Add Conv2DActiv

chainercv/links/__init__.py

@@ -1,3 +1,5 @@
+from chainercv.links.connection.conv_2d_activ import Conv2DActiv
+
 from chainercv.links.model.pixelwise_softmax_classifier import PixelwiseSoftmaxClassifier  # NOQA
 from chainercv.links.model.sequential_feature_extractor import SequentialFeatureExtractor  # NOQA
 

chainercv/links/connection/__init__.py

@@ -0,0 +1 @@
+from chainercv.links.connection.conv_2d_activ import Conv2DActiv  # NOQA

chainercv/links/connection/conv_2d_activ.py

@@ -0,0 +1,73 @@
+import chainer
+from chainer.functions import relu
+from chainer.links import Convolution2D
+
+
+class Conv2DActiv(chainer.Chain):
+    """Convolution2D --> Activation
+
+    This is a chain that does two-dimensional convolution
+    and applies an activation.
+
+    The arguments are the same as those of
+    :class:`chainer.links.Convolution2D`
+    except for :obj:`activ`.
+
+    Example:
+
+        There are sevaral ways to initialize a :class:`Conv2DActiv`.
+
+        1. Give the first three arguments explicitly:
+
+            >>> l = Conv2DActiv(5, 10, 3)
+
+        2. Omit :obj:`in_channels` or fill it with :obj:`None`:
+
+            In these ways, attributes are initialized at runtime based on
+            the channel size of the input.
+
+            >>> l = Conv2DActiv(None, 10, 3)
+            >>> l = Conv2DActiv(10, 3)
+
+    Args:
+        in_channels (int or None): Number of channels of input arrays.
+            If :obj:`None`, parameter initialization will be deferred until the
+            first forward data pass at which time the size will be determined.
+        out_channels (int): Number of channels of output arrays.
+        ksize (int or pair of ints): Size of filters (a.k.a. kernels).
+            :obj:`ksize=k` and :obj:`ksize=(k, k)` are equivalent.
+        stride (int or pair of ints): Stride of filter applications.
+            :obj:`stride=s` and :obj:`stride=(s, s)` are equivalent.
+        pad (int or pair of ints): Spatial padding width for input arrays.
+            :obj:`pad=p` and :obj:`pad=(p, p)` are equivalent.
+        nobias (bool): If :obj:`True`,
+            then this link does not use the bias term.
+        initialW (4-D array): Initial weight value. If :obj:`None`, the default
+            initializer is used.
+            May also be a callable that takes :obj:`numpy.ndarray` or
+            :obj:`cupy.ndarray` and edits its value.
+        initial_bias (1-D array): Initial bias value. If :obj:`None`, the bias
+            is set to 0.
+            May also be a callable that takes :obj:`numpy.ndarray` or
+            :obj:`cupy.ndarray` and edits its value.
+        activ (callable): An activation function. The default value is
+            :func:`chainer.functions.relu`.
+
+    """
+
+    def __init__(self, in_channels, out_channels, ksize=None,
+                 stride=1, pad=0, nobias=False, initialW=None,
+                 initial_bias=None, activ=relu):
+        if ksize is None:
+            out_channels, ksize, in_channels = in_channels, out_channels, None
+
+        self.activ = activ
+        super(Conv2DActiv, self).__init__()
+        with self.init_scope():
+            self.conv = Convolution2D(
+                in_channels, out_channels, ksize, stride, pad,
+                nobias, initialW, initial_bias)
+
+    def __call__(self, x):
+        h = self.conv(x)
+        return self.activ(h)

docs/source/reference/links.rst

@@ -34,3 +34,10 @@ Classifiers
 .. toctree::
 
    links/classifier
+
+
+Connection
+----------
+
+.. toctree::
+    links/connection

docs/source/reference/links/connection.rst

@@ -0,0 +1,9 @@
+Connection
+==========
+
+.. module:: chainercv.links.connection
+
+
+Conv2DActiv
+-----------
+.. autoclass:: Conv2DActiv

tests/links_tests/connection_tests/test_conv_2d_activ.py

@@ -0,0 +1,120 @@
+import unittest
+
+import numpy as np
+
+import chainer
+from chainer import cuda
+from chainer import testing
+from chainer.testing import attr
+
+from chainercv.links import Conv2DActiv
+
+
+def _add_one(x):
+    return x + 1
+
+
+@testing.parameterize(
+    {'args_style': 'explicit'},
+    {'args_style': 'None'},
+    {'args_style': 'omit'}
+)
+class TestConv2DActivForward(unittest.TestCase):
+
+    in_channels = 1
+    out_channels = 1
+    ksize = 3
+    stride = 1
+    pad = 1
+
+    def setUp(self):
+        self.x = np.random.uniform(
+            -1, 1, (5, self.in_channels, 5, 5)).astype(np.float32)
+
+        # Convolution is the identity function.
+        initialW = np.array([[0, 0, 0], [0, 1, 0], [0, 0, 0]],
+                            dtype=np.float32).reshape(1, 1, 3, 3)
+        initial_bias = 0
+        if self.args_style == 'explicit':
+            self.l = Conv2DActiv(
+                self.in_channels, self.out_channels, self.ksize,
+                self.stride, self.pad,
+                initialW=initialW, initial_bias=initial_bias,
+                activ=_add_one)
+        elif self.args_style == 'None':
+            self.l = Conv2DActiv(
+                None, self.out_channels, self.ksize, self.stride, self.pad,
+                initialW=initialW, initial_bias=initial_bias,
+                activ=_add_one)
+        elif self.args_style == 'omit':
+            self.l = Conv2DActiv(
+                self.out_channels, self.ksize, stride=self.stride,
+                pad=self.pad, initialW=initialW, initial_bias=initial_bias,
+                activ=_add_one)
+
+    def check_forward(self, x_data):
+        x = chainer.Variable(x_data)
+        y = self.l(x)
+
+        self.assertIsInstance(y, chainer.Variable)
+        self.assertIsInstance(y.data, self.l.xp.ndarray)
+
+        np.testing.assert_almost_equal(
+            cuda.to_cpu(y.data), cuda.to_cpu(x_data) + 1)
+
+    def test_forward_cpu(self):
+        self.check_forward(self.x)
+
+    @attr.gpu
+    def test_forward_gpu(self):
+        self.l.to_gpu()
+        self.check_forward(cuda.to_gpu(self.x))
+
+
+@testing.parameterize(
+    {'args_style': 'explicit'},
+    {'args_style': 'None'},
+    {'args_style': 'omit'}
+)
+class TestConv2DActivBackward(unittest.TestCase):
+
+    in_channels = 3
+    out_channels = 5
+    ksize = 3
+    stride = 1
+    pad = 1
+
+    def setUp(self):
+        self.x = np.random.uniform(
+            -1, 1, (5, self.in_channels, 5, 5)).astype(np.float32)
+        self.gy = np.random.uniform(
+            -1, 1, (5, self.out_channels, 5, 5)).astype(np.float32)
+
+        if self.args_style == 'explicit':
+            self.l = Conv2DActiv(
+                self.in_channels, self.out_channels, self.ksize,
+                self.stride, self.pad)
+        elif self.args_style == 'None':
+            self.l = Conv2DActiv(
+                None, self.out_channels, self.ksize, self.stride, self.pad)
+        elif self.args_style == 'omit':
+            self.l = Conv2DActiv(
+                self.out_channels, self.ksize, stride=self.stride,
+                pad=self.pad)
+
+    def check_backward(self, x_data, y_grad):
+        x = chainer.Variable(x_data)
+        y = self.l(x)
+        y.grad = y_grad
+        y.backward()
+
+    def test_backward_cpu(self):
+        self.check_backward(self.x, self.gy)
+
+    @attr.gpu
+    def test_backward_gpu(self):
+        self.l.to_gpu()
+        self.check_backward(cuda.to_gpu(self.x), cuda.to_gpu(self.gy))
+
+
+testing.run_module(__name__, __file__)