def test_argtopk_nd(self, shp, k_, dtype, sorted, idx_dtype):
ndim = len(shp)
for axis in range(-ndim, ndim):
if isinstance(k_, str):
k = eval(k_.replace("n", str(shp[axis])))
else:
k = k_
if k == 0:
continue
x = theano.tensor.tensor(
name="x", broadcastable=(False,) * len(shp), dtype=dtype
)
y = argtopk(x, k, axis=axis, sorted=sorted, idx_dtype=idx_dtype)
fn = theano.function([x], y, mode=self.mode)
assert any(
[isinstance(n.op, self.op_class) for n in fn.maker.fgraph.apply_nodes]
)
size = reduce(int.__mul__, shp)
xval = gen_unique_vector(size, dtype).reshape(shp)
yval = fn(xval)
idx = slice(-k, None) if k > 0 else slice(-k)
l = axis % ndim
r = ndim - l
idx = (slice(None),) * l + (idx,) + (slice(None),) * (r - 1)
goal = np.argsort(xval, axis=axis)[idx].astype(idx_dtype)
assert np.all(np.sort(yval, axis=axis) == np.sort(goal, axis=axis))
def test_argtopk_nd(self, shp, k_, dtype, sorted, idx_dtype):
ndim = len(shp)
for axis in range(-ndim, ndim):
if isinstance(k_, str):
k = eval(k_.replace('n', str(shp[axis])))
else:
k = k_
if k == 0:
continue
x = theano.tensor.tensor(
name='x', broadcastable=(False,) * len(shp), dtype=dtype)
y = argtopk(x, k, axis=axis, sorted=sorted, idx_dtype=idx_dtype)
fn = theano.function([x], y, mode=self.mode)
assert any([isinstance(n.op, self.op_class) for n in fn.maker.fgraph.apply_nodes])
size = reduce(int.__mul__, shp)
xval = gen_unique_vector(size, dtype).reshape(shp)
yval = fn(xval)
idx = slice(-k, None) if k > 0 else slice(-k)
l = axis % ndim
r = ndim - l
idx = (slice(None),) * l + (idx,) + (slice(None),) * (r - 1)
goal = np.argsort(xval, axis=axis)[idx].astype(idx_dtype)
assert np.all(np.sort(yval, axis=axis) == np.sort(goal, axis=axis))
def test_argtopk_sanity(self, dtype, idx_dtype, axis, sorted):
x = tensor.vector(name='x', dtype=dtype)
fn = theano.function([x], argtopk(x, 1, axis=axis, sorted=sorted, idx_dtype=idx_dtype))
xval = np.asarray([1]).astype(dtype)
yval = fn(xval)
assert yval == np.asarray([0], dtype=idx_dtype)
assert yval.dtype == np.dtype(idx_dtype)
def test_argtopk_sanity(self, dtype, idx_dtype, axis, sorted):
x = tensor.vector(name='x', dtype=dtype)
fn = theano.function([x],
argtopk(x, 1, axis=axis, sorted=sorted, idx_dtype=idx_dtype),
mode=self.mode)
assert any([isinstance(n.op, self.op_class) for n in fn.maker.fgraph.apply_nodes])
xval = np.asarray([1]).astype(dtype)
yval = fn(xval)
assert yval == np.asarray([0], dtype=idx_dtype)
assert yval.dtype == np.dtype(idx_dtype)
def test_argtopk_sanity(self, dtype, idx_dtype, axis, sorted):
x = tensor.vector(name='x', dtype=dtype)
fn = theano.function([x],
argtopk(x,
1,
axis=axis,
sorted=sorted,
idx_dtype=idx_dtype))
xval = np.asarray([1]).astype(dtype)
yval = fn(xval)
assert yval == np.asarray([0], dtype=idx_dtype)
assert yval.dtype == np.dtype(idx_dtype)
def test_argtopk_sanity(self, dtype, idx_dtype, axis, sorted):
x = tensor.vector(name="x", dtype=dtype)
fn = theano.function(
[x],
argtopk(x, 1, axis=axis, sorted=sorted, idx_dtype=idx_dtype),
mode=self.mode,
)
assert any(
[isinstance(n.op, self.op_class) for n in fn.maker.fgraph.apply_nodes]
)
xval = np.asarray([1]).astype(dtype)
yval = fn(xval)
assert yval == np.asarray([0], dtype=idx_dtype)
assert yval.dtype == np.dtype(idx_dtype)
def test_argtopk_1d_collision(self, size, k, dtype, sorted):
# with non-unique kth max value
if isinstance(k, str):
k = eval(k.replace('n', str(size)))
x = theano.tensor.vector(name='x', dtype=dtype)
y = argtopk(x, k, sorted=sorted, idx_dtype='int32')
fn = theano.function([x], y)
xval = np.repeat(np.random.uniform(-100., 100., size=size // 2).astype(dtype), 2)
xval = xval[np.random.permutation(size)]
yval = fn(xval)
idx = (slice(-k, None) if k > 0 else slice(-k))
goal = np.argsort(xval)[idx].astype('int32')
utt.assert_allclose(np.sort(xval[yval]), np.sort(xval[goal]))
def test_argtopk_1d_collision(self, size, k, dtype, sorted):
# with non-unique kth max value
if isinstance(k, str):
k = eval(k.replace('n', str(size)))
x = theano.tensor.vector(name='x', dtype=dtype)
y = argtopk(x, k, sorted=sorted, idx_dtype='int32')
fn = theano.function([x], y)
xval = np.repeat(
np.random.uniform(-100., 100., size=size // 2).astype(dtype), 2)
xval = xval[np.random.permutation(size)]
yval = fn(xval)
idx = (slice(-k, None) if k > 0 else slice(-k))
goal = np.argsort(xval)[idx].astype('int32')
utt.assert_allclose(np.sort(xval[yval]), np.sort(xval[goal]))
def test_argtopk_1d(self, size, k, dtype, sorted, idx_dtype):
if isinstance(k, str):
k = eval(k.replace('n', str(size)))
x = theano.tensor.vector(name='x', dtype=dtype)
y = argtopk(x, k, sorted=sorted, idx_dtype=idx_dtype)
fn = theano.function([x], y)
# assert local_useless_topk opt is done properly
assert 1 == len(fn.maker.fgraph.outputs[0].owner.outputs)
# generate a all-unique array
xval = gen_unique_vector(size, dtype)
yval = fn(xval)
idx = (slice(-k, None) if k > 0 else slice(-k))
goal = np.argsort(xval)[idx].astype(idx_dtype)
# due to uniqueness, we expect indices same
assert np.all(xval[np.sort(yval)] == xval[np.sort(goal)])
def test_argtopk_1d(self, size, k, dtype, sorted, idx_dtype):
if isinstance(k, str):
k = eval(k.replace('n', str(size)))
x = theano.tensor.vector(name='x', dtype=dtype)
y = argtopk(x, k, sorted=sorted, idx_dtype=idx_dtype)
fn = theano.function([x], y)
# assert local_useless_topk opt is done properly
assert 1 == len(fn.maker.fgraph.outputs[0].owner.outputs)
# generate a all-unique array
xval = gen_unique_vector(size, dtype)
yval = fn(xval)
idx = (slice(-k, None) if k > 0 else slice(-k))
goal = np.argsort(xval)[idx].astype(idx_dtype)
# due to uniqueness, we expect indices same
assert np.all(xval[np.sort(yval)] == xval[np.sort(goal)])
def test_argtopk_1d_collision(self, size, k, dtype, sorted):
# with non-unique kth max value
if isinstance(k, str):
k = eval(k.replace('n', str(size)))
x = theano.tensor.vector(name='x', dtype=dtype)
y = argtopk(x, k, sorted=sorted, idx_dtype='int32')
# DebugMode won't like the index change on collision on CPU
# So don't use DebugMode here.
mode = self.mode
if isinstance(self.mode, theano.compile.DebugMode):
mode = theano.Mode(optimizer=mode.optimizer)
fn = theano.function([x], y, mode=mode)
assert any([isinstance(n.op, self.op_class) for n in fn.maker.fgraph.apply_nodes])
xval = np.repeat(np.random.uniform(-100., 100., size=size // 2).astype(dtype), 2)
xval = xval[np.random.permutation(size)]
yval = fn(xval)
idx = (slice(-k, None) if k > 0 else slice(-k))
goal = np.argsort(xval)[idx].astype('int32')
utt.assert_allclose(np.sort(xval[yval]), np.sort(xval[goal]))
def test_argtopk_1d_collision(self, size, k, dtype, sorted):
# with non-unique kth max value
if isinstance(k, str):
k = eval(k.replace("n", str(size)))
x = theano.tensor.vector(name="x", dtype=dtype)
y = argtopk(x, k, sorted=sorted, idx_dtype="int32")
# DebugMode won't like the index change on collision on CPU
# So don't use DebugMode here.
mode = self.mode
if isinstance(self.mode, theano.compile.DebugMode):
mode = theano.Mode(optimizer=mode.optimizer)
fn = theano.function([x], y, mode=mode)
assert any(
[isinstance(n.op, self.op_class) for n in fn.maker.fgraph.apply_nodes]
)
xval = np.repeat(
np.random.uniform(-100.0, 100.0, size=size // 2).astype(dtype), 2
)
xval = xval[np.random.permutation(size)]
yval = fn(xval)
idx = slice(-k, None) if k > 0 else slice(-k)
goal = np.argsort(xval)[idx].astype("int32")
utt.assert_allclose(np.sort(xval[yval]), np.sort(xval[goal]))