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diff --git a/tests/models/ sp_transform/test_ sp_transform.py b/tests/models/ sp_transform/test_ sp_transform.py
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+++ b/tests/models/ sp_transform/test_ sp_transform.py	
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+# Copyright 2024 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""spectral transform testcase"""
+
+import os
+import sys
+import pytest
+import numpy as np
+
+import mindspore as ms
+from mindspore import Tensor, set_seed
+from mindspore import dtype as mstype
+
+PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../"))
+sys.path.append(PROJECT_ROOT)
+
+# pylint: disable=wrong-import-position
+from mindflow.cell.neural_operators.sp_transform import (
+    ConvCell, TransformCell, TransformCell1D, TransformCell2D, TransformCell3D, Dim
+)
+# pylint: enable=wrong-import-position
+
+set_seed(123456)
+
+
+# ======================================================================
+#  ConvCell Tests
+# ======================================================================
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+@pytest.mark.parametrize("mode", [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
+def test_convcell_forward(mode):
+    """
+    Feature: Test ConvCell in 1D/2D/3D.
+    Description: Check output shape for conv1d, conv2d, conv3d.
+    Expectation: Output shape matches input shape.
+    """
+    ms.set_context(mode=mode)
+
+    # 1D conv
+    x1 = Tensor(np.random.randn(2, 3, 16), mstype.float32)
+    net1 = ConvCell(dim=1, in_channels=3, out_channels=4, kernel_size=3)
+    y1 = net1(x1)
+    assert y1.shape == (2, 4, 16)
+
+    # 2D conv
+    x2 = Tensor(np.random.randn(2, 3, 16, 16), mstype.float32)
+    net2 = ConvCell(dim=2, in_channels=3, out_channels=5, kernel_size=3)
+    y2 = net2(x2)
+    assert y2.shape == (2, 5, 16, 16)
+
+    # 3D conv
+    x3 = Tensor(np.random.randn(2, 3, 8, 8, 8), mstype.float32)
+    net3 = ConvCell(dim=3, in_channels=3, out_channels=6, kernel_size=3)
+    y3 = net3(x3)
+    assert y3.shape == (2, 6, 8, 8, 8)
+
+
+# ======================================================================
+#  TransformCell1D
+# ======================================================================
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+@pytest.mark.parametrize("mode", [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
+def test_transformcell1d(mode):
+    """
+    Feature: Test TransformCell1D.
+    Description: Polynomial transform in 1D.
+    Expectation: matmul result has correct shape.
+    """
+    ms.set_context(mode=mode)
+
+    res = 16
+    modes = 5
+    batch, ch = 2, 3
+
+    transform = Tensor(np.random.randn(modes, res), mstype.float32)
+    net = TransformCell1D(transform)
+
+    x = Tensor(np.random.randn(batch, ch, res), mstype.float32)
+    y = net(x)
+
+    assert y.shape == (batch, ch, modes)
+
+
+# ======================================================================
+#  TransformCell2D
+# ======================================================================
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+@pytest.mark.parametrize("axis", [Dim.x, Dim.y])
+@pytest.mark.parametrize("mode", [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
+def test_transformcell2d(mode, axis):
+    """
+    Feature: Test TransformCell2D.
+    Description: Check 2D polynomial transform on both axes.
+    Expectation: Output shapes match transform rules.
+    """
+    ms.set_context(mode=mode)
+
+    res = 12
+    modes = 4
+    batch, ch = 2, 3
+
+    transform = Tensor(np.random.randn(modes, res), mstype.float32)
+    net = TransformCell2D(transform, axis)
+
+    x = Tensor(np.random.randn(batch, ch, res, res), mstype.float32)
+    y = net(x)
+
+    # For both axes, last transformed dimension becomes modes
+    assert y.shape in (
+        (batch, modes, ch, res),    # axis x
+        (batch, res, modes, ch)     # axis y
+    )
+
+
+# ======================================================================
+#  TransformCell3D
+# ======================================================================
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+@pytest.mark.parametrize("axis", [Dim.x, Dim.y, Dim.z])
+@pytest.mark.parametrize("mode", [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
+def test_transformcell3d(mode, axis):
+    """
+    Feature: Test TransformCell3D forward.
+    Description: Ensure shape correctness for 3D polynomial transform.
+    Expectation: One dimension is replaced with modes.
+    """
+    ms.set_context(mode=mode)
+
+    res = 10
+    modes = 6
+    batch, ch = 1, 2
+
+    transform = Tensor(np.random.randn(modes, res), mstype.float32)
+    net = TransformCell3D(transform, axis)
+
+    x = Tensor(np.random.randn(batch, ch, res, res, res), mstype.float32)
+    y = net(x)
+
+    # validate one dimension equals modes
+    assert modes in y.shape
+
+
+# ======================================================================
+#  TransformCell (Unified Interface)
+# ======================================================================
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
+@pytest.mark.parametrize("dim", [1, 2, 3])
+@pytest.mark.parametrize("mode", [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
+def test_transformcell_unified(dim, mode):
+    """
+    Feature: Test TransformCell wrapper dispatcher.
+    Description: Validate correct dim selection.
+    Expectation: Output has modes in one axis.
+    """
+    ms.set_context(mode=mode)
+
+    res = 10
+    modes = 4
+    batch, ch = 2, 3
+    transform = Tensor(np.random.randn(modes, res), mstype.float32)
+
+    net = TransformCell(dim=dim, transform=transform, axis=Dim.x)
+
+    if dim == 1:
+        x = Tensor(np.random.randn(batch, ch, res), mstype.float32)
+    elif dim == 2:
+        x = Tensor(np.random.randn(batch, ch, res, res), mstype.float32)
+    else:
+        x = Tensor(np.random.randn(batch, ch, res, res, res), mstype.float32)
+
+    y = net(x)
+
+    assert modes in y.shape