Merge remote-tracking branch 'origin/dygraph' into dygraph

PPOCRLabel/libs/canvas.py

@@ -624,6 +624,13 @@ class Canvas(QWidget):
             pal.setColor(self.backgroundRole(), QColor(232, 232, 232, 255))
             self.setPalette(pal)
 
+        # adaptive BBOX label & index font size
+        if self.pixmap:
+            h, w = self.pixmap.size().height(), self.pixmap.size().width()
+            fontszie = int(max(h, w) / 48)
+            for s in self.shapes:
+                s.fontsize = fontszie
+        
         p.end()
 
     def fillDrawing(self):

PPOCRLabel/libs/shape.py

@@ -66,6 +66,7 @@ class Shape(object):
             self.NEAR_VERTEX: (4, self.P_ROUND),
             self.MOVE_VERTEX: (1.5, self.P_SQUARE),
         }
+        self.fontsize = 8
 
         self._closed = False
 
@@ -156,7 +157,7 @@ class Shape(object):
                     min_y = min(min_y, point.y())
                 if min_x != sys.maxsize and min_y != sys.maxsize:
                     font = QFont()
-                    font.setPointSize(8)
+                    font.setPointSize(self.fontsize)
                     font.setBold(True)
                     painter.setFont(font)
                     if self.label is None:
@@ -174,7 +175,7 @@ class Shape(object):
                     min_y = min(min_y, point.y())
                 if min_x != sys.maxsize and min_y != sys.maxsize:
                     font = QFont()
-                    font.setPointSize(8)
+                    font.setPointSize(self.fontsize)
                     font.setBold(True)
                     painter.setFont(font)
                     text = ''

configs/rec/rec_r45_abinet.yml

@@ -8,7 +8,7 @@ Global:
   # evaluation is run every 2000 iterations
   eval_batch_step: [0, 2000]
   cal_metric_during_train: True
-  pretrained_model:
+  pretrained_model: ./pretrain_models/abinet_vl_pretrained
   checkpoints:
   save_inference_dir:
   use_visualdl: False

configs/rec/rec_vitstr_none_ce.yml

@@ -81,7 +81,7 @@ Train:
 Eval:
   dataset:
     name: LMDBDataSet
-    data_dir: ./train_data/data_lmdb_release/evaluaiton/
+    data_dir: ./train_data/data_lmdb_release/evaluation/
     transforms:
       - DecodeImage: # load image
           img_mode: BGR

ppstructure/table/predict_table.py

@@ -129,11 +129,25 @@ class TableSystem(object):
 
     def rebuild_table(self, structure_res, dt_boxes, rec_res):
         pred_structures, pred_bboxes = structure_res
+        dt_boxes, rec_res = self.filter_ocr_result(pred_bboxes,dt_boxes, rec_res)
         matched_index = self.match_result(dt_boxes, pred_bboxes)
         pred_html, pred = self.get_pred_html(pred_structures, matched_index,
                                              rec_res)
         return pred_html, pred
 
+    def filter_ocr_result(self, pred_bboxes,dt_boxes, rec_res):
+        y1 = pred_bboxes[:,1::2].min()
+        new_dt_boxes = []
+        new_rec_res = []
+
+        for box,rec in zip(dt_boxes, rec_res):
+            if np.max(box[1::2]) < y1:
+                continue
+            new_dt_boxes.append(box)
+            new_rec_res.append(rec)
+        return new_dt_boxes, new_rec_res
+
+
     def match_result(self, dt_boxes, pred_bboxes):
         matched = {}
         for i, gt_box in enumerate(dt_boxes):

test_tipc/benchmark_train.sh

@@ -21,6 +21,18 @@ function func_parser_params(){
     echo ${tmp}
 }
 
+function set_dynamic_epoch(){
+    string=$1
+    num=$2
+    _str=${string:1:6}
+    IFS="C"
+    arr=(${_str})
+    M=${arr[0]}
+    P=${arr[1]}
+    ep=`expr $num \* $M \* $P`
+    echo $ep
+}
+
 function func_sed_params(){
     filename=$1
     line=$2
@@ -143,6 +155,7 @@ else
         precision="amp"
     fi
 
+    epoch=$(set_dynamic_epoch $device_num $epoch)
     fp_items_list=($precision)
     batch_size_list=($batch_size)
     device_num_list=($device_num)