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!15206 fix mindspore.numpy.gcd/lcm/convolve 

From: @yanglf1121
Reviewed-by: @guoqi1024,@liangchenghui,@kingxian
Signed-off-by: @liangchenghui,@kingxian
This commit is contained in:
mindspore-ci-bot 2021-04-20 09:53:06 +08:00 committed by Gitee
parent 0fc5a9e978 33303d9e77
commit 30ed22848f
2 changed files with 16 additions and 16 deletions
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4
mindspore/numpy/array_creations.py
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@ -425,10 +425,10 @@ def arange(start, stop=None, step=None, dtype=None):
num = _ceil(stop - start)
out = _iota(mstype.float32, num) + start
elif stop is None: # (start, stop, step) -> (0, start, step)
num = _ceil(start / step)
num = _ceil((start + 0.0) / step)
out = _iota(mstype.float32, num) * step
else:
num = _ceil((stop - start) / step)
num = _ceil((stop - start + 0.0) / step)
out = _iota(mstype.float32, num) * step + start
return out.astype(dtype)

28
mindspore/numpy/math_ops.py
View File

@ -1237,9 +1237,7 @@ def log(x, dtype=None):
On CPU, the supported dtypes are np.float16, np.float32, and np.float64.
Args:
x (Tensor): Input array. For integer arguments with absolute value larger
than 1 the result is always zero because of the way Python handles
integer division. For integer zero the result is an overflow.
x (Tensor): Input array.
dtype (:class:`mindspore.dtype`, optional): defaults to None. Overrides the dtype of the
output Tensor.
@ -2088,7 +2086,7 @@ def trapz(y, x=None, dx=1.0, axis=-1):
def _gcd(x1, x2):
"""Calculates gcd without applying keyword arguments."""
dtype = _promote(F.dtype(x1), F.dtype(x2))
if _get_device() == 'CPU' and not _check_is_float(dtype):
if not _check_is_float(dtype):
# F.reduce_sum only supports float
x1 = F.cast(x1, mstype.float32)
x2 = F.cast(x2, mstype.float32)
@ -2173,7 +2171,7 @@ def lcm(x1, x2, dtype=None):
q2 = F.tensor_div(x2, common_divisor)
res = F.tensor_mul(F.tensor_mul(q1, q2), common_divisor)
dtype = F.dtype(res)
if _get_device() == 'CPU' and not _check_is_float(dtype):
if not _check_is_float(dtype):
# F.absolute only supports float
res = F.cast(res, mstype.float32)
return F.absolute(res).astype(dtype)
@ -2222,7 +2220,9 @@ def convolve(a, v, mode='full'):
a = asarray_const(a)
if not isinstance(v, Tensor):
v = asarray_const(v)
if a.size == 0 or v.size == 0:
a_size = F.shape_mul(a.shape)
v_size = F.shape_mul(v.shape)
if a_size == 0 or v_size == 0:
_raise_value_error("Inputs cannot be empty.")
a = _expand(a, 1)
v = _expand(v, 1)
@ -2231,22 +2231,22 @@ def convolve(a, v, mode='full'):
v = v.astype("float32")
if a.ndim != 1 or v.ndim != 1:
_raise_value_error("a and v must be 1-D tensor.")
if a.size < v.size:
if a_size < v_size:
a, v = v, a
v = v[::-1]
if mode not in ('same', 'full', 'valid'):
_raise_value_error("mode must be one of ['full', 'same', 'valid']")
if v.size > 1:
if v_size > 1:
if mode == 'same':
pad_left = _to_tensor(_list_comprehensions(v.size // 2, 0.0, True))
pad_right = _to_tensor(_list_comprehensions(v.size - v.size // 2 - 1, 0.0, True))
a = P.Concat(axis=0)((pad_left, a, pad_right))
pad_left = _to_tensor(_list_comprehensions(v_size // 2, 0.0, True))
pad_right = _to_tensor(_list_comprehensions(v_size - v_size // 2 - 1, 0.0, True))
a = P.Concat(0)((pad_left, a, pad_right))
elif mode == 'full':
pad = _to_tensor(_list_comprehensions(v.size - 1, 0.0, True))
a = P.Concat(axis=0)((pad, a, pad))
pad = _to_tensor(_list_comprehensions(v_size - 1, 0.0, True))
a = P.Concat(0)((pad, a, pad))
a = a.reshape(1, 1, 1, a.size)
v = v.reshape(1, 1, 1, v.size)
_conv = P.Conv2D(out_channel=1, kernel_size=(1, v.size), pad_mode="valid")
_conv = P.Conv2D(1, (1, v.size))
return _conv(a, v).reshape(-1).astype(final_dtype)