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fix det inference bug and optimize save path

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revert-162-develop
LDOUBLEV 5 years ago
parent ab8fd7d947
commit e3f171a3bb
16 changed files with 57 additions and 30 deletions
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4
configs/det/det_db_mv3.yml
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@ -4,7 +4,7 @@ Global:
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 2
save_model_dir: output
save_model_dir: ./output/det_db/
save_epoch_step: 200
eval_batch_step: 5000
train_batch_size_per_card: 16
@ -13,7 +13,7 @@ Global:
reader_yml: ./configs/det/det_db_icdar15_reader.yml
pretrain_weights: ./pretrain_models/MobileNetV3_pretrained/MobileNetV3_large_x0_5_pretrained/
checkpoints:
save_res_path: ./output/predicts_db.txt
save_res_path: ./output/det_db/predicts_db.txt
save_inference_dir:
Architecture:

8
configs/det/det_db_r50_vd.yml
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@ -4,7 +4,7 @@ Global:
epoch_num: 1200
log_smooth_window: 20
print_batch_step: 2
save_model_dir: output
save_model_dir: ./output/det_db/
save_epoch_step: 200
eval_batch_step: 5000
train_batch_size_per_card: 8
@ -12,8 +12,10 @@ Global:
image_shape: [3, 640, 640]
reader_yml: ./configs/det/det_db_icdar15_reader.yml
pretrain_weights: ./pretrain_models/ResNet50_vd_pretrained/
save_res_path: ./output/predicts_db.txt
save_res_path: ./output/det_db/predicts_db.txt
checkpoints:
save_inference_dir:
Architecture:
function: ppocr.modeling.architectures.det_model,DetModel

6
configs/det/det_east_mv3.yml
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@ -4,7 +4,7 @@ Global:
epoch_num: 100000
log_smooth_window: 20
print_batch_step: 5
save_model_dir: output
save_model_dir: ./output/det_east/
save_epoch_step: 200
eval_batch_step: 5000
train_batch_size_per_card: 16
@ -12,7 +12,9 @@ Global:
image_shape: [3, 512, 512]
reader_yml: ./configs/det/det_east_icdar15_reader.yml
pretrain_weights: ./pretrain_models/MobileNetV3_pretrained/MobileNetV3_large_x0_5_pretrained/
save_res_path: ./output/predicts_east.txt
checkpoints:
save_res_path: ./output/det_east/predicts_east.txt
save_inference_dir:
Architecture:
function: ppocr.modeling.architectures.det_model,DetModel

8
configs/det/det_east_r50_vd.yml
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@ -4,7 +4,7 @@ Global:
epoch_num: 100000
log_smooth_window: 20
print_batch_step: 5
save_model_dir: output
save_model_dir: ./output/det_east/
save_epoch_step: 200
eval_batch_step: 5000
train_batch_size_per_card: 8
@ -12,8 +12,10 @@ Global:
image_shape: [3, 512, 512]
reader_yml: ./configs/det/det_east_icdar15_reader.yml
pretrain_weights: ./pretrain_models/ResNet50_vd_pretrained/
save_res_path: ./output/predicts_east.txt
save_res_path: ./output/det_east/predicts_east.txt
checkpoints:
save_inference_dir:
Architecture:
function: ppocr.modeling.architectures.det_model,DetModel

6
configs/rec/rec_mv3_none_bilstm_ctc.yml
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@ -4,7 +4,7 @@ Global:
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: output
save_model_dir: output/rec
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
@ -14,7 +14,9 @@ Global:
character_type: en
loss_type: ctc
reader_yml: ./configs/rec/rec_benchmark_reader.yml
pretrain_weights:
pretrain_weights:
checkpoints:
save_inference_dir:
Architecture:
function: ppocr.modeling.architectures.rec_model,RecModel

6
configs/rec/rec_mv3_none_none_ctc.yml
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@ -4,7 +4,7 @@ Global:
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: output
save_model_dir: output/rec
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
@ -15,7 +15,9 @@ Global:
loss_type: ctc
reader_yml: ./configs/rec/rec_benchmark_reader.yml
pretrain_weights:
checkpoints:
save_inference_dir:
Architecture:
function: ppocr.modeling.architectures.rec_model,RecModel

6
configs/rec/rec_mv3_tps_bilstm_attn.yml
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@ -4,7 +4,7 @@ Global:
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: output
save_model_dir: output/rec
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
@ -14,7 +14,9 @@ Global:
character_type: en
loss_type: attention
reader_yml: ./configs/rec/rec_benchmark_reader.yml
pretrain_weights:
pretrain_weights:
checkpoints:
save_inference_dir:
Architecture:
function: ppocr.modeling.architectures.rec_model,RecModel

7
configs/rec/rec_mv3_tps_bilstm_ctc.yml
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@ -4,7 +4,7 @@ Global:
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: output
save_model_dir: output/rec
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
@ -14,7 +14,10 @@ Global:
character_type: en
loss_type: ctc
reader_yml: ./configs/rec/rec_benchmark_reader.yml
pretrain_weights:
pretrain_weights:
checkpoints:
save_inference_dir:
Architecture:
function: ppocr.modeling.architectures.rec_model,RecModel

6
configs/rec/rec_r34_vd_none_bilstm_ctc.yml
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@ -4,7 +4,7 @@ Global:
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: output
save_model_dir: output/rec
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
@ -14,7 +14,9 @@ Global:
character_type: en
loss_type: ctc
reader_yml: ./configs/rec/rec_benchmark_reader.yml
pretrain_weights:
pretrain_weights:
checkpoints:
save_inference_dir:
Architecture:
function: ppocr.modeling.architectures.rec_model,RecModel

6
configs/rec/rec_r34_vd_none_none_ctc.yml
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@ -4,7 +4,7 @@ Global:
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: output
save_model_dir: output/rec
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
@ -14,7 +14,9 @@ Global:
character_type: en
loss_type: ctc
reader_yml: ./configs/rec/rec_benchmark_reader.yml
pretrain_weights:
pretrain_weights:
checkpoints:
save_inference_dir:
Architecture:
function: ppocr.modeling.architectures.rec_model,RecModel

6
configs/rec/rec_r34_vd_tps_bilstm_attn.yml
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@ -4,7 +4,7 @@ Global:
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: output
save_model_dir: output/rec
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
@ -14,7 +14,9 @@ Global:
character_type: en
loss_type: attention
reader_yml: ./configs/rec/rec_benchmark_reader.yml
pretrain_weights:
pretrain_weights:
checkpoints:
save_inference_dir:
Architecture:
function: ppocr.modeling.architectures.rec_model,RecModel

4
configs/rec/rec_r34_vd_tps_bilstm_ctc.yml
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@ -4,7 +4,7 @@ Global:
epoch_num: 72
log_smooth_window: 20
print_batch_step: 10
save_model_dir: output
save_model_dir: output/rec
save_epoch_step: 3
eval_batch_step: 2000
train_batch_size_per_card: 256
@ -15,6 +15,8 @@ Global:
loss_type: ctc
reader_yml: ./configs/rec/rec_benchmark_reader.yml
pretrain_weights:
checkpoints:
save_inference_dir:
Architecture:
function: ppocr.modeling.architectures.rec_model,RecModel

2
ppocr/modeling/heads/det_db_head.py
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@ -196,7 +196,7 @@ class DBHead(object):
fuse = fluid.layers.concat(input=[p5, p4, p3, p2], axis=1)
shrink_maps = self.binarize(fuse)
if mode != "train":
return shrink_maps
return {"maps", shrink_maps}
threshold_maps = self.thresh(fuse)
binary_maps = self.step_function(shrink_maps, threshold_maps)
y = fluid.layers.concat(

1
ppocr/postprocess/db_postprocess.py
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@ -128,6 +128,7 @@ class DBPostProcess(object):
def __call__(self, outs_dict, ratio_list):
pred = outs_dict['maps']
pred = pred[:, 0, :, :]
segmentation = pred > self.thresh

9
tools/infer/predict_det.py
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@ -24,6 +24,7 @@ import copy
import numpy as np
import math
import time
import sys
class TextDetector(object):
@ -52,10 +53,10 @@ class TextDetector(object):
utility.create_predictor(args, mode="det")
def order_points_clockwise(self, pts):
#######
## https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
########
"""
reference from: https://github.com/jrosebr1/imutils/blob/master/imutils/perspective.py
# sort the points based on their x-coordinates
"""
xSorted = pts[np.argsort(pts[:, 0]), :]
# grab the left-most and right-most points from the sorted
@ -141,7 +142,7 @@ class TextDetector(object):
outs_dict['f_score'] = outputs[0]
outs_dict['f_geo'] = outputs[1]
else:
outs_dict['maps'] = [outputs[0]]
outs_dict['maps'] = outputs[0]
dt_boxes_list = self.postprocess_op(outs_dict, [ratio_list])
dt_boxes = dt_boxes_list[0]
dt_boxes = self.filter_tag_det_res(dt_boxes, ori_im.shape)

2
tools/program.py
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@ -219,6 +219,8 @@ def train_eval_det_run(config, exe, train_info_dict, eval_info_dict):
eval_batch_step = config['Global']['eval_batch_step']
save_epoch_step = config['Global']['save_epoch_step']
save_model_dir = config['Global']['save_model_dir']
if not os.path.exists(save_model_dir):
os.makedirs(save_model_dir)
train_stats = TrainingStats(log_smooth_window,
train_info_dict['fetch_name_list'])
best_eval_hmean = -1

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