Fix circular import

theano/tensor/nnet/Conv3D.py

@@ -83,10 +83,11 @@ class Conv3D(theano.Op):
 
         # Make sure the broadcasting pattern of the gradient is the the same
         # as the initial variable
-        dCdV = ConvTransp3D.convTransp3D(W, T.zeros_like(V[0, 0, 0, 0, :]), d, dCdH, V.shape[1:4])
+        dCdV = theano.tensor.nnet.convTransp3D(
+            W, T.zeros_like(V[0, 0, 0, 0, :]), d, dCdH, V.shape[1:4])
         dCdV = T.patternbroadcast(dCdV, V.broadcastable)
         WShape = W.shape
-        dCdW = ConvGrad3D.convGrad3D(V, d, WShape, dCdH)
+        dCdW = theano.tensor.nnet.convGrad3D(V, d, WShape, dCdH)
         dCdW = T.patternbroadcast(dCdW, W.broadcastable)
         dCdb = T.sum(dCdH, axis=(0, 1, 2, 3))
         dCdb = T.patternbroadcast(dCdb, b.broadcastable)
@@ -620,6 +621,3 @@ def computeH(V, W, b, d):
                                         #    print 'setting H[0] += '+str(w*v)+'   W['+str((j,z,k,l,m))+']='+str(w)+'   V['+str((i,d[0]*x+k,d[1]*y+l,d[2]*t+m,z))+']='+str(v)
                                         H[i, x, y, t, j] += w * v
     return H
-    
-from . import ConvGrad3D
-from . import ConvTransp3D

theano/tensor/nnet/ConvGrad3D.py

@@ -4,8 +4,6 @@ import numpy as N
 
 import theano
 from theano.tensor import basic as T
-from theano.tensor.nnet.Conv3D import conv3D
-from theano.tensor.nnet.ConvTransp3D import convTransp3D
 from theano.misc import strutil
 from theano.gradient import grad_undefined
 from theano.gradient import DisconnectedType
@@ -46,12 +44,12 @@ class ConvGrad3D(theano.Op):
         dLdA, = output_gradients
 
         z = T.zeros_like(C[0, 0, 0, 0, :])
-        dLdC = convTransp3D(dLdA, z, d, B, C.shape[1:4])
+        dLdC = theano.tensor.nnet.convTransp3D(dLdA, z, d, B, C.shape[1:4])
         # d actually does affect the outputs, so it's not disconnected
         dLdd = grad_undefined(self, 1, d)
         # The shape of the weights doesn't affect the output elements
         dLdWShape = DisconnectedType()()
-        dLdB = conv3D(C, dLdA, T.zeros_like(B[0, 0, 0, 0, :]), d)
+        dLdB = theano.tensor.nnet.conv3D(C, dLdA, T.zeros_like(B[0, 0, 0, 0, :]), d)
 
         return [dLdC, dLdd, dLdWShape, dLdB]
 

theano/tensor/nnet/ConvTransp3D.py

@@ -8,8 +8,6 @@ from theano.tensor import basic as T
 from theano.misc import strutil
 from theano.gradient import grad_undefined
 from theano.gradient import DisconnectedType
-from theano.tensor.nnet.Conv3D import conv3D
-from theano.tensor.nnet.ConvGrad3D import convGrad3D
 
 
 class ConvTransp3D(theano.Op):
@@ -51,9 +49,9 @@ class ConvTransp3D(theano.Op):
     def grad(self, inputs, output_gradients):
         W, b, d, H, RShape = inputs
         dCdR, = output_gradients
-        dCdH = conv3D(dCdR, W, T.zeros_like(H[0, 0, 0, 0, :]), d)
+        dCdH = theano.tensor.nnet.conv3D(dCdR, W, T.zeros_like(H[0, 0, 0, 0, :]), d)
         WShape = W.shape
-        dCdW = convGrad3D(dCdR, d, WShape, H)
+        dCdW = theano.tensor.nnet.convGrad3D(dCdR, d, WShape, H)
         dCdb = T.sum(dCdR, axis=(0, 1, 2, 3))
         # not differentiable, since d affects the output elements
         dCdd = grad_undefined(self, 2, d)