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denseteacher_fcos_r50_fpn_coco_semi010.yml
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denseteacher_fcos_r50_fpn_coco_semi010.yml
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_BASE_: [
'../../fcos/fcos_r50_fpn_iou_multiscale_2x_coco.yml',
'../_base_/coco_detection_percent_10.yml',
]
log_iter: 50
snapshot_epoch: 5
epochs: &epochs 240
weights: output/denseteacher_fcos_r50_fpn_coco_semi010/model_final
### pretrain and warmup config, choose one and comment another
# pretrain_weights: https://bj.bcebos.com/v1/paddledet/models/fcos_r50_fpn_2x_coco_sup010.pdparams # mAP=26.3
# semi_start_iters: 0
# ema_start_iters: 0
# use_warmup: &use_warmup False
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_cos_pretrained.pdparams
semi_start_iters: 5000
ema_start_iters: 3000
use_warmup: &use_warmup True
### global config
use_simple_ema: True
ema_decay: 0.9996
ssod_method: DenseTeacher
DenseTeacher:
train_cfg:
sup_weight: 1.0
unsup_weight: 1.0
loss_weight: {distill_loss_cls: 4.0, distill_loss_box: 1.0, distill_loss_quality: 1.0}
concat_sup_data: True
suppress: linear
ratio: 0.01
gamma: 2.0
test_cfg:
inference_on: teacher
### reader config
worker_num: 2
SemiTrainReader:
sample_transforms:
- Decode: {}
- RandomResize: {target_size: [[640, 1333], [672, 1333], [704, 1333], [736, 1333], [768, 1333], [800, 1333]], keep_ratio: True, interp: 1}
- RandomFlip: {}
weak_aug:
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: true}
strong_aug:
- StrongAugImage: {transforms: [
RandomColorJitter: {prob: 0.8, brightness: 0.4, contrast: 0.4, saturation: 0.4, hue: 0.1},
RandomErasingCrop: {},
RandomGaussianBlur: {prob: 0.5, sigma: [0.1, 2.0]},
RandomGrayscale: {prob: 0.2},
]}
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: true}
sup_batch_transforms:
- Permute: {}
- PadBatch: {pad_to_stride: 32}
- Gt2FCOSTarget:
object_sizes_boundary: [64, 128, 256, 512]
center_sampling_radius: 1.5
downsample_ratios: [8, 16, 32, 64, 128]
num_shift: 0. # default 0.5
multiply_strides_reg_targets: False
norm_reg_targets: True
unsup_batch_transforms:
- Permute: {}
- PadBatch: {pad_to_stride: 32}
sup_batch_size: 2
unsup_batch_size: 2
shuffle: True
drop_last: True
EvalReader:
sample_transforms:
- Decode: {}
- Resize: {target_size: [800, 1333], keep_ratio: True, interp: 1}
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- Permute: {}
batch_transforms:
- PadBatch: {pad_to_stride: 32}
batch_size: 1
TestReader:
sample_transforms:
- Decode: {}
- Resize: {target_size: [800, 1333], keep_ratio: True, interp: 1}
- NormalizeImage: {mean: [0.485, 0.456, 0.406], std: [0.229, 0.224, 0.225], is_scale: True}
- Permute: {}
batch_transforms:
- PadBatch: {pad_to_stride: 32}
batch_size: 1
fuse_normalize: True
### model config
architecture: FCOS
FCOS:
backbone: ResNet
neck: FPN
fcos_head: FCOSHead
ResNet:
depth: 50
variant: 'b'
norm_type: bn
freeze_at: 0 # res2
return_idx: [1, 2, 3]
num_stages: 4
FPN:
out_channel: 256
spatial_scales: [0.125, 0.0625, 0.03125]
extra_stage: 2
has_extra_convs: True
use_c5: False
FCOSHead:
fcos_feat:
name: FCOSFeat
feat_in: 256
feat_out: 256
num_convs: 4
norm_type: "gn"
use_dcn: False
fpn_stride: [8, 16, 32, 64, 128]
prior_prob: 0.01
norm_reg_targets: True
centerness_on_reg: True
num_shift: 0. # default 0.5
multiply_strides_reg_targets: False
sqrt_score: False
fcos_loss:
name: FCOSLoss
loss_alpha: 0.25
loss_gamma: 2.0
iou_loss_type: "giou"
reg_weights: 1.0
quality: "iou"
nms:
name: MultiClassNMS
nms_top_k: 1000
keep_top_k: 100
score_threshold: 0.025
nms_threshold: 0.6
### other config
epoch: *epochs
LearningRate:
base_lr: 0.01
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [*epochs]
use_warmup: *use_warmup
- !LinearWarmup
start_factor: 0.001
steps: 1000
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0001
type: L2
clip_grad_by_value: 1.0