remove unused code

configs/table/SLANet.yml

 Global:
   use_gpu: true
-  epoch_num: 400
+  epoch_num: 100
   log_smooth_window: 20
   print_batch_step: 20
   save_model_dir: ./output/SLANet
@@ -28,7 +28,10 @@ Optimizer:
   beta2: 0.999
   clip_norm: 5.0
   lr:
+    name: Piecewise
     learning_rate: 0.001
+    decay_epochs : [40, 50]
+    values : [0.001, 0.0001, 0.00005]
   regularizer:
     name: 'L2'
     factor: 0.00000
@@ -105,8 +108,8 @@ Train:
 Eval:
   dataset:
     name: PubTabDataSet
-    data_dir: /home/zhoujun20/table/PubTabNe/pubtabnet/val/
-    label_file_list: [/home/zhoujun20/table/PubTabNe/pubtabnet/val_500.jsonl]
+    data_dir: train_data/table/pubtabnet/val/
+    label_file_list: [train_data/table/pubtabnet/PubTabNet_2.0.0_val.jsonl]
     transforms:
       - DecodeImage: # load image
           img_mode: BGR

ppstructure/table/matcher.py

@@ -40,169 +40,6 @@ def compute_iou(rec1, rec2):
         return (intersect / (sum_area - intersect)) * 1.0
 
 
-def matcher_merge(ocr_bboxes, pred_bboxes):
-    all_dis = []
-    ious = []
-    matched = {}
-    for i, gt_box in enumerate(ocr_bboxes):
-        distances = []
-        for j, pred_box in enumerate(pred_bboxes):
-            # compute l1 distence and IOU between two boxes
-            distances.append((distance(gt_box, pred_box),
-                              1. - compute_iou(gt_box, pred_box)))
-        sorted_distances = distances.copy()
-        # select nearest cell
-        sorted_distances = sorted(
-            sorted_distances, key=lambda item: (item[1], item[0]))
-        if distances.index(sorted_distances[0]) not in matched.keys():
-            matched[distances.index(sorted_distances[0])] = [i]
-        else:
-            matched[distances.index(sorted_distances[0])].append(i)
-    return matched  #, sum(ious) / len(ious)
-
-
-def complex_num(pred_bboxes):
-    complex_nums = []
-    for bbox in pred_bboxes:
-        distances = []
-        temp_ious = []
-        for pred_bbox in pred_bboxes:
-            if bbox != pred_bbox:
-                distances.append(distance(bbox, pred_bbox))
-                temp_ious.append(compute_iou(bbox, pred_bbox))
-        complex_nums.append(temp_ious[distances.index(min(distances))])
-    return sum(complex_nums) / len(complex_nums)
-
-
-def get_rows(pred_bboxes):
-    pre_bbox = pred_bboxes[0]
-    res = []
-    step = 0
-    for i in range(len(pred_bboxes)):
-        bbox = pred_bboxes[i]
-        if bbox[1] - pre_bbox[1] > 2 or bbox[0] - pre_bbox[0] < 0:
-            break
-        else:
-            res.append(bbox)
-            step += 1
-    for i in range(step):
-        pred_bboxes.pop(0)
-    return res, pred_bboxes
-
-
-def refine_rows(pred_bboxes):  # 微调整行的框，使在一条水平线上
-    ys_1 = []
-    ys_2 = []
-    for box in pred_bboxes:
-        ys_1.append(box[1])
-        ys_2.append(box[3])
-    min_y_1 = sum(ys_1) / len(ys_1)
-    min_y_2 = sum(ys_2) / len(ys_2)
-    re_boxes = []
-    for box in pred_bboxes:
-        box[1] = min_y_1
-        box[3] = min_y_2
-        re_boxes.append(box)
-    return re_boxes
-
-
-def matcher_refine_row(gt_bboxes, pred_bboxes):
-    before_refine_pred_bboxes = pred_bboxes.copy()
-    pred_bboxes = []
-    while (len(before_refine_pred_bboxes) != 0):
-        row_bboxes, before_refine_pred_bboxes = get_rows(
-            before_refine_pred_bboxes)
-        print(row_bboxes)
-        pred_bboxes.extend(refine_rows(row_bboxes))
-    all_dis = []
-    ious = []
-    matched = {}
-    for i, gt_box in enumerate(gt_bboxes):
-        distances = []
-        #temp_ious = []
-        for j, pred_box in enumerate(pred_bboxes):
-            distances.append(distance(gt_box, pred_box))
-            #temp_ious.append(compute_iou(gt_box, pred_box))
-        #all_dis.append(min(distances))
-        #ious.append(temp_ious[distances.index(min(distances))])
-        if distances.index(min(distances)) not in matched.keys():
-            matched[distances.index(min(distances))] = [i]
-        else:
-            matched[distances.index(min(distances))].append(i)
-    return matched  #, sum(ious) / len(ious)
-
-
-#先挑选出一行，再进行匹配
-def matcher_structure_1(gt_bboxes, pred_bboxes_rows, pred_bboxes):
-    gt_box_index = 0
-    delete_gt_bboxes = gt_bboxes.copy()
-    match_bboxes_ready = []
-    matched = {}
-    while (len(delete_gt_bboxes) != 0):
-        row_bboxes, delete_gt_bboxes = get_rows(delete_gt_bboxes)
-        row_bboxes = sorted(row_bboxes, key=lambda key: key[0])
-        if len(pred_bboxes_rows) > 0:
-            match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
-        print(row_bboxes)
-        for i, gt_box in enumerate(row_bboxes):
-            #print(gt_box)
-            pred_distances = []
-            distances = []
-            for pred_bbox in pred_bboxes:
-                pred_distances.append(distance(gt_box, pred_bbox))
-            for j, pred_box in enumerate(match_bboxes_ready):
-                distances.append(distance(gt_box, pred_box))
-            index = pred_distances.index(min(distances))
-            #print('index', index)
-            if index not in matched.keys():
-                matched[index] = [gt_box_index]
-            else:
-                matched[index].append(gt_box_index)
-            gt_box_index += 1
-    return matched
-
-
-def matcher_structure(gt_bboxes, pred_bboxes_rows, pred_bboxes):
-    '''
-    gt_bboxes: 排序后
-    pred_bboxes: 
-    '''
-    pre_bbox = gt_bboxes[0]
-    matched = {}
-    match_bboxes_ready = []
-    match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
-    for i, gt_box in enumerate(gt_bboxes):
-
-        pred_distances = []
-        for pred_bbox in pred_bboxes:
-            pred_distances.append(distance(gt_box, pred_bbox))
-        distances = []
-        gap_pre = gt_box[1] - pre_bbox[1]
-        gap_pre_1 = gt_box[0] - pre_bbox[2]
-        #print(gap_pre, len(pred_bboxes_rows))
-        if (gap_pre_1 < 0 and len(pred_bboxes_rows) > 0):
-            match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
-        if len(pred_bboxes_rows) == 1:
-            match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
-        if len(match_bboxes_ready) == 0 and len(pred_bboxes_rows) > 0:
-            match_bboxes_ready.extend(pred_bboxes_rows.pop(0))
-        if len(match_bboxes_ready) == 0 and len(pred_bboxes_rows) == 0:
-            break
-        #print(match_bboxes_ready)
-        for j, pred_box in enumerate(match_bboxes_ready):
-            distances.append(distance(gt_box, pred_box))
-        index = pred_distances.index(min(distances))
-        #print(gt_box, index)
-        #match_bboxes_ready.pop(distances.index(min(distances)))
-        print(gt_box, match_bboxes_ready[distances.index(min(distances))])
-        if index not in matched.keys():
-            matched[index] = [i]
-        else:
-            matched[index].append(i)
-        pre_bbox = gt_box
-    return matched
-
-
 class TableMatch:
     def __init__(self, filter_ocr_result=False, use_master=False):
         self.filter_ocr_result = filter_ocr_result
@@ -225,14 +62,13 @@ class TableMatch:
     def match_result(self, dt_boxes, pred_bboxes):
         matched = {}
         for i, gt_box in enumerate(dt_boxes):
-            # gt_box = [np.min(gt_box[:, 0]), np.min(gt_box[:, 1]), np.max(gt_box[:, 0]), np.max(gt_box[:, 1])]
             distances = []
             for j, pred_box in enumerate(pred_bboxes):
                 distances.append((distance(gt_box, pred_box),
                                   1. - compute_iou(gt_box, pred_box)
-                                  ))  # 获取两两cell之间的L1距离和 1- IOU
+                                  ))  # compute iou and l1 distance
             sorted_distances = distances.copy()
-            # 根据距离和IOU挑选最"近"的cell
+            # select det box by iou and l1 distance
             sorted_distances = sorted(
                 sorted_distances, key=lambda item: (item[1], item[0]))
             if distances.index(sorted_distances[0]) not in matched.keys():