def test_infer_shape(self):
for ndim in range(4):
x = T.TensorType(config.floatX, [False] * ndim)()
shp = (numpy.arange(ndim) + 1) * 5
a = np.random.random(shp).astype(config.floatX)
for axis in self._possible_axis(ndim):
for dtype in tensor.discrete_dtypes:
r_var = T.scalar(dtype=dtype)
r = numpy.asarray(3, dtype=dtype)
if dtype in self.numpy_unsupported_dtypes:
self.assertRaises(TypeError, repeat, x, r_var)
else:
self._compile_and_check(
[x, r_var], [RepeatOp(axis=axis)(x, r_var)],
[a, r], self.op_class)
r_var = T.vector(dtype=dtype)
if axis is None:
r = np.random.random_integers(
5, size=a.size).astype(dtype)
elif a.size > 0:
r = np.random.random_integers(
5, size=a.shape[axis]).astype(dtype)
else:
r = np.random.random_integers(
5, size=(10, )).astype(dtype)
self._compile_and_check(
[x, r_var], [RepeatOp(axis=axis)(x, r_var)],
[a, r], self.op_class)
def test_broadcastable(self):
x = T.TensorType(config.floatX, [False, True, False])()
r = RepeatOp(axis=1)(x, 2)
self.assertEqual(r.broadcastable, (False, False, False))
r = RepeatOp(axis=1)(x, 1)
self.assertEqual(r.broadcastable, (False, True, False))
r = RepeatOp(axis=0)(x, 2)
self.assertEqual(r.broadcastable, (False, True, False))
def test_broadcastable(self):
x = T.TensorType(config.floatX, [False, True, False])()
r = RepeatOp(axis=1)(x, 2)
assert r.broadcastable == (False, False, False)
r = RepeatOp(axis=1)(x, 1)
assert r.broadcastable == (False, True, False)
r = RepeatOp(axis=0)(x, 2)
assert r.broadcastable == (False, True, False)
def setup_method(self):
super().setup_method()
self.op_class = RepeatOp
self.op = RepeatOp()
# uint64 always fails
# int64 and uint32 also fail if python int are 32-bit
if LOCAL_BITWIDTH == 64:
self.numpy_unsupported_dtypes = ("uint64",)
if LOCAL_BITWIDTH == 32:
self.numpy_unsupported_dtypes = ("uint32", "int64", "uint64")
def setUp(self):
super(TestRepeatOp, self).setUp()
self.op_class = RepeatOp
self.op = RepeatOp()
# uint64 always fails
# int64 and uint32 also fail if python int are 32-bit
ptr_bitwidth = theano.gof.local_bitwidth()
if ptr_bitwidth == 64:
self.numpy_unsupported_dtypes = ('uint64', )
if ptr_bitwidth == 32:
self.numpy_unsupported_dtypes = ('uint32', 'int64', 'uint64')
def setup_method(self):
super().setup_method()
self.op_class = RepeatOp
self.op = RepeatOp()
# uint64 always fails
# int64 and uint32 also fail if python int are 32-bit
ptr_bitwidth = theano.configdefaults.local_bitwidth()
if ptr_bitwidth == 64:
self.numpy_unsupported_dtypes = ("uint64", )
if ptr_bitwidth == 32:
self.numpy_unsupported_dtypes = ("uint32", "int64", "uint64")
def test_infer_shape(self):
for ndim in [1, 3]:
x = T.TensorType(config.floatX, [False] * ndim)()
shp = (np.arange(ndim) + 1) * 3
a = np.random.random(shp).astype(config.floatX)
for axis in self._possible_axis(ndim):
for dtype in ["int8", "uint8", "uint64"]:
r_var = T.scalar(dtype=dtype)
r = np.asarray(3, dtype=dtype)
if dtype in self.numpy_unsupported_dtypes:
r_var = T.vector(dtype=dtype)
with pytest.raises(TypeError):
repeat(x, r_var)
else:
self._compile_and_check(
[x, r_var],
[RepeatOp(axis=axis)(x, r_var)],
[a, r],
self.op_class,
)
r_var = T.vector(dtype=dtype)
if axis is None:
r = np.random.randint(1, 6,
size=a.size).astype(dtype)
elif a.size > 0:
r = np.random.randint(
1, 6, size=a.shape[axis]).astype(dtype)
else:
r = np.random.randint(1, 6,
size=(10, )).astype(dtype)
self._compile_and_check(
[x, r_var],
[RepeatOp(axis=axis)(x, r_var)],
[a, r],
self.op_class,
)
def test_grad(self):
for ndim in range(3):
a = np.random.random((10, ) * ndim).astype(config.floatX)
for axis in self._possible_axis(ndim):
utt.verify_grad(lambda x: RepeatOp(axis=axis)(x, 3), [a])
