Add more RPN models

configs/rpn_R_101_FPN_1x.yaml

+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/MSRA/R-101"
+  RPN_ONLY: True
+  BACKBONE:
+    CONV_BODY: "R-101-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 2000
+    FPN_POST_NMS_TOP_N_TEST: 2000
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000

configs/rpn_X_101_32x8d_FPN_1x.yaml

+MODEL:
+  META_ARCHITECTURE: "GeneralizedRCNN"
+  WEIGHT: "catalog://ImageNetPretrained/FAIR/20171220/X-101-32x8d"
+  RPN_ONLY: True
+  BACKBONE:
+    CONV_BODY: "R-101-FPN"
+    OUT_CHANNELS: 256
+  RPN:
+    USE_FPN: True
+    ANCHOR_STRIDE: (4, 8, 16, 32, 64)
+    PRE_NMS_TOP_N_TEST: 1000
+    POST_NMS_TOP_N_TEST: 2000
+    FPN_POST_NMS_TOP_N_TEST: 2000
+  RESNETS:
+    STRIDE_IN_1X1: False
+    NUM_GROUPS: 32
+    WIDTH_PER_GROUP: 8
+DATASETS:
+  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
+  TEST: ("coco_2014_minival",)
+DATALOADER:
+  SIZE_DIVISIBILITY: 32
+SOLVER:
+  BASE_LR: 0.02
+  WEIGHT_DECAY: 0.0001
+  STEPS: (60000, 80000)
+  MAX_ITER: 90000