Merge pull request #1 from nouiz/master

theanoconv3d2d/conv3d2d.py

+from theano.gradient import DisconnectedType
 from theano.gof import Op, Apply
 from theano import tensor
 
+
 def get_diagonal_subtensor_view(x, i0, i1):
     if x.shape[i0] < x.shape[i1]:
         raise NotImplementedError('is this allowed?')
@@ -12,44 +14,60 @@ 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:
             output_storage[0][0] = xview
         else:
             output_storage[0][0] = xview.copy()
+
     def grad(self, inputs, g_outputs):
         z = tensor.zeros_like(inputs[0])
         gx = inc_diagonal_subtensor(z, inputs[1], inputs[2], g_outputs[0])
-        return [gx] + [None] * (len(inputs)-1)
+        return [gx, DisconnectedType()(), DisconnectedType()()]
+
+    def connection_pattern(self, node):
+        rval = [[True], [False], [False]]
+        return rval
+
 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:
@@ -57,15 +75,22 @@ class IncDiagonalSubtensor(Op):
         xview = get_diagonal_subtensor_view(x, i0, i1)
         xview += amt
         output_storage[0][0] = x
+
     def grad(self, inputs, g_outputs):
         x, i0, i1, amt = inputs
         gy = g_outputs[0]
-        return [gy, None, None, diagonal_subtensor(gy, i0, i1)]
+        return [gy, DisconnectedType()(), DisconnectedType()(),
+                diagonal_subtensor(gy, i0, i1)]
+
+    def connection_pattern(self, node):
+        rval = [[True], [False], [False], [True]]
+        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):
+           signals_shape=None, filters_shape=None,
+           border_mode='valid', subsample=(1, 1, 1), **kwargs):
     """
     Convolve spatio-temporal filters with a movie.
 
@@ -87,8 +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],
@@ -106,29 +129,29 @@ def conv3d(signals, filters,
         raise NotImplementedError('height and width bordermodes must match')
 
     out_4d = tensor.nnet.conv2d(
-            signals.reshape(_signals_shape_4d),
-            filters.reshape(_filters_shape_4d),
-            image_shape=_signals_shape_4d,
-            filter_shape=_filters_shape_4d,
-            border_mode = border_mode[1]) #ignoring border_mode[2]
+        signals.reshape(_signals_shape_4d),
+        filters.reshape(_filters_shape_4d),
+        image_shape=_signals_shape_4d,
+        filter_shape=_filters_shape_4d,
+        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
     if border_mode[1] == 'valid':
         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,
             ))
     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,
             ))