flake8 for theano/tensor/nnet/Conv3D.py

theano/tensor/nnet/Conv3D.py

 from __future__ import print_function
+
+import numpy as N
 from six.moves import xrange
+
+from . import ConvGrad3D
+from . import ConvTransp3D
+
 import theano
 from theano.tensor import basic as T
-import numpy as N
-#from util import strutil
+# from util import strutil
 from theano.tensor.blas_headers import blas_header_text, blas_header_version
 from theano.tensor.blas import ldflags
 from theano.misc import strutil
@@ -72,12 +77,12 @@ class Conv3D(theano.Op):
 
     def grad(self, inputs, output_gradients):
         V, W, b, d = inputs
-        dCdH , = output_gradients
+        dCdH, = output_gradients
         # make all of these ops support broadcasting of scalar b to vector b and eplace the zeros_like in all their grads
         # print dCdH.broadcastable
         # print "dCdH.broadcastable"
         # quit(-1)
-        #dCdH = printing.Print("dCdH = ",["shape"])
+        # dCdH = printing.Print("dCdH = ",["shape"])
 
         # Make sure the broadcasting pattern of the gradient is the the same
         # as the initial variable
@@ -88,10 +93,11 @@ class Conv3D(theano.Op):
         dCdW = T.patternbroadcast(dCdW, W.broadcastable)
         dCdb = T.sum(dCdH, axis=(0, 1, 2, 3))
         dCdb = T.patternbroadcast(dCdb, b.broadcastable)
-        dCdd = grad_undefined(self, 3, inputs[3],
-                "The gradient of Conv3D with respect to the convolution" +\
-                " stride is undefined because Conv3D is only defined for" +\
-                " integer strides.")
+        dCdd = grad_undefined(
+            self, 3, inputs[3],
+            "The gradient of Conv3D with respect to the convolution"
+            " stride is undefined because Conv3D is only defined for"
+            " integer strides.")
 
         if 'name' in dir(dCdH) and dCdH.name is not None:
             dCdH_name = dCdH.name
@@ -113,11 +119,13 @@ class Conv3D(theano.Op):
         else:
             b_name = 'anon_b'
 
-        dCdV.name = 'Conv3D_dCdV(dCdH='+dCdH_name+',V='+V_name+')'
-        dCdW.name = 'Conv3D_dCdW(dCdH='+dCdH_name+',V='+V_name+',W='+W_name+')'
-        dCdb.name = 'Conv3D_dCdb(dCdH='+dCdH_name+',V='+V_name+',W='+W_name+',b='+b_name+')'
+        dCdV.name = 'Conv3D_dCdV(dCdH=' + dCdH_name + ',V=' + V_name + ')'
+        dCdW.name = ('Conv3D_dCdW(dCdH=' + dCdH_name + ',V=' + V_name +
+                     ',W=' + W_name + ')')
+        dCdb.name = ('Conv3D_dCdb(dCdH=' + dCdH_name + ',V=' + V_name +
+                     ',W=' + W_name + ',b=' + b_name + ')')
 
-        return [ dCdV, dCdW, dCdb, dCdd ]
+        return [dCdV, dCdW, dCdb, dCdd]
 
     def perform(self, node, inputs, output_storage):
         V, W, b, d = inputs
@@ -144,7 +152,7 @@ class Conv3D(theano.Op):
         output_width = T.floor((vidWidth - filterWidth) // dc) + 1
         output_dur = T.floor((vidDur - filterDur) // dt) + 1
 
-        rval = (batch_size,  output_height, output_width, output_dur, output_channels )
+        rval = (batch_size, output_height, output_width, output_dur, output_channels)
 
         return [rval]
 
@@ -155,7 +163,7 @@ class Conv3D(theano.Op):
         return ldflags()
 
     def c_compile_args(self):
-        flags =  ldflags(libs=False, flags=True)
+        flags = ldflags(libs=False, flags=True)
         return flags
 
     def c_lib_dirs(self):
@@ -170,7 +178,7 @@ class Conv3D(theano.Op):
 
         H = outputs[0]
 
-        codeSource =  """
+        codeSource = """
             ///////////// < code generated by Conv3D >
 
             //printf("\t\t\t\tConv3D c code\\n");
@@ -320,13 +328,13 @@ class Conv3D(theano.Op):
         VV, WV, bv, dv = node.inputs
         HV = node.outputs[0]
         if (theano.config.blas.ldflags and
-            VV.dtype == WV.dtype and HV.dtype == VV.dtype):
+                VV.dtype == WV.dtype and HV.dtype == VV.dtype):
             if VV.dtype == 'float64':
                 gemv = 'dgemv_'
             elif VV.dtype == 'float32':
                 gemv = 'sgemv_'
             else:
-                raise Exception('Unrecognized dtype for convolution '+V.value.dtype)
+                raise Exception('Unrecognized dtype for convolution ' + V.value.dtype)
 
             codeSource += """
             if (inputChannels > 20 && outputChannels > 20 && ws4 == sizeof(ELEM_AT(%(W)s,0)))
@@ -571,7 +579,7 @@ def computeH(V, W, b, d):
     outputChannels = W.shape[0]
     inputChannels = V.shape[4]
     if W.shape[4] != inputChannels:
-        raise Exception("W.shape[4] = "+str(W.shape[4])+" but inputChannels = "+str(inputChannels))
+        raise Exception("W.shape[4] = " + str(W.shape[4]) + " but inputChannels = " + str(inputChannels))
     filterHeight = W.shape[1]
     filterWidth = W.shape[2]
     filterDur = W.shape[3]
@@ -586,12 +594,12 @@ def computeH(V, W, b, d):
     assert dy > 0
     assert dt > 0
 
-    outputHeight = int( (vidHeight - filterHeight) / dx )+1
-    outputWidth = int( (vidWidth - filterWidth) / dy )+1
-    outputDur = int( (vidDur - filterDur) / dt ) + 1
+    outputHeight = int((vidHeight - filterHeight) / dx) + 1
+    outputWidth = int((vidWidth - filterWidth) / dy) + 1
+    outputDur = int((vidDur - filterDur) / dt) + 1
 
-    H =  N.zeros( (batchSize,  outputHeight,
-        outputWidth, outputDur, outputChannels ), dtype=V.dtype )
+    H = N.zeros((batchSize, outputHeight,
+                outputWidth, outputDur, outputChannels), dtype=V.dtype)
 
     # H[i,j,x,y,t] = b_j + sum_k sum_l sum_m sum_z W[j,z,k,l,m] V[i,z, dx*x+k,dy*y+l,dt*t+m]
     for i in xrange(0, H.shape[0]):
@@ -610,12 +618,8 @@ def computeH(V, W, b, d):
                                         # if (i,j,x,y,t) == (0,0,0,0,0):
                                         #    print (( W[j,z,k,l,m] , V[i,z,d[0]*x+k,d[1]*y+l,d[2]*t+m] ), (k,l,m) )
                                         w = W[j, k, l, m, z]
-                                        v = V[i, d[0]*x+k, d[1]*y+l, d[2]*t+m, z]
+                                        v = V[i, d[0] * x + k, d[1] * y + l, d[2] * t + m, z]
                                         # if i == 0 and x == 0 and y == 0 and t == 0 and j == 0:
                                         #    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/tests/test_flake8.py

@@ -88,7 +88,6 @@ whitelist_flake8 = [
     "tensor/signal/conv.py",
     "tensor/signal/tests/test_conv.py",
     "tensor/signal/tests/test_downsample.py",
-    "tensor/nnet/Conv3D.py",
     "tensor/nnet/__init__.py",
     "tensor/nnet/ConvTransp3D.py",
     "tensor/nnet/sigm.py",