pep8

theanoconv3d2d/conv3d2d.py

@@ -14,19 +14,24 @@ def get_diagonal_subtensor_view(x, i0, i1):
     xview.strides = strides
     return xview
 
+
 class DiagonalSubtensor(Op):
     def __init__(self, inplace):
         self.inplace = inplace
         if inplace:
-            self.view_map = {0:[0]}
+            self.view_map = {0: [0]}
+
     def __eq__(self, other):
         return type(self) == type(other) and self.inplace == other.inplace
+
     def __hash__(self):
         return hash((type(self), self.inplace))
+
     def make_node(self, x, i0, i1):
         _i0 = tensor.as_tensor_variable(i0)
         _i1 = tensor.as_tensor_variable(i1)
         return Apply(self, [x, _i0, _i1], [x.type()])
+
     def perform(self, node, inputs, output_storage):
         xview = get_diagonal_subtensor_view(*inputs)
         if self.inplace:
@@ -45,19 +50,24 @@ class DiagonalSubtensor(Op):
 
 diagonal_subtensor = DiagonalSubtensor(False)
 
+
 class IncDiagonalSubtensor(Op):
     def __init__(self, inplace):
         self.inplace = inplace
         if inplace:
-            self.destroy_map = {0:[0]}
+            self.destroy_map = {0: [0]}
+
     def __eq__(self, other):
         return type(self) == type(other) and self.inplace == other.inplace
+
     def __hash__(self):
         return hash((type(self), self.inplace))
+
     def make_node(self, x, i0, i1, amt):
         _i0 = tensor.as_tensor_variable(i0)
         _i1 = tensor.as_tensor_variable(i1)
         return Apply(self, [x, _i0, _i1, amt], [x.type()])
+
     def perform(self, node, inputs, output_storage):
         x, i0, i1, amt = inputs
         if not self.inplace:
@@ -77,9 +87,10 @@ class IncDiagonalSubtensor(Op):
         return rval
 inc_diagonal_subtensor = IncDiagonalSubtensor(False)
 
+
 def conv3d(signals, filters,
            signals_shape=None, filters_shape=None,
-        border_mode='valid', subsample=(1,1,1), **kwargs):
+           border_mode='valid', subsample=(1, 1, 1), **kwargs):
     """
     Convolve spatio-temporal filters with a movie.
 
@@ -101,8 +112,6 @@ def conv3d(signals, filters,
     _signals_shape_5d = signals.shape if signals_shape is None else signals_shape
     _filters_shape_5d = filters.shape if filters_shape is None else filters_shape
 
-
-
     _signals_shape_4d = (
         _signals_shape_5d[0] * _signals_shape_5d[1],
         _signals_shape_5d[2],
@@ -124,7 +133,7 @@ def conv3d(signals, filters,
         filters.reshape(_filters_shape_4d),
         image_shape=_signals_shape_4d,
         filter_shape=_filters_shape_4d,
-            border_mode = border_mode[1]) #ignoring border_mode[2]
+        border_mode = border_mode[1])  # ignoring border_mode[2]
 
     # reshape the output to restore its original size
     # shape = Ns, Ts, Nf, Tf, W-Wf+1, H-Hf+1
@@ -139,10 +148,10 @@ def conv3d(signals, filters,
             ))
     elif border_mode[1] == 'full':
         out_tmp = out_4d.reshape((
-            _signals_shape_5d[0], #Ns
-            _signals_shape_5d[1], #Ts
-            _filters_shape_5d[0], #Nf
-            _filters_shape_5d[1], #Tf
+            _signals_shape_5d[0],  # Ns
+            _signals_shape_5d[1],  # Ts
+            _filters_shape_5d[0],  # Nf
+            _filters_shape_5d[1],  # Tf
             _signals_shape_5d[3] + _filters_shape_5d[3] - 1,
             _signals_shape_5d[4] + _filters_shape_5d[4] - 1,
             ))