fix the docs

README.md

@@ -93,12 +93,6 @@ X2Paddle提供了工具解决如下问题，详见[tools/README.md](tools/README
 6. [X2Paddle添加内置的Caffe自定义层](./docs/user_guides/add_caffe_custom_layer.md)
 
 ## 更新历史
-2019.08.05  
-1. 统一tensorflow/caffe/onnx模型转换代码和对外接口
-2. 解决上一版caffe2fluid无法转换多分支模型的问题
-3. 解决Windows上保存模型无法加载的问题
-4. 新增optimizer，优化代码结构，合并conv、batch_norm的bias和激活函数  
-
 2020.12.09
 1. 新增PyTorch2Paddle转换方式，转换得到Paddle动态图代码，并动转静获得inference_model。
 方式一：trace方式，转换后的代码有模块划分，每个模块的功能与PyTorch相同。
@@ -107,8 +101,6 @@ X2Paddle提供了工具解决如下问题，详见[tools/README.md](tools/README
 3. 新增TensorFlow op（14个）：Neg、Greater、FloorMod、LogicalAdd、Prd、Equal、Conv3D、Ceil、AddN、DivNoNan、Where、MirrorPad、Size、TopKv2
 4. 新增Optimizer模块，主要包括op融合、op消除功能，转换后的代码可读性更强，进行预测时耗时更短。
 
-**如果你需要之前版本的tensorflow2fluid/caffe2fluid/onnx2fluid，可以继续访问release-0.9分支，获取之前版本的代码使用。**
-
 
 ## Acknowledgements
 

docs/introduction/op_list.md

@@ -61,7 +61,7 @@
 | 41 | MatMul   | 42 | Sum       | 43 | Transpose | 44 | BatchNormalization |
 | 45 | Squeeze  | 46 | Equal     | 47 | Identity  | 48 | GlobalAveragePool  |
 | 49 | MaxPool  | 50 | Conv      | 51 | Gemm      | 52 | NonZero            |
-| 53 | Abs      | 54 | Floor     |
+| 53 | Abs      | 54 | Floor     | 52 | ArgMax    |
 
 ## PyTorch
 Aten:
@@ -93,7 +93,8 @@ Aten:
 | 93 | aten::sub | 94 | aten::t |95|aten::tanh|96|aten::split|
 | 97 | aten::transpose | 98 | aten::to |99|aten::type\_as|100|aten::unsqueeze|
 | 101 | aten::upsample\_bilinear2d | 102 | aten::values |103|aten::view|104|aten::warn|
-| 105 | aten::where | 106 | aten::zeros |107|aten::zeros\_like|||
+| 105 | aten::where | 106 | aten::zeros |107|aten::zeros\_like|108|aten::bmm|
+| 109 | aten::sub\_ | 110 | aten:erf |111|aten::lstm|112|aten::gather|
 
 Prim:
 | 序号 | OP | 序号 | OP | 序号 | OP | 序号 | OP |

docs/introduction/x2paddle_model_zoo.md

@@ -5,28 +5,28 @@
 
 ## TensorFlow
 
-| 模型 | 代码 | 备注 |
-|------|----------|------|
-| SqueezeNet | [code](https://github.com/tensorflow/tpu/blob/master/models/official/squeezenet/squeezenet_model.py)|-|
-| MobileNet_V1 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |-|
-| MobileNet_V2 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |-|
-| ShuffleNet | [code](https://github.com/TropComplique/shufflenet-v2-tensorflow) |-|
-| mNASNet | [code](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet) |-|
-| EfficientNet | [code](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |-|
-| Inception_V3 | [code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v3.py) |-|
-| Inception_V4 | [code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v4.py) |-|
-| Inception_ResNet_V2 | [code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py) |-|
-| VGG16 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |-|
-| ResNet_V1_101 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |-|
-| ResNet_V2_101 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |-|
-| UNet | [code1](https://github.com/jakeret/tf_unet )/[code2](https://github.com/lyatdawn/Unet-Tensorflow) |-|
-| MTCNN | [code](https://github.com/AITTSMD/MTCNN-Tensorflow) |-|
-| YOLO-V3| [code](https://github.com/YunYang1994/tensorflow-yolov3) | -|
-| FALSR | [code](https://github.com/xiaomi-automl/FALSR) | 需使用参数without_data_format_optimization |
-| DCSCN | [code](https://modelzoo.co/model/dcscn-super-resolution) | 需使用参数without_data_format_optimization |
-| Bert（albert） | [code](https://github.com/google-research/albert#pre-trained-models) | 需使用参数without_data_format_optimization |
-| Bert（chinese_L-12_H-768_A-12） | [code](https://github.com/google-research/bert#pre-trained-models) | 需使用参数without_data_format_optimization |
-| Bert（multi_cased_L-12_H-768_A-12） | [code](https://github.com/google-research/bert#pre-trained-models) | 需使用参数without_data_format_optimization |
+| 模型 | 代码 |
+|------|----------|
+| SqueezeNet | [code](https://github.com/tensorflow/tpu/blob/master/models/official/squeezenet/squeezenet_model.py)|
+| MobileNet_V1 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |
+| MobileNet_V2 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |
+| ShuffleNet | [code](https://github.com/TropComplique/shufflenet-v2-tensorflow) |
+| mNASNet | [code](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet) |
+| EfficientNet | [code](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |
+| Inception_V3 | [code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v3.py) |
+| Inception_V4 | [code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v4.py) |
+| Inception_ResNet_V2 | [code](https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py) |
+| VGG16 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |
+| ResNet_V1_101 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |
+| ResNet_V2_101 | [code](https://github.com/tensorflow/models/tree/master/research/slim/nets) |
+| UNet | [code1](https://github.com/jakeret/tf_unet )/[code2](https://github.com/lyatdawn/Unet-Tensorflow) |
+| MTCNN | [code](https://github.com/AITTSMD/MTCNN-Tensorflow) |
+| YOLO-V3| [code](https://github.com/YunYang1994/tensorflow-yolov3) | 
+| FALSR | [code](https://github.com/xiaomi-automl/FALSR) |
+| DCSCN | [code](https://modelzoo.co/model/dcscn-super-resolution) |
+| Bert（albert） | [code](https://github.com/google-research/albert#pre-trained-models) |
+| Bert（chinese_L-12_H-768_A-12） | [code](https://github.com/google-research/bert#pre-trained-models) | 
+| Bert（multi_cased_L-12_H-768_A-12） | [code](https://github.com/google-research/bert#pre-trained-models) | 
 
 ## Caffe
 
@@ -72,8 +72,8 @@
 | EfficientNet | [pytorch(personal practice)](https://github.com/rwightman/gen-efficientnet-pytorch) |9|
 | SqueezeNet | [onnx official](https://s3.amazonaws.com/download.onnx/models/opset_9/squeezenet.tar.gz) |9|
 |Ultra-Light-Fast-Generic-Face-Detector-1MB| [onnx_model](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB/tree/master/models/onnx)|9 |
-|BERT| [pytorch(huggingface)](https://github.com/huggingface/transformers/blob/master/notebooks/04-onnx-export.ipynb)|11|转换时需指定input shape，见[文档Q3](FAQ.md)|
-|GPT2| [pytorch(huggingface)](https://github.com/huggingface/transformers/blob/master/notebooks/04-onnx-export.ipynb)|11|转换时需指定input shape，见[文档Q3](FAQ.md)|
+|BERT| [pytorch(huggingface)](https://github.com/huggingface/transformers/blob/master/notebooks/04-onnx-export.ipynb)|11|转换时需指定input shape，见[文档Q3](../user_guides/FAQ.md)|
+|GPT2| [pytorch(huggingface)](https://github.com/huggingface/transformers/blob/master/notebooks/04-onnx-export.ipynb)|11|转换时需指定input shape，见[文档Q3](../user_guides/FAQ.md)|
 
 
 ## PyTorch
@@ -96,3 +96,6 @@
 | FlaubertModel | [code](https://huggingface.co/transformers/model_doc/flaubert.html) |只支持trace模式|
 | Roberta| [code](https://huggingface.co/transformers/model_doc/roberta.html)  |只支持trace模式|
 | XLMRobertaForTokenClassification|[code](https://huggingface.co/transformers/model_doc/xlmroberta.html)  |只支持trace模式|
+| EasyOCR_detector|[code](https://github.com/JaidedAI/EasyOCR/blob/master/easyocr/detection.py)  |-|
+| EasyOCR_recognizer|[code](https://github.com/JaidedAI/EasyOCR/blob/master/easyocr/recognition.py)  |-|
+