Use shorter dimension references in conv3d2d.

theano/tensor/nnet/conv3d2d.py

@@ -221,18 +221,11 @@ def conv3d(signals, filters,
     else:
         _filters_shape_5d = filters_shape
 
-    _signals_shape_4d = (
-        _signals_shape_5d[0] * _signals_shape_5d[1],
-        _signals_shape_5d[2],
-        _signals_shape_5d[3],
-        _signals_shape_5d[4],
-    )
-    _filters_shape_4d = (
-        _filters_shape_5d[0] * _filters_shape_5d[1],
-        _filters_shape_5d[2],
-        _filters_shape_5d[3],
-        _filters_shape_5d[4],
-    )
+    Ns, Ts, C, Hs, Ws = _signals_shape_5d
+    Nf, Tf, C, Hf, Wf = _filters_shape_5d
+
+    _signals_shape_4d = (Ns * Ts, C, Hs, Ws)
+    _filters_shape_4d = (Nf * Tf, C, Hf, Wf)
 
     if border_mode[1] != border_mode[2]:
         raise NotImplementedError('height and width bordermodes must match')
@@ -251,25 +244,10 @@ def conv3d(signals, filters,
         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[3] - _filters_shape_5d[3] + 1,
-            _signals_shape_5d[4] - _filters_shape_5d[4] + 1,
-        ))
+        out_tmp = out_4d.reshape((Ns, Ts, Nf, Tf, Hs - Hf + 1, Ws - Wf + 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[3] + _filters_shape_5d[3] - 1,
-            _signals_shape_5d[4] + _filters_shape_5d[4] - 1,
-        ))
+        out_tmp = out_4d.reshape((Ns, Ts, Nf, Tf, Hs + Hf - 1, Ws + Wf - 1))
     elif border_mode[1] == 'same':
         raise NotImplementedError()
     else:
@@ -281,38 +259,19 @@ def conv3d(signals, filters,
         if _filters_shape_5d[1] != 1:
             out_5d = diagonal_subtensor(out_tmp, 1, 3).sum(axis=3)
         else:  # for Tf==1, no sum along Tf, the Ts-axis of the output is unchanged!
-            out_5d = out_tmp.reshape((
-                _signals_shape_5d[0],
-                _signals_shape_5d[1],
-                _filters_shape_5d[0],
-                _signals_shape_5d[3] - _filters_shape_5d[3] + 1,
-                _signals_shape_5d[4] - _filters_shape_5d[4] + 1,
-            ))
+            out_5d = out_tmp.reshape((Ns, Ts, Nf, Hs - Hf + 1, Ws - Wf + 1))
     elif border_mode[0] == 'full':
         if _filters_shape_5d[1] != 1:
             # pad out_tmp with zeros to have full convolution
             out_tmp_padded = tensor.zeros(dtype=out_tmp.dtype, shape=(
-                _signals_shape_5d[0],  # Ns
-                _signals_shape_5d[1] + 2 * (_filters_shape_5d[1] - 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,
+                Ns, Ts + 2 * (Tf - 1), Nf, Tf, Hs + Hf - 1, Ws + Wf - 1
             ))
             out_tmp_padded = tensor.set_subtensor(
-                out_tmp_padded[:,
-                               (_filters_shape_5d[1] - 1):(_signals_shape_5d[1] + _filters_shape_5d[1] - 1),
-                               :, :, :, :],
+                out_tmp_padded[:, (Tf - 1):(Ts + Tf - 1), :, :, :, :],
                 out_tmp)
             out_5d = diagonal_subtensor(out_tmp_padded, 1, 3).sum(axis=3)
         else:  # for tf==1, no sum along tf, the ts-axis of the output is unchanged!
-            out_5d = out_tmp.reshape((
-                _signals_shape_5d[0],
-                _signals_shape_5d[1],
-                _filters_shape_5d[0],
-                _signals_shape_5d[3] + _filters_shape_5d[3] - 1,
-                _signals_shape_5d[4] + _filters_shape_5d[4] - 1,
-            ))
+            out_5d = out_tmp.reshape((Ns, Ts, Nf, Hs + Hf - 1, Ws + Wf - 1))
     elif border_mode[0] == 'same':
         raise NotImplementedError('sequence border mode', border_mode[0])
     else: