mirror of https://github.com/RE-OWOD/RE-OWOD
122 lines
3.2 KiB
YAML
122 lines
3.2 KiB
YAML
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MODEL:
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META_ARCHITECTURE: "GeneralizedRCNN"
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BACKBONE:
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NAME: "build_resnet_fpn_backbone"
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RESNETS:
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OUT_FEATURES: ["res2", "res3", "res4", "res5"]
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FPN:
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IN_FEATURES: ["res2", "res3", "res4", "res5"]
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ANCHOR_GENERATOR:
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SIZES: [[32], [64], [128], [256], [512]] # One size for each in feature map
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ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps)
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RPN:
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IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
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PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level
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PRE_NMS_TOPK_TEST: 1000 # Per FPN level
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# Detectron1 uses 2000 proposals per-batch,
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# (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue)
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# which is approximately 1000 proposals per-image since the default batch size for FPN is 2.
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POST_NMS_TOPK_TRAIN: 1000
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POST_NMS_TOPK_TEST: 1000
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ROI_HEADS:
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NAME: "StandardROIHeads"
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IN_FEATURES: ["p2", "p3", "p4", "p5"]
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NUM_CLASSES: 1
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ROI_BOX_HEAD:
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NAME: "FastRCNNConvFCHead"
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NUM_FC: 2
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POOLER_RESOLUTION: 7
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ROI_MASK_HEAD:
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NAME: "MaskRCNNConvUpsampleHead"
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NUM_CONV: 4
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POOLER_RESOLUTION: 14
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DATASETS:
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TRAIN: ("base_coco_2017_train",)
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TEST: ("base_coco_2017_val", "densepose_chimps")
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CATEGORY_MAPS:
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"base_coco_2017_train":
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"16": 1 # bird -> person
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"17": 1 # cat -> person
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"18": 1 # dog -> person
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"19": 1 # horse -> person
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"20": 1 # sheep -> person
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"21": 1 # cow -> person
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"22": 1 # elephant -> person
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"23": 1 # bear -> person
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"24": 1 # zebra -> person
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"25": 1 # girafe -> person
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"base_coco_2017_val":
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"16": 1 # bird -> person
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"17": 1 # cat -> person
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"18": 1 # dog -> person
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"19": 1 # horse -> person
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"20": 1 # sheep -> person
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"21": 1 # cow -> person
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"22": 1 # elephant -> person
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"23": 1 # bear -> person
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"24": 1 # zebra -> person
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"25": 1 # girafe -> person
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WHITELISTED_CATEGORIES:
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"base_coco_2017_train":
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- 1 # person
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- 16 # bird
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- 17 # cat
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- 18 # dog
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- 19 # horse
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- 20 # sheep
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- 21 # cow
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- 22 # elephant
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- 23 # bear
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- 24 # zebra
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- 25 # girafe
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"base_coco_2017_val":
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- 1 # person
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- 16 # bird
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- 17 # cat
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- 18 # dog
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- 19 # horse
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- 20 # sheep
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- 21 # cow
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- 22 # elephant
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- 23 # bear
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- 24 # zebra
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- 25 # girafe
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BOOTSTRAP_DATASETS:
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- DATASET: "chimpnsee"
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RATIO: 1.0
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IMAGE_LOADER:
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TYPE: "video_keyframe"
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SELECT:
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STRATEGY: "random_k"
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NUM_IMAGES: 4
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TRANSFORM:
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TYPE: "resize"
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MIN_SIZE: 800
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MAX_SIZE: 1333
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BATCH_SIZE: 8
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NUM_WORKERS: 1
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INFERENCE:
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INPUT_BATCH_SIZE: 1
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OUTPUT_BATCH_SIZE: 1
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DATA_SAMPLER:
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# supported types:
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# densepose_uniform
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# densepose_UV_confidence
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# densepose_fine_segm_confidence
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# densepose_coarse_segm_confidence
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TYPE: "densepose_uniform"
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COUNT_PER_CLASS: 8
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FILTER:
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TYPE: "detection_score"
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MIN_VALUE: 0.8
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BOOTSTRAP_MODEL:
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WEIGHTS: ""
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SOLVER:
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IMS_PER_BATCH: 16
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BASE_LR: 0.02
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STEPS: (60000, 80000)
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MAX_ITER: 90000
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INPUT:
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MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
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VERSION: 2
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