forked from PulseFocusPlatform/PulseFocusPlatform
91 lines
1.6 KiB
YAML
91 lines
1.6 KiB
YAML
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architecture: RetinaNet
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max_iters: 90000
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use_gpu: true
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pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar
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weights: output/retinanet_r101_fpn_1x/model_final
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log_iter: 20
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snapshot_iter: 10000
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metric: COCO
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save_dir: output
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num_classes: 81
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RetinaNet:
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backbone: ResNet
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fpn: FPN
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retina_head: RetinaHead
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ResNet:
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norm_type: affine_channel
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norm_decay: 0.
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depth: 101
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feature_maps: [3, 4, 5]
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freeze_at: 2
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FPN:
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max_level: 7
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min_level: 3
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num_chan: 256
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spatial_scale: [0.03125, 0.0625, 0.125]
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has_extra_convs: true
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RetinaHead:
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num_convs_per_octave: 4
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num_chan: 256
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max_level: 7
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min_level: 3
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prior_prob: 0.01
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base_scale: 4
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num_scales_per_octave: 3
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anchor_generator:
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aspect_ratios: [1.0, 2.0, 0.5]
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variance: [1.0, 1.0, 1.0, 1.0]
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target_assign:
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positive_overlap: 0.5
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negative_overlap: 0.4
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gamma: 2.0
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alpha: 0.25
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sigma: 3.0151134457776365
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output_decoder:
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score_thresh: 0.05
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nms_thresh: 0.5
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pre_nms_top_n: 1000
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detections_per_im: 100
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nms_eta: 1.0
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LearningRate:
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base_lr: 0.01
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schedulers:
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- !PiecewiseDecay
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gamma: 0.1
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milestones: [60000, 80000]
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- !LinearWarmup
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start_factor: 0.3333333333333333
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steps: 500
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OptimizerBuilder:
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optimizer:
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momentum: 0.9
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type: Momentum
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regularizer:
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factor: 0.0001
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type: L2
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_READER_: 'faster_fpn_reader.yml'
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TrainReader:
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batch_size: 2
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batch_transforms:
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- !PadBatch
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pad_to_stride: 128
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EvalReader:
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batch_size: 2
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batch_transforms:
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- !PadBatch
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pad_to_stride: 128
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TestReader:
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batch_size: 1
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batch_transforms:
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- !PadBatch
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pad_to_stride: 128
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