提交 e83d5955 编写于 作者: qq_25193841's avatar qq_25193841

Merge remote-tracking branch 'origin/dygraph' into dygraph

......@@ -8,7 +8,7 @@ Global:
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [3000, 2000]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
pretrained_model: ./pretrain_models/ch_ppocr_mobile_v2.1_det_distill_train/best_accuracy
checkpoints:
save_inference_dir:
use_visualdl: False
......@@ -19,30 +19,26 @@ Architecture:
name: DistillationModel
algorithm: Distillation
Models:
Student:
pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
freeze_params: false
Teacher:
freeze_params: true
return_all_feats: false
model_type: det
algorithm: DB
Transform:
Backbone:
name: MobileNetV3
scale: 0.5
model_name: large
disable_se: True
name: ResNet
layers: 18
Neck:
name: DBFPN
out_channels: 96
out_channels: 256
Head:
name: DBHead
k: 50
Student2:
pretrained: ./pretrain_models/MobileNetV3_large_x0_5_pretrained
Student:
freeze_params: false
return_all_feats: false
model_type: det
algorithm: DB
Transform:
Backbone:
name: MobileNetV3
scale: 0.5
......@@ -54,23 +50,24 @@ Architecture:
Head:
name: DBHead
k: 50
Teacher:
pretrained: ./pretrain_models/ch_ppocr_server_v2.0_det_train/best_accuracy
freeze_params: true
Student2:
freeze_params: false
return_all_feats: false
model_type: det
algorithm: DB
Transform:
Backbone:
name: ResNet
layers: 18
name: MobileNetV3
scale: 0.5
model_name: large
disable_se: True
Neck:
name: DBFPN
out_channels: 256
out_channels: 96
Head:
name: DBHead
k: 50
Loss:
name: CombinedLoss
loss_config_list:
......
Global:
use_gpu: true
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/ch_db_mv3/
save_epoch_step: 1200
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [0, 400]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/student.pdparams
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_en/img_10.jpg
save_res_path: ./output/det_db/predicts_db.txt
Architecture:
model_type: det
algorithm: DB
Transform:
Backbone:
name: MobileNetV3
scale: 0.5
model_name: large
disable_se: True
Neck:
name: DBFPN
out_channels: 96
Head:
name: DBHead
k: 50
Loss:
name: DBLoss
balance_loss: true
main_loss_type: DiceLoss
alpha: 5
beta: 10
ohem_ratio: 3
Optimizer:
name: Adam
beta1: 0.9
beta2: 0.999
lr:
name: Cosine
learning_rate: 0.001
warmup_epoch: 2
regularizer:
name: 'L2'
factor: 0
PostProcess:
name: DBPostProcess
thresh: 0.3
box_thresh: 0.6
max_candidates: 1000
unclip_ratio: 1.5
Metric:
name: DetMetric
main_indicator: hmean
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization/
label_file_list:
- ./train_data/icdar2015/text_localization/train_icdar2015_label.txt
ratio_list: [1.0]
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- IaaAugment:
augmenter_args:
- { 'type': Fliplr, 'args': { 'p': 0.5 } }
- { 'type': Affine, 'args': { 'rotate': [-10, 10] } }
- { 'type': Resize, 'args': { 'size': [0.5, 3] } }
- EastRandomCropData:
size: [960, 960]
max_tries: 50
keep_ratio: true
- MakeBorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- MakeShrinkMap:
shrink_ratio: 0.4
min_text_size: 8
- 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', 'threshold_map', 'threshold_mask', 'shrink_map', 'shrink_mask'] # the order of the dataloader list
loader:
shuffle: True
drop_last: False
batch_size_per_card: 8
num_workers: 4
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/icdar2015/text_localization/
label_file_list:
- ./train_data/icdar2015/text_localization/test_icdar2015_label.txt
transforms:
- DecodeImage: # load image
img_mode: BGR
channel_first: False
- DetLabelEncode: # Class handling label
- DetResizeForTest:
# image_shape: [736, 1280]
- 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', 'ignore_tags']
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1 # must be 1
num_workers: 2
......@@ -27,7 +27,7 @@ from ppocr.data import build_dataloader
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.metrics import build_metric
from ppocr.utils.save_load import init_model, load_pretrained_params
from ppocr.utils.save_load import init_model, load_dygraph_params
from ppocr.utils.utility import print_dict
import tools.program as program
......@@ -60,7 +60,7 @@ def main():
else:
model_type = None
best_model_dict = init_model(config, model)
best_model_dict = load_dygraph_params(config, model, logger, None)
if len(best_model_dict):
logger.info('metric in ckpt ***************')
for k, v in best_model_dict.items():
......@@ -71,7 +71,7 @@ def main():
# start eval
metric = program.eval(model, valid_dataloader, post_process_class,
eval_class, model_type, use_srn)
eval_class, model_type, use_srn)
logger.info('metric eval ***************')
for k, v in metric.items():
logger.info('{}:{}'.format(k, v))
......
......@@ -34,23 +34,21 @@ import paddle
from ppocr.data import create_operators, transform
from ppocr.modeling.architectures import build_model
from ppocr.postprocess import build_post_process
from ppocr.utils.save_load import init_model
from ppocr.utils.save_load import init_model, load_dygraph_params
from ppocr.utils.utility import get_image_file_list
import tools.program as program
def draw_det_res(dt_boxes, config, img, img_name):
def draw_det_res(dt_boxes, config, img, img_name, save_path):
if len(dt_boxes) > 0:
import cv2
src_im = img
for box in dt_boxes:
box = box.astype(np.int32).reshape((-1, 1, 2))
cv2.polylines(src_im, [box], True, color=(255, 255, 0), thickness=2)
save_det_path = os.path.dirname(config['Global'][
'save_res_path']) + "/det_results/"
if not os.path.exists(save_det_path):
os.makedirs(save_det_path)
save_path = os.path.join(save_det_path, os.path.basename(img_name))
if not os.path.exists(save_path):
os.makedirs(save_path)
save_path = os.path.join(save_path, os.path.basename(img_name))
cv2.imwrite(save_path, src_im)
logger.info("The detected Image saved in {}".format(save_path))
......@@ -61,8 +59,7 @@ def main():
# build model
model = build_model(config['Architecture'])
init_model(config, model)
_ = load_dygraph_params(config, model, logger, None)
# build post process
post_process_class = build_post_process(config['PostProcess'])
......@@ -96,17 +93,41 @@ def main():
images = paddle.to_tensor(images)
preds = model(images)
post_result = post_process_class(preds, shape_list)
boxes = post_result[0]['points']
# write result
src_img = cv2.imread(file)
dt_boxes_json = []
for box in boxes:
tmp_json = {"transcription": ""}
tmp_json['points'] = box.tolist()
dt_boxes_json.append(tmp_json)
# parser boxes if post_result is dict
if isinstance(post_result, dict):
det_box_json = {}
for k in post_result.keys():
boxes = post_result[k][0]['points']
dt_boxes_list = []
for box in boxes:
tmp_json = {"transcription": ""}
tmp_json['points'] = box.tolist()
dt_boxes_list.append(tmp_json)
det_box_json[k] = dt_boxes_list
save_det_path = os.path.dirname(config['Global'][
'save_res_path']) + "/det_results_{}/".format(k)
draw_det_res(boxes, config, src_img, file, save_det_path)
else:
boxes = post_result[0]['points']
dt_boxes_json = []
# write result
for box in boxes:
tmp_json = {"transcription": ""}
tmp_json['points'] = box.tolist()
dt_boxes_json.append(tmp_json)
save_det_path = os.path.dirname(config['Global'][
'save_res_path']) + "/det_results/"
draw_det_res(boxes, config, src_img, file, save_det_path)
otstr = file + "\t" + json.dumps(dt_boxes_json) + "\n"
fout.write(otstr.encode())
src_img = cv2.imread(file)
draw_det_res(boxes, config, src_img, file)
save_det_path = os.path.dirname(config['Global'][
'save_res_path']) + "/det_results/"
draw_det_res(boxes, config, src_img, file, save_det_path)
logger.info("success!")
......
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