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examples/application/js/converter/README.md

@@ -1,3 +1,4 @@
+English | [简体中文](README_CN.md)
 # PaddleJsConverter
 
 ## Installation
@@ -26,4 +27,4 @@ pip3 install paddlejsconverter
 ```shell
 paddlejsconverter --modelPath=user_model_path --paramPath=user_model_params_path --outputDir=model_saved_path --useGPUOpt=True
 ```
-注意：useGPUOpt 选项默认不开启，如果模型用在 gpu backend（webgl/webgpu），则开启 useGPUOpt，如果模型运行在（wasm/plain js）则不要开启。
+Note: The option useGPUOpt is not turned on by default. Turn on useGPUOpt if the model is used on gpu backend (webgl/webgpu), don't turn on if is running on (wasm/plain js).

examples/application/js/converter/README_CN.md

@@ -0,0 +1,30 @@
+简体中文 ｜ [English](README.md)
+# PaddleJsConverter
+
+## Installation
+
+System Requirements:
+
+* paddlepaddle >= 2.0.0
+* paddlejslite >= 0.0.2
+* Python3： 3.5.1+ / 3.6 / 3.7
+* Python2： 2.7.15+
+
+#### Install PaddleJsConverter
+
+<img src="https://img.shields.io/pypi/v/paddlejsconverter" alt="version">
+
+```shell
+pip install paddlejsconverter
+
+# or
+pip3 install paddlejsconverter
+```
+
+
+## Usage
+
+```shell
+paddlejsconverter --modelPath=user_model_path --paramPath=user_model_params_path --outputDir=model_saved_path --useGPUOpt=True
+```
+注意：useGPUOpt 选项默认不开启，如果模型用在 gpu backend（webgl/webgpu），则开启 useGPUOpt，如果模型运行在（wasm/plain js）则不要开启。

examples/application/js/converter/RNN.md

@@ -1,3 +1,4 @@
+简体中文 ｜ [English](RNN_EN.md)
 # RNN算子计算过程
 
 ## 一、RNN理解
@@ -73,7 +74,7 @@ paddle源码实现：https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/
 
 计算方式：将rnn_matmul op输出结果分割成4份，每份执行不同激活函数计算，最后输出lstm_x_y.tmp_c[1,  1,  48]。x∈[0, 3]，y∈[0, 24]。
 详见算子实现：[rnn_cell](../paddlejs-backend-webgl/src/ops/shader/rnn/rnn_cell.ts)
-)
+
 
 4）rnn_hidden
 计算方式：将rnn_matmul op输出结果分割成4份，每份执行不同激活函数计算，最后输出lstm_x_y.tmp_h[1,  1,  48]。x∈[0, 3]，y∈[0, 24]。

examples/application/js/converter/RNN_EN.md

@@ -0,0 +1,80 @@
+English | [简体中文](RNN.md)
+# The computation process of RNN operator
+
+## 1. Understanding of RNN
+
+**RNN** is a recurrent neural network, including an input layer, a hidden layer and an output layer, which is specialized in processing sequential data.
+
+![RNN](https://user-images.githubusercontent.com/43414102/144739164-d6c4b9ff-d885-4812-8d05-5bf045d3a11b.png)
+paddle official document: https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/nn/RNN_cn.html#rnn
+
+paddle source code implementation: https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/fluid/operators/rnn_op.h#L812
+
+## 2. How to compute RNN 
+
+ At moment t, the input layer is ![图片](https://paddlejs.bj.bcebos.com/doc/xt.svg), hidden layer is ![图片](https://paddlejs.bj.bcebos.com/doc/st.svg), output layer is  ![图片](https://paddlejs.bj.bcebos.com/doc/ot.svg). As the picture above, ![图片](https://paddlejs.bj.bcebos.com/doc/st.svg)isn't just decided by  ![图片](https://paddlejs.bj.bcebos.com/doc/xt.svg),it is also related to ![图片](https://paddlejs.bj.bcebos.com/doc/st1.svg). The formula is as follows.:
+
+![RNN公式](https://user-images.githubusercontent.com/43414102/144739185-92724c8c-25f7-4559-9b1d-f1d76e65d965.jpeg)
+
+## 3.  RNN operator implementation in pdjs
+
+Because the gradient disappearance problem exists in RNN, and more contextual information cannot be obtained, **LSTM (Long Short Term Memory)** is used in CRNN, which is a special kind of RNN that can preserve long-term dependencies.
+
+Based on the image sequence, the two directions of context are mutually useful and complementary. Since the LSTM is unidirectional, two LSTMs, one forward and one backward, are combined into a **bidirectional LSTM**. In addition, multiple layers of bidirectional LSTMs can be stacked. ch_PP-OCRv2_rec_infer recognition model is using a two-layer bidirectional LSTM structure. The calculation process is shown as follows.
+
+#### Take ch_ppocr_mobile_v2.0_rec_infer model, rnn operator as an example
+```javascript
+{
+	Attr: {
+		mode: 'LSTM'
+		//  Whether bidirectional, if true, it is necessary to traverse both forward and reverse.
+		is_bidirec: true
+		// Number of hidden layers, representing the number of loops.
+		num_layers: 2
+	}
+	
+	Input: [
+		transpose_1.tmp_0[25, 1, 288]
+	]
+
+	PreState: [
+		fill_constant_batch_size_like_0.tmp_0[4, 1, 48],  
+		fill_constant_batch_size_like_1.tmp_0[4, 1, 48]
+	]
+
+	WeightList: [
+		lstm_cell_0.w_0[192, 288], lstm_cell_0.w_1[192, 48], 
+		lstm_cell_1.w_0[192, 288], lstm_cell_1.w_1[192, 48],
+		lstm_cell_2.w_0[192, 96], lstm_cell_2.w_1[192, 48], 
+		lstm_cell_3.w_0[192, 96], lstm_cell_3.w_1[192, 48],
+		lstm_cell_0.b_0[192], lstm_cell_0.b_1[192],
+		lstm_cell_1.b_0[192], lstm_cell_1.b_1[192],
+		lstm_cell_2.b_0[192], lstm_cell_2.b_1[192], 
+		lstm_cell_3.b_0[192], lstm_cell_3.b_1[192]
+	]
+
+	Output: [
+	    lstm_0.tmp_0[25, 1, 96]
+    ]
+}
+```
+
+#### Overall computation process
+![LSTM计算过程](https://user-images.githubusercontent.com/43414102/144739246-daf839ad-1d96-4e1d-8f34-38ed0bc5f288.png)
+#### Add op in rnn calculation
+1) rnn_origin
+Formula: blas.MatMul(Input,  WeightList_ih, blas_ih) + blas.MatMul(PreState,  WeightList_hh,  blas_hh)
+
+2) rnn_matmul
+Formula: rnn_matmul = rnn_origin +  Matmul( $ S_{t-1} $,  WeightList_hh)
+
+3) rnn_cell
+Method: Split the rnn_matmul op output into 4 copies, each copy performs a different activation function calculation, and finally outputs lstm_x_y.tmp_c[1,  1,  48]. x∈[0, 3], y∈[0, 24].
+For details, please refer to [rnn_cell](../paddlejs-backend-webgl/src/ops/shader/rnn/rnn_cell.ts).
+
+
+4) rnn_hidden
+Split the rnn_matmul op output into 4 copies, each copy performs a different activation function calculation, and finally outputs lstm_x_y.tmp_h[1,  1,  48]. x∈[0, 3], y∈[0, 24].
+For details, please refer to [rnn_hidden](../paddlejs-backend-webgl/src/ops/shader/rnn/rnn_hidden.ts).
+
+

examples/application/js/package/packages/paddlejs-models/humanseg_gpu/README.md

@@ -47,7 +47,7 @@ humanseg.drawMask(data, canvas3, back_canvas);
 
 ```js
 
-// 引入 humanseg sdk
+// import humanseg sdk
 import * as humanseg from '@paddle-js-models/humanseg/lib/index_gpu';
 
 // load humanseg model, use 398x224 shape model, and preheat