_to_quantize_model.html)。  
 
 [quantize_onnx_model.py](./tools/quantization/quantize_onnx_model.py): 量化 ONNX 模型
 
@@ -60,7 +60,7 @@ Model *model = new Model((const char *)espdl_model, fbs::MODEL_LOCATION_IN_FLASH
 model->run(inputs); // inputs 是一个张量或张量映射
 ```
 
-更多详细信息，请参阅 [tutorial/how_to_load_model](./tutorial/how_to_load_model_cn.md) 和 [mobilenet_v2 示例](./examples/mobilenet_v2/)。
+更多详细信息，请参阅 [使用 ESP-DL 加载模型](https://docs.espressif.com/projects/esp-dl/zh_CN/latest/tutorials/how_to_load_model.html) 和 [mobilenet_v2 示例](./examples/mobilenet_v2/)。
 
 
 ## Support models
@@ -73,6 +73,6 @@ model->run(inputs); // inputs 是一个张量或张量映射
 ## Suport Operators
 
 如果你有遇到不支持的算子，请将问题在[issues](https://github.com/espressif/esp-dl/issues)中反馈给我们，我们会尽快支持。  
-也欢迎大家贡献新的算子。
+也欢迎大家贡献新的算子, 具体方法请参考[创建新模块（算子）](https://docs.espressif.com/projects/esp-dl/zh_CN/latest/tutorials/how_to_add_a_new_module%28operator%29.html)。
 
 [算子支持状态](./operator_support_state.md)

esp-dl/idf_component.yml

@@ -1,4 +1,4 @@
-version: "3.0.0~1-rc.2"
+version: "3.0.0"
 license: "MIT"
 targets:
   - esp32s3

examples/mobilenet_v2/README.md

@@ -6,7 +6,7 @@
 
 Deploy [MobileNet_v2](https://arxiv.org/abs/1801.04381) model from [torchvision](https://pytorch.org/vision/0.18/models/generated/torchvision.models.mobilenet_v2.html).
 
-See [tutotial/how_to_deploy_mobilenet_v2](../../tutorial/how_to_deploy_mobilenet_v2_en.md) for more information.
+See [Deploying MobileNet_v2 Using ESP-DL](https://docs.espressif.com/projects/esp-dl/en/latest/tutorials/how_to_deploy_mobilenet.html) for more information.
 
 # Example Output
 After the flashing you should see the output at idf monitor:

operator_support_state.md

@@ -11,8 +11,8 @@ The rounding for ESP32-P4 is [rounding half to even](https://simple.wikipedia.or
 
 ## Support Operators
 
-The ESP-DL operator interface is aligned with ONNX. The opset 13 is recommended to export ONNX.    
-Currently, the following 30 operators have been implemented and tested. Some operators do not implement all functionalities and attributes. Please refer to the description of each operator or [test cases](./tools/ops_test/config/op_cfg.toml) for details.
+The ESP-DL operator interface is aligned with ONNX. The opset 13 is recommended to export ONNX.
+Currently, the following 31 operators have been implemented and tested. Some operators do not implement all functionalities and attributes. Please refer to the description of each operator or [test cases](./tools/ops_test/config/op_cfg.toml) for details.
 | Operator                                                                                                                                                     | int8     | int16    | Description                                 |
 |--------------------------------------------------------------------------------------------------------------------------------------------------------------|----------|----------|---------------------------------------------|
 | Add[(ESP-DL)](esp-dl/dl/module/include/dl_module_add.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__Add.html)                                             | ✔ | ✔ | Support up to 4D                            |
@@ -29,21 +29,22 @@ Currently, the following 30 operators have been implemented and tested. Some ope
 | HardSwish[(ESP-DL)](esp-dl/dl/module/include/dl_module_hard_swish.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__HardSwish.html)                          | ✔ | ✔ |                                             |
 | LeakyRelu[(ESP-DL)](esp-dl/dl/module/include/dl_module_leaky_relu.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__LeakyRelu.html)                          | ✔ | ✔ |                                             |
 | Log[(ESP-DL)](esp-dl/dl/module/include/dl_module_log.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__Log.html)                                             | ✔ | ✔ |                                             |
-| MatMul[(ESP-DL)](esp-dl/dl/module/include/dl_module_matmul.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__MatMul.html)                                    | ✔ | ✔ |                                             |
+| MatMul[(ESP-DL)](esp-dl/dl/module/include/dl_module_matmul.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__MatMul.html)                                    | ✔ | ✔ | Support up to 4D                            |
 | MaxPool[(ESP-DL)](esp-dl/dl/module/include/dl_module_max_pool.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__MaxPool.html)                                | ✔ | ✔ |                                             |
 | Mul[(ESP-DL)](esp-dl/dl/module/include/dl_module_mul.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__Mul.html)                                             | ✔ | ✔ | Support up to 4D                            |
 | Pad[(ESP-DL)](esp-dl/dl/module/include/dl_module_pad.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__Pad.html)                                             | ✔ | ✔ | Do not support wrap mode                    |
 | PRelu[(ESP-DL)](esp-dl/dl/module/include/dl_module_prelu.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__PRelu.html)                                       | ✔ | ✔ |                                             |
 | Reshape[(ESP-DL)](esp-dl/dl/module/include/dl_module_reshape.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__Reshape.html)                                 | ✔ | ✔ |                                             |
-| Resize[(ESP-DL)](esp-dl/dl/module/include/dl_module_resize.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__Resize.html)                                    | ✔ | ✔ | Only support nearest and do not support roi |
+| Resize[(ESP-DL)](esp-dl/dl/module/include/dl_module_resize.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__Resize.html)                                    | ✔ | ✖ | Only support nearest and do not support roi |
 | Sigmoid[(ESP-DL)](esp-dl/dl/module/include/dl_module_sigmoid.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__Sigmoid.html)                                 | ✔ | ✔ |                                             |
 | Slice[(ESP-DL)](esp-dl/dl/module/include/dl_module_slice.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__Slice.html)                                       | ✔ | ✔ |                                             |
 | Softmax[(ESP-DL)](esp-dl/dl/module/include/dl_module_softmax.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__Softmax.html)                                 | ✔ | ✔ | Dtype of output is float32                  |
+| Split[(ESP-DL)](esp-dl/dl/module/include/dl_module_split.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__Split.html)                                       | ✔ | ✔ |                                             |
 | Sqrt[(ESP-DL)](esp-dl/dl/module/include/dl_module_sqrt.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__Sqrt.html)                                          | ✔ | ✔ |                                             |
 | Squeeze[(ESP-DL)](esp-dl/dl/module/include/dl_module_squeeze.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__Squeeze.html)                                 | ✔ | ✔ |                                             |
 | Sub[(ESP-DL)](esp-dl/dl/module/include/dl_module_sub.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__Sub.html)                                             | ✔ | ✔ | Support up to 4D                            |
 | Tanh[(ESP-DL)](esp-dl/dl/module/include/dl_module_tanh.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__Tanh.html)                                          | ✔ | ✔ |                                             |
 | Transpose[(ESP-DL)](esp-dl/dl/module/include/dl_module_transpose.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__Transpose.html)                           | ✔ | ✔ |                                             |
 | Unsqueeze[(ESP-DL)](esp-dl/dl/module/include/dl_module_unsqueeze.hpp)[(ONNX)](https://onnx.ai/onnx/operators/onnx__Unsqueeze.html)                           | ✔ | ✔ |                                             |
 
-Generation Time: 2024-12-13 15:55:57
+Generation Time: 2024-12-20 17:28:29