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db_r50_ctw1500.yaml
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system:
mode: 0 # 0 for graph mode, 1 for pynative mode in MindSpore
distribute: False
amp_level: 'O0'
seed: 42
log_interval: 10
val_while_train: True
drop_overflow_update: False
model:
type: det
transform: null
backbone:
name: det_resnet50
pretrained: False
neck:
name: DBFPN
out_channels: 256
bias: False
head:
name: DBHead
k: 50
bias: False
adaptive: True
pretrained: https://download.mindspore.cn/toolkits/mindocr/dbnet/dbnet_resnet50_synthtext-40655acb.ckpt
postprocess:
name: DBPostprocess
box_type: poly # whether to output a polygon or a box
binary_thresh: 0.3 # binarization threshold
box_thresh: 0.6 # box score threshold
max_candidates: 1000
expand_ratio: 1.5 # coefficient for expanding predictions
metric:
name: DetMetric
main_indicator: f-score
loss:
name: DBLoss
eps: 1.0e-6
l1_scale: 10
bce_scale: 5
bce_replace: bceloss
scheduler:
scheduler: polynomial_decay
lr: 0.007
num_epochs: 1200
decay_rate: 0.9
warmup_epochs: 3
optimizer:
opt: SGD
filter_bias_and_bn: false
momentum: 0.9
weight_decay: 1.0e-4
# only used for mixed precision training
loss_scaler:
type: dynamic
loss_scale: 512
scale_factor: 2
scale_window: 1000
train:
ckpt_save_dir: './tmp_det'
dataset_sink_mode: True
dataset:
type: DetDataset
dataset_root: /data/ocr_datasets
data_dir: ctw1500/train_images
label_file: ctw1500/train_det_gt.txt
sample_ratio: 1.0
transform_pipeline:
- DecodeImage:
img_mode: RGB
to_float32: False
- DetLabelEncode:
- RandomColorAdjust:
brightness: 0.1255 # 32.0 / 255
saturation: 0.5
- RandomHorizontalFlip:
p: 0.5
- RandomScale:
scale_range: [ 0.5, 3.0 ]
p: 1.0
- RandomCropWithBBox:
max_tries: 10
min_crop_ratio: 0.1
crop_size: [ 640, 640 ]
p: 1.0
- ValidatePolygons:
- ShrinkBinaryMap:
min_text_size: 8
shrink_ratio: 0.55
- BorderMap:
shrink_ratio: 0.4
thresh_min: 0.3
thresh_max: 0.7
- NormalizeImage:
bgr_to_rgb: False
is_hwc: True
mean: imagenet
std: imagenet
- ToCHWImage:
# the order of the dataloader list, matching the network input and the input labels for the loss function, and optional data for debug/visualize
output_columns: [ 'image', 'binary_map', 'mask', 'thresh_map', 'thresh_mask' ] #'img_path']
# output_columns: ['image'] # for debug op performance
net_input_column_index: [0] # input indices for network forward func in output_columns
label_column_index: [1, 2, 3, 4] # input indices marked as label
loader:
shuffle: True
batch_size: 20
drop_remainder: True
num_workers: 20
eval:
ckpt_load_path: tmp_det/best.ckpt
dataset_sink_mode: False
dataset:
type: DetDataset
dataset_root: /data/ocr_datasets
data_dir: ctw1500/test_images
label_file: ctw1500/test_det_gt.txt
sample_ratio: 1.0
transform_pipeline:
- DecodeImage:
img_mode: RGB
to_float32: False
- DetLabelEncode:
- DetResize:
target_size: [ 1024, 1024 ] # h, w
keep_ratio: True
padding: True
- NormalizeImage:
bgr_to_rgb: False
is_hwc: True
mean: imagenet
std: imagenet
- ToCHWImage:
# the order of the dataloader list, matching the network input and the labels for evaluation
output_columns: [ 'image', 'polys', 'ignore_tags', 'shape_list' ]
net_input_column_index: [0] # input indices for network forward func in output_columns
label_column_index: [1, 2] # input indices marked as label
loader:
shuffle: False
batch_size: 1 # TODO: due to dynamic shape of polygons (num of boxes varies), BS has to be 1
drop_remainder: False
num_workers: 2