mirror of https://github.com/open-mmlab/mmocr.git
199 lines
6.2 KiB
Python
199 lines
6.2 KiB
Python
_base_ = [
|
|
'../../_base_/default_runtime.py',
|
|
'../../_base_/recog_models/robust_scanner.py'
|
|
]
|
|
|
|
# optimizer
|
|
optimizer = dict(type='Adam', lr=1e-3)
|
|
optimizer_config = dict(grad_clip=None)
|
|
# learning policy
|
|
lr_config = dict(policy='step', step=[3, 4])
|
|
total_epochs = 5
|
|
|
|
img_norm_cfg = dict(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
|
|
train_pipeline = [
|
|
dict(type='LoadImageFromFile'),
|
|
dict(
|
|
type='ResizeOCR',
|
|
height=48,
|
|
min_width=48,
|
|
max_width=160,
|
|
keep_aspect_ratio=True,
|
|
width_downsample_ratio=0.25),
|
|
dict(type='ToTensorOCR'),
|
|
dict(type='NormalizeOCR', **img_norm_cfg),
|
|
dict(
|
|
type='Collect',
|
|
keys=['img'],
|
|
meta_keys=[
|
|
'filename', 'ori_shape', 'img_shape', 'text', 'valid_ratio'
|
|
]),
|
|
]
|
|
test_pipeline = [
|
|
dict(type='LoadImageFromFile'),
|
|
dict(
|
|
type='MultiRotateAugOCR',
|
|
rotate_degrees=[0, 90, 270],
|
|
transforms=[
|
|
dict(
|
|
type='ResizeOCR',
|
|
height=48,
|
|
min_width=48,
|
|
max_width=160,
|
|
keep_aspect_ratio=True,
|
|
width_downsample_ratio=0.25),
|
|
dict(type='ToTensorOCR'),
|
|
dict(type='NormalizeOCR', **img_norm_cfg),
|
|
dict(
|
|
type='Collect',
|
|
keys=['img'],
|
|
meta_keys=[
|
|
'filename', 'ori_shape', 'img_shape', 'valid_ratio'
|
|
]),
|
|
])
|
|
]
|
|
|
|
dataset_type = 'OCRDataset'
|
|
|
|
prefix = 'data/mixture/'
|
|
|
|
train_img_prefix1 = prefix + 'icdar_2011/Challenge1_Training_Task3_Images_GT'
|
|
train_img_prefix2 = prefix + 'icdar_2013/recog_train_data/' + \
|
|
'Challenge2_Training_Task3_Images_GT'
|
|
train_img_prefix3 = prefix + 'icdar_2015/ch4_training_word_images_gt'
|
|
train_img_prefix4 = prefix + 'coco_text/train_words'
|
|
train_img_prefix5 = prefix + 'III5K'
|
|
train_img_prefix6 = prefix + 'SynthText_Add/SynthText_Add'
|
|
train_img_prefix7 = prefix + 'SynthText/synthtext/SynthText_patch_horizontal'
|
|
train_img_prefix8 = prefix + 'mnt/ramdisk/max/90kDICT32px'
|
|
|
|
train_ann_file1 = prefix + 'icdar_2011/training_label_fix.txt',
|
|
train_ann_file2 = prefix + 'icdar_2013/recog_train_data/train_label.txt',
|
|
train_ann_file3 = prefix + 'icdar_2015/training_label_fix.txt',
|
|
train_ann_file4 = prefix + 'coco_text/train_label.txt',
|
|
train_ann_file5 = prefix + 'III5K/train_label.txt',
|
|
train_ann_file6 = prefix + 'SynthText_Add/SynthText_Add/' + \
|
|
'annotationlist/label.lmdb',
|
|
train_ann_file7 = prefix + 'SynthText/synthtext/shuffle_labels.lmdb',
|
|
train_ann_file8 = prefix + 'mnt/ramdisk/max/90kDICT32px/shuffle_labels.lmdb'
|
|
|
|
train1 = dict(
|
|
type=dataset_type,
|
|
img_prefix=train_img_prefix1,
|
|
ann_file=train_ann_file1,
|
|
loader=dict(
|
|
type='HardDiskLoader',
|
|
repeat=20,
|
|
parser=dict(
|
|
type='LineStrParser',
|
|
keys=['filename', 'text'],
|
|
keys_idx=[0, 1],
|
|
separator=' ')),
|
|
pipeline=train_pipeline,
|
|
test_mode=False)
|
|
|
|
train2 = {key: value for key, value in train1.items()}
|
|
train2['img_prefix'] = train_img_prefix2
|
|
train2['ann_file'] = train_ann_file2
|
|
|
|
train3 = {key: value for key, value in train1.items()}
|
|
train3['img_prefix'] = train_img_prefix3
|
|
train3['ann_file'] = train_ann_file3
|
|
|
|
train4 = {key: value for key, value in train1.items()}
|
|
train4['img_prefix'] = train_img_prefix4
|
|
train4['ann_file'] = train_ann_file4
|
|
|
|
train5 = {key: value for key, value in train1.items()}
|
|
train5['img_prefix'] = train_img_prefix5
|
|
train5['ann_file'] = train_ann_file5
|
|
|
|
train6 = dict(
|
|
type=dataset_type,
|
|
img_prefix=train_img_prefix6,
|
|
ann_file=train_ann_file6,
|
|
loader=dict(
|
|
type='LmdbLoader',
|
|
repeat=1,
|
|
parser=dict(
|
|
type='LineStrParser',
|
|
keys=['filename', 'text'],
|
|
keys_idx=[0, 1],
|
|
separator=' ')),
|
|
pipeline=train_pipeline,
|
|
test_mode=False)
|
|
|
|
train7 = {key: value for key, value in train6.items()}
|
|
train7['img_prefix'] = train_img_prefix7
|
|
train7['ann_file'] = train_ann_file7
|
|
|
|
train8 = {key: value for key, value in train6.items()}
|
|
train8['img_prefix'] = train_img_prefix8
|
|
train8['ann_file'] = train_ann_file8
|
|
|
|
test_img_prefix1 = prefix + 'testset/IIIT5K/'
|
|
test_img_prefix2 = prefix + 'testset/svt/'
|
|
test_img_prefix3 = prefix + 'testset/icdar_2013/Challenge2_Test_Task3_Images/'
|
|
test_img_prefix4 = prefix + 'testset/icdar_2015/ch4_test_word_images_gt'
|
|
test_img_prefix5 = prefix + 'testset/svtp/'
|
|
test_img_prefix6 = prefix + 'testset/ct80/'
|
|
|
|
test_ann_file1 = prefix + 'testset/IIIT5K/label.txt'
|
|
test_ann_file2 = prefix + 'testset/svt/test_list.txt'
|
|
test_ann_file3 = prefix + 'testset/icdar_2013/test_label_1015.txt'
|
|
test_ann_file4 = prefix + 'testset/icdar_2015/test_label.txt'
|
|
test_ann_file5 = prefix + 'testset/svtp/imagelist.txt'
|
|
test_ann_file6 = prefix + 'testset/ct80/imagelist.txt'
|
|
|
|
test1 = dict(
|
|
type=dataset_type,
|
|
img_prefix=test_img_prefix1,
|
|
ann_file=test_ann_file1,
|
|
loader=dict(
|
|
type='HardDiskLoader',
|
|
repeat=1,
|
|
parser=dict(
|
|
type='LineStrParser',
|
|
keys=['filename', 'text'],
|
|
keys_idx=[0, 1],
|
|
separator=' ')),
|
|
pipeline=test_pipeline,
|
|
test_mode=True)
|
|
|
|
test2 = {key: value for key, value in test1.items()}
|
|
test2['img_prefix'] = test_img_prefix2
|
|
test2['ann_file'] = test_ann_file2
|
|
|
|
test3 = {key: value for key, value in test1.items()}
|
|
test3['img_prefix'] = test_img_prefix3
|
|
test3['ann_file'] = test_ann_file3
|
|
|
|
test4 = {key: value for key, value in test1.items()}
|
|
test4['img_prefix'] = test_img_prefix4
|
|
test4['ann_file'] = test_ann_file4
|
|
|
|
test5 = {key: value for key, value in test1.items()}
|
|
test5['img_prefix'] = test_img_prefix5
|
|
test5['ann_file'] = test_ann_file5
|
|
|
|
test6 = {key: value for key, value in test1.items()}
|
|
test6['img_prefix'] = test_img_prefix6
|
|
test6['ann_file'] = test_ann_file6
|
|
|
|
data = dict(
|
|
samples_per_gpu=64,
|
|
workers_per_gpu=2,
|
|
train=dict(
|
|
type='ConcatDataset',
|
|
datasets=[
|
|
train1, train2, train3, train4, train5, train6, train7, train8
|
|
]),
|
|
val=dict(
|
|
type='ConcatDataset',
|
|
datasets=[test1, test2, test3, test4, test5, test6]),
|
|
test=dict(
|
|
type='ConcatDataset',
|
|
datasets=[test1, test2, test3, test4, test5, test6]))
|
|
|
|
evaluation = dict(interval=1, metric='acc')
|