remove print in tests.

theano/tensor/tests/test_opt.py

@@ -502,9 +502,6 @@ class test_canonize(unittest.TestCase):
                 assert(out_dtype == out.dtype)
                 assert numpy.allclose(out, val_inputs[1])
                 topo = f.maker.fgraph.toposort()
-                print "ID TOPO", id, topo, sym_inputs
-                for r, t in f.maker.fgraph.shape_feature.shape_of.items():
-                    print '  ', r, t
                 if topo and not(len(topo)==1 and topo[0].op==deep_copy_op):
                     for node in topo[:-1]:
                         assert isinstance(node.op, Shape_i)
@@ -528,7 +525,6 @@ class test_canonize(unittest.TestCase):
                 out = f(*val_inputs)
                 assert numpy.allclose(out, (1 / val_inputs[1]))
                 topo = f.maker.fgraph.toposort()
-                print topo
                 elem = [t for t in topo if isinstance(t.op, T.Elemwise)]
                 assert len(elem) == nb_elemwise
                 assert isinstance(elem[0].op, (T.Elemwise, ))
@@ -727,7 +723,6 @@ class test_canonize(unittest.TestCase):
                 assert numpy.allclose(out, val_inputs[0] /
                     val_inputs[1] / val_inputs[2])
                 topo = f.maker.fgraph.toposort()
-                print topo
                 assert len(topo) == 2
                 assert isinstance(topo[0].op, (T.Elemwise, ))
                 assert isinstance(topo[0].op.scalar_op,
@@ -746,7 +741,6 @@ class test_canonize(unittest.TestCase):
                 assert numpy.allclose(out, val_inputs[0] / (
                     val_inputs[1] / val_inputs[2]))
                 topo = f.maker.fgraph.toposort()
-                print topo
                 assert len(topo) == 2
                 assert isinstance(topo[0].op, (T.Elemwise, ))
                 assert isinstance(topo[0].op.scalar_op,
@@ -798,13 +792,11 @@ def test_local_merge_abs():
 
     f = theano.function([y, z], (abs(y * z * -2)), mode=mode)
     f(y_val, z_val)
-    theano.printing.debugprint(f)
     assert isinstance(f.maker.fgraph.toposort()[1].op.scalar_op, scal.Abs)
     assert len(f.maker.fgraph.toposort()) == 2
 
     f = theano.function([x, y], abs(x / y), mode=mode)
     f(x_val, y_val)
-    theano.printing.debugprint(f)
     assert isinstance(f.maker.fgraph.toposort()[1].op.scalar_op, scal.Abs)
     assert len(f.maker.fgraph.toposort()) == 2
 
@@ -1511,17 +1503,13 @@ def test_log1p():
     # check trickier cases (and use different dtype)
     y = fmatrix()
     f = function([x, y], T.log(tensor.fill(y, 1) + (x)), mode=m)
-    print f.maker.fgraph.toposort()
     # the first three ops are Shape_i, Shape_i, and Dimshuffle
-    theano.printing.debugprint(f)
     assert [node.op for node in f.maker.fgraph.toposort()][3:] == [
         T.log1p, tensor.alloc]
     f = function([x, y], T.log(0 + (x) + tensor.fill(y, 1.0)), mode=m)
-    theano.printing.debugprint(f)
     assert [node.op for node in f.maker.fgraph.toposort()][3:] == [
         T.log1p, tensor.alloc]
     f = function([x, y], T.log(2 + (x) - tensor.fill(y, 1.0)), mode=m)
-    theano.printing.debugprint(f)
     assert [node.op for node in f.maker.fgraph.toposort()][3:] \
             == [T.log1p, tensor.alloc]
 
@@ -1533,14 +1521,12 @@ def test_log1p():
         # I was never sure if this optimization should work on complex numbers or not.
         z = tensor.zmatrix()
         f = function([z], T.log(1 + (z)), mode=m)
-        theano.printing.debugprint(f)
         assert [node.op for node in f.maker.fgraph.toposort()] == [T.log1p]
 
     if 1:
         # should work for int
         z = tensor.imatrix()
         f = function([z], T.log(1 + (z)), mode=m)
-        theano.printing.debugprint(f)
         assert [node.op for node in f.maker.fgraph.toposort()] == [T.log1p]
 
 
@@ -1559,14 +1545,12 @@ def test_log_add():
     y = dvector()
     f = function([x, y], T.log(T.exp(x) + T.exp(y)), mode=m)
 
-    theano.printing.debugprint(f)
-
-    print f([10000], [10000])  # causes overflow if handled incorrectly
+    f([10000], [10000])  # causes overflow if handled incorrectly
     assert numpy.isfinite(f([10000], [10000]))
     assert numpy.allclose(f([10000], [10000]), 10000 + numpy.log1p(1))
 
     #test that it give the same result when it don't overflow
-    print f([10], [10])  # don't causes overflow
+    f([10], [10])  # don't causes overflow
     assert numpy.allclose(f([10], [10]), 10 + numpy.log1p(1))
 
     # test that it also works with more than two args, (this currently fails)
@@ -1574,10 +1558,9 @@ def test_log_add():
     y = dvector()
     f = function([x, y], T.log(T.exp(x) + T.exp(y) + T.exp(x - y) + T.exp(
         x + y)), mode=m)
-    theano.printing.debugprint(f)
 
     try:
-        print f([10000], [10000])  # causes overflow if handled incorrectly
+        f([10000], [10000])  # causes overflow if handled incorrectly
         assert numpy.allclose(f([10000], [10000]), 20000)
     except AssertionError:
         raise KnownFailureTest(('log(add(exp)) is not stabilized when adding '
@@ -2192,8 +2175,8 @@ class test_local_subtensor_merge(unittest.TestCase):
                                 n_ok += 1
                                 f(x_val, b_v, e_v, s_v, i_v)
 
-            print 'shape: %s' % (x_s,)
-            print '%% OK: %f' % (float(n_ok) * 100 / (n_ok + n_index_err))
+            #print 'shape: %s' % (x_s,)
+            #print '%% OK: %f' % (float(n_ok) * 100 / (n_ok + n_index_err))
 
     @attr('slow')
     def test_none_slice(self):
@@ -2873,41 +2856,30 @@ def test_local_mul_specialize():
 
     f = function([v], v * 1, mode=mode)
     nodes = [node.op for node in f.maker.fgraph.toposort()]
-    print nodes
     nodes == [deep_copy_op]
 
     f = function([v], v * 0, mode=mode)
     nodes = [node.op for node in f.maker.fgraph.toposort()]
-    print nodes
     assert nodes == [Shape_i(0), T.alloc]
 
     f = function([v], v * (-1), mode=mode)
     nodes = [node.op for node in f.maker.fgraph.toposort()]
-    print nodes
     assert nodes == [T.neg]
 
     f = function([v, m], v * 1 * (-m), mode=mode)
     nodes = [node.op for node in f.maker.fgraph.toposort()]
-    print nodes
-    theano.printing.debugprint(f)
     assert nodes == [T.mul]
 
     f = function([v, m], v * 0 * (-m), mode=mode)
     nodes = [node.op for node in f.maker.fgraph.toposort()]
-    print nodes
-    theano.printing.debugprint(f)
     assert nodes == [Shape_i(0), T.alloc]
 
     f = function([v, m], v * (-1) * (-m), mode=mode)
     nodes = [node.op for node in f.maker.fgraph.toposort()]
-    print nodes
-    theano.printing.debugprint(f)
     assert nodes == [T.mul]
 
     f = function([v, m], v * (-1) * m, mode=mode)
     nodes = [node.op for node in f.maker.fgraph.toposort()]
-    print nodes
-    theano.printing.debugprint(f)
     assert nodes == [T.mul]
 
 
@@ -3078,7 +3050,6 @@ class T_useless_elemwise(unittest.TestCase):
         f2 = theano.function([x], T.eq(x, x), mode=self.mode)
         assert numpy.all(f2(vx) == numpy.ones((5, 4)))
         topo2 = f2.maker.fgraph.toposort()
-        print topo2
         #Shape_i{1}(<TensorType(float64, matrix)>), Shape_i{0}(<TensorType(float64, matrix)>), Alloc([[1]], Shape_i{0}.0, Shape_i{1}.0
         assert len(topo2) == 3
         assert isinstance(topo2[-1].op, T.Alloc)
@@ -3097,7 +3068,6 @@ class T_useless_elemwise(unittest.TestCase):
         f2 = theano.function([x], T.neq(x, x), mode=self.mode)
         assert numpy.all(f2(vx) == numpy.zeros((5, 4)))
         topo2 = f2.maker.fgraph.toposort()
-        print topo2
         assert len(topo2) == 3
         assert isinstance(topo2[-1].op, T.Alloc)
 
@@ -3114,7 +3084,6 @@ class T_useless_elemwise(unittest.TestCase):
         f2 = theano.function([x, y], T.mul(x, y), mode=self.mode)
         assert numpy.all(f2(vx, vy) == vx * vy)
         topo2 = f2.maker.fgraph.toposort()
-        print topo2
         assert len(topo2) == 1
         assert isinstance(topo2[0].op, T.Elemwise)
         assert isinstance(topo2[0].op.scalar_op, theano.scalar.Mul)
@@ -3132,7 +3101,6 @@ class T_useless_elemwise(unittest.TestCase):
         f2 = theano.function([x, y], T.add(x, y), mode=self.mode)
         assert numpy.all(f2(vx, vy) == vx + vy)
         topo2 = f2.maker.fgraph.toposort()
-        print topo2
         assert len(topo2) == 1
         assert isinstance(topo2[0].op, T.Elemwise)
         assert isinstance(topo2[0].op.scalar_op, theano.scalar.Add)
@@ -3264,20 +3232,17 @@ class T_local_erf(unittest.TestCase):
         x = T.vector()
 
         f = theano.function([x], 1 + T.erf(x), mode=self.mode)
-        print f.maker.fgraph.toposort()
         assert [n.op for n in f.maker.fgraph.toposort()] == [
             T.mul, T.erfc], f.maker.fgraph.toposort()
         f(val)
 
         f = theano.function([x], T.erf(x) + 1, mode=self.mode)
-        print f.maker.fgraph.toposort()
         assert [n.op for n in f.maker.fgraph.toposort()] == [
             T.mul, T.erfc], f.maker.fgraph.toposort()
         f(val)
 
         f = theano.function([x], T.erf(x) + 2, mode=self.mode)
         topo = f.maker.fgraph.toposort()
-        print topo
         assert len(topo) == 2
         assert topo[0].op == T.erf
         assert isinstance(topo[1].op, T.Elemwise)
@@ -3290,26 +3255,22 @@ class T_local_erf(unittest.TestCase):
         x = T.vector()
 
         f = theano.function([x], 1 - T.erf(x), mode=self.mode)
-        print f.maker.fgraph.toposort()
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc]\
             , f.maker.fgraph.toposort()
         print f(val)
 
         f = theano.function([x], 1 + (-T.erf(x)), mode=self.mode)
-        print f.maker.fgraph.toposort()
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc]\
             , f.maker.fgraph.toposort()
         print f(val)
 
         f = theano.function([x], (-T.erf(x)) + 1, mode=self.mode)
-        print f.maker.fgraph.toposort()
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc]\
             , f.maker.fgraph.toposort()
         print f(val)
 
         f = theano.function([x], 2 - T.erf(x), mode=self.mode)
         topo = f.maker.fgraph.toposort()
-        print topo
         assert len(topo) == 2, f.maker.fgraph.toposort()
         assert topo[0].op == T.erf, f.maker.fgraph.toposort()
         assert isinstance(topo[1].op, T.Elemwise), f.maker.fgraph.toposort()
@@ -3323,23 +3284,19 @@ class T_local_erf(unittest.TestCase):
         x = T.vector()
 
         f = theano.function([x], T.erf(x) - 1, mode=self.mode)
-        print f.maker.fgraph.toposort()
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc, T.mul]
         print f(val)
 
         f = theano.function([x], T.erf(x) + (-1), mode=self.mode)
-        print f.maker.fgraph.toposort()
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc, T.mul]
         print f(val)
 
         f = theano.function([x], -1 + T.erf(x), mode=self.mode)
-        print f.maker.fgraph.toposort()
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc, T.mul]
         print f(val)
 
         f = theano.function([x], T.erf(x) - 2, mode=self.mode)
         topo = f.maker.fgraph.toposort()
-        print topo
         assert len(topo) == 2
         assert topo[0].op == T.erf
         assert isinstance(topo[1].op, T.Elemwise)
@@ -3366,20 +3323,17 @@ class T_local_erfc(unittest.TestCase):
         x = T.vector('x')
 
         f = theano.function([x], 1 - T.erfc(x), mode=self.mode)
-        theano.printing.debugprint(f)
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erf]\
             , f.maker.fgraph.toposort()
         print f(val)
 
         f = theano.function([x], (-T.erfc(x)) + 1, mode=self.mode)
-        theano.printing.debugprint(f)
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erf]\
             , f.maker.fgraph.toposort()
         print f(val)
 
         f = theano.function([x], 2 - T.erfc(x), mode=self.mode)
         topo = f.maker.fgraph.toposort()
-        theano.printing.debugprint(f)
         assert len(topo) == 2, f.maker.fgraph.toposort()
         assert topo[0].op == T.erfc, f.maker.fgraph.toposort()
         assert isinstance(topo[1].op, T.Elemwise), f.maker.fgraph.toposort()
@@ -3394,19 +3348,16 @@ class T_local_erfc(unittest.TestCase):
         x = T.vector('x')
 
         f = theano.function([x], -1 + T.erfc(-x), mode=self.mode)
-        theano.printing.debugprint(f)
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erf]\
             , f.maker.fgraph.toposort()
         print f(val)
 
         f = theano.function([x], T.erfc(-x) - 1, mode=self.mode)
-        theano.printing.debugprint(f)
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erf]\
             , f.maker.fgraph.toposort()
         print f(val)
 
         f = theano.function([x], T.erfc(-x) + (-1), mode=self.mode)
-        theano.printing.debugprint(f)
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erf]\
             , f.maker.fgraph.toposort()
         print f(val)
@@ -3427,13 +3378,11 @@ class T_local_erfc(unittest.TestCase):
         mode_fusion.check_isfinite = False
 
         f = theano.function([x], T.log(T.erfc(x)), mode=mode)
-        #theano.printing.debugprint(f)
         assert len(f.maker.fgraph.apply_nodes) == 23, len(f.maker.fgraph.apply_nodes)
         assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
         assert all(numpy.isfinite(f(val)))
 
         f = theano.function([x], T.log(T.erfc(-x)), mode=mode)
-        #theano.printing.debugprint(f)
         assert len(f.maker.fgraph.apply_nodes) == 24, len(f.maker.fgraph.apply_nodes)
         assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
         assert all(numpy.isfinite(f(-val)))
@@ -3470,7 +3419,6 @@ class T_local_erfc(unittest.TestCase):
         mode_fusion.check_isfinite = False
 
         f = theano.function([x], T.grad(T.log(T.erfc(x)).sum(), x), mode=mode)
-        #theano.printing.debugprint(f)
         assert len(f.maker.fgraph.apply_nodes) == 23, len(f.maker.fgraph.apply_nodes)
         assert all(numpy.isfinite(f(val)))
         assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
@@ -3478,14 +3426,12 @@ class T_local_erfc(unittest.TestCase):
         #test with a different mul constant
         f = theano.function([x], T.mul(T.exp(T.neg(T.sqr(x))), -
             10.12837917) / T.erfc(x), mode=mode)
-        #theano.printing.debugprint(f)
         assert len(f.maker.fgraph.apply_nodes) == 23, len(f.maker.fgraph.apply_nodes)
         assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
         assert all(numpy.isfinite(f(val)))
 
         #test that we work without the mul
         f = theano.function([x], T.exp(T.neg(T.sqr(x))) / T.erfc(x), mode=mode)
-        #theano.printing.debugprint(f)
         assert len(f.maker.fgraph.apply_nodes) == 23, len(f.maker.fgraph.apply_nodes)
         assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
         assert all(numpy.isfinite(f(val)))
@@ -3493,14 +3439,12 @@ class T_local_erfc(unittest.TestCase):
         #test that we don't work if x!=y
         f = theano.function([x, y], T.exp(T.neg(T.sqr(x))) / T.erfc(
             y), mode=mode)
-        #theano.printing.debugprint(f)
         assert len(f.maker.fgraph.apply_nodes) == 5, len(f.maker.fgraph.apply_nodes)
         assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
         f(val, val - 3)
 
         #test that we work without the sqr and neg
         f = theano.function([x], T.exp(T.mul(-1, x, x)) / T.erfc(x), mode=mode)
-        #theano.printing.debugprint(f)
         assert len(f.maker.fgraph.apply_nodes) == 22, len(f.maker.fgraph.apply_nodes)
         assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
         assert all(numpy.isfinite(f(val)))
@@ -3508,7 +3452,6 @@ class T_local_erfc(unittest.TestCase):
         #test that it work correctly if x is x*2 in the graph.
         f = theano.function([x], T.grad(T.log(T.erfc(2 * x)).sum(),
              x), mode=mode)
-        #theano.printing.debugprint(f)
         assert len(f.maker.fgraph.apply_nodes) == 23, len(f.maker.fgraph.apply_nodes)
         assert numpy.isfinite(f(val)).all()
         assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
@@ -3587,7 +3530,6 @@ class test_local_remove_switch_const_cond(unittest.TestCase):
 
         z = theano.tensor.switch(1, x, y)
         f = theano.function([x, y], z, mode=self.mode)
-        #theano.printing.debugprint(f)
         assert len([node.op for node in f.maker.fgraph.toposort() if
                     isinstance(node.op, theano.tensor.Elemwise) and
                     not isinstance(node.op.scalar_op,theano.scalar.basic.Cast)]) == 0
@@ -3597,7 +3539,6 @@ class test_local_remove_switch_const_cond(unittest.TestCase):
 
         z = theano.tensor.switch(0, x, y)
         f = theano.function([x, y], z, mode=self.mode)
-        #theano.printing.debugprint(f)
         assert len([node.op for node in f.maker.fgraph.toposort() if
                     isinstance(node.op, theano.tensor.Elemwise)]) == 0
         vx = numpy.array([[1, 2, 3], [4, 5, 6]], dtype='int32')
@@ -3912,9 +3853,7 @@ class T_local_sum_dimshuffle(unittest.TestCase):
                 print i
                 f = theano.function([a, b, c, d], s, mode=self.mode,
                         on_unused_input='ignore')
-                theano.printing.debugprint(f)
                 g = f.maker.fgraph.toposort()
-                #print 'g =', g
                 assert isinstance(g[-1].op.scalar_op,
                                   theano.scalar.basic.TrueDiv)
                 f(a_val, b_val, c_val, d_val)
@@ -4157,8 +4096,6 @@ def test_local_div_to_inv():
     denom_m = denom_s.dimshuffle('x', 'x')
 
     out = num_v / denom_m
-    theano.printing.debugprint(out, print_type=True)
-    print out.broadcastable
     assert numpy.all(out.broadcastable == (True, False))
 
     f = theano.function([num_len_s, denom_s], out)