[Bug][Relax] Import failures on PaddleOCR and FasterRCNN-style models: Squeeze axes, shape Gather, and dynamic TopK

## Summary

During compatibility testing, two industrial-style ONNX graphs consistently fail at the TVM Relax ONNX importer stage, before tuning can start:

1. `PP-OCRv6_tiny.onnx`, exported from a PaddleOCR-style model, fails around `Squeeze -> Transpose`.
2. `FasterRCNN-12.onnx`, a detection graph with dynamic post-processing, fails around shape `Gather` and then dynamic `TopK`.

I would like to ask whether these are expected importer limitations in the current Relax ONNX frontend, and whether the preprocessing workarounds described below are recommended, or if there is a better official way to handle these graphs.

## Environment

```text
TVM version: 0.24.0
Python: 3.11.15
ONNX: 1.21.0
OS: Linux
Frontend used: tvm.relax.frontend.onnx.from_onnx
```

## Case 1: PP-OCRv6_tiny Squeeze axes are lost before Transpose

The model is `PP-OCRv6_tiny.onnx`, opset 11. The failing pattern in the ONNX graph is:

```text
Squeeze.0
inputs = ['p2o.pd_op.pool2d.0.0']
outputs = ['p2o.pd_op.squeeze.0.0']
attrs = {'axes': [2]}

Transpose.0
inputs = ['p2o.pd_op.squeeze.0.0']
outputs = ['p2o.pd_op.transpose.0.0']
attrs = {'perm': [0, 2, 1]}
```

Using either OCR runtime shape below gives the same failure:

```python
from tvm.relax.frontend.onnx import from_onnx
import onnx

model = onnx.load("PP-OCRv6_tiny.onnx")
from_onnx(
model,
shape_dict={"x": [1, 3, 48, 128]},
dtype_dict={"x": "float32"},
opset=11,
keep_params_in_input=False,
)
```

Observed error:

```text
Error converting operator Transpose, with inputs: [R.squeeze(lv151, axis=None)]
ValueError: Transpose: number of axes in perm attribute (3) must equal the number of input tensor dimensions (2)
```

The ONNX node has `Squeeze axes=[2]`, so the expected output rank should be 3 and `Transpose perm=[0,2,1]` should be valid. The TVM error shows `R.squeeze(..., axis=None)`, which suggests that the importer may be ignoring the ONNX `axes` attribute for this opset/node form.

### Related PaddleOCR Conv pattern

The same model also contains Paddle-style grouped/depthwise `1xK` conv patterns:

```text
node = Conv.35
inputs = ['p2o.pd_op.unsqueeze.1.0', 'p2o.pd_op.unsqueeze.0.0']
outputs = ['p2o.pd_op.depthwise_conv2d.9.0']
attrs = {
'dilations': [1, 1],
'kernel_shape': [1, 5],
'strides': [1, 1],
'group': 160,
'pads': [0, 2, 0, 2]
}
```

I experimented with two graph rewrites:

1. Normalize symmetric ONNX Conv pads from `[top,left,bottom,right]` to `[h,w]` for TVM.
2. Rewrite `Unsqueeze(axis=2) -> Conv(1xK) -> Squeeze(axis=2)` into an equivalent Conv1D form.

However, the first rewrite is not ONNX-spec-safe as a persisted ONNX model, because ONNX Runtime validation reports:

```text
Node (Conv.0) Op (Conv) [ShapeInferenceError] Attribute pads has incorrect size
```

So I currently treat this as a TVM-only workaround and avoid saving it as a general runtime ONNX graph.

## Case 2: FasterRCNN-12 dynamic detection post-processing

The model is `FasterRCNN-12.onnx`, opset 12. It contains a full detection post-processing graph:

```text
TopK: 7
NonMaxSuppression: 85
RoiAlign: 4
Resize: 3
Shape: 117
Gather: 798
```

Raw import fails first at shape `Gather`:

```text
Error converting operator Gather, with inputs: [R.shape([1, 3, 1, 1]), 2034]
AssertionError: Only constant indices supported for shape gather.
```

After freezing/stabilizing static shape subgraphs and importing the prepared runtime ONNX with:

```python
from_onnx(
model,
shape_dict={"image": [3, 224, 224]},
dtype_dict={"image": "float32"},
opset=12,
keep_params_in_input=False,
)
```

the next importer failure is dynamic `TopK`:

```text
Error converting operator TopK, with inputs: [R.sigmoid(lv242), v_5635]
ValueError: TopK k must be a constant
```

## Current workarounds in my pipeline

The pipeline currently does the following:

- Fold static shape subgraphs into initializers when this preserves ONNX Runtime validation.
- Fix missing or empty `Resize` ROI inputs.
- Rewrite static `Split` tensor inputs to `Slice` where TVM treats the second input as dynamic.
- Remove or bypass inference-time `Dropout`.
- Skip full detection post-processing graphs containing `NonMaxSuppression + dynamic TopK`.
- Skip Paddle-style degenerate grouped `1xK` conv graphs when the rewrite would make the persisted ONNX invalid.

## Questions

1. For opset 11 `Squeeze` with an `axes` attribute, should the Relax ONNX importer preserve the axes and emit `R.squeeze(..., axis=[2])` instead of `axis=None`?
2. Is the shape `Gather` failure expected when the input is `R.shape(...)` and the index is a scalar constant-like value?
3. Is dynamic `TopK k` unsupported by design in Relax ONNX import, or is there a recommended way to keep it symbolic?
4. For full detection graphs with `NonMaxSuppression + TopK`, does the TVM team recommend splitting the graph before import, or should users expect full-graph import to work eventually?
5. Are graph-level rewrites such as static shape folding, static Split-to-Slice, and `Unsqueeze-Conv-Squeeze -> Conv1D` considered reasonable preprocessing for TVM, or is there a more official path?

## Expected behavior

Ideally, TVM should import the valid ONNX graph or report a precise unsupported-pattern diagnostic. In the PP-OCRv6 case, the `Squeeze axes=[2]` attribute appears to be valid and should not reduce the tensor with `axis=None`.

## Attachments

log:

```text
[11:04:07] /home/perception/Nanmur/tvm/src/relax/ir/block_builder.cc:66: Warning: BlockBuilder destroyed with remaining blocks!
[11:04:07] /home/perception/Nanmur/tvm/src/relax/ir/block_builder.cc:66: Warning: BlockBuilder destroyed with remaining blocks!
[11:04:07] /home/perception/Nanmur/tvm/src/relax/ir/block_builder.cc:66: Warning: BlockBuilder destroyed with remaining blocks!
[11:04:08] /home/perception/Nanmur/tvm/src/relax/ir/block_builder.cc:66: Warning: BlockBuilder destroyed with remaining blocks!

==========================================================================================
PP-OCRv6_tiny raw ONNX -> TVM Relax from_onnx
==========================================================================================
shape_dict = {'x': [1, 3, 48, 1]}
dtype_dict = {'x': 'float32'}
Error converting operator Transpose, with inputs: [R.squeeze(lv151, axis=None)]
IMPORT_FAILED
ValueError: Transpose: number of axes in perm attribute (3) must equal the number of input tensor dimensions (2)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5173, in from_onnx
self._construct_nodes(graph)
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5382, in _construct_nodes
raise err
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5376, in _construct_nodes
op = self._convert_operator(op_name, inputs, attr, self.opset)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5476, in _convert_operator
sym = op_function(self.bb, inputs, attrs, [self._nodes, self._params])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 931, in _impl_v13
raise ValueError(
ValueError: Transpose: number of axes in perm attribute (3) must equal the number of input tensor dimensions (2)
[Preprocess] Normalized 37 symmetric Conv pads from 4D to 2D for TVM.

==========================================================================================
PP-OCRv6_tiny after symmetric Conv pads normalization
==========================================================================================
shape_dict = {'x': [1, 3, 48, 1]}
dtype_dict = {'x': 'float32'}
Error converting operator Transpose, with inputs: [R.squeeze(lv151, axis=None)]
IMPORT_FAILED
ValueError: Transpose: number of axes in perm attribute (3) must equal the number of input tensor dimensions (2)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5173, in from_onnx
self._construct_nodes(graph)
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5382, in _construct_nodes
raise err
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5376, in _construct_nodes
op = self._convert_operator(op_name, inputs, attr, self.opset)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5476, in _convert_operator
sym = op_function(self.bb, inputs, attrs, [self._nodes, self._params])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 931, in _impl_v13
raise ValueError(
ValueError: Transpose: number of axes in perm attribute (3) must equal the number of input tensor dimensions (2)
[Preprocess] Normalized 37 symmetric Conv pads from 4D to 2D for TVM.

==========================================================================================
PP-OCRv6_tiny after pads normalization + Unsqueeze-Conv-Squeeze to Conv1D rewrite
==========================================================================================
shape_dict = {'x': [1, 3, 48, 1]}
dtype_dict = {'x': 'float32'}
Error converting operator Transpose, with inputs: [R.squeeze(lv151, axis=None)]
IMPORT_FAILED
ValueError: Transpose: number of axes in perm attribute (3) must equal the number of input tensor dimensions (2)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5173, in from_onnx
self._construct_nodes(graph)
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5382, in _construct_nodes
raise err
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5376, in _construct_nodes
op = self._convert_operator(op_name, inputs, attr, self.opset)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5476, in _convert_operator
sym = op_function(self.bb, inputs, attrs, [self._nodes, self._params])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 931, in _impl_v13
raise ValueError(
ValueError: Transpose: number of axes in perm attribute (3) must equal the number of input tensor dimensions (2)

==========================================================================================
PP-OCRv6_tiny representative grouped 1xK Conv nodes
==========================================================================================
node= Conv.35 inputs= ['p2o.pd_op.unsqueeze.1.0', 'p2o.pd_op.unsqueeze.0.0'] outputs= ['p2o.pd_op.depthwise_conv2d.9.0'] attrs= {'dilations': [1, 1], 'kernel_shape': [1, 5], 'strides': [1, 1], 'group': 160, 'pads': [0, 2, 0, 2]}

==========================================================================================
FasterRCNN-12 raw ONNX -> TVM Relax from_onnx
==========================================================================================
shape_dict = {'image': [3, 1, 1]}
dtype_dict = {'image': 'float32'}
Error converting operator Gather, with inputs: [R.shape([1, 3, 1, 1]), 2034]
IMPORT_FAILED
AssertionError: Only constant indices supported for shape gather.
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5173, in from_onnx
self._construct_nodes(graph)
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5382, in _construct_nodes
raise err
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5376, in _construct_nodes
op = self._convert_operator(op_name, inputs, attr, self.opset)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5476, in _convert_operator
sym = op_function(self.bb, inputs, attrs, [self._nodes, self._params])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 1086, in _impl_v13
assert isinstance(indices, relax.Constant), (
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AssertionError: Only constant indices supported for shape gather.

==========================================================================================
FasterRCNN-12 prepared runtime_fp32.onnx -> TVM Relax from_onnx
==========================================================================================
shape_dict = {'image': [3, 224, 224]}
dtype_dict = {'image': 'float32'}
Error converting operator TopK, with inputs: [R.sigmoid(lv242), v_5635]
IMPORT_FAILED
ValueError: TopK k must be a constant
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5173, in from_onnx
self._construct_nodes(graph)
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5382, in _construct_nodes
raise err
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5376, in _construct_nodes
op = self._convert_operator(op_name, inputs, attr, self.opset)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 5476, in _convert_operator
sym = op_function(self.bb, inputs, attrs, [self._nodes, self._params])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/perception/Nanmur/tvm/python/tvm/relax/frontend/onnx/onnx_frontend.py", line 4154, in _impl_v11[11:04:08] /home/perception/Nanmur/tvm/src/relax/ir/block_builder.cc:66: Warning: BlockBuilder destroyed with remaining blocks!

raise ValueError("TopK k must be a constant")
ValueError: TopK k must be a constant

==========================================================================================

```

Start with python/tvm/relax/frontend/onnx/onnx_frontend.py, especially the Squeeze, Gather, and TopK conversion paths shown in the traceback. Run the provided from_onnx reproducers for PP-OCRv6_tiny.onnx and FasterRCNN-12.onnx, then determine whether these patterns can be imported or need explicit unsupported-pattern diagnostics. Done means the valid Squeeze case imports correctly and the remaining Gather or dynamic TopK limitations are reported precisely.

Written by the indexing model from the issue text.