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R50_depth_mlc_occatmask_hom_concat.yaml
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R50_depth_mlc_occatmask_hom_concat.yaml
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VERSION: 2
MODEL:
META_ARCHITECTURE: "GeneralizedRCNN"
WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
BACKBONE:
NAME: "build_resnet_fpn_backbone"
RESNETS:
OUT_FEATURES: ["res2", "res3", "res4", "res5"]
DEPTH: 50
PIXEL_MEAN: [127.5, 127.5, 127.5]
PIXEL_STD: [1, 1, 1]
RESNETS:
OUT_FEATURES: ["res2", "res3", "res4", "res5"]
DEPTH: 50
FPN:
IN_FEATURES: ["res2", "res3", "res4", "res5"]
ANCHOR_GENERATOR:
SIZES: [[16], [32], [64], [128], [256]] # One size for each in feature map
ASPECT_RATIOS: [[0.5, 1.0, 2.0]] # Three aspect ratios (same for all in feature maps)
RPN:
IN_FEATURES: ["p2", "p3", "p4", "p5", "p6"]
PRE_NMS_TOPK_TRAIN: 2000 # Per FPN level
PRE_NMS_TOPK_TEST: 1000 # Per FPN level
POST_NMS_TOPK_TRAIN: 1000
POST_NMS_TOPK_TEST: 1000
ROI_HEADS:
NAME: "ORCNNROIHeads"
IN_FEATURES: ["p2", "p3", "p4", "p5"]
NUM_CLASSES: 1
ROI_BOX_HEAD:
NAME: "MLCFastRCNNConvFCHead"
NUM_FC: 0
NUM_CONV: 4
POOLER_RESOLUTION: 7
CLS_AGNOSTIC_BBOX_REG: True
ROI_MASK_HEAD:
NAME: "AmodalMaskRCNNConvUpsampleHead"
NUM_CONV: 4
POOLER_RESOLUTION: 14
ROI_VISIBLE_MASK_HEAD:
NAME: "VisibleMaskRCNNConvUpsampleHead"
NUM_CONV: 4
POOLER_RESOLUTION: 14
BOXINST:
ENABLED: False
MASK_ON: True
OCC_CLS_AT_BOX: False
MULTI_LEVEL_CODING: True
OCC_CLS_AT_MASK: True
HIERARCHCIAL_OCCLUSION_MODELING: True
PREDICTION_ORDER: ["V", "A", "O"]
GUIDANCE_TYPE: "concat"
SOLVER:
IMS_PER_BATCH: 2
BASE_LR: 0.00125
STEPS: (60000, 80000)
MAX_ITER: 90000
WARMUP_ITERS: 5000
CHECKPOINT_PERIOD: 50000
CLIP_GRADIENTS:
ENABLED: True
DATASETS:
TRAIN: ("uoais_sim_train_amodal",)
TEST: ("uoais_sim_val_amodal",)
TEST:
EVAL_PERIOD: 90000 # no evaluation if 0
EVAL_TARGET: ["amodal_visible"]
INPUT:
MASK_FORMAT: "rle"
IMG_SIZE: (640, 480)
COLOR_AUGMENTATION: False
PERLIN_DISTORTION: True
CROP_RATIO: 0.5
AMODAL: True
DEPTH: True
DEPTH_ONLY: True
DEPTH_RANGE: [2500, 15000]
OUTPUT_DIR: "output/R50_depth_mlc_occatmask_hom_concat"
SEED: 7