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!45910 add function and Tensor clip clamp 

Merge pull request !45910 from emmmmtang/master
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i-robot 2022-11-26 18:35:50 +00:00 committed by Gitee
parent 77bc32c89a d8068b112e
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18 changed files with 309 additions and 138 deletions
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3
.jenkins/check/config/filter_pylint.txt
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@ -17,6 +17,7 @@
"mindspore/mindspore/python/mindspore/common/hook_handle.py" "protected-access"
"mindspore/mindspore/python/mindspore/common/dtype.py" "undefined-all-variable"
"mindspore/mindspore/python/mindspore/context.py" "protected-access"
"mindspore/mindspore/python/mindspore/ops/function/clip_func.py" "redefined-builtin"
"mindspore/mindspore/python/mindspore/ops/operations" "super-init-not-called"
"mindspore/mindspore/python/mindspore/ops/operations/_quant_ops.py" "unused-import"
"mindspore/mindspore/python/mindspore/ops/operations/nn_ops.py" "redefined-builtin"
@ -58,6 +59,7 @@
"mindspore/mindspore/python/mindspore/ops/function/__init__.py" "redefined-builtin"
"mindspore/mindspore/python/mindspore/ops/function/array_func.py" "redefined-builtin"
"mindspore/mindspore/python/mindspore/ops/function/array_func.py" "redefined-outer-name"
"mindspore/mindspore/python/mindspore/ops/function/clip_func.py" "redefined-builtin"
"mindspore/mindspore/python/mindspore/ops/function/math_func.py" "redefined-builtin"
"mindspore/mindspore/python/mindspore/ops/functional.py" "redefined-builtin"
"mindspore/mindspore/python/mindspore/ops/functional.py" "wildcard-import"
@ -65,6 +67,7 @@
"mindspore/mindspore/python/mindspore/_extends/parse/standard_method.py" "redefined-builtin"
"mindspore/mindspore/python/mindspore/_extends/parse/standard_method.py" "protected-access"
"mindspore/mindspore/python/mindspore/_extends/parse/standard_method.py" "len-as-condition"
"mindspore/mindspore/python/mindspore/_extends/parse/standard_method.py" "redefined-outer-name"
"mindspore/mindspore/python/mindspore/common/tensor.py" "redefined-builtin"
"mindspore/mindspore/python/mindspore/ops/function/array_func.py" "redefined-builtin"
"mindspore/mindspore/python/mindspore/ops/operations/array_ops.py" "redefined-builtin"

2
docs/api/api_python/mindspore.ops.rst
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@ -184,6 +184,8 @@ mindspore.ops
mindspore.ops.bitwise_or
mindspore.ops.bitwise_xor
mindspore.ops.ceil
mindspore.ops.clamp
mindspore.ops.clip
mindspore.ops.copysign
mindspore.ops.cos
mindspore.ops.cosh

6
docs/api/api_python/mindspore/Tensor/mindspore.Tensor.clamp.rst Normal file
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@ -0,0 +1,6 @@
mindspore.Tensor.clamp
=======================
.. py:method:: mindspore.Tensor.clamp(min, max)
详情请参考 :func:`mindspore.ops.clamp`。

22
docs/api/api_python/mindspore/Tensor/mindspore.Tensor.clip.rst
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@ -1,24 +1,6 @@
mindspore.Tensor.clip
=====================
.. py:method:: mindspore.Tensor.clip(xmin, xmax, dtype=None)
.. py:method:: mindspore.Tensor.clip(min, max)
裁剪Tensor中的值。
给定一个区间，区间外的值将被裁剪到区间边缘。
例如，如果指定的间隔为 :math:`[0, 1]` ，则小于0的值将变为0，大于1的值将变为1。
.. note::
目前不支持裁剪 `xmin=nan` 或 `xmax=nan` 。
参数：
- **xmin** (Tensor, scalar, None) - 最小值。如果值为None，则不在间隔的下边缘执行裁剪操作。`xmin` 或 `xmax` 只能有一个为None。
- **xmax** (Tensor, scalar, None) - 最大值。如果值为None，则不在间隔的上边缘执行裁剪操作。`xmin` 或 `xmax` 只能有一个为None。如果 `xmin` 或 `xmax` 是Tensor，则三个Tensor将被广播进行shape匹配。
- **dtype** (mindspore.dtype, 可选) - 覆盖输出Tensor的dtype。默认值为None。
返回：
Tensor，含有输入Tensor的元素，其中values < `xmin` 被替换为 `xmin` ，values > `xmax` 被替换为 `xmax` 。
异常：
- **TypeError** - 输入的类型与Tensor不一致。
- **ValueError** - 输入与Tensor的shape不能广播，或者 `xmin` 和 `xmax` 都是 `None` 。
:func:`mindspore.Tensor.clamp` 的别名。

1
docs/api/api_python/mindspore/mindspore.Tensor.rst
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@ -68,6 +68,7 @@ mindspore.Tensor
mindspore.Tensor.cholesky
mindspore.Tensor.cholesky_inverse
mindspore.Tensor.choose
mindspore.Tensor.clamp
mindspore.Tensor.clip
mindspore.Tensor.col2im
mindspore.Tensor.conj

37
docs/api/api_python/ops/mindspore.ops.func_clamp.rst Normal file
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@ -0,0 +1,37 @@
mindspore.ops.clamp
====================
.. py:function:: mindspore.ops.clamp(x, min=None, max=None)
将输入Tensor值裁剪到指定的最小值和最大值之间。
限制 :math:`x` 的范围，其 :math:`x` 的最小值为 `min` ，最大值为 `max` 。
.. math::
out_i= \left\{
\begin{array}{align}
max & \text{ if } x_i\ge max \\
x_i & \text{ if } min \lt x_i \lt max \\
min & \text{ if } x_i \le min \\
\end{array}\right.
.. note::
- `min` 和 `max` 不能同时为None；
- 当 `min` 为None，`max` 不为None时，Tensor中大于 `max` 的元素会变为 `max`；
- 当 `min` 不为None，`max` 为None时，Tensor中小于 `min` 的元素会变为 `min`；
- 当 `min` 大于 `max` 时，Tensor中所有元素的值会被置为 `max`；
- :math:`x` ， `min` 和 `max` 的数据类型需支持隐式类型转换，且不能为布尔型。
参数：
- **x** (Union(Tensor, list[Tensor], tuple[Tensor])) - `clamp` 的输入，类型为Tensor、Tensor的列表或元组。支持任意维度的Tensor。
- **min** (Union(Tensor, float, int)) - 指定最小值。默认值为None。
- **max** (Union(Tensor, float, int)) - 指定最大值。默认值为None。
返回：
Tensor、Tensor的列表或元组，表示裁剪后的Tensor。其shape和数据类型和 `x` 相同。
异常：
- **ValueError** - 如果 `min` 和 `max` 都为None。
- **TypeError** - 如果 `x` 的数据类型不在Tensor、list[Tensor]或tuple[Tensor]中。
- **TypeError** - 如果 `min` 的数据类型不为None、Tensor、float或int。
- **TypeError** - 如果 `max` 的数据类型不为None、Tensor、float或int。

6
docs/api/api_python/ops/mindspore.ops.func_clip.rst Normal file
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@ -0,0 +1,6 @@
mindspore.ops.clip
===================
.. py:function:: mindspore.ops.clip(x, min=None, max=None)
:func:`mindspore.ops.clamp` 的别名。

4
docs/api/api_python/ops/mindspore.ops.func_clip_by_value.rst
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@ -17,8 +17,8 @@
.. note::
- `clip_value_min` 和 `clip_value_max` 不能同时为None；
- 当 `clip_value_min` 为None, `clip_value_max` 不为None时，Tensor中大于 `clip_value_max` 的元素会变为 `clip_value_max`；
- 当 `clip_value_min` 不为None, `clip_value_max` 为None时，Tensor中小于 `clip_value_min` 的元素会变为 `clip_value_min`；
- 当 `clip_value_min` 为None，`clip_value_max` 不为None时，Tensor中大于 `clip_value_max` 的元素会变为 `clip_value_max`；
- 当 `clip_value_min` 不为None，`clip_value_max` 为None时，Tensor中小于 `clip_value_min` 的元素会变为 `clip_value_min`；
- 当 `clip_value_min` 大于 `clip_value_max` 时，Tensor中所有元素的值会被置为 `clip_value_max`；
- :math:`x` ， `clip_value_min` 和 `clip_value_max` 的数据类型需支持隐式类型转换，且不能为布尔型。

1
docs/api/api_python_en/Tensor_list.rst
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@ -74,6 +74,7 @@
mindspore.Tensor.cholesky
mindspore.Tensor.cholesky_inverse
mindspore.Tensor.choose
mindspore.Tensor.clamp
mindspore.Tensor.clip
mindspore.Tensor.col2im
mindspore.Tensor.conj

2
docs/api/api_python_en/mindspore.ops.rst
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@ -185,6 +185,8 @@ Element-by-Element Operations
mindspore.ops.bitwise_or
mindspore.ops.bitwise_xor
mindspore.ops.ceil
mindspore.ops.clip
mindspore.ops.clamp
mindspore.ops.copysign
mindspore.ops.cos
mindspore.ops.cosh

3
mindspore/ccsrc/pipeline/jit/resource.cc
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@ -272,7 +272,8 @@ BuiltInTypeMap &GetMethodMap() {
{"fill", std::string("fill")}, // P.fill()
{"fills", std::string("fills")}, // P.fills
{"ptp", std::string("ptp")}, // P.reduce_max() - P.reduce_min()
{"clip", std::string("clip")}, // P.maximum(P.minimum)
{"clamp", std::string("clamp")}, // clamp()
{"clip", std::string("clamp")}, // clamp()
{"__bool__", std::string("tensor_bool")}, // C.tensor_bool
{"argmax", std::string("argmax")}, // P.Argmax()
{"argmin", std::string("argmin")}, // P.Argmax()

66
mindspore/python/mindspore/_extends/parse/standard_method.py
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@ -1746,64 +1746,18 @@ def ptp(x, axis=None, keepdims=False):
return x.max(axis, keepdims) - x.min(axis, keepdims)
def clip(x, xmin, xmax, dtype=None):
def clamp(x, min=None, max=None):
"""
Clips (limits) the values in an array.
Given an interval, values outside the interval are clipped to the interval edges.
For example, if an interval of :math:`[0, 1]` is specified, values smaller than 0 become 0,
and values larger than 1 become 1.
Note:
Currently, clip with `nan` is not supported.
Args:
x (Tensor): Tensor containing elements to clip.
xmin (Tensor, scalar, None): Minimum value. If None, clipping is not performed
on lower interval edge. Not more than one of `xmin` and `xmax` may be None.
xmax (Tensor, scalar, None): Maximum value. If None, clipping is not performed
on upper interval edge. Not more than one of `xmin` and `xmax` may be None.
If `xmin` or `xmax` are tensors, then the three tensors will be broadcasted
to match their shapes.
dtype (:class:`mindspore.dtype`, optional): defaults to None. Overrides the dtype of the
output Tensor.
Returns:
Tensor, a tensor with the elements of `x`, but where values
< `xmin` are replaced with `xmin`, and those > `xmax` with `xmax`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor
>>> x = Tensor([1, 2, 3, -4, 0, 3, 2, 0]).astype("float32")
>>> output = x.clip(0, 2)
>>> print(output)
[1 2 2 0 0 2 2 0]
Clamps all elements in `x` into the range `[min, max]`.
"""
if xmin is None and xmax is None:
const_utils.raise_value_error("One of max or min must be given.")
is_scalar = False
if xmin is not None:
xmin = const_utils.make_tensor(xmin, x.dtype)
if x.ndim == 0 and xmin.ndim == 0:
x = F.maximum(x.reshape((1,)), xmin).squeeze()
else:
x = F.maximum(x, xmin)
if xmax is not None:
xmax = const_utils.make_tensor(xmax, x.dtype)
if x.ndim == 0 and xmax.ndim == 0:
x = F.minimum(x.reshape((1,)), xmax).squeeze()
else:
x = F.minimum(x, xmax)
if is_scalar:
return x.squeeze()
if dtype is not None:
dtype = check_astype_dtype_const(dtype)
if dtype != x.dtype:
return x.astype(dtype)
return x
return F.clamp(x, min, max)
def clip(x, min=None, max=None):
"""
Clamps all elements in `x` into the range `[min, max]`.
"""
return F.clamp(x, min, max)
def var(x, axis=None, ddof=0, keepdims=False):

68
mindspore/python/mindspore/common/tensor.py
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@ -2286,68 +2286,18 @@ class Tensor(Tensor_):
"""
return tensor_operator_registry.get('minimum')()(self, other)
def clip(self, xmin, xmax, dtype=None):
def clamp(self, min=None, max=None):
r"""
For details, please refer to :func:`mindspore.ops.clamp`.
"""
Clips (limits) the values in a Tensor.
self._init_check()
return tensor_operator_registry.get('clamp')(self, min, max)
Given an interval, values outside the interval are clipped to the interval edges.
For example, if an interval of :math:`[0, 1]` is specified, values smaller than 0 become 0,
and values larger than 1 become 1.
Note:
Currently, clip with `xmin=nan` or `xmax=nan` is not supported.
Args:
xmin (Tensor, scalar, None): Minimum value. If None, clipping is not performed
on the lower interval edge. Not more than one of `xmin` and `xmax` may be None.
xmax (Tensor, scalar, None): Maximum value. If None, clipping is not performed
on the upper interval edge. Not more than one of `xmin` and `xmax` may be None.
If `xmin` or `xmax` are tensors, then `xmin`, `xmax` and the given tensor
will be broadcasted to match their shapes.
dtype (:class:`mindspore.dtype`, optional): Overrides the dtype of the
output Tensor. Default is None.
Returns:
Tensor, a tensor with the elements of the input tensor, but where values
< `xmin` are replaced with `xmin`, and those > `xmax` with `xmax`.
Raises:
TypeError: If inputs have types not specified above.
ValueError: If the shapes of `x1` and `x2` cannot broadcast, or both `xmin` and `xmax` are `None`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor
>>> x = Tensor([1, 2, 3, -4, 0, 3, 2, 0]).astype("float32")
>>> y = x.clip(0, 2)
>>> print(y)
[1. 2. 2. 0. 0. 2. 2. 0.]
>>> t = Tensor([1, 1, 1, 1, 1, 1, 1, 1])
>>> y = x.clip(t, 2)
>>> print(y)
[1. 2. 2. 1. 1. 2. 2. 1.]
def clip(self, min=None, max=None):
r"""
Alias for :func:`mindspore.Tensor.clamp`.
"""
if xmin is None and xmax is None:
raise ValueError("For 'Tensor.clip', the argument 'xmin' and 'xman' cannot all be None.")
x = self
# F.maximum/minimum does not support when both operands are scalar
if xmin is not None:
xmin = Tensor(xmin).astype(x.dtype)
if x.ndim == 0 and xmin.ndim == 0:
x = tensor_operator_registry.get("maximum")(x.reshape((1,)), xmin).squeeze()
else:
x = tensor_operator_registry.get("maximum")(x, xmin)
if xmax is not None:
xmax = Tensor(xmax).astype(x.dtype)
if x.ndim == 0 and xmax.ndim == 0:
x = tensor_operator_registry.get("minimum")()(x.reshape((1,)), xmax).squeeze()
else:
x = tensor_operator_registry.get("minimum")()(x, xmax)
if dtype is not None and dtype != x.dtype:
return x.astype(dtype)
return x
return self.clamp(min, max)
def _init_check(self):
if self.has_init:

2
mindspore/python/mindspore/ops/function/__init__.py
View File

@ -542,6 +542,8 @@ from .sparse_unary_func import (
)
from .clip_func import (
clip_by_value,
clamp,
clip,
)
__all__ = []

80
mindspore/python/mindspore/ops/function/clip_func.py
View File

@ -22,6 +22,8 @@ from mindspore.common.tensor import Tensor
__all__ = [
'clip_by_value',
'clamp',
'clip',
]
hyper_map = C.HyperMap()
@ -81,6 +83,7 @@ def clip_by_value(x, clip_value_min=None, clip_value_max=None):
Examples:
>>> # case 1: the data type of x is Tensor
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> import numpy as np
>>> min_value = Tensor(5, mindspore.float32)
@ -131,3 +134,80 @@ def clip_by_value(x, clip_value_min=None, clip_value_max=None):
if isinstance(x, tuple):
results = tuple(results)
return results
def clamp(x, min=None, max=None):
r"""
Clamps tensor values to a specified min and max.
Limits the value of :math:`x` to a range, whose lower limit is `min` and upper limit is `max` .
.. math::
out_i= \left\{
\begin{array}{align}
max & \text{ if } x_i\ge max \\
x_i & \text{ if } min \lt x_i \lt max \\
min & \text{ if } x_i \le min \\
\end{array}\right.
Note:
- `min` and `max` cannot be None at the same time;
- When `min` is None and `max` is not None, the elements in Tensor larger than `max` will become `max`;
- When `min` is not None and `max` is None, the elements in Tensor smaller than `min` will become `min`;
- If `min` is greater than `max`, the value of all elements in Tensor will be set to `max`;
- The data type of `x`, `min` and `max` should support implicit type conversion and cannot be bool type.
Args:
x (Union(Tensor, list[Tensor], tuple[Tensor])): Input data, which type is Tensor or a list or tuple of Tensor.
The shape of Tensor is :math:`(N,*)` where :math:`*` means,
any number of additional dimensions.
min (Union(Tensor, float, int)): The minimum value. Default: None.
max (Union(Tensor, float, int)): The maximum value. Default: None.
Returns:
(Union(Tensor, tuple[Tensor], list[Tensor])), a clipped Tensor or a tuple or a list of clipped Tensor.
The data type and shape are the same as x.
Raises:
ValueError: If both `min` and `max` are None.
TypeError: If the type of `x` is not in Tensor or list[Tensor] or tuple[Tensor].
TypeError: If the type of `min` is not in None, Tensor, float or int.
TypeError: If the type of `max` is not in None, Tensor, float or int.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> # case 1: the data type of x is Tensor
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> import numpy as np
>>> min_value = Tensor(5, mindspore.float32)
>>> max_value = Tensor(20, mindspore.float32)
>>> x = Tensor(np.array([[1., 25., 5., 7.], [4., 11., 6., 21.]]), mindspore.float32)
>>> output = ops.clamp(x, min_value, max_value)
>>> print(output)
[[ 5. 20. 5. 7.]
[ 5. 11. 6. 20.]]
>>> # case 2: the data type of x is list[Tensor]
>>> min_value = 5
>>> max_value = 20
>>> x = Tensor(np.array([[1., 25., 5., 7.], [4., 11., 6., 21.]]), mindspore.float32)
>>> y = Tensor(np.array([[1., 25., 5., 7.], [4., 11., 6., 21.]]), mindspore.float32)
>>> output = ops.clamp([x,y], min_value, max_value)
>>> print(output)
[[[ 5. 20. 5. 7.]
[ 5. 11. 6. 20.]],
[[ 5. 20. 5. 7.]
[ 5. 11. 6. 20.]]]
"""
return clip_by_value(x, min, max)
def clip(x, min=None, max=None):
"""
Alias for ops.clamp.
For details, please refer to :func:`mindspore.ops.clamp`.
"""
return clamp(x, min, max)

1
mindspore/python/mindspore/ops/functional.py
View File

@ -238,6 +238,7 @@ tensor_operator_registry.register('erfinv', erfinv)
tensor_operator_registry.register('less_equal', less_equal)
tensor_operator_registry.register('lcm', lcm)
tensor_operator_registry.register('ldexp', ldexp)
tensor_operator_registry.register('clamp', clamp)
tensor_operator_registry.register('fold', fold)
tensor_operator_registry.register('unfold', unfold)
tensor_operator_registry.register('index_add', index_add)

60
tests/st/ops/test_ops_clip_func.py
View File

@ -24,6 +24,16 @@ class NetWorkClipByValue(nn.Cell):
return ops.clip_by_value(x, min_value, max_value)
class NetWorkClamp(nn.Cell):
def construct(self, x, min_value, max_value):
return ops.clamp(x, min_value, max_value)
class NetWorkClip(nn.Cell):
def construct(self, x, min_value, max_value):
return ops.clip(x, min_value, max_value)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_cpu
@ -73,3 +83,53 @@ def test_clip_by_value_list_tensor(mode):
assert np.allclose(output[0].asnumpy(), expect_output[0])
assert np.allclose(output[1].asnumpy(), expect_output[1])
assert np.allclose(output[2].asnumpy(), expect_output[2])
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_cpu
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
def test_ops_clamp(mode):
"""
Feature: ops.clamp
Description: Verify the result of clamp
Expectation: success
"""
ms.set_context(mode=mode)
x = Tensor(np.array([-0.5962, 0.4985, 0.2349, -0.4396, 0.4525]), ms.float32)
net = NetWorkClamp()
output_case_1 = net(x, -0.3, 0.4)
expect_output_case_1 = [-0.3, 0.4, 0.2349, -0.3, 0.4]
output_case_2 = net(x, 0.4, -0.3)
expect_output_case_2 = [-0.3, -0.3, -0.3, -0.3, -0.3]
assert np.allclose(output_case_1.asnumpy(), expect_output_case_1)
assert np.allclose(output_case_2.asnumpy(), expect_output_case_2)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_cpu
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
def test_ops_clip(mode):
"""
Feature: ops.clip
Description: Verify the result of clip
Expectation: success
"""
ms.set_context(mode=mode)
x = Tensor(np.array([-0.5962, 0.4985, 0.2349, -0.4396, 0.4525]), ms.float32)
net = NetWorkClip()
output_case_1 = net(x, -0.3, 0.4)
expect_output_case_1 = [-0.3, 0.4, 0.2349, -0.3, 0.4]
output_case_2 = net(x, 0.4, -0.3)
expect_output_case_2 = [-0.3, -0.3, -0.3, -0.3, -0.3]
assert np.allclose(output_case_1.asnumpy(), expect_output_case_1)
assert np.allclose(output_case_2.asnumpy(), expect_output_case_2)

83
tests/st/tensor/test_clamp.py Normal file
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@ -0,0 +1,83 @@
# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import numpy as np
import pytest
import mindspore as ms
import mindspore.nn as nn
from mindspore import Tensor
class ClampNet(nn.Cell):
def construct(self, x, min_value, max_value):
return x.clamp(min_value, max_value)
class ClipNet(nn.Cell):
def construct(self, x, min_value, max_value):
return x.clip(min_value, max_value)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_cpu
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
def test_clamp(mode):
"""
Feature: test Tensor.clamp
Description: Verify the result of Tensor.clamp
Expectation: expect correct forward result
"""
ms.set_context(mode=mode)
x_np = np.array([[1., 25., 5., 7.], [4., 11., 6., 21.]]).astype(np.float32)
x = Tensor(x_np, ms.float32)
net = ClampNet()
output_ms_case_1 = net(x, 5, 20)
expect_output_case_1 = np.clip(x_np, 5, 20)
output_ms_case_2 = net(x, 20, 5)
expect_output_case_2 = np.clip(x_np, 20, 5)
assert np.allclose(output_ms_case_1.asnumpy(), expect_output_case_1)
assert np.allclose(output_ms_case_2.asnumpy(), expect_output_case_2)
@pytest.mark.level0
@pytest.mark.platform_x86_cpu
@pytest.mark.platform_arm_cpu
@pytest.mark.platform_x86_gpu_training
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
def test_clip(mode):
"""
Feature: test Tensor.clip
Description: Verify the result of Tensor.clip
Expectation: expect correct forward result
"""
ms.set_context(mode=mode)
x_np = np.array([[1., 25., 5., 7.], [4., 11., 6., 21.]]).astype(np.float32)
x = Tensor(x_np, ms.float32)
net = ClipNet()
output_ms_case_1 = net(x, 5, 20)
expect_output_case_1 = np.clip(x_np, 5, 20)
output_ms_case_2 = net(x, 20, 5)
expect_output_case_2 = np.clip(x_np, 20, 5)
assert np.allclose(output_ms_case_1.asnumpy(), expect_output_case_1)
assert np.allclose(output_ms_case_2.asnumpy(), expect_output_case_2)