[Relax][ONNX] Fix Resize coordinate error with non-integer scales

python/tvm/relax/frontend/onnx/onnx_frontend.py

@@ -3230,14 +3230,18 @@ def _impl_v18(cls, bb, inputs, attr, params):
 
         use_dynamic_roi = roi_dynamic_vec is not None
 
-        # Convert scales to sizes if needed.
+        # Convert scales to sizes if needed, preserving the original spatial scales so
+        # the coordinate transformation uses the exact ONNX scale value rather than the
+        # lossy ratio derived from floor(input * scale) / input.
+        original_spatial_scales = None
         if scales is not None:
             if isinstance(scales, relax.Constant):
                 scales = scales.data.numpy()
             elif isinstance(scales, relax.expr.ShapeExpr):
                 scales = [int(val.value) for val in scales.values]
             else:
                 raise ValueError(f"Type {type(scales)} for scale is currently unsupported.")
+            original_spatial_scales = [float(s) for s in scales[2:]]
             sizes = []
 
             for i, dim in enumerate(x.struct_info.shape):
@@ -3279,8 +3283,24 @@ def _impl_v18(cls, bb, inputs, attr, params):
                 cubic_coeff_a,
                 exclude_outside,
                 extrapolation_value,
+                scales=original_spatial_scales,
             )
         elif ndims == 4:
+            if original_spatial_scales is not None:
+                return bb.emit_te(
+                    topi.image.resize2d,
+                    x,
+                    roi_static,
+                    sizes,
+                    "NCHW",
+                    topi_mode,
+                    coord_mode,
+                    rounding_method,
+                    cubic_coeff_a,
+                    exclude_outside,
+                    extrapolation_value,
+                    scales=original_spatial_scales,
+                )
             return relax.op.image.resize2d(
                 x,
                 size=relax.ShapeExpr(sizes),
@@ -3295,6 +3315,21 @@ def _impl_v18(cls, bb, inputs, attr, params):
             )
         else:  # ndims == 5
             roi3d = _topi_resize3d_roi_from_onnx_ncdhw_spatial(roi_static)
+            if original_spatial_scales is not None:
+                return bb.emit_te(
+                    topi.image.resize3d,
+                    x,
+                    roi3d,
+                    sizes,
+                    "NCDHW",
+                    topi_mode,
+                    coord_mode,
+                    rounding_method,
+                    cubic_coeff_a,
+                    exclude_outside,
+                    extrapolation_value,
+                    scales=original_spatial_scales,
+                )
             return relax.op.image.resize3d(
                 x,
                 size=relax.ShapeExpr(sizes),

python/tvm/topi/image/resize.py

@@ -145,9 +145,13 @@ def get_inx(
     start_x=0,
     end_x=-1,
     use_int_div=False,
+    scale_x_override=None,
 ):
     """Infer input x from output x with various coordinate transformation methods"""
-    scale_x = te.div(image_width.astype("float"), target_width.astype("float"))
+    if scale_x_override is not None:
+        scale_x = scale_x_override
+    else:
+        scale_x = te.div(image_width.astype("float"), target_width.astype("float"))
     if coordinate_transformation_mode == "half_pixel":
         in_x = (x + 0.5) * scale_x - 0.5
     elif coordinate_transformation_mode == "align_corners":
@@ -237,6 +241,7 @@ def _resize_1d(
     alpha=-0.5,
     exclude_outside=0,
     out_dtype=None,
+    scale_x=None,
 ):
     """Perform resize operation on the data with selected method and options.
 
@@ -315,7 +320,15 @@ def _cast_output(value, data_dtype="float32", out_dtype=None):
     if boxes is not None:
         # TODO(mbrookhart): Find an example of this
         raise NotImplementedError("resize1d with image boxes not yet implemented")
-    in_x = get_inx(x, image_width, target_width, coordinate_transformation_mode, roi[0], roi[1])
+    in_x = get_inx(
+        x,
+        image_width,
+        target_width,
+        coordinate_transformation_mode,
+        roi[0],
+        roi[1],
+        scale_x_override=scale_x,
+    )
 
     if method == "nearest_neighbor":
         if rounding_method == "":
@@ -383,6 +396,7 @@ def resize1d(
     extrapolation_value=0.0,
     out_dtype=None,
     output_shape=None,
+    scales=None,
 ):
     """Perform resize operation on the data.
 
@@ -472,6 +486,8 @@ def resize1d(
         if isinstance(size[i], int):
             size[i] = tvm.tirx.IntImm("int32", size[i])
 
+    scale_x = (1.0 / scales[0]) if scales is not None else None
+
     def compute_func(*indices):
         return _resize_1d(
             indices,
@@ -487,6 +503,7 @@ def compute_func(*indices):
             exclude_outside=bicubic_exclude,
             extrapolation_value=extrapolation_value,
             out_dtype=out_dtype,
+            scale_x=scale_x,
         )
 
     return te.compute(output_shape, compute_func, name="resize", tag=tag.INJECTIVE)
@@ -510,6 +527,8 @@ def _resize_2d(
     alpha=-0.5,
     exclude_outside=0,
     out_dtype=None,
+    scale_h=None,
+    scale_w=None,
 ):
     """Perform resize operation on the data with selected method and options.
 
@@ -618,6 +637,7 @@ def _cast_output(value, data_dtype="float32", out_dtype=None):
             roi[1],
             roi[3],
             width_use_int_div,
+            scale_x_override=scale_w,
         )
         in_y = get_inx(
             y,
@@ -627,6 +647,7 @@ def _cast_output(value, data_dtype="float32", out_dtype=None):
             roi[0],
             roi[2],
             height_use_int_div,
+            scale_x_override=scale_h,
         )
 
     if method == "nearest_neighbor":
@@ -756,6 +777,7 @@ def resize2d(
     extrapolation_value=0.0,
     out_dtype=None,
     output_shape=None,
+    scales=None,
 ):
     """Perform resize operation on the data.
 
@@ -839,6 +861,9 @@ def resize2d(
         if isinstance(size[i], int):
             size[i] = tvm.tirx.IntImm("int32", size[i])
 
+    scale_h = (1.0 / scales[0]) if scales is not None else None
+    scale_w = (1.0 / scales[1]) if scales is not None else None
+
     def compute_func(*indices):
         return _resize_2d(
             indices,
@@ -856,6 +881,8 @@ def compute_func(*indices):
             exclude_outside=bicubic_exclude,
             extrapolation_value=extrapolation_value,
             out_dtype=out_dtype,
+            scale_h=scale_h,
+            scale_w=scale_w,
         )
 
     return te.compute(output_shape, compute_func, name="resize", tag=tag.INJECTIVE)
@@ -976,6 +1003,9 @@ def _resize_3d(
     alpha=-0.5,
     exclude_outside=0,
     out_dtype=None,
+    scale_d=None,
+    scale_h=None,
+    scale_w=None,
 ):
     """Perform resize operation on the data with selected method and options.
 
@@ -1066,9 +1096,33 @@ def _cast_output(value, data_dtype="float32", out_dtype=None):
     if boxes is not None:
         # TODO(mbrookhart): Find an example of this
         raise NotImplementedError("resize1d with image boxes not yet implemented")
-    in_z = get_inx(z, image_depth, target_depth, coordinate_transformation_mode, roi[2], roi[5])
-    in_y = get_inx(y, image_height, target_height, coordinate_transformation_mode, roi[1], roi[4])
-    in_x = get_inx(x, image_width, target_width, coordinate_transformation_mode, roi[0], roi[3])
+    in_z = get_inx(
+        z,
+        image_depth,
+        target_depth,
+        coordinate_transformation_mode,
+        roi[2],
+        roi[5],
+        scale_x_override=scale_d,
+    )
+    in_y = get_inx(
+        y,
+        image_height,
+        target_height,
+        coordinate_transformation_mode,
+        roi[1],
+        roi[4],
+        scale_x_override=scale_h,
+    )
+    in_x = get_inx(
+        x,
+        image_width,
+        target_width,
+        coordinate_transformation_mode,
+        roi[0],
+        roi[3],
+        scale_x_override=scale_w,
+    )
 
     if method == "nearest_neighbor":
         if rounding_method == "":
@@ -1225,6 +1279,7 @@ def resize3d(
     extrapolation_value=0.0,
     out_dtype=None,
     output_shape=None,
+    scales=None,
 ):
     """Perform resize operation on the data.
 
@@ -1302,6 +1357,10 @@ def resize3d(
         if isinstance(size[i], int):
             size[i] = tvm.tirx.IntImm("int32", size[i])
 
+    scale_d = (1.0 / scales[0]) if scales is not None else None
+    scale_h = (1.0 / scales[1]) if scales is not None else None
+    scale_w = (1.0 / scales[2]) if scales is not None else None
+
     def compute_func(*indices):
         return _resize_3d(
             indices,
@@ -1321,6 +1380,9 @@ def compute_func(*indices):
             exclude_outside=bicubic_exclude,
             extrapolation_value=extrapolation_value,
             out_dtype=out_dtype,
+            scale_d=scale_d,
+            scale_h=scale_h,
+            scale_w=scale_w,
         )
 
     return te.compute(output_shape, compute_func, name="resize", tag=tag.INJECTIVE)

tests/python/relax/test_frontend_onnx.py

@@ -4012,6 +4012,114 @@ def _visit(expr):
     assert seen_resize3d
 
 
+@pytest.mark.parametrize(
+    "coord_mode, method",
+    [
+        ("half_pixel", "nearest"),
+        ("pytorch_half_pixel", "nearest"),
+        ("asymmetric", "nearest"),
+        ("half_pixel", "linear"),
+    ],
+)
+def test_resize_noninteger_scales_2d(coord_mode, method):
+    """Non-integer scales must use the original scale in coordinate transformation.
+
+    floor(3 * 2.5) = 7, so the recomputed ratio 3/7 = 0.4286 differs from 1/2.5 = 0.4,
+    causing wrong pixel mapping at boundary positions before the fix.
+    """
+    nearest_mode_kwargs = {}
+    if method == "nearest":
+        nearest_mode_kwargs["nearest_mode"] = "round_prefer_floor"
+    resize_node = helper.make_node(
+        "Resize",
+        ["X", "", "scales"],
+        ["Y"],
+        mode=method,
+        coordinate_transformation_mode=coord_mode,
+        **nearest_mode_kwargs,
+    )
+    graph = helper.make_graph(
+        [resize_node],
+        "resize_noninteger_2d",
+        inputs=[helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 3, 3])],
+        initializer=[
+            helper.make_tensor("scales", TensorProto.FLOAT, [4], [1.0, 1.0, 2.5, 2.5])
+        ],
+        outputs=[helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 7, 7])],
+    )
+    check_correctness(helper.make_model(graph), opset=18)
+
+
+def test_resize_noninteger_scales_1d():
+    resize_node = helper.make_node(
+        "Resize",
+        ["X", "", "scales"],
+        ["Y"],
+        mode="nearest",
+        coordinate_transformation_mode="half_pixel",
+        nearest_mode="round_prefer_floor",
+    )
+    graph = helper.make_graph(
+        [resize_node],
+        "resize_noninteger_1d",
+        inputs=[helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 5])],
+        initializer=[helper.make_tensor("scales", TensorProto.FLOAT, [3], [1.0, 1.0, 1.5])],
+        outputs=[helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 7])],
+    )
+    check_correctness(helper.make_model(graph), opset=18)
+
+
+def test_resize_noninteger_scales_3d():
+    resize_node = helper.make_node(
+        "Resize",
+        ["X", "", "scales"],
+        ["Y"],
+        mode="nearest",
+        coordinate_transformation_mode="asymmetric",
+        nearest_mode="floor",
+    )
+    graph = helper.make_graph(
+        [resize_node],
+        "resize_noninteger_3d",
+        inputs=[
+            helper.make_tensor_value_info("X", TensorProto.FLOAT, [1, 1, 3, 3, 3])
+        ],
+        initializer=[
+            helper.make_tensor("scales", TensorProto.FLOAT, [5], [1.0, 1.0, 1.5, 1.5, 1.5])
+        ],
+        outputs=[helper.make_tensor_value_info("Y", TensorProto.FLOAT, [1, 1, 4, 4, 4])],
+    )
+    check_correctness(helper.make_model(graph), opset=18)
+
+
+@pytest.mark.parametrize(
+    "input_shape,scales,output_shape",
+    [
+        ([1, 1, 4, 4], [1.0, 1.0, 2.0, 2.0], [1, 1, 8, 8]),
+        ([1, 1, 3, 3], [1.0, 1.0, 3.0, 3.0], [1, 1, 9, 9]),
+    ],
+)
+def test_resize_integer_scales_regression(input_shape, scales, output_shape):
+    resize_node = helper.make_node(
+        "Resize",
+        ["X", "", "scales"],
+        ["Y"],
+        mode="nearest",
+        coordinate_transformation_mode="half_pixel",
+        nearest_mode="round_prefer_floor",
+    )
+    graph = helper.make_graph(
+        [resize_node],
+        "resize_integer_scales",
+        inputs=[helper.make_tensor_value_info("X", TensorProto.FLOAT, input_shape)],
+        initializer=[
+            helper.make_tensor("scales", TensorProto.FLOAT, [len(scales)], scales)
+        ],
+        outputs=[helper.make_tensor_value_info("Y", TensorProto.FLOAT, output_shape)],
+    )
+    check_correctness(helper.make_model(graph), opset=18)
+
+
 def test_einsum():
     eqn = "ij->i"
     einsum_node = helper.make_node("Einsum", ["x"], ["y"], equation=eqn)