Merge branch 'dygraph' of https://github.com/PaddlePaddle/PaddleOCR into table_pr

configs/sr/sr_tsrn_transformer_strock.yml

+Global:
+  use_gpu: true
+  epoch_num: 500
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: ./output/sr/sr_tsrn_transformer_strock/
+  save_epoch_step: 3
+  # evaluation is run every 2000 iterations
+  eval_batch_step: [0, 1000]
+  cal_metric_during_train: False
+  pretrained_model:
+  checkpoints:
+  save_inference_dir: sr_output
+  use_visualdl: False
+  infer_img: doc/imgs_words_en/word_52.png
+  # for data or label process
+  character_dict_path: ./train_data/srdata/english_decomposition.txt
+  max_text_length: 100
+  infer_mode: False
+  use_space_char: False
+  save_res_path: ./output/sr/predicts_gestalt.txt
+
+Optimizer:
+  name: Adam
+  beta1: 0.5
+  beta2: 0.999
+  clip_norm: 0.25
+  lr:
+    learning_rate: 0.0001
+
+Architecture:
+  model_type: sr
+  algorithm: Gestalt
+  Transform:
+    name: TSRN
+    STN: True
+    infer_mode: False
+
+Loss:
+  name: StrokeFocusLoss
+  character_dict_path: ./train_data/srdata/english_decomposition.txt
+
+PostProcess:
+  name: None
+
+Metric:
+  name: SRMetric
+  main_indicator: all
+
+Train:
+  dataset:
+    name: LMDBDataSetSR
+    data_dir: ./train_data/srdata/train
+    transforms:
+      - SRResize:
+          imgH: 32
+          imgW: 128
+          down_sample_scale: 2
+      - SRLabelEncode: # Class handling label
+      - KeepKeys:
+          keep_keys: ['img_lr', 'img_hr', 'length', 'input_tensor', 'label'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    batch_size_per_card: 16
+    drop_last: True
+    num_workers: 4
+
+Eval:
+  dataset:
+    name: LMDBDataSetSR
+    data_dir: ./train_data/srdata/test
+    transforms:
+      - SRResize:
+          imgH: 32
+          imgW: 128
+          down_sample_scale: 2
+      - SRLabelEncode: # Class handling label
+      - KeepKeys:
+          keep_keys: ['img_lr', 'img_hr','length', 'input_tensor', 'label'] # dataloader will return list in this order
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 16
+    num_workers: 4
+

doc/doc_ch/algorithm_sr_gestalt.md

+# Text Gestalt
+
+- [1. 算法简介](#1)
+- [2. 环境配置](#2)
+- [3. 模型训练、评估、预测](#3)
+    - [3.1 训练](#3-1)
+    - [3.2 评估](#3-2)
+    - [3.3 预测](#3-3)
+- [4. 推理部署](#4)
+    - [4.1 Python推理](#4-1)
+    - [4.2 C++推理](#4-2)
+    - [4.3 Serving服务化部署](#4-3)
+    - [4.4 更多推理部署](#4-4)
+- [5. FAQ](#5)
+
+<a name="1"></a>
+## 1. 算法简介
+
+论文信息：
+> [Text Gestalt: Stroke-Aware Scene Text Image Super-Resolution](https://arxiv.org/pdf/2112.08171.pdf)
+
+> Chen, Jingye and Yu, Haiyang and Ma, Jianqi and Li, Bin and Xue, Xiangyang
+
+> AAAI, 2022
+
+参考[FudanOCR](https://github.com/FudanVI/FudanOCR/tree/main/text-gestalt) 数据下载说明，在TextZoom测试集合上超分算法效果如下：
+
+|模型|骨干网络|PSNR_Avg|SSIM_Avg|配置文件|下载链接|
+|---|---|---|---|---|---|
+|Text Gestalt|tsrn|19.28|0.6560| [configs/sr/sr_tsrn_transformer_strock.yml](../../configs/sr/sr_tsrn_transformer_strock.yml)|[训练模型](https://paddleocr.bj.bcebos.com/sr_tsrn_transformer_strock_train.tar)|
+
+
+<a name="2"></a>
+## 2. 环境配置
+请先参考[《运行环境准备》](./environment.md)配置PaddleOCR运行环境，参考[《项目克隆》](./clone.md)克隆项目代码。
+
+
+<a name="3"></a>
+## 3. 模型训练、评估、预测
+
+请参考[文本识别训练教程](./recognition.md)。PaddleOCR对代码进行了模块化，训练不同的识别模型只需要**更换配置文件**即可。
+
+- 训练
+
+在完成数据准备后，便可以启动训练，训练命令如下：
+
+```
+#单卡训练（训练周期长，不建议）
+python3 tools/train.py -c configs/sr/sr_tsrn_transformer_strock.yml
+
+#多卡训练，通过--gpus参数指定卡号
+python3 -m paddle.distributed.launch --gpus '0,1,2,3'  tools/train.py -c configs/sr/sr_tsrn_transformer_strock.yml
+
+```
+
+- 评估
+
+```
+# GPU 评估， Global.pretrained_model 为待测权重
+python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
+```
+
+- 预测：
+
+```
+# 预测使用的配置文件必须与训练一致
+python3 tools/infer_sr.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words_en/word_52.png
+```
+
+![](../imgs_words_en/word_52.png)
+
+执行命令后，上面图像的超分结果如下：
+
+![](../imgs_results/sr_word_52.png)
+
+<a name="4"></a>
+## 4. 推理部署
+
+<a name="4-1"></a>
+### 4.1 Python推理
+
+首先将文本超分训练过程中保存的模型，转换成inference model。以 Text-Gestalt 训练的[模型](https://paddleocr.bj.bcebos.com/sr_tsrn_transformer_strock_train.tar) 为例，可以使用如下命令进行转换：
+```shell
+python3 tools/export_model.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=./inference/sr_out
+```
+Text-Gestalt 文本超分模型推理，可以执行如下命令：
+```
+python3 tools/infer/predict_sr.py --sr_model_dir=./inference/sr_out --image_dir=doc/imgs_words_en/word_52.png --sr_image_shape=3,32,128
+
+```
+
+执行命令后，图像的超分结果如下：
+
+![](../imgs_results/sr_word_52.png)
+
+<a name="4-2"></a>
+### 4.2 C++推理
+
+暂未支持
+
+<a name="4-3"></a>
+### 4.3 Serving服务化部署
+
+暂未支持
+
+<a name="4-4"></a>
+### 4.4 更多推理部署
+
+暂未支持
+
+<a name="5"></a>
+## 5. FAQ
+
+
+## 引用
+
+```bibtex
+@inproceedings{chen2022text,
+  title={Text gestalt: Stroke-aware scene text image super-resolution},
+  author={Chen, Jingye and Yu, Haiyang and Ma, Jianqi and Li, Bin and Xue, Xiangyang},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={36},
+  number={1},
+  pages={285--293},
+  year={2022}
+}
+```

doc/doc_en/algorithm_sr_gestalt_en.md

+# Text Gestalt
+
+- [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:
+> [Text Gestalt: Stroke-Aware Scene Text Image Super-Resolution](https://arxiv.org/pdf/2112.08171.pdf)
+
+> Chen, Jingye and Yu, Haiyang and Ma, Jianqi and Li, Bin and Xue, Xiangyang
+
+> AAAI, 2022
+
+Referring to the [FudanOCR](https://github.com/FudanVI/FudanOCR/tree/main/text-gestalt) data download instructions, the effect of the super-score algorithm on the TextZoom test set is as follows:
+
+|Model|Backbone|config|Acc|Download link|
+|---|---|---|---|---|---|
+|Text Gestalt|tsrn|19.28|0.6560| [configs/sr/sr_tsrn_transformer_strock.yml](../../configs/sr/sr_tsrn_transformer_strock.yml)|[train model](https://paddleocr.bj.bcebos.com/sr_tsrn_transformer_strock_train.tar)|
+
+
+<a name="2"></a>
+## 2. Environment
+Please refer to ["Environment Preparation"](./environment_en.md) to configure the PaddleOCR environment, and refer to ["Project Clone"](./clone_en.md) to clone the project code.
+
+
+<a name="3"></a>
+## 3. Model Training / Evaluation / Prediction
+
+Please refer to [Text Recognition Tutorial](./recognition_en.md). PaddleOCR modularizes the code, and training different models only requires **changing the configuration file**.
+
+Training:
+
+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/sr/sr_tsrn_transformer_strock.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 configs/sr/sr_tsrn_transformer_strock.yml
+
+```
+
+
+Evaluation:
+
+```
+# GPU evaluation
+python3 -m paddle.distributed.launch --gpus '0' tools/eval.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy
+```
+
+Prediction:
+
+```
+# The configuration file used for prediction must match the training
+
+python3 tools/infer_sr.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.infer_img=doc/imgs_words_en/word_52.png
+```
+
+![](../imgs_words_en/word_52.png)
+
+After executing the command, the super-resolution result of the above image is as follows:
+
+![](../imgs_results/sr_word_52.png)
+
+<a name="4"></a>
+## 4. Inference and Deployment
+
+<a name="4-1"></a>
+### 4.1 Python Inference
+
+First, the model saved during the training process is converted into an inference model. ( [Model download link](https://paddleocr.bj.bcebos.com/sr_tsrn_transformer_strock_train.tar) ), you can use the following command to convert:
+
+```shell
+python3 tools/export_model.py -c configs/sr/sr_tsrn_transformer_strock.yml -o Global.pretrained_model={path/to/weights}/best_accuracy Global.save_inference_dir=./inference/sr_out
+```
+
+For Text-Gestalt super-resolution model inference, the following commands can be executed:
+
+```
+python3 tools/infer/predict_sr.py --sr_model_dir=./inference/sr_out --image_dir=doc/imgs_words_en/word_52.png --sr_image_shape=3,32,128
+
+```
+
+After executing the command, the super-resolution result of the above image is as follows:
+
+![](../imgs_results/sr_word_52.png)
+
+
+<a name="4-2"></a>
+### 4.2 C++ Inference
+
+Not supported
+
+<a name="4-3"></a>
+### 4.3 Serving
+
+Not supported
+
+<a name="4-4"></a>
+### 4.4 More
+
+Not supported
+
+<a name="5"></a>
+## 5. FAQ
+
+
+## Citation
+
+```bibtex
+@inproceedings{chen2022text,
+  title={Text gestalt: Stroke-aware scene text image super-resolution},
+  author={Chen, Jingye and Yu, Haiyang and Ma, Jianqi and Li, Bin and Xue, Xiangyang},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={36},
+  number={1},
+  pages={285--293},
+  year={2022}
+}
+```

ppocr/data/__init__.py

@@ -34,7 +34,7 @@ import paddle.distributed as dist
 
 from ppocr.data.imaug import transform, create_operators
 from ppocr.data.simple_dataset import SimpleDataSet
-from ppocr.data.lmdb_dataset import LMDBDataSet
+from ppocr.data.lmdb_dataset import LMDBDataSet, LMDBDataSetSR
 from ppocr.data.pgnet_dataset import PGDataSet
 from ppocr.data.pubtab_dataset import PubTabDataSet
 
@@ -54,7 +54,8 @@ def build_dataloader(config, mode, device, logger, seed=None):
     config = copy.deepcopy(config)
 
     support_dict = [
-        'SimpleDataSet', 'LMDBDataSet', 'PGDataSet', 'PubTabDataSet'
+        'SimpleDataSet', 'LMDBDataSet', 'PGDataSet', 'PubTabDataSet',
+        'LMDBDataSetSR'
     ]
     module_name = config[mode]['dataset']['name']
     assert module_name in support_dict, Exception(

ppocr/data/imaug/label_ops.py

@@ -1243,6 +1243,54 @@ class ABINetLabelEncode(BaseRecLabelEncode):
         return dict_character
 
 
+class SRLabelEncode(BaseRecLabelEncode):
+    def __init__(self,
+                 max_text_length,
+                 character_dict_path=None,
+                 use_space_char=False,
+                 **kwargs):
+        super(SRLabelEncode, self).__init__(max_text_length,
+                                            character_dict_path, use_space_char)
+        self.dic = {}
+        with open(character_dict_path, 'r') as fin:
+            for line in fin.readlines():
+                line = line.strip()
+                character, sequence = line.split()
+                self.dic[character] = sequence
+        english_stroke_alphabet = '0123456789'
+        self.english_stroke_dict = {}
+        for index in range(len(english_stroke_alphabet)):
+            self.english_stroke_dict[english_stroke_alphabet[index]] = index
+
+    def encode(self, label):
+        stroke_sequence = ''
+        for character in label:
+            if character not in self.dic:
+                continue
+            else:
+                stroke_sequence += self.dic[character]
+        stroke_sequence += '0'
+        label = stroke_sequence
+
+        length = len(label)
+
+        input_tensor = np.zeros(self.max_text_len).astype("int64")
+        for j in range(length - 1):
+            input_tensor[j + 1] = self.english_stroke_dict[label[j]]
+
+        return length, input_tensor
+
+    def __call__(self, data):
+        text = data['label']
+        length, input_tensor = self.encode(text)
+
+        data["length"] = length
+        data["input_tensor"] = input_tensor
+        if text is None:
+            return None
+        return data
+
+
 class SPINLabelEncode(AttnLabelEncode):
     """ Convert between text-label and text-index """
 

ppocr/data/imaug/operators.py

@@ -24,6 +24,7 @@ import six
 import cv2
 import numpy as np
 import math
+from PIL import Image
 
 
 class DecodeImage(object):
@@ -440,3 +441,52 @@ class KieResize(object):
         points[:, 0::2] = np.clip(points[:, 0::2], 0, img_shape[1])
         points[:, 1::2] = np.clip(points[:, 1::2], 0, img_shape[0])
         return points
+
+
+class SRResize(object):
+    def __init__(self,
+                 imgH=32,
+                 imgW=128,
+                 down_sample_scale=4,
+                 keep_ratio=False,
+                 min_ratio=1,
+                 mask=False,
+                 infer_mode=False,
+                 **kwargs):
+        self.imgH = imgH
+        self.imgW = imgW
+        self.keep_ratio = keep_ratio
+        self.min_ratio = min_ratio
+        self.down_sample_scale = down_sample_scale
+        self.mask = mask
+        self.infer_mode = infer_mode
+
+    def __call__(self, data):
+        imgH = self.imgH
+        imgW = self.imgW
+        images_lr = data["image_lr"]
+        transform2 = ResizeNormalize(
+            (imgW // self.down_sample_scale, imgH // self.down_sample_scale))
+        images_lr = transform2(images_lr)
+        data["img_lr"] = images_lr
+        if self.infer_mode:
+            return data
+
+        images_HR = data["image_hr"]
+        label_strs = data["label"]
+        transform = ResizeNormalize((imgW, imgH))
+        images_HR = transform(images_HR)
+        data["img_hr"] = images_HR
+        return data
+
+
+class ResizeNormalize(object):
+    def __init__(self, size, interpolation=Image.BICUBIC):
+        self.size = size
+        self.interpolation = interpolation
+
+    def __call__(self, img):
+        img = img.resize(self.size, self.interpolation)
+        img_numpy = np.array(img).astype("float32")
+        img_numpy = img_numpy.transpose((2, 0, 1)) / 255
+        return img_numpy

ppocr/data/lmdb_dataset.py

@@ -16,6 +16,9 @@ import os
 from paddle.io import Dataset
 import lmdb
 import cv2
+import string
+import six
+from PIL import Image
 
 from .imaug import transform, create_operators
 
@@ -116,3 +119,58 @@ class LMDBDataSet(Dataset):
 
     def __len__(self):
         return self.data_idx_order_list.shape[0]
+
+
+class LMDBDataSetSR(LMDBDataSet):
+    def buf2PIL(self, txn, key, type='RGB'):
+        imgbuf = txn.get(key)
+        buf = six.BytesIO()
+        buf.write(imgbuf)
+        buf.seek(0)
+        im = Image.open(buf).convert(type)
+        return im
+
+    def str_filt(self, str_, voc_type):
+        alpha_dict = {
+            'digit': string.digits,
+            'lower': string.digits + string.ascii_lowercase,
+            'upper': string.digits + string.ascii_letters,
+            'all': string.digits + string.ascii_letters + string.punctuation
+        }
+        if voc_type == 'lower':
+            str_ = str_.lower()
+        for char in str_:
+            if char not in alpha_dict[voc_type]:
+                str_ = str_.replace(char, '')
+        return str_
+
+    def get_lmdb_sample_info(self, txn, index):
+        self.voc_type = 'upper'
+        self.max_len = 100
+        self.test = False
+        label_key = b'label-%09d' % index
+        word = str(txn.get(label_key).decode())
+        img_HR_key = b'image_hr-%09d' % index  # 128*32
+        img_lr_key = b'image_lr-%09d' % index  # 64*16
+        try:
+            img_HR = self.buf2PIL(txn, img_HR_key, 'RGB')
+            img_lr = self.buf2PIL(txn, img_lr_key, 'RGB')
+        except IOError or len(word) > self.max_len:
+            return self[index + 1]
+        label_str = self.str_filt(word, self.voc_type)
+        return img_HR, img_lr, label_str
+
+    def __getitem__(self, idx):
+        lmdb_idx, file_idx = self.data_idx_order_list[idx]
+        lmdb_idx = int(lmdb_idx)
+        file_idx = int(file_idx)
+        sample_info = self.get_lmdb_sample_info(self.lmdb_sets[lmdb_idx]['txn'],
+                                                file_idx)
+        if sample_info is None:
+            return self.__getitem__(np.random.randint(self.__len__()))
+        img_HR, img_lr, label_str = sample_info
+        data = {'image_hr': img_HR, 'image_lr': img_lr, 'label': label_str}
+        outs = transform(data, self.ops)
+        if outs is None:
+            return self.__getitem__(np.random.randint(self.__len__()))
+        return outs

ppocr/losses/__init__.py

@@ -57,6 +57,9 @@ from .table_master_loss import TableMasterLoss
 # vqa token loss
 from .vqa_token_layoutlm_loss import VQASerTokenLayoutLMLoss
 
+# sr loss
+from .stroke_focus_loss import StrokeFocusLoss
+
 
 def build_loss(config):
     support_dict = [
@@ -64,7 +67,8 @@ def build_loss(config):
         'ClsLoss', 'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss',
         'CELoss', 'TableAttentionLoss', 'SARLoss', 'AsterLoss', 'SDMGRLoss',
         'VQASerTokenLayoutLMLoss', 'LossFromOutput', 'PRENLoss', 'MultiLoss',
-        'TableMasterLoss', 'SPINAttentionLoss', 'VLLoss', 'SLALoss'
+        'TableMasterLoss', 'SPINAttentionLoss', 'VLLoss', 'StrokeFocusLoss',
+        'SLALoss'
     ]
     config = copy.deepcopy(config)
     module_name = config.pop('name')

ppocr/losses/stroke_focus_loss.py

+# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This code is refer from:
+https://github.com/FudanVI/FudanOCR/blob/main/text-gestalt/loss/stroke_focus_loss.py
+"""
+import cv2
+import sys
+import time
+import string
+import random
+import numpy as np
+import paddle.nn as nn
+import paddle
+
+
+class StrokeFocusLoss(nn.Layer):
+    def __init__(self, character_dict_path=None, **kwargs):
+        super(StrokeFocusLoss, self).__init__(character_dict_path)
+        self.mse_loss = nn.MSELoss()
+        self.ce_loss = nn.CrossEntropyLoss()
+        self.l1_loss = nn.L1Loss()
+        self.english_stroke_alphabet = '0123456789'
+        self.english_stroke_dict = {}
+        for index in range(len(self.english_stroke_alphabet)):
+            self.english_stroke_dict[self.english_stroke_alphabet[
+                index]] = index
+
+        stroke_decompose_lines = open(character_dict_path, 'r').readlines()
+        self.dic = {}
+        for line in stroke_decompose_lines:
+            line = line.strip()
+            character, sequence = line.split()
+            self.dic[character] = sequence
+
+    def forward(self, pred, data):
+
+        sr_img = pred["sr_img"]
+        hr_img = pred["hr_img"]
+
+        mse_loss = self.mse_loss(sr_img, hr_img)
+        word_attention_map_gt = pred["word_attention_map_gt"]
+        word_attention_map_pred = pred["word_attention_map_pred"]
+
+        hr_pred = pred["hr_pred"]
+        sr_pred = pred["sr_pred"]
+
+        attention_loss = paddle.nn.functional.l1_loss(word_attention_map_gt,
+                                                      word_attention_map_pred)
+
+        loss = (mse_loss + attention_loss * 50) * 100
+
+        return {
+            "mse_loss": mse_loss,
+            "attention_loss": attention_loss,
+            "loss": loss
+        }

ppocr/metrics/__init__.py

@@ -30,13 +30,13 @@ from .table_metric import TableMetric
 from .kie_metric import KIEMetric
 from .vqa_token_ser_metric import VQASerTokenMetric
 from .vqa_token_re_metric import VQAReTokenMetric
-
+from .sr_metric import SRMetric
 
 def build_metric(config):
     support_dict = [
         "DetMetric", "DetFCEMetric", "RecMetric", "ClsMetric", "E2EMetric",
         "DistillationMetric", "TableMetric", 'KIEMetric', 'VQASerTokenMetric',
-        'VQAReTokenMetric'
+        'VQAReTokenMetric', 'SRMetric'
     ]
 
     config = copy.deepcopy(config)

ppocr/metrics/rec_metric.py

@@ -16,6 +16,7 @@ import Levenshtein
 import string
 
 
+
 class RecMetric(object):
     def __init__(self,
                  main_indicator='acc',

ppocr/metrics/sr_metric.py

+# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+https://github.com/FudanVI/FudanOCR/blob/main/text-gestalt/utils/ssim_psnr.py
+"""
+
+from math import exp
+
+import paddle
+import paddle.nn.functional as F
+import paddle.nn as nn
+import string
+
+
+class SSIM(nn.Layer):
+    def __init__(self, window_size=11, size_average=True):
+        super(SSIM, self).__init__()
+        self.window_size = window_size
+        self.size_average = size_average
+        self.channel = 1
+        self.window = self.create_window(window_size, self.channel)
+
+    def gaussian(self, window_size, sigma):
+        gauss = paddle.to_tensor([
+            exp(-(x - window_size // 2)**2 / float(2 * sigma**2))
+            for x in range(window_size)
+        ])
+        return gauss / gauss.sum()
+
+    def create_window(self, window_size, channel):
+        _1D_window = self.gaussian(window_size, 1.5).unsqueeze(1)
+        _2D_window = _1D_window.mm(_1D_window.t()).unsqueeze(0).unsqueeze(0)
+        window = _2D_window.expand([channel, 1, window_size, window_size])
+        return window
+
+    def _ssim(self, img1, img2, window, window_size, channel,
+              size_average=True):
+        mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
+        mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
+
+        mu1_sq = mu1.pow(2)
+        mu2_sq = mu2.pow(2)
+        mu1_mu2 = mu1 * mu2
+
+        sigma1_sq = F.conv2d(
+            img1 * img1, window, padding=window_size // 2,
+            groups=channel) - mu1_sq
+        sigma2_sq = F.conv2d(
+            img2 * img2, window, padding=window_size // 2,
+            groups=channel) - mu2_sq
+        sigma12 = F.conv2d(
+            img1 * img2, window, padding=window_size // 2,
+            groups=channel) - mu1_mu2
+
+        C1 = 0.01**2
+        C2 = 0.03**2
+
+        ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / (
+            (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
+
+        if size_average:
+            return ssim_map.mean()
+        else:
+            return ssim_map.mean([1, 2, 3])
+
+    def ssim(self, img1, img2, window_size=11, size_average=True):
+        (_, channel, _, _) = img1.shape
+        window = self.create_window(window_size, channel)
+
+        return self._ssim(img1, img2, window, window_size, channel,
+                          size_average)
+
+    def forward(self, img1, img2):
+        (_, channel, _, _) = img1.shape
+
+        if channel == self.channel and self.window.dtype == img1.dtype:
+            window = self.window
+        else:
+            window = self.create_window(self.window_size, channel)
+
+            self.window = window
+            self.channel = channel
+
+        return self._ssim(img1, img2, window, self.window_size, channel,
+                          self.size_average)
+
+
+class SRMetric(object):
+    def __init__(self, main_indicator='all', **kwargs):
+        self.main_indicator = main_indicator
+        self.eps = 1e-5
+        self.psnr_result = []
+        self.ssim_result = []
+        self.calculate_ssim = SSIM()
+        self.reset()
+
+    def reset(self):
+        self.correct_num = 0
+        self.all_num = 0
+        self.norm_edit_dis = 0
+        self.psnr_result = []
+        self.ssim_result = []
+
+    def calculate_psnr(self, img1, img2):
+        # img1 and img2 have range [0, 1]
+        mse = ((img1 * 255 - img2 * 255)**2).mean()
+        if mse == 0:
+            return float('inf')
+        return 20 * paddle.log10(255.0 / paddle.sqrt(mse))
+
+    def _normalize_text(self, text):
+        text = ''.join(
+            filter(lambda x: x in (string.digits + string.ascii_letters), text))
+        return text.lower()
+
+    def __call__(self, pred_label, *args, **kwargs):
+        metric = {}
+        images_sr = pred_label["sr_img"]
+        images_hr = pred_label["hr_img"]
+        psnr = self.calculate_psnr(images_sr, images_hr)
+        ssim = self.calculate_ssim(images_sr, images_hr)
+        self.psnr_result.append(psnr)
+        self.ssim_result.append(ssim)
+
+    def get_metric(self):
+        """
+        return metrics {
+                 'acc': 0,
+                 'norm_edit_dis': 0,
+            }
+        """
+        self.psnr_avg = sum(self.psnr_result) / len(self.psnr_result)
+        self.psnr_avg = round(self.psnr_avg.item(), 6)
+        self.ssim_avg = sum(self.ssim_result) / len(self.ssim_result)
+        self.ssim_avg = round(self.ssim_avg.item(), 6)
+
+        self.all_avg = self.psnr_avg + self.ssim_avg
+
+        self.reset()
+        return {
+            'psnr_avg': self.psnr_avg,
+            "ssim_avg": self.ssim_avg,
+            "all": self.all_avg
+        }

ppocr/modeling/architectures/base_model.py

@@ -14,6 +14,7 @@
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
+
 from paddle import nn
 from ppocr.modeling.transforms import build_transform
 from ppocr.modeling.backbones import build_backbone
@@ -46,9 +47,13 @@ class BaseModel(nn.Layer):
             in_channels = self.transform.out_channels
 
         # build backbone, backbone is need for del, rec and cls
-        config["Backbone"]['in_channels'] = in_channels
-        self.backbone = build_backbone(config["Backbone"], model_type)
-        in_channels = self.backbone.out_channels
+        if 'Backbone' not in config or config['Backbone'] is None:
+            self.use_backbone = False
+        else:
+            self.use_backbone = True
+            config["Backbone"]['in_channels'] = in_channels
+            self.backbone = build_backbone(config["Backbone"], model_type)
+            in_channels = self.backbone.out_channels
 
         # build neck
         # for rec, neck can be cnn,rnn or reshape(None)
@@ -77,7 +82,8 @@ class BaseModel(nn.Layer):
         y = dict()
         if self.use_transform:
             x = self.transform(x)
-        x = self.backbone(x)
+        if self.use_backbone:
+            x = self.backbone(x)
         if isinstance(x, dict):
             y.update(x)
         else:
@@ -109,4 +115,4 @@ class BaseModel(nn.Layer):
             else:
                 return {final_name: x}
         else:
-            return x
+            return x
\ No newline at end of file

ppocr/modeling/heads/sr_rensnet_transformer.py

+# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This code is refer from:
+https://github.com/FudanVI/FudanOCR/blob/main/text-gestalt/loss/transformer_english_decomposition.py
+"""
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+import math, copy
+import numpy as np
+
+# stroke-level alphabet
+alphabet = '0123456789'
+
+
+def get_alphabet_len():
+    return len(alphabet)
+
+
+def subsequent_mask(size):
+    """Generate a square mask for the sequence. The masked positions are filled with float('-inf').
+        Unmasked positions are filled with float(0.0).
+    """
+    mask = paddle.ones([1, size, size], dtype='float32')
+    mask_inf = paddle.triu(
+        paddle.full(
+            shape=[1, size, size], dtype='float32', fill_value='-inf'),
+        diagonal=1)
+    mask = mask + mask_inf
+    padding_mask = paddle.equal(mask, paddle.to_tensor(1, dtype=mask.dtype))
+    return padding_mask
+
+
+def clones(module, N):
+    return nn.LayerList([copy.deepcopy(module) for _ in range(N)])
+
+
+def masked_fill(x, mask, value):
+    y = paddle.full(x.shape, value, x.dtype)
+    return paddle.where(mask, y, x)
+
+
+def attention(query, key, value, mask=None, dropout=None, attention_map=None):
+    d_k = query.shape[-1]
+    scores = paddle.matmul(query,
+                           paddle.transpose(key, [0, 1, 3, 2])) / math.sqrt(d_k)
+
+    if mask is not None:
+        scores = masked_fill(scores, mask == 0, float('-inf'))
+    else:
+        pass
+
+    p_attn = F.softmax(scores, axis=-1)
+
+    if dropout is not None:
+        p_attn = dropout(p_attn)
+    return paddle.matmul(p_attn, value), p_attn
+
+
+class MultiHeadedAttention(nn.Layer):
+    def __init__(self, h, d_model, dropout=0.1, compress_attention=False):
+        super(MultiHeadedAttention, self).__init__()
+        assert d_model % h == 0
+        self.d_k = d_model // h
+        self.h = h
+        self.linears = clones(nn.Linear(d_model, d_model), 4)
+        self.attn = None
+        self.dropout = nn.Dropout(p=dropout, mode="downscale_in_infer")
+        self.compress_attention = compress_attention
+        self.compress_attention_linear = nn.Linear(h, 1)
+
+    def forward(self, query, key, value, mask=None, attention_map=None):
+        if mask is not None:
+            mask = mask.unsqueeze(1)
+        nbatches = query.shape[0]
+
+        query, key, value = \
+            [paddle.transpose(l(x).reshape([nbatches, -1, self.h, self.d_k]), [0,2,1,3])
+             for l, x in zip(self.linears, (query, key, value))]
+
+        x, attention_map = attention(
+            query,
+            key,
+            value,
+            mask=mask,
+            dropout=self.dropout,
+            attention_map=attention_map)
+
+        x = paddle.reshape(
+            paddle.transpose(x, [0, 2, 1, 3]),
+            [nbatches, -1, self.h * self.d_k])
+
+        return self.linears[-1](x), attention_map
+
+
+class ResNet(nn.Layer):
+    def __init__(self, num_in, block, layers):
+        super(ResNet, self).__init__()
+
+        self.conv1 = nn.Conv2D(num_in, 64, kernel_size=3, stride=1, padding=1)
+        self.bn1 = nn.BatchNorm2D(64, use_global_stats=True)
+        self.relu1 = nn.ReLU()
+        self.pool = nn.MaxPool2D((2, 2), (2, 2))
+
+        self.conv2 = nn.Conv2D(64, 128, kernel_size=3, stride=1, padding=1)
+        self.bn2 = nn.BatchNorm2D(128, use_global_stats=True)
+        self.relu2 = nn.ReLU()
+
+        self.layer1_pool = nn.MaxPool2D((2, 2), (2, 2))
+        self.layer1 = self._make_layer(block, 128, 256, layers[0])
+        self.layer1_conv = nn.Conv2D(256, 256, 3, 1, 1)
+        self.layer1_bn = nn.BatchNorm2D(256, use_global_stats=True)
+        self.layer1_relu = nn.ReLU()
+
+        self.layer2_pool = nn.MaxPool2D((2, 2), (2, 2))
+        self.layer2 = self._make_layer(block, 256, 256, layers[1])
+        self.layer2_conv = nn.Conv2D(256, 256, 3, 1, 1)
+        self.layer2_bn = nn.BatchNorm2D(256, use_global_stats=True)
+        self.layer2_relu = nn.ReLU()
+
+        self.layer3_pool = nn.MaxPool2D((2, 2), (2, 2))
+        self.layer3 = self._make_layer(block, 256, 512, layers[2])
+        self.layer3_conv = nn.Conv2D(512, 512, 3, 1, 1)
+        self.layer3_bn = nn.BatchNorm2D(512, use_global_stats=True)
+        self.layer3_relu = nn.ReLU()
+
+        self.layer4_pool = nn.MaxPool2D((2, 2), (2, 2))
+        self.layer4 = self._make_layer(block, 512, 512, layers[3])
+        self.layer4_conv2 = nn.Conv2D(512, 1024, 3, 1, 1)
+        self.layer4_conv2_bn = nn.BatchNorm2D(1024, use_global_stats=True)
+        self.layer4_conv2_relu = nn.ReLU()
+
+    def _make_layer(self, block, inplanes, planes, blocks):
+
+        if inplanes != planes:
+            downsample = nn.Sequential(
+                nn.Conv2D(inplanes, planes, 3, 1, 1),
+                nn.BatchNorm2D(
+                    planes, use_global_stats=True), )
+        else:
+            downsample = None
+        layers = []
+        layers.append(block(inplanes, planes, downsample))
+        for i in range(1, blocks):
+            layers.append(block(planes, planes, downsample=None))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu1(x)
+        x = self.pool(x)
+
+        x = self.conv2(x)
+        x = self.bn2(x)
+        x = self.relu2(x)
+
+        x = self.layer1_pool(x)
+        x = self.layer1(x)
+        x = self.layer1_conv(x)
+        x = self.layer1_bn(x)
+        x = self.layer1_relu(x)
+
+        x = self.layer2(x)
+        x = self.layer2_conv(x)
+        x = self.layer2_bn(x)
+        x = self.layer2_relu(x)
+
+        x = self.layer3(x)
+        x = self.layer3_conv(x)
+        x = self.layer3_bn(x)
+        x = self.layer3_relu(x)
+
+        x = self.layer4(x)
+        x = self.layer4_conv2(x)
+        x = self.layer4_conv2_bn(x)
+        x = self.layer4_conv2_relu(x)
+
+        return x
+
+
+class Bottleneck(nn.Layer):
+    def __init__(self, input_dim):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2D(input_dim, input_dim, 1)
+        self.bn1 = nn.BatchNorm2D(input_dim, use_global_stats=True)
+        self.relu = nn.ReLU()
+
+        self.conv2 = nn.Conv2D(input_dim, input_dim, 3, 1, 1)
+        self.bn2 = nn.BatchNorm2D(input_dim, use_global_stats=True)
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class PositionalEncoding(nn.Layer):
+    "Implement the PE function."
+
+    def __init__(self, dropout, dim, max_len=5000):
+        super(PositionalEncoding, self).__init__()
+        self.dropout = nn.Dropout(p=dropout, mode="downscale_in_infer")
+
+        pe = paddle.zeros([max_len, dim])
+        position = paddle.arange(0, max_len, dtype=paddle.float32).unsqueeze(1)
+        div_term = paddle.exp(
+            paddle.arange(0, dim, 2).astype('float32') *
+            (-math.log(10000.0) / dim))
+        pe[:, 0::2] = paddle.sin(position * div_term)
+        pe[:, 1::2] = paddle.cos(position * div_term)
+        pe = paddle.unsqueeze(pe, 0)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        x = x + self.pe[:, :paddle.shape(x)[1]]
+        return self.dropout(x)
+
+
+class PositionwiseFeedForward(nn.Layer):
+    "Implements FFN equation."
+
+    def __init__(self, d_model, d_ff, dropout=0.1):
+        super(PositionwiseFeedForward, self).__init__()
+        self.w_1 = nn.Linear(d_model, d_ff)
+        self.w_2 = nn.Linear(d_ff, d_model)
+        self.dropout = nn.Dropout(dropout, mode="downscale_in_infer")
+
+    def forward(self, x):
+        return self.w_2(self.dropout(F.relu(self.w_1(x))))
+
+
+class Generator(nn.Layer):
+    "Define standard linear + softmax generation step."
+
+    def __init__(self, d_model, vocab):
+        super(Generator, self).__init__()
+        self.proj = nn.Linear(d_model, vocab)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        out = self.proj(x)
+        return out
+
+
+class Embeddings(nn.Layer):
+    def __init__(self, d_model, vocab):
+        super(Embeddings, self).__init__()
+        self.lut = nn.Embedding(vocab, d_model)
+        self.d_model = d_model
+
+    def forward(self, x):
+        embed = self.lut(x) * math.sqrt(self.d_model)
+        return embed
+
+
+class LayerNorm(nn.Layer):
+    "Construct a layernorm module (See citation for details)."
+
+    def __init__(self, features, eps=1e-6):
+        super(LayerNorm, self).__init__()
+        self.a_2 = self.create_parameter(
+            shape=[features],
+            default_initializer=paddle.nn.initializer.Constant(1.0))
+        self.b_2 = self.create_parameter(
+            shape=[features],
+            default_initializer=paddle.nn.initializer.Constant(0.0))
+        self.eps = eps
+
+    def forward(self, x):
+        mean = x.mean(-1, keepdim=True)
+        std = x.std(-1, keepdim=True)
+        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2
+
+
+class Decoder(nn.Layer):
+    def __init__(self):
+        super(Decoder, self).__init__()
+
+        self.mask_multihead = MultiHeadedAttention(
+            h=16, d_model=1024, dropout=0.1)
+        self.mul_layernorm1 = LayerNorm(1024)
+
+        self.multihead = MultiHeadedAttention(h=16, d_model=1024, dropout=0.1)
+        self.mul_layernorm2 = LayerNorm(1024)
+
+        self.pff = PositionwiseFeedForward(1024, 2048)
+        self.mul_layernorm3 = LayerNorm(1024)
+
+    def forward(self, text, conv_feature, attention_map=None):
+        text_max_length = text.shape[1]
+        mask = subsequent_mask(text_max_length)
+        result = text
+        result = self.mul_layernorm1(result + self.mask_multihead(
+            text, text, text, mask=mask)[0])
+        b, c, h, w = conv_feature.shape
+        conv_feature = paddle.transpose(
+            conv_feature.reshape([b, c, h * w]), [0, 2, 1])
+        word_image_align, attention_map = self.multihead(
+            result,
+            conv_feature,
+            conv_feature,
+            mask=None,
+            attention_map=attention_map)
+        result = self.mul_layernorm2(result + word_image_align)
+        result = self.mul_layernorm3(result + self.pff(result))
+
+        return result, attention_map
+
+
+class BasicBlock(nn.Layer):
+    def __init__(self, inplanes, planes, downsample):
+        super(BasicBlock, self).__init__()
+        self.conv1 = nn.Conv2D(
+            inplanes, planes, kernel_size=3, stride=1, padding=1)
+        self.bn1 = nn.BatchNorm2D(planes, use_global_stats=True)
+        self.relu = nn.ReLU()
+        self.conv2 = nn.Conv2D(
+            planes, planes, kernel_size=3, stride=1, padding=1)
+        self.bn2 = nn.BatchNorm2D(planes, use_global_stats=True)
+        self.downsample = downsample
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample != None:
+            residual = self.downsample(residual)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Encoder(nn.Layer):
+    def __init__(self):
+        super(Encoder, self).__init__()
+        self.cnn = ResNet(num_in=1, block=BasicBlock, layers=[1, 2, 5, 3])
+
+    def forward(self, input):
+        conv_result = self.cnn(input)
+        return conv_result
+
+
+class Transformer(nn.Layer):
+    def __init__(self, in_channels=1):
+        super(Transformer, self).__init__()
+
+        word_n_class = get_alphabet_len()
+        self.embedding_word_with_upperword = Embeddings(512, word_n_class)
+        self.pe = PositionalEncoding(dim=512, dropout=0.1, max_len=5000)
+
+        self.encoder = Encoder()
+        self.decoder = Decoder()
+        self.generator_word_with_upperword = Generator(1024, word_n_class)
+
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.initializer.XavierNormal(p)
+
+    def forward(self, image, text_length, text_input, attention_map=None):
+        if image.shape[1] == 3:
+            R = image[:, 0:1, :, :]
+            G = image[:, 1:2, :, :]
+            B = image[:, 2:3, :, :]
+            image = 0.299 * R + 0.587 * G + 0.114 * B
+
+        conv_feature = self.encoder(image)  # batch, 1024, 8, 32
+        max_length = max(text_length)
+        text_input = text_input[:, :max_length]
+
+        text_embedding = self.embedding_word_with_upperword(
+            text_input)  # batch, text_max_length, 512
+        postion_embedding = self.pe(
+            paddle.zeros(text_embedding.shape))  # batch, text_max_length, 512
+        text_input_with_pe = paddle.concat([text_embedding, postion_embedding],
+                                           2)  # batch, text_max_length, 1024
+        batch, seq_len, _ = text_input_with_pe.shape
+
+        text_input_with_pe, word_attention_map = self.decoder(
+            text_input_with_pe, conv_feature)
+
+        word_decoder_result = self.generator_word_with_upperword(
+            text_input_with_pe)
+
+        if self.training:
+            total_length = paddle.sum(text_length)
+            probs_res = paddle.zeros([total_length, get_alphabet_len()])
+            start = 0
+
+            for index, length in enumerate(text_length):
+                length = int(length.numpy())
+                probs_res[start:start + length, :] = word_decoder_result[
+                    index, 0:0 + length, :]
+
+                start = start + length
+
+            return probs_res, word_attention_map, None
+        else:
+            return word_decoder_result

ppocr/modeling/transforms/__init__.py

@@ -18,10 +18,10 @@ __all__ = ['build_transform']
 def build_transform(config):
     from .tps import TPS
     from .stn import STN_ON
+    from .tsrn import TSRN
     from .gaspin_transformer import GA_SPIN_Transformer as GA_SPIN
 
-
-    support_dict = ['TPS', 'STN_ON', 'GA_SPIN']
+    support_dict = ['TPS', 'STN_ON', 'GA_SPIN', 'TSRN']
 
     module_name = config.pop('name')
     assert module_name in support_dict, Exception(

ppocr/modeling/transforms/tps_spatial_transformer.py

@@ -153,4 +153,4 @@ class TPSSpatialTransformer(nn.Layer):
         # the input to grid_sample is normalized [-1, 1], but what we get is [0, 1]
         grid = 2.0 * grid - 1.0
         output_maps = grid_sample(input, grid, canvas=None)
-        return output_maps, source_coordinate
+        return output_maps, source_coordinate
\ No newline at end of file

ppocr/modeling/transforms/tsrn.py

+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This code is refer from:
+https://github.com/FudanVI/FudanOCR/blob/main/text-gestalt/model/tsrn.py
+"""
+
+import math
+import paddle
+import paddle.nn.functional as F
+from paddle import nn
+from collections import OrderedDict
+import sys
+import numpy as np
+import warnings
+import math, copy
+import cv2
+
+warnings.filterwarnings("ignore")
+
+from .tps_spatial_transformer import TPSSpatialTransformer
+from .stn import STN as STN_model
+from ppocr.modeling.heads.sr_rensnet_transformer import Transformer
+
+
+class TSRN(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 scale_factor=2,
+                 width=128,
+                 height=32,
+                 STN=False,
+                 srb_nums=5,
+                 mask=False,
+                 hidden_units=32,
+                 infer_mode=False,
+                 **kwargs):
+        super(TSRN, self).__init__()
+        in_planes = 3
+        if mask:
+            in_planes = 4
+        assert math.log(scale_factor, 2) % 1 == 0
+        upsample_block_num = int(math.log(scale_factor, 2))
+        self.block1 = nn.Sequential(
+            nn.Conv2D(
+                in_planes, 2 * hidden_units, kernel_size=9, padding=4),
+            nn.PReLU())
+        self.srb_nums = srb_nums
+        for i in range(srb_nums):
+            setattr(self, 'block%d' % (i + 2),
+                    RecurrentResidualBlock(2 * hidden_units))
+
+        setattr(
+            self,
+            'block%d' % (srb_nums + 2),
+            nn.Sequential(
+                nn.Conv2D(
+                    2 * hidden_units,
+                    2 * hidden_units,
+                    kernel_size=3,
+                    padding=1),
+                nn.BatchNorm2D(2 * hidden_units)))
+
+        block_ = [
+            UpsampleBLock(2 * hidden_units, 2)
+            for _ in range(upsample_block_num)
+        ]
+        block_.append(
+            nn.Conv2D(
+                2 * hidden_units, in_planes, kernel_size=9, padding=4))
+        setattr(self, 'block%d' % (srb_nums + 3), nn.Sequential(*block_))
+        self.tps_inputsize = [height // scale_factor, width // scale_factor]
+        tps_outputsize = [height // scale_factor, width // scale_factor]
+        num_control_points = 20
+        tps_margins = [0.05, 0.05]
+        self.stn = STN
+        if self.stn:
+            self.tps = TPSSpatialTransformer(
+                output_image_size=tuple(tps_outputsize),
+                num_control_points=num_control_points,
+                margins=tuple(tps_margins))
+
+            self.stn_head = STN_model(
+                in_channels=in_planes,
+                num_ctrlpoints=num_control_points,
+                activation='none')
+        self.out_channels = in_channels
+
+        self.r34_transformer = Transformer()
+        for param in self.r34_transformer.parameters():
+            param.trainable = False
+        self.infer_mode = infer_mode
+
+    def forward(self, x):
+        output = {}
+        if self.infer_mode:
+            output["lr_img"] = x
+            y = x
+        else:
+            output["lr_img"] = x[0]
+            output["hr_img"] = x[1]
+            y = x[0]
+        if self.stn and self.training:
+            _, ctrl_points_x = self.stn_head(y)
+            y, _ = self.tps(y, ctrl_points_x)
+        block = {'1': self.block1(y)}
+        for i in range(self.srb_nums + 1):
+            block[str(i + 2)] = getattr(self,
+                                        'block%d' % (i + 2))(block[str(i + 1)])
+
+        block[str(self.srb_nums + 3)] = getattr(self, 'block%d' % (self.srb_nums + 3)) \
+            ((block['1'] + block[str(self.srb_nums + 2)]))
+
+        sr_img = paddle.tanh(block[str(self.srb_nums + 3)])
+
+        output["sr_img"] = sr_img
+
+        if self.training:
+            hr_img = x[1]
+            length = x[2]
+            input_tensor = x[3]
+
+            # add transformer 
+            sr_pred, word_attention_map_pred, _ = self.r34_transformer(
+                sr_img, length, input_tensor)
+
+            hr_pred, word_attention_map_gt, _ = self.r34_transformer(
+                hr_img, length, input_tensor)
+
+            output["hr_img"] = hr_img
+            output["hr_pred"] = hr_pred
+            output["word_attention_map_gt"] = word_attention_map_gt
+            output["sr_pred"] = sr_pred
+            output["word_attention_map_pred"] = word_attention_map_pred
+
+        return output
+
+
+class RecurrentResidualBlock(nn.Layer):
+    def __init__(self, channels):
+        super(RecurrentResidualBlock, self).__init__()
+        self.conv1 = nn.Conv2D(channels, channels, kernel_size=3, padding=1)
+        self.bn1 = nn.BatchNorm2D(channels)
+        self.gru1 = GruBlock(channels, channels)
+        self.prelu = mish()
+        self.conv2 = nn.Conv2D(channels, channels, kernel_size=3, padding=1)
+        self.bn2 = nn.BatchNorm2D(channels)
+        self.gru2 = GruBlock(channels, channels)
+
+    def forward(self, x):
+        residual = self.conv1(x)
+        residual = self.bn1(residual)
+        residual = self.prelu(residual)
+        residual = self.conv2(residual)
+        residual = self.bn2(residual)
+        residual = self.gru1(residual.transpose([0, 1, 3, 2])).transpose(
+            [0, 1, 3, 2])
+
+        return self.gru2(x + residual)
+
+
+class UpsampleBLock(nn.Layer):
+    def __init__(self, in_channels, up_scale):
+        super(UpsampleBLock, self).__init__()
+        self.conv = nn.Conv2D(
+            in_channels, in_channels * up_scale**2, kernel_size=3, padding=1)
+
+        self.pixel_shuffle = nn.PixelShuffle(up_scale)
+        self.prelu = mish()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.pixel_shuffle(x)
+        x = self.prelu(x)
+        return x
+
+
+class mish(nn.Layer):
+    def __init__(self, ):
+        super(mish, self).__init__()
+        self.activated = True
+
+    def forward(self, x):
+        if self.activated:
+            x = x * (paddle.tanh(F.softplus(x)))
+        return x
+
+
+class GruBlock(nn.Layer):
+    def __init__(self, in_channels, out_channels):
+        super(GruBlock, self).__init__()
+        assert out_channels % 2 == 0
+        self.conv1 = nn.Conv2D(
+            in_channels, out_channels, kernel_size=1, padding=0)
+        self.gru = nn.GRU(out_channels,
+                          out_channels // 2,
+                          direction='bidirectional')
+
+    def forward(self, x):
+        # x: b, c, w, h
+        x = self.conv1(x)
+        x = x.transpose([0, 2, 3, 1])  # b, w, h, c
+        batch_size, w, h, c = x.shape
+        x = x.reshape([-1, h, c])  # b*w, h, c  
+        x, _ = self.gru(x)
+        x = x.reshape([-1, w, h, c])
+        x = x.transpose([0, 3, 1, 2])
+        return x

ppocr/utils/save_load.py

@@ -148,10 +148,14 @@ def load_pretrained_params(model, path):
         "The {}.pdparams does not exists!".format(path)
 
     params = paddle.load(path + '.pdparams')
+
     state_dict = model.state_dict()
+
     new_state_dict = {}
     is_float16 = False
+    
     for k1 in params.keys():
+
         if k1 not in state_dict.keys():
             logger.warning("The pretrained params {} not in model".format(k1))
         else:

tools/export_model.py

@@ -78,6 +78,12 @@ def export_single_model(model,
                 shape=[None, 3, 64, 512], dtype="float32"),
         ]
         model = to_static(model, input_spec=other_shape)
+    elif arch_config["model_type"] == "sr":
+        other_shape = [
+            paddle.static.InputSpec(
+                shape=[None, 3, 16, 64], dtype="float32")
+        ]
+        model = to_static(model, input_spec=other_shape)
     elif arch_config["algorithm"] == "ViTSTR":
         other_shape = [
             paddle.static.InputSpec(
@@ -195,6 +201,9 @@ def main():
         else:  # base rec model
             config["Architecture"]["Head"]["out_channels"] = char_num
 
+    # for sr algorithm
+    if config["Architecture"]["model_type"] == "sr":
+        config['Architecture']["Transform"]['infer_mode'] = True
     model = build_model(config["Architecture"])
     load_model(config, model, model_type=config['Architecture']["model_type"])
     model.eval()

tools/infer/predict_sr.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import os
+import sys
+from PIL import Image
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.insert(0, __dir__)
+sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '../..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+import cv2
+import numpy as np
+import math
+import time
+import traceback
+import paddle
+
+import tools.infer.utility as utility
+from ppocr.postprocess import build_post_process
+from ppocr.utils.logging import get_logger
+from ppocr.utils.utility import get_image_file_list, check_and_read_gif
+
+logger = get_logger()
+
+
+class TextSR(object):
+    def __init__(self, args):
+        self.sr_image_shape = [int(v) for v in args.sr_image_shape.split(",")]
+        self.sr_batch_num = args.sr_batch_num
+
+        self.predictor, self.input_tensor, self.output_tensors, self.config = \
+            utility.create_predictor(args, 'sr', logger)
+        self.benchmark = args.benchmark
+        if args.benchmark:
+            import auto_log
+            pid = os.getpid()
+            gpu_id = utility.get_infer_gpuid()
+            self.autolog = auto_log.AutoLogger(
+                model_name="sr",
+                model_precision=args.precision,
+                batch_size=args.sr_batch_num,
+                data_shape="dynamic",
+                save_path=None,  #args.save_log_path,
+                inference_config=self.config,
+                pids=pid,
+                process_name=None,
+                gpu_ids=gpu_id if args.use_gpu else None,
+                time_keys=[
+                    'preprocess_time', 'inference_time', 'postprocess_time'
+                ],
+                warmup=0,
+                logger=logger)
+
+    def resize_norm_img(self, img):
+        imgC, imgH, imgW = self.sr_image_shape
+        img = img.resize((imgW // 2, imgH // 2), Image.BICUBIC)
+        img_numpy = np.array(img).astype("float32")
+        img_numpy = img_numpy.transpose((2, 0, 1)) / 255
+        return img_numpy
+
+    def __call__(self, img_list):
+        img_num = len(img_list)
+        batch_num = self.sr_batch_num
+        st = time.time()
+        st = time.time()
+        all_result = [] * img_num
+        if self.benchmark:
+            self.autolog.times.start()
+        for beg_img_no in range(0, img_num, batch_num):
+            end_img_no = min(img_num, beg_img_no + batch_num)
+            norm_img_batch = []
+            imgC, imgH, imgW = self.sr_image_shape
+            for ino in range(beg_img_no, end_img_no):
+                norm_img = self.resize_norm_img(img_list[ino])
+                norm_img = norm_img[np.newaxis, :]
+                norm_img_batch.append(norm_img)
+
+            norm_img_batch = np.concatenate(norm_img_batch)
+            norm_img_batch = norm_img_batch.copy()
+            if self.benchmark:
+                self.autolog.times.stamp()
+            self.input_tensor.copy_from_cpu(norm_img_batch)
+            self.predictor.run()
+            outputs = []
+            for output_tensor in self.output_tensors:
+                output = output_tensor.copy_to_cpu()
+                outputs.append(output)
+            if len(outputs) != 1:
+                preds = outputs
+            else:
+                preds = outputs[0]
+            all_result.append(outputs)
+        if self.benchmark:
+            self.autolog.times.end(stamp=True)
+        return all_result, time.time() - st
+
+
+def main(args):
+    image_file_list = get_image_file_list(args.image_dir)
+    text_recognizer = TextSR(args)
+    valid_image_file_list = []
+    img_list = []
+
+    # warmup 2 times
+    if args.warmup:
+        img = np.random.uniform(0, 255, [16, 64, 3]).astype(np.uint8)
+        for i in range(2):
+            res = text_recognizer([img] * int(args.sr_batch_num))
+
+    for image_file in image_file_list:
+        img, flag = check_and_read_gif(image_file)
+        if not flag:
+            img = Image.open(image_file).convert("RGB")
+        if img is None:
+            logger.info("error in loading image:{}".format(image_file))
+            continue
+        valid_image_file_list.append(image_file)
+        img_list.append(img)
+    try:
+        preds, _ = text_recognizer(img_list)
+        for beg_no in range(len(preds)):
+            sr_img = preds[beg_no][1]
+            lr_img = preds[beg_no][0]
+            for i in (range(sr_img.shape[0])):
+                fm_sr = (sr_img[i] * 255).transpose(1, 2, 0).astype(np.uint8)
+                fm_lr = (lr_img[i] * 255).transpose(1, 2, 0).astype(np.uint8)
+                img_name_pure = os.path.split(valid_image_file_list[
+                    beg_no * args.sr_batch_num + i])[-1]
+                cv2.imwrite("infer_result/sr_{}".format(img_name_pure),
+                            fm_sr[:, :, ::-1])
+                logger.info("The visualized image saved in infer_result/sr_{}".
+                            format(img_name_pure))
+
+    except Exception as E:
+        logger.info(traceback.format_exc())
+        logger.info(E)
+        exit()
+    if args.benchmark:
+        text_recognizer.autolog.report()
+
+
+if __name__ == "__main__":
+    main(utility.parse_args())

tools/infer/utility.py

@@ -121,6 +121,11 @@ def init_args():
     parser.add_argument("--use_pdserving", type=str2bool, default=False)
     parser.add_argument("--warmup", type=str2bool, default=False)
 
+    # SR parmas
+    parser.add_argument("--sr_model_dir", type=str)
+    parser.add_argument("--sr_image_shape", type=str, default="3, 32, 128")
+    parser.add_argument("--sr_batch_num", type=int, default=1)
+
     #
     parser.add_argument(
         "--draw_img_save_dir", type=str, default="./inference_results")
@@ -156,6 +161,8 @@ def create_predictor(args, mode, logger):
         model_dir = args.table_model_dir
     elif mode == 'ser':
         model_dir = args.ser_model_dir
+    elif mode == "sr":
+        model_dir = args.sr_model_dir
     elif mode == 'layout':
         model_dir = args.layout_model_dir
     else:

tools/infer_sr.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+import os
+import sys
+import json
+from PIL import Image
+import cv2
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.insert(0, __dir__)
+sys.path.insert(0, os.path.abspath(os.path.join(__dir__, '..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+import paddle
+
+from ppocr.data import create_operators, transform
+from ppocr.modeling.architectures import build_model
+from ppocr.postprocess import build_post_process
+from ppocr.utils.save_load import load_model
+from ppocr.utils.utility import get_image_file_list
+import tools.program as program
+
+
+def main():
+    global_config = config['Global']
+
+    # build post process
+    post_process_class = build_post_process(config['PostProcess'],
+                                            global_config)
+
+    # sr transform
+    config['Architecture']["Transform"]['infer_mode'] = True
+
+    model = build_model(config['Architecture'])
+
+    load_model(config, model)
+
+    # create data ops
+    transforms = []
+    for op in config['Eval']['dataset']['transforms']:
+        op_name = list(op)[0]
+        if 'Label' in op_name:
+            continue
+        elif op_name in ['SRResize']:
+            op[op_name]['infer_mode'] = True
+        elif op_name == 'KeepKeys':
+            op[op_name]['keep_keys'] = ['imge_lr']
+        transforms.append(op)
+    global_config['infer_mode'] = True
+    ops = create_operators(transforms, global_config)
+
+    save_res_path = config['Global'].get('save_res_path', "./infer_result")
+    if not os.path.exists(os.path.dirname(save_res_path)):
+        os.makedirs(os.path.dirname(save_res_path))
+
+    model.eval()
+    for file in get_image_file_list(config['Global']['infer_img']):
+        logger.info("infer_img: {}".format(file))
+        img = Image.open(file).convert("RGB")
+        data = {'image_lr': img}
+        batch = transform(data, ops)
+        images = np.expand_dims(batch[0], axis=0)
+        images = paddle.to_tensor(images)
+
+        preds = model(images)
+        sr_img = preds["sr_img"][0]
+        lr_img = preds["lr_img"][0]
+        fm_sr = (sr_img.numpy() * 255).transpose(1, 2, 0).astype(np.uint8)
+        fm_lr = (lr_img.numpy() * 255).transpose(1, 2, 0).astype(np.uint8)
+        img_name_pure = os.path.split(file)[-1]
+        cv2.imwrite("infer_result/sr_{}".format(img_name_pure),
+                    fm_sr[:, :, ::-1])
+        logger.info("The visualized image saved in infer_result/sr_{}".format(
+            img_name_pure))
+
+    logger.info("success!")
+
+
+if __name__ == '__main__':
+    config, device, logger, vdl_writer = program.preprocess()
+    main()

tools/program.py

@@ -25,6 +25,8 @@ import datetime
 import paddle
 import paddle.distributed as dist
 from tqdm import tqdm
+import cv2
+import numpy as np
 from argparse import ArgumentParser, RawDescriptionHelpFormatter
 
 from ppocr.utils.stats import TrainingStats
@@ -262,6 +264,7 @@ def train(config,
                 config, 'Train', device, logger, seed=epoch)
             max_iter = len(train_dataloader) - 1 if platform.system(
             ) == "Windows" else len(train_dataloader)
+
         for idx, batch in enumerate(train_dataloader):
             profiler.add_profiler_step(profiler_options)
             train_reader_cost += time.time() - reader_start
@@ -289,7 +292,7 @@ def train(config,
             else:
                 if model_type == 'table' or extra_input:
                     preds = model(images, data=batch[1:])
-                elif model_type in ["kie", 'vqa']:
+                elif model_type in ["kie", 'vqa', 'sr']:
                     preds = model(batch)
                 else:
                     preds = model(images)
@@ -297,11 +300,12 @@ def train(config,
                 avg_loss = loss['loss']
                 avg_loss.backward()
                 optimizer.step()
+
             optimizer.clear_grad()
 
             if cal_metric_during_train and epoch % calc_epoch_interval == 0:  # only rec and cls need
                 batch = [item.numpy() for item in batch]
-                if model_type in ['kie']:
+                if model_type in ['kie', 'sr']:
                     eval_class(preds, batch)
                 elif model_type in ['table']:
                     post_result = post_process_class(preds, batch)
@@ -347,8 +351,8 @@ def train(config,
                     len(train_dataloader) - idx - 1) * eta_meter.avg
                 eta_sec_format = str(datetime.timedelta(seconds=int(eta_sec)))
                 strs = 'epoch: [{}/{}], global_step: {}, {}, avg_reader_cost: ' \
-                       '{:.5f} s, avg_batch_cost: {:.5f} s, avg_samples: {}, ' \
-                       'ips: {:.5f} samples/s, eta: {}'.format(
+                    '{:.5f} s, avg_batch_cost: {:.5f} s, avg_samples: {}, ' \
+                    'ips: {:.5f} samples/s, eta: {}'.format(
                     epoch, epoch_num, global_step, logs,
                     train_reader_cost / print_batch_step,
                     train_batch_cost / print_batch_step,
@@ -480,6 +484,7 @@ def eval(model,
             leave=True)
         max_iter = len(valid_dataloader) - 1 if platform.system(
         ) == "Windows" else len(valid_dataloader)
+        sum_images = 0
         for idx, batch in enumerate(valid_dataloader):
             if idx >= max_iter:
                 break
@@ -493,6 +498,20 @@ def eval(model,
                         preds = model(images, data=batch[1:])
                     elif model_type in ["kie", 'vqa']:
                         preds = model(batch)
+                    elif model_type in ['sr']:
+                        preds = model(batch)
+                        sr_img = preds["sr_img"]
+                        lr_img = preds["lr_img"]
+
+                        for i in (range(sr_img.shape[0])):
+                            fm_sr = (sr_img[i].numpy() * 255).transpose(
+                                1, 2, 0).astype(np.uint8)
+                            fm_lr = (lr_img[i].numpy() * 255).transpose(
+                                1, 2, 0).astype(np.uint8)
+                            cv2.imwrite("output/images/{}_{}_sr.jpg".format(
+                                sum_images, i), fm_sr)
+                            cv2.imwrite("output/images/{}_{}_lr.jpg".format(
+                                sum_images, i), fm_lr)
                     else:
                         preds = model(images)
             else:
@@ -500,6 +519,20 @@ def eval(model,
                     preds = model(images, data=batch[1:])
                 elif model_type in ["kie", 'vqa']:
                     preds = model(batch)
+                elif model_type in ['sr']:
+                    preds = model(batch)
+                    sr_img = preds["sr_img"]
+                    lr_img = preds["lr_img"]
+
+                    for i in (range(sr_img.shape[0])):
+                        fm_sr = (sr_img[i].numpy() * 255).transpose(
+                            1, 2, 0).astype(np.uint8)
+                        fm_lr = (lr_img[i].numpy() * 255).transpose(
+                            1, 2, 0).astype(np.uint8)
+                        cv2.imwrite("output/images/{}_{}_sr.jpg".format(
+                            sum_images, i), fm_sr)
+                        cv2.imwrite("output/images/{}_{}_lr.jpg".format(
+                            sum_images, i), fm_lr)
                 else:
                     preds = model(images)
 
@@ -517,12 +550,15 @@ def eval(model,
             elif model_type in ['table', 'vqa']:
                 post_result = post_process_class(preds, batch_numpy)
                 eval_class(post_result, batch_numpy)
+            elif model_type in ['sr']:
+                eval_class(preds, batch_numpy)
             else:
                 post_result = post_process_class(preds, batch_numpy[1])
                 eval_class(post_result, batch_numpy)
 
             pbar.update(1)
             total_frame += len(images)
+            sum_images += 1
         # Get final metric，eg. acc or hmean
         metric = eval_class.get_metric()
 
@@ -617,7 +653,7 @@ def preprocess(is_train=False):
         'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE',
         'SEED', 'SDMGR', 'LayoutXLM', 'LayoutLM', 'LayoutLMv2', 'PREN', 'FCE',
         'SVTR', 'ViTSTR', 'ABINet', 'DB++', 'TableMaster', 'SPIN', 'VisionLAN',
-        'SLANet'
+        'Gestalt', 'SLANet'
     ]
 
     if use_xpu: