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| VERSION: 2 | |
| MODEL: | |
| META_ARCHITECTURE: "GeneralizedRCNN" | |
| BACKBONE: | |
| NAME: "build_resnet_fpn_backbone" | |
| RESNETS: | |
| OUT_FEATURES: ["res2", "res3", "res4", "res5"] | |
| FPN: | |
| IN_FEATURES: ["res2", "res3", "res4", "res5"] | |
| ANCHOR_GENERATOR: | |
| SIZES: [[32], [64], [128], [256], [512]] # 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 | |
| # Detectron1 uses 2000 proposals per-batch, | |
| # (See "modeling/rpn/rpn_outputs.py" for details of this legacy issue) | |
| # which is approximately 1000 proposals per-image since the default batch size for FPN is 2. | |
| POST_NMS_TOPK_TRAIN: 1000 | |
| POST_NMS_TOPK_TEST: 1000 | |
| DENSEPOSE_ON: True | |
| ROI_HEADS: | |
| NAME: "DensePoseROIHeads" | |
| IN_FEATURES: ["p2", "p3", "p4", "p5"] | |
| NUM_CLASSES: 1 | |
| ROI_BOX_HEAD: | |
| NAME: "FastRCNNConvFCHead" | |
| NUM_FC: 2 | |
| POOLER_RESOLUTION: 7 | |
| POOLER_SAMPLING_RATIO: 2 | |
| POOLER_TYPE: "ROIAlign" | |
| ROI_DENSEPOSE_HEAD: | |
| NAME: "DensePoseV1ConvXHead" | |
| POOLER_TYPE: "ROIAlign" | |
| NUM_COARSE_SEGM_CHANNELS: 2 | |
| DATASETS: | |
| TRAIN: ("densepose_coco_2014_train", "densepose_coco_2014_valminusminival") | |
| TEST: ("densepose_coco_2014_minival",) | |
| SOLVER: | |
| IMS_PER_BATCH: 16 | |
| BASE_LR: 0.01 | |
| STEPS: (60000, 80000) | |
| MAX_ITER: 90000 | |
| WARMUP_FACTOR: 0.1 | |
| INPUT: | |
| MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800) | |