commented out test_maxpool. bring it back later after reviewing

tests/test_blas.py

@@ -47,23 +47,28 @@ def test_gemm():
 
     assert numpy.allclose(numpy.dot(a0, bval)+numpy.exp(cval), a.value)
 
-def test_maxpool():
-    """TODO: test the gpu version!!! """
-    for d0, d1, r_true, r_false in [(4,4,[[[[5,7],[13,15]]]],[[[[5,7],[13,15]]]]),
-                                    (5,5,[[[[6, 8],[ 16, 18], [ 21, 23]]]],
-                                     [[[[6, 8, 9],[ 16, 18, 19], [ 21, 23, 24]]]])]:
-        for border,ret in [(True,r_true),(False, r_false)]:
-            ret=numpy.array(ret)
-            a = tcn.blas.DownsampleFactorMax((2,2),border)
-            dmatrix4 = tensor.TensorType("float32", (False, False, False, False))
-            b = dmatrix4()
-            f = pfunc([b], [a(b)])
-            
-            bval = numpy.arange(0,d0*d1).reshape(1,1,d0,d1)
-            r = f(bval)[0]
-#            print bval, bval.shape, border
-            print r, r.shape
-            assert (ret==r).all()
+if 0:
+    # This is commented out because it doesn't make sense...
+    # tcn.blas has no op called DownsampleFactorMax
+    # tcn.blas has an op called GpuDownsampleFactorMax, but that op requires arguments that are
+    # CudaNdarrayType variables... so rethink this test?
+    def test_maxpool():
+        """TODO: test the gpu version!!! """
+        for d0, d1, r_true, r_false in [(4,4,[[[[5,7],[13,15]]]],[[[[5,7],[13,15]]]]),
+                                        (5,5,[[[[6, 8],[ 16, 18], [ 21, 23]]]],
+                                         [[[[6, 8, 9],[ 16, 18, 19], [ 21, 23, 24]]]])]:
+            for border,ret in [(True,r_true),(False, r_false)]:
+                ret=numpy.array(ret)
+                a = tcn.blas.DownsampleFactorMax((2,2),border)
+                dmatrix4 = tensor.TensorType("float32", (False, False, False, False))
+                b = dmatrix4()
+                f = pfunc([b], [a(b)])
+                
+                bval = numpy.arange(0,d0*d1).reshape(1,1,d0,d1)
+                r = f(bval)[0]
+    #            print bval, bval.shape, border
+                print r, r.shape
+                assert (ret==r).all()
 
 def test_downsample():