Merge pull request #4270 from LDOUBLEV/sdmgr

[SDMGR]add kie

configs/kie/kie_unet_sdmgr.yml

@@ -0,0 +1,111 @@
+Global:
+  use_gpu: True
+  epoch_num: 300
+  log_smooth_window: 20
+  print_batch_step: 50
+  save_model_dir: ./output/kie_5/
+  save_epoch_step: 50
+  # evaluation is run every 5000 iterations after the 4000th iteration
+  eval_batch_step: [ 0, 80 ]
+  # 1. If pretrained_model is saved in static mode, such as classification pretrained model
+  #    from static branch, load_static_weights must be set as True.
+  # 2. If you want to finetune the pretrained models we provide in the docs,
+  #    you should set load_static_weights as False.
+  load_static_weights: False
+  cal_metric_during_train: False
+  pretrained_model: ./output/kie_4/best_accuracy
+  checkpoints:
+  save_inference_dir:
+  use_visualdl: False
+  class_path: ./train_data/wildreceipt/class_list.txt
+  infer_img: ./train_data/wildreceipt/1.txt
+  save_res_path: ./output/sdmgr_kie/predicts_kie.txt
+  img_scale: [ 1024, 512 ]
+
+Architecture:
+  model_type: kie
+  algorithm: SDMGR
+  Transform:
+  Backbone:
+    name: Kie_backbone
+  Head:
+    name: SDMGRHead
+
+Loss:
+  name: SDMGRLoss
+
+Optimizer:
+  name: Adam
+  beta1: 0.9
+  beta2: 0.999
+  lr:
+    name: Piecewise
+    learning_rate: 0.001
+    decay_epochs: [ 60, 80, 100]
+    values: [ 0.001, 0.0001, 0.00001]
+    warmup_epoch: 2
+  regularizer:
+    name: 'L2'
+    factor: 0.00005
+
+PostProcess:
+  name: None
+
+Metric:
+  name: KIEMetric
+  main_indicator: hmean
+
+Train:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/wildreceipt/
+    label_file_list: [ './train_data/wildreceipt/wildreceipt_train.txt' ]
+    ratio_list: [ 1.0 ]
+    transforms:
+      - DecodeImage: # load image
+          img_mode: RGB
+          channel_first: False
+      - NormalizeImage:
+          scale: 1
+          mean: [ 123.675, 116.28, 103.53 ]
+          std: [ 58.395, 57.12, 57.375 ]
+          order: 'hwc'
+      - KieLabelEncode: # Class handling label
+          character_dict_path: ./train_data/wildreceipt/dict.txt
+      - KieResize:
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'relations', 'texts', 'points', 'labels', 'tag', 'shape'] # dataloader will return list in this order
+  loader:
+    shuffle: True
+    drop_last: False
+    batch_size_per_card: 4
+    num_workers: 4
+
+Eval:
+  dataset:
+    name: SimpleDataSet
+    data_dir: ./train_data/wildreceipt
+    label_file_list:
+      - ./train_data/wildreceipt/wildreceipt_test.txt
+      # - /paddle/data/PaddleOCR/train_data/wildreceipt/1.txt
+    transforms:
+      - DecodeImage: # load image
+          img_mode: RGB
+          channel_first: False
+      - KieLabelEncode: # Class handling label
+          character_dict_path: ./train_data/wildreceipt/dict.txt
+      - KieResize:
+      - NormalizeImage:
+          scale: 1
+          mean: [ 123.675, 116.28, 103.53 ]
+          std: [ 58.395, 57.12, 57.375 ]
+          order: 'hwc'
+      - ToCHWImage:
+      - KeepKeys:
+          keep_keys: [ 'image', 'relations', 'texts', 'points', 'labels', 'tag', 'ori_image', 'ori_boxes', 'shape']
+  loader:
+    shuffle: False
+    drop_last: False
+    batch_size_per_card: 1 # must be 1
+    num_workers: 4

doc/doc_ch/algorithm_overview.md

@@ -21,6 +21,7 @@ PaddleOCR开源的文本检测算法列表：
 - [x]  EAST([paper](https://arxiv.org/abs/1704.03155))[1]
 - [x]  SAST([paper](https://arxiv.org/abs/1908.05498))[4]
 - [x]  PSENet([paper](https://arxiv.org/abs/1903.12473v2)）
+- [x]  SDMGR([paper](https://arxiv.org/pdf/2103.14470.pdf))
 
 在ICDAR2015文本检测公开数据集上，算法效果如下：
 |模型|骨干网络|precision|recall|Hmean|下载链接|
@@ -32,6 +33,7 @@ PaddleOCR开源的文本检测算法列表：
 |SAST|ResNet50_vd|91.39%|83.77%|87.42%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_r50_vd_sast_icdar15_v2.0_train.tar)|
 |PSE|ResNet50_vd|85.81%|79.53%|82.55%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_r50_vd_pse_v2.0_train.tar)|
 |PSE|MobileNetV3|82.20%|70.48%|75.89%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/en_det/det_mv3_pse_v2.0_train.tar)|
+|SDMGR|VGG16|-|-|87.11%|[训练模型](https://paddleocr.bj.bcebos.com/dygraph_v2.1/kie/kie_vgg16.tar)|
 
 在Total-text文本检测公开数据集上，算法效果如下：
 

ppocr/data/imaug/label_ops.py

@@ -19,6 +19,7 @@ from __future__ import unicode_literals
 
 import numpy as np
 import string
+from shapely.geometry import LineString, Point, Polygon
 import json
 
 from ppocr.utils.logging import get_logger
@@ -286,6 +287,168 @@ class E2ELabelEncodeTrain(object):
         return data
 
 
+class KieLabelEncode(object):
+    def __init__(self, character_dict_path, norm=10, directed=False, **kwargs):
+        super(KieLabelEncode, self).__init__()
+        self.dict = dict({'': 0})
+        with open(character_dict_path, 'r') as fr:
+            idx = 1
+            for line in fr:
+                char = line.strip()
+                self.dict[char] = idx
+                idx += 1
+        self.norm = norm
+        self.directed = directed
+
+    def compute_relation(self, boxes):
+        """Compute relation between every two boxes."""
+        x1s, y1s = boxes[:, 0:1], boxes[:, 1:2]
+        x2s, y2s = boxes[:, 4:5], boxes[:, 5:6]
+        ws, hs = x2s - x1s + 1, np.maximum(y2s - y1s + 1, 1)
+        dxs = (x1s[:, 0][None] - x1s) / self.norm
+        dys = (y1s[:, 0][None] - y1s) / self.norm
+        xhhs, xwhs = hs[:, 0][None] / hs, ws[:, 0][None] / hs
+        whs = ws / hs + np.zeros_like(xhhs)
+        relations = np.stack([dxs, dys, whs, xhhs, xwhs], -1)
+        bboxes = np.concatenate([x1s, y1s, x2s, y2s], -1).astype(np.float32)
+        return relations, bboxes
+
+    def pad_text_indices(self, text_inds):
+        """Pad text index to same length."""
+        max_len = 300
+        recoder_len = max([len(text_ind) for text_ind in text_inds])
+        padded_text_inds = -np.ones((len(text_inds), max_len), np.int32)
+        for idx, text_ind in enumerate(text_inds):
+            padded_text_inds[idx, :len(text_ind)] = np.array(text_ind)
+        return padded_text_inds, recoder_len
+
+    def list_to_numpy(self, ann_infos):
+        """Convert bboxes, relations, texts and labels to ndarray."""
+        boxes, text_inds = ann_infos['points'], ann_infos['text_inds']
+        boxes = np.array(boxes, np.int32)
+        relations, bboxes = self.compute_relation(boxes)
+
+        labels = ann_infos.get('labels', None)
+        if labels is not None:
+            labels = np.array(labels, np.int32)
+            edges = ann_infos.get('edges', None)
+            if edges is not None:
+                labels = labels[:, None]
+                edges = np.array(edges)
+                edges = (edges[:, None] == edges[None, :]).astype(np.int32)
+                if self.directed:
+                    edges = (edges & labels == 1).astype(np.int32)
+                np.fill_diagonal(edges, -1)
+                labels = np.concatenate([labels, edges], -1)
+        padded_text_inds, recoder_len = self.pad_text_indices(text_inds)
+        max_num = 300
+        temp_bboxes = np.zeros([max_num, 4])
+        h, _ = bboxes.shape
+        temp_bboxes[:h, :h] = bboxes
+
+        temp_relations = np.zeros([max_num, max_num, 5])
+        temp_relations[:h, :h, :] = relations
+
+        temp_padded_text_inds = np.zeros([max_num, max_num])
+        temp_padded_text_inds[:h, :] = padded_text_inds
+
+        temp_labels = np.zeros([max_num, max_num])
+        temp_labels[:h, :h + 1] = labels
+
+        tag = np.array([h, recoder_len])
+        return dict(
+            image=ann_infos['image'],
+            points=temp_bboxes,
+            relations=temp_relations,
+            texts=temp_padded_text_inds,
+            labels=temp_labels,
+            tag=tag)
+
+    def convert_canonical(self, points_x, points_y):
+
+        assert len(points_x) == 4
+        assert len(points_y) == 4
+
+        points = [Point(points_x[i], points_y[i]) for i in range(4)]
+
+        polygon = Polygon([(p.x, p.y) for p in points])
+        min_x, min_y, _, _ = polygon.bounds
+        points_to_lefttop = [
+            LineString([points[i], Point(min_x, min_y)]) for i in range(4)
+        ]
+        distances = np.array([line.length for line in points_to_lefttop])
+        sort_dist_idx = np.argsort(distances)
+        lefttop_idx = sort_dist_idx[0]
+
+        if lefttop_idx == 0:
+            point_orders = [0, 1, 2, 3]
+        elif lefttop_idx == 1:
+            point_orders = [1, 2, 3, 0]
+        elif lefttop_idx == 2:
+            point_orders = [2, 3, 0, 1]
+        else:
+            point_orders = [3, 0, 1, 2]
+
+        sorted_points_x = [points_x[i] for i in point_orders]
+        sorted_points_y = [points_y[j] for j in point_orders]
+
+        return sorted_points_x, sorted_points_y
+
+    def sort_vertex(self, points_x, points_y):
+
+        assert len(points_x) == 4
+        assert len(points_y) == 4
+
+        x = np.array(points_x)
+        y = np.array(points_y)
+        center_x = np.sum(x) * 0.25
+        center_y = np.sum(y) * 0.25
+
+        x_arr = np.array(x - center_x)
+        y_arr = np.array(y - center_y)
+
+        angle = np.arctan2(y_arr, x_arr) * 180.0 / np.pi
+        sort_idx = np.argsort(angle)
+
+        sorted_points_x, sorted_points_y = [], []
+        for i in range(4):
+            sorted_points_x.append(points_x[sort_idx[i]])
+            sorted_points_y.append(points_y[sort_idx[i]])
+
+        return self.convert_canonical(sorted_points_x, sorted_points_y)
+
+    def __call__(self, data):
+        import json
+        label = data['label']
+        annotations = json.loads(label)
+        boxes, texts, text_inds, labels, edges = [], [], [], [], []
+        for ann in annotations:
+            box = ann['points']
+            x_list = [box[i][0] for i in range(4)]
+            y_list = [box[i][1] for i in range(4)]
+            sorted_x_list, sorted_y_list = self.sort_vertex(x_list, y_list)
+            sorted_box = []
+            for x, y in zip(sorted_x_list, sorted_y_list):
+                sorted_box.append(x)
+                sorted_box.append(y)
+            boxes.append(sorted_box)
+            text = ann['transcription']
+            texts.append(ann['transcription'])
+            text_ind = [self.dict[c] for c in text if c in self.dict]
+            text_inds.append(text_ind)
+            labels.append(ann['label'])
+            edges.append(ann.get('edge', 0))
+        ann_infos = dict(
+            image=data['image'],
+            points=boxes,
+            texts=texts,
+            text_inds=text_inds,
+            edges=edges,
+            labels=labels)
+
+        return self.list_to_numpy(ann_infos)
+
+
 class AttnLabelEncode(BaseRecLabelEncode):
     """ Convert between text-label and text-index """
 

ppocr/data/imaug/operators.py

@@ -111,7 +111,6 @@ class NormalizeImage(object):
         from PIL import Image
         if isinstance(img, Image.Image):
             img = np.array(img)
-
         assert isinstance(img,
                           np.ndarray), "invalid input 'img' in NormalizeImage"
         data['image'] = (
@@ -367,3 +366,53 @@ class E2EResizeForTest(object):
         ratio_w = resize_w / float(w)
 
         return im, (ratio_h, ratio_w)
+
+
+class KieResize(object):
+    def __init__(self, **kwargs):
+        super(KieResize, self).__init__()
+        self.max_side, self.min_side = kwargs['img_scale'][0], kwargs[
+            'img_scale'][1]
+
+    def __call__(self, data):
+        img = data['image']
+        points = data['points']
+        src_h, src_w, _ = img.shape
+        im_resized, scale_factor, [ratio_h, ratio_w
+                                   ], [new_h, new_w] = self.resize_image(img)
+        resize_points = self.resize_boxes(img, points, scale_factor)
+        data['ori_image'] = img
+        data['ori_boxes'] = points
+        data['points'] = resize_points
+        data['image'] = im_resized
+        data['shape'] = np.array([new_h, new_w])
+        return data
+
+    def resize_image(self, img):
+        norm_img = np.zeros([1024, 1024, 3], dtype='float32')
+        scale = [512, 1024]
+        h, w = img.shape[:2]
+        max_long_edge = max(scale)
+        max_short_edge = min(scale)
+        scale_factor = min(max_long_edge / max(h, w),
+                           max_short_edge / min(h, w))
+        resize_w, resize_h = int(w * float(scale_factor) + 0.5), int(h * float(
+            scale_factor) + 0.5)
+        max_stride = 32
+        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(img, (resize_w, resize_h))
+        new_h, new_w = im.shape[:2]
+        w_scale = new_w / w
+        h_scale = new_h / h
+        scale_factor = np.array(
+            [w_scale, h_scale, w_scale, h_scale], dtype=np.float32)
+        norm_img[:new_h, :new_w, :] = im
+        return norm_img, scale_factor, [h_scale, w_scale], [new_h, new_w]
+
+    def resize_boxes(self, im, points, scale_factor):
+        points = points * scale_factor
+        img_shape = im.shape[:2]
+        points[:, 0::2] = np.clip(points[:, 0::2], 0, img_shape[1])
+        points[:, 1::2] = np.clip(points[:, 1::2], 0, img_shape[0])
+        return points

ppocr/losses/__init__.py

@@ -35,6 +35,7 @@ from .cls_loss import ClsLoss
 
 # e2e loss
 from .e2e_pg_loss import PGLoss
+from .kie_sdmgr_loss import SDMGRLoss
 
 # basic loss function
 from .basic_loss import DistanceLoss
@@ -50,7 +51,7 @@ def build_loss(config):
     support_dict = [
         'DBLoss', 'PSELoss', 'EASTLoss', 'SASTLoss', 'CTCLoss', 'ClsLoss',
         'AttentionLoss', 'SRNLoss', 'PGLoss', 'CombinedLoss', 'NRTRLoss',
-        'TableAttentionLoss', 'SARLoss', 'AsterLoss'
+        'TableAttentionLoss', 'SARLoss', 'AsterLoss', 'SDMGRLoss'
     ]
     config = copy.deepcopy(config)
     module_name = config.pop('name')

ppocr/losses/kie_sdmgr_loss.py

@@ -0,0 +1,113 @@
+# copyright (c) 2021 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
+
+
+class SDMGRLoss(nn.Layer):
+    def __init__(self, node_weight=1.0, edge_weight=1.0, ignore=0):
+        super().__init__()
+        self.loss_node = nn.CrossEntropyLoss(ignore_index=ignore)
+        self.loss_edge = nn.CrossEntropyLoss(ignore_index=-1)
+        self.node_weight = node_weight
+        self.edge_weight = edge_weight
+        self.ignore = ignore
+
+    def pre_process(self, gts, tag):
+        gts, tag = gts.numpy(), tag.numpy().tolist()
+        temp_gts = []
+        batch = len(tag)
+        for i in range(batch):
+            num, recoder_len = tag[i][0], tag[i][1]
+            temp_gts.append(
+                paddle.to_tensor(
+                    gts[i, :num, :num + 1], dtype='int64'))
+        return temp_gts
+
+    def accuracy(self, pred, target, topk=1, thresh=None):
+        """Calculate accuracy according to the prediction and target.
+
+        Args:
+            pred (torch.Tensor): The model prediction, shape (N, num_class)
+            target (torch.Tensor): The target of each prediction, shape (N, )
+            topk (int | tuple[int], optional): If the predictions in ``topk``
+                matches the target, the predictions will be regarded as
+                correct ones. Defaults to 1.
+            thresh (float, optional): If not None, predictions with scores under
+                this threshold are considered incorrect. Default to None.
+
+        Returns:
+            float | tuple[float]: If the input ``topk`` is a single integer,
+                the function will return a single float as accuracy. If
+                ``topk`` is a tuple containing multiple integers, the
+                function will return a tuple containing accuracies of
+                each ``topk`` number.
+        """
+        assert isinstance(topk, (int, tuple))
+        if isinstance(topk, int):
+            topk = (topk, )
+            return_single = True
+        else:
+            return_single = False
+
+        maxk = max(topk)
+        if pred.shape[0] == 0:
+            accu = [pred.new_tensor(0.) for i in range(len(topk))]
+            return accu[0] if return_single else accu
+        pred_value, pred_label = paddle.topk(pred, maxk, axis=1)
+        pred_label = pred_label.transpose(
+            [1, 0])  # transpose to shape (maxk, N)
+        correct = paddle.equal(pred_label,
+                               (target.reshape([1, -1]).expand_as(pred_label)))
+        res = []
+        for k in topk:
+            correct_k = paddle.sum(correct[:k].reshape([-1]).astype('float32'),
+                                   axis=0,
+                                   keepdim=True)
+            res.append(
+                paddle.multiply(correct_k,
+                                paddle.to_tensor(100.0 / pred.shape[0])))
+        return res[0] if return_single else res
+
+    def forward(self, pred, batch):
+        node_preds, edge_preds = pred
+        gts, tag = batch[4], batch[5]
+        gts = self.pre_process(gts, tag)
+        node_gts, edge_gts = [], []
+        for gt in gts:
+            node_gts.append(gt[:, 0])
+            edge_gts.append(gt[:, 1:].reshape([-1]))
+        node_gts = paddle.concat(node_gts)
+        edge_gts = paddle.concat(edge_gts)
+
+        node_valids = paddle.nonzero(node_gts != self.ignore).reshape([-1])
+        edge_valids = paddle.nonzero(edge_gts != -1).reshape([-1])
+        loss_node = self.loss_node(node_preds, node_gts)
+        loss_edge = self.loss_edge(edge_preds, edge_gts)
+        loss = self.node_weight * loss_node + self.edge_weight * loss_edge
+        return dict(
+            loss=loss,
+            loss_node=loss_node,
+            loss_edge=loss_edge,
+            acc_node=self.accuracy(
+                paddle.gather(node_preds, node_valids),
+                paddle.gather(node_gts, node_valids)),
+            acc_edge=self.accuracy(
+                paddle.gather(edge_preds, edge_valids),
+                paddle.gather(edge_gts, edge_valids)))

ppocr/metrics/__init__.py

@@ -27,10 +27,13 @@ from .cls_metric import ClsMetric
 from .e2e_metric import E2EMetric
 from .distillation_metric import DistillationMetric
 from .table_metric import TableMetric
+from .kie_metric import KIEMetric
+
 
 def build_metric(config):
     support_dict = [
-        "DetMetric", "RecMetric", "ClsMetric", "E2EMetric", "DistillationMetric", "TableMetric"
+        "DetMetric", "RecMetric", "ClsMetric", "E2EMetric",
+        "DistillationMetric", "TableMetric", 'KIEMetric'
     ]
 
     config = copy.deepcopy(config)

ppocr/metrics/kie_metric.py

@@ -0,0 +1,70 @@
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import paddle
+
+__all__ = ['KIEMetric']
+
+
+class KIEMetric(object):
+    def __init__(self, main_indicator='hmean', **kwargs):
+        self.main_indicator = main_indicator
+        self.reset()
+        self.node = []
+        self.gt = []
+
+    def __call__(self, preds, batch, **kwargs):
+        nodes, _ = preds
+        gts, tag = batch[4].squeeze(0), batch[5].tolist()[0]
+        gts = gts[:tag[0], :1].reshape([-1])
+        self.node.append(nodes.numpy())
+        self.gt.append(gts)
+        # result = self.compute_f1_score(nodes, gts)
+        # self.results.append(result)
+
+    def compute_f1_score(self, preds, gts):
+        ignores = [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 25]
+        C = preds.shape[1]
+        classes = np.array(sorted(set(range(C)) - set(ignores)))
+        hist = np.bincount(
+            (gts * C).astype('int64') + preds.argmax(1), minlength=C
+            **2).reshape([C, C]).astype('float32')
+        diag = np.diag(hist)
+        recalls = diag / hist.sum(1).clip(min=1)
+        precisions = diag / hist.sum(0).clip(min=1)
+        f1 = 2 * recalls * precisions / (recalls + precisions).clip(min=1e-8)
+        return f1[classes]
+
+    def combine_results(self, results):
+        node = np.concatenate(self.node, 0)
+        gts = np.concatenate(self.gt, 0)
+        results = self.compute_f1_score(node, gts)
+        data = {'hmean': results.mean()}
+        return data
+
+    def get_metric(self):
+
+        metircs = self.combine_results(self.results)
+        self.reset()
+        return metircs
+
+    def reset(self):
+        self.results = []  # clear results
+        self.node = []
+        self.gt = []

ppocr/modeling/backbones/__init__.py

@@ -35,7 +35,14 @@ def build_backbone(config, model_type):
         ]
     elif model_type == "e2e":
         from .e2e_resnet_vd_pg import ResNet
-        support_dict = ["ResNet"]
+        support_dict = ['ResNet']
+    elif model_type == 'kie':
+        from .kie_unet_sdmgr import Kie_backbone
+        support_dict = ['Kie_backbone']
+    elif model_type == "table":
+        from .table_resnet_vd import ResNet
+        from .table_mobilenet_v3 import MobileNetV3
+        support_dict = ["ResNet", "MobileNetV3"]
     else:
         raise NotImplementedError
 

ppocr/modeling/backbones/kie_unet_sdmgr.py

@@ -0,0 +1,186 @@
+# copyright (c) 2021 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 numpy as np
+import cv2
+
+__all__ = ["Kie_backbone"]
+
+
+class Encoder(nn.Layer):
+    def __init__(self, num_channels, num_filters):
+        super(Encoder, self).__init__()
+        self.conv1 = nn.Conv2D(
+            num_channels,
+            num_filters,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias_attr=False)
+        self.bn1 = nn.BatchNorm(num_filters, act='relu')
+
+        self.conv2 = nn.Conv2D(
+            num_filters,
+            num_filters,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias_attr=False)
+        self.bn2 = nn.BatchNorm(num_filters, act='relu')
+
+        self.pool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
+
+    def forward(self, inputs):
+        x = self.conv1(inputs)
+        x = self.bn1(x)
+        x = self.conv2(x)
+        x = self.bn2(x)
+        x_pooled = self.pool(x)
+        return x, x_pooled
+
+
+class Decoder(nn.Layer):
+    def __init__(self, num_channels, num_filters):
+        super(Decoder, self).__init__()
+
+        self.conv1 = nn.Conv2D(
+            num_channels,
+            num_filters,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias_attr=False)
+        self.bn1 = nn.BatchNorm(num_filters, act='relu')
+
+        self.conv2 = nn.Conv2D(
+            num_filters,
+            num_filters,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias_attr=False)
+        self.bn2 = nn.BatchNorm(num_filters, act='relu')
+
+        self.conv0 = nn.Conv2D(
+            num_channels,
+            num_filters,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias_attr=False)
+        self.bn0 = nn.BatchNorm(num_filters, act='relu')
+
+    def forward(self, inputs_prev, inputs):
+        x = self.conv0(inputs)
+        x = self.bn0(x)
+        x = paddle.nn.functional.interpolate(
+            x, scale_factor=2, mode='bilinear', align_corners=False)
+        x = paddle.concat([inputs_prev, x], axis=1)
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.conv2(x)
+        x = self.bn2(x)
+        return x
+
+
+class UNet(nn.Layer):
+    def __init__(self):
+        super(UNet, self).__init__()
+        self.down1 = Encoder(num_channels=3, num_filters=16)
+        self.down2 = Encoder(num_channels=16, num_filters=32)
+        self.down3 = Encoder(num_channels=32, num_filters=64)
+        self.down4 = Encoder(num_channels=64, num_filters=128)
+        self.down5 = Encoder(num_channels=128, num_filters=256)
+
+        self.up1 = Decoder(32, 16)
+        self.up2 = Decoder(64, 32)
+        self.up3 = Decoder(128, 64)
+        self.up4 = Decoder(256, 128)
+        self.out_channels = 16
+
+    def forward(self, inputs):
+        x1, _ = self.down1(inputs)
+        _, x2 = self.down2(x1)
+        _, x3 = self.down3(x2)
+        _, x4 = self.down4(x3)
+        _, x5 = self.down5(x4)
+
+        x = self.up4(x4, x5)
+        x = self.up3(x3, x)
+        x = self.up2(x2, x)
+        x = self.up1(x1, x)
+        return x
+
+
+class Kie_backbone(nn.Layer):
+    def __init__(self, in_channels, **kwargs):
+        super(Kie_backbone, self).__init__()
+        self.out_channels = 16
+        self.img_feat = UNet()
+        self.maxpool = nn.MaxPool2D(kernel_size=7)
+
+    def bbox2roi(self, bbox_list):
+        rois_list = []
+        rois_num = []
+        for img_id, bboxes in enumerate(bbox_list):
+            rois_num.append(bboxes.shape[0])
+            rois_list.append(bboxes)
+        rois = paddle.concat(rois_list, 0)
+        rois_num = paddle.to_tensor(rois_num, dtype='int32')
+        return rois, rois_num
+
+    def pre_process(self, img, relations, texts, gt_bboxes, tag, img_size):
+        img, relations, texts, gt_bboxes, tag, img_size = img.numpy(
+        ), relations.numpy(), texts.numpy(), gt_bboxes.numpy(), tag.numpy(
+        ).tolist(), img_size.numpy()
+        temp_relations, temp_texts, temp_gt_bboxes = [], [], []
+        h, w = int(np.max(img_size[:, 0])), int(np.max(img_size[:, 1]))
+        img = paddle.to_tensor(img[:, :, :h, :w])
+        batch = len(tag)
+        for i in range(batch):
+            num, recoder_len = tag[i][0], tag[i][1]
+            temp_relations.append(
+                paddle.to_tensor(
+                    relations[i, :num, :num, :], dtype='float32'))
+            temp_texts.append(
+                paddle.to_tensor(
+                    texts[i, :num, :recoder_len], dtype='float32'))
+            temp_gt_bboxes.append(
+                paddle.to_tensor(
+                    gt_bboxes[i, :num, ...], dtype='float32'))
+        return img, temp_relations, temp_texts, temp_gt_bboxes
+
+    def forward(self, inputs):
+        img = inputs[0]
+        relations, texts, gt_bboxes, tag, img_size = inputs[1], inputs[
+            2], inputs[3], inputs[5], inputs[-1]
+        img, relations, texts, gt_bboxes = self.pre_process(
+            img, relations, texts, gt_bboxes, tag, img_size)
+        x = self.img_feat(img)
+        boxes, rois_num = self.bbox2roi(gt_bboxes)
+        feats = paddle.fluid.layers.roi_align(
+            x,
+            boxes,
+            spatial_scale=1.0,
+            pooled_height=7,
+            pooled_width=7,
+            rois_num=rois_num)
+        feats = self.maxpool(feats).squeeze(-1).squeeze(-1)
+        return [relations, texts, feats]

ppocr/modeling/heads/__init__.py

@@ -33,14 +33,19 @@ def build_head(config):
 
     # cls head
     from .cls_head import ClsHead
+
+    #kie head
+    from .kie_sdmgr_head import SDMGRHead
+
+    from .table_att_head import TableAttentionHead
+
     support_dict = [
         'DBHead', 'PSEHead', 'EASTHead', 'SASTHead', 'CTCHead', 'ClsHead',
         'AttentionHead', 'SRNHead', 'PGHead', 'Transformer',
-        'TableAttentionHead', 'SARHead', 'AsterHead'
+        'TableAttentionHead', 'SARHead', 'AsterHead', 'SDMGRHead'
     ]
 
     #table head
-    from .table_att_head import TableAttentionHead
 
     module_name = config.pop('name')
     assert module_name in support_dict, Exception('head only support {}'.format(

ppocr/modeling/heads/kie_sdmgr_head.py

@@ -0,0 +1,206 @@
+# copyright (c) 2021 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 SDMGRHead(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 num_chars=92,
+                 visual_dim=16,
+                 fusion_dim=1024,
+                 node_input=32,
+                 node_embed=256,
+                 edge_input=5,
+                 edge_embed=256,
+                 num_gnn=2,
+                 num_classes=26,
+                 bidirectional=False):
+        super().__init__()
+
+        self.fusion = Block([visual_dim, node_embed], node_embed, fusion_dim)
+        self.node_embed = nn.Embedding(num_chars, node_input, 0)
+        hidden = node_embed // 2 if bidirectional else node_embed
+        self.rnn = nn.LSTM(
+            input_size=node_input, hidden_size=hidden, num_layers=1)
+        self.edge_embed = nn.Linear(edge_input, edge_embed)
+        self.gnn_layers = nn.LayerList(
+            [GNNLayer(node_embed, edge_embed) for _ in range(num_gnn)])
+        self.node_cls = nn.Linear(node_embed, num_classes)
+        self.edge_cls = nn.Linear(edge_embed, 2)
+
+    def forward(self, input, targets):
+        relations, texts, x = input
+        node_nums, char_nums = [], []
+        for text in texts:
+            node_nums.append(text.shape[0])
+            char_nums.append(paddle.sum((text > -1).astype(int), axis=-1))
+
+        max_num = max([char_num.max() for char_num in char_nums])
+        all_nodes = paddle.concat([
+            paddle.concat(
+                [text, paddle.zeros(
+                    (text.shape[0], max_num - text.shape[1]))], -1)
+            for text in texts
+        ])
+        temp = paddle.clip(all_nodes, min=0).astype(int)
+        embed_nodes = self.node_embed(temp)
+        rnn_nodes, _ = self.rnn(embed_nodes)
+
+        b, h, w = rnn_nodes.shape
+        nodes = paddle.zeros([b, w])
+        all_nums = paddle.concat(char_nums)
+        valid = paddle.nonzero((all_nums > 0).astype(int))
+        temp_all_nums = (
+            paddle.gather(all_nums, valid) - 1).unsqueeze(-1).unsqueeze(-1)
+        temp_all_nums = paddle.expand(temp_all_nums, [
+            temp_all_nums.shape[0], temp_all_nums.shape[1], rnn_nodes.shape[-1]
+        ])
+        temp_all_nodes = paddle.gather(rnn_nodes, valid)
+        N, C, A = temp_all_nodes.shape
+        one_hot = F.one_hot(
+            temp_all_nums[:, 0, :], num_classes=C).transpose([0, 2, 1])
+        one_hot = paddle.multiply(
+            temp_all_nodes, one_hot.astype("float32")).sum(axis=1, keepdim=True)
+        t = one_hot.expand([N, 1, A]).squeeze(1)
+        nodes = paddle.scatter(nodes, valid.squeeze(1), t)
+
+        if x is not None:
+            nodes = self.fusion([x, nodes])
+
+        all_edges = paddle.concat(
+            [rel.reshape([-1, rel.shape[-1]]) for rel in relations])
+        embed_edges = self.edge_embed(all_edges.astype('float32'))
+        embed_edges = F.normalize(embed_edges)
+
+        for gnn_layer in self.gnn_layers:
+            nodes, cat_nodes = gnn_layer(nodes, embed_edges, node_nums)
+
+        node_cls, edge_cls = self.node_cls(nodes), self.edge_cls(cat_nodes)
+        return node_cls, edge_cls
+
+
+class GNNLayer(nn.Layer):
+    def __init__(self, node_dim=256, edge_dim=256):
+        super().__init__()
+        self.in_fc = nn.Linear(node_dim * 2 + edge_dim, node_dim)
+        self.coef_fc = nn.Linear(node_dim, 1)
+        self.out_fc = nn.Linear(node_dim, node_dim)
+        self.relu = nn.ReLU()
+
+    def forward(self, nodes, edges, nums):
+        start, cat_nodes = 0, []
+        for num in nums:
+            sample_nodes = nodes[start:start + num]
+            cat_nodes.append(
+                paddle.concat([
+                    paddle.expand(sample_nodes.unsqueeze(1), [-1, num, -1]),
+                    paddle.expand(sample_nodes.unsqueeze(0), [num, -1, -1])
+                ], -1).reshape([num**2, -1]))
+            start += num
+        cat_nodes = paddle.concat([paddle.concat(cat_nodes), edges], -1)
+        cat_nodes = self.relu(self.in_fc(cat_nodes))
+        coefs = self.coef_fc(cat_nodes)
+
+        start, residuals = 0, []
+        for num in nums:
+            residual = F.softmax(
+                -paddle.eye(num).unsqueeze(-1) * 1e9 +
+                coefs[start:start + num**2].reshape([num, num, -1]), 1)
+            residuals.append((residual * cat_nodes[start:start + num**2]
+                              .reshape([num, num, -1])).sum(1))
+            start += num**2
+
+        nodes += self.relu(self.out_fc(paddle.concat(residuals)))
+        return [nodes, cat_nodes]
+
+
+class Block(nn.Layer):
+    def __init__(self,
+                 input_dims,
+                 output_dim,
+                 mm_dim=1600,
+                 chunks=20,
+                 rank=15,
+                 shared=False,
+                 dropout_input=0.,
+                 dropout_pre_lin=0.,
+                 dropout_output=0.,
+                 pos_norm='before_cat'):
+        super().__init__()
+        self.rank = rank
+        self.dropout_input = dropout_input
+        self.dropout_pre_lin = dropout_pre_lin
+        self.dropout_output = dropout_output
+        assert (pos_norm in ['before_cat', 'after_cat'])
+        self.pos_norm = pos_norm
+        # Modules
+        self.linear0 = nn.Linear(input_dims[0], mm_dim)
+        self.linear1 = (self.linear0
+                        if shared else nn.Linear(input_dims[1], mm_dim))
+        self.merge_linears0 = nn.LayerList()
+        self.merge_linears1 = nn.LayerList()
+        self.chunks = self.chunk_sizes(mm_dim, chunks)
+        for size in self.chunks:
+            ml0 = nn.Linear(size, size * rank)
+            self.merge_linears0.append(ml0)
+            ml1 = ml0 if shared else nn.Linear(size, size * rank)
+            self.merge_linears1.append(ml1)
+        self.linear_out = nn.Linear(mm_dim, output_dim)
+
+    def forward(self, x):
+        x0 = self.linear0(x[0])
+        x1 = self.linear1(x[1])
+        bs = x1.shape[0]
+        if self.dropout_input > 0:
+            x0 = F.dropout(x0, p=self.dropout_input, training=self.training)
+            x1 = F.dropout(x1, p=self.dropout_input, training=self.training)
+        x0_chunks = paddle.split(x0, self.chunks, -1)
+        x1_chunks = paddle.split(x1, self.chunks, -1)
+        zs = []
+        for x0_c, x1_c, m0, m1 in zip(x0_chunks, x1_chunks, self.merge_linears0,
+                                      self.merge_linears1):
+            m = m0(x0_c) * m1(x1_c)  # bs x split_size*rank
+            m = m.reshape([bs, self.rank, -1])
+            z = paddle.sum(m, 1)
+            if self.pos_norm == 'before_cat':
+                z = paddle.sqrt(F.relu(z)) - paddle.sqrt(F.relu(-z))
+                z = F.normalize(z)
+            zs.append(z)
+        z = paddle.concat(zs, 1)
+        if self.pos_norm == 'after_cat':
+            z = paddle.sqrt(F.relu(z)) - paddle.sqrt(F.relu(-z))
+            z = F.normalize(z)
+
+        if self.dropout_pre_lin > 0:
+            z = F.dropout(z, p=self.dropout_pre_lin, training=self.training)
+        z = self.linear_out(z)
+        if self.dropout_output > 0:
+            z = F.dropout(z, p=self.dropout_output, training=self.training)
+        return z
+
+    def chunk_sizes(self, dim, chunks):
+        split_size = (dim + chunks - 1) // chunks
+        sizes_list = [split_size] * chunks
+        sizes_list[-1] = sizes_list[-1] - (sum(sizes_list) - dim)
+        return sizes_list

ppocr/postprocess/__init__.py

@@ -45,6 +45,8 @@ def build_post_process(config, global_config=None):
 
     config = copy.deepcopy(config)
     module_name = config.pop('name')
+    if module_name == "None":
+        return
     if global_config is not None:
         config.update(global_config)
     assert module_name in support_dict, Exception(

requirements.txt

@@ -12,4 +12,5 @@ cython
 lxml
 premailer
 openpyxl
-fasttext==0.9.1
+fasttext==0.9.1
+

tools/eval.py

@@ -54,7 +54,8 @@ def main():
             config['Architecture']["Head"]['out_channels'] = char_num
 
     model = build_model(config['Architecture'])
-    extra_input = config['Architecture']['algorithm'] in ["SRN", "SAR"]
+    extra_input = config['Architecture'][
+        'algorithm'] in ["SRN", "NRTR", "SAR", "SEED"]
     if "model_type" in config['Architecture'].keys():
         model_type = config['Architecture']['model_type']
     else:
@@ -68,7 +69,6 @@ def main():
 
     # build metric
     eval_class = build_metric(config['Metric'])
-
     # start eval
     metric = program.eval(model, valid_dataloader, post_process_class,
                           eval_class, model_type, extra_input)

tools/infer_kie.py

@@ -0,0 +1,139 @@
+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import paddle.nn.functional as F
+
+import os
+import sys
+
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(__dir__)
+sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
+
+os.environ["FLAGS_allocator_strategy"] = 'auto_growth'
+
+import cv2
+import paddle
+
+from ppocr.data import create_operators, transform
+from ppocr.modeling.architectures import build_model
+from ppocr.utils.save_load import init_model
+import tools.program as program
+
+
+def read_class_list(filepath):
+    dict = {}
+    with open(filepath, "r") as f:
+        lines = f.readlines()
+        for line in lines:
+            key, value = line.split(" ")
+            dict[key] = value.rstrip()
+    return dict
+
+
+def draw_kie_result(batch, node, idx_to_cls, count):
+    img = batch[6].copy()
+    boxes = batch[7]
+    h, w = img.shape[:2]
+    pred_img = np.ones((h, w * 2, 3), dtype=np.uint8) * 255
+    max_value, max_idx = paddle.max(node, -1), paddle.argmax(node, -1)
+    node_pred_label = max_idx.numpy().tolist()
+    node_pred_score = max_value.numpy().tolist()
+
+    for i, box in enumerate(boxes):
+        if i >= len(node_pred_label):
+            break
+        new_box = [[box[0], box[1]], [box[2], box[1]], [box[2], box[3]],
+                   [box[0], box[3]]]
+        Pts = np.array([new_box], np.int32)
+        cv2.polylines(
+            img, [Pts.reshape((-1, 1, 2))],
+            True,
+            color=(255, 255, 0),
+            thickness=1)
+        x_min = int(min([point[0] for point in new_box]))
+        y_min = int(min([point[1] for point in new_box]))
+
+        pred_label = str(node_pred_label[i])
+        if pred_label in idx_to_cls:
+            pred_label = idx_to_cls[pred_label]
+        pred_score = '{:.2f}'.format(node_pred_score[i])
+        text = pred_label + '(' + pred_score + ')'
+        cv2.putText(pred_img, text, (x_min * 2, y_min),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
+    vis_img = np.ones((h, w * 3, 3), dtype=np.uint8) * 255
+    vis_img[:, :w] = img
+    vis_img[:, w:] = pred_img
+    save_kie_path = os.path.dirname(config['Global']['save_res_path']) + "/kie_results/"
+    if not os.path.exists(save_kie_path):
+        os.makedirs(save_kie_path)
+    save_path = os.path.join(save_kie_path, str(count) + ".png")
+    cv2.imwrite(save_path, vis_img)
+    logger.info("The Kie Image saved in {}".format(save_path))
+
+
+def main():
+    global_config = config['Global']
+
+    # build model
+    model = build_model(config['Architecture'])
+    init_model(config, model, logger)
+
+    # create data ops
+    transforms = []
+    for op in config['Eval']['dataset']['transforms']:
+        transforms.append(op)
+
+    data_dir = config['Eval']['dataset']['data_dir']
+
+    ops = create_operators(transforms, global_config)
+
+    save_res_path = config['Global']['save_res_path']
+    class_path = config['Global']['class_path']
+    idx_to_cls = read_class_list(class_path)
+    if not os.path.exists(os.path.dirname(save_res_path)):
+        os.makedirs(os.path.dirname(save_res_path))
+
+    model.eval()
+    with open(save_res_path, "wb") as fout:
+        with open(config['Global']['infer_img'], "rb") as f:
+            lines = f.readlines()
+            for index, data_line in enumerate(lines):
+                data_line = data_line.decode('utf-8')
+                substr = data_line.strip("\n").split("\t")
+                img_path, label = data_dir + "/" + substr[0], substr[1]
+                data = {'img_path': img_path, 'label': label}
+                with open(data['img_path'], 'rb') as f:
+                    img = f.read()
+                    data['image'] = img
+                batch = transform(data, ops)
+                batch_pred = [0] * len(batch)
+                for i in range(len(batch)):
+                    batch_pred[i] = paddle.to_tensor(
+                        np.expand_dims(
+                            batch[i], axis=0))
+                node, edge = model(batch_pred)
+                node = F.softmax(node, -1)
+                draw_kie_result(batch, node, idx_to_cls, index)
+    logger.info("success!")
+
+
+if __name__ == '__main__':
+    config, device, logger, vdl_writer = program.preprocess()
+    main()

tools/program.py

@@ -227,6 +227,10 @@ def train(config,
             images = batch[0]
             if use_srn:
                 model_average = True
+            if model_type == 'table' or extra_input:
+                preds = model(images, data=batch[1:])
+            if model_type == "kie":
+                preds = model(batch)
 
             train_start = time.time()
             # use amp
@@ -266,7 +270,7 @@ def train(config,
 
             if cal_metric_during_train:  # only rec and cls need
                 batch = [item.numpy() for item in batch]
-                if model_type == 'table':
+                if model_type in ['table', 'kie']:
                     eval_class(preds, batch)
                 else:
                     post_result = post_process_class(preds, batch[1])
@@ -399,17 +403,20 @@ def eval(model,
             start = time.time()
             if model_type == 'table' or extra_input:
                 preds = model(images, data=batch[1:])
+            if model_type == "kie":
+                preds = model(batch)
             else:
                 preds = model(images)
             batch = [item.numpy() for item in batch]
             # Obtain usable results from post-processing methods
             total_time += time.time() - start
             # Evaluate the results of the current batch
-            if model_type == 'table':
+            if model_type in ['table', 'kie']:
                 eval_class(preds, batch)
             else:
                 post_result = post_process_class(preds, batch[1])
                 eval_class(post_result, batch)
+
             pbar.update(1)
             total_frame += len(images)
         # Get final metric，eg. acc or hmean
@@ -498,8 +505,13 @@ def preprocess(is_train=False):
     assert alg in [
         'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
         'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn', 'SAR', 'PSE',
-        'SEED'
+        'SEED', 'SDMGR'
     ]
+    windows_not_support_list = ['PSE']
+    if platform.system() == "Windows" and alg in windows_not_support_list:
+        logger.warning('{} is not support in Windows now'.format(
+            windows_not_support_list))
+        sys.exit()
 
     device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'
     device = paddle.set_device(device)