[ONNX] Fix dtype for NonMaxSuppression (#7056)

Co-authored-by: Nikita Shulga <nshulga@fb.com>
Co-authored-by: Philip Meier <github.pmeier@posteo.de>

torchvision/ops/_register_onnx_ops.py

@@ -11,21 +11,25 @@
 def _register_custom_op():
     from torch.onnx.symbolic_helper import parse_args
     from torch.onnx.symbolic_opset11 import select, squeeze, unsqueeze
-    from torch.onnx.symbolic_opset9 import _cast_Long
 
     @parse_args("v", "v", "f")
     def symbolic_multi_label_nms(g, boxes, scores, iou_threshold):
         boxes = unsqueeze(g, boxes, 0)
         scores = unsqueeze(g, unsqueeze(g, scores, 0), 0)
         max_output_per_class = g.op("Constant", value_t=torch.tensor([sys.maxsize], dtype=torch.long))
         iou_threshold = g.op("Constant", value_t=torch.tensor([iou_threshold], dtype=torch.float))
-        nms_out = g.op("NonMaxSuppression", boxes, scores, max_output_per_class, iou_threshold)
+        nms_out = g.op(
+            "NonMaxSuppression",
+            g.op("Cast", boxes, to_i=torch.onnx.TensorProtoDataType.FLOAT),
+            g.op("Cast", scores, to_i=torch.onnx.TensorProtoDataType.FLOAT),
+            max_output_per_class,
+            iou_threshold,
+        )
         return squeeze(g, select(g, nms_out, 1, g.op("Constant", value_t=torch.tensor([2], dtype=torch.long))), 1)
 
     def _process_batch_indices_for_roi_align(g, rois):
-        return _cast_Long(
-            g, squeeze(g, select(g, rois, 1, g.op("Constant", value_t=torch.tensor([0], dtype=torch.long))), 1), False
-        )
+        indices = squeeze(g, select(g, rois, 1, g.op("Constant", value_t=torch.tensor([0], dtype=torch.long))), 1)
+        return g.op("Cast", indices, to_i=torch.onnx.TensorProtoDataType.INT64)
 
     def _process_rois_for_roi_align(g, rois):
         return select(g, rois, 1, g.op("Constant", value_t=torch.tensor([1, 2, 3, 4], dtype=torch.long)))