GitOrigin-RevId: a1bd1102a6
release-1.5
@@ -165,7 +165,7 @@ def _remove_axis(inp: Tensor, axis) -> Tensor: | |||||
return list(map(int, axis)) | return list(map(int, axis)) | ||||
axis = get_axes() | axis = get_axes() | ||||
axis = sorted(i + inp.ndim if i < 0 else i for i in axis) | |||||
axis = utils._normalize_axis(inp.ndim, axis) | |||||
axis = [a - i for i, a in enumerate(axis)] | axis = [a - i for i, a in enumerate(axis)] | ||||
op = builtin.RemoveAxis(axis=axis) | op = builtin.RemoveAxis(axis=axis) | ||||
@@ -190,8 +190,7 @@ def _reduce(mode): | |||||
op = builtin.Reduce(mode=mode, axis=0) | op = builtin.Reduce(mode=mode, axis=0) | ||||
(result,) = apply(op, data) | (result,) = apply(op, data) | ||||
elif isinstance(axis, collections.abc.Iterable): | elif isinstance(axis, collections.abc.Iterable): | ||||
axis = list(axis) | |||||
axis.sort(reverse=True) | |||||
axis = utils._normalize_axis(self.ndim, axis, reverse=True) | |||||
for ai in axis: | for ai in axis: | ||||
op = builtin.Reduce(mode=mode, axis=ai) | op = builtin.Reduce(mode=mode, axis=ai) | ||||
(data,) = apply(op, data) | (data,) = apply(op, data) | ||||
@@ -199,6 +198,7 @@ def _reduce(mode): | |||||
data = _remove_axis(data, ai) | data = _remove_axis(data, ai) | ||||
result = data | result = data | ||||
else: | else: | ||||
# builtin.Reduce already accept negtive axis | |||||
op = builtin.Reduce(mode=mode, axis=axis) | op = builtin.Reduce(mode=mode, axis=axis) | ||||
(result,) = apply(op, data) | (result,) = apply(op, data) | ||||
@@ -178,3 +178,28 @@ def make_shape_tuple(shape): | |||||
s = [] | s = [] | ||||
_expand_int(s, shape) | _expand_int(s, shape) | ||||
return tuple(s) | return tuple(s) | ||||
def _normalize_axis( | |||||
ndim: int, axis: Union[int, Iterable], reverse=False | |||||
) -> Union[int, list]: | |||||
def convert(x): | |||||
x_org = x | |||||
if x < 0: | |||||
x = ndim + x | |||||
assert ( | |||||
x >= 0 and x < ndim | |||||
), "axis {} is out of bounds for tensor of dimension {}".format(x_org, ndim) | |||||
return x | |||||
if isinstance(axis, int): | |||||
return convert(axis) | |||||
elif isinstance(axis, Iterable): | |||||
axis_org = axis | |||||
axis = list(sorted(map(convert, axis), reverse=reverse)) | |||||
for i in range(len(axis) - 1): | |||||
assert axis[i] != axis[i + 1], "axis {} contains duplicated indices".format( | |||||
axis_org | |||||
) | |||||
return axis | |||||
raise |
@@ -466,9 +466,13 @@ def argmin( | |||||
0 | 0 | ||||
""" | """ | ||||
if axis is None: | |||||
assert not keepdims, "can not set axis=None and keepdims=True" | |||||
inp = inp.flatten() | |||||
axis = 0 | |||||
axis = utils._normalize_axis(inp.ndim, axis, reverse=True) | |||||
if isinstance(axis, collections.abc.Iterable): | if isinstance(axis, collections.abc.Iterable): | ||||
axis = list(axis) | |||||
axis.sort(reverse=True) | |||||
for ai in axis: | for ai in axis: | ||||
op = builtin.Argmin(axis=ai) | op = builtin.Argmin(axis=ai) | ||||
@@ -479,11 +483,6 @@ def argmin( | |||||
return inp | return inp | ||||
if axis is None: | |||||
assert not keepdims, "can not set axis=None and keepdims=True" | |||||
inp = inp.flatten() | |||||
axis = 0 | |||||
op = builtin.Argmin(axis=axis) | op = builtin.Argmin(axis=axis) | ||||
(result,) = apply(op, inp) | (result,) = apply(op, inp) | ||||
if not keepdims: | if not keepdims: | ||||
@@ -525,9 +524,13 @@ def argmax( | |||||
5 | 5 | ||||
""" | """ | ||||
if axis is None: | |||||
assert not keepdims, "can not set axis=None and keepdims=True" | |||||
inp = inp.flatten() | |||||
axis = 0 | |||||
axis = utils._normalize_axis(inp.ndim, axis, reverse=True) | |||||
if isinstance(axis, collections.abc.Iterable): | if isinstance(axis, collections.abc.Iterable): | ||||
axis = list(axis) | |||||
axis.sort(reverse=True) | |||||
for ai in axis: | for ai in axis: | ||||
op = builtin.Argmax(axis=ai) | op = builtin.Argmax(axis=ai) | ||||
@@ -538,11 +541,6 @@ def argmax( | |||||
return inp | return inp | ||||
if axis is None: | |||||
assert not keepdims, "can not set axis=None and keepdims=True" | |||||
inp = inp.flatten() | |||||
axis = 0 | |||||
op = builtin.Argmax(axis=axis) | op = builtin.Argmax(axis=axis) | ||||
(result,) = apply(op, inp) | (result,) = apply(op, inp) | ||||
if not keepdims: | if not keepdims: | ||||
@@ -811,3 +811,19 @@ def test_assert_not_equal(): | |||||
y = F.zeros(shape, dtype=np.float32) + 1.1 | y = F.zeros(shape, dtype=np.float32) + 1.1 | ||||
with pytest.raises(RuntimeError): | with pytest.raises(RuntimeError): | ||||
z = F.utils._assert_equal(x, y) | z = F.utils._assert_equal(x, y) | ||||
def test_neg_axis(): | |||||
x = tensor(np.random.normal(0, 1, (32, 5))) | |||||
y = F.argmax(x, axis=-1) | |||||
yy = F.argmax(x, axis=1) | |||||
np.testing.assert_equal(y.numpy(), yy.numpy()) | |||||
y = F.argmax(x, axis=(-1, -2)) | |||||
yy = F.argmax(x, axis=(0, 1)) | |||||
np.testing.assert_equal(y.numpy(), yy.numpy()) | |||||
y = F.argmin(x, axis=(-1, -2)) | |||||
yy = F.argmin(x, axis=(0, 1)) | |||||
np.testing.assert_equal(y.numpy(), yy.numpy()) |
@@ -9,6 +9,7 @@ | |||||
from functools import partial | from functools import partial | ||||
import numpy as np | import numpy as np | ||||
import pytest | |||||
from utils import opr_test | from utils import opr_test | ||||
import megengine.functional as F | import megengine.functional as F | ||||
@@ -48,6 +49,14 @@ def common_test_reduce(opr, ref_opr): | |||||
ref_fn=lambda x: ref_opr(x, axis=axis).astype(np.int32), | ref_fn=lambda x: ref_opr(x, axis=axis).astype(np.int32), | ||||
axis=axis, | axis=axis, | ||||
) | ) | ||||
# test negative axis | |||||
axis = axis - len(data1_shape) | |||||
opr_test( | |||||
cases, | |||||
opr, | |||||
ref_fn=lambda x: ref_opr(x, axis=axis).astype(np.int32), | |||||
axis=axis, | |||||
) | |||||
def test_sum(): | def test_sum(): | ||||
@@ -137,3 +146,14 @@ def test_normalize(): | |||||
cases[0]["input"][0, 0, 0, :] = 0 | cases[0]["input"][0, 0, 0, :] = 0 | ||||
cases[1]["input"][0, 0, 0, :] = 0 | cases[1]["input"][0, 0, 0, :] = 0 | ||||
opr_test(cases, partial(F.normalize, axis=3), ref_fn=partial(np_normalize, axis=3)) | opr_test(cases, partial(F.normalize, axis=3), ref_fn=partial(np_normalize, axis=3)) | ||||
def test_sum_neg_axis(): | |||||
shape = (2, 3) | |||||
data = np.random.random(shape).astype(np.float32) | |||||
for axis in (-1, -2, (-2, 1), (-1, 0)): | |||||
get = F.sum(tensor(data), axis=axis) | |||||
ref = np.sum(data, axis=axis) | |||||
np.testing.assert_allclose(get.numpy(), ref, rtol=1e-6) | |||||
with pytest.raises(AssertionError): | |||||
F.sum(tensor(data), axis=(-1, 1)) |