[Fix][Relax][ONNX] Import Resize with empty scales tensor and symbolic sizes

[LiRunGuo]

Models exported from PyTorch with F.interpolate(x, size=...) currently fail to import through from_onnx, and the failure comes from how the Resize converter unpacks its optional inputs rather than from the resize itself. There are two separate triggers.

At opset 12 and below, the PyTorch exporter does not omit roi and scales when sizes is used; it passes them as empty constant tensors. The converter only treated a missing input as "not provided", so it saw both scales and sizes and failed the Only one of scales and sizes can be provided in Resize assertion. The ONNX spec treats an empty tensor here the same as an omitted input, so this PR maps zero-element constants to None before the check.

At opset 13 and above, when the model has a dynamic batch dimension, the exporter builds sizes as Concat(Shape(x)[:2], [H_out, W_out]). That reaches the converter as a ShapeExpr whose leading entries are symbolic, and int(val.value) raised AttributeError: 'Var' object has no attribute 'value'. Only the spatial entries are used, and relax.op.image.resize2d/resize3d already accept symbolic output extents, so the converter now keeps non-constant entries as PrimExprs instead of forcing them to Python ints. As a side effect, resizing to another tensor's runtime height and width (sizes taken from a symbolic Shape) also imports.

A minimal reproduction:

import io, torch, torch.nn.functional as F, onnx
from tvm.relax.frontend.onnx import from_onnx

class M(torch.nn.Module):
    def forward(self, x):
        return F.interpolate(x, size=(8, 8), mode="nearest")

for opset, dynamic_axes in [(11, {}), (13, {"x": {0: "N"}})]:
    buf = io.BytesIO()
    torch.onnx.export(M(), torch.randn(1, 3, 4, 4), buf, opset_version=opset,
                      input_names=["x"], dynamic_axes=dynamic_axes, dynamo=False)
    from_onnx(onnx.load_from_string(buf.getvalue()))
# opset 11: AssertionError: Only one of scales and sizes can be provided in Resize.
# opset 13: AttributeError: 'Var' object has no attribute 'value'

The new tests cover the empty-scales pattern with static and symbolic batch, sizes computed from Shape(x) with a symbolic batch, and spatial sizes taken from a second input with symbolic height and width. Each checks the imported output shape and compares against onnxruntime; all four fail without this change. I also checked PyTorch exports of nearest and bilinear F.interpolate at opsets 11, 13 and 17 with batch sizes 1 and 3, which match onnxruntime to within 2.4e-7.

[yongwww]

@tvm-bot rerun