support v0.6

configs/new_baselines/mask_rcnn_R_50_FPN_400ep_LSJ.py

+from .mask_rcnn_R_50_FPN_100ep_LSJ import (
+    dataloader,
+    lr_multiplier,
+    model,
+    optimizer,
+    train,
+)
+
+train.max_iter *= 4  # 100ep -> 400ep
+
+lr_multiplier.scheduler.milestones = [
+    milestone * 4 for milestone in lr_multiplier.scheduler.milestones
+]
+lr_multiplier.scheduler.num_updates = train.max_iter

configs/new_baselines/mask_rcnn_R_50_FPN_50ep_LSJ.py

+from .mask_rcnn_R_50_FPN_100ep_LSJ import (
+    dataloader,
+    lr_multiplier,
+    model,
+    optimizer,
+    train,
+)
+
+train.max_iter //= 2  # 100ep -> 50ep
+
+lr_multiplier.scheduler.milestones = [
+    milestone // 2 for milestone in lr_multiplier.scheduler.milestones
+]
+lr_multiplier.scheduler.num_updates = train.max_iter

configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ.py

+from .mask_rcnn_R_50_FPN_100ep_LSJ import (
+    dataloader,
+    lr_multiplier,
+    model,
+    optimizer,
+    train,
+)
+from detectron2.config import LazyCall as L
+from detectron2.modeling.backbone import RegNet
+from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock
+
+# Config source:
+# https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-InstanceSegmentation/mask_rcnn_regnetx_4gf_dds_fpn_1x.py  # 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,
+    norm="SyncBN",
+    out_features=["s1", "s2", "s3", "s4"],
+)
+model.pixel_std = [57.375, 57.120, 58.395]
+
+# RegNets benefit from enabling cudnn benchmark mode
+train.cudnn_benchmark = True

configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_200ep_LSJ.py

+from .mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ import (
+    dataloader,
+    lr_multiplier,
+    model,
+    optimizer,
+    train,
+)
+
+train.max_iter *= 2  # 100ep -> 200ep
+
+lr_multiplier.scheduler.milestones = [
+    milestone * 2 for milestone in lr_multiplier.scheduler.milestones
+]
+lr_multiplier.scheduler.num_updates = train.max_iter

configs/new_baselines/mask_rcnn_regnetx_4gf_dds_FPN_400ep_LSJ.py

+from .mask_rcnn_regnetx_4gf_dds_FPN_100ep_LSJ import (
+    dataloader,
+    lr_multiplier,
+    model,
+    optimizer,
+    train,
+)
+
+train.max_iter *= 4  # 100ep -> 400ep
+
+lr_multiplier.scheduler.milestones = [
+    milestone * 4 for milestone in lr_multiplier.scheduler.milestones
+]
+lr_multiplier.scheduler.num_updates = train.max_iter

configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ.py

+from .mask_rcnn_R_50_FPN_100ep_LSJ import (
+    dataloader,
+    lr_multiplier,
+    model,
+    optimizer,
+    train,
+)
+from detectron2.config import LazyCall as L
+from detectron2.modeling.backbone import RegNet
+from detectron2.modeling.backbone.regnet import SimpleStem, ResBottleneckBlock
+
+# Config source:
+# https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-InstanceSegmentation/mask_rcnn_regnety_4gf_dds_fpn_1x.py  # 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,
+    norm="SyncBN",
+    out_features=["s1", "s2", "s3", "s4"],
+)
+model.pixel_std = [57.375, 57.120, 58.395]
+
+# RegNets benefit from enabling cudnn benchmark mode
+train.cudnn_benchmark = True

configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_200ep_LSJ.py

+from .mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ import (
+    dataloader,
+    lr_multiplier,
+    model,
+    optimizer,
+    train,
+)
+
+train.max_iter *= 2  # 100ep -> 200ep
+
+lr_multiplier.scheduler.milestones = [
+    milestone * 2 for milestone in lr_multiplier.scheduler.milestones
+]
+lr_multiplier.scheduler.num_updates = train.max_iter

configs/new_baselines/mask_rcnn_regnety_4gf_dds_FPN_400ep_LSJ.py

+from .mask_rcnn_regnety_4gf_dds_FPN_100ep_LSJ import (
+    dataloader,
+    lr_multiplier,
+    model,
+    optimizer,
+    train,
+)
+
+train.max_iter *= 4  # 100ep -> 400ep
+
+lr_multiplier.scheduler.milestones = [
+    milestone * 4 for milestone in lr_multiplier.scheduler.milestones
+]
+lr_multiplier.scheduler.num_updates = train.max_iter

configs/quick_schedules/README.md

+These are quick configs for performance or accuracy regression tracking purposes.
+
+* `*instance_test.yaml`: can train on 2 GPUs. They are used to test whether the training can
+  successfully finish. They are not expected to produce reasonable training results.
+* `*inference_acc_test.yaml`: They should be run using `--eval-only`. They run inference using pre-trained models and verify
+  the results are as expected.
+* `*training_acc_test.yaml`: They should be trained on 8 GPUs. They finish in about an hour and verify the training accuracy
+  is within the normal range.

configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_inference_acc_test.yaml

+_BASE_: "../Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml"
+MODEL:
+  WEIGHTS: "detectron2://Misc/cascade_mask_rcnn_R_50_FPN_3x/144998488/model_final_480dd8.pkl"
+DATASETS:
+  TEST: ("coco_2017_val_100",)
+TEST:
+  EXPECTED_RESULTS: [["bbox", "AP", 50.18, 0.02], ["segm", "AP",  43.87, 0.02]]

configs/quick_schedules/cascade_mask_rcnn_R_50_FPN_instant_test.yaml

+_BASE_: "../Misc/cascade_mask_rcnn_R_50_FPN_3x.yaml"
+DATASETS:
+  TRAIN: ("coco_2017_val_100",)
+  TEST: ("coco_2017_val_100",)
+SOLVER:
+  BASE_LR: 0.005
+  STEPS: (30,)
+  MAX_ITER: 40
+  IMS_PER_BATCH: 4
+DATALOADER:
+  NUM_WORKERS: 2

configs/quick_schedules/fast_rcnn_R_50_FPN_inference_acc_test.yaml

+_BASE_: "../COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml"
+MODEL:
+  WEIGHTS: "detectron2://COCO-Detection/fast_rcnn_R_50_FPN_1x/137635226/model_final_e5f7ce.pkl"
+DATASETS:
+  TEST: ("coco_2017_val_100",)
+TEST:
+  EXPECTED_RESULTS: [["bbox", "AP", 45.70, 0.02]]

configs/quick_schedules/fast_rcnn_R_50_FPN_instant_test.yaml

+_BASE_: "../COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml"
+MODEL:
+  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
+DATASETS:
+  TRAIN: ("coco_2017_val_100",)
+  PROPOSAL_FILES_TRAIN: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_val_box_proposals_ee0dad.pkl", )
+  TEST: ("coco_2017_val_100",)
+  PROPOSAL_FILES_TEST: ("detectron2://COCO-Detection/rpn_R_50_FPN_1x/137258492/coco_2017_val_box_proposals_ee0dad.pkl", )
+SOLVER:
+  BASE_LR: 0.005
+  STEPS: (30,)
+  MAX_ITER: 40
+  IMS_PER_BATCH: 4
+DATALOADER:
+  NUM_WORKERS: 2

configs/quick_schedules/keypoint_rcnn_R_50_FPN_inference_acc_test.yaml

+_BASE_: "../COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x.yaml"
+MODEL:
+  WEIGHTS: "detectron2://COCO-Keypoints/keypoint_rcnn_R_50_FPN_3x/137849621/model_final_a6e10b.pkl"
+DATASETS:
+  TEST: ("keypoints_coco_2017_val_100",)
+TEST:
+  EXPECTED_RESULTS: [["bbox", "AP", 52.47, 0.02], ["keypoints", "AP", 67.36, 0.02]]

configs/quick_schedules/keypoint_rcnn_R_50_FPN_instant_test.yaml

+_BASE_: "../Base-RCNN-FPN.yaml"
+MODEL:
+  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
+  KEYPOINT_ON: True
+  ROI_HEADS:
+    NUM_CLASSES: 1
+DATASETS:
+  TRAIN: ("keypoints_coco_2017_val_100",)
+  TEST: ("keypoints_coco_2017_val_100",)
+SOLVER:
+  BASE_LR: 0.005
+  STEPS: (30,)
+  MAX_ITER: 40
+  IMS_PER_BATCH: 4
+DATALOADER:
+  NUM_WORKERS: 2

configs/quick_schedules/keypoint_rcnn_R_50_FPN_normalized_training_acc_test.yaml

+_BASE_: "../Base-RCNN-FPN.yaml"
+MODEL:
+  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
+  KEYPOINT_ON: True
+  RESNETS:
+    DEPTH: 50
+  ROI_HEADS:
+    BATCH_SIZE_PER_IMAGE: 256
+    NUM_CLASSES: 1
+  ROI_KEYPOINT_HEAD:
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    NORMALIZE_LOSS_BY_VISIBLE_KEYPOINTS: False
+    LOSS_WEIGHT: 4.0
+  ROI_BOX_HEAD:
+    SMOOTH_L1_BETA: 1.0  # Keypoint AP degrades when using plain L1 loss
+  RPN:
+    SMOOTH_L1_BETA: 0.2  # Keypoint AP degrades when using plain L1 loss
+DATASETS:
+  TRAIN: ("keypoints_coco_2017_val",)
+  TEST: ("keypoints_coco_2017_val",)
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
+SOLVER:
+  WARMUP_FACTOR: 0.33333333
+  WARMUP_ITERS: 100
+  STEPS: (5500, 5800)
+  MAX_ITER: 6000
+TEST:
+  EXPECTED_RESULTS: [["bbox", "AP", 55.35, 1.0], ["keypoints", "AP", 76.91, 1.0]]

configs/quick_schedules/keypoint_rcnn_R_50_FPN_training_acc_test.yaml

+_BASE_: "../Base-RCNN-FPN.yaml"
+MODEL:
+  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
+  KEYPOINT_ON: True
+  RESNETS:
+    DEPTH: 50
+  ROI_HEADS:
+    BATCH_SIZE_PER_IMAGE: 256
+    NUM_CLASSES: 1
+  ROI_KEYPOINT_HEAD:
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+  ROI_BOX_HEAD:
+    SMOOTH_L1_BETA: 1.0  # Keypoint AP degrades when using plain L1 loss
+  RPN:
+    SMOOTH_L1_BETA: 0.2  # Keypoint AP degrades when using plain L1 loss
+DATASETS:
+  TRAIN: ("keypoints_coco_2017_val",)
+  TEST: ("keypoints_coco_2017_val",)
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
+SOLVER:
+  WARMUP_FACTOR: 0.33333333
+  WARMUP_ITERS: 100
+  STEPS: (5500, 5800)
+  MAX_ITER: 6000
+TEST:
+  EXPECTED_RESULTS: [["bbox", "AP", 53.5, 1.0], ["keypoints", "AP", 72.4, 1.0]]

configs/quick_schedules/mask_rcnn_R_50_C4_GCV_instant_test.yaml

+_BASE_: "../Base-RCNN-C4.yaml"
+MODEL:
+  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2017_val_100",)
+  TEST: ("coco_2017_val_100",)
+SOLVER:
+  BASE_LR: 0.001
+  STEPS: (30,)
+  MAX_ITER: 40
+  IMS_PER_BATCH: 4
+  CLIP_GRADIENTS:
+    ENABLED: True
+    CLIP_TYPE: "value"
+    CLIP_VALUE: 1.0
+DATALOADER:
+  NUM_WORKERS: 2

configs/quick_schedules/mask_rcnn_R_50_C4_inference_acc_test.yaml

+_BASE_: "../COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x.yaml"
+MODEL:
+  WEIGHTS: "detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_C4_3x/137849525/model_final_4ce675.pkl"
+DATASETS:
+  TEST: ("coco_2017_val_100",)
+TEST:
+  EXPECTED_RESULTS: [["bbox", "AP", 47.37, 0.02], ["segm", "AP", 40.99, 0.02]]

configs/quick_schedules/mask_rcnn_R_50_C4_instant_test.yaml

+_BASE_: "../Base-RCNN-C4.yaml"
+MODEL:
+  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2017_val_100",)
+  TEST: ("coco_2017_val_100",)
+SOLVER:
+  BASE_LR: 0.001
+  STEPS: (30,)
+  MAX_ITER: 40
+  IMS_PER_BATCH: 4
+DATALOADER:
+  NUM_WORKERS: 2