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Merge pull request #609 from tink2123/adaptation_ch 

Adaptation chinese for SRN
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littletomatodonkey 2020-08-27 10:19:14 +08:00 committed by GitHub
parent 0d4ccd456b e3fd6db0bf
commit 2bdaea5656
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7 changed files with 163 additions and 67 deletions
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2
configs/rec/rec_r50fpn_vd_none_srn.yml
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@ -27,7 +27,7 @@ Architecture:
function: ppocr.modeling.architectures.rec_model,RecModel
Backbone:
function: ppocr.modeling.backbones.rec_resnet50_fpn,ResNet
function: ppocr.modeling.backbones.rec_resnet_fpn,ResNet
layers: 50
Head:

2
doc/doc_en/FAQ_en.md
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@ -45,7 +45,7 @@ At present, the open source model, dataset and magnitude are as follows:
Among them, the public datasets are opensourced, users can search and download by themselves, or refer to [Chinese data set](./datasets_en.md), synthetic data is not opensourced, users can use open-source synthesis tools to synthesize data themselves. Current available synthesis tools include [text_renderer](https://github.com/Sanster/text_renderer), [SynthText](https://github.com/ankush-me/SynthText), [TextRecognitionDataGenerator](https://github.com/Belval/TextRecognitionDataGenerator), etc.
10. **Error in using the model with TPS module for prediction**
Error message: Input(X) dims[3] and Input(Grid) dims[2] should be equal, but received X dimension[3](108) != Grid dimension[2](100)
Error message: Input(X) dims[3] and Input(Grid) dims[2] should be equal, but received X dimension[3]\(108) != Grid dimension[2]\(100)
Solution：TPS does not support variable shape. Please set --rec_image_shape='3,32,100' and --rec_char_type='en'
11. **Custom dictionary used during training, the recognition results show that words do not appear in the dictionary**

20
ppocr/data/rec/dataset_traversal.py
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@ -214,6 +214,8 @@ class SimpleReader(object):
self.mode = params['mode']
self.infer_img = params['infer_img']
self.use_tps = False
if "num_heads" in params:
self.num_heads = params['num_heads']
if "tps" in params:
self.use_tps = True
self.use_distort = False
@ -251,6 +253,13 @@ class SimpleReader(object):
img = cv2.imread(single_img)
if img.shape[-1] == 1 or len(list(img.shape)) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
if self.loss_type == 'srn':
norm_img = process_image_srn(
img=img,
image_shape=self.image_shape,
num_heads=self.num_heads,
max_text_length=self.max_text_length)
else:
norm_img = process_image(
img=img,
image_shape=self.image_shape,
@ -286,6 +295,17 @@ class SimpleReader(object):
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
label = substr[1]
if self.loss_type == "srn":
outs = process_image_srn(
img=img,
image_shape=self.image_shape,
num_heads=self.num_heads,
max_text_length=self.max_text_length,
label=label,
char_ops=self.char_ops,
loss_type=self.loss_type)
else:
outs = process_image(
img=img,
image_shape=self.image_shape,

12
ppocr/data/rec/img_tools.py
View File

@ -410,7 +410,8 @@ def resize_norm_img_srn(img, image_shape):
def srn_other_inputs(image_shape,
num_heads,
max_text_length):
max_text_length,
char_num):
imgC, imgH, imgW = image_shape
feature_dim = int((imgH / 8) * (imgW / 8))
@ -418,7 +419,7 @@ def srn_other_inputs(image_shape,
encoder_word_pos = np.array(range(0, feature_dim)).reshape((feature_dim, 1)).astype('int64')
gsrm_word_pos = np.array(range(0, max_text_length)).reshape((max_text_length, 1)).astype('int64')
lbl_weight = np.array([37] * max_text_length).reshape((-1,1)).astype('int64')
lbl_weight = np.array([int(char_num-1)] * max_text_length).reshape((-1,1)).astype('int64')
gsrm_attn_bias_data = np.ones((1, max_text_length, max_text_length))
gsrm_slf_attn_bias1 = np.triu(gsrm_attn_bias_data, 1).reshape([-1, 1, max_text_length, max_text_length])
@ -441,17 +442,18 @@ def process_image_srn(img,
loss_type=None):
norm_img = resize_norm_img_srn(img, image_shape)
norm_img = norm_img[np.newaxis, :]
char_num = char_ops.get_char_num()
[lbl_weight, encoder_word_pos, gsrm_word_pos, gsrm_slf_attn_bias1, gsrm_slf_attn_bias2] = \
srn_other_inputs(image_shape, num_heads, max_text_length)
srn_other_inputs(image_shape, num_heads, max_text_length,char_num)
if label is not None:
char_num = char_ops.get_char_num()
text = char_ops.encode(label)
if len(text) == 0 or len(text) > max_text_length:
return None
else:
if loss_type == "srn":
text_padded = [37] * max_text_length
text_padded = [int(char_num-1)] * max_text_length
for i in range(len(text)):
text_padded[i] = text[i]
lbl_weight[i] = [1.0]

124
ppocr/modeling/backbones/rec_resnet50_fpn.py → ppocr/modeling/backbones/rec_resnet_fpn.py
View File

@ -22,12 +22,12 @@ import paddle
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
__all__ = ["ResNet", "ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152"]
__all__ = [
"ResNet", "ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152"
]
Trainable = True
w_nolr = fluid.ParamAttr(
trainable = Trainable)
w_nolr = fluid.ParamAttr(trainable=Trainable)
train_parameters = {
"input_size": [3, 224, 224],
"input_mean": [0.485, 0.456, 0.406],
@ -40,12 +40,12 @@ train_parameters = {
}
}
class ResNet():
def __init__(self, params):
self.layers = params['layers']
self.params = train_parameters
def __call__(self, input):
layers = self.layers
supported_layers = [18, 34, 50, 101, 152]
@ -64,7 +64,12 @@ class ResNet():
num_filters = [64, 128, 256, 512]
conv = self.conv_bn_layer(
input=input, num_filters=64, filter_size=7, stride=2, act='relu', name="conv1")
input=input,
num_filters=64,
filter_size=7,
stride=2,
act='relu',
name="conv1")
F = []
if layers >= 50:
for block in range(len(depth)):
@ -79,7 +84,25 @@ class ResNet():
conv = self.bottleneck_block(
input=conv,
num_filters=num_filters[block],
stride=stride_list[block] if i == 0 else 1, name=conv_name)
stride=stride_list[block] if i == 0 else 1,
name=conv_name)
F.append(conv)
else:
for block in range(len(depth)):
for i in range(depth[block]):
conv_name = "res" + str(block + 2) + chr(97 + i)
if i == 0 and block != 0:
stride = (2, 1)
else:
stride = (1, 1)
conv = self.basic_block(
input=conv,
num_filters=num_filters[block],
stride=stride,
if_first=block == i == 0,
name=conv_name)
F.append(conv)
base = F[-1]
@ -88,17 +111,40 @@ class ResNet():
if (w, h) == base.shape[2:]:
base = base
else:
base = fluid.layers.conv2d_transpose( input=base, num_filters=c,filter_size=4, stride=2,
padding=1,act=None,
base = fluid.layers.conv2d_transpose(
input=base,
num_filters=c,
filter_size=4,
stride=2,
padding=1,
act=None,
param_attr=w_nolr,
bias_attr=w_nolr)
base = fluid.layers.batch_norm(base, act = "relu", param_attr=w_nolr, bias_attr=w_nolr)
base = fluid.layers.batch_norm(
base, act="relu", param_attr=w_nolr, bias_attr=w_nolr)
base = fluid.layers.concat([base, F[i]], axis=1)
base = fluid.layers.conv2d(base, num_filters=c, filter_size=1, param_attr=w_nolr, bias_attr=w_nolr)
base = fluid.layers.conv2d(base, num_filters=c, filter_size=3,padding = 1, param_attr=w_nolr, bias_attr=w_nolr)
base = fluid.layers.batch_norm(base, act = "relu", param_attr=w_nolr, bias_attr=w_nolr)
base = fluid.layers.conv2d(
base,
num_filters=c,
filter_size=1,
param_attr=w_nolr,
bias_attr=w_nolr)
base = fluid.layers.conv2d(
base,
num_filters=c,
filter_size=3,
padding=1,
param_attr=w_nolr,
bias_attr=w_nolr)
base = fluid.layers.batch_norm(
base, act="relu", param_attr=w_nolr, bias_attr=w_nolr)
base = fluid.layers.conv2d(base, num_filters=512, filter_size=1,bias_attr=w_nolr,param_attr=w_nolr)
base = fluid.layers.conv2d(
base,
num_filters=512,
filter_size=1,
bias_attr=w_nolr,
param_attr=w_nolr)
return base
@ -119,7 +165,8 @@ class ResNet():
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights",trainable = Trainable),
param_attr=ParamAttr(
name=name + "_weights", trainable=Trainable),
bias_attr=False,
name=name + '.conv2d.output.1')
@ -127,11 +174,14 @@ class ResNet():
bn_name = "bn_" + name
else:
bn_name = "bn" + name[3:]
return fluid.layers.batch_norm(input=conv,
return fluid.layers.batch_norm(
input=conv,
act=act,
name=bn_name + '.output.1',
param_attr=ParamAttr(name=bn_name + '_scale',trainable = Trainable),
bias_attr=ParamAttr(bn_name + '_offset',trainable = Trainable),
param_attr=ParamAttr(
name=bn_name + '_scale', trainable=Trainable),
bias_attr=ParamAttr(
bn_name + '_offset', trainable=Trainable),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance', )
@ -148,7 +198,11 @@ class ResNet():
def bottleneck_block(self, input, num_filters, stride, name):
conv0 = self.conv_bn_layer(
input=input, num_filters=num_filters, filter_size=1, act='relu', name=name + "_branch2a")
input=input,
num_filters=num_filters,
filter_size=1,
act='relu',
name=name + "_branch2a")
conv1 = self.conv_bn_layer(
input=conv0,
num_filters=num_filters,
@ -157,16 +211,36 @@ class ResNet():
act='relu',
name=name + "_branch2b")
conv2 = self.conv_bn_layer(
input=conv1, num_filters=num_filters * 4, filter_size=1, act=None, name=name + "_branch2c")
input=conv1,
num_filters=num_filters * 4,
filter_size=1,
act=None,
name=name + "_branch2c")
short = self.shortcut(input, num_filters * 4, stride, is_first=False, name=name + "_branch1")
short = self.shortcut(
input,
num_filters * 4,
stride,
is_first=False,
name=name + "_branch1")
return fluid.layers.elementwise_add(x=short, y=conv2, act='relu', name=name + ".add.output.5")
return fluid.layers.elementwise_add(
x=short, y=conv2, act='relu', name=name + ".add.output.5")
def basic_block(self, input, num_filters, stride, is_first, name):
conv0 = self.conv_bn_layer(input=input, num_filters=num_filters, filter_size=3, act='relu', stride=stride,
conv0 = self.conv_bn_layer(
input=input,
num_filters=num_filters,
filter_size=3,
act='relu',
stride=stride,
name=name + "_branch2a")
conv1 = self.conv_bn_layer(input=conv0, num_filters=num_filters, filter_size=3, act=None,
conv1 = self.conv_bn_layer(
input=conv0,
num_filters=num_filters,
filter_size=3,
act=None,
name=name + "_branch2b")
short = self.shortcut(input, num_filters, stride, is_first, name=name + "_branch1")
short = self.shortcut(
input, num_filters, stride, is_first, name=name + "_branch1")
return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')

8
ppocr/utils/character.py
View File

@ -26,8 +26,6 @@ class CharacterOps(object):
self.character_type = config['character_type']
self.loss_type = config['loss_type']
self.max_text_len = config['max_text_length']
if self.loss_type == "srn" and self.character_type != "en":
raise Exception("SRN can only support in character_type == en")
if self.character_type == "en":
self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
dict_character = list(self.character_str)
@ -160,13 +158,15 @@ def cal_predicts_accuracy_srn(char_ops,
acc_num = 0
img_num = 0
char_num = char_ops.get_char_num()
total_len = preds.shape[0]
img_num = int(total_len / max_text_len)
for i in range(img_num):
cur_label = []
cur_pred = []
for j in range(max_text_len):
if labels[j + i * max_text_len] != 37: #0
if labels[j + i * max_text_len] != int(char_num-1): #0
cur_label.append(labels[j + i * max_text_len][0])
else:
break
@ -178,7 +178,7 @@ def cal_predicts_accuracy_srn(char_ops,
elif j == len(cur_label) and j == max_text_len:
acc_num += 1
break
elif j == len(cur_label) and preds[j + i * max_text_len][0] == 37:
elif j == len(cur_label) and preds[j + i * max_text_len][0] == int(char_num-1):
acc_num += 1
break
acc = acc_num * 1.0 / img_num

4
tools/infer_rec.py
View File

@ -140,12 +140,12 @@ def main():
preds = preds.reshape(-1)
preds_text = char_ops.decode(preds)
elif loss_type == "srn":
cur_pred = []
char_num = char_ops.get_char_num()
preds = np.array(predict[0])
preds = preds.reshape(-1)
probs = np.array(predict[1])
ind = np.argmax(probs, axis=1)
valid_ind = np.where(preds != 37)[0]
valid_ind = np.where(preds != int(char_num-1))[0]
if len(valid_ind) == 0:
continue
score = np.mean(probs[valid_ind, ind[valid_ind]])