!6607 delete SoftmaxCrossEntropyExpand interface

Merge pull request !6607 from ghzl/delete-SoftmaxCrossEntropyExpand

mindspore/nn/loss/__init__.py

@@ -20,9 +20,8 @@ It shows how well the model works on a dataset and the optimization target which
 """
 
 from .loss import L1Loss, MSELoss, SmoothL1Loss, \
-    SoftmaxCrossEntropyWithLogits, SoftmaxCrossEntropyExpand, CosineEmbeddingLoss
+    SoftmaxCrossEntropyWithLogits, CosineEmbeddingLoss
 
 __all__ = ['L1Loss', 'MSELoss', 'SmoothL1Loss',
            'SoftmaxCrossEntropyWithLogits',
-           'SoftmaxCrossEntropyExpand',
            'CosineEmbeddingLoss']

mindspore/nn/loss/loss.py

@@ -262,70 +262,6 @@ class SoftmaxCrossEntropyWithLogits(_Loss):
         return self.get_loss(x)
 
 
-class SoftmaxCrossEntropyExpand(Cell):
-    r"""
-    Computes softmax cross entropy between logits and labels. Implemented by expanded formula.
-
-    This is a wrapper of several functions.
-
-    .. math::
-        \ell(x_i, t_i) = -log\left(\frac{\exp(x_{t_i})}{\sum_j \exp(x_j)}\right),
-    where :math:`x_i` is a 1D score Tensor, :math:`t_i` is the target class.
-
-    Note:
-        When argument sparse is set to True, the format of the label is the index
-        ranging from :math:`0` to :math:`C - 1` instead of one-hot vectors.
-
-    Args:
-        sparse(bool): Specifies whether labels use sparse format or not. Default: False.
-
-    Inputs:
-        - **input_data** (Tensor) - Tensor of shape :math:`(x_1, x_2, ..., x_R)`.
-        - **label** (Tensor) - Tensor of shape :math:`(y_1, y_2, ..., y_S)`.
-
-    Outputs:
-        Tensor, a scalar tensor including the mean loss.
-
-    Examples:
-        >>> loss = nn.SoftmaxCrossEntropyExpand(sparse=True)
-        >>> input_data = Tensor(np.ones([64, 512]), dtype=mindspore.float32)
-        >>> label = Tensor(np.ones([64]), dtype=mindspore.int32)
-        >>> loss(input_data, label)
-    """
-    def __init__(self, sparse=False):
-        super(SoftmaxCrossEntropyExpand, self).__init__()
-        self.exp = P.Exp()
-        self.reduce_sum = P.ReduceSum(keep_dims=True)
-        self.onehot = P.OneHot()
-        self.on_value = Tensor(1.0, mstype.float32)
-        self.off_value = Tensor(0.0, mstype.float32)
-        self.div = P.Div()
-        self.log = P.Log()
-        self.sum_cross_entropy = P.ReduceSum(keep_dims=False)
-        self.mul = P.Mul()
-        self.mul2 = P.Mul()
-        self.cast = P.Cast()
-        self.reduce_mean = P.ReduceMean(keep_dims=False)
-        self.sparse = sparse
-        self.reduce_max = P.ReduceMax(keep_dims=True)
-        self.sub = P.Sub()
-
-    def construct(self, logit, label):
-        logit_max = self.reduce_max(logit, -1)
-        exp = self.exp(self.sub(logit, logit_max))
-        exp_sum = self.reduce_sum(exp, -1)
-        softmax_result = self.div(exp, exp_sum)
-        if self.sparse:
-            label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
-
-        softmax_result_log = self.log(softmax_result)
-        loss = self.sum_cross_entropy((self.mul(softmax_result_log, label)), -1)
-        loss = self.mul2(F.scalar_to_array(-1.0), loss)
-        loss = self.reduce_mean(loss, -1)
-
-        return loss
-
-
 @constexpr
 def _check_reduced_shape_valid(ori_shape, reduced_shape, axis, cls_name):
     validator.check_reduce_shape(ori_shape, reduced_shape, axis, cls_name)

tests/ut/python/nn/test_loss.py

@@ -17,7 +17,6 @@ import numpy as np
 
 import mindspore.nn as nn
 from mindspore import Tensor
-from mindspore.common.api import _executor
 from ..ut_filter import non_graph_engine
 
 
@@ -54,15 +53,6 @@ def test_SoftmaxCrossEntropyWithLogits_reduce():
     loss(logits, labels)
 
 
-def test_SoftmaxCrossEntropyExpand():
-    from mindspore import context
-    context.set_context(mode=context.GRAPH_MODE)
-    loss = nn.SoftmaxCrossEntropyExpand()
-
-    logits = Tensor(np.random.randint(0, 9, [100, 10]).astype(np.float32))
-    labels = Tensor(np.random.randint(0, 9, [10,]).astype(np.float32))
-    _executor.compile(loss, logits, labels)
-
 def test_cosine_embedding_loss():
     """ test CosineEmbeddingLoss """
     loss = nn.CosineEmbeddingLoss()

tests/ut/python/parallel/test_softmax_cross_entropy_expand.py

@@ -1,31 +0,0 @@
-# Copyright 2019 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
-
-from mindspore import Tensor
-from mindspore import context
-from mindspore.common import dtype as mstype
-from mindspore.common.api import _executor
-from mindspore.nn.loss.loss import SoftmaxCrossEntropyExpand
-
-
-def test_SoftmaxCrossEntropy():
-    net = SoftmaxCrossEntropyExpand(sparse=True)
-    context.set_auto_parallel_context(parallel_mode="auto_parallel")
-    logit = Tensor(np.ones([64, 512]), dtype=mstype.float32)
-    label = Tensor(np.ones([64]), dtype=mstype.int32)
-    context.set_auto_parallel_context(device_num=8, global_rank=0)
-    net.set_auto_parallel()
-    _executor.compile(net, logit, label)