yews.transform under cover with 100% coverage.

tests/test_transforms.py

@@ -5,7 +5,19 @@
 import torch
 import numpy as np
 
+
+class DummpyBaseTransform(transforms.BaseTransform):
+
+    def __init__(self, a=0, b=1):
+        self.a = a
+        self.b = b
+
+    def __call__(self, data):
+        return data
+
+
 class TestIsNumpyWaveform:
+
     def test_single_channel_waveform_vector(self):
         wav = np.empty(10)
         assert F._is_numpy_waveform(wav)
@@ -26,7 +38,9 @@ def test_invalid_waveform_wrong_type(self):
         wav = torch.tensor(10)
         assert not F._is_numpy_waveform(wav)
 
+
 class TestToTensor:
+
     def test_type_exception(self):
         wav = torch.tensor(10)
         with pytest.raises(TypeError):
@@ -42,7 +56,19 @@ def test_multi_channel_waveform(self):
         tensor = torch.zeros(3, 10,dtype=torch.float)
         assert torch.allclose(F._to_tensor(wav), tensor)
 
+class TestBaseTransform:
+
+    def test_raise_call_notimplementederror(self):
+        with pytest.raises(NotImplementedError):
+            t = transforms.BaseTransform()
+            t(0)
+
+    def test_repr(self):
+        t = transforms.BaseTransform()
+        assert type(t.__repr__()) is str
+
 class TestMandatoryMethods:
+
     def test_call_method(self):
         assert all([hasattr(getattr(transforms, t), '__call__') for t in
                     transforms.transforms.__all__])
@@ -51,6 +77,13 @@ def test_repr_method(self):
         assert all([hasattr(getattr(transforms, t), '__repr__') for t in
                     transforms.transforms.__all__])
 
+
+class TestComposeTransform:
+
+    def test_repr(self):
+        t = transforms.Compose([DummpyBaseTransform()])
+        assert type(t.__repr__()) is str
+
 class TestTransformCorrectness:
     def test_compose(self):
         wav = np.array([1, 3])
@@ -80,3 +113,7 @@ def test_cut_waveform_value(self):
         assert np.allclose(transforms.CutWaveform(100, 1900)(wav),
                            wav[:, 100:1900])
 
+    def test_soft_clip(self):
+        wav = np.array([-1, -0.5, 0, 0.5, 1])
+        assert np.allclose(transforms.SoftClip()(wav),
+                           np.array([0.26894142, 0.37754067, 0.5, 0.62245933, 0.73105858]))

yews/transforms/__init__.py

@@ -1 +1,2 @@
+from .base import BaseTransform, Compose
 from .transforms import *

yews/transforms/base.py

@@ -0,0 +1,55 @@
+class BaseTransform(object):
+    """An abstract class representing a Transform.
+
+    All other transform should subclass it. All subclasses should override
+    ``__call__`` which performs the transform.
+
+    Args:
+        root (object): Source of the dataset.
+        sample_transform (callable, optional): A function/transform that takes
+            a sample and returns a transformed version.
+        target_transform (callable, optional): A function/transform that takes
+            a target and transform it.
+
+    Attributes:
+        samples (dataset-like object): Dataset-like object for samples.
+        targets (dataset-like object): Dataset-like object for targets.
+
+    """
+
+    def __call__(self, data):
+        raise NotImplementedError
+
+    def __repr__(self):
+        head = self.__class__.__name__
+        content = [f"{key} = {val}" for key, val in self.__dict__.items()]
+        body = ", ".join(content)
+        return f"{head}({body})"
+
+
+class Compose(BaseTransform):
+    """Composes several transforms together.
+    Args:
+        transforms (list of ``Transform`` objects): list of transforms to compose.
+    Example:
+        >>> transforms.Compose([
+        >>>     transforms.CenterCrop(10),
+        >>>     transforms.ToTensor(),
+        >>> ])
+    """
+
+    def __init__(self, transforms):
+        self.transforms = transforms
+
+    def __call__(self, wav):
+        for t in self.transforms:
+            wav = t(wav)
+        return wav
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + '('
+        for t in self.transforms:
+            format_string += '\n'
+            format_string += '    {0}'.format(t)
+        format_string += '\n)'
+        return format_string

yews/transforms/transforms.py

@@ -1,42 +1,14 @@
+from .base import BaseTransform
 from . import functional as F
 
 __all__ = [
-    "Compose",
     "ToTensor",
     "ZeroMean",
     "SoftClip",
     "CutWaveform",
 ]
 
-class Compose(object):
-    """Composes several transforms together.
-    Args:
-        transforms (list of ``Transform`` objects): list of transforms to compose.
-    Example:
-        >>> transforms.Compose([
-        >>>     transforms.CenterCrop(10),
-        >>>     transforms.ToTensor(),
-        >>> ])
-    """
-
-    def __init__(self, transforms):
-        self.transforms = transforms
-
-    def __call__(self, wav):
-        for t in self.transforms:
-            wav = t(wav)
-        return wav
-
-    def __repr__(self):
-        format_string = self.__class__.__name__ + '('
-        for t in self.transforms:
-            format_string += '\n'
-            format_string += '    {0}'.format(t)
-        format_string += '\n)'
-        return format_string
-
-
-class ToTensor(object):
+class ToTensor(BaseTransform):
     """Convert a ``numpy.ndarray`` to tensor.
 
     Converts a numpy.ndarray (C x S) to a torch.FloatTensor of shape (C x S).
@@ -51,11 +23,8 @@ def __call__(self, wav):
         """
         return F._to_tensor(wav)
 
-    def __repr__(self):
-        return self.__class__.__name__ + '()'
-
 
-class SoftClip(object):
+class SoftClip(BaseTransform):
     """Soft clip input to compress large amplitude signals
 
     """
@@ -66,11 +35,8 @@ def __init__(self, scale=1):
     def __call__(self, wav):
         return F.expit(wav * self.scale)
 
-    def __repr__(self):
-        return self.__class__.__name__ + f'(scale = {self.scale})'
 
-
-class ZeroMean(object):
+class ZeroMean(BaseTransform):
     """Remove mean from each waveforms
 
     """
@@ -80,11 +46,8 @@ def __call__(self, wav):
         wav -= wav.mean(axis=0)
         return wav.T
 
-    def __repr__(self):
-        return self.__class__.__name__ + '()'
-
 
-class CutWaveform(object):
+class CutWaveform(BaseTransform):
     """Cut a portion of waveform.
 
     """
@@ -96,5 +59,3 @@ def __init__(self, samplestart, sampleend):
     def __call__(self, wav):
         return wav[:, self.start:self.end]
 
-    def __repr__(self):
-        return self.__call__.__name__ + f'(start = {self.start}, end = {self.end})'