Merge pull request #3621 from shabanian/tensor_signal_pep8

theano/tensor/signal/conv.py

@@ -3,8 +3,6 @@ Contains a wrapper function for tensor.nnet.ConvOp, which can be used to perform
 generic 2D convolution.
 
 """
-__docformat__ = "restructuredtext en"
-
 import warnings
 
 import theano
@@ -12,6 +10,10 @@ import theano.tensor as tensor
 from theano.tensor.nnet import conv
 
 import logging
+
+__docformat__ = "restructuredtext en"
+
+
 _logger = logging.getLogger("theano.tensor.signal.conv")
 
 
@@ -52,7 +54,7 @@ def conv2d(input, filters, image_shape=None, filter_shape=None,
     assert input.ndim in (2, 3)
     assert filters.ndim in (2, 3)
 
-    ### use shape information if it is given to us ###
+    # use shape information if it is given to us ###
     if filter_shape and image_shape:
         if input.ndim == 3:
             bsize = image_shape[0]
@@ -69,7 +71,7 @@ def conv2d(input, filters, image_shape=None, filter_shape=None,
         nkern, kshp = None, None
         bsize, imshp = None, None
 
-    ### reshape tensors to 4D, for compatibility with ConvOp ###
+    # reshape tensors to 4D, for compatibility with ConvOp ###
     if input.ndim == 3:
         sym_bsize = input.shape[0]
     else:
@@ -86,10 +88,10 @@ def conv2d(input, filters, image_shape=None, filter_shape=None,
     new_filter_shape = tensor.join(0, tensor.stack([sym_nkern, 1]), filters.shape[-2:])
     filters4D = tensor.reshape(filters, new_filter_shape, ndim=4)
 
-    ### perform actual convolution ###
+    # perform actual convolution ###
     op = conv.ConvOp(output_mode=border_mode,
-                dx=subsample[0], dy=subsample[1],
-                imshp=imshp, kshp=kshp, nkern=nkern, bsize=bsize, **kargs)
+                     dx=subsample[0], dy=subsample[1],
+                     imshp=imshp, kshp=kshp, nkern=nkern, bsize=bsize, **kargs)
 
     output = op(input4D, filters4D)
 

theano/tensor/signal/tests/test_conv.py

@@ -31,7 +31,7 @@ class TestSignalConv2D(unittest.TestCase):
         if filter_dim != 3:
             nkern = 1
 
-        ############# THEANO IMPLEMENTATION ############
+        # THEANO IMPLEMENTATION ############
         # we create a symbolic function so that verify_grad can work
         def sym_conv2d(input, filters):
             return conv.conv2d(input, filters)
@@ -44,9 +44,8 @@ class TestSignalConv2D(unittest.TestCase):
         filter_data = numpy.random.random(filter_shape)
         theano_output = theano_conv(image_data, filter_data)
 
-        ############# REFERENCE IMPLEMENTATION ############
-        out_shape2d = numpy.array(image_shape[-2:]) -\
-                      numpy.array(filter_shape[-2:]) + 1
+        # REFERENCE IMPLEMENTATION ############
+        out_shape2d = numpy.array(image_shape[-2:]) - numpy.array(filter_shape[-2:]) + 1
         ref_output = numpy.zeros(tuple(out_shape2d))
 
         # reshape as 3D input tensors to make life easier
@@ -76,7 +75,7 @@ class TestSignalConv2D(unittest.TestCase):
                 self.assertTrue(_allclose(theano_output4d[b, k, :, :],
                                           output2d))
 
-        ############# TEST GRADIENT ############
+        # TEST GRADIENT ############
         if verify_grad:
             utt.verify_grad(sym_conv2d, [image_data, filter_data])
 
@@ -87,8 +86,8 @@ class TestSignalConv2D(unittest.TestCase):
         signal.conv.conv2d can support inputs and filters of type
         matrix or tensor3.
         """
-        if (not theano.tensor.nnet.conv.imported_scipy_signal and
-            theano.config.cxx == ""):
+        if(not theano.tensor.nnet.conv.imported_scipy_signal and
+           theano.config.cxx == ""):
             raise SkipTest("conv2d tests need SciPy or a c++ compiler")
 
         self.validate((1, 4, 5), (2, 2, 3), out_dim=4, verify_grad=True)