Support export of CollectAndDistributeFpnRpnProposalsOp

Author: orionr

tools/convert_pkl_to_pb.py

@@ -119,10 +119,41 @@ def unscope_name(name):
 
 
 def reset_names(names):
-    for i in range(0, len(names)):
+    for i in range(len(names)):
         names[i] = unscope_name(names[i])
 
 
+def convert_collect_and_distribute(
+    op, blobs,
+    roi_canonical_scale,
+    roi_canonical_level,
+    roi_max_level,
+    roi_min_level,
+    rpn_max_level,
+    rpn_min_level,
+    rpn_post_nms_topN,
+):
+    print('Converting CollectAndDistributeFpnRpnProposals'
+          ' Python -> C++:\n{}'.format(op))
+    assert op.name.startswith('CollectAndDistributeFpnRpnProposalsOp'), \
+        'Not valid CollectAndDistributeFpnRpnProposalsOp'
+
+    inputs = [x for x in op.input]
+    ret = core.CreateOperator(
+        'CollectAndDistributeFpnRpnProposals',
+        inputs,
+        list(op.output),
+        roi_canonical_scale=roi_canonical_scale,
+        roi_canonical_level=roi_canonical_level,
+        roi_max_level=roi_max_level,
+        roi_min_level=roi_min_level,
+        rpn_max_level=rpn_max_level,
+        rpn_min_level=rpn_min_level,
+        rpn_post_nms_topN=rpn_post_nms_topN,
+    )
+    return ret
+
+
 def convert_gen_proposals(
     op, blobs,
     rpn_pre_nms_topN,
@@ -131,19 +162,19 @@ def convert_gen_proposals(
     rpn_min_size,
 ):
     print('Converting GenerateProposals Python -> C++:\n{}'.format(op))
-    assert op.name.startswith("GenerateProposalsOp"), "Not valid GenerateProposalsOp"
+    assert op.name.startswith('GenerateProposalsOp'), 'Not valid GenerateProposalsOp'
 
-    spatial_scale = mutils.get_op_arg_valf(op, "spatial_scale", None)
+    spatial_scale = mutils.get_op_arg_valf(op, 'spatial_scale', None)
     assert spatial_scale is not None
 
     inputs = [x for x in op.input]
-    anchor_name = "anchor"
+    anchor_name = 'anchor'
     inputs.append(anchor_name)
     blobs[anchor_name] = get_anchors(spatial_scale)
     print('anchors {}'.format(blobs[anchor_name]))
 
     ret = core.CreateOperator(
-        "GenerateProposals",
+        'GenerateProposals',
         inputs,
         list(op.output),
         spatial_scale=spatial_scale,
@@ -153,7 +184,6 @@ def convert_gen_proposals(
         min_size=rpn_min_size,
         correct_transform_coords=True,
     )
-
     return ret, anchor_name
 
 
@@ -183,25 +213,48 @@ def convert_op_name(op):
         reset_names(op.output)
         return [op]
 
-    @op_filter(type="Python", inputs=['rpn_cls_probs', 'rpn_bbox_pred', 'im_info'])
-    def convert_gen_proposal(op_in):
-        gen_proposals_op, ext_input = convert_gen_proposals(
-            op_in, blobs,
-            rpn_min_size=float(cfg.TEST.RPN_MIN_SIZE),
-            rpn_post_nms_topN=cfg.TEST.RPN_POST_NMS_TOP_N,
-            rpn_pre_nms_topN=cfg.TEST.RPN_PRE_NMS_TOP_N,
-            rpn_nms_thres=cfg.TEST.RPN_NMS_THRESH,
-        )
-        net.external_input.extend([ext_input])
-        return [gen_proposals_op]
+    @op_filter()
+    def convert_python(op):
+        if op.type == 'Python':
+            if op.name.startswith('GenerateProposalsOp'):
+                gen_proposals_op, ext_input = convert_gen_proposals(
+                    op, blobs,
+                    rpn_min_size=float(cfg.TEST.RPN_MIN_SIZE),
+                    rpn_post_nms_topN=cfg.TEST.RPN_POST_NMS_TOP_N,
+                    rpn_pre_nms_topN=cfg.TEST.RPN_PRE_NMS_TOP_N,
+                    rpn_nms_thres=cfg.TEST.RPN_NMS_THRESH,
+                )
+                net.external_input.extend([ext_input])
+                return [gen_proposals_op]
+            elif op.name.startswith('CollectAndDistributeFpnRpnProposalsOp'):
+                collect_dist_op = convert_collect_and_distribute(
+                    op, blobs,
+                    roi_canonical_scale=cfg.FPN.ROI_CANONICAL_SCALE,
+                    roi_canonical_level=cfg.FPN.ROI_CANONICAL_LEVEL,
+                    roi_max_level=cfg.FPN.ROI_MAX_LEVEL,
+                    roi_min_level=cfg.FPN.ROI_MIN_LEVEL,
+                    rpn_max_level=cfg.FPN.RPN_MAX_LEVEL,
+                    rpn_min_level=cfg.FPN.RPN_MIN_LEVEL,
+                    rpn_post_nms_topN=cfg.TEST.RPN_POST_NMS_TOP_N,
+                )
+                return [collect_dist_op]
+            else:
+                raise ValueError('Failed to convert Python op {}'.format(
+                    op.name))
+        return [op]
 
-    @op_filter(input_has='rois')
+    @op_filter()
     def convert_rpn_rois(op):
-        for j in range(0, len(op.input)):
+        for j in range(len(op.input)):
             if op.input[j] == 'rois':
                 print('Converting op {} input name: rois -> rpn_rois:\n{}'.format(
                     op.type, op))
                 op.input[j] = 'rpn_rois'
+        for j in range(len(op.output)):
+            if op.output[j] == 'rois':
+                print('Converting op {} output name: rois -> rpn_rois:\n{}'.format(
+                    op.type, op))
+                op.output[j] = 'rpn_rois'
         return [op]
 
     @op_filter(type_in=['StopGradient', 'Alias'])
@@ -211,7 +264,7 @@ def convert_remove_op(op):
 
     convert_op_in_proto(net, convert_op_name)
     convert_op_in_proto(net, [
-        convert_gen_proposal, convert_rpn_rois, convert_remove_op
+        convert_python, convert_rpn_rois, convert_remove_op
     ])
 
     reset_names(net.external_input)
@@ -267,6 +320,7 @@ def convert_model_gpu(args, net, init_net):
     cdo_cpu = mutils.get_device_option_cpu()
 
     CPU_OPS = [
+        ["CollectAndDistributeFpnRpnProposals", None],
         ["GenerateProposals", None],
         ["BBoxTransform", None],
         ["BoxWithNMSLimit", None],
@@ -457,7 +511,7 @@ def run_model_pb(args, net, init_net, im, check_blobs):
         )
 
     try:
-        workspace.RunNet(net.Proto().name)
+        workspace.RunNet(net)
         scores = workspace.FetchBlob('score_nms')
         classids = workspace.FetchBlob('class_nms')
         boxes = workspace.FetchBlob('bbox_nms')
@@ -515,13 +569,16 @@ def main():
         merge_cfg_from_list(args.opts)
     cfg.NUM_GPUS = 1
     assert_and_infer_cfg()
-    logger.info('Conerting model with config:')
+    logger.info('Converting model with config:')
     logger.info(pprint.pformat(cfg))
 
-    assert not cfg.MODEL.KEYPOINTS_ON, "Keypoint model not supported."
-    assert not cfg.MODEL.MASK_ON, "Mask model not supported."
-    assert not cfg.FPN.FPN_ON, "FPN not supported."
-    assert not cfg.RETINANET.RETINANET_ON, "RetinaNet model not supported."
+    # script will stop when it can't find an operator rather
+    # than stopping based on these flags
+    #
+    # assert not cfg.MODEL.KEYPOINTS_ON, "Keypoint model not supported."
+    # assert not cfg.MODEL.MASK_ON, "Mask model not supported."
+    # assert not cfg.FPN.FPN_ON, "FPN not supported."
+    # assert not cfg.RETINANET.RETINANET_ON, "RetinaNet model not supported."
 
     # load model from cfg
     model, blobs = load_model(args)