flake8 for theano/tensor/nnet/ConvTransp3D.py

theano/tensor/nnet/ConvTransp3D.py

 from __future__ import print_function
+
 import numpy as N
 from six.moves import xrange
+
+import theano
 from theano.tensor import basic as T
 from theano.misc import strutil
-import theano
 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):
@@ -31,12 +35,15 @@ class ConvTransp3D(theano.Op):
         else:
             RShape_ = T.as_tensor_variable([-1, -1, -1])
 
-        return theano.Apply(self, inputs=[W_, b_, d_, H_, RShape_], outputs=[ T.TensorType(H_.dtype, (False, False, False, False, False))() ] )
+        return theano.Apply(self,
+                            inputs=[W_, b_, d_, H_, RShape_],
+                            outputs=[T.TensorType(H_.dtype,
+                                     (False, False, False, False, False))()])
 
     def infer_shape(self, node, input_shapes):
         W, b, d, H, RShape = node.inputs
         W_shape, b_shape, d_shape, H_shape, RShape_shape = input_shapes
-        return [(H_shape[0],  RShape[0], RShape[1], RShape[2], W_shape[4])]
+        return [(H_shape[0], RShape[0], RShape[1], RShape[2], W_shape[4])]
 
     def connection_pattern(self, node):
         return [[True], [True], [True], [True], [False]]
@@ -73,11 +80,13 @@ class ConvTransp3D(theano.Op):
         else:
             b_name = 'anon_b'
 
-        dCdW.name = 'ConvTransp3D_dCdW.H='+H_name+',dCdR='+dCdR_name+',W='+W_name
-        dCdb.name = 'ConvTransp3D_dCdb.H='+H_name+',dCdR='+dCdR_name+',W='+W_name+',b='+b_name
+        dCdW.name = ('ConvTransp3D_dCdW.H=' + H_name + ',dCdR=' + dCdR_name +
+                     ',W=' + W_name)
+        dCdb.name = ('ConvTransp3D_dCdb.H=' + H_name + ',dCdR=' + dCdR_name +
+                     ',W=' + W_name + ',b=' + b_name)
         dCdH.name = 'ConvTransp3D_dCdH.H=' + H_name + ',dCdR=' + dCdR_name
 
-        return [dCdW,  dCdb, dCdd, dCdH, dCdRShape]
+        return [dCdW, dCdb, dCdd, dCdH, dCdRShape]
 
     def perform(self, node, inputs, output_storage):
         W, b, d, H, RShape = inputs
@@ -335,7 +344,7 @@ def computeR(W, b, d, H, Rshape=None):
     assert len(b.shape) == 1
     assert len(d) == 3
 
-    outputChannels,  filterHeight, filterWidth, filterDur, \
+    outputChannels, filterHeight, filterWidth, filterDur, \
         inputChannels = W.shape
     batchSize, outputHeight, outputWidth, outputDur, \
         outputChannelsAgain = H.shape
@@ -367,7 +376,7 @@ def computeR(W, b, d, H, Rshape=None):
     # print "video size: "+str((videoHeight, videoWidth, videoDur))
 
     R = N.zeros((batchSize, videoHeight,
-            videoWidth, videoDur, inputChannels), dtype=H.dtype)
+                videoWidth, videoDur, inputChannels), dtype=H.dtype)
 
     # R[i,j,r,c,t] = b_j + sum_{rc,rk | d \circ rc + rk = r} sum_{cc,ck | ...} sum_{tc,tk | ...} sum_k W[k, j, rk, ck, tk] * H[i,k,rc,cc,tc]
     for i in xrange(0, batchSize):
@@ -404,8 +413,8 @@ def computeR(W, b, d, H, Rshape=None):
                                     if tk < 0:
                                         break
 
-                                    R[
-                                        i, r, c, t, j] += N.dot(W[:, rk, ck, tk, j], H[i, rc, cc, tc, :] )
+                                    R[i, r, c, t, j] += N.dot(
+                                        W[:, rk, ck, tk, j], H[i, rc, cc, tc, :])
 
                                     tc += 1
                                 ""  # close loop over tc
@@ -421,7 +430,3 @@ def computeR(W, b, d, H, Rshape=None):
     ""  # close loop over i
 
     return R
-
-
-from theano.tensor.nnet.Conv3D import conv3D
-from theano.tensor.nnet.ConvGrad3D import convGrad3D

theano/tests/test_flake8.py

@@ -89,7 +89,6 @@ whitelist_flake8 = [
     "tensor/signal/tests/test_conv.py",
     "tensor/signal/tests/test_downsample.py",
     "tensor/nnet/__init__.py",
-    "tensor/nnet/ConvTransp3D.py",
     "tensor/nnet/sigm.py",
     "tensor/nnet/ConvGrad3D.py",
     "tensor/nnet/conv3d2d.py",