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

deploy/pdserving/rec_web_server.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 paddle_serving_client import Client
-from paddle_serving_app.reader import OCRReader
-import cv2
-import sys
-import numpy as np
-import os
-from paddle_serving_client import Client
-from paddle_serving_app.reader import Sequential, URL2Image, ResizeByFactor
-from paddle_serving_app.reader import Div, Normalize, Transpose
-from paddle_serving_app.reader import DBPostProcess, FilterBoxes, GetRotateCropImage, SortedBoxes
-if sys.argv[1] == 'gpu':
-    from paddle_serving_server_gpu.web_service import WebService
-elif sys.argv[1] == 'cpu':
-    from paddle_serving_server.web_service import WebService
-import time
-import re
-import base64
-
-
-class OCRService(WebService):
-    def init_rec(self):
-        self.ocr_reader = OCRReader()
-
-    def preprocess(self, feed=[], fetch=[]):
-        # TODO: to handle batch rec images
-        img_list = []
-        for feed_data in feed:
-            data = base64.b64decode(feed_data["image"].encode('utf8'))
-            data = np.fromstring(data, np.uint8)
-            im = cv2.imdecode(data, cv2.IMREAD_COLOR)
-            img_list.append(im)
-        feed_list = []
-        max_wh_ratio = 0
-        for i, boximg in enumerate(img_list):
-            h, w = boximg.shape[0:2]
-            wh_ratio = w * 1.0 / h
-            max_wh_ratio = max(max_wh_ratio, wh_ratio)
-        for img in img_list:
-            norm_img = self.ocr_reader.resize_norm_img(img, max_wh_ratio)
-            feed = {"image": norm_img}
-            feed_list.append(feed)
-        fetch = ["ctc_greedy_decoder_0.tmp_0", "softmax_0.tmp_0"]
-        return feed_list, fetch
-
-    def postprocess(self, feed={}, fetch=[], fetch_map=None):
-        rec_res = self.ocr_reader.postprocess(fetch_map, with_score=True)
-        res_lst = []
-        for res in rec_res:
-            res_lst.append(res[0])
-        res = {"res": res_lst}
-        return res
-
-
-ocr_service = OCRService(name="ocr")
-ocr_service.load_model_config("ocr_rec_model")
-ocr_service.init_rec()
-if sys.argv[1] == 'gpu':
-    ocr_service.set_gpus("0")
-    ocr_service.prepare_server(workdir="workdir", port=9292, device="gpu", gpuid=0)
-elif sys.argv[1] == 'cpu':
-    ocr_service.prepare_server(workdir="workdir", port=9292, device="cpu")
-ocr_service.run_rpc_service()
-ocr_service.run_web_service()

doc/doc_ch/recognition.md

@@ -37,8 +37,6 @@ ln -sf <path/to/dataset> <path/to/paddle_ocr>/train_data/dataset
 
 若您本地没有数据集，可以在官网下载 [icdar2015](http://rrc.cvc.uab.es/?ch=4&com=downloads) 数据，用于快速验证。也可以参考[DTRB](https://github.com/clovaai/deep-text-recognition-benchmark#download-lmdb-dataset-for-traininig-and-evaluation-from-here)，下载 benchmark 所需的lmdb格式数据集。
 
-如果希望复现SRN的论文指标，需要下载离线[增广数据](https://pan.baidu.com/s/1-HSZ-ZVdqBF2HaBZ5pRAKA),提取码: y3ry。增广数据是由MJSynth和SynthText做旋转和扰动得到的。数据下载完成后请解压到 {your_path}/PaddleOCR/train_data/data_lmdb_release/training/ 路径下。
-
 <a name="自定义数据集"></a>
 * 使用自己数据集
 
@@ -65,7 +63,7 @@ wget -P ./train_data/ic15_data  https://paddleocr.bj.bcebos.com/dataset/rec_gt_t
 wget -P ./train_data/ic15_data  https://paddleocr.bj.bcebos.com/dataset/rec_gt_test.txt
 ```
 
-PaddleOCR 也提供了数据格式转换脚本，可以将官网 label 转换支持的数据格式。 数据转换工具在 `train_data/gen_label.py`, 这里以训练集为例：
+PaddleOCR 也提供了数据格式转换脚本，可以将官网 label 转换支持的数据格式。 数据转换工具在 `ppocr/utils/gen_label.py`, 这里以训练集为例：
 
 ```
 # 将官网下载的标签文件转换为 rec_gt_label.txt
@@ -116,9 +114,9 @@ n
 
 word_dict.txt 每行有一个单字，将字符与数字索引映射在一起，“and” 将被映射成 [2 5 1]
 
-`ppocr/utils/ppocr_keys_v1.txt` 是一个包含6623个字符的中文字典，
+`ppocr/utils/ppocr_keys_v1.txt` 是一个包含6623个字符的中文字典
 
-`ppocr/utils/ic15_dict.txt` 是一个包含36个字符的英文字典，
+`ppocr/utils/ic15_dict.txt` 是一个包含36个字符的英文字典
 
 `ppocr/utils/dict/french_dict.txt` 是一个包含118个字符的法文字典
 
@@ -128,6 +126,8 @@ word_dict.txt 每行有一个单字，将字符与数字索引映射在一起，
 
 `ppocr/utils/dict/german_dict.txt` 是一个包含131个字符的德文字典
 
+`ppocr/utils/dict/en_dict.txt` 是一个包含63个字符的英文字典
+
 
 您可以按需使用。
 
@@ -155,10 +155,10 @@ PaddleOCR提供了训练脚本、评估脚本和预测脚本，本节将以 CRNN
 ```
 cd PaddleOCR/
 # 下载MobileNetV3的预训练模型
-wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/rec_mv3_none_bilstm_ctc.tar
+wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar
 # 解压模型参数
 cd pretrain_models
-tar -xf rec_mv3_none_bilstm_ctc.tar && rm -rf rec_mv3_none_bilstm_ctc.tar
+tar -xf rec_mv3_none_bilstm_ctc_v2.0_train.tar && rm -rf rec_mv3_none_bilstm_ctc_v2.0_train.tar
 ```
 
 开始训练:
@@ -204,9 +204,7 @@ PaddleOCR支持训练和评估交替进行, 可以在 `configs/rec/rec_icdar15_t
 | rec_mv3_tps_bilstm_attn.yml |  RARE |   Mobilenet_v3 large 0.5 |  tps   |  BiLSTM |  attention  |
 | 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_r34_vd_tps_bilstm_attn.yml | RARE | Resnet34_vd | tps | BiLSTM | attention |
 | rec_r34_vd_tps_bilstm_ctc.yml | STARNet | Resnet34_vd | tps | BiLSTM | ctc |
-| rec_r50fpn_vd_none_srn.yml | SRN | Resnet50_fpn_vd | None | rnn | srn |
 
 训练中文数据，推荐使用[rec_chinese_lite_train_v2.0.yml](../../configs/rec/ch_ppocr_v2.0/rec_chinese_lite_train_v2.0.yml)，如您希望尝试其他算法在中文数据集上的效果，请参考下列说明修改配置文件：
 

doc/doc_en/recognition_en.md

@@ -120,6 +120,9 @@ In `word_dict.txt`, there is a single word in each line, which maps characters a
 
 `ppocr/utils/dict/german_dict.txt` is a German dictionary with 131 characters
 
+`ppocr/utils/dict/en_dict.txt` is a English dictionary with 63 characters
+
+
 You can use it on demand.
 
 The current multi-language model is still in the demo stage and will continue to optimize the model and add languages. **You are very welcome to provide us with dictionaries and fonts in other languages**,
@@ -149,10 +152,10 @@ 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/rec_mv3_none_bilstm_ctc.tar
+wget -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/rec_mv3_none_bilstm_ctc_v2.0_train.tar
 # Decompress model parameters
 cd pretrain_models
-tar -xf rec_mv3_none_bilstm_ctc.tar && rm -rf rec_mv3_none_bilstm_ctc.tar
+tar -xf rec_mv3_none_bilstm_ctc_v2.0_train.tar && rm -rf rec_mv3_none_bilstm_ctc_v2.0_train.tar
 ```
 
 Start training:
@@ -194,7 +197,6 @@ If the evaluation set is large, the test will be time-consuming. It is recommend
 | rec_mv3_tps_bilstm_attn.yml |  RARE |   Mobilenet_v3 large 0.5 |  tps   |  BiLSTM |  attention  |
 | 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_r34_vd_tps_bilstm_attn.yml | RARE | Resnet34_vd | tps | BiLSTM | attention |
 | rec_r34_vd_tps_bilstm_ctc.yml | STARNet | Resnet34_vd | tps | BiLSTM | ctc |
 
 For training Chinese data, it is recommended to use

ppocr/data/__init__.py

@@ -67,6 +67,7 @@ def build_dataloader(config, mode, device, logger):
     drop_last = loader_config['drop_last']
     num_workers = loader_config['num_workers']
 
+    use_shared_memory = False
     if mode == "Train":
         #Distribute data to multiple cards
         batch_sampler = DistributedBatchSampler(
@@ -74,6 +75,7 @@ def build_dataloader(config, mode, device, logger):
             batch_size=batch_size,
             shuffle=False,
             drop_last=drop_last)
+        use_shared_memory = True
     else:
         #Distribute data to single card
         batch_sampler = BatchSampler(
@@ -87,6 +89,7 @@ def build_dataloader(config, mode, device, logger):
         batch_sampler=batch_sampler,
         places=device,
         num_workers=num_workers,
-        return_list=True)
+        return_list=True,
+        use_shared_memory=use_shared_memory)
 
     return data_loader

ppocr/data/imaug/rec_img_aug.py

@@ -35,12 +35,13 @@ from .text_image_aug import tia_perspective, tia_stretch, tia_distort
 
 
 class RecAug(object):
-    def __init__(self, use_tia=True, **kwargsz):
+    def __init__(self, use_tia=True, aug_prob=0.4, **kwargs):
         self.use_tia = use_tia
+        self.aug_prob = aug_prob
 
     def __call__(self, data):
         img = data['image']
-        img = warp(img, 10, self.use_tia)
+        img = warp(img, 10, self.use_tia, self.aug_prob)
         data['image'] = img
         return data
 
@@ -329,7 +330,7 @@ def get_warpAffine(config):
     return rz
 
 
-def warp(img, ang, use_tia=True):
+def warp(img, ang, use_tia=True, prob=0.4):
     """
     warp
     """
@@ -338,8 +339,6 @@ def warp(img, ang, use_tia=True):
     config.make(w, h, ang)
     new_img = img
 
-    prob = 0.4
-
     if config.distort:
         img_height, img_width = img.shape[0:2]
         if random.random() <= prob and img_height >= 20 and img_width >= 20:

ppocr/postprocess/db_postprocess.py

@@ -40,7 +40,7 @@ class DBPostProcess(object):
         self.max_candidates = max_candidates
         self.unclip_ratio = unclip_ratio
         self.min_size = 3
-        self.dilation_kernel = None if not use_dilation else [[1, 1], [1, 1]]
+        self.dilation_kernel = None if not use_dilation else np.array([[1, 1], [1, 1]])
 
     def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
         '''

ppocr/utils/ic15_dict.txt

@@ -33,4 +33,4 @@ v
 w
 x
 y
-z
+z
\ No newline at end of file

tools/infer/predict_det.py

@@ -63,6 +63,7 @@ class TextDetector(object):
             postprocess_params["box_thresh"] = args.det_db_box_thresh
             postprocess_params["max_candidates"] = 1000
             postprocess_params["unclip_ratio"] = args.det_db_unclip_ratio
+            postprocess_params["use_dilation"] = True
         else:
             logger.info("unknown det_algorithm:{}".format(self.det_algorithm))
             sys.exit(0)
@@ -111,7 +112,7 @@ class TextDetector(object):
             box = self.clip_det_res(box, img_height, img_width)
             rect_width = int(np.linalg.norm(box[0] - box[1]))
             rect_height = int(np.linalg.norm(box[0] - box[3]))
-            if rect_width <= 10 or rect_height <= 10:
+            if rect_width <= 3 or rect_height <= 3:
                 continue
             dt_boxes_new.append(box)
         dt_boxes = np.array(dt_boxes_new)

tools/test_hubserving.py

@@ -17,8 +17,9 @@ __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(__dir__)
 sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
 
-from ppocr.utils.utility import initial_logger
-logger = initial_logger()
+from ppocr.utils.logging import get_logger
+logger = get_logger()
+
 import cv2
 import numpy as np
 import time