From 5dfcc98306ff5482f230ca0929ef0fc3c4697639 Mon Sep 17 00:00:00 2001
From: WenmuZhou <572459439@qq.com>
Date: Mon, 27 Jun 2022 09:37:07 +0000
Subject: [PATCH] fix bug
---
ppocr/data/imaug/label_ops.py | 12 ++++--
ppocr/losses/table_att_loss.py | 55 +++++++++++++++-----------
ppocr/metrics/table_metric.py | 6 ++-
ppocr/modeling/heads/table_att_head.py | 15 ++++---
4 files changed, 54 insertions(+), 34 deletions(-)
diff --git a/ppocr/data/imaug/label_ops.py b/ppocr/data/imaug/label_ops.py
index a55869a6..d63901a0 100644
--- a/ppocr/data/imaug/label_ops.py
+++ b/ppocr/data/imaug/label_ops.py
@@ -591,7 +591,7 @@ class TableLabelEncode(AttnLabelEncode):
replace_empty_cell_token=False,
merge_no_span_structure=False,
learn_empty_box=False,
- point_num=4,
+ point_num=2,
**kwargs):
self.max_text_len = max_text_length
self.lower = False
@@ -669,13 +669,15 @@ class TableLabelEncode(AttnLabelEncode):
# encode box
bboxes = np.zeros(
- (self._max_text_len, self.point_num), dtype=np.float32)
+ (self._max_text_len, self.point_num * 2), dtype=np.float32)
bbox_masks = np.zeros((self._max_text_len, 1), dtype=np.float32)
bbox_idx = 0
+
for i, token in enumerate(structure):
if self.idx2char[token] in self.td_token:
- if 'bbox' in cells[bbox_idx]:
+ if 'bbox' in cells[bbox_idx] and len(cells[bbox_idx][
+ 'tokens']) > 0:
bbox = cells[bbox_idx]['bbox'].copy()
bbox = np.array(bbox, dtype=np.float32).reshape(-1)
bboxes[i] = bbox
@@ -723,11 +725,13 @@ class TableMasterLabelEncode(TableLabelEncode):
replace_empty_cell_token=False,
merge_no_span_structure=False,
learn_empty_box=False,
- point_num=4,
+ point_num=2,
**kwargs):
super(TableMasterLabelEncode, self).__init__(
max_text_length, character_dict_path, replace_empty_cell_token,
merge_no_span_structure, learn_empty_box, point_num, **kwargs)
+ self.pad_idx = self.dict[self.pad_str]
+ self.unknown_idx = self.dict[self.unknown_str]
@property
def _max_text_len(self):
diff --git a/ppocr/losses/table_att_loss.py b/ppocr/losses/table_att_loss.py
index d7fd99e6..4bdccad3 100644
--- a/ppocr/losses/table_att_loss.py
+++ b/ppocr/losses/table_att_loss.py
@@ -21,15 +21,21 @@ from paddle import nn
from paddle.nn import functional as F
from paddle import fluid
+
class TableAttentionLoss(nn.Layer):
- def __init__(self, structure_weight, loc_weight, use_giou=False, giou_weight=1.0, **kwargs):
+ def __init__(self,
+ structure_weight,
+ loc_weight,
+ use_giou=False,
+ giou_weight=1.0,
+ **kwargs):
super(TableAttentionLoss, self).__init__()
self.loss_func = nn.CrossEntropyLoss(weight=None, reduction='none')
self.structure_weight = structure_weight
self.loc_weight = loc_weight
self.use_giou = use_giou
self.giou_weight = giou_weight
-
+
def giou_loss(self, preds, bbox, eps=1e-7, reduction='mean'):
'''
:param preds:[[x1,y1,x2,y2], [x1,y1,x2,y2],,,]
@@ -48,9 +54,10 @@ class TableAttentionLoss(nn.Layer):
inters = iw * ih
# union
- uni = (preds[:, 2] - preds[:, 0] + 1e-3) * (preds[:, 3] - preds[:, 1] + 1e-3
- ) + (bbox[:, 2] - bbox[:, 0] + 1e-3) * (
- bbox[:, 3] - bbox[:, 1] + 1e-3) - inters + eps
+ uni = (preds[:, 2] - preds[:, 0] + 1e-3) * (
+ preds[:, 3] - preds[:, 1] + 1e-3) + (bbox[:, 2] - bbox[:, 0] + 1e-3
+ ) * (bbox[:, 3] - bbox[:, 1] +
+ 1e-3) - inters + eps
# ious
ious = inters / uni
@@ -80,30 +87,34 @@ class TableAttentionLoss(nn.Layer):
structure_probs = predicts['structure_probs']
structure_targets = batch[1].astype("int64")
structure_targets = structure_targets[:, 1:]
- if len(batch) == 6:
- structure_mask = batch[5].astype("int64")
- structure_mask = structure_mask[:, 1:]
- structure_mask = paddle.reshape(structure_mask, [-1])
- structure_probs = paddle.reshape(structure_probs, [-1, structure_probs.shape[-1]])
+ structure_probs = paddle.reshape(structure_probs,
+ [-1, structure_probs.shape[-1]])
structure_targets = paddle.reshape(structure_targets, [-1])
structure_loss = self.loss_func(structure_probs, structure_targets)
-
- if len(batch) == 6:
- structure_loss = structure_loss * structure_mask
-
-# structure_loss = paddle.sum(structure_loss) * self.structure_weight
+
structure_loss = paddle.mean(structure_loss) * self.structure_weight
-
+
loc_preds = predicts['loc_preds']
loc_targets = batch[2].astype("float32")
- loc_targets_mask = batch[4].astype("float32")
+ loc_targets_mask = batch[3].astype("float32")
loc_targets = loc_targets[:, 1:, :]
loc_targets_mask = loc_targets_mask[:, 1:, :]
- loc_loss = F.mse_loss(loc_preds * loc_targets_mask, loc_targets) * self.loc_weight
+ loc_loss = F.mse_loss(loc_preds * loc_targets_mask,
+ loc_targets) * self.loc_weight
if self.use_giou:
- loc_loss_giou = self.giou_loss(loc_preds * loc_targets_mask, loc_targets) * self.giou_weight
+ loc_loss_giou = self.giou_loss(loc_preds * loc_targets_mask,
+ loc_targets) * self.giou_weight
total_loss = structure_loss + loc_loss + loc_loss_giou
- return {'loss':total_loss, "structure_loss":structure_loss, "loc_loss":loc_loss, "loc_loss_giou":loc_loss_giou}
+ return {
+ 'loss': total_loss,
+ "structure_loss": structure_loss,
+ "loc_loss": loc_loss,
+ "loc_loss_giou": loc_loss_giou
+ }
else:
- total_loss = structure_loss + loc_loss
- return {'loss':total_loss, "structure_loss":structure_loss, "loc_loss":loc_loss}
\ No newline at end of file
+ total_loss = structure_loss + loc_loss
+ return {
+ 'loss': total_loss,
+ "structure_loss": structure_loss,
+ "loc_loss": loc_loss
+ }
diff --git a/ppocr/metrics/table_metric.py b/ppocr/metrics/table_metric.py
index 17f3dc92..26f577a0 100644
--- a/ppocr/metrics/table_metric.py
+++ b/ppocr/metrics/table_metric.py
@@ -31,6 +31,8 @@ class TableStructureMetric(object):
gt_structure_batch_list):
pred_str = ''.join(pred)
target_str = ''.join(target)
+ # pred_str = pred_str.replace('','').replace('','').replace('','').replace('','')
+ # target_str = target_str.replace('','').replace('','').replace('','').replace('','')
if pred_str == target_str:
correct_num += 1
all_num += 1
@@ -131,10 +133,10 @@ class TableMetric(object):
self.bbox_metric.reset()
def format_box(self, box):
- if self.point_num == 4:
+ if self.point_num == 2:
x1, y1, x2, y2 = box
box = [[x1, y1], [x2, y1], [x2, y2], [x1, y2]]
- elif self.point_num == 8:
+ elif self.point_num == 4:
x1, y1, x2, y2, x3, y3, x4, y4 = box
box = [[x1, y1], [x2, y2], [x3, y3], [x4, y4]]
return box
diff --git a/ppocr/modeling/heads/table_att_head.py b/ppocr/modeling/heads/table_att_head.py
index b6471389..4f39d625 100644
--- a/ppocr/modeling/heads/table_att_head.py
+++ b/ppocr/modeling/heads/table_att_head.py
@@ -31,16 +31,18 @@ class TableAttentionHead(nn.Layer):
loc_type,
in_max_len=488,
max_text_length=800,
+ out_channels=30,
+ point_num=2,
**kwargs):
super(TableAttentionHead, self).__init__()
self.input_size = in_channels[-1]
self.hidden_size = hidden_size
- self.elem_num = 30
+ self.out_channels = out_channels
self.max_text_length = max_text_length
self.structure_attention_cell = AttentionGRUCell(
- self.input_size, hidden_size, self.elem_num, use_gru=False)
- self.structure_generator = nn.Linear(hidden_size, self.elem_num)
+ self.input_size, hidden_size, self.out_channels, use_gru=False)
+ self.structure_generator = nn.Linear(hidden_size, self.out_channels)
self.loc_type = loc_type
self.in_max_len = in_max_len
@@ -53,7 +55,8 @@ class TableAttentionHead(nn.Layer):
self.loc_fea_trans = nn.Linear(625, self.max_text_length + 1)
else:
self.loc_fea_trans = nn.Linear(256, self.max_text_length + 1)
- self.loc_generator = nn.Linear(self.input_size + hidden_size, 4)
+ self.loc_generator = nn.Linear(self.input_size + hidden_size,
+ point_num * 2)
def _char_to_onehot(self, input_char, onehot_dim):
input_ont_hot = F.one_hot(input_char, onehot_dim)
@@ -77,7 +80,7 @@ class TableAttentionHead(nn.Layer):
structure = targets[0]
for i in range(self.max_text_length + 1):
elem_onehots = self._char_to_onehot(
- structure[:, i], onehot_dim=self.elem_num)
+ structure[:, i], onehot_dim=self.out_channels)
(outputs, hidden), alpha = self.structure_attention_cell(
hidden, fea, elem_onehots)
output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
@@ -104,7 +107,7 @@ class TableAttentionHead(nn.Layer):
i = 0
while i < max_text_length + 1:
elem_onehots = self._char_to_onehot(
- temp_elem, onehot_dim=self.elem_num)
+ temp_elem, onehot_dim=self.out_channels)
(outputs, hidden), alpha = self.structure_attention_cell(
hidden, fea, elem_onehots)
output_hiddens.append(paddle.unsqueeze(outputs, axis=1))
--
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