[ONNX] misc improvements (#7249)

Co-authored-by: Nikita Shulga <[email protected]>

Author: justinchuby

test/test_onnx.py

@@ -34,7 +34,7 @@ def run_model(
         opset_version: Optional[int] = None,
     ):
         if opset_version is None:
-            opset_version = _register_onnx_ops.base_onnx_opset_version
+            opset_version = _register_onnx_ops.BASE_ONNX_OPSET_VERSION
 
         model.eval()
 
@@ -139,7 +139,7 @@ def test_roi_align(self):
         self.run_model(model, [(x, single_roi)])
 
     def test_roi_align_aligned(self):
-        supported_onnx_version = _register_onnx_ops._onnx_opset_version_16
+        supported_onnx_version = _register_onnx_ops._ONNX_OPSET_VERSION_16
         x = torch.rand(1, 1, 10, 10, dtype=torch.float32)
         single_roi = torch.tensor([[0, 1.5, 1.5, 3, 3]], dtype=torch.float32)
         model = ops.RoIAlign((5, 5), 1, 2, aligned=True)
@@ -166,7 +166,7 @@ def test_roi_align_aligned(self):
         self.run_model(model, [(x, single_roi)], opset_version=supported_onnx_version)
 
     def test_roi_align_malformed_boxes(self):
-        supported_onnx_version = _register_onnx_ops._onnx_opset_version_16
+        supported_onnx_version = _register_onnx_ops._ONNX_OPSET_VERSION_16
         x = torch.randn(1, 1, 10, 10, dtype=torch.float32)
         single_roi = torch.tensor([[0, 2, 0.3, 1.5, 1.5]], dtype=torch.float32)
         model = ops.RoIAlign((5, 5), 1, 1, aligned=True)

torchvision/ops/_register_onnx_ops.py

@@ -2,96 +2,106 @@
 import warnings
 
 import torch
+from torch.onnx import symbolic_opset11 as opset11
+from torch.onnx.symbolic_helper import parse_args
 
-_onnx_opset_version_11 = 11
-_onnx_opset_version_16 = 16
-base_onnx_opset_version = _onnx_opset_version_11
+_ONNX_OPSET_VERSION_11 = 11
+_ONNX_OPSET_VERSION_16 = 16
+BASE_ONNX_OPSET_VERSION = _ONNX_OPSET_VERSION_11
 
 
-def _register_custom_op():
-    from torch.onnx.symbolic_helper import parse_args
-    from torch.onnx.symbolic_opset11 import select, squeeze, unsqueeze
-
-    @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",
-            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):
-        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)))
-
-    def _process_sampling_ratio_for_roi_align(g, sampling_ratio: int):
-        if sampling_ratio < 0:
-            warnings.warn(
-                "ONNX export for RoIAlign with a non-zero sampling_ratio is not supported. "
-                "The model will be exported with a sampling_ratio of 0."
-            )
-            sampling_ratio = 0
-        return sampling_ratio
-
-    @parse_args("v", "v", "f", "i", "i", "i", "i")
-    def roi_align_opset11(g, input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned):
-        batch_indices = _process_batch_indices_for_roi_align(g, rois)
-        rois = _process_rois_for_roi_align(g, rois)
-        if aligned:
-            warnings.warn(
-                "ROIAlign with aligned=True is only supported in opset >= 16. "
-                "Please export with opset 16 or higher, or use aligned=False."
-            )
-        sampling_ratio = _process_sampling_ratio_for_roi_align(g, sampling_ratio)
-        return g.op(
-            "RoiAlign",
-            input,
-            rois,
-            batch_indices,
-            spatial_scale_f=spatial_scale,
-            output_height_i=pooled_height,
-            output_width_i=pooled_width,
-            sampling_ratio_i=sampling_ratio,
-        )
+@parse_args("v", "v", "f")
+def symbolic_multi_label_nms(g, boxes, scores, iou_threshold):
+    boxes = opset11.unsqueeze(g, boxes, 0)
+    scores = opset11.unsqueeze(g, opset11.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))
+
+    # Cast boxes and scores to float32 in case they are float64 inputs
+    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 opset11.squeeze(
+        g, opset11.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):
+    indices = opset11.squeeze(
+        g, opset11.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 opset11.select(g, rois, 1, g.op("Constant", value_t=torch.tensor([1, 2, 3, 4], dtype=torch.long)))
 
-    @parse_args("v", "v", "f", "i", "i", "i", "i")
-    def roi_align_opset16(g, input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned):
-        batch_indices = _process_batch_indices_for_roi_align(g, rois)
-        rois = _process_rois_for_roi_align(g, rois)
-        coordinate_transformation_mode = "half_pixel" if aligned else "output_half_pixel"
-        sampling_ratio = _process_sampling_ratio_for_roi_align(g, sampling_ratio)
-        return g.op(
-            "RoiAlign",
-            input,
-            rois,
-            batch_indices,
-            coordinate_transformation_mode_s=coordinate_transformation_mode,
-            spatial_scale_f=spatial_scale,
-            output_height_i=pooled_height,
-            output_width_i=pooled_width,
-            sampling_ratio_i=sampling_ratio,
+
+def _process_sampling_ratio_for_roi_align(g, sampling_ratio: int):
+    if sampling_ratio < 0:
+        warnings.warn(
+            "ONNX export for RoIAlign with a non-zero sampling_ratio is not supported. "
+            "The model will be exported with a sampling_ratio of 0."
         )
+        sampling_ratio = 0
+    return sampling_ratio
+
 
-    @parse_args("v", "v", "f", "i", "i")
-    def roi_pool(g, input, rois, spatial_scale, pooled_height, pooled_width):
-        roi_pool = g.op(
-            "MaxRoiPool", input, rois, pooled_shape_i=(pooled_height, pooled_width), spatial_scale_f=spatial_scale
+@parse_args("v", "v", "f", "i", "i", "i", "i")
+def roi_align_opset11(g, input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned):
+    batch_indices = _process_batch_indices_for_roi_align(g, rois)
+    rois = _process_rois_for_roi_align(g, rois)
+    if aligned:
+        warnings.warn(
+            "ROIAlign with aligned=True is only supported in opset >= 16. "
+            "Please export with opset 16 or higher, or use aligned=False."
         )
-        return roi_pool, None
+    sampling_ratio = _process_sampling_ratio_for_roi_align(g, sampling_ratio)
+    return g.op(
+        "RoiAlign",
+        input,
+        rois,
+        batch_indices,
+        spatial_scale_f=spatial_scale,
+        output_height_i=pooled_height,
+        output_width_i=pooled_width,
+        sampling_ratio_i=sampling_ratio,
+    )
+
+
+@parse_args("v", "v", "f", "i", "i", "i", "i")
+def roi_align_opset16(g, input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio, aligned):
+    batch_indices = _process_batch_indices_for_roi_align(g, rois)
+    rois = _process_rois_for_roi_align(g, rois)
+    coordinate_transformation_mode = "half_pixel" if aligned else "output_half_pixel"
+    sampling_ratio = _process_sampling_ratio_for_roi_align(g, sampling_ratio)
+    return g.op(
+        "RoiAlign",
+        input,
+        rois,
+        batch_indices,
+        coordinate_transformation_mode_s=coordinate_transformation_mode,
+        spatial_scale_f=spatial_scale,
+        output_height_i=pooled_height,
+        output_width_i=pooled_width,
+        sampling_ratio_i=sampling_ratio,
+    )
 
-    from torch.onnx import register_custom_op_symbolic
 
-    register_custom_op_symbolic("torchvision::nms", symbolic_multi_label_nms, _onnx_opset_version_11)
-    register_custom_op_symbolic("torchvision::roi_align", roi_align_opset11, _onnx_opset_version_11)
-    register_custom_op_symbolic("torchvision::roi_align", roi_align_opset16, _onnx_opset_version_16)
-    register_custom_op_symbolic("torchvision::roi_pool", roi_pool, _onnx_opset_version_11)
+@parse_args("v", "v", "f", "i", "i")
+def roi_pool(g, input, rois, spatial_scale, pooled_height, pooled_width):
+    roi_pool = g.op(
+        "MaxRoiPool", input, rois, pooled_shape_i=(pooled_height, pooled_width), spatial_scale_f=spatial_scale
+    )
+    return roi_pool, None
+
+
+def _register_custom_op():
+    torch.onnx.register_custom_op_symbolic("torchvision::nms", symbolic_multi_label_nms, _ONNX_OPSET_VERSION_11)
+    torch.onnx.register_custom_op_symbolic("torchvision::roi_align", roi_align_opset11, _ONNX_OPSET_VERSION_11)
+    torch.onnx.register_custom_op_symbolic("torchvision::roi_align", roi_align_opset16, _ONNX_OPSET_VERSION_16)
+    torch.onnx.register_custom_op_symbolic("torchvision::roi_pool", roi_pool, _ONNX_OPSET_VERSION_11)