!45200 add marginrankingloss ops
Merge pull request !45200 from 冯一航/add_marginrankingloss_ops
This commit is contained in:
commit
572bdc1158
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@ -36,6 +36,7 @@ mindspore.ops.function
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mindspore.ops.lp_pool1d
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mindspore.ops.lp_pool2d
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mindspore.ops.lrn
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mindspore.ops.margin_ranking_loss
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mindspore.ops.max_pool3d
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mindspore.ops.max_unpool1d
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mindspore.ops.max_unpool2d
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@ -3,4 +3,6 @@ mindspore.Tensor.arcsinh
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.. py:method:: mindspore.Tensor.arcsinh()
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参考 `Tensor.asinh() <https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/Tensor/mindspore.Tensor.asinh.html>`_。
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Tensor.asinh()的别名。
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详情请参考 :func:`mindspore.ops.asinh`。
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@ -3,4 +3,6 @@ mindspore.Tensor.arctanh
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.. py:method:: mindspore.Tensor.arctanh()
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参考 `Tensor.atanh() <https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/Tensor/mindspore.Tensor.atanh.html>`_。
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Tensor.atanh()的别名。
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详情请参考 :func:`mindspore.ops.atanh`。
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@ -0,0 +1,6 @@
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mindspore.ops.margin_ranking_loss
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==================================
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.. py:function:: mindspore.ops.margin_ranking_loss(input1, input2, target, margin=0.0, reduction='mean')
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详情请参考 :class:`mindspore.nn.MarginRankingLoss`。
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@ -36,6 +36,7 @@ Neural Network
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mindspore.ops.lp_pool1d
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mindspore.ops.lp_pool2d
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mindspore.ops.lrn
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mindspore.ops.margin_ranking_loss
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mindspore.ops.max_pool3d
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mindspore.ops.max_unpool1d
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mindspore.ops.max_unpool2d
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@ -3651,8 +3651,8 @@ class Tensor(Tensor_):
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def arcsinh(self):
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r"""
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See `Tensor.asinh()
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<https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/Tensor/mindspore.Tensor.asinh.html>`_.
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Alias for Tensor.asinh().
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For details, please refer to :func:`mindspore.ops.asinh`.
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"""
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self._init_check()
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return tensor_operator_registry.get('asinh')(self)
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@ -3673,8 +3673,8 @@ class Tensor(Tensor_):
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def arctanh(self):
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r"""
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See `Tensor.atanh()
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<https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/Tensor/mindspore.Tensor.atanh.html>`_.
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Alias for Tensor.atanh().
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For details, please refer to :func:`mindspore.ops.atanh`.
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"""
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self._init_check()
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return tensor_operator_registry.get('atanh')(self)
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@ -525,19 +525,12 @@ class MarginRankingLoss(LossBase):
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def __init__(self, margin=0.0, reduction='mean'):
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"""Initialize MarginRankingLoss."""
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super(MarginRankingLoss, self).__init__(reduction)
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self.margin = validator.check_value_type("margin", margin, [float], self.cls_name)
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self.reduction = reduction
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self.margin = margin
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self.maximum = P.Maximum()
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def construct(self, input1, input2, target):
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_check_is_tensor('input1', input1, self.cls_name)
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_check_is_tensor('input2', input2, self.cls_name)
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_check_is_tensor('target', target, self.cls_name)
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F.same_type_shape(input1, input2)
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F.same_type_shape(target, input1)
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x = self.maximum(0, -target * (input1 - input2) + self.margin)
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return self.get_loss(x)
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x = ops.margin_ranking_loss(input1, input2, target, self.margin, self.reduction)
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return x
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class SmoothL1Loss(LossBase):
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@ -328,6 +328,7 @@ from .nn_func import (
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log_softmax,
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lrn,
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mish,
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margin_ranking_loss,
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max_unpool1d,
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max_unpool2d,
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max_unpool3d,
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@ -2645,6 +2645,66 @@ def mish(x):
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return mish_(x)
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@constexpr
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def _check_value_type(arg_name, arg_value, valid_types, prim_name=None):
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"""Checks whether a value is instance of some types."""
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return validator.check_value_type(arg_name, arg_value, valid_types, prim_name)
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@constexpr(check=False)
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def _check_is_tensor(param_name, input_data, cls_name):
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"""Internal function, used to check whether the input data is Tensor."""
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if input_data is not None and not isinstance(ops.typeof(input_data), mstype.tensor_type):
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raise TypeError(f"For '{cls_name}', the '{param_name}' must be '{mstype.tensor_type}', "
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f"but got '{ops.typeof(input_data)}'")
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def _get_axis(x):
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"""Get a range of axis for input."""
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shape = ops.shape(x)
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length = ops.tuple_len(shape)
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perm = ops.make_range(0, length)
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return perm
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def _get_loss(x, reduction, cls_name, weights=1.0):
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"""Calculate the loss with reduction and weights."""
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if reduction not in ('mean', 'sum', 'none'):
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raise ValueError(f"For '{cls_name}', the 'reduction' must be in ['mean', 'sum', 'none'], "
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f"but got {reduction}.")
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reduce_mean = P.ReduceMean()
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reduce_sum = P.ReduceSum()
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mul = P.Mul()
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cast = P.Cast()
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input_dtype = x.dtype
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x = cast(x, mstype.float32)
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weights = cast(weights, mstype.float32)
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x = mul(weights, x)
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if reduction == 'mean':
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x = reduce_mean(x, _get_axis(x))
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if reduction == 'sum':
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x = reduce_sum(x, _get_axis(x))
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x = cast(x, input_dtype)
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return x
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def margin_ranking_loss(input1, input2, target, margin=0.0, reduction='mean'):
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"""
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For details, please refer to :class:`mindspore.nn.MarginRankingLoss`.
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"""
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margin = _check_value_type("margin", margin, [float], "margin_ranking_loss")
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_check_is_tensor('input1', input1, "margin_ranking_loss")
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_check_is_tensor('input2', input2, "margin_ranking_loss")
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_check_is_tensor('target', target, "margin_ranking_loss")
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maximum = P.Maximum()
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ops.same_type_shape(input1, input2)
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ops.same_type_shape(target, input1)
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x = maximum(0, -target * (input1 - input2) + margin)
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return _get_loss(x, reduction, "margin_ranking_loss")
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def max_pool3d(x, kernel_size, stride=None, padding=0, dilation=1, ceil_mode=False, return_indices=False):
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r"""
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Performs a 3D max pooling on the input Tensor.
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@ -4325,6 +4385,7 @@ __all__ = [
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'relu6',
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'conv3d',
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'glu',
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'margin_ranking_loss',
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'multi_margin_loss',
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'multi_label_margin_loss',
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'elu',
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@ -24,7 +24,7 @@ from mindspore import Tensor
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class MarginRankingLoss(nn.Cell):
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def __init__(self, reduction="none"):
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super(MarginRankingLoss, self).__init__()
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self.margin_ranking_loss = nn.MarginRankingLoss(reduction=reduction)
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self.margin_ranking_loss = nn.MarginRankingLoss(margin=0.0, reduction=reduction)
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def construct(self, x, y, label):
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return self.margin_ranking_loss(x, y, label)
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@ -43,60 +43,24 @@ target = Tensor(np.array([-1, -1, 1]), ms.float32)
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
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def test_margin_ranking_loss_none(mode):
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@pytest.mark.parametrize('reduction', ["none", "mean", "sum"])
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def test_margin_ranking_loss(mode, reduction):
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"""
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Feature: test MarginRankingLoss op with reduction none.
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Description: Verify the result of MarginRankingLoss.
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Expectation: expect correct forward result.
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"""
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ms.set_context(mode=mode)
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loss = MarginRankingLoss('none')
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loss = MarginRankingLoss(reduction=reduction)
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output = loss(input1, input2, target)
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if reduction == 'none':
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expect_output = np.array([0.98759997, 0., 2.7003999])
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assert np.allclose(output.asnumpy(), expect_output)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
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def test_margin_ranking_loss_sum(mode):
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"""
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Feature: test MarginRankingLoss op with reduction sum.
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Description: Verify the result of MarginRankingLoss.
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Expectation: expect correct forward result.
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"""
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ms.set_context(mode=mode)
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loss = MarginRankingLoss('sum')
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output = loss(input1, input2, target)
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elif reduction == 'sum':
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expect_output = np.array(3.6879997)
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assert np.allclose(output.asnumpy(), expect_output)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
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def test_margin_ranking_loss_mean(mode):
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"""
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Feature: test MarginRankingLoss op with reduction mean.
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Description: Verify the result of MarginRankingLoss.
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Expectation: expect correct forward result.
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"""
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ms.set_context(mode=mode)
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loss = MarginRankingLoss('mean')
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output = loss(input1, input2, target)
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else:
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expect_output = np.array(1.2293333)
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assert np.allclose(output.asnumpy(), expect_output)
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assert np.allclose(output.asnumpy(), expect_output)
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@pytest.mark.level0
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@ -113,6 +77,7 @@ def test_tensor_dim(mode):
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Description: Verify the result of dim.
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Expectation: expect correct forward result.
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"""
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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@ -0,0 +1,62 @@
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# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore as ms
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import mindspore.nn as nn
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import mindspore.ops as ops
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from mindspore import Tensor
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class MarginRankingLoss(nn.Cell):
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def __init__(self, reduction):
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super(MarginRankingLoss, self).__init__()
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self.reduction = reduction
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def construct(self, x, y, label, margin):
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return ops.margin_ranking_loss(x, y, label, margin=margin, reduction=self.reduction)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
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@pytest.mark.parametrize('reduction', ["none", "mean", "sum"])
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def test_margin_ranking_loss(mode, reduction):
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"""
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Feature: test MarginRankingLoss op.
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Description: Verify the result of MarginRankingLoss.
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Expectation: expect correct forward result.
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"""
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ms.set_context(mode=mode)
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loss = MarginRankingLoss(reduction)
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input1 = Tensor(np.array([0.3864, -2.4093, -1.4076]), ms.float32)
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input2 = Tensor(np.array([-0.6012, -1.6681, 1.2928]), ms.float32)
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target = Tensor(np.array([-1, -1, 1]), ms.float32)
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output = loss(input1, input2, target, 0.0)
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if reduction == 'none':
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expect_output = np.array([0.98759997, 0., 2.7003999])
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elif reduction == 'sum':
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expect_output = np.array(3.6879997)
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else:
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expect_output = np.array(1.2293333)
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assert np.allclose(output.asnumpy(), expect_output)
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@ -0,0 +1,45 @@
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# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore as ms
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import mindspore.nn as nn
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import mindspore.ops as ops
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from mindspore import Tensor
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class MarginRankingLoss(nn.Cell):
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def __init__(self, reduction):
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super(MarginRankingLoss, self).__init__()
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self.reduction = reduction
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def construct(self, x, y, label, margin):
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return ops.margin_ranking_loss(x, y, label, margin, reduction=self.reduction)
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@pytest.mark.parametrize('reduction', ["none", "mean", "sum"])
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def test_margin_ranking_loss(reduction):
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"""
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Feature: test MarginRankingLoss op with reduction none.
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Description: Verify the result of MarginRankingLoss.
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Expectation: expect correct forward result.
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"""
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loss = MarginRankingLoss(reduction)
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input1 = Tensor(np.array([0.3864, -2.4093, -1.4076]), ms.float32)
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input2 = Tensor(np.array([-0.6012, -1.6681, 1.2928]), ms.float32)
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target = Tensor(np.array([-1, -1, 1]), ms.float32)
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loss(input1, input2, target, 0.0)
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