Add support for onnx FasterRCNN model (#3393)

* Add support for onnx FasterRCNN model

* Add documentation

model-optimizer/extensions/front/onnx/faster_rcnn.json

@@ -0,0 +1,20 @@
+[
+  {
+    "custom_attributes":
+    {
+      "max_detections_per_image": 100,
+      "max_delta_log_wh": 4.135166645050049,
+      "score_threshold": 0.05,
+      "nms_threshold": 0.5,
+      "post_nms_count": 2000,
+      "input_fpn_heads": ["486", "454", "422", "390"],
+      "do_outputs": ["6371", "6373", "6375"],
+      "box_regressions_input_node": "2614",
+      "class_predicitons_node": "2615",
+      "ROIFeatureExtractor2_input": "2335",
+      "ROIFeatureExtractor2_output": "2592"
+    },
+    "id": "ONNXMaskRCNNReplacement",
+    "match_kind": "general"
+  }
+]

model-optimizer/extensions/front/onnx/mask_rcnn.json

@@ -6,7 +6,14 @@
       "max_delta_log_wh": 4.135166645050049,
       "score_threshold": 0.05,
       "nms_threshold": 0.5,
-      "post_nms_count": 2000
+      "post_nms_count": 2000,
+      "input_fpn_heads": ["486", "454", "422", "390"],
+      "do_outputs": ["6530", "6532", "6534"],
+      "box_regressions_input_node": "2773",
+      "class_predicitons_node": "2774",
+      "ROIFeatureExtractor1_output": "6795",
+      "ROIFeatureExtractor2_input": "2490",
+      "ROIFeatureExtractor2_output": "2751"
     },
     "id": "ONNXMaskRCNNReplacement",
     "match_kind": "general"

model-optimizer/extensions/front/onnx/mask_rcnn_conversion.py

@@ -28,8 +28,6 @@ from mo.graph.graph import Graph
 from mo.graph.graph import Node
 from mo.ops.reshape import Reshape
 
-input_fpn_heads = ('486', '454', '422', '390')
-
 
 class ONNXMaskRCNNTransformation(FrontReplacementFromConfigFileGeneral):
     """
@@ -45,24 +43,25 @@ class ONNXMaskRCNNTransformation(FrontReplacementFromConfigFileGeneral):
     replacement_id = 'ONNXMaskRCNNReplacement'
 
     def run_before(self):
-        # the node "2774" which is used in this transformation is of op SoftMaxONNX. But operations of op SoftMaxONNX
+        # the class_predicitons_node which is used in this transformation is of op SoftMaxONNX. But operations of op SoftMaxONNX
         # will be replaced with a transformation SoftmaxONNXFrontReplacer
         return [SoftmaxONNXFrontReplacer]
 
     def transform_graph(self, graph: Graph, replacement_descriptions: dict):
-        insert_ExperimentalDetectronROIFeatureExtractor2(graph)
+        insert_ExperimentalDetectronROIFeatureExtractor2(graph, replacement_descriptions)
         insert_do(graph, replacement_descriptions)
-        insert_ExperimentalDetectronROIFeatureExtractor1(graph)
+        insert_ExperimentalDetectronROIFeatureExtractor1(graph, replacement_descriptions)
 
 
-def insert_do(graph: Graph, replacement_descriptions):
-    do_outputs = ['6530', '6532', '6534']
+def insert_do(graph: Graph, replacement_descriptions: dict):
+    do_outputs = replacement_descriptions['do_outputs']
     prior_boxes_node = Node(graph, 'ROIFeatureExtractor_2')
     num_classes = 81
+    box_regressions_input_node = Node(graph, replacement_descriptions['box_regressions_input_node'])
     box_regressions_node = create_op_node_with_second_input(graph, Reshape, int64_array([-1, 4 * num_classes]),
-                                                            dict(name='box_regressions'), Node(graph, '2773'))
+                                                            dict(name='box_regressions'), box_regressions_input_node)
 
-    class_predicitons_node = Node(graph, '2774')
+    class_predicitons_node = Node(graph, replacement_descriptions['class_predicitons_node'])
     im_info_node = Parameter(graph, {"name": 'im_info', 'shape': int64_array([1, 3])}).create_node()
 
     do_node = ExperimentalDetectronDetectionOutput(graph, {'name': 'DetectionOutput',
@@ -92,8 +91,12 @@ def insert_do(graph: Graph, replacement_descriptions):
     do_node.out_port(1).get_connection().insert_node(Cast(graph, {'dst_type': np.int64}).create_node())
 
 
-def insert_ExperimentalDetectronROIFeatureExtractor1(graph: Graph):
-    old_output_node = Node(graph, '6795')
+def insert_ExperimentalDetectronROIFeatureExtractor1(graph: Graph, replacement_descriptions: dict):
+    if 'ROIFeatureExtractor1_output' not in replacement_descriptions:
+        # In case of Faster-RCNN this transformation is not needed and this attribute shouldn't be set
+        return
+    input_fpn_heads = replacement_descriptions['input_fpn_heads']
+    old_output_node = Node(graph, replacement_descriptions['ROIFeatureExtractor1_output'])
     input_fpn_head_nodes = [Node(graph, node_id) for node_id in input_fpn_heads]
     fpn_roi_align = ExperimentalDetectronROIFeatureExtractor(graph, {'name': 'ROIFeatureExtractor_1',
                                                                      'distribute_rois_between_levels': 1,
@@ -110,8 +113,9 @@ def insert_ExperimentalDetectronROIFeatureExtractor1(graph: Graph):
     old_output_node.out_port(0).get_connection().set_source(fpn_roi_align.out_port(0))
 
 
-def insert_ExperimentalDetectronROIFeatureExtractor2(graph: Graph):
-    old_output_node = Node(graph, '2751')
+def insert_ExperimentalDetectronROIFeatureExtractor2(graph: Graph, replacement_descriptions: dict):
+    input_fpn_heads = replacement_descriptions['input_fpn_heads']
+    old_output_node = Node(graph, replacement_descriptions['ROIFeatureExtractor2_output'])
     input_fpn_head_nodes = [Node(graph, node_id) for node_id in input_fpn_heads]
     fpn_roi_align = ExperimentalDetectronROIFeatureExtractor(graph, {'name': 'ROIFeatureExtractor_2',
                                                                      'distribute_rois_between_levels': 1,
@@ -121,7 +125,7 @@ def insert_ExperimentalDetectronROIFeatureExtractor2(graph: Graph):
                                                                      'pyramid_scales': int64_array(
                                                                          [4, 8, 16, 32, 64]),
                                                                      'sampling_ratio': 2, }).create_node()
-    fpn_roi_align.in_port(0).connect(Node(graph, '2490').out_port(0))
+    fpn_roi_align.in_port(0).connect(Node(graph, replacement_descriptions['ROIFeatureExtractor2_input']).out_port(0))
     for ind, fpn_node in enumerate(input_fpn_head_nodes):
         fpn_roi_align.in_port(ind + 1).connect(fpn_node.out_port(0))