update configs

configs/Base-RCNN-FPN-4gpu.yaml

@@ -1,44 +0,0 @@
-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
-  ROI_HEADS:
-    NAME: "StandardROIHeads"
-    IN_FEATURES: ["p2", "p3", "p4", "p5"]
-  ROI_BOX_HEAD:
-    NAME: "FastRCNNConvFCHead"
-    NUM_FC: 2
-    POOLER_RESOLUTION: 7
-  ROI_MASK_HEAD:
-    NAME: "MaskRCNNConvUpsampleHead"
-    NUM_CONV: 4
-    POOLER_RESOLUTION: 14
-DATASETS:
-  TRAIN: ("coco_2017_train",)
-  #TEST: ("coco_2017_val",)
-  #TEST: ("lvis_v0.5_val_cocofied",)
-  TEST: ("coco_2017_test-dev",)
-SOLVER:
-  IMS_PER_BATCH: 16 #8 #16
-  BASE_LR: 0.02 # 0.02
-  STEPS: (60000, 80000)
-  MAX_ITER: 90000
-INPUT:
-  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
-VERSION: 2

configs/Base-RCNN-FPN.yaml

@@ -31,7 +31,8 @@ MODEL:
     POOLER_RESOLUTION: 14
 DATASETS:
   TRAIN: ("coco_2017_train",)
-  TEST: ("coco_2017_val",)
+  #TEST: ("coco_2017_val",)
+  TEST: ("coco_2017_test-dev",)
 SOLVER:
   IMS_PER_BATCH: 16 #16
   BASE_LR: 0.02

configs/COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml

@@ -1,17 +0,0 @@
-_BASE_: "../Base-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  LOAD_PROPOSALS: True
-  RESNETS:
-    DEPTH: 50
-  PROPOSAL_GENERATOR:
-    NAME: "PrecomputedProposals"
-DATASETS:
-  TRAIN: ("coco_2017_train",)
-  PROPOSAL_FILES_TRAIN: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_train_box_proposals_21bc3a.pkl", )
-  TEST: ("coco_2017_val",)
-  PROPOSAL_FILES_TEST: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_val_box_proposals_ee0dad.pkl", )
-DATALOADER:
-  # proposals are part of the dataset_dicts, and take a lot of RAM
-  NUM_WORKERS: 2

configs/COCO-Detection/faster_rcnn_R_101_C4_3x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-C4.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-Detection/faster_rcnn_R_101_DC5_3x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-DilatedC5.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-Detection/faster_rcnn_R_50_C4_1x.yaml

@@ -1,6 +0,0 @@
-_BASE_: "../Base-RCNN-C4.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 50

configs/COCO-Detection/faster_rcnn_R_50_C4_3x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-C4.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 50
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-Detection/faster_rcnn_R_50_DC5_1x.yaml

@@ -1,6 +0,0 @@
-_BASE_: "../Base-RCNN-DilatedC5.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 50

configs/COCO-Detection/faster_rcnn_R_50_DC5_3x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-DilatedC5.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 50
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml

@@ -1,6 +0,0 @@
-_BASE_: "../Base-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 50

configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 50
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml

@@ -1,13 +0,0 @@
-_BASE_: "../Base-RCNN-FPN.yaml"
-MODEL:
-  MASK_ON: False
-  WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl"
-  PIXEL_STD: [57.375, 57.120, 58.395]
-  RESNETS:
-    STRIDE_IN_1X1: False  # this is a C2 model
-    NUM_GROUPS: 32
-    WIDTH_PER_GROUP: 8
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-Detection/retinanet_R_101_FPN_3x.yaml

@@ -1,8 +0,0 @@
-_BASE_: "../Base-RetinaNet.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-Detection/retinanet_R_50_FPN_1x.py

@@ -1,9 +0,0 @@
-from ..common.optim import SGD as optimizer
-from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
-from ..common.data.coco import dataloader
-from ..common.models.retinanet import model
-from ..common.train import train
-
-dataloader.train.mapper.use_instance_mask = False
-model.backbone.bottom_up.freeze_at = 2
-optimizer.lr = 0.01

configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml

@@ -1,5 +0,0 @@
-_BASE_: "../Base-RetinaNet.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  RESNETS:
-    DEPTH: 50

configs/COCO-Detection/retinanet_R_50_FPN_3x.yaml

@@ -1,8 +0,0 @@
-_BASE_: "../Base-RetinaNet.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  RESNETS:
-    DEPTH: 50
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-Detection/rpn_R_50_C4_1x.yaml

@@ -1,10 +0,0 @@
-_BASE_: "../Base-RCNN-C4.yaml"
-MODEL:
-  META_ARCHITECTURE: "ProposalNetwork"
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 50
-  RPN:
-    PRE_NMS_TOPK_TEST: 12000
-    POST_NMS_TOPK_TEST: 2000

configs/COCO-Detection/rpn_R_50_FPN_1x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-FPN.yaml"
-MODEL:
-  META_ARCHITECTURE: "ProposalNetwork"
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: False
-  RESNETS:
-    DEPTH: 50
-  RPN:
-    POST_NMS_TOPK_TEST: 2000

configs/COCO-InstanceSegmentation/mask_rcnn_R_101_C4_3x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-C4.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  MASK_ON: True
-  RESNETS:
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-InstanceSegmentation/mask_rcnn_R_101_DC5_3x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-DilatedC5.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  MASK_ON: True
-  RESNETS:
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x_4gpu_transfiner.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-FPN-4gpu.yaml"
-MODEL:
-  WEIGHTS: "./init_weights/model_final_a3ec72.pkl"
-  MASK_ON: True
-  RESNETS:
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x_4gpu_transfiner_lvis.yaml

@@ -1,12 +0,0 @@
-_BASE_: "../Base-RCNN-FPN-4gpu.yaml"
-MODEL:
-  WEIGHTS: "./init_weights/model_final_a3ec72.pkl"
-  MASK_ON: True
-  RESNETS:
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000
-DATASETS:
-  TEST: ("lvis_v0.5_val_cocofied",)
-

configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.py

@@ -1,7 +0,0 @@
-from ..common.train import train
-from ..common.optim import SGD as optimizer
-from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
-from ..common.data.coco import dataloader
-from ..common.models.mask_rcnn_c4 import model
-
-model.backbone.freeze_at = 2

configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.yaml

@@ -1,6 +0,0 @@
-_BASE_: "../Base-RCNN-C4.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: True
-  RESNETS:
-    DEPTH: 50

configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-C4.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: True
-  RESNETS:
-    DEPTH: 50
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_1x.yaml

@@ -1,6 +0,0 @@
-_BASE_: "../Base-RCNN-DilatedC5.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: True
-  RESNETS:
-    DEPTH: 50

configs/COCO-InstanceSegmentation/mask_rcnn_R_50_DC5_3x.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-DilatedC5.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: True
-  RESNETS:
-    DEPTH: 50
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.py

@@ -1,7 +0,0 @@
-from ..common.optim import SGD as optimizer
-from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
-from ..common.data.coco import dataloader
-from ..common.models.mask_rcnn_fpn import model
-from ..common.train import train
-
-model.backbone.bottom_up.freeze_at = 2

configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_4gpu.yaml

@@ -1,6 +0,0 @@
-_BASE_: "../Base-RCNN-FPN-4gpu.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: True
-  RESNETS:
-    DEPTH: 50

configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_4gpu_transfiner.yaml

@@ -1,6 +0,0 @@
-_BASE_: "../Base-RCNN-FPN-4gpu.yaml"
-MODEL:
-  WEIGHTS: "./init_weights/model_final_a54504.pkl"
-  MASK_ON: True
-  RESNETS:
-    DEPTH: 50

configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x_giou.yaml

@@ -1,12 +0,0 @@
-_BASE_: "../Base-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  MASK_ON: True
-  RESNETS:
-    DEPTH: 50
-  RPN:
-    BBOX_REG_LOSS_TYPE: "giou"
-    BBOX_REG_LOSS_WEIGHT: 2.0
-  ROI_BOX_HEAD:
-    BBOX_REG_LOSS_TYPE: "giou"
-    BBOX_REG_LOSS_WEIGHT: 10.0

configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x_4gpu_transfiner.yaml

@@ -1,9 +0,0 @@
-_BASE_: "../Base-RCNN-FPN-4gpu.yaml"
-MODEL:
-  WEIGHTS: "./init_weights/model_final_f10217.pkl"
-  MASK_ON: True
-  RESNETS:
-    DEPTH: 50
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x_4gpu_transfiner_lvis.yaml

@@ -1,12 +0,0 @@
-_BASE_: "../Base-RCNN-FPN-4gpu.yaml"
-MODEL:
-  WEIGHTS: "./init_weights/model_final_f10217.pkl"
-  MASK_ON: True
-  RESNETS:
-    DEPTH: 50
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000
-DATASETS:
-  TEST: ("lvis_v0.5_val_cocofied",)
-

configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x.yaml

@@ -1,13 +0,0 @@
-_BASE_: "../Base-RCNN-FPN.yaml"
-MODEL:
-  MASK_ON: True
-  WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl"
-  PIXEL_STD: [57.375, 57.120, 58.395]
-  RESNETS:
-    STRIDE_IN_1X1: False  # this is a C2 model
-    NUM_GROUPS: 32
-    WIDTH_PER_GROUP: 8
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-InstanceSegmentation/mask_rcnn_X_101_32x8d_FPN_3x_transfiner.yaml

@@ -1,13 +0,0 @@
-_BASE_: "../Base-RCNN-FPN-4gpu.yaml"
-MODEL:
-  MASK_ON: True
-  WEIGHTS: "./init_weights/model_final_x101.pkl"
-  PIXEL_STD: [57.375, 57.120, 58.395]
-  RESNETS:
-    STRIDE_IN_1X1: False  # this is a C2 model
-    NUM_GROUPS: 32
-    WIDTH_PER_GROUP: 8
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-InstanceSegmentation/mask_rcnn_regnetx_4gf_dds_fpn_1x.py

@@ -1,34 +0,0 @@
-from ..common.optim import SGD as optimizer
-from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
-from ..common.data.coco import dataloader
-from ..common.models.mask_rcnn_fpn import model
-from ..common.train import train
-
-from detectron2.config import LazyCall as L
-from detectron2.modeling.backbone import RegNet
-from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock
-
-
-# Replace default ResNet with RegNetX-4GF from the DDS paper. Config source:
-# https://github.com/facebookresearch/pycls/blob/2c152a6e5d913e898cca4f0a758f41e6b976714d/configs/dds_baselines/regnetx/RegNetX-4.0GF_dds_8gpu.yaml#L4-L9  # noqa
-model.backbone.bottom_up = L(RegNet)(
-    stem_class=SimpleStem,
-    stem_width=32,
-    block_class=ResBottleneckBlock,
-    depth=23,
-    w_a=38.65,
-    w_0=96,
-    w_m=2.43,
-    group_width=40,
-    freeze_at=2,
-    norm="FrozenBN",
-    out_features=["s1", "s2", "s3", "s4"],
-)
-model.pixel_std = [57.375, 57.120, 58.395]
-
-optimizer.weight_decay = 5e-5
-train.init_checkpoint = (
-    "https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906383/RegNetX-4.0GF_dds_8gpu.pyth"
-)
-# RegNets benefit from enabling cudnn benchmark mode
-train.cudnn_benchmark = True

configs/COCO-InstanceSegmentation/mask_rcnn_regnety_4gf_dds_fpn_1x.py

@@ -1,35 +0,0 @@
-from ..common.optim import SGD as optimizer
-from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
-from ..common.data.coco import dataloader
-from ..common.models.mask_rcnn_fpn import model
-from ..common.train import train
-
-from detectron2.config import LazyCall as L
-from detectron2.modeling.backbone import RegNet
-from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock
-
-
-# Replace default ResNet with RegNetY-4GF from the DDS paper. Config source:
-# https://github.com/facebookresearch/pycls/blob/2c152a6e5d913e898cca4f0a758f41e6b976714d/configs/dds_baselines/regnety/RegNetY-4.0GF_dds_8gpu.yaml#L4-L10  # noqa
-model.backbone.bottom_up = L(RegNet)(
-    stem_class=SimpleStem,
-    stem_width=32,
-    block_class=ResBottleneckBlock,
-    depth=22,
-    w_a=31.41,
-    w_0=96,
-    w_m=2.24,
-    group_width=64,
-    se_ratio=0.25,
-    freeze_at=2,
-    norm="FrozenBN",
-    out_features=["s1", "s2", "s3", "s4"],
-)
-model.pixel_std = [57.375, 57.120, 58.395]
-
-optimizer.weight_decay = 5e-5
-train.init_checkpoint = (
-    "https://dl.fbaipublicfiles.com/pycls/dds_baselines/160906838/RegNetY-4.0GF_dds_8gpu.pyth"
-)
-# RegNets benefit from enabling cudnn benchmark mode
-train.cudnn_benchmark = True

configs/COCO-Keypoints/Base-Keypoint-RCNN-FPN.yaml

@@ -1,15 +0,0 @@
-_BASE_: "../Base-RCNN-FPN.yaml"
-MODEL:
-  KEYPOINT_ON: True
-  ROI_HEADS:
-    NUM_CLASSES: 1
-  ROI_BOX_HEAD:
-    SMOOTH_L1_BETA: 0.5  # Keypoint AP degrades (though box AP improves) when using plain L1 loss
-  RPN:
-    # Detectron1 uses 2000 proposals per-batch, but this option is per-image in detectron2.
-    # 1000 proposals per-image is found to hurt box AP.
-    # Therefore we increase it to 1500 per-image.
-    POST_NMS_TOPK_TRAIN: 1500
-DATASETS:
-  TRAIN: ("keypoints_coco_2017_train",)
-  TEST: ("keypoints_coco_2017_val",)

configs/COCO-Keypoints/keypoint_rcnn_R_101_FPN_3x.yaml

@@ -1,8 +0,0 @@
-_BASE_: "Base-Keypoint-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.py

@@ -1,7 +0,0 @@
-from ..common.optim import SGD as optimizer
-from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier
-from ..common.data.coco_keypoint import dataloader
-from ..common.models.keypoint_rcnn_fpn import model
-from ..common.train import train
-
-model.backbone.bottom_up.freeze_at = 2

configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_1x.yaml

@@ -1,5 +0,0 @@
-_BASE_: "Base-Keypoint-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  RESNETS:
-    DEPTH: 50

configs/COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml

@@ -1,8 +0,0 @@
-_BASE_: "Base-Keypoint-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
-  RESNETS:
-    DEPTH: 50
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-Keypoints/keypoint_rcnn_X_101_32x8d_FPN_3x.yaml

@@ -1,12 +0,0 @@
-_BASE_: "Base-Keypoint-RCNN-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/FAIR/X-101-32x8d.pkl"
-  PIXEL_STD: [57.375, 57.120, 58.395]
-  RESNETS:
-    STRIDE_IN_1X1: False  # this is a C2 model
-    NUM_GROUPS: 32
-    WIDTH_PER_GROUP: 8
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000

configs/COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml

@@ -1,11 +0,0 @@
-_BASE_: "../Base-RCNN-FPN.yaml"
-MODEL:
-  META_ARCHITECTURE: "PanopticFPN"
-  MASK_ON: True
-  SEM_SEG_HEAD:
-    LOSS_WEIGHT: 0.5
-DATASETS:
-  TRAIN: ("coco_2017_train_panoptic_separated",)
-  TEST: ("coco_2017_val_panoptic_separated",)
-DATALOADER:
-  FILTER_EMPTY_ANNOTATIONS: False

configs/COCO-PanopticSegmentation/panoptic_fpn_R_101_3x.yaml

@@ -1,8 +0,0 @@
-_BASE_: "Base-Panoptic-FPN.yaml"
-MODEL:
-  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-101.pkl"
-  RESNETS:
-    DEPTH: 101
-SOLVER:
-  STEPS: (210000, 250000)
-  MAX_ITER: 270000