SavitzkyGolay simple layer checks for PyTorch > 1.5.1 if padding mode not zeros, and version dependent tests added.

Signed-off-by: Christian Baker <christian.baker@kcl.ac.uk>

Author: crnbaker

monai/networks/layers/simplelayers.py

@@ -39,7 +39,7 @@
     "LLTM",
     "Reshape",
     "separable_filtering",
-    "SavitskyGolayFilter",
+    "SavitzkyGolayFilter",
     "HilbertTransform",
     "ChannelPad",
 ]
@@ -174,12 +174,18 @@ def separable_filtering(
         x: the input image. must have shape (batch, channels, H[, W, ...]).
         kernels: kernel along each spatial dimension.
             could be a single kernel (duplicated for all dimension), or `spatial_dims` number of kernels.
-        mode (string, optional): padding mode passed to convolution class. ``'zeros'``, ``'reflect'``, ``'replicate'`` or
-        ``'circular'``. Default: ``'zeros'``. See torch.nn.Conv1d() for more information.
+        mode (string, optional): padding mode passed to convolution class. ``'zeros'``, ``'reflect'``, ``'replicate'``
+            or ``'circular'``. Default: ``'zeros'``. Modes other than ``'zeros'`` require PyTorch version >= 1.5.1. See
+            torch.nn.Conv1d() for more information.
 
     Raises:
         TypeError: When ``x`` is not a ``torch.Tensor``.
     """
+
+    pytorch_version_pre_1_5_1 = tuple(int(x[0]) for x in torch.__version__.split(".")[:3]) < (1, 5, 1)
+    if (mode != "zeros") and pytorch_version_pre_1_5_1:
+        raise InvalidPyTorchVersionError("1.5.1", f"Padding mode '{mode}'")
+
     if not torch.is_tensor(x):
         raise TypeError(f"x must be a torch.Tensor but is {type(x).__name__}.")
 
@@ -219,9 +225,9 @@ def _conv(input_: torch.Tensor, d: int) -> torch.Tensor:
     return _conv(x, spatial_dims - 1)
 
 
-class SavitskyGolayFilter(nn.Module):
+class SavitzkyGolayFilter(nn.Module):
     """
-    Convolve a Tensor along a particular axis with a Savitsky-Golay kernel.
+    Convolve a Tensor along a particular axis with a Savitzky-Golay kernel.
 
     Args:
         window_length: Length of the filter window, must be a positive odd integer.
@@ -247,7 +253,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
             x: Tensor or array-like to filter. Must be real, in shape ``[Batch, chns, spatial1, spatial2, ...]`` and
                 have a device type of ``'cpu'``.
         Returns:
-            torch.Tensor: ``x`` filtered by Savitsky-Golay kernel with window length ``self.window_length`` using
+            torch.Tensor: ``x`` filtered by Savitzky-Golay kernel with window length ``self.window_length`` using
             polynomials of order ``self.order``, along axis specified in ``self.axis``.
         """
 

tests/test_savitzky_golay_filter.py

@@ -1,136 +1,162 @@
-# Copyright 2020 MONAI Consortium
-# 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.
-
-import unittest
-
-import numpy as np
-import torch
-from parameterized import parameterized
-
-from monai.networks.layers import SavitskyGolayFilter
-from tests.utils import skip_if_no_cuda
-
-# Zero-padding trivial tests
-
-TEST_CASE_SINGLE_VALUE = [
-    {"window_length": 3, "order": 1},
-    torch.Tensor([1.0]).unsqueeze(0).unsqueeze(0),  # Input data: Single value
-    torch.Tensor([1 / 3]).unsqueeze(0).unsqueeze(0),  # Expected output: With a window length of 3 and polyorder 1
-    # output should be equal to mean of 0, 1 and 0 = 1/3 (because input will be zero-padded and a linear fit performed)
-    1e-15,  # absolute tolerance
-]
-
-TEST_CASE_1D = [
-    {"window_length": 3, "order": 1},
-    torch.Tensor([1.0, 1.0, 1.0]).unsqueeze(0).unsqueeze(0),  # Input data
-    torch.Tensor([2 / 3, 1.0, 2 / 3])
-    .unsqueeze(0)
-    .unsqueeze(0),  # Expected output: zero padded, so linear interpolation
-    # over length-3 windows will result in output of [2/3, 1, 2/3].
-    1e-15,  # absolute tolerance
-]
-
-TEST_CASE_2D_AXIS_2 = [
-    {"window_length": 3, "order": 1},  # along default axis (2, first spatial dim)
-    torch.ones((3, 2)).unsqueeze(0).unsqueeze(0),
-    torch.Tensor([[2 / 3, 2 / 3], [1.0, 1.0], [2 / 3, 2 / 3]]).unsqueeze(0).unsqueeze(0),
-    1e-15,  # absolute tolerance
-]
-
-TEST_CASE_2D_AXIS_3 = [
-    {"window_length": 3, "order": 1, "axis": 3},  # along axis 3 (second spatial dim)
-    torch.ones((2, 3)).unsqueeze(0).unsqueeze(0),
-    torch.Tensor([[2 / 3, 1.0, 2 / 3], [2 / 3, 1.0, 2 / 3]]).unsqueeze(0).unsqueeze(0),
-    1e-15,  # absolute tolerance
-]
-
-# Replicated-padding trivial tests
-
-TEST_CASE_SINGLE_VALUE_REP = [
-    {"window_length": 3, "order": 1, "mode": "replicate"},
-    torch.Tensor([1.0]).unsqueeze(0).unsqueeze(0),  # Input data: Single value
-    torch.Tensor([1.0]).unsqueeze(0).unsqueeze(0),  # Expected output: With a window length of 3 and polyorder 1
-    # output will be equal to mean of [1, 1, 1] = 1 (input will be nearest-neighbour-padded and a linear fit performed)
-    1e-15,  # absolute tolerance
-]
-
-TEST_CASE_1D_REP = [
-    {"window_length": 3, "order": 1, "mode": "replicate"},
-    torch.Tensor([1.0, 1.0, 1.0]).unsqueeze(0).unsqueeze(0),  # Input data
-    torch.Tensor([1.0, 1.0, 1.0]).unsqueeze(0).unsqueeze(0),  # Expected output: zero padded, so linear interpolation
-    # over length-3 windows will result in output of [2/3, 1, 2/3].
-    1e-15,  # absolute tolerance
-]
-
-TEST_CASE_2D_AXIS_2_REP = [
-    {"window_length": 3, "order": 1, "mode": "replicate"},  # along default axis (2, first spatial dim)
-    torch.ones((3, 2)).unsqueeze(0).unsqueeze(0),
-    torch.Tensor([[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]).unsqueeze(0).unsqueeze(0),
-    1e-15,  # absolute tolerance
-]
-
-TEST_CASE_2D_AXIS_3_REP = [
-    {"window_length": 3, "order": 1, "axis": 3, "mode": "replicate"},  # along axis 3 (second spatial dim)
-    torch.ones((2, 3)).unsqueeze(0).unsqueeze(0),
-    torch.Tensor([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]).unsqueeze(0).unsqueeze(0),
-    1e-15,  # absolute tolerance
-]
-
-# Sine smoothing
-
-TEST_CASE_SINE_SMOOTH = [
-    {"window_length": 3, "order": 1},
-    # Sine wave with period equal to savgol window length (windowed to reduce edge effects).
-    torch.as_tensor(np.sin(2 * np.pi * 1 / 3 * np.arange(100)) * np.hanning(100)).unsqueeze(0).unsqueeze(0),
-    # Should be smoothed out to zeros
-    torch.zeros(100).unsqueeze(0).unsqueeze(0),
-    # tolerance chosen by examining output of SciPy.signal.savgol_filter when provided the above input
-    2e-2,  # absolute tolerance
-]
-
-
-class TestSavitskyGolayCPU(unittest.TestCase):
-    @parameterized.expand(
-        [
-            TEST_CASE_SINGLE_VALUE,
-            TEST_CASE_1D,
-            TEST_CASE_2D_AXIS_2,
-            TEST_CASE_2D_AXIS_3,
-            TEST_CASE_SINGLE_VALUE_REP,
-            TEST_CASE_1D_REP,
-            TEST_CASE_2D_AXIS_2_REP,
-            TEST_CASE_2D_AXIS_3_REP,
-            TEST_CASE_SINE_SMOOTH,
-        ]
-    )
-    def test_value(self, arguments, image, expected_data, atol):
-        result = SavitskyGolayFilter(**arguments)(image)
-        np.testing.assert_allclose(result, expected_data, atol=atol)
-
-
-@skip_if_no_cuda
-class TestSavitskyGolayGPU(unittest.TestCase):
-    @parameterized.expand(
-        [
-            TEST_CASE_SINGLE_VALUE,
-            TEST_CASE_1D,
-            TEST_CASE_2D_AXIS_2,
-            TEST_CASE_2D_AXIS_3,
-            TEST_CASE_SINGLE_VALUE_REP,
-            TEST_CASE_1D_REP,
-            TEST_CASE_2D_AXIS_2_REP,
-            TEST_CASE_2D_AXIS_3_REP,
-            TEST_CASE_SINE_SMOOTH,
-        ]
-    )
-    def test_value(self, arguments, image, expected_data, atol):
-        result = SavitskyGolayFilter(**arguments)(image.to(device="cuda"))
-        np.testing.assert_allclose(result.cpu(), expected_data, atol=atol)
+# Copyright 2020 MONAI Consortium
+# 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.
+
+import unittest
+
+import numpy as np
+import torch
+from parameterized import parameterized
+
+from monai.networks.layers import SavitzkyGolayFilter
+from monai.utils import InvalidPyTorchVersionError
+from tests.utils import SkipIfAtLeastPyTorchVersion, SkipIfBeforePyTorchVersion, skip_if_no_cuda
+
+# Zero-padding trivial tests
+
+TEST_CASE_SINGLE_VALUE = [
+    {"window_length": 3, "order": 1},
+    torch.Tensor([1.0]).unsqueeze(0).unsqueeze(0),  # Input data: Single value
+    torch.Tensor([1 / 3]).unsqueeze(0).unsqueeze(0),  # Expected output: With a window length of 3 and polyorder 1
+    # output should be equal to mean of 0, 1 and 0 = 1/3 (because input will be zero-padded and a linear fit performed)
+    1e-15,  # absolute tolerance
+]
+
+TEST_CASE_1D = [
+    {"window_length": 3, "order": 1},
+    torch.Tensor([1.0, 1.0, 1.0]).unsqueeze(0).unsqueeze(0),  # Input data
+    torch.Tensor([2 / 3, 1.0, 2 / 3])
+    .unsqueeze(0)
+    .unsqueeze(0),  # Expected output: zero padded, so linear interpolation
+    # over length-3 windows will result in output of [2/3, 1, 2/3].
+    1e-15,  # absolute tolerance
+]
+
+TEST_CASE_2D_AXIS_2 = [
+    {"window_length": 3, "order": 1},  # along default axis (2, first spatial dim)
+    torch.ones((3, 2)).unsqueeze(0).unsqueeze(0),
+    torch.Tensor([[2 / 3, 2 / 3], [1.0, 1.0], [2 / 3, 2 / 3]]).unsqueeze(0).unsqueeze(0),
+    1e-15,  # absolute tolerance
+]
+
+TEST_CASE_2D_AXIS_3 = [
+    {"window_length": 3, "order": 1, "axis": 3},  # along axis 3 (second spatial dim)
+    torch.ones((2, 3)).unsqueeze(0).unsqueeze(0),
+    torch.Tensor([[2 / 3, 1.0, 2 / 3], [2 / 3, 1.0, 2 / 3]]).unsqueeze(0).unsqueeze(0),
+    1e-15,  # absolute tolerance
+]
+
+# Replicated-padding trivial tests
+
+TEST_CASE_SINGLE_VALUE_REP = [
+    {"window_length": 3, "order": 1, "mode": "replicate"},
+    torch.Tensor([1.0]).unsqueeze(0).unsqueeze(0),  # Input data: Single value
+    torch.Tensor([1.0]).unsqueeze(0).unsqueeze(0),  # Expected output: With a window length of 3 and polyorder 1
+    # output will be equal to mean of [1, 1, 1] = 1 (input will be nearest-neighbour-padded and a linear fit performed)
+    1e-15,  # absolute tolerance
+]
+
+TEST_CASE_1D_REP = [
+    {"window_length": 3, "order": 1, "mode": "replicate"},
+    torch.Tensor([1.0, 1.0, 1.0]).unsqueeze(0).unsqueeze(0),  # Input data
+    torch.Tensor([1.0, 1.0, 1.0]).unsqueeze(0).unsqueeze(0),  # Expected output: zero padded, so linear interpolation
+    # over length-3 windows will result in output of [2/3, 1, 2/3].
+    1e-15,  # absolute tolerance
+]
+
+TEST_CASE_2D_AXIS_2_REP = [
+    {"window_length": 3, "order": 1, "mode": "replicate"},  # along default axis (2, first spatial dim)
+    torch.ones((3, 2)).unsqueeze(0).unsqueeze(0),
+    torch.Tensor([[1.0, 1.0], [1.0, 1.0], [1.0, 1.0]]).unsqueeze(0).unsqueeze(0),
+    1e-15,  # absolute tolerance
+]
+
+TEST_CASE_2D_AXIS_3_REP = [
+    {"window_length": 3, "order": 1, "axis": 3, "mode": "replicate"},  # along axis 3 (second spatial dim)
+    torch.ones((2, 3)).unsqueeze(0).unsqueeze(0),
+    torch.Tensor([[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]).unsqueeze(0).unsqueeze(0),
+    1e-15,  # absolute tolerance
+]
+
+# Sine smoothing
+
+TEST_CASE_SINE_SMOOTH = [
+    {"window_length": 3, "order": 1},
+    # Sine wave with period equal to savgol window length (windowed to reduce edge effects).
+    torch.as_tensor(np.sin(2 * np.pi * 1 / 3 * np.arange(100)) * np.hanning(100)).unsqueeze(0).unsqueeze(0),
+    # Should be smoothed out to zeros
+    torch.zeros(100).unsqueeze(0).unsqueeze(0),
+    # tolerance chosen by examining output of SciPy.signal.savgol_filter when provided the above input
+    2e-2,  # absolute tolerance
+]
+
+
+class TestSavitzkyGolayCPU(unittest.TestCase):
+    @parameterized.expand(
+        [
+            TEST_CASE_SINGLE_VALUE,
+            TEST_CASE_1D,
+            TEST_CASE_2D_AXIS_2,
+            TEST_CASE_2D_AXIS_3,
+            TEST_CASE_SINE_SMOOTH,
+        ]
+    )
+    def test_value(self, arguments, image, expected_data, atol):
+        result = SavitzkyGolayFilter(**arguments)(image)
+        np.testing.assert_allclose(result, expected_data, atol=atol)
+
+
+@SkipIfBeforePyTorchVersion((1, 5, 1))
+class TestSavitzkyGolayCPUREP(unittest.TestCase):
+    @parameterized.expand(
+        [TEST_CASE_SINGLE_VALUE_REP, TEST_CASE_1D_REP, TEST_CASE_2D_AXIS_2_REP, TEST_CASE_2D_AXIS_3_REP]
+    )
+    def test_value(self, arguments, image, expected_data, atol):
+        result = SavitzkyGolayFilter(**arguments)(image)
+        np.testing.assert_allclose(result, expected_data, atol=atol)
+
+
+@skip_if_no_cuda
+class TestSavitzkyGolayGPU(unittest.TestCase):
+    @parameterized.expand(
+        [
+            TEST_CASE_SINGLE_VALUE,
+            TEST_CASE_1D,
+            TEST_CASE_2D_AXIS_2,
+            TEST_CASE_2D_AXIS_3,
+            TEST_CASE_SINE_SMOOTH,
+        ]
+    )
+    def test_value(self, arguments, image, expected_data, atol):
+        result = SavitzkyGolayFilter(**arguments)(image.to(device="cuda"))
+        np.testing.assert_allclose(result.cpu(), expected_data, atol=atol)
+
+
+@skip_if_no_cuda
+@SkipIfBeforePyTorchVersion((1, 5, 1))
+class TestSavitzkyGolayGPUREP(unittest.TestCase):
+    @parameterized.expand(
+        [
+            TEST_CASE_SINGLE_VALUE_REP,
+            TEST_CASE_1D_REP,
+            TEST_CASE_2D_AXIS_2_REP,
+            TEST_CASE_2D_AXIS_3_REP,
+        ]
+    )
+    def test_value(self, arguments, image, expected_data, atol):
+        result = SavitzkyGolayFilter(**arguments)(image.to(device="cuda"))
+        np.testing.assert_allclose(result.cpu(), expected_data, atol=atol)
+
+
+@SkipIfAtLeastPyTorchVersion((1, 5, 1))
+class TestSavitzkyGolayInvalidPyTorch(unittest.TestCase):
+    def test_invalid_pytorch_error(self):
+        with self.assertRaisesRegex(InvalidPyTorchVersionError, "version"):
+            SavitzkyGolayFilter(3, 1, mode="replicate")(torch.ones((1, 1, 10, 10)))