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fce_icdar15.yaml
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fce_icdar15.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:
resume: False
type: det
transform: null
backbone:
name: det_resnet50
pretrained: True
neck:
name: FCEFPN
out_channels: 256
head:
name: FCEHead
fourier_degree: 5
postprocess:
name: FCEPostprocess
box_type: 'quad'
alpha: 1.2
beta: 1.0
scales: [ 8, 16, 32 ]
metric:
name: DetMetric
main_indicator: f-score
loss:
name: FCELoss
fourier_degree: 5
num_sample: 50
scheduler:
scheduler: polynomial_decay
lr: 0.002
min_lr: 0.0000002
num_epochs: 1500
warmup_epochs: 0
optimizer:
opt: momentum
weight_decay: 5.0e-4
momentum: 0.9
filter_bias_and_bn: False
train:
ckpt_save_dir: './tmp_det'
dataset_sink_mode: True
ema: True
dataset:
type: DetDataset
dataset_root: /data/ocr_datasets
data_dir: ic15/det/train/ch4_training_images
label_file: ic15/det/train/det_gt.txt
sample_ratio: 1.0
transform_pipeline:
- DecodeImage:
img_mode: RGB
to_float32: False
- DetLabelEncode:
- DetResize:
target_size: [ 800, 800 ]
keep_ratio: True
padding: True
- RandomScale:
scale_range: [ 0.75,2.5 ]
p: 1.0
- RandomHorizontalFlip:
p: 0.5
- RandomRotate:
degrees: [ -10, 10 ]
expand_canvas: False
p: 1.0
- RandomCropWithBBox:
max_tries: 10
min_crop_ratio: 0.1
crop_size: [ 640, 640 ]
p: 1.0
- ValidatePolygons:
- RandomColorAdjust:
brightness: 0.1255 # 32.0 / 255
saturation: 0.5
- NormalizeImage:
bgr_to_rgb: False
is_hwc: True
mean: imagenet
std: imagenet
- FCENetTargets:
fourier_degree: 5
level_proportion_range: [ [ 0, 0.25 ], [ 0.2, 0.65 ], [ 0.55, 1.0 ] ]
- 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', 'p3_maps','p4_maps','p5_maps' ]
# 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 ] # input indices marked as label
loader:
shuffle: True
batch_size: 8
drop_remainder: True
num_workers: 8
eval:
ckpt_load_path: './best.ckpt'
dataset_sink_mode: False
dataset:
type: DetDataset
dataset_root: /data/ocr_datasets
data_dir: ic15/det/test/ch4_test_images
label_file: ic15/det/test/det_gt.txt
sample_ratio: 1.0
shuffle: False
transform_pipeline:
- DecodeImage:
img_mode: RGB
to_float32: False
- DetLabelEncode:
- DetResize:
target_size: [ 800, 800 ]
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','img_path','texts', 'shape_list' ]
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
# num_keys_of_labels: 2 # num labels
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: 1