fix test TestConvOp.test_convolution. We only support 4d images and kernels.

theano/sandbox/test_conv.py

@@ -158,6 +158,9 @@ def exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp, kshps, nkerns, unroll
 
 
 class TestConvOp(unittest.TestCase):
+    """NOTE: we test only when we pass 4d tensor.
+    """
+
     def setUp(self):
         utt.seed_rng()
 
@@ -170,7 +173,7 @@ class TestConvOp(unittest.TestCase):
         if 0:
             # fixed parameters
             bsize = 10     # batch size
-            imshp = (28,28)# image shape
+            imshp = (1,28,28)# image shape
             kshps = [(5,5),(6,7),(12,8)]   # kernel shaped
             nkern = 5      # nb kernel
             ssizes = ((1,1),(2,2),(3,3),(4,4))#step size
@@ -178,7 +181,7 @@ class TestConvOp(unittest.TestCase):
         elif 0:
             # fixed parameters
             bsize = 10     # batch size
-            imshp = (50,50)# image shape
+            imshp = (1,50,50)# image shape
             print >> sys.stderr, "WARNING: only square shape tested"
             kshps = [(12,12), (12,12)]
             nkern = 20      # nb kernel
@@ -187,7 +190,7 @@ class TestConvOp(unittest.TestCase):
         elif 0:
             # fixed parameters
             bsize = 7     # batch size
-            imshp = (5,4)# image shape
+            imshp = (1,5,4)# image shape
             kshps = [(2,3)]
             nkern = 6      # nb kernel
             ssizes = [(1,1)] #step size
@@ -195,7 +198,7 @@ class TestConvOp(unittest.TestCase):
         else:
             # fixed parameters
             bsize = 7     # batch size
-            imshp = (5,4)# image shape
+            imshp = (1,5,4)# image shape
             kshps = [(2,3)]
             nkern = 6      # nb kernel
             ssizes = [(1,1)] #step size
@@ -204,13 +207,13 @@ class TestConvOp(unittest.TestCase):
         # TODO: ask Fred about this
         # this combination trigered a bug.
         #        bsize=1
-        #        imshp=(9,9)#fail with 9,9
+        #        imshp=(1,9,9)#fail with 9,9
         #        kshp=(2,2)
         #        nkern=5
         #        ssizes=((1,1),)
         # this combination trigered a bug.
         #        bsize = 1     # batch size
-        #        imshp = (3,3)# image shape
+        #        imshp = (1,3,3)# image shape
         #        kshp = (2,3)#(5,5)   # kernel shaped
         #        nkern = 1      # nb kernel
         #        ssizes = ((1,1),)#(2,2),(3,3),(4,4))#step size
@@ -257,34 +260,34 @@ class TestConvOp(unittest.TestCase):
 
                     # compute with ConvOp
                     dmatrix3=T.TensorType('float64', (False, False, False))
-                    inputs=dmatrix3()
-                    kerns3=dmatrix3()
+                    inputs4=dmatrix4()
+                    kerns4=dmatrix4()
                     bia=T.dscalar()
-                    conv_op = ConvOp(imshp, kshp, nkern, bsize, ss[0],ss[1], conv_mode)(inputs, kerns3)
-                    f2 = function([inputs, kerns3], conv_op, mode=Mode(linker="c"))
-                    f3 = function([inputs, kerns3], conv_op, mode=Mode(linker="py"))
+                    conv_op = ConvOp(imshp, kshp, nkern, bsize, ss[0],ss[1], conv_mode)(inputs4, kerns4)
+                    f2 = function([inputs4, kerns4], conv_op, mode=Mode(linker="c"))
+                    f3 = function([inputs4, kerns4], conv_op, mode=Mode(linker="py"))
 
                     ttime1 = time.time()
-                    out2_ = f2(img2d, filtersflipped)
-                    out2__ = out2_#[:,:,0::ss[0],0::ss[1]]
+                    out2_ = f2(img2d, filtersflipped.reshape(nkern,1,*kshp))
+                    out2__ = out2_
                     tconvop += [time.time() - ttime1]
                     out2___ = out2__.copy()
                     out2 = out2___ + biasvals.reshape(1,nkern,1,1)
-                    out3_ = f3(img2d, filtersflipped)
-                    out3__ = out3_#[:,:,0::ss[0],0::ss[1]]
+                    out3_ = f3(img2d, filtersflipped.reshape(nkern,1,*kshp))
+                    out3__ = out3_
                     out3___ = out3__.copy()
                     out3 = out3___ + biasvals.reshape(1,nkern,1,1)
                     assert (N.abs(out2_-out3_)<1e-5).all()
 
                     # REFERENCE IMPLEMENTATION: compute output with convolve2d
-                    fulloutshp = N.array(imshp) - N.array(kshp) + 1 if conv_mode=='valid'\
-                             else N.array(imshp) + N.array(kshp) - 1
+                    fulloutshp = N.array(imshp[1:]) - N.array(kshp) + 1 if conv_mode=='valid'\
+                             else N.array(imshp[1:]) + N.array(kshp) - 1
                     ntime1 = time.time()
                     refout = N.zeros((bsize,)+tuple(fulloutshp)+(nkern,))
                     for b in range(bsize):
                         for n in range(nkern):
                             refout[b,...,n] = convolve2d(\
-                                    img2d[b,:,:], filtersflipped[n,...],conv_mode)
+                                    img2d[b,0,:,:], filtersflipped[n,...],conv_mode)
                     tscipy += [time.time() - ntime1]
 
                     # need to flatten images