moving fpn message to fpn.proto

research/object_detection/protos/faster_rcnn.proto

@@ -8,6 +8,7 @@ import "object_detection/protos/hyperparams.proto";
 import "object_detection/protos/image_resizer.proto";
 import "object_detection/protos/losses.proto";
 import "object_detection/protos/post_processing.proto";
+import "object_detection/protos/fpn.proto";
 
 // Configuration for Faster R-CNN models.
 // See meta_architectures/faster_rcnn_meta_arch.py and models/model_builder.py

research/object_detection/protos/fpn.proto

+syntax = "proto2";
+
+package object_detection.protos;
+
+// Configuration for Feature Pyramid Networks.
+message FeaturePyramidNetworks {
+  // We recommend to use multi_resolution_feature_map_generator with FPN, and
+  // the levels there must match the levels defined below for better
+  // performance.
+  // Correspondence from FPN levels to Resnet/Mobilenet V1 feature maps:
+  // FPN Level        Resnet Feature Map      Mobilenet-V1 Feature Map
+  //     2               Block 1                Conv2d_3_pointwise
+  //     3               Block 2                Conv2d_5_pointwise
+  //     4               Block 3                Conv2d_11_pointwise
+  //     5               Block 4                Conv2d_13_pointwise
+  //     6               Bottomup_5             bottom_up_Conv2d_14
+  //     7               Bottomup_6             bottom_up_Conv2d_15
+  //     8               Bottomup_7             bottom_up_Conv2d_16
+  //     9               Bottomup_8             bottom_up_Conv2d_17
+
+  // minimum level in feature pyramid
+  optional int32 min_level = 1 [default = 3];
+
+  // maximum level in feature pyramid
+  optional int32 max_level = 2 [default = 7];
+
+  // channel depth for additional coarse feature layers.
+  optional int32 additional_layer_depth = 3 [default = 256];
+}