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new 072c849439 [Test][TFLite] Add unit tests for RESIZE_BILINEAR and
RESIZE_NEAREST_NEIGHBOR ops (#19365)
072c849439 is described below
commit 072c849439f887aec6f70e061de0eba2e3d9b811
Author: Kryptonite <[email protected]>
AuthorDate: Tue Apr 7 04:04:13 2026 +0300
[Test][TFLite] Add unit tests for RESIZE_BILINEAR and
RESIZE_NEAREST_NEIGHBOR ops (#19365)
Partially closes #18971.
Add parametrized unit tests for `RESIZE_BILINEAR` and
`RESIZE_NEAREST_NEIGHBOR` following the existing `verify()` +
`tf.Module` pattern in the file.
## Tests added (13 total)
### `RESIZE_BILINEAR` (7 cases via `test_resize_bilinear`)
| Case | Input shape | Output size | `coordinate_transformation_mode` |
|------|-------------|-------------|----------------------------------|
| upsample, default | `(1, 4, 4, 1)` | `8×8` | `half_pixel` |
| downsample, default | `(1, 8, 8, 3)` | `4×4` | `half_pixel` |
| align_corners | `(1, 4, 4, 1)` | `7×7` | `align_corners` |
| half_pixel_centers | `(1, 4, 4, 2)` | `8×8` | `half_pixel` |
| multichannel / batch > 1 | `(2, 6, 6, 16)` | `12×12` | `half_pixel` |
| identity | `(1, 5, 5, 3)` | `5×5` | `half_pixel` |
| non-square | `(1, 4, 8, 1)` | `8×16` | `half_pixel` |
### `RESIZE_NEAREST_NEIGHBOR` (6 cases via
`test_resize_nearest_neighbor`)
| Case | Input shape | Output size | `coordinate_transformation_mode` |
`rounding_method` |
|------|-------------|-------------|----------------------------------|-------------------|
| upsample, default | `(1, 2, 2, 1)` | `4×4` | `half_pixel` |
`round_prefer_ceil` |
| downsample, default | `(1, 8, 8, 3)` | `4×4` | `half_pixel` |
`round_prefer_ceil` |
| align_corners | `(1, 4, 4, 1)` | `7×7` | `align_corners` | `""` |
| multichannel / batch > 1 | `(4, 3, 3, 8)` | `6×6` | `half_pixel` |
`round_prefer_ceil` |
| non-square | `(1, 4, 8, 1)` | `8×16` | `half_pixel` |
`round_prefer_ceil` |
| identity | `(1, 3, 3, 2)` | `3×3` | `half_pixel` | `round_prefer_ceil`
|
## Implementation notes
- `Expected` modules are built programmatically via `relax.BlockBuilder`
rather than TVMScript — the TVMScript parser does not accept runtime
variables in type annotations, which would be required inside a
parametrized test.
- All 13 new tests pass.
- `test_fill`, `test_batch_matmul`, and `test_batch_matmul_adj` were
already failing on unmodified `main` before this PR. No regressions
introduced.
- Tested with Python 3.10.20, TensorFlow 2.9.0, NumPy 1.26.4.
---
tests/python/relax/test_frontend_tflite.py | 71 ++++++++++++++++++++++++++++++
1 file changed, 71 insertions(+)
diff --git a/tests/python/relax/test_frontend_tflite.py
b/tests/python/relax/test_frontend_tflite.py
index d5149fb161..275e162b81 100644
--- a/tests/python/relax/test_frontend_tflite.py
+++ b/tests/python/relax/test_frontend_tflite.py
@@ -1183,5 +1183,76 @@ def test_nms_v5_ir():
# Bounding boxes / scores tensor bounds checks
assert f"R.Tensor(({max_output_size},)" in ir
+
+def _make_resize_expected(input_shape, output_size, method,
coordinate_transformation_mode, rounding_method):
+ """Build an Expected IRModule programmatically to avoid TVMScript variable
scope limitations."""
+ bb = relax.BlockBuilder()
+ x = relax.Var("x", relax.TensorStructInfo(input_shape, "float32"))
+ with bb.function("main", [x]):
+ with bb.dataflow():
+ gv = bb.emit_output(
+ relax.op.image.resize2d(
+ x,
+ size=relax.ShapeExpr([output_size[0], output_size[1]]),
+ roi=[0.0, 0.0, 0.0, 0.0],
+ layout="NHWC",
+ method=method,
+
coordinate_transformation_mode=coordinate_transformation_mode,
+ rounding_method=rounding_method,
+ cubic_alpha=-0.75,
+ cubic_exclude=0,
+ extrapolation_value=0.0,
+ out_dtype="void",
+ )
+ )
+ bb.emit_func_output(gv)
+ mod = bb.get()
+ mod["main"] = mod["main"].with_attr("num_input", 1)
+ return mod
+
+
[email protected](
+ "input_shape, output_size, tf_op, coordinate_transformation_mode",
+ [
+ ((1, 4, 4, 1), [8, 8], lambda x: tf.image.resize(x, [8, 8],
method="bilinear"), "half_pixel"),
+ ((1, 8, 8, 3), [4, 4], lambda x: tf.image.resize(x, [4, 4],
method="bilinear"), "half_pixel"),
+ ((1, 4, 4, 1), [7, 7], lambda x:
tf.compat.v1.image.resize_bilinear(x, [7, 7], align_corners=True),
"align_corners"),
+ ((1, 4, 4, 2), [8, 8], lambda x:
tf.compat.v1.image.resize_bilinear(x, [8, 8], half_pixel_centers=True),
"half_pixel"),
+ ((2, 6, 6, 16), [12, 12], lambda x: tf.image.resize(x, [12, 12],
method="bilinear"), "half_pixel"),
+ ((1, 5, 5, 3), [5, 5], lambda x: tf.image.resize(x, [5, 5],
method="bilinear"), "half_pixel"),
+ ((1, 4, 8, 1), [8, 16], lambda x: tf.image.resize(x, [8, 16],
method="bilinear"), "half_pixel"),
+ ],
+)
+def test_resize_bilinear(input_shape, output_size, tf_op,
coordinate_transformation_mode):
+ class ResizeBilinear(tf.Module):
+ @tf.function(input_signature=[tf.TensorSpec(shape=input_shape,
dtype=tf.float32)])
+ def func(self, x):
+ return tf_op(x)
+
+ expected = _make_resize_expected(input_shape, output_size, "linear",
coordinate_transformation_mode, "")
+ verify(ResizeBilinear, expected)
+
+
[email protected](
+ "input_shape, output_size, tf_op, coordinate_transformation_mode,
rounding_method",
+ [
+ ((1, 2, 2, 1), [4, 4], lambda x: tf.image.resize(x, [4, 4],
method="nearest"), "half_pixel",
"round_prefer_ceil"),
+ ((1, 8, 8, 3), [4, 4], lambda x: tf.image.resize(x, [4, 4],
method="nearest"), "half_pixel",
"round_prefer_ceil"),
+ ((1, 4, 4, 1), [7, 7], lambda x:
tf.compat.v1.image.resize_nearest_neighbor(x, [7, 7], align_corners=True),
"align_corners", ""),
+ ((4, 3, 3, 8), [6, 6], lambda x: tf.image.resize(x, [6, 6],
method="nearest"), "half_pixel",
"round_prefer_ceil"),
+ ((1, 4, 8, 1), [8, 16], lambda x: tf.image.resize(x, [8, 16],
method="nearest"), "half_pixel",
"round_prefer_ceil"),
+ ((1, 3, 3, 2), [3, 3], lambda x: tf.image.resize(x, [3, 3],
method="nearest"), "half_pixel",
"round_prefer_ceil"),
+ ],
+)
+def test_resize_nearest_neighbor(input_shape, output_size, tf_op,
coordinate_transformation_mode, rounding_method):
+ class ResizeNearest(tf.Module):
+ @tf.function(input_signature=[tf.TensorSpec(shape=input_shape,
dtype=tf.float32)])
+ def func(self, x):
+ return tf_op(x)
+
+ expected = _make_resize_expected(input_shape, output_size,
"nearest_neighbor", coordinate_transformation_mode, rounding_method)
+ verify(ResizeNearest, expected)
+
+
if __name__ == "__main__":
pytest.main(["-s", __file__])
