Merge pull request #537 from tink2123/add_srn

Add SRN

README_cn.md

@@ -122,7 +122,10 @@ PaddleOCR开源的文本识别算法列表：
 - [x]  Rosetta([paper](https://arxiv.org/abs/1910.05085))
 - [x]  STAR-Net([paper](http://www.bmva.org/bmvc/2016/papers/paper043/index.html))
 - [x]  RARE([paper](https://arxiv.org/abs/1603.03915v1))
-- [ ]  SRN([paper](https://arxiv.org/abs/2003.12294))(百度自研, coming soon)
+- [x]  SRN([paper](https://arxiv.org/abs/2003.12294))(百度自研)
+
+*备注：* SRN模型使用了数据扰动方法对上述提到对两个训练集进行增广,增广后的数据可以在[百度网盘](todo)上下载。
+原始论文使用两阶段训练平均精度为89.74%，PaddleOCR中使用one-stage训练，平均精度为88.33%。两种预训练权重均在[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)中。
 
 参考[DTRB](https://arxiv.org/abs/1904.01906)文字识别训练和评估流程，使用MJSynth和SynthText两个文字识别数据集训练，在IIIT, SVT, IC03, IC13, IC15, SVTP, CUTE数据集上进行评估，算法效果如下：
 
@@ -136,6 +139,7 @@ PaddleOCR开源的文本识别算法列表：
 |STAR-Net|MobileNetV3|81.56%|rec_mv3_tps_bilstm_ctc|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_ctc.tar)|
 |RARE|Resnet34_vd|84.90%|rec_r34_vd_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_r34_vd_tps_bilstm_attn.tar)|
 |RARE|MobileNetV3|83.32%|rec_mv3_tps_bilstm_attn|[下载链接](https://paddleocr.bj.bcebos.com/rec_mv3_tps_bilstm_attn.tar)|
+|SRN|Resnet50_vd_fpn|88.33%|rec_r50fpn_vd_none_srn|[下载链接](https://paddleocr.bj.bcebos.com/SRN/rec_r50fpn_vd_none_srn.tar)|
 
 使用[LSVT](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_ch/datasets.md#1icdar2019-lsvt)街景数据集根据真值将图crop出来30w数据，进行位置校准。此外基于LSVT语料生成500w合成数据训练中文模型，相关配置和预训练文件如下：  
 

configs/rec/rec_r50fpn_vd_none_srn.yml

+Global:
+  algorithm: SRN
+  use_gpu: true
+  epoch_num: 72
+  log_smooth_window: 20
+  print_batch_step: 10
+  save_model_dir: output/rec_pvam_withrotate
+  save_epoch_step: 1
+  eval_batch_step: 8000
+  train_batch_size_per_card: 64
+  test_batch_size_per_card: 1
+  image_shape: [1, 64, 256]
+  max_text_length: 25
+  character_type: en
+  loss_type: srn
+  num_heads: 8
+  average_window: 0.15
+  max_average_window: 15625
+  min_average_window: 10000
+  reader_yml: ./configs/rec/rec_benchmark_reader.yml
+  pretrain_weights: 
+  checkpoints:
+  save_inference_dir:
+  infer_img:
+
+Architecture:
+  function: ppocr.modeling.architectures.rec_model,RecModel
+
+Backbone:
+  function: ppocr.modeling.backbones.rec_resnet50_fpn,ResNet
+  layers: 50
+ 
+Head:
+  function: ppocr.modeling.heads.rec_srn_all_head,SRNPredict
+  encoder_type: rnn
+  num_encoder_TUs: 2
+  num_decoder_TUs: 4
+  hidden_dims: 512
+  SeqRNN:
+    hidden_size: 256
+    
+Loss:
+  function: ppocr.modeling.losses.rec_srn_loss,SRNLoss
+
+Optimizer:
+  function: ppocr.optimizer,AdamDecay
+  base_lr: 0.0001
+  beta1: 0.9
+  beta2: 0.999

doc/doc_ch/config.md

@@ -32,6 +32,9 @@
 |      loss_type           |    设置 loss 类型              |       ctc         |    支持两种loss： ctc / attention |
 |       distort            |    设置是否使用数据增强          |       false       |  设置为true时，将在训练时随机进行扰动，支持的扰动操作可阅读[img_tools.py](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/ppocr/data/rec/img_tools.py)                 |
 |       use_space_char     |    设置是否识别空格             |        false      |          仅在 character_type=ch 时支持空格                 |
+|      average_window      |    ModelAverage优化器中的窗口长度计算比例 |  0.15       |       目前仅应用与SRN |
+|      max_average_window  |    平均值计算窗口长度的最大值   |   15625              | 推荐设置为一轮训练中mini-batchs的数目|
+|      min_average_window  |    平均值计算窗口长度的最小值  |    10000              |      \          |
 |      reader_yml          |    设置reader配置文件          |  ./configs/rec/rec_icdar15_reader.yml  |  \          |
 |      pretrain_weights    |    加载预训练模型路径      |  ./pretrain_models/CRNN/best_accuracy  |  \          |
 |      checkpoints         |    加载模型参数路径            |       None        |    用于中断后加载参数继续训练 |

ppocr/data/rec/dataset_traversal.py

@@ -26,7 +26,7 @@ from ppocr.utils.utility import initial_logger
 from ppocr.utils.utility import get_image_file_list
 logger = initial_logger()
 
-from .img_tools import process_image, get_img_data
+from .img_tools import process_image, process_image_srn, get_img_data
 
 
 class LMDBReader(object):
@@ -43,6 +43,9 @@ class LMDBReader(object):
         self.mode = params['mode']
         self.drop_last = False
         self.use_tps = False
+        self.num_heads = None
+        if "num_heads" in params:
+            self.num_heads = params['num_heads']
         if "tps" in params:
             self.ues_tps = True
         self.use_distort = False
@@ -119,12 +122,19 @@ class LMDBReader(object):
                     img = cv2.imread(single_img)
                     if img.shape[-1] == 1 or len(list(img.shape)) == 2:
                         img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
-                    norm_img = process_image(
-                        img=img,
-                        image_shape=self.image_shape,
-                        char_ops=self.char_ops,
-                        tps=self.use_tps,
-                        infer_mode=True)
+                    if self.loss_type == 'srn':
+                        norm_img = process_image_srn(
+                            img=img,
+                            image_shape=self.image_shape,
+                            num_heads=self.num_heads,
+                            max_text_length=self.max_text_length)
+                    else:
+                        norm_img = process_image(
+                            img=img,
+                            image_shape=self.image_shape,
+                            char_ops=self.char_ops,
+                            tps=self.use_tps,
+                            infer_mode=True)
                     yield norm_img
             else:
                 lmdb_sets = self.load_hierarchical_lmdb_dataset()
@@ -144,14 +154,25 @@ class LMDBReader(object):
                             if sample_info is None:
                                 continue
                             img, label = sample_info
-                            outs = process_image(
-                                img=img,
-                                image_shape=self.image_shape,
-                                label=label,
-                                char_ops=self.char_ops,
-                                loss_type=self.loss_type,
-                                max_text_length=self.max_text_length,
-                                distort=self.use_distort)
+                            outs = []
+                            if self.loss_type == "srn":
+                                outs = process_image_srn(
+                                    img=img,
+                                    image_shape=self.image_shape,
+                                    num_heads=self.num_heads,
+                                    max_text_length=self.max_text_length,
+                                    label=label,
+                                    char_ops=self.char_ops,
+                                    loss_type=self.loss_type)
+
+                            else:
+                                outs = process_image(
+                                    img=img,
+                                    image_shape=self.image_shape,
+                                    label=label,
+                                    char_ops=self.char_ops,
+                                    loss_type=self.loss_type,
+                                    max_text_length=self.max_text_length)
                             if outs is None:
                                 continue
                             yield outs

ppocr/data/rec/img_tools.py

@@ -381,3 +381,84 @@ def process_image(img,
                 assert False, "Unsupport loss_type %s in process_image"\
                     % loss_type
     return (norm_img)
+
+def resize_norm_img_srn(img, image_shape):
+    imgC, imgH, imgW = image_shape
+
+    img_black = np.zeros((imgH, imgW))
+    im_hei = img.shape[0]
+    im_wid = img.shape[1]
+
+    if im_wid <= im_hei * 1:
+        img_new = cv2.resize(img, (imgH * 1, imgH))
+    elif im_wid <= im_hei * 2:
+        img_new = cv2.resize(img, (imgH * 2, imgH))
+    elif im_wid <= im_hei * 3:
+        img_new = cv2.resize(img, (imgH * 3, imgH))
+    else:
+        img_new = cv2.resize(img, (imgW, imgH))
+
+    img_np = np.asarray(img_new)
+    img_np = cv2.cvtColor(img_np, cv2.COLOR_BGR2GRAY)
+    img_black[:, 0:img_np.shape[1]] = img_np
+    img_black = img_black[:, :, np.newaxis]
+
+    row, col, c = img_black.shape
+    c = 1
+
+    return np.reshape(img_black, (c, row, col)).astype(np.float32)
+
+def srn_other_inputs(image_shape,
+                     num_heads,
+                     max_text_length):
+
+    imgC, imgH, imgW = image_shape
+    feature_dim = int((imgH / 8) * (imgW / 8))
+
+    encoder_word_pos = np.array(range(0, feature_dim)).reshape((feature_dim, 1)).astype('int64')
+    gsrm_word_pos = np.array(range(0, max_text_length)).reshape((max_text_length, 1)).astype('int64')
+
+    lbl_weight = np.array([37] * max_text_length).reshape((-1,1)).astype('int64')
+
+    gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length)) 
+    gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape([-1, 1, max_text_length, max_text_length])
+    gsrm_slf_attn_bias1 = np.tile(gsrm_slf_attn_bias1, [1, num_heads, 1, 1]) * [-1e9] 
+
+    gsrm_slf_attn_bias2 = np.tril(gsrm_attn_bias_data, -1).reshape([-1, 1, max_text_length, max_text_length])
+    gsrm_slf_attn_bias2 = np.tile(gsrm_slf_attn_bias2, [1, num_heads, 1, 1]) * [-1e9] 
+
+    encoder_word_pos = encoder_word_pos[np.newaxis, :]
+    gsrm_word_pos = gsrm_word_pos[np.newaxis, :]
+
+    return [lbl_weight, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2]
+
+def process_image_srn(img,
+                      image_shape,
+                      num_heads,
+                      max_text_length,
+                      label=None,
+                      char_ops=None,
+                      loss_type=None):
+    norm_img = resize_norm_img_srn(img, image_shape)
+    norm_img = norm_img[np.newaxis, :]
+    [lbl_weight, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
+        srn_other_inputs(image_shape, num_heads, max_text_length)
+
+    if label is not None:
+        char_num = char_ops.get_char_num()
+        text = char_ops.encode(label)
+        if len(text) == 0 or len(text) > max_text_length:
+            return None
+        else:
+            if loss_type == "srn":
+                text_padded = [37] * max_text_length
+                for i in range(len(text)):
+                    text_padded[i] = text[i]
+                    lbl_weight[i] = [1.0]
+                text_padded = np.array(text_padded)
+                text = text_padded.reshape(-1, 1)
+                return (norm_img, text,encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2,lbl_weight)
+            else:
+                assert False, "Unsupport loss_type %s in process_image"\
+                    % loss_type
+    return (norm_img, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2)

ppocr/modeling/architectures/rec_model.py

@@ -58,6 +58,10 @@ class RecModel(object):
         self.loss_type = global_params['loss_type']
         self.image_shape = global_params['image_shape']
         self.max_text_length = global_params['max_text_length']
+        if "num_heads" in params:
+            self.num_heads = global_params["num_heads"]
+        else:
+            self.num_heads = None
 
     def create_feed(self, mode):
         image_shape = deepcopy(self.image_shape)
@@ -77,6 +81,48 @@ class RecModel(object):
                     lod_level=1)
                 feed_list = [image, label_in, label_out]
                 labels = {'label_in': label_in, 'label_out': label_out}
+            elif self.loss_type == "srn":
+                encoder_word_pos = fluid.data(
+                    name="encoder_word_pos",
+                    shape=[
+                        -1, int((image_shape[-2] / 8) * (image_shape[-1] / 8)),
+                        1
+                    ],
+                    dtype="int64")
+                gsrm_word_pos = fluid.data(
+                    name="gsrm_word_pos",
+                    shape=[-1, self.max_text_length, 1],
+                    dtype="int64")
+                gsrm_slf_attn_bias1 = fluid.data(
+                    name="gsrm_slf_attn_bias1",
+                    shape=[
+                        -1, self.num_heads, self.max_text_length,
+                        self.max_text_length
+                    ],
+                    dtype="float32")
+                gsrm_slf_attn_bias2 = fluid.data(
+                    name="gsrm_slf_attn_bias2",
+                    shape=[
+                        -1, self.num_heads, self.max_text_length,
+                        self.max_text_length
+                    ],
+                    dtype="float32")
+                lbl_weight = fluid.layers.data(
+                    name="lbl_weight", shape=[-1, 1], dtype='int64')
+                label = fluid.data(
+                    name='label', shape=[-1, 1], dtype='int32', lod_level=1)
+                feed_list = [
+                    image, label, encoder_word_pos, gsrm_word_pos,
+                    gsrm_slf_attn_bias1, gsrm_slf_attn_bias2, lbl_weight
+                ]
+                labels = {
+                    'label': label,
+                    'encoder_word_pos': encoder_word_pos,
+                    'gsrm_word_pos': gsrm_word_pos,
+                    'gsrm_slf_attn_bias1': gsrm_slf_attn_bias1,
+                    'gsrm_slf_attn_bias2': gsrm_slf_attn_bias2,
+                    'lbl_weight': lbl_weight
+                }
             else:
                 label = fluid.data(
                     name='label', shape=[None, 1], dtype='int32', lod_level=1)
@@ -88,6 +134,8 @@ class RecModel(object):
                 use_double_buffer=True,
                 iterable=False)
         else:
+            labels = None
+            loader = None
             if self.char_type == "ch" and self.infer_img:
                 image_shape[-1] = -1
                 if self.tps != None:
@@ -98,8 +146,42 @@ class RecModel(object):
                     )
                     image_shape = deepcopy(self.image_shape)
             image = fluid.data(name='image', shape=image_shape, dtype='float32')
-            labels = None
-            loader = None
+            if self.loss_type == "srn":
+                encoder_word_pos = fluid.data(
+                    name="encoder_word_pos",
+                    shape=[
+                        -1, int((image_shape[-2] / 8) * (image_shape[-1] / 8)),
+                        1
+                    ],
+                    dtype="int64")
+                gsrm_word_pos = fluid.data(
+                    name="gsrm_word_pos",
+                    shape=[-1, self.max_text_length, 1],
+                    dtype="int64")
+                gsrm_slf_attn_bias1 = fluid.data(
+                    name="gsrm_slf_attn_bias1",
+                    shape=[
+                        -1, self.num_heads, self.max_text_length,
+                        self.max_text_length
+                    ],
+                    dtype="float32")
+                gsrm_slf_attn_bias2 = fluid.data(
+                    name="gsrm_slf_attn_bias2",
+                    shape=[
+                        -1, self.num_heads, self.max_text_length,
+                        self.max_text_length
+                    ],
+                    dtype="float32")
+                feed_list = [
+                    image, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1,
+                    gsrm_slf_attn_bias2
+                ]
+                labels = {
+                    'encoder_word_pos': encoder_word_pos,
+                    'gsrm_word_pos': gsrm_word_pos,
+                    'gsrm_slf_attn_bias1': gsrm_slf_attn_bias1,
+                    'gsrm_slf_attn_bias2': gsrm_slf_attn_bias2
+                }
         return image, labels, loader
 
     def __call__(self, mode):
@@ -117,13 +199,27 @@ class RecModel(object):
                 label = labels['label_out']
             else:
                 label = labels['label']
-            outputs = {'total_loss':loss, 'decoded_out':\
-                decoded_out, 'label':label}
+            if self.loss_type == 'srn':
+                total_loss, img_loss, word_loss = self.loss(predicts, labels)
+                outputs = {
+                    'total_loss': total_loss,
+                    'img_loss': img_loss,
+                    'word_loss': word_loss,
+                    'decoded_out': decoded_out,
+                    'label': label
+                }
+            else:
+                outputs = {'total_loss':loss, 'decoded_out':\
+                    decoded_out, 'label':label}
             return loader, outputs
+
         elif mode == "export":
             predict = predicts['predict']
             if self.loss_type == "ctc":
                 predict = fluid.layers.softmax(predict)
+            if self.loss_type == "srn":
+                raise Exception(
+                    "Warning! SRN does not support export model currently")
             return [image, {'decoded_out': decoded_out, 'predicts': predict}]
         else:
             predict = predicts['predict']

ppocr/modeling/backbones/rec_resnet50_fpn.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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.param_attr import ParamAttr
+
+
+__all__ = ["ResNet", "ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152"]
+
+Trainable = True
+w_nolr = fluid.ParamAttr(
+        trainable = Trainable)
+train_parameters = {
+    "input_size": [3, 224, 224],
+    "input_mean": [0.485, 0.456, 0.406],
+    "input_std": [0.229, 0.224, 0.225],
+    "learning_strategy": {
+        "name": "piecewise_decay",
+        "batch_size": 256,
+        "epochs": [30, 60, 90],
+        "steps": [0.1, 0.01, 0.001, 0.0001]
+    }
+}
+
+class ResNet():
+    def __init__(self, params):
+        self.layers = params['layers']
+        self.params = train_parameters
+
+
+    def __call__(self, input):
+        layers = self.layers
+        supported_layers = [18, 34, 50, 101, 152]
+        assert layers in supported_layers, \
+            "supported layers are {} but input layer is {}".format(supported_layers, layers)
+
+        if layers == 18:
+            depth = [2, 2, 2, 2]
+        elif layers == 34 or layers == 50:
+            depth = [3, 4, 6, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        stride_list = [(2,2),(2,2),(1,1),(1,1)]
+        num_filters = [64, 128, 256, 512]
+
+        conv = self.conv_bn_layer(
+            input=input, num_filters=64, filter_size=7, stride=2, act='relu', name="conv1")
+        F = [] 
+        if layers >= 50:
+            for block in range(len(depth)):
+                for i in range(depth[block]):
+                    if layers in [101, 152] and block == 2:
+                        if i == 0:
+                            conv_name = "res" + str(block + 2) + "a"
+                        else:
+                            conv_name = "res" + str(block + 2) + "b" + str(i)
+                    else:
+                        conv_name = "res" + str(block + 2) + chr(97 + i)
+                    conv = self.bottleneck_block(
+                        input=conv,
+                        num_filters=num_filters[block],
+                        stride=stride_list[block]  if i == 0 else 1, name=conv_name)
+                F.append(conv)
+
+        base = F[-1]
+        for i in [-2, -3]:  
+            b, c, w, h = F[i].shape
+            if (w,h) == base.shape[2:]:
+                base = base
+            else:
+                base = fluid.layers.conv2d_transpose( input=base, num_filters=c,filter_size=4, stride=2,
+                    padding=1,act=None,
+                    param_attr=w_nolr,
+                    bias_attr=w_nolr)
+                base = fluid.layers.batch_norm(base, act = "relu", param_attr=w_nolr, bias_attr=w_nolr)
+            base = fluid.layers.concat([base, F[i]], axis=1)
+            base = fluid.layers.conv2d(base, num_filters=c, filter_size=1, param_attr=w_nolr, bias_attr=w_nolr)
+            base = fluid.layers.conv2d(base, num_filters=c, filter_size=3,padding = 1, param_attr=w_nolr, bias_attr=w_nolr)
+            base = fluid.layers.batch_norm(base, act = "relu", param_attr=w_nolr, bias_attr=w_nolr)
+
+        base = fluid.layers.conv2d(base, num_filters=512, filter_size=1,bias_attr=w_nolr,param_attr=w_nolr)
+
+        return base
+
+    def conv_bn_layer(self,
+                      input,
+                      num_filters,
+                      filter_size,
+                      stride=1,
+                      groups=1,
+                      act=None,
+                      name=None):
+        conv = fluid.layers.conv2d(
+            input=input,
+            num_filters=num_filters,
+            filter_size= 2  if stride==(1,1)  else filter_size,
+            dilation = 2 if stride==(1,1) else 1,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            param_attr=ParamAttr(name=name + "_weights",trainable = Trainable),
+            bias_attr=False,
+            name=name + '.conv2d.output.1')
+
+        if name == "conv1":
+            bn_name = "bn_" + name
+        else:
+            bn_name = "bn" + name[3:]
+        return fluid.layers.batch_norm(input=conv,
+                                       act=act,
+                                       name=bn_name + '.output.1',
+                                       param_attr=ParamAttr(name=bn_name + '_scale',trainable = Trainable),
+                                       bias_attr=ParamAttr(bn_name + '_offset',trainable = Trainable),
+                                       moving_mean_name=bn_name + '_mean',
+                                       moving_variance_name=bn_name + '_variance', )
+
+    def shortcut(self, input, ch_out, stride, is_first, name):
+        ch_in = input.shape[1]
+        if ch_in != ch_out or stride != 1 or is_first == True:
+            if stride == (1,1):
+                return self.conv_bn_layer(input, ch_out, 1, 1, name=name)
+            else: #stride == (2,2)
+                return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
+                
+        else:
+            return input
+
+    def bottleneck_block(self, input, num_filters, stride, name):
+        conv0 = self.conv_bn_layer(
+            input=input, num_filters=num_filters, filter_size=1, act='relu', name=name + "_branch2a")
+        conv1 = self.conv_bn_layer(
+            input=conv0,
+            num_filters=num_filters,
+            filter_size=3,
+            stride=stride,
+            act='relu',
+            name=name + "_branch2b")
+        conv2 = self.conv_bn_layer(
+            input=conv1, num_filters=num_filters * 4, filter_size=1, act=None, name=name + "_branch2c")
+
+        short = self.shortcut(input, num_filters * 4, stride, is_first=False, name=name + "_branch1")
+
+        return fluid.layers.elementwise_add(x=short, y=conv2, act='relu', name=name + ".add.output.5")
+
+    def basic_block(self, input, num_filters, stride, is_first, name):
+        conv0 = self.conv_bn_layer(input=input, num_filters=num_filters, filter_size=3, act='relu', stride=stride,
+                                   name=name + "_branch2a")
+        conv1 = self.conv_bn_layer(input=conv0, num_filters=num_filters, filter_size=3, act=None,
+                                   name=name + "_branch2b")
+        short = self.shortcut(input, num_filters, stride, is_first, name=name + "_branch1")
+        return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')

ppocr/modeling/heads/rec_srn_all_head.py

+#copyright (c) 2019 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid.param_attr import ParamAttr
+import numpy as np
+from .self_attention.model import wrap_encoder
+from .self_attention.model import wrap_encoder_forFeature
+gradient_clip = 10
+
+
+class SRNPredict(object):
+    def __init__(self, params):
+        super(SRNPredict, self).__init__()
+        self.char_num = params['char_num']
+        self.max_length = params['max_text_length']
+
+        self.num_heads = params['num_heads']
+        self.num_encoder_TUs = params['num_encoder_TUs']
+        self.num_decoder_TUs = params['num_decoder_TUs']
+        self.hidden_dims = params['hidden_dims']
+
+    def pvam(self, inputs, others):
+
+        b, c, h, w = inputs.shape
+        conv_features = fluid.layers.reshape(x=inputs, shape=[-1, c, h * w])
+        conv_features = fluid.layers.transpose(x=conv_features, perm=[0, 2, 1])
+
+        #===== Transformer encoder =====
+        b, t, c = conv_features.shape
+        encoder_word_pos = others["encoder_word_pos"]
+        gsrm_word_pos = others["gsrm_word_pos"]
+
+        enc_inputs = [conv_features, encoder_word_pos, None]
+        word_features = wrap_encoder_forFeature(
+            src_vocab_size=-1,
+            max_length=t,
+            n_layer=self.num_encoder_TUs,
+            n_head=self.num_heads,
+            d_key=int(self.hidden_dims / self.num_heads),
+            d_value=int(self.hidden_dims / self.num_heads),
+            d_model=self.hidden_dims,
+            d_inner_hid=self.hidden_dims,
+            prepostprocess_dropout=0.1,
+            attention_dropout=0.1,
+            relu_dropout=0.1,
+            preprocess_cmd="n",
+            postprocess_cmd="da",
+            weight_sharing=True,
+            enc_inputs=enc_inputs, )
+        fluid.clip.set_gradient_clip(
+            fluid.clip.GradientClipByValue(gradient_clip))
+
+        #===== Parallel Visual Attention Module =====
+        b, t, c = word_features.shape
+
+        word_features = fluid.layers.fc(word_features, c, num_flatten_dims=2)
+        word_features_ = fluid.layers.reshape(word_features, [-1, 1, t, c])
+        word_features_ = fluid.layers.expand(word_features_,
+                                             [1, self.max_length, 1, 1])
+        word_pos_feature = fluid.layers.embedding(gsrm_word_pos,
+                                                  [self.max_length, c])
+        word_pos_ = fluid.layers.reshape(word_pos_feature,
+                                         [-1, self.max_length, 1, c])
+        word_pos_ = fluid.layers.expand(word_pos_, [1, 1, t, 1])
+        temp = fluid.layers.elementwise_add(
+            word_features_, word_pos_, act='tanh')
+
+        attention_weight = fluid.layers.fc(input=temp,
+                                           size=1,
+                                           num_flatten_dims=3,
+                                           bias_attr=False)
+        attention_weight = fluid.layers.reshape(
+            x=attention_weight, shape=[-1, self.max_length, t])
+        attention_weight = fluid.layers.softmax(input=attention_weight, axis=-1)
+
+        pvam_features = fluid.layers.matmul(attention_weight,
+                                            word_features)  #[b, max_length, c]
+
+        return pvam_features
+
+    def gsrm(self, pvam_features, others):
+
+        #===== GSRM Visual-to-semantic embedding block =====
+        b, t, c = pvam_features.shape
+        word_out = fluid.layers.fc(
+            input=fluid.layers.reshape(pvam_features, [-1, c]),
+            size=self.char_num,
+            act="softmax")
+        #word_out.stop_gradient = True
+        word_ids = fluid.layers.argmax(word_out, axis=1)
+        word_ids.stop_gradient = True
+        word_ids = fluid.layers.reshape(x=word_ids, shape=[-1, t, 1])
+
+        #===== GSRM Semantic reasoning block =====
+        """
+        This module is achieved through bi-transformers,
+        ngram_feature1 is the froward one, ngram_fetaure2 is the backward one
+        """
+        pad_idx = self.char_num
+        gsrm_word_pos = others["gsrm_word_pos"]
+        gsrm_slf_attn_bias1 = others["gsrm_slf_attn_bias1"]
+        gsrm_slf_attn_bias2 = others["gsrm_slf_attn_bias2"]
+
+        def prepare_bi(word_ids):
+            """
+            prepare bi for gsrm
+            word1 for forward; word2 for backward
+            """
+            word1 = fluid.layers.cast(word_ids, "float32")
+            word1 = fluid.layers.pad(word1, [0, 0, 1, 0, 0, 0],
+                                     pad_value=1.0 * pad_idx)
+            word1 = fluid.layers.cast(word1, "int64")
+            word1 = word1[:, :-1, :]
+            word2 = word_ids
+            return word1, word2
+
+        word1, word2 = prepare_bi(word_ids)
+        word1.stop_gradient = True
+        word2.stop_gradient = True
+        enc_inputs_1 = [word1, gsrm_word_pos, gsrm_slf_attn_bias1]
+        enc_inputs_2 = [word2, gsrm_word_pos, gsrm_slf_attn_bias2]
+
+        gsrm_feature1 = wrap_encoder(
+            src_vocab_size=self.char_num + 1,
+            max_length=self.max_length,
+            n_layer=self.num_decoder_TUs,
+            n_head=self.num_heads,
+            d_key=int(self.hidden_dims / self.num_heads),
+            d_value=int(self.hidden_dims / self.num_heads),
+            d_model=self.hidden_dims,
+            d_inner_hid=self.hidden_dims,
+            prepostprocess_dropout=0.1,
+            attention_dropout=0.1,
+            relu_dropout=0.1,
+            preprocess_cmd="n",
+            postprocess_cmd="da",
+            weight_sharing=True,
+            enc_inputs=enc_inputs_1, )
+        gsrm_feature2 = wrap_encoder(
+            src_vocab_size=self.char_num + 1,
+            max_length=self.max_length,
+            n_layer=self.num_decoder_TUs,
+            n_head=self.num_heads,
+            d_key=int(self.hidden_dims / self.num_heads),
+            d_value=int(self.hidden_dims / self.num_heads),
+            d_model=self.hidden_dims,
+            d_inner_hid=self.hidden_dims,
+            prepostprocess_dropout=0.1,
+            attention_dropout=0.1,
+            relu_dropout=0.1,
+            preprocess_cmd="n",
+            postprocess_cmd="da",
+            weight_sharing=True,
+            enc_inputs=enc_inputs_2, )
+        gsrm_feature2 = fluid.layers.pad(gsrm_feature2, [0, 0, 0, 1, 0, 0],
+                                         pad_value=0.)
+        gsrm_feature2 = gsrm_feature2[:, 1:, ]
+        gsrm_features = gsrm_feature1 + gsrm_feature2
+
+        b, t, c = gsrm_features.shape
+
+        gsrm_out = fluid.layers.matmul(
+            x=gsrm_features,
+            y=fluid.default_main_program().global_block().var(
+                "src_word_emb_table"),
+            transpose_y=True)
+        b, t, c = gsrm_out.shape
+        gsrm_out = fluid.layers.softmax(input=fluid.layers.reshape(gsrm_out,
+                                                                   [-1, c]))
+
+        return gsrm_features, word_out, gsrm_out
+
+    def vsfd(self, pvam_features, gsrm_features):
+
+        #===== Visual-Semantic Fusion Decoder Module =====
+        b, t, c1 = pvam_features.shape
+        b, t, c2 = gsrm_features.shape
+        combine_features_ = fluid.layers.concat(
+            [pvam_features, gsrm_features], axis=2)
+        img_comb_features_ = fluid.layers.reshape(
+            x=combine_features_, shape=[-1, c1 + c2])
+        img_comb_features_map = fluid.layers.fc(input=img_comb_features_,
+                                                size=c1,
+                                                act="sigmoid")
+        img_comb_features_map = fluid.layers.reshape(
+            x=img_comb_features_map, shape=[-1, t, c1])
+        combine_features = img_comb_features_map * pvam_features + (
+            1.0 - img_comb_features_map) * gsrm_features
+        img_comb_features = fluid.layers.reshape(
+            x=combine_features, shape=[-1, c1])
+
+        fc_out = fluid.layers.fc(input=img_comb_features,
+                                 size=self.char_num,
+                                 act="softmax")
+        return fc_out
+
+    def __call__(self, inputs, others, mode=None):
+
+        pvam_features = self.pvam(inputs, others)
+        gsrm_features, word_out, gsrm_out = self.gsrm(pvam_features, others)
+        final_out = self.vsfd(pvam_features, gsrm_features)
+
+        _, decoded_out = fluid.layers.topk(input=final_out, k=1)
+        predicts = {
+            'predict': final_out,
+            'decoded_out': decoded_out,
+            'word_out': word_out,
+            'gsrm_out': gsrm_out
+        }
+
+        return predicts

ppocr/modeling/losses/rec_srn_loss.py

+#copyright (c) 2019 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.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+
+import paddle
+import paddle.fluid as fluid
+
+
+class SRNLoss(object):
+    def __init__(self, params):
+        super(SRNLoss, self).__init__()
+        self.char_num = params['char_num']
+
+    def __call__(self, predicts, others):
+        predict = predicts['predict']
+        word_predict = predicts['word_out']
+        gsrm_predict = predicts['gsrm_out']
+        label = others['label']
+        lbl_weight = others['lbl_weight']
+
+        casted_label = fluid.layers.cast(x=label, dtype='int64')
+        cost_word = fluid.layers.cross_entropy(
+            input=word_predict, label=casted_label)
+        cost_gsrm = fluid.layers.cross_entropy(
+            input=gsrm_predict, label=casted_label)
+        cost_vsfd = fluid.layers.cross_entropy(
+            input=predict, label=casted_label)
+
+        cost_word = fluid.layers.reshape(
+            x=fluid.layers.reduce_sum(cost_word), shape=[1])
+        cost_gsrm = fluid.layers.reshape(
+            x=fluid.layers.reduce_sum(cost_gsrm), shape=[1])
+        cost_vsfd = fluid.layers.reshape(
+            x=fluid.layers.reduce_sum(cost_vsfd), shape=[1])
+
+        sum_cost = fluid.layers.sum(
+            [cost_word, cost_vsfd * 2.0, cost_gsrm * 0.15])
+
+        return [sum_cost, cost_vsfd, cost_word]

ppocr/utils/character.py

@@ -25,6 +25,9 @@ class CharacterOps(object):
     def __init__(self, config):
         self.character_type = config['character_type']
         self.loss_type = config['loss_type']
+        self.max_text_len = config['max_text_length']
+        if self.loss_type == "srn" and self.character_type != "en":
+            raise Exception("SRN can only support in character_type == en")
         if self.character_type == "en":
             self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
             dict_character = list(self.character_str)
@@ -54,6 +57,8 @@ class CharacterOps(object):
         self.end_str = "eos"
         if self.loss_type == "attention":
             dict_character = [self.beg_str, self.end_str] + dict_character
+        elif self.loss_type == "srn":
+            dict_character = dict_character + [self.beg_str, self.end_str]
         self.dict = {}
         for i, char in enumerate(dict_character):
             self.dict[char] = i
@@ -147,6 +152,39 @@ def cal_predicts_accuracy(char_ops,
     return acc, acc_num, img_num
 
 
+def cal_predicts_accuracy_srn(char_ops,
+                              preds,
+                              labels,
+                              max_text_len,
+                              is_debug=False):
+    acc_num = 0
+    img_num = 0
+
+    total_len = preds.shape[0]
+    img_num = int(total_len / max_text_len)
+    for i in range(img_num):
+        cur_label = []
+        cur_pred = []
+        for j in range(max_text_len):
+            if labels[j + i * max_text_len] != 37:  #0
+                cur_label.append(labels[j + i * max_text_len][0])
+            else:
+                break
+
+        for j in range(max_text_len + 1):
+            if j < len(cur_label) and preds[j + i * max_text_len][
+                    0] != cur_label[j]:
+                break
+            elif j == len(cur_label) and j == max_text_len:
+                acc_num += 1
+                break
+            elif j == len(cur_label) and preds[j + i * max_text_len][0] == 37:
+                acc_num += 1
+                break
+    acc = acc_num * 1.0 / img_num
+    return acc, acc_num, img_num
+
+
 def convert_rec_attention_infer_res(preds):
     img_num = preds.shape[0]
     target_lod = [0]

tools/eval_utils/eval_rec_utils.py

@@ -29,7 +29,7 @@ FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
 logging.basicConfig(level=logging.INFO, format=FORMAT)
 logger = logging.getLogger(__name__)
 
-from ppocr.utils.character import cal_predicts_accuracy
+from ppocr.utils.character import cal_predicts_accuracy, cal_predicts_accuracy_srn
 from ppocr.utils.character import convert_rec_label_to_lod
 from ppocr.utils.character import convert_rec_attention_infer_res
 from ppocr.utils.utility import create_module
@@ -60,22 +60,60 @@ def eval_rec_run(exe, config, eval_info_dict, mode):
         for ino in range(img_num):
             img_list.append(data[ino][0])
             label_list.append(data[ino][1])
-        img_list = np.concatenate(img_list, axis=0)
-        outs = exe.run(eval_info_dict['program'], \
+
+        if config['Global']['loss_type'] != "srn":
+            img_list = np.concatenate(img_list, axis=0)
+            outs = exe.run(eval_info_dict['program'], \
                        feed={'image': img_list}, \
                        fetch_list=eval_info_dict['fetch_varname_list'], \
                        return_numpy=False)
-        preds = np.array(outs[0])
-        if preds.shape[1] != 1:
-            preds, preds_lod = convert_rec_attention_infer_res(preds)
+            preds = np.array(outs[0])
+
+            if config['Global']['loss_type'] == "attention":
+                preds, preds_lod = convert_rec_attention_infer_res(preds)
+            else:
+                preds_lod = outs[0].lod()[0]
+            labels, labels_lod = convert_rec_label_to_lod(label_list)
+            acc, acc_num, sample_num = cal_predicts_accuracy(
+                char_ops, preds, preds_lod, labels, labels_lod,
+                is_remove_duplicate)
         else:
-            preds_lod = outs[0].lod()[0]
-        labels, labels_lod = convert_rec_label_to_lod(label_list)
-        acc, acc_num, sample_num = cal_predicts_accuracy(
-            char_ops, preds, preds_lod, labels, labels_lod, is_remove_duplicate)
+            encoder_word_pos_list = []
+            gsrm_word_pos_list = []
+            gsrm_slf_attn_bias1_list = []
+            gsrm_slf_attn_bias2_list = []
+            for ino in range(img_num):
+                encoder_word_pos_list.append(data[ino][2])
+                gsrm_word_pos_list.append(data[ino][3])
+                gsrm_slf_attn_bias1_list.append(data[ino][4])
+                gsrm_slf_attn_bias2_list.append(data[ino][5])
+
+            img_list = np.concatenate(img_list, axis=0)
+            label_list = np.concatenate(label_list, axis=0)
+            encoder_word_pos_list = np.concatenate(
+                encoder_word_pos_list, axis=0).astype(np.int64)
+            gsrm_word_pos_list = np.concatenate(
+                gsrm_word_pos_list, axis=0).astype(np.int64)
+            gsrm_slf_attn_bias1_list = np.concatenate(
+                gsrm_slf_attn_bias1_list, axis=0).astype(np.float32)
+            gsrm_slf_attn_bias2_list = np.concatenate(
+                gsrm_slf_attn_bias2_list, axis=0).astype(np.float32)
+
+            labels = label_list
+
+            outs = exe.run(eval_info_dict['program'], \
+                       feed={'image': img_list, 'encoder_word_pos': encoder_word_pos_list,
+                             'gsrm_word_pos': gsrm_word_pos_list, 'gsrm_slf_attn_bias1': gsrm_slf_attn_bias1_list,
+                             'gsrm_slf_attn_bias2': gsrm_slf_attn_bias2_list}, \
+                       fetch_list=eval_info_dict['fetch_varname_list'], \
+                       return_numpy=False)
+            preds = np.array(outs[0])
+            acc, acc_num, sample_num = cal_predicts_accuracy_srn(
+                char_ops, preds, labels, config['Global']['max_text_length'])
+
         total_acc_num += acc_num
         total_sample_num += sample_num
-        logger.info("eval batch id: {}, acc: {}".format(total_batch_num, acc))
+        #logger.info("eval batch id: {}, acc: {}".format(total_batch_num, acc))
         total_batch_num += 1
     avg_acc = total_acc_num * 1.0 / total_sample_num
     metrics = {'avg_acc': avg_acc, "total_acc_num": total_acc_num, \

tools/infer/predict_rec.py

@@ -40,7 +40,8 @@ class TextRecognizer(object):
         char_ops_params = {
             "character_type": args.rec_char_type,
             "character_dict_path": args.rec_char_dict_path,
-            "use_space_char": args.use_space_char
+            "use_space_char": args.use_space_char,
+            "max_text_length": args.max_text_length
         }
         if self.rec_algorithm != "RARE":
             char_ops_params['loss_type'] = 'ctc'

tools/infer/utility.py

@@ -59,6 +59,7 @@ def parse_args():
     parser.add_argument("--rec_image_shape", type=str, default="3, 32, 320")
     parser.add_argument("--rec_char_type", type=str, default='ch')
     parser.add_argument("--rec_batch_num", type=int, default=30)
+    parser.add_argument("--max_text_length", type=int, default=25)
     parser.add_argument(
         "--rec_char_dict_path",
         type=str,

tools/infer_rec.py

@@ -64,7 +64,6 @@ def main():
     exe = fluid.Executor(place)
 
     rec_model = create_module(config['Architecture']['function'])(params=config)
-
     startup_prog = fluid.Program()
     eval_prog = fluid.Program()
     with fluid.program_guard(eval_prog, startup_prog):
@@ -86,10 +85,36 @@ def main():
     for i in range(max_img_num):
         logger.info("infer_img:%s" % infer_list[i])
         img = next(blobs)
-        predict = exe.run(program=eval_prog,
-                          feed={"image": img},
-                          fetch_list=fetch_varname_list,
-                          return_numpy=False)
+        if loss_type != "srn":
+            predict = exe.run(program=eval_prog,
+                              feed={"image": img},
+                              fetch_list=fetch_varname_list,
+                              return_numpy=False)
+        else:
+            encoder_word_pos_list = []
+            gsrm_word_pos_list = []
+            gsrm_slf_attn_bias1_list = []
+            gsrm_slf_attn_bias2_list = []
+            encoder_word_pos_list.append(img[1])
+            gsrm_word_pos_list.append(img[2])
+            gsrm_slf_attn_bias1_list.append(img[3])
+            gsrm_slf_attn_bias2_list.append(img[4])
+
+            encoder_word_pos_list = np.concatenate(
+                encoder_word_pos_list, axis=0).astype(np.int64)
+            gsrm_word_pos_list = np.concatenate(
+                gsrm_word_pos_list, axis=0).astype(np.int64)
+            gsrm_slf_attn_bias1_list = np.concatenate(
+                gsrm_slf_attn_bias1_list, axis=0).astype(np.float32)
+            gsrm_slf_attn_bias2_list = np.concatenate(
+                gsrm_slf_attn_bias2_list, axis=0).astype(np.float32)
+
+            predict = exe.run(program=eval_prog, \
+                       feed={'image': img[0], 'encoder_word_pos': encoder_word_pos_list,
+                             'gsrm_word_pos': gsrm_word_pos_list, 'gsrm_slf_attn_bias1': gsrm_slf_attn_bias1_list,
+                             'gsrm_slf_attn_bias2': gsrm_slf_attn_bias2_list}, \
+                       fetch_list=fetch_varname_list, \
+                       return_numpy=False)
         if loss_type == "ctc":
             preds = np.array(predict[0])
             preds = preds.reshape(-1)
@@ -114,7 +139,18 @@ def main():
                 score = np.mean(probs[0, 1:end_pos[1]])
             preds = preds.reshape(-1)
             preds_text = char_ops.decode(preds)
-
+        elif loss_type == "srn":
+            cur_pred = []
+            preds = np.array(predict[0])
+            preds = preds.reshape(-1)
+            probs = np.array(predict[1])
+            ind = np.argmax(probs, axis=1)
+            valid_ind = np.where(preds != 37)[0]
+            if len(valid_ind) == 0:
+                continue
+            score = np.mean(probs[valid_ind, ind[valid_ind]])
+            preds = preds[:valid_ind[-1] + 1]
+            preds_text = char_ops.decode(preds)
         logger.info("\t index: {}".format(preds))
         logger.info("\t word : {}".format(preds_text))
         logger.info("\t score: {}".format(score))

tools/program.py

@@ -32,7 +32,8 @@ from eval_utils.eval_det_utils import eval_det_run
 from eval_utils.eval_rec_utils import eval_rec_run
 from ppocr.utils.save_load import save_model
 import numpy as np
-from ppocr.utils.character import cal_predicts_accuracy, CharacterOps
+from ppocr.utils.character import cal_predicts_accuracy, cal_predicts_accuracy_srn, CharacterOps
+
 
 class ArgsParser(ArgumentParser):
     def __init__(self):
@@ -176,8 +177,16 @@ def build(config, main_prog, startup_prog, mode):
             fetch_name_list = list(outputs.keys())
             fetch_varname_list = [outputs[v].name for v in fetch_name_list]
             opt_loss_name = None
+            model_average = None
+            img_loss_name = None
+            word_loss_name = None
             if mode == "train":
                 opt_loss = outputs['total_loss']
+                # srn loss
+                #img_loss = outputs['img_loss']
+                #word_loss = outputs['word_loss']
+                #img_loss_name = img_loss.name
+                #word_loss_name = word_loss.name
                 opt_params = config['Optimizer']
                 optimizer = create_module(opt_params['function'])(opt_params)
                 optimizer.minimize(opt_loss)
@@ -185,7 +194,17 @@ def build(config, main_prog, startup_prog, mode):
                 global_lr = optimizer._global_learning_rate()
                 fetch_name_list.insert(0, "lr")
                 fetch_varname_list.insert(0, global_lr.name)
-    return (dataloader, fetch_name_list, fetch_varname_list, opt_loss_name)
+                if "loss_type" in config["Global"]:
+                    if config['Global']["loss_type"] == 'srn':
+                        model_average = fluid.optimizer.ModelAverage(
+                            config['Global']['average_window'],
+                            min_average_window=config['Global'][
+                                'min_average_window'],
+                            max_average_window=config['Global'][
+                                'max_average_window'])
+
+    return (dataloader, fetch_name_list, fetch_varname_list, opt_loss_name,
+            model_average)
 
 
 def build_export(config, main_prog, startup_prog):
@@ -329,14 +348,20 @@ def train_eval_rec_run(config, exe, train_info_dict, eval_info_dict):
                 lr = np.mean(np.array(train_outs[fetch_map['lr']]))
                 preds_idx = fetch_map['decoded_out']
                 preds = np.array(train_outs[preds_idx])
-                preds_lod = train_outs[preds_idx].lod()[0]
                 labels_idx = fetch_map['label']
                 labels = np.array(train_outs[labels_idx])
-                labels_lod = train_outs[labels_idx].lod()[0]
 
-                acc, acc_num, img_num = cal_predicts_accuracy(
-                    config['Global']['char_ops'], preds, preds_lod, labels,
-                    labels_lod)
+                if config['Global']['loss_type'] != 'srn':
+                    preds_lod = train_outs[preds_idx].lod()[0]
+                    labels_lod = train_outs[labels_idx].lod()[0]
+
+                    acc, acc_num, img_num = cal_predicts_accuracy(
+                        config['Global']['char_ops'], preds, preds_lod, labels,
+                        labels_lod)
+                else:
+                    acc, acc_num, img_num = cal_predicts_accuracy_srn(
+                        config['Global']['char_ops'], preds, labels,
+                        config['Global']['max_text_length'])
                 t2 = time.time()
                 train_batch_elapse = t2 - t1
                 stats = {'loss': loss, 'acc': acc}
@@ -350,6 +375,9 @@ def train_eval_rec_run(config, exe, train_info_dict, eval_info_dict):
 
                 if train_batch_id > 0 and\
                     train_batch_id % eval_batch_step == 0:
+                    model_average = train_info_dict['model_average']
+                    if model_average != None:
+                        model_average.apply(exe)
                     metrics = eval_rec_run(exe, config, eval_info_dict, "eval")
                     eval_acc = metrics['avg_acc']
                     eval_sample_num = metrics['total_sample_num']
@@ -375,6 +403,7 @@ def train_eval_rec_run(config, exe, train_info_dict, eval_info_dict):
             save_model(train_info_dict['train_program'], save_path)
     return
 
+
 def preprocess():
     FLAGS = ArgsParser().parse_args()
     config = load_config(FLAGS.config)
@@ -386,8 +415,8 @@ def preprocess():
     check_gpu(use_gpu)
 
     alg = config['Global']['algorithm']
-    assert alg in ['EAST', 'DB', 'Rosetta', 'CRNN', 'STARNet', 'RARE']
-    if alg in ['Rosetta', 'CRNN', 'STARNet', 'RARE']:
+    assert alg in ['EAST', 'DB', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN']
+    if alg in ['Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN']:
         config['Global']['char_ops'] = CharacterOps(config['Global'])
 
     place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()

tools/train.py

@@ -52,6 +52,7 @@ def main():
     train_fetch_name_list = train_build_outputs[1]
     train_fetch_varname_list = train_build_outputs[2]
     train_opt_loss_name = train_build_outputs[3]
+    model_average = train_build_outputs[-1]
 
     eval_program = fluid.Program()
     eval_build_outputs = program.build(
@@ -85,7 +86,8 @@ def main():
         'train_program':train_program,\
         'reader':train_loader,\
         'fetch_name_list':train_fetch_name_list,\
-        'fetch_varname_list':train_fetch_varname_list}
+        'fetch_varname_list':train_fetch_varname_list,\
+        'model_average': model_average}
 
     eval_info_dict = {'program':eval_program,\
         'reader':eval_reader,\