122 lines
3.4 KiB
YAML
122 lines
3.4 KiB
YAML
Global:
|
||
use_gpu: True
|
||
epoch_num: 600
|
||
log_smooth_window: 20
|
||
print_batch_step: 10
|
||
save_model_dir: ./output/pgnet_r50_vd_totaltext/
|
||
save_epoch_step: 10
|
||
# evaluation is run every 0 iterationss after the 1000th iteration
|
||
eval_batch_step: [ 0, 1000 ]
|
||
cal_metric_during_train: False
|
||
pretrained_model:
|
||
checkpoints:
|
||
save_inference_dir:
|
||
use_visualdl: False
|
||
infer_img:
|
||
infer_visual_type: EN # two mode: EN is for english datasets, CN is for chinese datasets
|
||
valid_set: totaltext # two mode: totaltext valid curved words, partvgg valid non-curved words
|
||
save_res_path: ./output/pgnet_r50_vd_totaltext/predicts_pgnet.txt
|
||
character_dict_path: ppocr/utils/ic15_dict.txt
|
||
character_type: EN
|
||
max_text_length: 50 # the max length in seq
|
||
max_text_nums: 30 # the max seq nums in a pic
|
||
tcl_len: 64
|
||
|
||
Architecture:
|
||
model_type: e2e
|
||
algorithm: PGNet
|
||
Transform:
|
||
Backbone:
|
||
name: ResNet
|
||
layers: 50
|
||
Neck:
|
||
name: PGFPN
|
||
Head:
|
||
name: PGHead
|
||
character_dict_path: ppocr/utils/ic15_dict.txt # the same as Global:character_dict_path
|
||
|
||
Loss:
|
||
name: PGLoss
|
||
tcl_bs: 64
|
||
max_text_length: 50 # the same as Global: max_text_length
|
||
max_text_nums: 30 # the same as Global：max_text_nums
|
||
pad_num: 36 # the length of dict for pad
|
||
|
||
Optimizer:
|
||
name: Adam
|
||
beta1: 0.9
|
||
beta2: 0.999
|
||
lr:
|
||
name: Cosine
|
||
learning_rate: 0.001
|
||
warmup_epoch: 50
|
||
regularizer:
|
||
name: 'L2'
|
||
factor: 0.0001
|
||
|
||
PostProcess:
|
||
name: PGPostProcess
|
||
score_thresh: 0.5
|
||
mode: fast # fast or slow two ways
|
||
point_gather_mode: align # same as PGProcessTrain: point_gather_mode
|
||
|
||
Metric:
|
||
name: E2EMetric
|
||
mode: A # two ways for eval, A: label from txt, B: label from gt_mat
|
||
gt_mat_dir: ./train_data/total_text/gt # the dir of gt_mat
|
||
character_dict_path: ppocr/utils/ic15_dict.txt
|
||
main_indicator: f_score_e2e
|
||
|
||
Train:
|
||
dataset:
|
||
name: PGDataSet
|
||
data_dir: ./train_data/total_text/train
|
||
label_file_list: [./train_data/total_text/train/train.txt]
|
||
ratio_list: [1.0]
|
||
transforms:
|
||
- DecodeImage: # load image
|
||
img_mode: BGR
|
||
channel_first: False
|
||
- E2ELabelEncodeTrain:
|
||
- PGProcessTrain:
|
||
batch_size: 14 # same as loader: batch_size_per_card
|
||
use_resize: True
|
||
use_random_crop: False
|
||
min_crop_size: 24
|
||
min_text_size: 4
|
||
max_text_size: 512
|
||
point_gather_mode: align # two mode: align and none, align mode is better than none mode
|
||
- KeepKeys:
|
||
keep_keys: [ 'images', 'tcl_maps', 'tcl_label_maps', 'border_maps','direction_maps', 'training_masks', 'label_list', 'pos_list', 'pos_mask' ] # dataloader will return list in this order
|
||
loader:
|
||
shuffle: True
|
||
drop_last: True
|
||
batch_size_per_card: 14
|
||
num_workers: 16
|
||
|
||
Eval:
|
||
dataset:
|
||
name: PGDataSet
|
||
data_dir: ./train_data/total_text/test
|
||
label_file_list: [./train_data/total_text/test/test.txt]
|
||
transforms:
|
||
- DecodeImage: # load image
|
||
img_mode: BGR
|
||
channel_first: False
|
||
- E2ELabelEncodeTest:
|
||
- E2EResizeForTest:
|
||
max_side_len: 768
|
||
- NormalizeImage:
|
||
scale: 1./255.
|
||
mean: [ 0.485, 0.456, 0.406 ]
|
||
std: [ 0.229, 0.224, 0.225 ]
|
||
order: 'hwc'
|
||
- ToCHWImage:
|
||
- KeepKeys:
|
||
keep_keys: [ 'image', 'shape', 'polys', 'texts', 'ignore_tags', 'img_id']
|
||
loader:
|
||
shuffle: False
|
||
drop_last: False
|
||
batch_size_per_card: 1 # must be 1
|
||
num_workers: 2
|