push v0.1.3 version commit bd2ea47

configs/Misc/panoptic_fpn_R_101_dconv_cascade_gn_3x.yaml

+# A large PanopticFPN for demo purposes.
+# Use GN on backbone to support semantic seg.
+# Use Cascade + Deform Conv to improve localization.
+_BASE_: "../COCO-PanopticSegmentation/Base-Panoptic-FPN.yaml"
+MODEL:
+  WEIGHTS: "catalog://ImageNetPretrained/FAIR/R-101-GN"
+  RESNETS:
+    DEPTH: 101
+    NORM: "GN"
+    DEFORM_ON_PER_STAGE: [False, True, True, True]
+    STRIDE_IN_1X1: False
+  FPN:
+    NORM: "GN"
+  ROI_HEADS:
+    NAME: CascadeROIHeads
+  ROI_BOX_HEAD:
+    CLS_AGNOSTIC_BBOX_REG: True
+  ROI_MASK_HEAD:
+    NORM: "GN"
+  RPN:
+    POST_NMS_TOPK_TRAIN: 2000
+SOLVER:
+  STEPS: (105000, 125000)
+  MAX_ITER: 135000
+  IMS_PER_BATCH: 32
+  BASE_LR: 0.04

configs/Misc/scratch_mask_rcnn_R_50_FPN_3x_gn.yaml

+_BASE_: "mask_rcnn_R_50_FPN_3x_gn.yaml"
+MODEL:
+  # Train from random initialization.
+  WEIGHTS: ""
+  # It makes sense to divide by STD when training from scratch
+  # But it seems to make no difference on the results and C2's models didn't do this.
+  # So we keep things consistent with C2.
+  # PIXEL_STD: [57.375, 57.12, 58.395]
+  MASK_ON: True
+  BACKBONE:
+    FREEZE_AT: 0
+# NOTE: Please refer to Rethinking ImageNet Pre-training https://arxiv.org/abs/1811.08883
+# to learn what you need for training from scratch.

configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_gn.yaml

+_BASE_: "mask_rcnn_R_50_FPN_3x_gn.yaml"
+MODEL:
+  PIXEL_STD: [57.375, 57.12, 58.395]
+  WEIGHTS: ""
+  MASK_ON: True
+  RESNETS:
+    STRIDE_IN_1X1: False
+  BACKBONE:
+    FREEZE_AT: 0
+SOLVER:
+  # 9x schedule
+  IMS_PER_BATCH: 64  # 4x the standard
+  STEPS: (187500, 197500)  # last 60/4==15k and last 20/4==5k
+  MAX_ITER: 202500   # 90k * 9 / 4
+  BASE_LR: 0.08
+TEST:
+  EVAL_PERIOD: 2500
+# NOTE: Please refer to Rethinking ImageNet Pre-training https://arxiv.org/abs/1811.08883
+# to learn what you need for training from scratch.

configs/Misc/scratch_mask_rcnn_R_50_FPN_9x_syncbn.yaml

+_BASE_: "mask_rcnn_R_50_FPN_3x_syncbn.yaml"
+MODEL:
+  PIXEL_STD: [57.375, 57.12, 58.395]
+  WEIGHTS: ""
+  MASK_ON: True
+  RESNETS:
+    STRIDE_IN_1X1: False
+  BACKBONE:
+    FREEZE_AT: 0
+SOLVER:
+  # 9x schedule
+  IMS_PER_BATCH: 64  # 4x the standard
+  STEPS: (187500, 197500)  # last 60/4==15k and last 20/4==5k
+  MAX_ITER: 202500   # 90k * 9 / 4
+  BASE_LR: 0.08
+TEST:
+  EVAL_PERIOD: 2500
+# NOTE: Please refer to Rethinking ImageNet Pre-training https://arxiv.org/abs/1811.08883
+# to learn what you need for training from scratch.

configs/Misc/semantic_R_50_FPN_1x.yaml

+_BASE_: "../Base-RCNN-FPN.yaml"
+MODEL:
+  META_ARCHITECTURE: "SemanticSegmentor"
+  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
+  RESNETS:
+    DEPTH: 50
+DATASETS:
+  TRAIN: ("coco_2017_train_panoptic_stuffonly",)
+  TEST: ("coco_2017_val_panoptic_stuffonly",)
+INPUT:
+  MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)

configs/PascalVOC-Detection/faster_rcnn_R_50_C4.yaml

+_BASE_: "../Base-RCNN-C4.yaml"
+MODEL:
+  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
+  MASK_ON: False
+  RESNETS:
+    DEPTH: 50
+  ROI_HEADS:
+    NUM_CLASSES: 20
+INPUT:
+  MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
+  MIN_SIZE_TEST: 800
+DATASETS:
+  TRAIN: ('voc_2007_trainval', 'voc_2012_trainval')
+  TEST: ('voc_2007_test',)
+SOLVER:
+  STEPS: (12000, 16000)
+  MAX_ITER: 18000  # 17.4 epochs
+  WARMUP_ITERS: 100

configs/PascalVOC-Detection/faster_rcnn_R_50_FPN.yaml

+_BASE_: "../Base-RCNN-FPN.yaml"
+MODEL:
+  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
+  MASK_ON: False
+  RESNETS:
+    DEPTH: 50
+  ROI_HEADS:
+    NUM_CLASSES: 20
+INPUT:
+  MIN_SIZE_TRAIN: (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
+  MIN_SIZE_TEST: 800
+DATASETS:
+  TRAIN: ('voc_2007_trainval', 'voc_2012_trainval')
+  TEST: ('voc_2007_test',)
+SOLVER:
+  STEPS: (12000, 16000)
+  MAX_ITER: 18000  # 17.4 epochs
+  WARMUP_ITERS: 100

configs/quick_schedules/README.md

+These are quick configs for performance or accuracy regression tracking purposes.

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
+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

configs/quick_schedules/mask_rcnn_R_50_C4_training_acc_test.yaml

+_BASE_: "../Base-RCNN-C4.yaml"
+MODEL:
+  WEIGHTS: "detectron2://ImageNetPretrained/MSRA/R-50.pkl"
+  ROI_HEADS:
+    BATCH_SIZE_PER_IMAGE: 256
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2017_val",)
+  TEST: ("coco_2017_val",)
+INPUT:
+  MIN_SIZE_TRAIN: (600,)
+  MAX_SIZE_TRAIN: 1000
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1000
+SOLVER:
+  IMS_PER_BATCH: 8  # base uses 16
+  WARMUP_FACTOR: 0.33333
+  WARMUP_ITERS: 100
+  STEPS: (11000, 11600)
+  MAX_ITER: 12000
+TEST:
+  EXPECTED_RESULTS: [["bbox", "AP", 41.88, 0.7], ["segm", "AP", 33.79, 0.5]]