Add PGNet

configs/det/det_r50_vd_sast_icdar15.yml

@@ -20,6 +20,7 @@ Global:
   infer_img:
   save_res_path: ./output/sast_r50_vd_ic15/predicts_sast.txt
 
+
 Architecture:
   model_type: det
   algorithm: SAST

configs/e2e/e2e_r50_vd_pg.yml

+Global:
+  use_gpu: False
+  epoch_num: 600
+  log_smooth_window: 20
+  print_batch_step: 2
+  save_model_dir: ./output/pg_r50_vd_tt/
+  save_epoch_step: 1
+  # evaluation is run every 5000 iterationss after the 4000th iteration
+  eval_batch_step: [ 0, 1000 ]
+  # if pretrained_model is saved in static mode, load_static_weights must set to True
+  load_static_weights: False
+  cal_metric_during_train: False
+  pretrained_model:
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  infer_img:
+  save_res_path: ./output/pg_r50_vd_tt/predicts_pg.txt
+
+Architecture:
+  model_type: e2e
+  algorithm: PG
+  Transform:
+  Backbone:
+    name: ResNet
+    layers: 50
+  Neck:
+    name: PGFPN
+    model_name: large
+  Head:
+    name: PGHead
+    model_name: large
+
+Loss:
+  name: PGLoss
+
+#Optimizer:
+#  name: Adam
+#  beta1: 0.9
+#  beta2: 0.999
+#  lr:
+#    name: Cosine
+#    learning_rate: 0.001
+#    warmup_epoch: 1
+#  regularizer:
+#    name: 'L2'
+#    factor: 0
+
+Optimizer:
+  name: RMSProp
+  lr:
+    name: Piecewise
+    learning_rate: 0.001
+    decay_epochs: [ 40, 80, 120, 160, 200 ]
+    values: [ 0.001, 0.00033, 0.0001, 0.000033, 0.00001 ]
+  regularizer:
+    name: 'L2'
+    factor: 0.00005
+
+PostProcess:
+  name: PGPostProcess
+  score_thresh: 0.8
+  cover_thresh: 0.1
+  nms_thresh: 0.2
+
+Metric:
+  name: E2EMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: PGDateSet
+    label_file_list:
+    ratio_list:
+    data_format: textnet  # textnet/partvgg
+    Lexicon_Table: [ '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z' ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - PGProcessTrain:
+          batch_size: 14
+          data_format: icdar
+          tcl_len: 64
+          min_crop_size: 24
+          min_text_size: 4
+          max_text_size: 512
+      - KeepKeys:
+          keep_keys: [ 'images', 'tcl_maps', 'tcl_label_maps', 'border_maps','direction_maps', 'training_masks', 'label_list', 'pos_list', 'pos_mask' ] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    drop_last: True
+    batch_size_per_card: 1
+    num_workers: 8
+
+Eval:
+  dataset:
+    name: PGDateSet
+    data_dir: ./train_data/
+    label_file_list:
+    transforms:
+      - DecodeImage: # load image
+          img_mode: BGR
+          channel_first: False
+      - E2ELabelEncode:
+          label_list: [ '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z' ]
+      - E2EResizeForTest:
+          valid_set: totaltext
+          max_side_len: 768
+      - NormalizeImage:
+          scale: 1./255.
+          mean: [ 0.485, 0.456, 0.406 ]
+          std: [ 0.229, 0.224, 0.225 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'shape', 'polys', 'strs', 'tags' ]
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 2
\ No newline at end of file

ppocr/data/__init__.py

@@ -34,6 +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.pgnet_dataset import PGDateSet
 
 __all__ = ['build_dataloader', 'transform', 'create_operators']
 
@@ -54,7 +55,8 @@ signal.signal(signal.SIGTERM, term_mp)
 def build_dataloader(config, mode, device, logger, seed=None):
     config = copy.deepcopy(config)
 
-    support_dict = ['SimpleDataSet', 'LMDBDataSet']
+
+    support_dict = ['SimpleDataSet', 'LMDBDateSet', 'PGDateSet']
     module_name = config[mode]['dataset']['name']
     assert module_name in support_dict, Exception(
         'DataSet only support {}'.format(support_dict))

ppocr/data/imaug/__init__.py

@@ -28,6 +28,7 @@ from .label_ops import *
 
 from .east_process import *
 from .sast_process import *
+from .pg_process import *
 
 
 def transform(data, ops=None):

ppocr/data/imaug/label_ops.py

@@ -34,6 +34,25 @@ class ClsLabelEncode(object):
         return data
 
 
+class E2ELabelEncode(object):
+    def __init__(self, label_list, **kwargs):
+        self.label_list = label_list
+
+    def __call__(self, data):
+        text_label_index_list, temp_text = [], []
+        texts = data['strs']
+        for text in texts:
+            text = text.upper()
+            temp_text = []
+            for c_ in text:
+                if c_ in self.label_list:
+                    temp_text.append(self.label_list.index(c_))
+            temp_text = temp_text + [36] * (50 - len(temp_text))
+            text_label_index_list.append(temp_text)
+        data['strs'] = np.array(text_label_index_list)
+        return data
+
+
 class DetLabelEncode(object):
     def __init__(self, **kwargs):
         pass

ppocr/data/imaug/operators.py

@@ -223,3 +223,74 @@ class DetResizeForTest(object):
         ratio_w = resize_w / float(w)
 
         return img, [ratio_h, ratio_w]
+
+
+class E2EResizeForTest(object):
+    def __init__(self, **kwargs):
+        super(E2EResizeForTest, self).__init__()
+        self.max_side_len = kwargs['max_side_len']
+        self.valid_set = kwargs['valid_set']
+
+    def __call__(self, data):
+        img = data['image']
+        src_h, src_w, _ = img.shape
+        if self.valid_set == 'totaltext':
+            im_resized, [ratio_h, ratio_w] = self.resize_image_for_totaltext(
+                img, max_side_len=self.max_side_len)
+        else:
+            im_resized, (ratio_h, ratio_w) = self.resize_image(
+                img, max_side_len=self.max_side_len)
+        data['image'] = im_resized
+        data['shape'] = np.array([src_h, src_w, ratio_h, ratio_w])
+        return data
+
+    def resize_image_for_totaltext(self, im, max_side_len=512):
+        """
+        """
+        h, w, _ = im.shape
+
+        resize_w = w
+        resize_h = h
+        ratio = 1.25
+        if h * ratio > max_side_len:
+            ratio = float(max_side_len) / resize_h
+        resize_h = int(resize_h * ratio)
+        resize_w = int(resize_w * ratio)
+
+        max_stride = 128
+        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+        im = cv2.resize(im, (int(resize_w), int(resize_h)))
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+        return im, (ratio_h, ratio_w)
+
+    def resize_image(self, im, max_side_len=512):
+        """
+        resize image to a size multiple of max_stride which is required by the network
+        :param im: the resized image
+        :param max_side_len: limit of max image size to avoid out of memory in gpu
+        :return: the resized image and the resize ratio
+        """
+        h, w, _ = im.shape
+
+        resize_w = w
+        resize_h = h
+
+        # Fix the longer side
+        if resize_h > resize_w:
+            ratio = float(max_side_len) / resize_h
+        else:
+            ratio = float(max_side_len) / resize_w
+
+        resize_h = int(resize_h * ratio)
+        resize_w = int(resize_w * ratio)
+
+        max_stride = 128
+        resize_h = (resize_h + max_stride - 1) // max_stride * max_stride
+        resize_w = (resize_w + max_stride - 1) // max_stride * max_stride
+        im = cv2.resize(im, (int(resize_w), int(resize_h)))
+        ratio_h = resize_h / float(h)
+        ratio_w = resize_w / float(w)
+
+        return im, (ratio_h, ratio_w)

ppocr/data/pgnet_dataset.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.
+import numpy as np
+import os
+from paddle.io import Dataset
+from .imaug import transform, create_operators
+import random
+
+
+class PGDateSet(Dataset):
+    def __init__(self, config, mode, logger):
+        super(PGDateSet, self).__init__()
+
+        self.logger = logger
+        global_config = config['Global']
+        dataset_config = config[mode]['dataset']
+        loader_config = config[mode]['loader']
+
+        label_file_list = dataset_config.pop('label_file_list')
+        data_source_num = len(label_file_list)
+        ratio_list = dataset_config.get("ratio_list", [1.0])
+        if isinstance(ratio_list, (float, int)):
+            ratio_list = [float(ratio_list)] * int(data_source_num)
+        self.data_format = dataset_config.get('data_format', 'icdar')
+        assert len(
+            ratio_list
+        ) == data_source_num, "The length of ratio_list should be the same as the file_list."
+        # self.data_dir = dataset_config['data_dir']
+        self.do_shuffle = loader_config['shuffle']
+
+        logger.info("Initialize indexs of datasets:%s" % label_file_list)
+        self.data_lines = self.get_image_info_list(label_file_list, ratio_list,
+                                                   self.data_format)
+        self.data_idx_order_list = list(range(len(self.data_lines)))
+        if mode.lower() == "train":
+            self.shuffle_data_random()
+
+        self.ops = create_operators(dataset_config['transforms'], global_config)
+
+    def shuffle_data_random(self):
+        if self.do_shuffle:
+            random.shuffle(self.data_lines)
+        return
+
+    def extract_polys(self, poly_txt_path):
+        """
+        Read text_polys, txt_tags, txts from give txt file.
+        """
+        text_polys, txt_tags, txts = [], [], []
+        with open(poly_txt_path) as f:
+            for line in f.readlines():
+                poly_str, txt = line.strip().split('\t')
+                poly = map(float, poly_str.split(','))
+                text_polys.append(
+                    np.array(
+                        list(poly), dtype=np.float32).reshape(-1, 2))
+                txts.append(txt)
+                if txt == '###':
+                    txt_tags.append(True)
+                else:
+                    txt_tags.append(False)
+
+        return np.array(list(map(np.array, text_polys))), \
+               np.array(txt_tags, dtype=np.bool), txts
+
+    def extract_info_textnet(self, im_fn, img_dir=''):
+        """
+        Extract information from line in textnet format.
+        """
+        info_list = im_fn.split('\t')
+        img_path = ''
+        for ext in ['.jpg', '.png', '.jpeg', '.JPG']:
+            if os.path.exists(os.path.join(img_dir, info_list[0] + ext)):
+                img_path = os.path.join(img_dir, info_list[0] + ext)
+                break
+
+        if img_path == '':
+            print('Image {0} NOT found in {1}, and it will be ignored.'.format(
+                info_list[0], img_dir))
+
+        nBox = (len(info_list) - 1) // 9
+        wordBBs, txts, txt_tags = [], [], []
+        for n in range(0, nBox):
+            wordBB = list(map(float, info_list[n * 9 + 1:(n + 1) * 9]))
+            txt = info_list[(n + 1) * 9]
+            wordBBs.append([[wordBB[0], wordBB[1]], [wordBB[2], wordBB[3]],
+                            [wordBB[4], wordBB[5]], [wordBB[6], wordBB[7]]])
+            txts.append(txt)
+            if txt == '###':
+                txt_tags.append(True)
+            else:
+                txt_tags.append(False)
+        return img_path, np.array(wordBBs, dtype=np.float32), txt_tags, txts
+
+    def get_image_info_list(self, file_list, ratio_list, data_format='textnet'):
+        if isinstance(file_list, str):
+            file_list = [file_list]
+        data_lines = []
+        for idx, data_source in enumerate(file_list):
+            image_files = []
+            if data_format == 'icdar':
+                image_files = [
+                    (data_source, x)
+                    for x in os.listdir(os.path.join(data_source, 'rgb'))
+                    if x.split('.')[-1] in ['jpg', 'png', 'jpeg', 'JPG']
+                ]
+            elif data_format == 'textnet':
+                with open(data_source) as f:
+                    image_files = [(data_source, x.strip())
+                                   for x in f.readlines()]
+            else:
+                print("Unrecognized data format...")
+                exit(-1)
+            image_files = random.sample(
+                image_files, round(len(image_files) * ratio_list[idx]))
+            data_lines.extend(image_files)
+        return data_lines
+
+    def __getitem__(self, idx):
+        file_idx = self.data_idx_order_list[idx]
+        data_path, data_line = self.data_lines[file_idx]
+        try:
+            if self.data_format == 'icdar':
+                im_path = os.path.join(data_path, 'rgb', data_line)
+                poly_path = os.path.join(data_path, 'poly',
+                                         data_line.split('.')[0] + '.txt')
+                text_polys, text_tags, text_strs = self.extract_polys(poly_path)
+            else:
+                image_dir = os.path.join(os.path.dirname(data_path), 'image')
+                im_path, text_polys, text_tags, text_strs = self.extract_info_textnet(
+                    data_line, image_dir)
+
+            data = {
+                'img_path': im_path,
+                'polys': text_polys,
+                'tags': text_tags,
+                'strs': text_strs
+            }
+            with open(data['img_path'], 'rb') as f:
+                img = f.read()
+                data['image'] = img
+            outs = transform(data, self.ops)
+        except Exception as e:
+            self.logger.error(
+                "When parsing line {}, error happened with msg: {}".format(
+                    self.data_idx_order_list[idx], e))
+            outs = None
+        if outs is None:
+            return self.__getitem__(np.random.randint(self.__len__()))
+        return outs
+
+    def __len__(self):
+        return len(self.data_idx_order_list)

ppocr/losses/__init__.py

@@ -29,10 +29,11 @@ def build_loss(config):
     # cls loss
     from .cls_loss import ClsLoss
 
+    # e2e loss
+    from .e2e_pg_loss import PGLoss
     support_dict = [
         'DBLoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss', 'AttentionLoss',
-        'SRNLoss'
-    ]
+        'SRNLoss', 'PGLoss']
 
     config = copy.deepcopy(config)
     module_name = config.pop('name')

ppocr/losses/e2e_pg_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
+
+from paddle import nn
+import paddle
+import numpy as np
+import copy
+
+from .det_basic_loss import BalanceLoss, MaskL1Loss, DiceLoss
+
+
+class PGLoss(nn.Layer):
+    """
+    Differentiable Binarization (DB) Loss Function
+    args:
+        param (dict): the super paramter for DB Loss
+    """
+
+    def __init__(self, alpha=5, beta=10, eps=1e-6, **kwargs):
+        super(PGLoss, self).__init__()
+        self.alpha = alpha
+        self.beta = beta
+        self.dice_loss = DiceLoss(eps=eps)
+
+    def org_tcl_rois(self, batch_size, pos_lists, pos_masks, label_lists):
+        """
+        """
+        pos_lists_, pos_masks_, label_lists_ = [], [], []
+        img_bs = batch_size
+        tcl_bs = 64
+        ngpu = int(batch_size / img_bs)
+        img_ids = np.array(pos_lists, dtype=np.int32)[:, 0, 0].copy()
+        pos_lists_split, pos_masks_split, label_lists_split = [], [], []
+        for i in range(ngpu):
+            pos_lists_split.append([])
+            pos_masks_split.append([])
+            label_lists_split.append([])
+
+        for i in range(img_ids.shape[0]):
+            img_id = img_ids[i]
+            gpu_id = int(img_id / img_bs)
+            img_id = img_id % img_bs
+            pos_list = pos_lists[i].copy()
+            pos_list[:, 0] = img_id
+            pos_lists_split[gpu_id].append(pos_list)
+            pos_masks_split[gpu_id].append(pos_masks[i].copy())
+            label_lists_split[gpu_id].append(copy.deepcopy(label_lists[i]))
+        # repeat or delete
+        for i in range(ngpu):
+            vp_len = len(pos_lists_split[i])
+            if vp_len <= tcl_bs:
+                for j in range(0, tcl_bs - vp_len):
+                    pos_list = pos_lists_split[i][j].copy()
+                    pos_lists_split[i].append(pos_list)
+                    pos_mask = pos_masks_split[i][j].copy()
+                    pos_masks_split[i].append(pos_mask)
+                    label_list = copy.deepcopy(label_lists_split[i][j])
+                    label_lists_split[i].append(label_list)
+            else:
+                for j in range(0, vp_len - tcl_bs):
+                    c_len = len(pos_lists_split[i])
+                    pop_id = np.random.permutation(c_len)[0]
+                    pos_lists_split[i].pop(pop_id)
+                    pos_masks_split[i].pop(pop_id)
+                    label_lists_split[i].pop(pop_id)
+        # merge
+        for i in range(ngpu):
+            pos_lists_.extend(pos_lists_split[i])
+            pos_masks_.extend(pos_masks_split[i])
+            label_lists_.extend(label_lists_split[i])
+        return pos_lists_, pos_masks_, label_lists_
+
+    def pre_process(self, label_list, pos_list, pos_mask):
+        label_list = label_list.numpy()
+        b, h, w, c = label_list.shape
+        pos_list = pos_list.numpy()
+        pos_mask = pos_mask.numpy()
+        pos_list_t = []
+        pos_mask_t = []
+        label_list_t = []
+        for i in range(b):
+            for j in range(30):
+                if pos_mask[i, j].any():
+                    pos_list_t.append(pos_list[i][j])
+                    pos_mask_t.append(pos_mask[i][j])
+                    label_list_t.append(label_list[i][j])
+        pos_list, pos_mask, label_list = self.org_tcl_rois(
+            b, pos_list_t, pos_mask_t, label_list_t)
+        label = []
+        tt = [l.tolist() for l in label_list]
+        for i in range(64):
+            k = 0
+            for j in range(50):
+                if tt[i][j][0] != 36:
+                    k += 1
+                else:
+                    break
+            label.append(k)
+        label = paddle.to_tensor(label)
+        label = paddle.cast(label, dtype='int64')
+        pos_list = paddle.to_tensor(pos_list)
+        pos_mask = paddle.to_tensor(pos_mask)
+        label_list = paddle.squeeze(paddle.to_tensor(label_list), axis=2)
+        label_list = paddle.cast(label_list, dtype='int32')
+        return pos_list, pos_mask, label_list, label
+
+    def border_loss(self, f_border, l_border, l_score, l_mask):
+        l_border_split, l_border_norm = paddle.tensor.split(
+            l_border, num_or_sections=[4, 1], axis=1)
+        f_border_split = f_border
+        b, c, h, w = l_border_norm.shape
+        l_border_norm_split = paddle.expand(
+            x=l_border_norm, shape=[b, 4 * c, h, w])
+        b, c, h, w = l_score.shape
+        l_border_score = paddle.expand(x=l_score, shape=[b, 4 * c, h, w])
+        b, c, h, w = l_mask.shape
+        l_border_mask = paddle.expand(x=l_mask, shape=[b, 4 * c, h, w])
+        border_diff = l_border_split - f_border_split
+        abs_border_diff = paddle.abs(border_diff)
+        border_sign = abs_border_diff < 1.0
+        border_sign = paddle.cast(border_sign, dtype='float32')
+        border_sign.stop_gradient = True
+        border_in_loss = 0.5 * abs_border_diff * abs_border_diff * border_sign + \
+                         (abs_border_diff - 0.5) * (1.0 - border_sign)
+        border_out_loss = l_border_norm_split * border_in_loss
+        border_loss = paddle.sum(border_out_loss * l_border_score * l_border_mask) / \
+                      (paddle.sum(l_border_score * l_border_mask) + 1e-5)
+        return border_loss
+
+    def direction_loss(self, f_direction, l_direction, l_score, l_mask):
+        l_direction_split, l_direction_norm = paddle.tensor.split(
+            l_direction, num_or_sections=[2, 1], axis=1)
+        f_direction_split = f_direction
+        b, c, h, w = l_direction_norm.shape
+        l_direction_norm_split = paddle.expand(
+            x=l_direction_norm, shape=[b, 2 * c, h, w])
+        b, c, h, w = l_score.shape
+        l_direction_score = paddle.expand(x=l_score, shape=[b, 2 * c, h, w])
+        b, c, h, w = l_mask.shape
+        l_direction_mask = paddle.expand(x=l_mask, shape=[b, 2 * c, h, w])
+        direction_diff = l_direction_split - f_direction_split
+        abs_direction_diff = paddle.abs(direction_diff)
+        direction_sign = abs_direction_diff < 1.0
+        direction_sign = paddle.cast(direction_sign, dtype='float32')
+        direction_sign.stop_gradient = True
+        direction_in_loss = 0.5 * abs_direction_diff * abs_direction_diff * direction_sign + \
+                            (abs_direction_diff - 0.5) * (1.0 - direction_sign)
+        direction_out_loss = l_direction_norm_split * direction_in_loss
+        direction_loss = paddle.sum(direction_out_loss * l_direction_score * l_direction_mask) / \
+                         (paddle.sum(l_direction_score * l_direction_mask) + 1e-5)
+        return direction_loss
+
+    def ctcloss(self, f_char, tcl_pos, tcl_mask, tcl_label, label_t):
+        f_char = paddle.transpose(f_char, [0, 2, 3, 1])
+        tcl_pos = paddle.reshape(tcl_pos, [-1, 3])
+        tcl_pos = paddle.cast(tcl_pos, dtype=int)
+        f_tcl_char = paddle.gather_nd(f_char, tcl_pos)
+        f_tcl_char = paddle.reshape(f_tcl_char,
+                                    [-1, 64, 37])  # len(Lexicon_Table)+1
+        f_tcl_char_fg, f_tcl_char_bg = paddle.split(f_tcl_char, [36, 1], axis=2)
+        f_tcl_char_bg = f_tcl_char_bg * tcl_mask + (1.0 - tcl_mask) * 20.0
+        b, c, l = tcl_mask.shape
+        tcl_mask_fg = paddle.expand(x=tcl_mask, shape=[b, c, 36 * l])
+        tcl_mask_fg.stop_gradient = True
+        f_tcl_char_fg = f_tcl_char_fg * tcl_mask_fg + (1.0 - tcl_mask_fg) * (
+            -20.0)
+        f_tcl_char_mask = paddle.concat([f_tcl_char_fg, f_tcl_char_bg], axis=2)
+        f_tcl_char_ld = paddle.transpose(f_tcl_char_mask, (1, 0, 2))
+        N, B, _ = f_tcl_char_ld.shape
+        input_lengths = paddle.to_tensor([N] * B, dtype='int64')
+        cost = paddle.nn.functional.ctc_loss(
+            log_probs=f_tcl_char_ld,
+            labels=tcl_label,
+            input_lengths=input_lengths,
+            label_lengths=label_t,
+            blank=36,
+            reduction='none')
+        cost = cost.mean()
+        return cost
+
+    def forward(self, predicts, labels):
+        images, tcl_maps, tcl_label_maps, border_maps \
+            , direction_maps, training_masks, label_list, pos_list, pos_mask = labels
+        # for all the batch_size
+        pos_list, pos_mask, label_list, label_t = self.pre_process(
+            label_list, pos_list, pos_mask)
+
+        f_score, f_boder, f_direction, f_char = predicts
+        score_loss = self.dice_loss(f_score, tcl_maps, training_masks)
+        border_loss = self.border_loss(f_boder, border_maps, tcl_maps,
+                                       training_masks)
+        direction_loss = self.direction_loss(f_direction, direction_maps,
+                                             tcl_maps, training_masks)
+        ctc_loss = self.ctcloss(f_char, pos_list, pos_mask, label_list, label_t)
+        loss_all = score_loss + border_loss + direction_loss + 5 * ctc_loss
+
+        losses = {
+            'loss': loss_all,
+            "score_loss": score_loss,
+            "border_loss": border_loss,
+            "direction_loss": direction_loss,
+            "ctc_loss": ctc_loss
+        }
+        return losses

ppocr/metrics/__init__.py

@@ -26,8 +26,9 @@ def build_metric(config):
     from .det_metric import DetMetric
     from .rec_metric import RecMetric
     from .cls_metric import ClsMetric
+    from .e2e_metric import E2EMetric
 
-    support_dict = ['DetMetric', 'RecMetric', 'ClsMetric']
+    support_dict = ['DetMetric', 'RecMetric', 'ClsMetric', 'E2EMetric']
 
     config = copy.deepcopy(config)
     module_name = config.pop('name')

ppocr/metrics/e2e_metric.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
+
+__all__ = ['E2EMetric']
+
+from ppocr.utils.e2e_metric.Deteval import *
+
+
+class E2EMetric(object):
+    def __init__(self, main_indicator='f_score_e2e', **kwargs):
+        self.label_list = [
+            '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C',
+            'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',
+            'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'
+        ]
+        self.main_indicator = main_indicator
+        self.reset()
+
+    def __call__(self, preds, batch, **kwargs):
+        '''
+       batch: a list produced by dataloaders.
+           image: np.ndarray  of shape (N, C, H, W).
+           ratio_list: np.ndarray  of shape(N,2)
+           polygons: np.ndarray  of shape (N, K, 4, 2), the polygons of objective regions.
+           ignore_tags: np.ndarray  of shape (N, K), indicates whether a region is ignorable or not.
+       preds: a list of dict produced by post process
+            points: np.ndarray of shape (N, K, 4, 2), the polygons of objective regions.
+       '''
+
+        gt_polyons_batch = batch[2]
+        temp_gt_strs_batch = batch[3]
+        ignore_tags_batch = batch[4]
+        gt_strs_batch = []
+        temp_gt_strs_batch = temp_gt_strs_batch[0].tolist()
+        for temp_list in temp_gt_strs_batch:
+            t = ""
+            for index in temp_list:
+                if index < 36:
+                    t += self.label_list[index]
+            gt_strs_batch.append(t)
+
+        for pred, gt_polyons, gt_strs, ignore_tags in zip(
+                preds, gt_polyons_batch, gt_strs_batch, ignore_tags_batch):
+            # prepare gt
+            gt_info_list = [{
+                'points': gt_polyon,
+                'text': gt_str,
+                'ignore': ignore_tag
+            } for gt_polyon, gt_str, ignore_tag in
+                            zip(gt_polyons, gt_strs, ignore_tags)]
+            # prepare det
+            e2e_info_list = [{
+                'points': det_polyon,
+                'text': pred_str
+            } for det_polyon, pred_str in zip(pred['points'], preds['strs'])]
+            result = get_socre(gt_info_list, e2e_info_list)
+            self.results.append(result)
+
+    def get_metric(self):
+        """
+        return metrics {
+                 'precision': 0,
+                 'recall': 0,
+                 'hmean': 0
+            }
+        """
+        metircs = combine_results(self.results)
+        self.reset()
+        return metircs
+
+    def reset(self):
+        self.results = []  # clear results

ppocr/metrics/eval_det_iou.py

@@ -200,7 +200,8 @@ class DetectionIoUEvaluator(object):
         methodPrecision = 0 if numGlobalCareDet == 0 else float(
             matchedSum) / numGlobalCareDet
         methodHmean = 0 if methodRecall + methodPrecision == 0 else 2 * \
-            methodRecall * methodPrecision / (methodRecall + methodPrecision)
+                                                                    methodRecall * methodPrecision / (
+                                                                            methodRecall + methodPrecision)
         # print(methodRecall, methodPrecision, methodHmean)
         # sys.exit(-1)
         methodMetrics = {

ppocr/modeling/backbones/__init__.py

@@ -26,6 +26,9 @@ def build_backbone(config, model_type):
         from .rec_resnet_vd import ResNet
         from .rec_resnet_fpn import ResNetFPN
         support_dict = ['MobileNetV3', 'ResNet', 'ResNetFPN']
+    elif model_type == 'e2e':
+        from .e2e_resnet_vd_pg import ResNet
+        support_dict = ['ResNet']
     else:
         raise NotImplementedError
 

ppocr/modeling/backbones/e2e_resnet_vd_pg.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 paddle
+from paddle import ParamAttr
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+__all__ = ["ResNet"]
+
+
+class ConvBNLayer(nn.Layer):
+    def __init__(
+            self,
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=1,
+            groups=1,
+            is_vd_mode=False,
+            act=None,
+            name=None, ):
+        super(ConvBNLayer, self).__init__()
+
+        self.is_vd_mode = is_vd_mode
+        self._pool2d_avg = nn.AvgPool2D(
+            kernel_size=2, stride=2, padding=0, ceil_mode=True)
+        self._conv = nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=(kernel_size - 1) // 2,
+            groups=groups,
+            weight_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False)
+        if name == "conv1":
+            bn_name = "bn_" + name
+        else:
+            bn_name = "bn" + name[3:]
+        self._batch_norm = nn.BatchNorm(
+            out_channels,
+            act=act,
+            param_attr=ParamAttr(name=bn_name + '_scale'),
+            bias_attr=ParamAttr(bn_name + '_offset'),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance')
+
+    def forward(self, inputs):
+        # if self.is_vd_mode:
+        #     inputs = self._pool2d_avg(inputs)
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+class BottleneckBlock(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride,
+                 shortcut=True,
+                 if_first=False,
+                 name=None):
+        super(BottleneckBlock, self).__init__()
+
+        self.conv0 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            act='relu',
+            name=name + "_branch2a")
+        self.conv1 = ConvBNLayer(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=stride,
+            act='relu',
+            name=name + "_branch2b")
+        self.conv2 = ConvBNLayer(
+            in_channels=out_channels,
+            out_channels=out_channels * 4,
+            kernel_size=1,
+            act=None,
+            name=name + "_branch2c")
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                in_channels=in_channels,
+                out_channels=out_channels * 4,
+                kernel_size=1,
+                stride=stride,
+                is_vd_mode=False if if_first else True,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+        conv2 = self.conv2(conv1)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = paddle.add(x=short, y=conv2)
+        y = F.relu(y)
+        return y
+
+
+class BasicBlock(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride,
+                 shortcut=True,
+                 if_first=False,
+                 name=None):
+        super(BasicBlock, self).__init__()
+        self.stride = stride
+        self.conv0 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            stride=stride,
+            act='relu',
+            name=name + "_branch2a")
+        self.conv1 = ConvBNLayer(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=3,
+            act=None,
+            name=name + "_branch2b")
+
+        if not shortcut:
+            self.short = ConvBNLayer(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=1,
+                stride=1,
+                is_vd_mode=False if if_first else True,
+                name=name + "_branch1")
+
+        self.shortcut = shortcut
+
+    def forward(self, inputs):
+        y = self.conv0(inputs)
+        conv1 = self.conv1(y)
+
+        if self.shortcut:
+            short = inputs
+        else:
+            short = self.short(inputs)
+        y = paddle.add(x=short, y=conv1)
+        y = F.relu(y)
+        return y
+
+
+class ResNet(nn.Layer):
+    def __init__(self, in_channels=3, layers=50, **kwargs):
+        super(ResNet, self).__init__()
+
+        self.layers = layers
+        supported_layers = [18, 34, 50, 101, 152, 200]
+        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]
+            depth = [3, 4, 6, 3, 3]
+        elif layers == 101:
+            depth = [3, 4, 23, 3]
+        elif layers == 152:
+            depth = [3, 8, 36, 3]
+        elif layers == 200:
+            depth = [3, 12, 48, 3]
+        num_channels = [64, 256, 512, 1024,
+                        2048] if layers >= 50 else [64, 64, 128, 256]
+        num_filters = [64, 128, 256, 512, 512]
+
+        self.conv1_1 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=64,
+            kernel_size=7,
+            stride=2,
+            act='relu',
+            name="conv1_1")
+        self.pool2d_max = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
+
+        self.stages = []
+        self.out_channels = [3, 64]
+        # num_filters = [64, 128, 256, 512, 512]
+        if layers >= 50:
+            for block in range(len(depth)):
+                block_list = []
+                shortcut = False
+                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)
+                    bottleneck_block = self.add_sublayer(
+                        'bb_%d_%d' % (block, i),
+                        BottleneckBlock(
+                            in_channels=num_channels[block]
+                            if i == 0 else num_filters[block] * 4,
+                            out_channels=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut,
+                            if_first=block == i == 0,
+                            name=conv_name))
+                    shortcut = True
+                    block_list.append(bottleneck_block)
+                self.out_channels.append(num_filters[block] * 4)
+                self.stages.append(nn.Sequential(*block_list))
+        else:
+            for block in range(len(depth)):
+                block_list = []
+                shortcut = False
+                for i in range(depth[block]):
+                    conv_name = "res" + str(block + 2) + chr(97 + i)
+                    basic_block = self.add_sublayer(
+                        'bb_%d_%d' % (block, i),
+                        BasicBlock(
+                            in_channels=num_channels[block]
+                            if i == 0 else num_filters[block],
+                            out_channels=num_filters[block],
+                            stride=2 if i == 0 and block != 0 else 1,
+                            shortcut=shortcut,
+                            if_first=block == i == 0,
+                            name=conv_name))
+                    shortcut = True
+                    block_list.append(basic_block)
+                self.out_channels.append(num_filters[block])
+                self.stages.append(nn.Sequential(*block_list))
+
+    def forward(self, inputs):
+        out = [inputs]
+        y = self.conv1_1(inputs)
+        out.append(y)
+        y = self.pool2d_max(y)
+        for block in self.stages:
+            y = block(y)
+            out.append(y)
+        return out

ppocr/modeling/heads/__init__.py

@@ -20,6 +20,7 @@ def build_head(config):
     from .det_db_head import DBHead
     from .det_east_head import EASTHead
     from .det_sast_head import SASTHead
+    from .e2e_pg_head import PGHead
 
     # rec head
     from .rec_ctc_head import CTCHead
@@ -30,8 +31,8 @@ def build_head(config):
     from .cls_head import ClsHead
     support_dict = [
         'DBHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead', 'AttentionHead',
-        'SRNHead'
-    ]
+        'SRNHead', 'PGHead']
+
 
     module_name = config.pop('name')
     assert module_name in support_dict, Exception('head only support {}'.format(

ppocr/modeling/heads/e2e_pg_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
+from paddle import nn
+import paddle.nn.functional as F
+from paddle import ParamAttr
+
+
+class ConvBNLayer(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride,
+                 padding,
+                 groups=1,
+                 if_act=True,
+                 act=None,
+                 name=None):
+        super(ConvBNLayer, self).__init__()
+        self.if_act = if_act
+        self.act = act
+        self.conv = nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            weight_attr=ParamAttr(name=name + '_weights'),
+            bias_attr=False)
+
+        self.bn = nn.BatchNorm(
+            num_channels=out_channels,
+            act=act,
+            param_attr=ParamAttr(name="bn_" + name + "_scale"),
+            bias_attr=ParamAttr(name="bn_" + name + "_offset"),
+            moving_mean_name="bn_" + name + "_mean",
+            moving_variance_name="bn_" + name + "_variance",
+            use_global_stats=False)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        return x
+
+
+class PGHead(nn.Layer):
+    """
+    """
+
+    def __init__(self, in_channels, model_name, **kwargs):
+        super(PGHead, self).__init__()
+        self.model_name = model_name
+        self.conv_f_score1 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=64,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_score{}".format(1))
+        self.conv_f_score2 = ConvBNLayer(
+            in_channels=64,
+            out_channels=64,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            act='relu',
+            name="conv_f_score{}".format(2))
+        self.conv_f_score3 = ConvBNLayer(
+            in_channels=64,
+            out_channels=128,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_score{}".format(3))
+
+        self.conv1 = nn.Conv2D(
+            in_channels=128,
+            out_channels=1,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=1,
+            weight_attr=ParamAttr(name="conv_f_score{}".format(4)),
+            bias_attr=False)
+
+        self.conv_f_boder1 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=64,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_boder{}".format(1))
+        self.conv_f_boder2 = ConvBNLayer(
+            in_channels=64,
+            out_channels=64,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            act='relu',
+            name="conv_f_boder{}".format(2))
+        self.conv_f_boder3 = ConvBNLayer(
+            in_channels=64,
+            out_channels=128,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_boder{}".format(3))
+        self.conv2 = nn.Conv2D(
+            in_channels=128,
+            out_channels=4,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=1,
+            weight_attr=ParamAttr(name="conv_f_boder{}".format(4)),
+            bias_attr=False)
+        self.conv_f_char1 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=128,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_char{}".format(1))
+        self.conv_f_char2 = ConvBNLayer(
+            in_channels=128,
+            out_channels=128,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            act='relu',
+            name="conv_f_char{}".format(2))
+        self.conv_f_char3 = ConvBNLayer(
+            in_channels=128,
+            out_channels=256,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_char{}".format(3))
+        self.conv_f_char4 = ConvBNLayer(
+            in_channels=256,
+            out_channels=256,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            act='relu',
+            name="conv_f_char{}".format(4))
+        self.conv_f_char5 = ConvBNLayer(
+            in_channels=256,
+            out_channels=256,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_char{}".format(5))
+        self.conv3 = nn.Conv2D(
+            in_channels=256,
+            out_channels=6625,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=1,
+            weight_attr=ParamAttr(name="conv_f_char{}".format(6)),
+            bias_attr=False)
+
+        self.conv_f_direc1 = ConvBNLayer(
+            in_channels=in_channels,
+            out_channels=64,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_direc{}".format(1))
+        self.conv_f_direc2 = ConvBNLayer(
+            in_channels=64,
+            out_channels=64,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            act='relu',
+            name="conv_f_direc{}".format(2))
+        self.conv_f_direc3 = ConvBNLayer(
+            in_channels=64,
+            out_channels=128,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            act='relu',
+            name="conv_f_direc{}".format(3))
+        self.conv4 = nn.Conv2D(
+            in_channels=128,
+            out_channels=2,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            groups=1,
+            weight_attr=ParamAttr(name="conv_f_direc{}".format(4)),
+            bias_attr=False)
+
+    def forward(self, x):
+        f_score = self.conv_f_score1(x)
+        f_score = self.conv_f_score2(f_score)
+        f_score = self.conv_f_score3(f_score)
+        f_score = self.conv1(f_score)
+        f_score = F.sigmoid(f_score)
+
+        # f_boder
+        f_boder = self.conv_f_boder1(x)
+        f_boder = self.conv_f_boder2(f_boder)
+        f_boder = self.conv_f_boder3(f_boder)
+        f_boder = self.conv2(f_boder)
+
+        f_char = self.conv_f_char1(x)
+        f_char = self.conv_f_char2(f_char)
+        f_char = self.conv_f_char3(f_char)
+        f_char = self.conv_f_char4(f_char)
+        f_char = self.conv_f_char5(f_char)
+        f_char = self.conv3(f_char)
+
+        f_direction = self.conv_f_direc1(x)
+        f_direction = self.conv_f_direc2(f_direction)
+        f_direction = self.conv_f_direc3(f_direction)
+        f_direction = self.conv4(f_direction)
+
+        return f_score, f_boder, f_direction, f_char

ppocr/modeling/necks/__init__.py

@@ -14,12 +14,14 @@
 
 __all__ = ['build_neck']
 
+
 def build_neck(config):
     from .db_fpn import DBFPN
     from .east_fpn import EASTFPN
     from .sast_fpn import SASTFPN
     from .rnn import SequenceEncoder
-    support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder']
+    from .pg_fpn import PGFPN
+    support_dict = ['DBFPN', 'EASTFPN', 'SASTFPN', 'SequenceEncoder', 'PGFPN']
 
     module_name = config.pop('name')
     assert module_name in support_dict, Exception('neck only support {}'.format(

ppocr/modeling/necks/pg_fpn.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 paddle
+from paddle import nn
+import paddle.nn.functional as F
+from paddle import ParamAttr
+
+
+class ConvBNLayer(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 groups=1,
+                 is_vd_mode=False,
+                 act=None,
+                 name=None):
+        super(ConvBNLayer, self).__init__()
+
+        self.is_vd_mode = is_vd_mode
+        self._pool2d_avg = nn.AvgPool2D(
+            kernel_size=2, stride=2, padding=0, ceil_mode=True)
+        self._conv = nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=(kernel_size - 1) // 2,
+            groups=groups,
+            weight_attr=ParamAttr(name=name + "_weights"),
+            bias_attr=False)
+        if name == "conv1":
+            bn_name = "bn_" + name
+        else:
+            bn_name = "bn" + name[3:]
+        self._batch_norm = nn.BatchNorm(
+            out_channels,
+            act=act,
+            param_attr=ParamAttr(name=bn_name + '_scale'),
+            bias_attr=ParamAttr(bn_name + '_offset'),
+            moving_mean_name=bn_name + '_mean',
+            moving_variance_name=bn_name + '_variance',
+            use_global_stats=False)
+
+    def forward(self, inputs):
+        # if self.is_vd_mode:
+        #     inputs = self._pool2d_avg(inputs)
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+class DeConvBNLayer(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=4,
+                 stride=2,
+                 padding=1,
+                 groups=1,
+                 if_act=True,
+                 act=None,
+                 name=None):
+        super(DeConvBNLayer, self).__init__()
+
+        self.if_act = if_act
+        self.act = act
+        self.deconv = nn.Conv2DTranspose(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            weight_attr=ParamAttr(name=name + '_weights'),
+            bias_attr=False)
+        self.bn = nn.BatchNorm(
+            num_channels=out_channels,
+            act=act,
+            param_attr=ParamAttr(name="bn_" + name + "_scale"),
+            bias_attr=ParamAttr(name="bn_" + name + "_offset"),
+            moving_mean_name="bn_" + name + "_mean",
+            moving_variance_name="bn_" + name + "_variance",
+            use_global_stats=False)
+
+    def forward(self, x):
+        x = self.deconv(x)
+        x = self.bn(x)
+        return x
+
+
+class FPN_Up_Fusion(nn.Layer):
+    def __init__(self, in_channels):
+        super(FPN_Up_Fusion, self).__init__()
+        in_channels = in_channels[::-1]
+        out_channels = [256, 256, 192, 192, 128]
+
+        self.h0_conv = ConvBNLayer(
+            in_channels[0], out_channels[0], 1, 1, act=None, name='conv_h0')
+        self.h1_conv = ConvBNLayer(
+            in_channels[1], out_channels[1], 1, 1, act=None, name='conv_h1')
+        self.h2_conv = ConvBNLayer(
+            in_channels[2], out_channels[2], 1, 1, act=None, name='conv_h2')
+        self.h3_conv = ConvBNLayer(
+            in_channels[3], out_channels[3], 1, 1, act=None, name='conv_h3')
+        self.h4_conv = ConvBNLayer(
+            in_channels[4], out_channels[4], 1, 1, act=None, name='conv_h4')
+
+        self.dconv0 = DeConvBNLayer(
+            in_channels=out_channels[0],
+            out_channels=out_channels[1],
+            name="dconv_{}".format(0))
+        self.dconv1 = DeConvBNLayer(
+            in_channels=out_channels[1],
+            out_channels=out_channels[2],
+            act=None,
+            name="dconv_{}".format(1))
+        self.dconv2 = DeConvBNLayer(
+            in_channels=out_channels[2],
+            out_channels=out_channels[3],
+            act=None,
+            name="dconv_{}".format(2))
+        self.dconv3 = DeConvBNLayer(
+            in_channels=out_channels[3],
+            out_channels=out_channels[4],
+            act=None,
+            name="dconv_{}".format(3))
+        self.conv_g1 = ConvBNLayer(
+            in_channels=out_channels[1],
+            out_channels=out_channels[1],
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv_g{}".format(1))
+        self.conv_g2 = ConvBNLayer(
+            in_channels=out_channels[2],
+            out_channels=out_channels[2],
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv_g{}".format(2))
+        self.conv_g3 = ConvBNLayer(
+            in_channels=out_channels[3],
+            out_channels=out_channels[3],
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv_g{}".format(3))
+        self.conv_g4 = ConvBNLayer(
+            in_channels=out_channels[4],
+            out_channels=out_channels[4],
+            kernel_size=3,
+            stride=1,
+            act='relu',
+            name="conv_g{}".format(4))
+        self.convf = ConvBNLayer(
+            in_channels=out_channels[4],
+            out_channels=out_channels[4],
+            kernel_size=1,
+            stride=1,
+            act=None,
+            name="conv_f{}".format(4))
+
+    def _add_relu(self, x1, x2):
+        x = paddle.add(x=x1, y=x2)
+        x = F.relu(x)
+        return x
+
+    def forward(self, x):
+        f = x[2:][::-1]
+        h0 = self.h0_conv(f[0])
+        h1 = self.h1_conv(f[1])
+        h2 = self.h2_conv(f[2])
+        h3 = self.h3_conv(f[3])
+        h4 = self.h4_conv(f[4])
+
+        g0 = self.dconv0(h0)
+
+        g1 = self.dconv2(self.conv_g2(self._add_relu(g0, h1)))
+        g2 = self.dconv2(self.conv_g2(self._add_relu(g1, h2)))
+        g3 = self.dconv3(self.conv_g2(self._add_relu(g2, h3)))
+        g4 = self.dconv4(self.conv_g2(self._add_relu(g3, h4)))
+        return g4
+
+
+class FPN_Down_Fusion(nn.Layer):
+    def __init__(self, in_channels):
+        super(FPN_Down_Fusion, self).__init__()
+        out_channels = [32, 64, 128]
+
+        self.h0_conv = ConvBNLayer(
+            in_channels[0], out_channels[0], 3, 1, act=None, name='FPN_d1')
+        self.h1_conv = ConvBNLayer(
+            in_channels[1], out_channels[1], 3, 1, act=None, name='FPN_d2')
+        self.h2_conv = ConvBNLayer(
+            in_channels[2], out_channels[2], 3, 1, act=None, name='FPN_d3')
+
+        self.g0_conv = ConvBNLayer(
+            out_channels[0], out_channels[1], 3, 2, act=None, name='FPN_d4')
+
+        self.g1_conv = nn.Sequential(
+            ConvBNLayer(
+                out_channels[1],
+                out_channels[1],
+                3,
+                1,
+                act='relu',
+                name='FPN_d5'),
+            ConvBNLayer(
+                out_channels[1], out_channels[2], 3, 2, act=None,
+                name='FPN_d6'))
+
+        self.g2_conv = nn.Sequential(
+            ConvBNLayer(
+                out_channels[2],
+                out_channels[2],
+                3,
+                1,
+                act='relu',
+                name='FPN_d7'),
+            ConvBNLayer(
+                out_channels[2], out_channels[2], 1, 1, act=None,
+                name='FPN_d8'))
+
+    def forward(self, x):
+        f = x[:3]
+        h0 = self.h0_conv(f[0])
+        h1 = self.h1_conv(f[1])
+        h2 = self.h2_conv(f[2])
+        g0 = self.g0_conv(h0)
+        g1 = paddle.add(x=g0, y=h1)
+        g1 = F.relu(g1)
+        g1 = self.g1_conv(g1)
+        g2 = paddle.add(x=g1, y=h2)
+        g2 = F.relu(g2)
+        g2 = self.g2_conv(g2)
+        return g2
+
+
+class PGFPN(nn.Layer):
+    def __init__(self, in_channels, with_cab=False, **kwargs):
+        super(PGFPN, self).__init__()
+        self.in_channels = in_channels
+        self.with_cab = with_cab
+        self.FPN_Down_Fusion = FPN_Down_Fusion(self.in_channels)
+        self.FPN_Up_Fusion = FPN_Up_Fusion(self.in_channels)
+        self.out_channels = 128
+
+    def forward(self, x):
+        # down fpn
+        f_down = self.FPN_Down_Fusion(x)
+
+        # up fpn
+        f_up = self.FPN_Up_Fusion(x)
+
+        # fusion
+        f_common = paddle.add(x=f_down, y=f_up)
+        f_common = F.relu(f_common)
+
+        return f_common

ppocr/postprocess/__init__.py

@@ -28,10 +28,11 @@ def build_post_process(config, global_config=None):
     from .sast_postprocess import SASTPostProcess
     from .rec_postprocess import CTCLabelDecode, AttnLabelDecode, SRNLabelDecode
     from .cls_postprocess import ClsPostProcess
+    from .pg_postprocess import PGPostProcess
 
     support_dict = [
         'DBPostProcess', 'EASTPostProcess', 'SASTPostProcess', 'CTCLabelDecode',
-        'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode'
+        'AttnLabelDecode', 'ClsPostProcess', 'SRNLabelDecode', 'PGPostProcess'
     ]
 
     config = copy.deepcopy(config)

ppocr/postprocess/pg_postprocess.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 os
+import sys
+
+__dir__ = os.path.dirname(__file__)
+sys.path.append(__dir__)
+sys.path.append(os.path.join(__dir__, '..'))
+
+import numpy as np
+from .locality_aware_nms import nms_locality
+from ppocr.utils.e2e_utils.extract_textpoint import *
+from ppocr.utils.e2e_utils.ski_thin import *
+from ppocr.utils.e2e_utils.visual import *
+import paddle
+import cv2
+import time
+
+
+class PGPostProcess(object):
+    """
+    The post process for SAST.
+    """
+
+    def __init__(self,
+                 score_thresh=0.5,
+                 nms_thresh=0.2,
+                 sample_pts_num=2,
+                 shrink_ratio_of_width=0.3,
+                 expand_scale=1.0,
+                 tcl_map_thresh=0.5,
+                 **kwargs):
+        self.result_path = ""
+        self.valid_set = 'totaltext'
+        self.Lexicon_Table = [
+            '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C',
+            'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P',
+            'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'
+        ]
+        self.score_thresh = score_thresh
+        self.nms_thresh = nms_thresh
+        self.sample_pts_num = sample_pts_num
+        self.shrink_ratio_of_width = shrink_ratio_of_width
+        self.expand_scale = expand_scale
+        self.tcl_map_thresh = tcl_map_thresh
+
+        # c++ la-nms is faster, but only support python 3.5
+        self.is_python35 = False
+        if sys.version_info.major == 3 and sys.version_info.minor == 5:
+            self.is_python35 = True
+
+    def __call__(self, outs_dict, shape_list):
+        p_score, p_border, p_direction, p_char = outs_dict[:4]
+        p_score = p_score[0].numpy()
+        p_border = p_border[0].numpy()
+        p_direction = p_direction[0].numpy()
+        p_char = p_char[0].numpy()
+        src_h, src_w, ratio_h, ratio_w = shape_list[0]
+        if self.valid_set != 'totaltext':
+            is_curved = False
+        else:
+            is_curved = True
+        instance_yxs_list = generate_pivot_list(
+            p_score,
+            p_char,
+            p_direction,
+            score_thresh=self.score_thresh,
+            is_backbone=True,
+            is_curved=is_curved)
+        p_char = np.expand_dims(p_char, axis=0)
+        p_char = paddle.to_tensor(p_char)
+        char_seq_idx_set = []
+        for i in range(len(instance_yxs_list)):
+            gather_info_lod = paddle.to_tensor(instance_yxs_list[i])
+            f_char_map = paddle.transpose(p_char, [0, 2, 3, 1])
+            featyre_seq = paddle.gather_nd(f_char_map, gather_info_lod)
+            featyre_seq = np.expand_dims(featyre_seq.numpy(), axis=0)
+            t = len(featyre_seq[0])
+            featyre_seq = paddle.to_tensor(featyre_seq)
+            l = np.array([[t]]).astype(np.int64)
+            length = paddle.to_tensor(l)
+            seq_pred = paddle.fluid.layers.ctc_greedy_decoder(
+                input=featyre_seq, blank=36, input_length=length)
+            seq_pred1 = seq_pred[0].numpy().tolist()[0]
+            seq_len = seq_pred[1].numpy()[0][0]
+            temp_t = []
+            for x in seq_pred1[:seq_len]:
+                temp_t.append(x)
+            char_seq_idx_set.append(temp_t)
+        seq_strs = []
+        for char_idx_set in char_seq_idx_set:
+            pr_str = ''.join([self.Lexicon_Table[pos] for pos in char_idx_set])
+            seq_strs.append(pr_str)
+        poly_list = []
+        keep_str_list = []
+        all_point_list = []
+        all_point_pair_list = []
+        for yx_center_line, keep_str in zip(instance_yxs_list, seq_strs):
+            if len(yx_center_line) == 1:
+                print('the length of tcl point is less than 2, repeat')
+                yx_center_line.append(yx_center_line[-1])
+
+            # expand corresponding offset for total-text.
+            offset_expand = 1.0
+            if self.valid_set == 'totaltext':
+                offset_expand = 1.2
+
+            point_pair_list = []
+            for batch_id, y, x in yx_center_line:
+                offset = p_border[:, y, x].reshape(2, 2)
+                if offset_expand != 1.0:
+                    offset_length = np.linalg.norm(
+                        offset, axis=1, keepdims=True)
+                    expand_length = np.clip(
+                        offset_length * (offset_expand - 1),
+                        a_min=0.5,
+                        a_max=3.0)
+                    offset_detal = offset / offset_length * expand_length
+                    offset = offset + offset_detal
+                ori_yx = np.array([y, x], dtype=np.float32)
+                point_pair = (ori_yx + offset)[:, ::-1] * 4.0 / np.array(
+                    [ratio_w, ratio_h]).reshape(-1, 2)
+                point_pair_list.append(point_pair)
+
+                # for visualization
+                all_point_list.append([
+                    int(round(x * 4.0 / ratio_w)),
+                    int(round(y * 4.0 / ratio_h))
+                ])
+                all_point_pair_list.append(point_pair.round().astype(np.int32)
+                                           .tolist())
+
+            # ndarry: (x, 2)
+            detected_poly, pair_length_info = point_pair2poly(point_pair_list)
+            print('expand along width. {}'.format(detected_poly.shape))
+            detected_poly = expand_poly_along_width(
+                detected_poly, shrink_ratio_of_width=0.2)
+            detected_poly[:, 0] = np.clip(
+                detected_poly[:, 0], a_min=0, a_max=src_w)
+            detected_poly[:, 1] = np.clip(
+                detected_poly[:, 1], a_min=0, a_max=src_h)
+
+            if len(keep_str) < 2:
+                print('--> too short, {}'.format(keep_str))
+                continue
+
+            keep_str_list.append(keep_str)
+            if self.valid_set == 'partvgg':
+                middle_point = len(detected_poly) // 2
+                detected_poly = detected_poly[
+                    [0, middle_point - 1, middle_point, -1], :]
+                poly_list.append(detected_poly)
+            elif self.valid_set == 'totaltext':
+                poly_list.append(detected_poly)
+            else:
+                print('--> Not supported format.')
+                exit(-1)
+        data = {
+            'points': poly_list,
+            'strs': keep_str_list,
+        }
+        # visualization
+        # if self.save_visualization:
+        #     visualize_e2e_result(im_fn, poly_list, keep_str_list, src_im)
+        #     visualize_point_result(im_fn, all_point_list, all_point_pair_list, src_im)
+
+        # save detected boxes
+        # txt_dir = (result_path[:-1] if result_path.endswith('/') else result_path) + '_txt_anno'
+        # if not os.path.exists(txt_dir):
+        #     os.makedirs(txt_dir)
+        # res_file = os.path.join(txt_dir, '{}.txt'.format(im_prefix))
+        # with open(res_file, 'w') as f:
+        #     for i_box, box in enumerate(poly_list):
+        #         seq_str = keep_str_list[i_box]
+        #         box = np.round(box).astype('int32')
+        #         box_str = ','.join(str(s) for s in (box.flatten().tolist()))
+        #         f.write('{}\t{}\r\n'.format(box_str, seq_str))
+        return data

ppocr/postprocess/sast_postprocess.py

@@ -18,6 +18,7 @@ from __future__ import print_function
 
 import os
 import sys
+
 __dir__ = os.path.dirname(__file__)
 sys.path.append(__dir__)
 sys.path.append(os.path.join(__dir__, '..'))
@@ -67,11 +68,15 @@ class SASTPostProcess(object):
             point_list[point_num - 1 - idx] = point_pair[1]
         return np.array(point_list).reshape(-1, 2)
 
-    def shrink_quad_along_width(self, quad, begin_width_ratio=0., end_width_ratio=1.):
+    def shrink_quad_along_width(self,
+                                quad,
+                                begin_width_ratio=0.,
+                                end_width_ratio=1.):
         """ 
         Generate shrink_quad_along_width.
         """
-        ratio_pair = np.array([[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
+        ratio_pair = np.array(
+            [[begin_width_ratio], [end_width_ratio]], dtype=np.float32)
         p0_1 = quad[0] + (quad[1] - quad[0]) * ratio_pair
         p3_2 = quad[3] + (quad[2] - quad[3]) * ratio_pair
         return np.array([p0_1[0], p0_1[1], p3_2[1], p3_2[0]])
@@ -81,16 +86,23 @@ class SASTPostProcess(object):
         expand poly along width.
         """
         point_num = poly.shape[0]
-        left_quad = np.array([poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
+        left_quad = np.array(
+            [poly[0], poly[1], poly[-2], poly[-1]], dtype=np.float32)
         left_ratio = -shrink_ratio_of_width * np.linalg.norm(left_quad[0] - left_quad[3]) / \
                      (np.linalg.norm(left_quad[0] - left_quad[1]) + 1e-6)
-        left_quad_expand = self.shrink_quad_along_width(left_quad, left_ratio, 1.0)
-        right_quad = np.array([poly[point_num // 2 - 2], poly[point_num // 2 - 1],
-                            poly[point_num // 2], poly[point_num // 2 + 1]], dtype=np.float32)
+        left_quad_expand = self.shrink_quad_along_width(left_quad, left_ratio,
+                                                        1.0)
+        right_quad = np.array(
+            [
+                poly[point_num // 2 - 2], poly[point_num // 2 - 1],
+                poly[point_num // 2], poly[point_num // 2 + 1]
+            ],
+            dtype=np.float32)
         right_ratio = 1.0 + \
                       shrink_ratio_of_width * np.linalg.norm(right_quad[0] - right_quad[3]) / \
                       (np.linalg.norm(right_quad[0] - right_quad[1]) + 1e-6)
-        right_quad_expand = self.shrink_quad_along_width(right_quad, 0.0, right_ratio)
+        right_quad_expand = self.shrink_quad_along_width(right_quad, 0.0,
+                                                         right_ratio)
         poly[0] = left_quad_expand[0]
         poly[-1] = left_quad_expand[-1]
         poly[point_num // 2 - 1] = right_quad_expand[1]
@@ -121,12 +133,10 @@ class SASTPostProcess(object):
         """
         compute area of a quad.
         """
-        edge = [
-            (quad[1][0] - quad[0][0]) * (quad[1][1] + quad[0][1]),
+        edge = [(quad[1][0] - quad[0][0]) * (quad[1][1] + quad[0][1]),
                 (quad[2][0] - quad[1][0]) * (quad[2][1] + quad[1][1]),
                 (quad[3][0] - quad[2][0]) * (quad[3][1] + quad[2][1]),
-            (quad[0][0] - quad[3][0]) * (quad[0][1] + quad[3][1])
-        ]
+                (quad[0][0] - quad[3][0]) * (quad[0][1] + quad[3][1])]
         return np.sum(edge) / 2.
 
     def nms(self, dets):
@@ -157,7 +167,8 @@ class SASTPostProcess(object):
         m = quads.shape[0]
         gt_tc = np.mean(quads, axis=1)  # (m, 2)
 
-        pred_tc_tile = np.tile(pred_tc[:, np.newaxis, :], (1, m, 1)) # (n, m, 2)
+        pred_tc_tile = np.tile(pred_tc[:, np.newaxis, :],
+                               (1, m, 1))  # (n, m, 2)
         gt_tc_tile = np.tile(gt_tc[np.newaxis, :, :], (n, 1, 1))  # (n, m, 2)
         dist_mat = np.linalg.norm(pred_tc_tile - gt_tc_tile, axis=2)  # (n, m)
         xy_text_assign = np.argmin(dist_mat, axis=1) + 1  # (n,)
@@ -169,26 +180,47 @@ class SASTPostProcess(object):
         """
         Estimate sample points number.
         """
-        eh = (np.linalg.norm(quad[0] - quad[3]) + np.linalg.norm(quad[1] - quad[2])) / 2.0
-        ew = (np.linalg.norm(quad[0] - quad[1]) + np.linalg.norm(quad[2] - quad[3])) / 2.0
+        eh = (np.linalg.norm(quad[0] - quad[3]) +
+              np.linalg.norm(quad[1] - quad[2])) / 2.0
+        ew = (np.linalg.norm(quad[0] - quad[1]) +
+              np.linalg.norm(quad[2] - quad[3])) / 2.0
 
         dense_sample_pts_num = max(2, int(ew))
-        dense_xy_center_line = xy_text[np.linspace(0, xy_text.shape[0] - 1, dense_sample_pts_num,
-                                                endpoint=True, dtype=np.float32).astype(np.int32)]
-
-        dense_xy_center_line_diff = dense_xy_center_line[1:] - dense_xy_center_line[:-1]
-        estimate_arc_len = np.sum(np.linalg.norm(dense_xy_center_line_diff, axis=1))
+        dense_xy_center_line = xy_text[np.linspace(
+            0,
+            xy_text.shape[0] - 1,
+            dense_sample_pts_num,
+            endpoint=True,
+            dtype=np.float32).astype(np.int32)]
+
+        dense_xy_center_line_diff = dense_xy_center_line[
+            1:] - dense_xy_center_line[:-1]
+        estimate_arc_len = np.sum(
+            np.linalg.norm(
+                dense_xy_center_line_diff, axis=1))
 
         sample_pts_num = max(2, int(estimate_arc_len / eh))
         return sample_pts_num
 
-    def detect_sast(self, tcl_map, tvo_map, tbo_map, tco_map, ratio_w, ratio_h, src_w, src_h, 
-                shrink_ratio_of_width=0.3, tcl_map_thresh=0.5, offset_expand=1.0, out_strid=4.0):
+    def detect_sast(self,
+                    tcl_map,
+                    tvo_map,
+                    tbo_map,
+                    tco_map,
+                    ratio_w,
+                    ratio_h,
+                    src_w,
+                    src_h,
+                    shrink_ratio_of_width=0.3,
+                    tcl_map_thresh=0.5,
+                    offset_expand=1.0,
+                    out_strid=4.0):
         """
         first resize the tcl_map, tvo_map and tbo_map to the input_size, then restore the polys
         """
         # restore quad
-        scores, quads, xy_text = self.restore_quad(tcl_map, tcl_map_thresh, tvo_map)
+        scores, quads, xy_text = self.restore_quad(tcl_map, tcl_map_thresh,
+                                                   tvo_map)
         dets = np.hstack((quads, scores)).astype(np.float32, copy=False)
         dets = self.nms(dets)
         if dets.shape[0] == 0:
@@ -202,7 +234,8 @@ class SASTPostProcess(object):
 
         # instance segmentation
         # instance_count, instance_label_map = cv2.connectedComponents(tcl_map.astype(np.uint8), connectivity=8)
-        instance_count, instance_label_map = self.cluster_by_quads_tco(tcl_map, tcl_map_thresh, quads, tco_map)
+        instance_count, instance_label_map = self.cluster_by_quads_tco(
+            tcl_map, tcl_map_thresh, quads, tco_map)
 
         # restore single poly with tcl instance.
         poly_list = []
@@ -214,8 +247,8 @@ class SASTPostProcess(object):
                 continue
 
             #
-            len1 = float(np.linalg.norm(quad[0] -quad[1]))
-            len2 = float(np.linalg.norm(quad[1] -quad[2]))
+            len1 = float(np.linalg.norm(quad[0] - quad[1]))
+            len2 = float(np.linalg.norm(quad[1] - quad[2]))
             min_len = min(len1, len2)
             if min_len < 3:
                 continue
@@ -231,9 +264,11 @@ class SASTPostProcess(object):
                 continue
 
             # sort xy_text
-            left_center_pt = np.array([[(quad[0, 0] + quad[-1, 0]) / 2.0,
+            left_center_pt = np.array(
+                [[(quad[0, 0] + quad[-1, 0]) / 2.0,
                   (quad[0, 1] + quad[-1, 1]) / 2.0]])  # (1, 2)
-            right_center_pt = np.array([[(quad[1, 0] + quad[2, 0]) / 2.0,
+            right_center_pt = np.array(
+                [[(quad[1, 0] + quad[2, 0]) / 2.0,
                   (quad[1, 1] + quad[2, 1]) / 2.0]])  # (1, 2)
             proj_unit_vec = (right_center_pt - left_center_pt) / \
                             (np.linalg.norm(right_center_pt - left_center_pt) + 1e-6)
@@ -245,28 +280,40 @@ class SASTPostProcess(object):
                 sample_pts_num = self.estimate_sample_pts_num(quad, xy_text)
             else:
                 sample_pts_num = self.sample_pts_num
-            xy_center_line = xy_text[np.linspace(0, xy_text.shape[0] - 1, sample_pts_num,
-                                                endpoint=True, dtype=np.float32).astype(np.int32)]
+            xy_center_line = xy_text[np.linspace(
+                0,
+                xy_text.shape[0] - 1,
+                sample_pts_num,
+                endpoint=True,
+                dtype=np.float32).astype(np.int32)]
 
             point_pair_list = []
             for x, y in xy_center_line:
                 # get corresponding offset
                 offset = tbo_map[y, x, :].reshape(2, 2)
                 if offset_expand != 1.0:
-                    offset_length = np.linalg.norm(offset, axis=1, keepdims=True)
-                    expand_length = np.clip(offset_length * (offset_expand - 1), a_min=0.5, a_max=3.0)
+                    offset_length = np.linalg.norm(
+                        offset, axis=1, keepdims=True)
+                    expand_length = np.clip(
+                        offset_length * (offset_expand - 1),
+                        a_min=0.5,
+                        a_max=3.0)
                     offset_detal = offset / offset_length * expand_length
                     offset = offset + offset_detal
                     # original point
                 ori_yx = np.array([y, x], dtype=np.float32)
-                point_pair = (ori_yx +  offset)[:, ::-1]* out_strid / np.array([ratio_w, ratio_h]).reshape(-1, 2) 
+                point_pair = (ori_yx + offset)[:, ::-1] * out_strid / np.array(
+                    [ratio_w, ratio_h]).reshape(-1, 2)
                 point_pair_list.append(point_pair)
 
             # ndarry: (x, 2), expand poly along width
             detected_poly = self.point_pair2poly(point_pair_list)
-            detected_poly = self.expand_poly_along_width(detected_poly, shrink_ratio_of_width)
-            detected_poly[:, 0] = np.clip(detected_poly[:, 0], a_min=0, a_max=src_w)
-            detected_poly[:, 1] = np.clip(detected_poly[:, 1], a_min=0, a_max=src_h)
+            detected_poly = self.expand_poly_along_width(detected_poly,
+                                                         shrink_ratio_of_width)
+            detected_poly[:, 0] = np.clip(
+                detected_poly[:, 0], a_min=0, a_max=src_w)
+            detected_poly[:, 1] = np.clip(
+                detected_poly[:, 1], a_min=0, a_max=src_h)
             poly_list.append(detected_poly)
 
         return poly_list
@@ -285,16 +332,24 @@ class SASTPostProcess(object):
         img_num = len(shape_list)
         poly_lists = []
         for ino in range(img_num):
-            p_score = score_list[ino].transpose((1,2,0))
-            p_border = border_list[ino].transpose((1,2,0))
-            p_tvo = tvo_list[ino].transpose((1,2,0))
-            p_tco = tco_list[ino].transpose((1,2,0))
+            p_score = score_list[ino].transpose((1, 2, 0))
+            p_border = border_list[ino].transpose((1, 2, 0))
+            p_tvo = tvo_list[ino].transpose((1, 2, 0))
+            p_tco = tco_list[ino].transpose((1, 2, 0))
             src_h, src_w, ratio_h, ratio_w = shape_list[ino]
 
-            poly_list = self.detect_sast(p_score, p_tvo, p_border, p_tco, ratio_w, ratio_h, src_w, src_h, 
+            poly_list = self.detect_sast(
+                p_score,
+                p_tvo,
+                p_border,
+                p_tco,
+                ratio_w,
+                ratio_h,
+                src_w,
+                src_h,
                 shrink_ratio_of_width=self.shrink_ratio_of_width,
-                                         tcl_map_thresh=self.tcl_map_thresh, offset_expand=self.expand_scale)
+                tcl_map_thresh=self.tcl_map_thresh,
+                offset_expand=self.expand_scale)
             poly_lists.append({'points': np.array(poly_list)})
 
         return poly_lists
-

ppocr/utils/e2e_metric/polygon_fast.py

+import numpy as np
+from shapely.geometry import Polygon
+#import Polygon
+"""
+:param det_x: [1, N] Xs of detection's vertices 
+:param det_y: [1, N] Ys of detection's vertices
+:param gt_x: [1, N] Xs of groundtruth's vertices
+:param gt_y: [1, N] Ys of groundtruth's vertices
+
+##############
+All the calculation of 'AREA' in this script is handled by:
+1) First generating a binary mask with the polygon area filled up with 1's
+2) Summing up all the 1's
+"""
+
+
+def area(x, y):
+    polygon = Polygon(np.stack([x, y], axis=1))
+    return float(polygon.area)
+
+
+def approx_area_of_intersection(det_x, det_y, gt_x, gt_y):
+    """
+    This helper determine if both polygons are intersecting with each others with an approximation method.
+    Area of intersection represented by the minimum bounding rectangular [xmin, ymin, xmax, ymax]
+    """
+    det_ymax = np.max(det_y)
+    det_xmax = np.max(det_x)
+    det_ymin = np.min(det_y)
+    det_xmin = np.min(det_x)
+
+    gt_ymax = np.max(gt_y)
+    gt_xmax = np.max(gt_x)
+    gt_ymin = np.min(gt_y)
+    gt_xmin = np.min(gt_x)
+
+    all_min_ymax = np.minimum(det_ymax, gt_ymax)
+    all_max_ymin = np.maximum(det_ymin, gt_ymin)
+
+    intersect_heights = np.maximum(0.0, (all_min_ymax - all_max_ymin))
+
+    all_min_xmax = np.minimum(det_xmax, gt_xmax)
+    all_max_xmin = np.maximum(det_xmin, gt_xmin)
+    intersect_widths = np.maximum(0.0, (all_min_xmax - all_max_xmin))
+
+    return intersect_heights * intersect_widths
+
+
+def area_of_intersection(det_x, det_y, gt_x, gt_y):
+    p1 = Polygon(np.stack([det_x, det_y], axis=1)).buffer(0)
+    p2 = Polygon(np.stack([gt_x, gt_y], axis=1)).buffer(0)
+    return float(p1.intersection(p2).area)
+
+
+def area_of_union(det_x, det_y, gt_x, gt_y):
+    p1 = Polygon(np.stack([det_x, det_y], axis=1)).buffer(0)
+    p2 = Polygon(np.stack([gt_x, gt_y], axis=1)).buffer(0)
+    return float(p1.union(p2).area)
+
+
+def iou(det_x, det_y, gt_x, gt_y):
+    return area_of_intersection(det_x, det_y, gt_x, gt_y) / (
+        area_of_union(det_x, det_y, gt_x, gt_y) + 1.0)
+
+
+def iod(det_x, det_y, gt_x, gt_y):
+    """
+    This helper determine the fraction of intersection area over detection area
+    """
+    return area_of_intersection(det_x, det_y, gt_x, gt_y) / (
+        area(det_x, det_y) + 1.0)

ppocr/utils/e2e_utils/ski_thin.py

+"""
+Algorithms for computing the skeleton of a binary image
+"""
+
+import numpy as np
+from scipy import ndimage as ndi
+
+G123_LUT = np.array(
+    [
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0,
+        0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0,
+        1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0,
+        1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0
+    ],
+    dtype=np.bool)
+
+G123P_LUT = np.array(
+    [
+        0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1,
+        0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
+        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+    ],
+    dtype=np.bool)
+
+
+def thin(image, max_iter=None):
+    """
+    Perform morphological thinning of a binary image.
+    Parameters
+    ----------
+    image : binary (M, N) ndarray
+        The image to be thinned.
+    max_iter : int, number of iterations, optional
+        Regardless of the value of this parameter, the thinned image
+        is returned immediately if an iteration produces no change.
+        If this parameter is specified it thus sets an upper bound on
+        the number of iterations performed.
+    Returns
+    -------
+    out : ndarray of bool
+        Thinned image.
+    See also
+    --------
+    skeletonize, medial_axis
+    Notes
+    -----
+    This algorithm [1]_ works by making multiple passes over the image,
+    removing pixels matching a set of criteria designed to thin
+    connected regions while preserving eight-connected components and
+    2 x 2 squares [2]_. In each of the two sub-iterations the algorithm
+    correlates the intermediate skeleton image with a neighborhood mask,
+    then looks up each neighborhood in a lookup table indicating whether
+    the central pixel should be deleted in that sub-iteration.
+    References
+    ----------
+    .. [1] Z. Guo and R. W. Hall, "Parallel thinning with
+           two-subiteration algorithms," Comm. ACM, vol. 32, no. 3,
+           pp. 359-373, 1989. :DOI:`10.1145/62065.62074`
+    .. [2] Lam, L., Seong-Whan Lee, and Ching Y. Suen, "Thinning
+           Methodologies-A Comprehensive Survey," IEEE Transactions on
+           Pattern Analysis and Machine Intelligence, Vol 14, No. 9,
+           p. 879, 1992. :DOI:`10.1109/34.161346`
+    Examples
+    --------
+    >>> square = np.zeros((7, 7), dtype=np.uint8)
+    >>> square[1:-1, 2:-2] = 1
+    >>> square[0, 1] =  1
+    >>> square
+    array([[0, 1, 0, 0, 0, 0, 0],
+           [0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 1, 1, 1, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
+    >>> skel = thin(square)
+    >>> skel.astype(np.uint8)
+    array([[0, 1, 0, 0, 0, 0, 0],
+           [0, 0, 1, 0, 0, 0, 0],
+           [0, 0, 0, 1, 0, 0, 0],
+           [0, 0, 0, 1, 0, 0, 0],
+           [0, 0, 0, 1, 0, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0],
+           [0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
+    """
+    # convert image to uint8 with values in {0, 1}
+    skel = np.asanyarray(image, dtype=bool).astype(np.uint8)
+
+    # neighborhood mask
+    mask = np.array([[8, 4, 2], [16, 0, 1], [32, 64, 128]], dtype=np.uint8)
+
+    # iterate until convergence, up to the iteration limit
+    max_iter = max_iter or np.inf
+    n_iter = 0
+    n_pts_old, n_pts_new = np.inf, np.sum(skel)
+    while n_pts_old != n_pts_new and n_iter < max_iter:
+        n_pts_old = n_pts_new
+
+        # perform the two "subiterations" described in the paper
+        for lut in [G123_LUT, G123P_LUT]:
+            # correlate image with neighborhood mask
+            N = ndi.correlate(skel, mask, mode='constant')
+            # take deletion decision from this subiteration's LUT
+            D = np.take(lut, N)
+            # perform deletion
+            skel[D] = 0
+
+        n_pts_new = np.sum(skel)  # count points after thinning
+        n_iter += 1
+
+    return skel.astype(np.bool)

tools/program.py

@@ -44,6 +44,7 @@ class ArgsParser(ArgumentParser):
 
     def parse_args(self, argv=None):
         args = super(ArgsParser, self).parse_args(argv)
+        args.config = '/Users/hongyongjie/project/PaddleOCR/configs/e2e/e2e_r50_vd_pg.yml'
         assert args.config is not None, \
             "Please specify --config=configure_file_path."
         args.opt = self._parse_opt(args.opt)
@@ -374,7 +375,8 @@ def preprocess(is_train=False):
 
     alg = config['Architecture']['algorithm']
     assert alg in [
-        'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS'
+        'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
+        'CLS', 'PG'
     ]
 
     device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'