forked from PulseFocusPlatform/PulseFocusPlatform
105 lines
1.9 KiB
YAML
105 lines
1.9 KiB
YAML
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architecture: MaskRCNN
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use_gpu: true
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max_iters: 360000
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snapshot_iter: 10000
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log_iter: 20
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save_dir: output
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pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
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metric: COCO
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weights: output/mask_rcnn_r50_2x/model_final
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num_classes: 81
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MaskRCNN:
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backbone: ResNet
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rpn_head: RPNHead
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roi_extractor: RoIAlign
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bbox_assigner: BBoxAssigner
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bbox_head: BBoxHead
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mask_assigner: MaskAssigner
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mask_head: MaskHead
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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: 50
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feature_maps: 4
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freeze_at: 2
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ResNetC5:
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depth: 50
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norm_type: affine_channel
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RPNHead:
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anchor_generator:
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anchor_sizes: [32, 64, 128, 256, 512]
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aspect_ratios: [0.5, 1.0, 2.0]
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stride: [16.0, 16.0]
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variance: [1.0, 1.0, 1.0, 1.0]
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rpn_target_assign:
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rpn_batch_size_per_im: 256
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rpn_fg_fraction: 0.5
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rpn_negative_overlap: 0.3
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rpn_positive_overlap: 0.7
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rpn_straddle_thresh: 0.0
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train_proposal:
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min_size: 0.0
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nms_thresh: 0.7
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pre_nms_top_n: 12000
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post_nms_top_n: 2000
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test_proposal:
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min_size: 0.0
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nms_thresh: 0.7
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pre_nms_top_n: 6000
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post_nms_top_n: 1000
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RoIAlign:
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resolution: 14
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spatial_scale: 0.0625
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sampling_ratio: 0
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BBoxHead:
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head: ResNetC5
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nms:
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keep_top_k: 100
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nms_threshold: 0.5
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normalized: false
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score_threshold: 0.05
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MaskHead:
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dilation: 1
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conv_dim: 256
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resolution: 14
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BBoxAssigner:
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batch_size_per_im: 512
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bbox_reg_weights: [0.1, 0.1, 0.2, 0.2]
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bg_thresh_hi: 0.5
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bg_thresh_lo: 0.0
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fg_fraction: 0.25
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fg_thresh: 0.5
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MaskAssigner:
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resolution: 14
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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: [240000, 320000]
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#start the warm up from base_lr * start_factor
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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_: 'mask_reader.yml'
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