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Merge pull request #989 from WenmuZhou/dygraph 

yml文件删掉一些个人路径
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dyning 2020-10-27 13:38:43 +08:00 committed by GitHub
parent f1048e296e e1b39945b8
commit 7d09cd1928
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10 changed files with 213 additions and 103 deletions
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8
configs/det/det_mv3_db.yml
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@ -10,8 +10,8 @@ Global:
# if pretrained_model is saved in static mode, load_static_weights must set to True
load_static_weights: True
cal_metric_during_train: False
pretrained_model: /home/zhoujun20/pretrain_models/MobileNetV3_large_x0_5_pretrained
checkpoints: #./output/det_db_0.001_DiceLoss_256_pp_config_2.0b_4gpu/best_accuracy
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
checkpoints:
save_inference_dir:
use_visualdl: True
infer_img: doc/imgs_en/img_10.jpg
@ -22,9 +22,7 @@ Optimizer:
beta1: 0.9
beta2: 0.999
learning_rate:
# name: Cosine
lr: 0.001
# warmup_epoch: 0
regularizer:
name: 'L2'
factor: 0
@ -98,7 +96,7 @@ TRAIN:
order: 'hwc'
- ToCHWImage:
- keepKeys:
keep_keys: ['image','threshold_map','threshold_mask','shrink_map','shrink_mask'] # dataloader将按照此顺序返回list
keep_keys: ['image','threshold_map','threshold_mask','shrink_map','shrink_mask'] # dataloader will return list in this order
loader:
shuffle: True
drop_last: False

18
configs/det/det_r50_vd_db.yml
View File

@ -3,15 +3,15 @@ Global:
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 2
save_model_dir: ./output/20201015_r50/
save_model_dir: ./output/det_r50_vd/
save_epoch_step: 1200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: 8
# if pretrained_model is saved in static mode, load_static_weights must set to True
load_static_weights: True
cal_metric_during_train: False
pretrained_model: /home/zhoujun20/pretrain_models/ResNet50_vd_ssld_pretrained/
checkpoints: #./output/det_db_0.001_DiceLoss_256_pp_config_2.0b_4gpu/best_accuracy
pretrained_model: ./pretrain_models/ResNet50_vd_ssld_pretrained/
checkpoints:
save_inference_dir:
use_visualdl: True
infer_img: doc/imgs_en/img_10.jpg
@ -22,9 +22,7 @@ Optimizer:
beta1: 0.9
beta2: 0.999
learning_rate:
# name: Cosine
lr: 0.001
# warmup_epoch: 0
regularizer:
name: 'L2'
factor: 0
@ -65,9 +63,9 @@ Metric:
TRAIN:
dataset:
name: SimpleDataSet
data_dir: /home/zhoujun20/detection/
data_dir: ./detection/
file_list:
- /home/zhoujun20/detection/train_icdar2015_label.txt # dataset1
- ./detection/train_icdar2015_label.txt # dataset1
ratio_list: [1.0]
transforms:
- DecodeImage: # load image
@ -97,7 +95,7 @@ TRAIN:
order: 'hwc'
- ToCHWImage:
- keepKeys:
keep_keys: ['image','threshold_map','threshold_mask','shrink_map','shrink_mask'] # dataloader将按照此顺序返回list
keep_keys: ['image','threshold_map','threshold_mask','shrink_map','shrink_mask'] # dataloader will return list in this order
loader:
shuffle: True
drop_last: False
@ -107,9 +105,9 @@ TRAIN:
EVAL:
dataset:
name: SimpleDataSet
data_dir: /home/zhoujun20/detection/
data_dir: ./detection/
file_list:
- /home/zhoujun20/detection/test_icdar2015_label.txt
- ./detection/test_icdar2015_label.txt
transforms:
- DecodeImage: # load image
img_mode: BGR

18
configs/rec/rec_mv3_none_bilstm_ctc.yml
View File

@ -3,7 +3,7 @@ Global:
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/test/
save_model_dir: ./output/rec/mv3_none_bilstm_ctc/
save_epoch_step: 500
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: 127
@ -11,7 +11,7 @@ Global:
load_static_weights: True
cal_metric_during_train: True
pretrained_model:
checkpoints: #output/rec/rec_crnn/best_accuracy
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words/ch/word_1.jpg
@ -29,9 +29,7 @@ Optimizer:
beta1: 0.9
beta2: 0.999
learning_rate:
name: Cosine
lr: 0.001
warmup_epoch: 4
regularizer:
name: 'L2'
factor: 0.00001
@ -66,9 +64,9 @@ Metric:
TRAIN:
dataset:
name: SimpleDataSet
data_dir: /home/zhoujun20/rec
data_dir: ./rec
file_list:
- /home/zhoujun20/rec/real_data.txt # dataset1
- ./rec/train.txt # dataset1
ratio_list: [ 0.4,0.6 ]
transforms:
- DecodeImage: # load image
@ -79,7 +77,7 @@ TRAIN:
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
loader:
batch_size: 256
shuffle: True
@ -89,9 +87,9 @@ TRAIN:
EVAL:
dataset:
name: SimpleDataSet
data_dir: /home/zhoujun20/rec
data_dir: ./rec
file_list:
- /home/zhoujun20/rec/label_val_all.txt
- ./rec/val.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
@ -100,7 +98,7 @@ EVAL:
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False

16
configs/rec/rec_mv3_none_bilstm_ctc_lmdb.yml
View File

@ -3,7 +3,7 @@ Global:
epoch_num: 500
log_smooth_window: 20
print_batch_step: 1
save_model_dir: ./output/rec/test/
save_model_dir: ./output/rec/mv3_none_bilstm_ctc/
save_epoch_step: 500
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: 1016
@ -11,13 +11,13 @@ Global:
load_static_weights: True
cal_metric_during_train: True
pretrained_model:
checkpoints: #output/rec/rec_crnn/best_accuracy
checkpoints:
save_inference_dir:
use_visualdl: True
infer_img: doc/imgs_words/ch/word_1.jpg
# for data or label process
max_text_length: 80
character_dict_path: /home/zhoujun20/rec/lmdb/dict.txt
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
character_type: 'ch'
use_space_char: True
infer_mode: False
@ -29,9 +29,7 @@ Optimizer:
beta1: 0.9
beta2: 0.999
learning_rate:
name: Cosine
lr: 0.0005
warmup_epoch: 1
regularizer:
name: 'L2'
factor: 0.00001
@ -67,7 +65,7 @@ TRAIN:
dataset:
name: LMDBDateSet
file_list:
- /home/zhoujun20/rec/lmdb/train # dataset1
- ./rec/lmdb/train # dataset1
ratio_list: [ 0.4,0.6 ]
transforms:
- DecodeImage: # load image
@ -78,7 +76,7 @@ TRAIN:
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
loader:
batch_size: 256
shuffle: True
@ -89,7 +87,7 @@ EVAL:
dataset:
name: LMDBDateSet
file_list:
- /home/zhoujun20/rec/lmdb/val
- ./rec/lmdb/val
transforms:
- DecodeImage: # load image
img_mode: BGR
@ -98,7 +96,7 @@ EVAL:
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False

32
configs/rec/rec_mv3_none_none_ctc_lmdb.yml
View File

@ -1,25 +1,25 @@
Global:
use_gpu: false
epoch_num: 500
epoch_num: 72
log_smooth_window: 20
print_batch_step: 1
save_model_dir: ./output/rec/test/
print_batch_step: 10
save_model_dir: ./output/rec/mv3_none_none_ctc/
save_epoch_step: 500
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: 1016
eval_batch_step: 2000
# if pretrained_model is saved in static mode, load_static_weights must set to True
load_static_weights: True
cal_metric_during_train: True
pretrained_model:
checkpoints: #output/rec/rec_crnn/best_accuracy
checkpoints:
save_inference_dir:
use_visualdl: True
infer_img: doc/imgs_words/ch/word_1.jpg
# for data or label process
max_text_length: 80
character_dict_path: /home/zhoujun20/rec/lmdb/dict.txt
max_text_length: 25
character_dict_path:
character_type: 'en'
use_space_char: True
use_space_char: False
infer_mode: False
use_tps: False
@ -29,9 +29,7 @@ Optimizer:
beta1: 0.9
beta2: 0.999
learning_rate:
name: Cosine
lr: 0.0005
warmup_epoch: 1
regularizer:
name: 'L2'
factor: 0.00001
@ -43,7 +41,7 @@ Architecture:
Backbone:
name: MobileNetV3
scale: 0.5
model_name: small
model_name: large
small_stride: [ 1, 2, 2, 2 ]
Neck:
name: SequenceEncoder
@ -66,7 +64,7 @@ TRAIN:
dataset:
name: LMDBDateSet
file_list:
- /Users/zhoujun20/Downloads/evaluation_new # dataset1
- ./rec/train # dataset1
ratio_list: [ 0.4,0.6 ]
transforms:
- DecodeImage: # load image
@ -75,9 +73,9 @@ TRAIN:
- CTCLabelEncode: # Class handling label
- RecAug:
- RecResizeImg:
image_shape: [ 3,32,320 ]
image_shape: [ 3,32,100 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
loader:
batch_size: 256
shuffle: True
@ -88,16 +86,16 @@ EVAL:
dataset:
name: LMDBDateSet
file_list:
- /home/zhoujun20/rec/lmdb/val
- ./rec/val/
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecResizeImg:
image_shape: [ 3,32,320 ]
image_shape: [ 3,32,100 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False

18
configs/rec/rec_r34_vd_none_bilstm_ctc.yml
View File

@ -3,7 +3,7 @@ Global:
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/test/
save_model_dir: ./output/rec/res34_none_bilstm_ctc/
save_epoch_step: 500
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: 127
@ -11,7 +11,7 @@ Global:
load_static_weights: True
cal_metric_during_train: True
pretrained_model:
checkpoints: #output/rec/rec_crnn/best_accuracy
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words/ch/word_1.jpg
@ -29,9 +29,7 @@ Optimizer:
beta1: 0.9
beta2: 0.999
learning_rate:
name: Cosine
lr: 0.001
warmup_epoch: 4
regularizer:
name: 'L2'
factor: 0.00001
@ -64,9 +62,9 @@ Metric:
TRAIN:
dataset:
name: SimpleDataSet
data_dir: /home/zhoujun20/rec
data_dir: ./rec
file_list:
- /home/zhoujun20/rec/real_data.txt # dataset1
- ./rec/train.txt # dataset1
ratio_list: [ 0.4,0.6 ]
transforms:
- DecodeImage: # load image
@ -77,7 +75,7 @@ TRAIN:
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
loader:
batch_size: 256
shuffle: True
@ -87,9 +85,9 @@ TRAIN:
EVAL:
dataset:
name: SimpleDataSet
data_dir: /home/zhoujun20/rec
data_dir: ./rec
file_list:
- /home/zhoujun20/rec/label_val_all.txt
- ./rec/val.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
@ -98,7 +96,7 @@ EVAL:
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader将按照此顺序返回list
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False

103
configs/rec/rec_r34_vd_none_none_ctc.yml Normal file
View File

@ -0,0 +1,103 @@
Global:
use_gpu: false
epoch_num: 500
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/rec/res34_none_none_ctc/
save_epoch_step: 500
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: 127
# if pretrained_model is saved in static mode, load_static_weights must set to True
load_static_weights: True
cal_metric_during_train: True
pretrained_model:
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_words/ch/word_1.jpg
# for data or label process
max_text_length: 80
character_dict_path: ppocr/utils/ppocr_keys_v1.txt
character_type: 'ch'
use_space_char: False
infer_mode: False
use_tps: False
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
learning_rate:
lr: 0.001
regularizer:
name: 'L2'
factor: 0.00001
Architecture:
type: rec
algorithm: CRNN
Transform:
Backbone:
name: ResNet
layers: 34
Neck:
name: SequenceEncoder
encoder_type: reshape
Head:
name: CTC
fc_decay: 0.00001
Loss:
name: CTCLoss
PostProcess:
name: CTCLabelDecode
Metric:
name: RecMetric
main_indicator: acc
TRAIN:
dataset:
name: SimpleDataSet
data_dir: ./rec
file_list:
- ./rec/train.txt # dataset1
ratio_list: [ 0.4,0.6 ]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecAug:
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
loader:
batch_size: 256
shuffle: True
drop_last: True
num_workers: 8
EVAL:
dataset:
name: SimpleDataSet
data_dir: ./rec
file_list:
- ./rec/val.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- CTCLabelEncode: # Class handling label
- RecResizeImg:
image_shape: [ 3,32,320 ]
- keepKeys:
keep_keys: [ 'image','label','length' ] # dataloader will return list in this order
loader:
shuffle: False
drop_last: False
batch_size: 256
num_workers: 8

44
ppocr/modeling/architectures/model.py
View File

@ -21,7 +21,6 @@ __dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.append('/home/zhoujun20/PaddleOCR')
import paddle
from paddle import nn
from ppocr.modeling.transform import build_transform
from ppocr.modeling.backbones import build_backbone
@ -72,12 +71,10 @@ class Model(nn.Layer):
config['Neck']['in_channels'] = in_channels
self.neck = build_neck(config['Neck'])
in_channels = self.neck.out_channels
# # build head, head is need for del, rec and cls
# # build head, head is need for det, rec and cls
config["Head"]['in_channels'] = in_channels
self.head = build_head(config["Head"])
# @paddle.jit.to_static
def forward(self, x):
if self.use_transform:
x = self.transform(x)
@ -85,41 +82,4 @@ class Model(nn.Layer):
if self.use_neck:
x = self.neck(x)
x = self.head(x)
return x
def check_static():
import numpy as np
from ppocr.utils.save_load import load_dygraph_pretrain
from ppocr.utils.logging import get_logger
from tools import program
config = program.load_config('configs/rec/rec_r34_vd_none_bilstm_ctc.yml')
logger = get_logger()
np.random.seed(0)
data = np.random.rand(1, 3, 32, 320).astype(np.float32)
paddle.disable_static()
config['Architecture']['in_channels'] = 3
config['Architecture']["Head"]['out_channels'] = 6624
model = Model(config['Architecture'])
model.eval()
load_dygraph_pretrain(
model,
logger,
'/Users/zhoujun20/Desktop/code/PaddleOCR/cnn_ctc/cnn_ctc',
load_static_weights=True)
x = paddle.to_tensor(data)
y = model(x)
for y1 in y:
print(y1.shape)
static_out = np.load(
'/Users/zhoujun20/Desktop/code/PaddleOCR/output/conv.npy')
diff = y.numpy() - static_out
print(y.shape, static_out.shape, diff.mean())
if __name__ == '__main__':
check_static()
return x

3
ppocr/modeling/heads/rec_ctc_head.py
View File

@ -20,6 +20,7 @@ import math
import paddle
from paddle import ParamAttr, nn
from paddle.nn import functional as F
def get_para_bias_attr(l2_decay, k, name):
@ -48,4 +49,6 @@ class CTC(nn.Layer):
def forward(self, x, labels=None):
predicts = self.fc(x)
if not self.training:
predicts = F.softmax(predicts, axis=2)
return predicts

56
tools/infer/utility.py
View File

@ -14,11 +14,14 @@
import argparse
import os
import sys
import cv2
import numpy as np
import json
from PIL import Image, ImageDraw, ImageFont
import math
from paddle.fluid.core import AnalysisConfig
from paddle.fluid.core import create_paddle_predictor
def parse_args():
@ -71,6 +74,59 @@ def parse_args():
return parser.parse_args()
def create_predictor(args, mode, logger):
if mode == "det":
model_dir = args.det_model_dir
elif mode == 'cls':
model_dir = args.cls_model_dir
else:
model_dir = args.rec_model_dir
if model_dir is None:
logger.info("not find {} model file path {}".format(mode, model_dir))
sys.exit(0)
model_file_path = model_dir + "/model"
params_file_path = model_dir + "/params"
if not os.path.exists(model_file_path):
logger.info("not find model file path {}".format(model_file_path))
sys.exit(0)
if not os.path.exists(params_file_path):
logger.info("not find params file path {}".format(params_file_path))
sys.exit(0)
config = AnalysisConfig(model_file_path, params_file_path)
if args.use_gpu:
config.enable_use_gpu(args.gpu_mem, 0)
else:
config.disable_gpu()
config.set_cpu_math_library_num_threads(6)
if args.enable_mkldnn:
# cache 10 different shapes for mkldnn to avoid memory leak
config.set_mkldnn_cache_capacity(10)
config.enable_mkldnn()
# config.enable_memory_optim()
config.disable_glog_info()
if args.use_zero_copy_run:
config.delete_pass("conv_transpose_eltwiseadd_bn_fuse_pass")
config.switch_use_feed_fetch_ops(False)
else:
config.switch_use_feed_fetch_ops(True)
predictor = create_paddle_predictor(config)
input_names = predictor.get_input_names()
for name in input_names:
input_tensor = predictor.get_input_tensor(name)
output_names = predictor.get_output_names()
output_tensors = []
for output_name in output_names:
output_tensor = predictor.get_output_tensor(output_name)
output_tensors.append(output_tensor)
return predictor, input_tensor, output_tensors
def draw_text_det_res(dt_boxes, img_path):
src_im = cv2.imread(img_path)
for box in dt_boxes: