fixed python implement of ConvOp (bug with mutable parametrs being stored in

theano/sandbox/conv.py

@@ -19,13 +19,16 @@ class ConvOp(Op):
     """
 
     def __init__(self, imshp, kshp, nkern, bsize, dx, dy, output_mode='valid'):
+
+        imshp = tuple(imshp)
         if len(imshp)==2:
             self.imshp = (1,)+imshp
         elif len(imshp)==3:
             self.imshp = imshp
         else:
             raise Exception("bad len for imshp")
-        self.kshp = kshp
+
+        self.kshp = tuple(kshp)
         self.nkern = nkern
         self.bsize=bsize
         self.dx=dx
@@ -33,15 +36,13 @@ class ConvOp(Op):
         if self.dx!=1 or self.dy!=1:
             print "Warning, dx!=1 or dy!=1 only supported in python mode!"
             raise NotImplementedError()
+
+        self.outshp = getFilterOutShp(self.imshp, kshp, (dx,dy), output_mode)
         self.out_mode = output_mode
         if not self.out_mode in ["valid", "full"]:
             raise Exception("Mode %s not implemented"%self.out_mode)
-        self.fulloutshp = N.array(self.imshp[1:]) - N.array(self.kshp) + 1 \
-            if self.out_mode=='valid'\
-            else N.array(self.imshp[1:]) + N.array(self.kshp) - 1
-
-        assert ((N.array(self.imshp[1:])-self.kshp)>=0).all()
-        assert N.prod(self.fulloutshp)>0
+       
+        assert (self.outshp >= 0).all()
 
 #    def __eq__(self, other):
 #        raise Error("Not implemented")
@@ -52,8 +53,6 @@ class ConvOp(Op):
     def make_node(self, inputs, kerns):
         #all kernels must have the same shape!
         #output_mode only valid and full are supported!
-
-        self.outshp = getFilterOutShp(self.imshp, self.kshp, (self.dx,self.dy), self.out_mode)
         self.dtype = inputs.dtype
         assert kerns.dtype==self.dtype
   
@@ -68,25 +67,14 @@ class ConvOp(Op):
         By default if len(img2d.shape)==3, we
         """
         if z[0] is None:
-            z[0] = N.zeros((self.bsize,)+(self.nkern,)+tuple(self.fulloutshp))
+            z[0] = N.zeros((self.bsize,)+(self.nkern,)+tuple(self.outshp))
         zz=z[0]
         val = _valfrommode(self.out_mode)
         bval = _bvalfromboundary('fill')
-        if len(img2d.shape)==2 and self.imshp[0]==1 and self.bsize==1:
-            img2d = img2d.reshape((1,1)+img2d.shape)
-        elif len(img2d.shape)==3 and self.imshp[0]==1 and self.bsize!=1:
-            img2d = img2d.reshape((img2d.shape[0],)+(1,)+img2d.shape[1:])            
-        elif len(img2d.shape)==3:
-            img2d = img2d.reshape((1,)+(img2d.shape[0],)+img2d.shape[1:])
-        elif len(img2d.shape)==3 and self.imshp[0]==1 and self.bsize==1:
-            img2d = img2d.reshape((1,1)+img2d.shape[1:])
-        elif len(img2d.shape)!=4: raise Exception("bad img2d shape.")
-        
-        if len(filtersflipped.shape)==3 and self.imshp[0]==1:
-            assert self.imshp[0]==1
-            filtersflipped = filtersflipped.reshape((filtersflipped.shape[0],)+(1,)+filtersflipped.shape[1:])
-        elif len(filtersflipped.shape)!=4: raise Exception("Bad filtersflipped shape")
 
+        img2d = img2d.reshape((self.bsize,)+ self.imshp)
+        filtersflipped = filtersflipped.reshape((self.nkern,self.imshp[0])+self.kshp)
+        
         for b in range(self.bsize):
             for n in range(self.nkern):
                 zz[b,n,...].fill(0)
@@ -140,7 +128,6 @@ class ConvOp(Op):
         filters = filters[:,:,::-1,::-1]
         nkern = self.imshp[0]
         imshp = N.hstack((self.nkern,self.outshp))
-
         din = ConvOp(imshp, self.kshp, nkern, self.bsize, 
                      1,1, output_mode=mode)(gz,filters)
 
@@ -420,7 +407,12 @@ def convolve2(kerns, kshp, nkern, images, imshp, bsize, step=(1,1),
 
     kernrshp   = tensor.as_tensor([nkern, nvis_dim] + list(kshp))
     kerntensor = tensor.reshape(kerns, kernrshp)
-   
+ 
+    print '*** convolve2 ***'
+    print 'imshp = ', imshp
+    print 'kshp = ', kshp
+    print 'nkern = ', nkern
+    print 'bsize = ', bsize
     convop = ConvOp(imshp, kshp, nkern, bsize, 1, 1, output_mode=mode)
     convout = convop(imtensor, kerntensor)
    

theano/sandbox/test_conv.py

@@ -307,13 +307,19 @@ class TestConvOp(unittest.TestCase):
         bsize = 2
         imgs  = T.dmatrix('imgs')
         kerns = T.dmatrix('kerns')
-     
+    
+        kshps = [(3,3)]
         for mode in 'valid', 'full':
+
+            # 'full' mode should support kernels bigger than the input
+            if mode == 'full':
+                kshps.append((12,12))
+
             for imshp in (5,5),(2,5,5),(2,10,10): # (12,10), (3,12,11):
                 visdim = 1 if len(imshp)!=3 else imshp[0]
-                print 'visdim = ', visdim
-                for kshp in (3,3),:# (6,7):
+                for kshp in kshps:
                     imgvals = N.random.random(N.hstack((bsize,imshp)))
+                    print 'imgvals.shape = ', imgvals.shape
                     imgvals = imgvals.reshape(bsize,-1)
 
                     if visdim == 1: