Merge pull request #2888 from nouiz/tests

theano/sandbox/cuda/basic_ops.py

@@ -3070,7 +3070,7 @@ class GpuJoin(tensor.Join, GpuOp):
         axis, cndas = axis_and_tensors[0], axis_and_tensors[1:]
         # In case axis is numpy.int8 and has no __index__() method
         axis = int(axis)
-        ndim = tensors[0].ndim
+        ndim = cndas[0].ndim
         if axis < -ndim:
             raise IndexError("Join axis %d out of bounds [0, %d)" %
                              (axis, ndim))

theano/sandbox/cuda/tests/test_basic_ops.py

@@ -304,7 +304,9 @@ def test_careduce():
 
 def test_flatten():
     x = cuda.fmatrix('x')
-    f = theano.function([x], x.flatten())
+    f = theano.function([x], x.flatten(), mode=mode_with_gpu)
+    assert any([node for node in f.maker.fgraph.toposort()
+                if isinstance(node.op, B.GpuFlatten)])
     assert len(f([[0., 0.], [0., 0.]]).shape) == 1
 
 

theano/scan_module/scan_op.py

@@ -245,7 +245,7 @@ class Scan(PureOp):
                                     "that it shouldn't be the case")
 
             for out in self.outputs:
-                if isinstance(inp.type, GpuArrayType):
+                if isinstance(out.type, GpuArrayType):
                     raise TypeError("Inconsistency in the inner graph of "
                                     "scan '%s' : one of the outputs to the "
                                     "inner graph is of type GpuArrayType but "

theano/tensor/tests/test_elemwise.py

@@ -27,6 +27,7 @@ def FunctionGraph(i, o):
 class test_DimShuffle(unittest_tools.InferShapeTester):
     op = DimShuffle
     type = TensorType
+    dtype = theano.config.floatX
 
     def with_linker(self, linker):
         for xsh, shuffle, zsh in [((2, 3), (1, 'x', 0), (3, 1, 2)),
@@ -40,25 +41,25 @@ class test_DimShuffle(unittest_tools.InferShapeTester):
                                   ((1, 1, 1), (), ()),
                                   ((1,), ('x', 'x'), (1, 1))]:
             ib = [(entry == 1) for entry in xsh]
-            x = self.type('float64', ib)('x')
+            x = self.type(self.dtype, ib)('x')
             e = self.op(ib, shuffle)(x)
             f = copy(linker).accept(FunctionGraph([x], [e])).make_function()
-            assert f(numpy.ones(xsh)).shape == zsh
+            assert f(numpy.ones(xsh, dtype=self.dtype)).shape == zsh
             # test that DimShuffle.infer_shape work correctly
-            x = self.type('float64', ib)('x')
+            x = self.type(self.dtype, ib)('x')
             e = self.op(ib, shuffle)(x)
             f = copy(linker).accept(FunctionGraph([x],
                                                   [e.shape])).make_function()
-            assert all(f(numpy.ones(xsh))) == all(zsh)
+            assert all(f(numpy.ones(xsh, dtype=self.dtype))) == all(zsh)
 
         # Test when we drop a axis that is not broadcastable
         ib = [False, True, False]
-        x = self.type('float64', ib)('x')
+        x = self.type(self.dtype, ib)('x')
         self.assertRaises(ValueError, self.op, ib, shuffle)
 
         # Test when we drop a axis that don't have shape 1
         ib = [True, True, False]
-        x = self.type('float64', ib)('x')
+        x = self.type(self.dtype, ib)('x')
         e = self.op(ib, (1, 2))(x)
         f = copy(linker).accept(FunctionGraph([x], [e.shape])).make_function()
         self.assertRaises(TypeError, f, numpy.ones((2, 1, 4)))
@@ -66,7 +67,7 @@ class test_DimShuffle(unittest_tools.InferShapeTester):
         # Test that we can't take a dimensions multiple time
         xsh, shuffle, zsh = ((1, 1, 4), (0, 1, 2, 0), (1, 4))
         ib = [False, True, False]
-        x = self.type('float64', ib)('x')
+        x = self.type(self.dtype, ib)('x')
         self.assertRaises(ValueError, DimShuffle, ib, shuffle)
 
     def test_perform(self):
@@ -89,15 +90,15 @@ class test_DimShuffle(unittest_tools.InferShapeTester):
                              ((1, 1, 1), ()),
                              ((1,), ('x', 'x'))]:
             ib = [(entry == 1) for entry in xsh]
-            adtens = self.type('float64', ib)('x')
-            adtens_val = numpy.ones(xsh)
+            adtens = self.type(self.dtype, ib)('x')
+            adtens_val = numpy.ones(xsh, dtype=self.dtype)
             self._compile_and_check([adtens],
                                     [self.op(ib, shuffle)(adtens)],
                                     [adtens_val], self.op,
                                     warn=False)
 
     def test_too_big_rank(self):
-        x = self.type('float64', broadcastable=())()
+        x = self.type(self.dtype, broadcastable=())()
         y = x.dimshuffle(('x',) * (numpy.MAXDIMS + 1))
         self.assertRaises(ValueError, y.eval, {x: 0})