Merge branch 'PaddlePaddle:dygraph' into dygraph

f3c0bc8f · topduke · GitHub · a340d95b · e03e52a2 · f3c0bc8f
Unverified Commit f3c0bc8f authored May 01, 2022 by topduke Committed by GitHub May 01, 2022
7 changed files
--- a/doc/doc_en/algorithm_rec_seed_en.md
+++ b/doc/doc_en/algorithm_rec_seed_en.md
+# SEED
+
+- [1. Introduction](#1)
+- [2. Environment](#2)
+- [3. Model Training / Evaluation / Prediction](#3)
+    - [3.1 Training](#3-1)
+    - [3.2 Evaluation](#3-2)
+    - [3.3 Prediction](#3-3)
+- [4. Inference and Deployment](#4)
+    - [4.1 Python Inference](#4-1)
+    - [4.2 C++ Inference](#4-2)
+    - [4.3 Serving](#4-3)
+    - [4.4 More](#4-4)
+- [5. FAQ](#5)
+
+<a name="1"></a>
+## 1. Introduction
+
+Paper:
+> [SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition](https://arxiv.org/pdf/2005.10977.pdf)
+
+> Qiao, Zhi and Zhou, Yu and Yang, Dongbao and Zhou, Yucan and Wang, Weiping
+
+> CVPR, 2020
+
+Using MJSynth and SynthText two text recognition datasets for training, and evaluating on IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE datasets, the algorithm reproduction effect is as follows:
+
+|Model|Backbone|ACC|config|Download link|
+| --- | --- | --- | --- | --- |
+|SEED|Aster_Resnet| 85.2% | [configs/rec/rec_resnet_stn_bilstm_att.yml](../../configs/rec/rec_resnet_stn_bilstm_att.yml) | [训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/rec/rec_resnet_stn_bilstm_att.tar) |
+
+<a name="2"></a>
+## 2. Environment
+Please refer to ["Environment Preparation"](./environment.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone.md) to clone the project code.
+
+
+<a name="3"></a>
+## 3. Model Training / Evaluation / Prediction
+
+Please refer to [Text Recognition Tutorial](./recognition.md). PaddleOCR modularizes the code, and training different recognition models only requires **changing the configuration file**.
+
+Training:
+
+The SEED model needs to additionally load the [language model](https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.en.300.bin.gz) trained by FastText, and install the fasttext dependencies:
+
+```
+python3 -m pip install fasttext==0.9.1
+```
+
+Specifically, after the data preparation is completed, the training can be started. The training command is as follows:
+
+```
+#Single GPU training (long training period, not recommended)
+python3 tools/train.py -c configs/rec/rec_resnet_stn_bilstm_att.yml
+
+#Multi GPU training, specify the gpu number through the --gpus parameter
+python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c rec_resnet_stn_bilstm_att.yml
+```
+
+Evaluation:
+
+```
+# GPU evaluation
+python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
+```
+
+Prediction:
+
+```
+# The configuration file used for prediction must match the training
+python3 tools/infer_rec.py -c configs/rec/rec_resnet_stn_bilstm_att.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/en/word_1.png
+```
+
+<a name="4"></a>
+## 4. Inference and Deployment
+
+<a name="4-1"></a>
+### 4.1 Python Inference
+
+Not support
+
+<a name="4-2"></a>
+### 4.2 C++ Inference
+
+Not support
+
+<a name="4-3"></a>
+### 4.3 Serving
+
+Not support
+
+<a name="4-4"></a>
+### 4.4 More
+
+Not support
+
+<a name="5"></a>
+## 5. FAQ
+
+
+## Citation
+
+```bibtex
+@inproceedings{qiao2020seed,
+  title={Seed: Semantics enhanced encoder-decoder framework for scene text recognition},
+  author={Qiao, Zhi and Zhou, Yu and Yang, Dongbao and Zhou, Yucan and Wang, Weiping},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={13528--13537},
+  year={2020}
+}
+```
--- a/doc/doc_en/ppocr_introduction_en.md
+++ b/doc/doc_en/ppocr_introduction_en.md
@@ -121,7 +121,7 @@ For more tutorials, including model training, model compression, deployment, etc
 | Model introduction                                           | Model name                   | Recommended scene | Detection model                                              | Direction classifier                                         | Recognition model                                            |
 | ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
 | Chinese and English ultra-lightweight PP-OCRv3 model（16.2M）     | ch_PP-OCRv3_xx          | Mobile & Server | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_distill_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_train.tar) |
-| English ultra-lightweight PP-OCRv3 model（13.4M）     | en_PP-OCRv3_xx          | Mobile & Server | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_distill_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/ch_ppocr_mobile_v2.0_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar) |
+| English ultra-lightweight PP-OCRv3 model（13.4M）     | en_PP-OCRv3_xx          | Mobile & Server | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_det_distill_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) | [inference model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar) |
 | Chinese and English ultra-lightweight PP-OCRv2 model（11.6M） |  ch_PP-OCRv2_xx |Mobile & Server|[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_det_distill_train.tar)| [inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/ch_PP-OCRv2_rec_train.tar)|
 | Chinese and English ultra-lightweight PP-OCR model (9.4M)       | ch_ppocr_mobile_v2.0_xx      | Mobile & server   |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar)      |
 | Chinese and English general PP-OCR model (143.4M)               | ch_ppocr_server_v2.0_xx      | Server            |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar)    |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar)    |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_train.tar)  |

--- a/doc/doc_en/recognition_en.md
+++ b/doc/doc_en/recognition_en.md
@@ -28,7 +28,34 @@

 To prepare datasets, refer to [ocr_datasets](./dataset/ocr_datasets.md) .

-If you want to reproduce the paper SAR, you need to download extra dataset [SynthAdd](https://pan.baidu.com/share/init?surl=uV0LtoNmcxbO-0YA7Ch4dg), extraction code: 627x. Besides, icdar2013, icdar2015, cocotext, IIIT5k datasets are also used to train. For specific details, please refer to the paper SAR.
+PaddleOCR provides label files for training the icdar2015 dataset, which can be downloaded in the following ways:
+
+```
+# Training set label
+wget -P ./train_data/ic15_data  https://paddleocr.bj.bcebos.com/dataset/rec_gt_train.txt
+# Test Set Label
+wget -P ./train_data/ic15_data  https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt
+```
+
+PaddleOCR also provides a data format conversion script, which can convert ICDAR official website label to a data format
+supported by PaddleOCR. The data conversion tool is in `ppocr/utils/gen_label.py`, here is the training set as an example:
+
+```
+# convert the official gt to rec_gt_label.txt
+python gen_label.py --mode="rec" --input_path="{path/of/origin/label}" --output_label="rec_gt_label.txt"
+```
+
+The data format is as follows, (a) is the original picture, (b) is the Ground Truth text file corresponding to each picture:
+
+![](../datasets/icdar_rec.png)
+
+
+- Multilingual dataset
+
+The multi-language model training method is the same as the Chinese model. The training data set is 100w synthetic data. A small amount of fonts and test data can be downloaded using the following two methods.
+* [Baidu Netdisk](https://pan.baidu.com/s/1bS_u207Rm7YbY33wOECKDA) ,Extraction code:frgi.
+* [Google drive](https://drive.google.com/file/d/18cSWX7wXSy4G0tbKJ0d9PuIaiwRLHpjA/view)
+

 <a name="Dictionary"></a>
 ### 1.2 Dictionary
@@ -101,11 +128,11 @@ First download the pretrain model, you can download the trained model to finetun

 ```
 cd PaddleOCR/
-# Download the pre-trained model of MobileNetV3
-wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar
+# Download the pre-trained model of en_PP-OCRv3
+wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/PP-OCRv3/english/en_PP-OCRv3_rec_train.tar
 # Decompress model parameters
 cd pretrain_models
-tar -xf rec_mv3_none_bilstm_ctc_v2.0_train.tar && rm -rf rec_mv3_none_bilstm_ctc_v2.0_train.tar
+tar -xf en_PP-OCRv3_rec_train.tar && rm -rf en_PP-OCRv3_rec_train.tar
 ```

 Start training:
@@ -115,9 +142,10 @@ Start training:
 # Training icdar15 English data and The training log will be automatically saved as train.log under "{save_model_dir}"

 #specify the single card training(Long training time, not recommended)
-python3 tools/train.py -c configs/rec/rec_icdar15_train.yml
+python3 tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=en_PP-OCRv3_rec_train/best_accuracy
+
 #specify the card number through --gpus
-python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/rec/rec_icdar15_train.yml
+python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=en_PP-OCRv3_rec_train/best_accuracy
 ```


@@ -125,31 +153,13 @@ PaddleOCR supports alternating training and evaluation. You can modify `eval_bat

 If the evaluation set is large, the test will be time-consuming. It is recommended to reduce the number of evaluations, or evaluate after training.

-* Tip: You can use the `-c` parameter to select multiple model configurations under the `configs/rec/` path for training. The recognition algorithms supported by PaddleOCR are:
-
-
-| Configuration file |  Algorithm |   backbone |   trans   |   seq      |     pred     |
-| :--------: |  :-------:   | :-------:  |   :-------:   |   :-----:   |  :-----:   |
-| [rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml) |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  |
-| [rec_chinese_common_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_common_train_v2.0.yml) |  CRNN | ResNet34_vd |  None   |  BiLSTM |  ctc  |
-| rec_chinese_lite_train.yml |  CRNN |   Mobilenet_v3 small 0.5 |  None   |  BiLSTM |  ctc  |
-| rec_chinese_common_train.yml |  CRNN |   ResNet34_vd |  None   |  BiLSTM |  ctc  |
-| rec_icdar15_train.yml |  CRNN |   Mobilenet_v3 large 0.5 |  None   |  BiLSTM |  ctc  |
-| rec_mv3_none_bilstm_ctc.yml |  CRNN |   Mobilenet_v3 large 0.5 |  None   |  BiLSTM |  ctc  |
-| rec_mv3_none_none_ctc.yml |  Rosetta |   Mobilenet_v3 large 0.5 |  None   |  None |  ctc  |
-| rec_r34_vd_none_bilstm_ctc.yml |  CRNN |   Resnet34_vd |  None   |  BiLSTM |  ctc  |
-| rec_r34_vd_none_none_ctc.yml |  Rosetta |   Resnet34_vd |  None   |  None |  ctc  |
-| rec_mv3_tps_bilstm_att.yml |  CRNN |   Mobilenet_v3 |  TPS   |  BiLSTM |  att  |
-| rec_r34_vd_tps_bilstm_att.yml |  CRNN |   Resnet34_vd |  TPS   |  BiLSTM |  att  |
-| rec_r50fpn_vd_none_srn.yml    | SRN | Resnet50_fpn_vd    | None    | rnn | srn |
-| rec_mtb_nrtr.yml    | NRTR | nrtr_mtb    | None    | transformer encoder | transformer decoder |
-| rec_r31_sar.yml               | SAR | ResNet31 | None | LSTM encoder | LSTM decoder |
+* Tip: You can use the `-c` parameter to select multiple model configurations under the `configs/rec/` path for training. The recognition algorithms supported at [rec_algorithm](https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_en/algorithm_overview.md):


 For training Chinese data, it is recommended to use
-[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
-co
-Take `rec_chinese_lite_train_v2.0.yml` as an example:
+[ch_PP-OCRv3_rec_distillation.yml](../../configs/rec/PP-OCRv3/ch_PP-OCRv3_rec_distillation.yml). If you want to try the result of other algorithms on the Chinese data set, please refer to the following instructions to modify the configuration file:
+
+Take `ch_PP-OCRv3_rec_distillation.yml` as an example:
 ```
 Global:
  ...
@@ -183,7 +193,7 @@ Train:
      ...
      - RecResizeImg:
          # Modify image_shape to fit long text
-          image_shape: [3, 32, 320]
+          image_shape: [3, 48, 320]
      ...
  loader:
    ...
@@ -203,7 +213,7 @@ Eval:
      ...
      - RecResizeImg:
          # Modify image_shape to fit long text
-          image_shape: [3, 32, 320]
+          image_shape: [3, 48, 320]
      ...
  loader:
    # Eval batch_size for Single card
@@ -380,11 +390,12 @@ Running on a DCU device requires setting the environment variable `export HIP_VI
 <a name="31-evaluation"></a>
 ### 3.1 Evaluation

-The model parameters during training are saved in the `Global.save_model_dir` directory by default. When evaluating indicators, you need to set `Global.checkpoints` to point to the saved parameter file. The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/rec_icdar15_train.yml` file.
+The model parameters during training are saved in the `Global.save_model_dir` directory by default. When evaluating indicators, you need to set `Global.checkpoints` to point to the saved parameter file. The evaluation dataset can be set by modifying the `Eval.dataset.label_file_list` field in the `configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml` file.
+

 ```
 # GPU evaluation, Global.checkpoints is the weight to be tested
-python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/rec_icdar15_train.yml -o Global.checkpoints={path/to/weights}/best_accuracy
+python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.checkpoints={path/to/weights}/best_accuracy
 ```

 <a name="32-test"></a>
@@ -417,7 +428,7 @@ Among them, best_accuracy.* is the best model on the evaluation set; iter_epoch_

 ```
 # Predict English results
-python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/en/word_1.jpg
+python3 tools/infer_rec.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model={path/to/weights}/best_accuracy  Global.infer_img=doc/imgs_words/en/word_1.png
 ```


@@ -436,7 +447,7 @@ The configuration file used for prediction must be consistent with the training.

 ```
 # Predict Chinese results
-python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.load_static_weights=false Global.infer_img=doc/imgs_words/ch/word_1.jpg
+python3 tools/infer_rec.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words/ch/word_1.jpg
 ```

 Input image:
@@ -467,7 +478,7 @@ The recognition model is converted to the inference model in the same way as the
 # Global.pretrained_model parameter Set the training model address to be converted without adding the file suffix .pdmodel, .pdopt or .pdparams.
 # Global.save_inference_dir Set the address where the converted model will be saved.

-python3 tools/export_model.py -c configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml -o Global.pretrained_model=./ch_lite/ch_ppocr_mobile_v2.0_rec_train/best_accuracy  Global.save_inference_dir=./inference/rec_crnn/
+python3 tools/export_model.py -c configs/rec/PP-OCRv3/en_PP-OCRv3_rec.yml -o Global.pretrained_model=en_PP-OCRv3_rec_train/best_accuracy  Global.save_inference_dir=./inference/en_PP-OCRv3_rec/
 ```

 If you have a model trained on your own dataset with a different dictionary file, please make sure that you modify the `character_dict_path` in the configuration file to your dictionary file path.
@@ -475,7 +486,8 @@ If you have a model trained on your own dataset with a different dictionary file
 After the conversion is successful, there are three files in the model save directory:

 ```
-inference/rec_crnn/
+
+inference/en_PP-OCRv3_rec/
    ├── inference.pdiparams         # The parameter file of recognition inference model
    ├── inference.pdiparams.info    # The parameter information of recognition inference model, which can be ignored
    └── inference.pdmodel           # The program file of recognition model

--- a/ppocr/optimizer/optimizer.py
+++ b/ppocr/optimizer/optimizer.py
@@ -43,12 +43,15 @@ class Momentum(object):
        self.grad_clip = grad_clip

    def __call__(self, model):
+        train_params = [
+            param for param in model.parameters() if param.trainable is True
+        ]
        opt = optim.Momentum(
            learning_rate=self.learning_rate,
            momentum=self.momentum,
            weight_decay=self.weight_decay,
            grad_clip=self.grad_clip,
-            parameters=model.parameters())
+            parameters=train_params)
        return opt


@@ -76,6 +79,9 @@ class Adam(object):
        self.lazy_mode = lazy_mode

    def __call__(self, model):
+        train_params = [
+            param for param in model.parameters() if param.trainable is True
+        ]
        opt = optim.Adam(
            learning_rate=self.learning_rate,
            beta1=self.beta1,
@@ -85,7 +91,7 @@ class Adam(object):
            grad_clip=self.grad_clip,
            name=self.name,
            lazy_mode=self.lazy_mode,
-            parameters=model.parameters())
+            parameters=train_params)
        return opt


@@ -118,6 +124,9 @@ class RMSProp(object):
        self.grad_clip = grad_clip

    def __call__(self, model):
+        train_params = [
+            param for param in model.parameters() if param.trainable is True
+        ]
        opt = optim.RMSProp(
            learning_rate=self.learning_rate,
            momentum=self.momentum,
@@ -125,7 +134,7 @@ class RMSProp(object):
            epsilon=self.epsilon,
            weight_decay=self.weight_decay,
            grad_clip=self.grad_clip,
-            parameters=model.parameters())
+            parameters=train_params)
        return opt


@@ -149,6 +158,9 @@ class Adadelta(object):
        self.name = name

    def __call__(self, model):
+        train_params = [
+            param for param in model.parameters() if param.trainable is True
+        ]
        opt = optim.Adadelta(
            learning_rate=self.learning_rate,
            epsilon=self.epsilon,
@@ -156,7 +168,7 @@ class Adadelta(object):
            weight_decay=self.weight_decay,
            grad_clip=self.grad_clip,
            name=self.name,
-            parameters=model.parameters())
+            parameters=train_params)
        return opt


@@ -190,10 +202,13 @@ class AdamW(object):
        self.one_dim_param_no_weight_decay = one_dim_param_no_weight_decay

    def __call__(self, model):
-        parameters = model.parameters()
+        parameters = [
+            param for param in model.parameters() if param.trainable is True
+        ]

        self.no_weight_decay_param_name_list = [
-            p.name for n, p in model.named_parameters() if any(nd in n for nd in self.no_weight_decay_name_list)
+            p.name for n, p in model.named_parameters()
+            if any(nd in n for nd in self.no_weight_decay_name_list)
        ]

        if self.one_dim_param_no_weight_decay:

--- a/ppocr/utils/utility.py
+++ b/ppocr/utils/utility.py
@@ -60,7 +60,7 @@ def get_image_file_list(img_file):
        raise Exception("not found any img file in {}".format(img_file))

    img_end = {'jpg', 'bmp', 'png', 'jpeg', 'rgb', 'tif', 'tiff', 'gif'}
-    if os.path.isfile(img_file) and _check_image_file(file_path):
+    if os.path.isfile(img_file) and _check_image_file(img_file):
        imgs_lists.append(img_file)
    elif os.path.isdir(img_file):
        for single_file in os.listdir(img_file):

--- a/tools/eval.py
+++ b/tools/eval.py
@@ -20,8 +20,8 @@ import os
 import sys

 __dir__ = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(__dir__)
-sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
+sys.path.insert(0, __dir__)
+sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))

 from ppocr.data import build_dataloader
 from ppocr.modeling.architectures import build_model

--- a/tools/infer/predict_system.py
+++ b/tools/infer/predict_system.py
@@ -195,7 +195,7 @@ def main(args):
        text_sys.text_detector.autolog.report()
        text_sys.text_recognizer.autolog.report()

-    with open(os.path.join(draw_img_save_dir, "system_results.txt"), 'w') as f:
+    with open(os.path.join(draw_img_save_dir, "system_results.txt"), 'w', encoding='utf-8') as f:
        f.writelines(save_results)