add dcn from mmdetection (facebookresearch#693)

* make pixel indexes 0-based for bounding box in pascal voc dataset

* replacing all instances of torch.distributed.deprecated with torch.distributed

* replacing all instances of torch.distributed.deprecated with torch.distributed

* add GroupNorm

* add GroupNorm -- sort out yaml files

* use torch.nn.GroupNorm instead, replace 'use_gn' with 'conv_block' and use 'BaseStem'&'Bottleneck' to simply codes

* modification on 'group_norm' and 'conv_with_kaiming_uniform' function

* modification on yaml files in configs/gn_baselines/ and reduce the amount of indentation and code duplication

* use 'kaiming_uniform' to initialize resnet, disable gn after fc layer, and add dilation into ResNetHead

* agnostic-regression for bbox

* please set 'STRIDE_IN_1X1' to be 'False' when backbone use GN

* add README.md for GN

* add dcn from mmdetection

.gitignore

@@ -28,3 +28,4 @@ dist/
 # project dirs
 /datasets
 /models
+/output

configs/dcn/README.md

@@ -0,0 +1,31 @@
+### Reference 
+1 [Deformable ConvNets v2: More Deformable, Better Results](https://arxiv.org/pdf/1811.11168.pdf)  
+2 third-party: [mmdetection](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn)  
+
+### Performance
+|      case                   | bbox AP | mask AP |
+|----------------------------:|--------:|:-------:|
+| R-50-FPN-dcn (implement)    |  39.8   |  -      |
+| R-50-FPN-dcn (mmdetection)  |  40.0   |  -      |
+| R-50-FPN-mdcn (implement)   |  40.0   |  -      |
+| R-50-FPN-mdcn (mmdetection) |  40.3   |  -      |
+| R-50-FPN-dcn (implement)    |  40.8   |  36.8   |
+| R-50-FPN-dcn (mmdetection)  |  41.1   |  37.2   |
+| R-50-FPN-dcn (implement)    |  40.7   |  36.7   |
+| R-50-FPN-dcn (mmdetection)  |  41.4   |  37.4   |
+
+
+### Note
+see [dcn-v2](https://github.com/open-mmlab/mmdetection/blob/master/MODEL_ZOO.md#deformable-convolution-v2) in `mmdetection` for more details.  
+
+
+### Usage
+add these three lines
+```
+MODEL:
+	RESNETS:
+		# corresponding to C2,C3,C4,C5
+		STAGE_WITH_DCN: (False, True, True, True)
+		WITH_MODULATED_DCN: True
+		DEFORMABLE_GROUPS: 1
+```

configs/dcn/e2e_faster_rcnn_dconv_R_50_FPN_1x.yaml

@@ -0,0 +1,44 @@
+INPUT:
+  MIN_SIZE_TRAIN: (800,)
+  MAX_SIZE_TRAIN: 1333
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1333
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+  RESNETS:
+    BACKBONE_OUT_CHANNELS: 256
+    STAGE_WITH_DCN: (False, True, True, True)
+    WITH_MODULATED_DCN: False
+    DEFORMABLE_GROUPS: 1
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  # Assume 8 gpus
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+  IMS_PER_BATCH: 16
+TEST:
+  IMS_PER_BATCH: 8

configs/dcn/e2e_faster_rcnn_mdconv_R_50_FPN_1x.yaml

@@ -0,0 +1,44 @@
+INPUT:
+  MIN_SIZE_TRAIN: (800,)
+  MAX_SIZE_TRAIN: 1333
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1333
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+  RESNETS:
+    BACKBONE_OUT_CHANNELS: 256
+    STAGE_WITH_DCN: (False, True, True, True)
+    WITH_MODULATED_DCN: True
+    DEFORMABLE_GROUPS: 1
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  # Assume 8 gpus
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+  IMS_PER_BATCH: 16
+TEST:
+  IMS_PER_BATCH: 8

configs/dcn/e2e_mask_rcnn_dconv_R_50_FPN_1x.yaml

@@ -0,0 +1,54 @@
+INPUT:
+  MIN_SIZE_TRAIN: (800,)
+  MAX_SIZE_TRAIN: 1333
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1333
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+  RESNETS:
+    BACKBONE_OUT_CHANNELS: 256
+    STAGE_WITH_DCN: (False, True, True, True)
+    WITH_MODULATED_DCN: False
+    DEFORMABLE_GROUPS: 1
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  # Assume 8 gpus
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+  IMS_PER_BATCH: 16
+TEST:
+  IMS_PER_BATCH: 8
+

configs/dcn/e2e_mask_rcnn_mdconv_R_50_FPN_1x.yaml

@@ -0,0 +1,53 @@
+INPUT:
+  MIN_SIZE_TRAIN: (800,)
+  MAX_SIZE_TRAIN: 1333
+  MIN_SIZE_TEST: 800
+  MAX_SIZE_TEST: 1333
+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-50"
+  BACKBONE:
+    CONV_BODY: "R-50-FPN"
+  RESNETS:
+    BACKBONE_OUT_CHANNELS: 256
+    STAGE_WITH_DCN: (False, True, True, True)
+    WITH_MODULATED_DCN: True
+    DEFORMABLE_GROUPS: 1
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TRAIN: 2000
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 1000
+    FPN_POST_NMS_TOP_N_TEST: 1000
+  ROI_HEADS:
+    USE_FPN: True
+  ROI_BOX_HEAD:
+    POOLER_RESOLUTION: 7
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    POOLER_SAMPLING_RATIO: 2
+    FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
+    PREDICTOR: "FPNPredictor"
+  ROI_MASK_HEAD:
+    POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
+    FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
+    PREDICTOR: "MaskRCNNC4Predictor"
+    POOLER_RESOLUTION: 14
+    POOLER_SAMPLING_RATIO: 2
+    RESOLUTION: 28
+    SHARE_BOX_FEATURE_EXTRACTOR: False
+  MASK_ON: True
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  # Assume 8 gpus
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000
+  IMS_PER_BATCH: 16
+TEST:
+  IMS_PER_BATCH: 8

maskrcnn_benchmark/config/defaults.py

@@ -274,6 +274,10 @@
 _C.MODEL.RESNETS.RES2_OUT_CHANNELS = 256
 _C.MODEL.RESNETS.STEM_OUT_CHANNELS = 64
 
+_C.MODEL.RESNETS.STAGE_WITH_DCN = (False, False, False, False)
+_C.MODEL.RESNETS.WITH_MODULATED_DCN = False
+_C.MODEL.RESNETS.DEFORMABLE_GROUPS = 1
+
 
 # ---------------------------------------------------------------------------- #
 # RetinaNet Options (Follow the Detectron version)