move Metrics from mindspore.nn to mindspore.train
This commit is contained in:
lvyufeng
parent
2b2edc30f4
commit
208147d55b
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@ -263,42 +263,6 @@ Dropout层
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mindspore.nn.SGD
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mindspore.nn.thor
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评价指标
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--------
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.. mscnplatformautosummary::
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:toctree: nn
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:nosignatures:
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:template: classtemplate.rst
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mindspore.nn.Accuracy
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mindspore.nn.auc
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mindspore.nn.BleuScore
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mindspore.nn.ConfusionMatrix
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mindspore.nn.ConfusionMatrixMetric
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mindspore.nn.CosineSimilarity
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mindspore.nn.Dice
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mindspore.nn.F1
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mindspore.nn.Fbeta
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mindspore.nn.HausdorffDistance
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mindspore.nn.get_metric_fn
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mindspore.nn.Loss
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mindspore.nn.MAE
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mindspore.nn.MeanSurfaceDistance
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mindspore.nn.Metric
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mindspore.nn.MSE
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mindspore.nn.names
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mindspore.nn.OcclusionSensitivity
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mindspore.nn.Perplexity
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mindspore.nn.Precision
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mindspore.nn.Recall
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mindspore.nn.ROC
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mindspore.nn.RootMeanSquareDistance
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mindspore.nn.rearrange_inputs
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mindspore.nn.Top1CategoricalAccuracy
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mindspore.nn.Top5CategoricalAccuracy
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mindspore.nn.TopKCategoricalAccuracy
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动态学习率
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-----------
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@ -26,3 +26,48 @@ mindspore.train
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mindspore.train.ReduceLROnPlateau
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mindspore.train.RunContext
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mindspore.train.TimeMonitor
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评价指标
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--------
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.. mscnplatformautosummary::
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:toctree: mindspore
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:nosignatures:
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:template: classtemplate.rst
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mindspore.train.Accuracy
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mindspore.train.BleuScore
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mindspore.train.ConfusionMatrix
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mindspore.train.ConfusionMatrixMetric
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mindspore.train.CosineSimilarity
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mindspore.train.Dice
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mindspore.train.F1
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mindspore.train.Fbeta
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mindspore.train.HausdorffDistance
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mindspore.train.Loss
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mindspore.train.MAE
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mindspore.train.MeanSurfaceDistance
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mindspore.train.Metric
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mindspore.train.MSE
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mindspore.train.OcclusionSensitivity
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mindspore.train.Perplexity
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mindspore.train.Precision
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mindspore.train.Recall
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mindspore.train.ROC
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mindspore.train.RootMeanSquareDistance
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mindspore.train.Top1CategoricalAccuracy
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mindspore.train.Top5CategoricalAccuracy
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mindspore.train.TopKCategoricalAccuracy
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工具
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----
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.. mscnplatformautosummary::
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:toctree: mindspore
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:nosignatures:
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:template: classtemplate.rst
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mindspore.train.auc
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mindspore.train.get_metric_fn
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mindspore.train.names
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mindspore.train.rearrange_inputs
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@ -27,7 +27,7 @@ mindspore.SummaryLandscape
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- mindspore.Model：用户的模型。
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- mindspore.nn.Cell：用户的网络。
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- mindspore.dataset：创建loss所需要的用户数据集。
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- mindspore.nn.Metrics：用户的评估指标。
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- mindspore.train.Metrics：用户的评估指标。
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- **collect_landscape** (Union[dict, None]) - 创建loss地形图所用的参数含义与SummaryCollector同名字段一致。此处设置的目的是允许用户可以自由修改创建loss地形图参数。默认值：None。
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@ -1,7 +1,7 @@
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mindspore.nn.Accuracy
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=====================
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mindspore.train.Accuracy
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=========================
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.. py:class:: mindspore.nn.Accuracy(eval_type='classification')
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.. py:class:: mindspore.train.Accuracy(eval_type='classification')
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计算数据分类的正确率，包括二分类和多分类。
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@ -1,7 +1,7 @@
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mindspore.nn.BleuScore
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======================
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mindspore.train.BleuScore
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==========================
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.. py:class:: mindspore.nn.BleuScore(n_gram=4, smooth=False)
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.. py:class:: mindspore.train.BleuScore(n_gram=4, smooth=False)
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计算BLEU分数。BLEU指的是具有一个或多个引用的机器翻译文本的metric。
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@ -1,11 +1,11 @@
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mindspore.nn.ConfusionMatrix
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============================
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mindspore.train.ConfusionMatrix
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================================
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.. py:class:: mindspore.nn.ConfusionMatrix(num_classes, normalize='no_norm', threshold=0.5)
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.. py:class:: mindspore.train.ConfusionMatrix(num_classes, normalize='no_norm', threshold=0.5)
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计算混淆矩阵(confusion matrix)，通常用于评估分类模型的性能，包括二分类和多分类场景。
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如果只想使用混淆矩阵，请使用该类。如果想计算"PPV"、"TPR"、"TNR"等，请使用'mindspore.nn.ConfusionMatrixMetric'类。
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如果只想使用混淆矩阵，请使用该类。如果想计算"PPV"、"TPR"、"TNR"等，请使用'mindspore.train.ConfusionMatrixMetric'类。
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参数：
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- **num_classes** (int) - 数据集中的类别数量。
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@ -1,7 +1,7 @@
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mindspore.nn.ConfusionMatrixMetric
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==================================
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mindspore.train.ConfusionMatrixMetric
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======================================
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.. py:class:: mindspore.nn.ConfusionMatrixMetric(skip_channel=True, metric_name='sensitivity', calculation_method=False, decrease='mean')
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.. py:class:: mindspore.train.ConfusionMatrixMetric(skip_channel=True, metric_name='sensitivity', calculation_method=False, decrease='mean')
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计算与混淆矩阵相关的度量。
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@ -9,7 +9,7 @@ mindspore.nn.ConfusionMatrixMetric
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此函数支持计算参数metric_name中描述中列出的所有度量名称。
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如果要使用混淆矩阵计算，如"PPV"、"TPR"、"TNR"，请使用此类。
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如果只想计算混淆矩阵，请使用'mindspore.nn.ConfusionMatrix'。
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如果只想计算混淆矩阵，请使用'mindspore.train.ConfusionMatrix'。
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参数：
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- **skip_channel** (bool) - 是否跳过预测输出的第一个通道的度量计算。默认值：True。
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@ -1,7 +1,7 @@
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mindspore.nn.Dice
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==================
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mindspore.train.Dice
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=====================
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.. py:class:: mindspore.nn.Dice(smooth=1e-5)
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.. py:class:: mindspore.train.Dice(smooth=1e-5)
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集合相似性度量。
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@ -1,10 +1,10 @@
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mindspore.nn.F1
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mindspore.train.F1
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=====================
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.. py:class:: mindspore.nn.F1
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.. py:class:: mindspore.train.F1
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计算F1 score。F1是Fbeta的特殊情况，即beta为1。
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有关更多详细信息，请参阅类 :class:`mindspore.nn.Fbeta`。
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有关更多详细信息，请参阅类 :class:`mindspore.train.Fbeta`。
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.. math::
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F_1=\frac{2\cdot true\_positive}{2\cdot true\_positive + false\_negative + false\_positive}
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@ -1,7 +1,7 @@
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mindspore.nn.Fbeta
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==================
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mindspore.train.Fbeta
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======================
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.. py:class:: mindspore.nn.Fbeta(beta)
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.. py:class:: mindspore.train.Fbeta(beta)
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计算Fbeta评分。
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@ -1,7 +1,7 @@
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mindspore.nn.HausdorffDistance
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mindspore.train.HausdorffDistance
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============================================
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.. py:class:: mindspore.nn.HausdorffDistance(distance_metric='euclidean', percentile=None, directed=False, crop=True)
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.. py:class:: mindspore.train.HausdorffDistance(distance_metric='euclidean', percentile=None, directed=False, crop=True)
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计算Hausdorff距离。Hausdorff距离是两个点集之间两点的最小距离的最大值，度量了两个点集间的最大不匹配程度。
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@ -1,7 +1,7 @@
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mindspore.nn.Loss
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=================
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mindspore.train.Loss
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====================
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.. py:class:: mindspore.nn.Loss
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.. py:class:: mindspore.train.Loss
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计算loss的平均值。如果每 :math:`n` 次迭代调用一次 `update` 方法，则计算结果为：
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@ -1,7 +1,7 @@
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mindspore.nn.MAE
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================
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mindspore.train.MAE
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====================
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.. py:class:: mindspore.nn.MAE
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.. py:class:: mindspore.train.MAE
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计算平均绝对误差MAE（Mean Absolute Error）。
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@ -1,7 +1,7 @@
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mindspore.nn.MSE
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================
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mindspore.train.MSE
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====================
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.. py:class:: mindspore.nn.MSE
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.. py:class:: mindspore.train.MSE
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测量均方差MSE（Mean Squared Error）。
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@ -1,7 +1,7 @@
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mindspore.nn.MeanSurfaceDistance
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mindspore.train.MeanSurfaceDistance
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===============================================
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.. py:class:: mindspore.nn.MeanSurfaceDistance(symmetric=False, distance_metric='euclidean')
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.. py:class:: mindspore.train.MeanSurfaceDistance(symmetric=False, distance_metric='euclidean')
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计算从 `y_pred` 到 `y` 的平均表面距离。通常情况下，用来衡量分割任务中，预测情况和真实情况之间的差异度。
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@ -1,12 +1,12 @@
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mindspore.nn.Metric
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====================
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mindspore.train.Metric
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=======================
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.. py:class:: mindspore.nn.Metric
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.. py:class:: mindspore.train.Metric
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用于计算评估指标的基类。
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在计算评估指标时需要调用 `clear` 、 `update` 和 `eval` 三个方法，在继承该类自定义评估指标时，也需要实现这三个方法。其中，`update` 用于计算中间过程的内部结果，`eval` 用于计算最终评估结果，`clear` 用于重置中间结果。
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请勿直接使用该类，需使用子类如 :class:`mindspore.nn.MAE` 、 :class:`mindspore.nn.Recall` 等。
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请勿直接使用该类，需使用子类如 :class:`mindspore.train.MAE` 、 :class:`mindspore.train.Recall` 等。
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.. py:method:: clear()
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:abstractmethod:
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@ -36,7 +36,7 @@ mindspore.nn.Metric
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给定(label0, label1, logits)作为 `update` 的输入，将 `indexes` 设置为[2, 1]，则最终使用(logits, label1)作为 `update` 的真实输入。
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.. note::
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在继承该类自定义评估函数时，需要用装饰器 `mindspore.nn.rearrange_inputs` 修饰 `update` 方法，否则配置的 `indexes` 值不生效。
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在继承该类自定义评估函数时，需要用装饰器 `mindspore.train.rearrange_inputs` 修饰 `update` 方法，否则配置的 `indexes` 值不生效。
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参数：
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- **indexes** (List(int)) - logits和标签的目标顺序。
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@ -1,7 +1,7 @@
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mindspore.nn.OcclusionSensitivity
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mindspore.train.OcclusionSensitivity
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=============================================
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.. py:class:: mindspore.nn.OcclusionSensitivity(pad_val=0.0, margin=2, n_batch=128, b_box=None)
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.. py:class:: mindspore.train.OcclusionSensitivity(pad_val=0.0, margin=2, n_batch=128, b_box=None)
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用于计算神经网络对给定图像的遮挡灵敏度（Occlusion Sensitivity），表示了图像的哪些部分对神经网络的分类决策最重要。
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@ -1,7 +1,7 @@
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mindspore.nn.Perplexity
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mindspore.train.Perplexity
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===========================
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.. py:class:: mindspore.nn.Perplexity(ignore_label=None)
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.. py:class:: mindspore.train.Perplexity(ignore_label=None)
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计算困惑度（perplexity）。困惑度是衡量一个概率分布或语言模型好坏的标准。低困惑度表明语言模型可以很好地预测样本。计算方式如下：
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mindspore.nn.Precision
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======================
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mindspore.train.Precision
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==========================
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.. py:class:: mindspore.nn.Precision(eval_type='classification')
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.. py:class:: mindspore.train.Precision(eval_type='classification')
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计算数据分类的精度，包括单标签场景和多标签场景。
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mindspore.nn.Recall
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=====================
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mindspore.train.Recall
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=======================
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.. py:class:: mindspore.nn.Recall(eval_type='classification')
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.. py:class:: mindspore.train.Recall(eval_type='classification')
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计算数据分类的召回率，包括单标签场景和多标签场景。
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mindspore.nn.RootMeanSquareDistance
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======================================
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mindspore.train.RootMeanSquareDistance
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=======================================
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.. py:class:: mindspore.nn.RootMeanSquareDistance(symmetric=False, distance_metric='euclidean')
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.. py:class:: mindspore.train.RootMeanSquareDistance(symmetric=False, distance_metric='euclidean')
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计算从 `y_pred` 到 `y` 的均方根表面距离。
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mindspore.nn.Top1CategoricalAccuracy
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====================================
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mindspore.train.Top1CategoricalAccuracy
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========================================
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.. py:class:: mindspore.nn.Top1CategoricalAccuracy
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.. py:class:: mindspore.train.Top1CategoricalAccuracy
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计算top-1分类正确率。此类是TopKCategoricalAccuracy的特殊类。有关更多详细信息，请参阅 :class:`.TopKCategoricalAccuracy`。
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mindspore.nn.Top5CategoricalAccuracy
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=====================================
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mindspore.train.Top5CategoricalAccuracy
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========================================
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.. py:class:: mindspore.nn.Top5CategoricalAccuracy
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.. py:class:: mindspore.train.Top5CategoricalAccuracy
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计算top-5分类正确率。此类是TopKCategoricalAccuracy的特殊类。有关更多详细信息，请参阅 :class:`.TopKCategoricalAccuracy`。
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mindspore.nn.TopKCategoricalAccuracy
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====================================
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mindspore.train.TopKCategoricalAccuracy
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========================================
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.. py:class:: mindspore.nn.TopKCategoricalAccuracy(k)
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.. py:class:: mindspore.train.TopKCategoricalAccuracy(k)
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计算top-k分类正确率。
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mindspore.nn.auc
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================
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mindspore.train.auc
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====================
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.. py:function:: mindspore.nn.auc(x, y, reorder=False)
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.. py:function:: mindspore.train.auc(x, y, reorder=False)
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使用梯形法则计算曲线下面积AUC（Area Under the Curve，AUC）。这是一个一般函数，给定曲线上的点，
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用于计算ROC (Receiver Operating Curve, ROC) 曲线下的面积。
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mindspore.nn.get_metric_fn
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===========================
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mindspore.train.get_metric_fn
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==============================
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.. py:function:: mindspore.nn.get_metric_fn(name, *args, **kwargs)
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.. py:function:: mindspore.train.get_metric_fn(name, *args, **kwargs)
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根据输入的 `name` 获取metric的方法。
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参数：
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- **name** (str) - metric的方法名，可以通过 :class:`mindspore.nn.names` 接口获取。
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- **name** (str) - metric的方法名，可以通过 :class:`mindspore.train.names` 接口获取。
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- **args** - metric函数的参数。
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- **kwargs** - metric函数的关键字参数。
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mindspore.nn.names
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==================
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mindspore.train.names
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======================
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.. py:function:: mindspore.nn.names()
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.. py:function:: mindspore.train.names()
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获取所有metric的名称。
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|
|
@ -1,11 +1,11 @@
|
|||
mindspore.nn.rearrange_inputs
|
||||
==============================
|
||||
mindspore.train.rearrange_inputs
|
||||
=================================
|
||||
|
||||
.. py:function:: mindspore.nn.rearrange_inputs(func)
|
||||
.. py:function:: mindspore.train.rearrange_inputs(func)
|
||||
|
||||
此装饰器用于根据类的 `indexes` 属性对输入重新排列。
|
||||
|
||||
此装饰器目前用于 :class:`mindspore.nn.Metric` 类的 `update` 方法。
|
||||
此装饰器目前用于 :class:`mindspore.train.Metric` 类的 `update` 方法。
|
||||
|
||||
参数：
|
||||
- **func** (Callable) - 要装饰的候选函数，其输入将被重新排列。
|
||||
|
|
@ -263,42 +263,6 @@ Optimizer
|
|||
mindspore.nn.SGD
|
||||
mindspore.nn.thor
|
||||
|
||||
Evaluation Metrics
|
||||
------------------
|
||||
|
||||
.. msplatformautosummary::
|
||||
:toctree: nn
|
||||
:nosignatures:
|
||||
:template: classtemplate.rst
|
||||
|
||||
mindspore.nn.Accuracy
|
||||
mindspore.nn.auc
|
||||
mindspore.nn.BleuScore
|
||||
mindspore.nn.ConfusionMatrix
|
||||
mindspore.nn.ConfusionMatrixMetric
|
||||
mindspore.nn.CosineSimilarity
|
||||
mindspore.nn.Dice
|
||||
mindspore.nn.F1
|
||||
mindspore.nn.Fbeta
|
||||
mindspore.nn.HausdorffDistance
|
||||
mindspore.nn.get_metric_fn
|
||||
mindspore.nn.Loss
|
||||
mindspore.nn.MAE
|
||||
mindspore.nn.MeanSurfaceDistance
|
||||
mindspore.nn.Metric
|
||||
mindspore.nn.MSE
|
||||
mindspore.nn.names
|
||||
mindspore.nn.OcclusionSensitivity
|
||||
mindspore.nn.Perplexity
|
||||
mindspore.nn.Precision
|
||||
mindspore.nn.Recall
|
||||
mindspore.nn.ROC
|
||||
mindspore.nn.RootMeanSquareDistance
|
||||
mindspore.nn.rearrange_inputs
|
||||
mindspore.nn.Top1CategoricalAccuracy
|
||||
mindspore.nn.Top5CategoricalAccuracy
|
||||
mindspore.nn.TopKCategoricalAccuracy
|
||||
|
||||
Dynamic Learning Rate
|
||||
---------------------
|
||||
|
||||
|
|
|
|||
|
|
@ -30,3 +30,48 @@ Callback
|
|||
mindspore.train.ReduceLROnPlateau
|
||||
mindspore.train.RunContext
|
||||
mindspore.train.TimeMonitor
|
||||
|
||||
Evaluation Metrics
|
||||
------------------
|
||||
|
||||
.. msplatformautosummary::
|
||||
:toctree: mindspore
|
||||
:nosignatures:
|
||||
:template: classtemplate.rst
|
||||
|
||||
mindspore.train.Accuracy
|
||||
mindspore.train.BleuScore
|
||||
mindspore.train.ConfusionMatrix
|
||||
mindspore.train.ConfusionMatrixMetric
|
||||
mindspore.train.CosineSimilarity
|
||||
mindspore.train.Dice
|
||||
mindspore.train.F1
|
||||
mindspore.train.Fbeta
|
||||
mindspore.train.HausdorffDistance
|
||||
mindspore.train.Loss
|
||||
mindspore.train.MAE
|
||||
mindspore.train.MeanSurfaceDistance
|
||||
mindspore.train.Metric
|
||||
mindspore.train.MSE
|
||||
mindspore.train.OcclusionSensitivity
|
||||
mindspore.train.Perplexity
|
||||
mindspore.train.Precision
|
||||
mindspore.train.Recall
|
||||
mindspore.train.ROC
|
||||
mindspore.train.RootMeanSquareDistance
|
||||
mindspore.train.Top1CategoricalAccuracy
|
||||
mindspore.train.Top5CategoricalAccuracy
|
||||
mindspore.train.TopKCategoricalAccuracy
|
||||
|
||||
Utils
|
||||
-----
|
||||
|
||||
.. msplatformautosummary::
|
||||
:toctree: mindspore
|
||||
:nosignatures:
|
||||
:template: classtemplate.rst
|
||||
|
||||
mindspore.train.auc
|
||||
mindspore.train.get_metric_fn
|
||||
mindspore.train.names
|
||||
mindspore.train.rearrange_inputs
|
||||
|
|
|
|||
|
|
@ -19,7 +19,7 @@ Pre-defined building blocks or computing units to construct neural networks.
|
|||
"""
|
||||
from __future__ import absolute_import
|
||||
|
||||
from mindspore.nn import layer, loss, optim, metrics, wrap, grad, probability, sparse, dynamic_lr, reinforcement
|
||||
from mindspore.nn import layer, loss, optim, wrap, grad, metrics, probability, sparse, dynamic_lr, reinforcement
|
||||
from mindspore.nn.learning_rate_schedule import *
|
||||
from mindspore.nn.dynamic_lr import *
|
||||
from mindspore.nn.cell import Cell, GraphCell
|
||||
|
|
|
|||
|
|
@ -0,0 +1,53 @@
|
|||
# Copyright 2020 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
Metrics from mindspore.train.metrics
|
||||
"""
|
||||
|
||||
from mindspore.train.metrics import Accuracy, HausdorffDistance, MAE, MSE, Metric, \
|
||||
rearrange_inputs, Precision, Recall, Fbeta, F1, Dice, ROC, auc, \
|
||||
TopKCategoricalAccuracy, Top1CategoricalAccuracy, Top5CategoricalAccuracy, Loss, \
|
||||
MeanSurfaceDistance, RootMeanSquareDistance, BleuScore, CosineSimilarity, \
|
||||
OcclusionSensitivity, Perplexity, ConfusionMatrixMetric, ConfusionMatrix, \
|
||||
names, get_metric_fn, get_metrics
|
||||
|
||||
__all__ = [
|
||||
"names",
|
||||
"get_metric_fn",
|
||||
"get_metrics",
|
||||
"Accuracy",
|
||||
"MAE", "MSE",
|
||||
"Metric", "rearrange_inputs",
|
||||
"Precision",
|
||||
"HausdorffDistance",
|
||||
"Recall",
|
||||
"Fbeta",
|
||||
"BleuScore",
|
||||
"CosineSimilarity",
|
||||
"OcclusionSensitivity",
|
||||
"F1",
|
||||
"Dice",
|
||||
"ROC",
|
||||
"auc",
|
||||
"TopKCategoricalAccuracy",
|
||||
"Top1CategoricalAccuracy",
|
||||
"Top5CategoricalAccuracy",
|
||||
"Loss",
|
||||
"MeanSurfaceDistance",
|
||||
"RootMeanSquareDistance",
|
||||
"Perplexity",
|
||||
"ConfusionMatrix",
|
||||
"ConfusionMatrixMetric",
|
||||
]
|
||||
|
|
@ -32,6 +32,7 @@ from mindspore.train.callback import Callback, LossMonitor, TimeMonitor, ModelCh
|
|||
History, LambdaCallback, ReduceLROnPlateau, EarlyStopping
|
||||
from mindspore.train.summary import SummaryRecord
|
||||
from mindspore.train.train_thor import ConvertNetUtils, ConvertModelUtils
|
||||
from mindspore.train.metrics import *
|
||||
|
||||
__all__ = ["Model", "DatasetHelper", "amp", "connect_network_with_dataset", "build_train_network", "LossScaleManager",
|
||||
"FixedLossScaleManager", "DynamicLossScaleManager", "save_checkpoint", "load_checkpoint",
|
||||
|
|
@ -40,3 +41,4 @@ __all__ = ["Model", "DatasetHelper", "amp", "connect_network_with_dataset", "bui
|
|||
__all__.extend(callback.__all__)
|
||||
__all__.extend(summary.__all__)
|
||||
__all__.extend(train_thor.__all__)
|
||||
__all__.extend(metrics.__all__)
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@ from mindspore.train.summary.enums import PluginEnum
|
|||
from mindspore.train.anf_ir_pb2 import DataType
|
||||
from mindspore.train._utils import check_value_type, _make_directory
|
||||
from mindspore.train.dataset_helper import DatasetHelper
|
||||
from mindspore.nn.metrics import get_metrics
|
||||
from mindspore.train.metrics import get_metrics
|
||||
from mindspore import context
|
||||
|
||||
# if there is no path, you need to set to empty list
|
||||
|
|
@ -265,7 +265,7 @@ class SummaryLandscape:
|
|||
- mindspore.Model: User's model object.
|
||||
- mindspore.nn.Cell: User's network object.
|
||||
- mindspore.dataset: User's dataset object for create loss landscape.
|
||||
- mindspore.nn.Metrics: User's metrics object.
|
||||
- mindspore.train.Metrics: User's metrics object.
|
||||
collect_landscape (Union[dict, None]): The meaning of the parameters
|
||||
when creating loss landscape is consistent with the fields
|
||||
with the same name in SummaryCollector. The purpose of setting here
|
||||
|
|
|
|||
|
|
@ -20,25 +20,25 @@ on the evaluation dataset. It's used to choose the best model.
|
|||
"""
|
||||
from __future__ import absolute_import
|
||||
|
||||
from mindspore.nn.metrics.accuracy import Accuracy
|
||||
from mindspore.nn.metrics.hausdorff_distance import HausdorffDistance
|
||||
from mindspore.nn.metrics.error import MAE, MSE
|
||||
from mindspore.nn.metrics.metric import Metric, rearrange_inputs
|
||||
from mindspore.nn.metrics.precision import Precision
|
||||
from mindspore.nn.metrics.recall import Recall
|
||||
from mindspore.nn.metrics.fbeta import Fbeta, F1
|
||||
from mindspore.nn.metrics.dice import Dice
|
||||
from mindspore.nn.metrics.roc import ROC
|
||||
from mindspore.nn.metrics.auc import auc
|
||||
from mindspore.nn.metrics.topk import TopKCategoricalAccuracy, Top1CategoricalAccuracy, Top5CategoricalAccuracy
|
||||
from mindspore.nn.metrics.loss import Loss
|
||||
from mindspore.nn.metrics.mean_surface_distance import MeanSurfaceDistance
|
||||
from mindspore.nn.metrics.root_mean_square_surface_distance import RootMeanSquareDistance
|
||||
from mindspore.nn.metrics.bleu_score import BleuScore
|
||||
from mindspore.nn.metrics.cosine_similarity import CosineSimilarity
|
||||
from mindspore.nn.metrics.occlusion_sensitivity import OcclusionSensitivity
|
||||
from mindspore.nn.metrics.perplexity import Perplexity
|
||||
from mindspore.nn.metrics.confusion_matrix import ConfusionMatrixMetric, ConfusionMatrix
|
||||
from mindspore.train.metrics.accuracy import Accuracy
|
||||
from mindspore.train.metrics.hausdorff_distance import HausdorffDistance
|
||||
from mindspore.train.metrics.error import MAE, MSE
|
||||
from mindspore.train.metrics.metric import Metric, rearrange_inputs
|
||||
from mindspore.train.metrics.precision import Precision
|
||||
from mindspore.train.metrics.recall import Recall
|
||||
from mindspore.train.metrics.fbeta import Fbeta, F1
|
||||
from mindspore.train.metrics.dice import Dice
|
||||
from mindspore.train.metrics.roc import ROC
|
||||
from mindspore.train.metrics.auc import auc
|
||||
from mindspore.train.metrics.topk import TopKCategoricalAccuracy, Top1CategoricalAccuracy, Top5CategoricalAccuracy
|
||||
from mindspore.train.metrics.loss import Loss
|
||||
from mindspore.train.metrics.mean_surface_distance import MeanSurfaceDistance
|
||||
from mindspore.train.metrics.root_mean_square_surface_distance import RootMeanSquareDistance
|
||||
from mindspore.train.metrics.bleu_score import BleuScore
|
||||
from mindspore.train.metrics.cosine_similarity import CosineSimilarity
|
||||
from mindspore.train.metrics.occlusion_sensitivity import OcclusionSensitivity
|
||||
from mindspore.train.metrics.perplexity import Perplexity
|
||||
from mindspore.train.metrics.confusion_matrix import ConfusionMatrixMetric, ConfusionMatrix
|
||||
|
||||
__all__ = [
|
||||
"names",
|
||||
|
|
@ -113,7 +113,7 @@ def get_metric_fn(name, *args, **kwargs):
|
|||
Gets the metric method based on the input name.
|
||||
|
||||
Args:
|
||||
name (str): The name of metric method. Names can be obtained by `mindspore.nn.names` .
|
||||
name (str): The name of metric method. Names can be obtained by `mindspore.train.names` .
|
||||
object for the currently supported metrics.
|
||||
args: Arguments for the metric function.
|
||||
kwargs: Keyword arguments for the metric function.
|
||||
|
|
@ -17,7 +17,7 @@ from __future__ import absolute_import
|
|||
|
||||
import numpy as np
|
||||
|
||||
from mindspore.nn.metrics.metric import EvaluationBase, rearrange_inputs, _check_onehot_data
|
||||
from mindspore.train.metrics.metric import EvaluationBase, rearrange_inputs, _check_onehot_data
|
||||
|
||||
|
||||
class Accuracy(EvaluationBase):
|
||||
|
|
@ -42,11 +42,12 @@ class Accuracy(EvaluationBase):
|
|||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> import mindspore
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import Accuracy
|
||||
>>>
|
||||
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]), mindspore.float32)
|
||||
>>> y = Tensor(np.array([1, 0, 1]), mindspore.float32)
|
||||
>>> metric = nn.Accuracy('classification')
|
||||
>>> metric = Accuracy('classification')
|
||||
>>> metric.clear()
|
||||
>>> metric.update(x, y)
|
||||
>>> accuracy = metric.eval()
|
||||
|
|
@ -39,15 +39,15 @@ def auc(x, y, reorder=False):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn
|
||||
>>> from mindspore.train import ROC, auc
|
||||
>>>
|
||||
>>> y_pred = np.array([[3, 0, 1], [1, 3, 0], [1, 0, 2]])
|
||||
>>> y = np.array([[0, 2, 1], [1, 2, 1], [0, 0, 1]])
|
||||
>>> metric = nn.ROC(pos_label=2)
|
||||
>>> metric = ROC(pos_label=2)
|
||||
>>> metric.clear()
|
||||
>>> metric.update(y_pred, y)
|
||||
>>> fpr, tpr, thre = metric.eval()
|
||||
>>> output = nn.auc(fpr, tpr)
|
||||
>>> output = auc(fpr, tpr)
|
||||
>>> print(output)
|
||||
0.5357142857142857
|
||||
"""
|
||||
|
|
@ -19,7 +19,7 @@ from collections import Counter
|
|||
import numpy as np
|
||||
|
||||
from mindspore._checkparam import Validator as validator
|
||||
from mindspore.nn.metrics.metric import Metric, rearrange_inputs
|
||||
from mindspore.train.metrics.metric import Metric, rearrange_inputs
|
||||
|
||||
|
||||
class BleuScore(Metric):
|
||||
|
|
@ -38,12 +38,12 @@ class BleuScore(Metric):
|
|||
``Ascend`` ``GPU`` ``CPU``
|
||||
|
||||
Examples:
|
||||
>>> import mindspore.nn as nn
|
||||
>>> import mindspore.train import BleuScore
|
||||
>>>
|
||||
>>> candidate_corpus = [['i', 'have', 'a', 'pen', 'on', 'my', 'desk']]
|
||||
>>> reference_corpus = [[['i', 'have', 'a', 'pen', 'in', 'my', 'desk'],
|
||||
... ['there', 'is', 'a', 'pen', 'on', 'the', 'desk']]]
|
||||
>>> metric = nn.BleuScore()
|
||||
>>> metric = BleuScore()
|
||||
>>> metric.clear()
|
||||
>>> metric.update(candidate_corpus, reference_corpus)
|
||||
>>> bleu_score = metric.eval()
|
||||
|
|
@ -18,7 +18,7 @@ from __future__ import absolute_import
|
|||
import numpy as np
|
||||
|
||||
from mindspore._checkparam import Validator as validator
|
||||
from mindspore.nn.metrics.metric import Metric, rearrange_inputs
|
||||
from mindspore.train.metrics.metric import Metric, rearrange_inputs
|
||||
|
||||
|
||||
class ConfusionMatrix(Metric):
|
||||
|
|
@ -27,7 +27,7 @@ class ConfusionMatrix(Metric):
|
|||
including binary classification and multiple classification.
|
||||
|
||||
If you only need confusion matrix, use this class. If you want to calculate other metrics, such as 'PPV',
|
||||
'TPR', 'TNR', etc., use class 'mindspore.nn.ConfusionMatrixMetric'.
|
||||
'TPR', 'TNR', etc., use class 'mindspore.train.ConfusionMatrixMetric'.
|
||||
|
||||
Args:
|
||||
num_classes (int): Number of classes in the dataset.
|
||||
|
|
@ -45,11 +45,12 @@ class ConfusionMatrix(Metric):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import ConfusionMatrix
|
||||
>>>
|
||||
>>> x = Tensor(np.array([1, 0, 1, 0]))
|
||||
>>> y = Tensor(np.array([1, 0, 0, 1]))
|
||||
>>> metric = nn.ConfusionMatrix(num_classes=2, normalize='no_norm', threshold=0.5)
|
||||
>>> metric = ConfusionMatrix(num_classes=2, normalize='no_norm', threshold=0.5)
|
||||
>>> metric.clear()
|
||||
>>> metric.update(x, y)
|
||||
>>> output = metric.eval()
|
||||
|
|
@ -174,9 +175,10 @@ class ConfusionMatrixMetric(Metric):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import ConfusionMatrixMetric
|
||||
>>>
|
||||
>>> metric = nn.ConfusionMatrixMetric(skip_channel=True, metric_name="tpr",
|
||||
>>> metric = ConfusionMatrixMetric(skip_channel=True, metric_name="tpr",
|
||||
... calculation_method=False, decrease="mean")
|
||||
>>> metric.clear()
|
||||
>>> x = Tensor(np.array([[[0], [1]], [[1], [0]]]))
|
||||
|
|
@ -18,7 +18,7 @@ from __future__ import absolute_import
|
|||
import numpy as np
|
||||
|
||||
from mindspore._checkparam import Validator as validator
|
||||
from mindspore.nn.metrics.metric import Metric, rearrange_inputs
|
||||
from mindspore.train.metrics.metric import Metric, rearrange_inputs
|
||||
|
||||
|
||||
class CosineSimilarity(Metric):
|
||||
|
|
@ -35,10 +35,10 @@ class CosineSimilarity(Metric):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn
|
||||
>>> from mindspore.train import CosineSimilarity
|
||||
>>>
|
||||
>>> test_data = np.array([[1, 3, 4, 7], [2, 4, 2, 5], [3, 1, 5, 8]])
|
||||
>>> metric = nn.CosineSimilarity()
|
||||
>>> metric = CosineSimilarity()
|
||||
>>> metric.clear()
|
||||
>>> metric.update(test_data)
|
||||
>>> square_matrix = metric.eval()
|
||||
|
|
@ -18,7 +18,7 @@ from __future__ import absolute_import
|
|||
import numpy as np
|
||||
|
||||
from mindspore._checkparam import Validator as validator
|
||||
from mindspore.nn.metrics.metric import Metric, rearrange_inputs
|
||||
from mindspore.train.metrics.metric import Metric, rearrange_inputs
|
||||
|
||||
|
||||
class Dice(Metric):
|
||||
|
|
@ -40,11 +40,12 @@ class Dice(Metric):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import Dice
|
||||
>>>
|
||||
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
|
||||
>>> y = Tensor(np.array([[0, 1], [1, 0], [0, 1]]))
|
||||
>>> metric = nn.Dice(smooth=1e-5)
|
||||
>>> metric = Dice(smooth=1e-5)
|
||||
>>> metric.clear()
|
||||
>>> metric.update(x, y)
|
||||
>>> dice = metric.eval()
|
||||
|
|
@ -17,7 +17,7 @@ from __future__ import absolute_import
|
|||
|
||||
import numpy as np
|
||||
|
||||
from mindspore.nn.metrics.metric import Metric, rearrange_inputs
|
||||
from mindspore.train.metrics.metric import Metric, rearrange_inputs
|
||||
|
||||
|
||||
class MAE(Metric):
|
||||
|
|
@ -38,11 +38,12 @@ class MAE(Metric):
|
|||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> import mindspore
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import MAE
|
||||
>>>
|
||||
>>> x = Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32)
|
||||
>>> y = Tensor(np.array([0.1, 0.25, 0.7, 0.9]), mindspore.float32)
|
||||
>>> error = nn.MAE()
|
||||
>>> error = MAE()
|
||||
>>> error.clear()
|
||||
>>> error.update(x, y)
|
||||
>>> result = error.eval()
|
||||
|
|
@ -114,11 +115,12 @@ class MSE(Metric):
|
|||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> import mindspore
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import MSE
|
||||
>>>
|
||||
>>> x = Tensor(np.array([0.1, 0.2, 0.6, 0.9]), mindspore.float32)
|
||||
>>> y = Tensor(np.array([0.1, 0.25, 0.5, 0.9]), mindspore.float32)
|
||||
>>> error = nn.MSE()
|
||||
>>> error = MSE()
|
||||
>>> error.clear()
|
||||
>>> error.update(x, y)
|
||||
>>> result = error.eval()
|
||||
|
|
@ -19,7 +19,7 @@ import sys
|
|||
import numpy as np
|
||||
|
||||
from mindspore._checkparam import Validator as validator
|
||||
from mindspore.nn.metrics.metric import Metric, rearrange_inputs, _check_onehot_data
|
||||
from mindspore.train.metrics.metric import Metric, rearrange_inputs, _check_onehot_data
|
||||
|
||||
|
||||
class Fbeta(Metric):
|
||||
|
|
@ -40,11 +40,12 @@ class Fbeta(Metric):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import Fbeta
|
||||
>>>
|
||||
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
|
||||
>>> y = Tensor(np.array([1, 0, 1]))
|
||||
>>> metric = nn.Fbeta(1)
|
||||
>>> metric = Fbeta(1)
|
||||
>>> metric.clear()
|
||||
>>> metric.update(x, y)
|
||||
>>> fbeta = metric.eval()
|
||||
|
|
@ -141,7 +142,7 @@ class Fbeta(Metric):
|
|||
class F1(Fbeta):
|
||||
r"""
|
||||
Calculates the F1 score. F1 is a special case of Fbeta when beta is 1.
|
||||
Refer to class :class:`mindspore.nn.Fbeta` for more details.
|
||||
Refer to class :class:`mindspore.train.Fbeta` for more details.
|
||||
|
||||
.. math::
|
||||
F_1=\frac{2\cdot true\_positive}{2\cdot true\_positive + false\_negative + false\_positive}
|
||||
|
|
@ -151,11 +152,12 @@ class F1(Fbeta):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import F1
|
||||
>>>
|
||||
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
|
||||
>>> y = Tensor(np.array([1, 0, 1]))
|
||||
>>> metric = nn.F1()
|
||||
>>> metric = F1()
|
||||
>>> metric.update(x, y)
|
||||
>>> result = metric.eval()
|
||||
>>> print(result)
|
||||
|
|
@ -22,7 +22,7 @@ import numpy as np
|
|||
|
||||
from mindspore.common.tensor import Tensor
|
||||
from mindspore._checkparam import Validator as validator
|
||||
from mindspore.nn.metrics.metric import Metric, rearrange_inputs
|
||||
from mindspore.train.metrics.metric import Metric, rearrange_inputs
|
||||
|
||||
|
||||
class _ROISpatialData(metaclass=ABCMeta):
|
||||
|
|
@ -98,11 +98,12 @@ class HausdorffDistance(Metric):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import HausdorffDistance
|
||||
>>>
|
||||
>>> x = Tensor(np.array([[3, 0, 1], [1, 3, 0], [1, 0, 2]]))
|
||||
>>> y = Tensor(np.array([[0, 2, 1], [1, 2, 1], [0, 0, 1]]))
|
||||
>>> metric = nn.HausdorffDistance()
|
||||
>>> metric = HausdorffDistance()
|
||||
>>> metric.clear()
|
||||
>>> metric.update(x, y, 0)
|
||||
>>> mean_average_distance = metric.eval()
|
||||
|
|
@ -15,7 +15,7 @@
|
|||
"""Loss for evaluation"""
|
||||
from __future__ import absolute_import
|
||||
|
||||
from mindspore.nn.metrics.metric import Metric, rearrange_inputs
|
||||
from mindspore.train.metrics.metric import Metric, rearrange_inputs
|
||||
|
||||
|
||||
class Loss(Metric):
|
||||
|
|
@ -32,10 +32,11 @@ class Loss(Metric):
|
|||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> import mindspore
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import Loss
|
||||
>>>
|
||||
>>> x = Tensor(np.array(0.2), mindspore.float32)
|
||||
>>> loss = nn.Loss()
|
||||
>>> loss = Loss()
|
||||
>>> loss.clear()
|
||||
>>> loss.update(x)
|
||||
>>> result = loss.eval()
|
||||
|
|
@ -19,7 +19,7 @@ from scipy.ndimage import morphology
|
|||
import numpy as np
|
||||
|
||||
from mindspore._checkparam import Validator as validator
|
||||
from mindspore.nn.metrics.metric import Metric, rearrange_inputs
|
||||
from mindspore.train.metrics.metric import Metric, rearrange_inputs
|
||||
|
||||
|
||||
class MeanSurfaceDistance(Metric):
|
||||
|
|
@ -61,10 +61,11 @@ class MeanSurfaceDistance(Metric):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import MeanSurfaceDistance
|
||||
>>> x = Tensor(np.array([[3, 0, 1], [1, 3, 0], [1, 0, 2]]))
|
||||
>>> y = Tensor(np.array([[0, 2, 1], [1, 2, 1], [0, 0, 1]]))
|
||||
>>> metric = nn.MeanSurfaceDistance(symmetric=False, distance_metric="euclidean")
|
||||
>>> metric = MeanSurfaceDistance(symmetric=False, distance_metric="euclidean")
|
||||
>>> metric.clear()
|
||||
>>> metric.update(x, y, 0)
|
||||
>>> mean_average_distance = metric.eval()
|
||||
|
|
@ -28,7 +28,7 @@ def rearrange_inputs(func):
|
|||
"""
|
||||
This decorator is used to rearrange the inputs according to its `indexes` attribute of the class.
|
||||
|
||||
This decorator is currently applied on the `update` of :class:`mindspore.nn.Metric`.
|
||||
This decorator is currently applied on the `update` of :class:`mindspore.train.Metric`.
|
||||
|
||||
Args:
|
||||
func (Callable): A candidate function to be wrapped whose input will be rearranged.
|
||||
|
|
@ -79,7 +79,7 @@ class Metric(metaclass=ABCMeta):
|
|||
result, and `clear` will reinitialize the intermediate results.
|
||||
|
||||
Never use this class directly, but instantiate one of its subclasses instead, for examples,
|
||||
:class:`mindspore.nn.MAE`, :class:`mindspore.nn.Recall` etc.
|
||||
:class:`mindspore.train.MAE`, :class:`mindspore.train.Recall` etc.
|
||||
|
||||
Supported Platforms:
|
||||
``Ascend`` ``GPU`` ``CPU``
|
||||
|
|
@ -123,7 +123,7 @@ class Metric(metaclass=ABCMeta):
|
|||
|
||||
Note:
|
||||
When customize a metric, decorate the `update` function with the decorator
|
||||
:func:`mindspore.nn.rearrange_inputs` for the `indexes` to take effect.
|
||||
:func:`mindspore.train.rearrange_inputs` for the `indexes` to take effect.
|
||||
|
||||
Args:
|
||||
indexes (List(int)): The order of logits and labels to be rearranged.
|
||||
|
|
@ -136,12 +136,13 @@ class Metric(metaclass=ABCMeta):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import Accuracy
|
||||
>>>
|
||||
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
|
||||
>>> y = Tensor(np.array([1, 0, 1]))
|
||||
>>> y2 = Tensor(np.array([0, 0, 1]))
|
||||
>>> metric = nn.Accuracy('classification').set_indexes([0, 2])
|
||||
>>> metric = Accuracy('classification').set_indexes([0, 2])
|
||||
>>> metric.clear()
|
||||
>>> # indexes is [0, 2], using x as logits, y2 as label.
|
||||
>>> metric.update(x, y, y2)
|
||||
|
|
@ -20,7 +20,7 @@ import numpy as np
|
|||
from mindspore import nn
|
||||
from mindspore.common.tensor import Tensor
|
||||
from mindspore._checkparam import Validator as validator
|
||||
from mindspore.nn.metrics.metric import Metric, rearrange_inputs
|
||||
from mindspore.train.metrics.metric import Metric, rearrange_inputs
|
||||
|
||||
try:
|
||||
from tqdm import trange
|
||||
|
|
@ -55,6 +55,7 @@ class OcclusionSensitivity(Metric):
|
|||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore.train import OcclusionSensitivity
|
||||
>>>
|
||||
>>> class DenseNet(nn.Cell):
|
||||
... def __init__(self):
|
||||
|
|
@ -69,7 +70,7 @@ class OcclusionSensitivity(Metric):
|
|||
>>> model = DenseNet()
|
||||
>>> test_data = np.array([[0.1, 0.2, 0.3, 0.4]]).astype(np.float32)
|
||||
>>> label = np.array(1).astype(np.int32)
|
||||
>>> metric = nn.OcclusionSensitivity()
|
||||
>>> metric = OcclusionSensitivity()
|
||||
>>> metric.clear()
|
||||
>>> metric.update(model, test_data, label)
|
||||
>>> score = metric.eval()
|
||||
|
|
@ -19,7 +19,7 @@ import math
|
|||
import numpy as np
|
||||
|
||||
from mindspore._checkparam import Validator as validator
|
||||
from mindspore.nn.metrics.metric import Metric, rearrange_inputs
|
||||
from mindspore.train.metrics.metric import Metric, rearrange_inputs
|
||||
|
||||
|
||||
class Perplexity(Metric):
|
||||
|
|
@ -41,10 +41,11 @@ class Perplexity(Metric):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import Perplexity
|
||||
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
|
||||
>>> y = Tensor(np.array([1, 0, 1]))
|
||||
>>> metric = nn.Perplexity(ignore_label=None)
|
||||
>>> metric = Perplexity(ignore_label=None)
|
||||
>>> metric.clear()
|
||||
>>> metric.update(x, y)
|
||||
>>> perplexity = metric.eval()
|
||||
|
|
@ -19,7 +19,7 @@ import sys
|
|||
import numpy as np
|
||||
|
||||
from mindspore._checkparam import Validator as validator
|
||||
from mindspore.nn.metrics.metric import EvaluationBase, rearrange_inputs, _check_onehot_data
|
||||
from mindspore.train.metrics.metric import EvaluationBase, rearrange_inputs, _check_onehot_data
|
||||
|
||||
|
||||
class Precision(EvaluationBase):
|
||||
|
|
@ -43,11 +43,12 @@ class Precision(EvaluationBase):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import Precision
|
||||
>>>
|
||||
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
|
||||
>>> y = Tensor(np.array([1, 0, 1]))
|
||||
>>> metric = nn.Precision('classification')
|
||||
>>> metric = Precision('classification')
|
||||
>>> metric.clear()
|
||||
>>> metric.update(x, y)
|
||||
>>> precision = metric.eval()
|
||||
|
|
@ -19,7 +19,7 @@ import sys
|
|||
import numpy as np
|
||||
|
||||
from mindspore._checkparam import Validator as validator
|
||||
from mindspore.nn.metrics.metric import EvaluationBase, rearrange_inputs, _check_onehot_data
|
||||
from mindspore.train.metrics.metric import EvaluationBase, rearrange_inputs, _check_onehot_data
|
||||
|
||||
|
||||
class Recall(EvaluationBase):
|
||||
|
|
@ -44,11 +44,12 @@ class Recall(EvaluationBase):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import Recall
|
||||
>>>
|
||||
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
|
||||
>>> y = Tensor(np.array([1, 0, 1]))
|
||||
>>> metric = nn.Recall('classification')
|
||||
>>> metric = Recall('classification')
|
||||
>>> metric.clear()
|
||||
>>> metric.update(x, y)
|
||||
>>> recall = metric.eval()
|
||||
|
|
@ -18,7 +18,7 @@ from __future__ import absolute_import
|
|||
import numpy as np
|
||||
|
||||
from mindspore._checkparam import Validator as validator
|
||||
from mindspore.nn.metrics.metric import Metric, rearrange_inputs, _binary_clf_curve
|
||||
from mindspore.train.metrics.metric import Metric, rearrange_inputs, _binary_clf_curve
|
||||
|
||||
|
||||
class ROC(Metric):
|
||||
|
|
@ -38,12 +38,13 @@ class ROC(Metric):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import ROC
|
||||
>>>
|
||||
>>> # 1) binary classification example
|
||||
>>> x = Tensor(np.array([3, 1, 4, 2]))
|
||||
>>> y = Tensor(np.array([0, 1, 2, 3]))
|
||||
>>> metric = nn.ROC(pos_label=2)
|
||||
>>> metric = ROC(pos_label=2)
|
||||
>>> metric.clear()
|
||||
>>> metric.update(x, y)
|
||||
>>> fpr, tpr, thresholds = metric.eval()
|
||||
|
|
@ -58,7 +59,7 @@ class ROC(Metric):
|
|||
>>> x = Tensor(np.array([[0.28, 0.55, 0.15, 0.05], [0.10, 0.20, 0.05, 0.05], [0.20, 0.05, 0.15, 0.05],
|
||||
... [0.05, 0.05, 0.05, 0.75]]))
|
||||
>>> y = Tensor(np.array([0, 1, 2, 3]))
|
||||
>>> metric = nn.ROC(class_num=4)
|
||||
>>> metric = ROC(class_num=4)
|
||||
>>> metric.clear()
|
||||
>>> metric.update(x, y)
|
||||
>>> fpr, tpr, thresholds = metric.eval()
|
||||
|
|
@ -19,7 +19,7 @@ from scipy.ndimage import morphology
|
|||
import numpy as np
|
||||
|
||||
from mindspore._checkparam import Validator as validator
|
||||
from mindspore.nn.metrics.metric import Metric, rearrange_inputs
|
||||
from mindspore.train.metrics.metric import Metric, rearrange_inputs
|
||||
|
||||
|
||||
class RootMeanSquareDistance(Metric):
|
||||
|
|
@ -60,11 +60,12 @@ class RootMeanSquareDistance(Metric):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import RootMeanSquareDistance
|
||||
>>>
|
||||
>>> x = Tensor(np.array([[3, 0, 1], [1, 3, 0], [1, 0, 2]]))
|
||||
>>> y = Tensor(np.array([[0, 2, 1], [1, 2, 1], [0, 0, 1]]))
|
||||
>>> metric = nn.RootMeanSquareDistance(symmetric=False, distance_metric="euclidean")
|
||||
>>> metric = RootMeanSquareDistance(symmetric=False, distance_metric="euclidean")
|
||||
>>> metric.clear()
|
||||
>>> metric.update(x, y, 0)
|
||||
>>> root_mean_square_distance = metric.eval()
|
||||
|
|
@ -17,7 +17,7 @@ from __future__ import absolute_import
|
|||
|
||||
import numpy as np
|
||||
|
||||
from mindspore.nn.metrics.metric import Metric, rearrange_inputs, _check_onehot_data
|
||||
from mindspore.train.metrics.metric import Metric, rearrange_inputs, _check_onehot_data
|
||||
|
||||
|
||||
class TopKCategoricalAccuracy(Metric):
|
||||
|
|
@ -37,12 +37,13 @@ class TopKCategoricalAccuracy(Metric):
|
|||
Examples:
|
||||
>>> import mindspore
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import TopKCategoricalAccuracy
|
||||
>>>
|
||||
>>> x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.],
|
||||
... [0.9, 0.6, 0.2, 0.01, 0.3]]), mindspore.float32)
|
||||
>>> y = Tensor(np.array([2, 0, 1]), mindspore.float32)
|
||||
>>> topk = nn.TopKCategoricalAccuracy(3)
|
||||
>>> topk = TopKCategoricalAccuracy(3)
|
||||
>>> topk.clear()
|
||||
>>> topk.update(x, y)
|
||||
>>> output = topk.eval()
|
||||
|
|
@ -120,12 +121,13 @@ class Top1CategoricalAccuracy(TopKCategoricalAccuracy):
|
|||
|
||||
Examples:
|
||||
>>> import numpy as np
|
||||
>>> from mindspore import nn, Tensor
|
||||
>>> from mindspore import Tensor
|
||||
>>> from mindspore.train import Top1CategoricalAccuracy
|
||||
>>>
|
||||
>>> x = Tensor(np.array([[0.2, 0.5, 0.3, 0.6, 0.2], [0.1, 0.35, 0.5, 0.2, 0.],
|
||||
... [0.9, 0.6, 0.2, 0.01, 0.3]]), mindspore.float32)
|
||||
>>> y = Tensor(np.array([2, 0, 1]), mindspore.float32)
|
||||
>>> topk = nn.Top1CategoricalAccuracy()
|
||||
>>> topk = Top1CategoricalAccuracy()
|
||||
>>> topk.clear()
|
||||
>>> topk.update(x, y)
|
||||
>>> output = topk.eval()
|
||||
|
|
@ -27,7 +27,7 @@ from mindspore import log as logger
|
|||
from mindspore.train.serialization import save_checkpoint, load_checkpoint
|
||||
from mindspore.train.callback._checkpoint import ModelCheckpoint, _chg_ckpt_file_name_if_same_exist
|
||||
from mindspore.common.tensor import Tensor
|
||||
from mindspore.nn.metrics import get_metrics, get_metric_fn
|
||||
from mindspore.train.metrics import get_metrics, get_metric_fn
|
||||
from mindspore._checkparam import check_input_data, check_output_data, Validator
|
||||
from mindspore.train.callback import _InternalCallbackParam, RunContext, _CallbackManager, Callback, TimeMonitor
|
||||
from mindspore.train.callback import __all__ as internal_cb_names
|
||||
|
|
@ -37,7 +37,7 @@ from mindspore.parallel._utils import _get_parallel_mode, _get_device_num, _get_
|
|||
_reset_op_id_with_offset
|
||||
from mindspore.parallel._ps_context import _is_role_worker, _is_role_pserver, _is_role_sched, _is_ps_mode, \
|
||||
_cache_enable, _enable_distributed_mindrt
|
||||
from mindspore.nn.metrics import Loss
|
||||
from mindspore.train.metrics import Loss
|
||||
from mindspore import nn
|
||||
from mindspore.boost import AutoBoost
|
||||
from mindspore.context import ParallelMode
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ import numpy as np
|
|||
import mindspore.communication.management as distributedTool
|
||||
import mindspore.nn as nn
|
||||
from mindspore import context
|
||||
from mindspore.nn.metrics import Accuracy
|
||||
from mindspore.train.metrics import Accuracy
|
||||
from mindspore.train import Model
|
||||
from mindspore.train.callback import LossMonitor, TimeMonitor
|
||||
from tests.models.official.cv.lenet.src.dataset import create_dataset
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@ import mindspore.dataset.vision as CV
|
|||
import mindspore.nn as nn
|
||||
from mindspore.common import dtype as mstype
|
||||
from mindspore.dataset.vision import Inter
|
||||
from mindspore.nn.metrics import Accuracy
|
||||
from mindspore.train.metrics import Accuracy
|
||||
from mindspore.train import Model
|
||||
from mindspore.train.callback import LossMonitor
|
||||
from mindspore.common.initializer import TruncatedNormal
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ from mindspore.ops import composite as C
|
|||
from mindspore.ops import operations as P
|
||||
from mindspore.nn import Dropout
|
||||
from mindspore.nn.optim import Adam
|
||||
from mindspore.nn.metrics import Metric
|
||||
from mindspore.train.metrics import Metric
|
||||
from mindspore import nn, Tensor, ParameterTuple, Parameter
|
||||
from mindspore.common.initializer import Uniform, initializer
|
||||
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
|
||||
|
|
|
|||
|
|
@ -26,7 +26,7 @@ from mindspore import log as logger
|
|||
from mindspore.common import dtype as mstype
|
||||
from mindspore.common.tensor import Tensor
|
||||
from mindspore.nn.learning_rate_schedule import LearningRateSchedule, PolynomialDecayLR, WarmUpLR
|
||||
from mindspore.nn.metrics import Metric
|
||||
from mindspore.train.metrics import Metric
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.train.callback import Callback
|
||||
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@
|
|||
# ============================================================================
|
||||
"""mIou."""
|
||||
import numpy as np
|
||||
from mindspore.nn.metrics.metric import Metric
|
||||
from mindspore.train.metrics import Metric
|
||||
|
||||
|
||||
def confuse_matrix(target, pred, n):
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ from mindspore import Tensor, ParameterTuple
|
|||
from mindspore.common import dtype as mstype
|
||||
from mindspore.dataset.vision import Inter
|
||||
from mindspore.nn import Dense, TrainOneStepCell, WithLossCell, ForwardValueAndGrad
|
||||
from mindspore.nn.metrics import Accuracy
|
||||
from mindspore.train.metrics import Accuracy
|
||||
from mindspore.nn.optim import Momentum
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.ops import functional as F
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@
|
|||
|
||||
import mindspore.context as context
|
||||
from mindspore import set_seed
|
||||
from mindspore.nn.metrics import Accuracy
|
||||
from mindspore.train.metrics import Accuracy
|
||||
from mindspore.train import Model
|
||||
from mindspore.train.callback import LossMonitor, TimeMonitor
|
||||
from mindspore.communication.management import init
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@
|
|||
|
||||
import mindspore.context as context
|
||||
from mindspore import set_seed
|
||||
from mindspore.nn.metrics import Accuracy
|
||||
from mindspore.train.metrics import Accuracy
|
||||
from mindspore.train import Model
|
||||
from mindspore.train.callback import TimeMonitor
|
||||
from mindspore.communication.management import init
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ import pytest
|
|||
|
||||
from mindspore import dataset as ds
|
||||
from mindspore import nn, Tensor, context
|
||||
from mindspore.nn.metrics import Accuracy
|
||||
from mindspore.train.metrics import Accuracy
|
||||
from mindspore.nn.optim import Momentum
|
||||
from mindspore.dataset.transforms import transforms as C
|
||||
from mindspore.dataset.vision import transforms as CV
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ from mindspore.nn import EmbeddingLookup, SoftmaxCrossEntropyWithLogits
|
|||
from mindspore.nn import Adam
|
||||
from mindspore.train import Model
|
||||
from mindspore.train.callback import CheckpointConfig, ModelCheckpoint
|
||||
from mindspore.nn.metrics import Accuracy
|
||||
from mindspore.train.metrics import Accuracy
|
||||
from mindspore.common import set_seed
|
||||
from mindspore.communication.management import get_rank
|
||||
import mindspore.ops.operations as op
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@ import mindspore.dataset.vision as CV
|
|||
import mindspore.nn as nn
|
||||
from mindspore.common import dtype as mstype
|
||||
from mindspore.dataset.vision import Inter
|
||||
from mindspore.nn.metrics import Accuracy
|
||||
from mindspore.train.metrics import Accuracy
|
||||
from mindspore.train import Model
|
||||
from mindspore.train.callback import LossMonitor
|
||||
from mindspore.common.initializer import TruncatedNormal
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ from mindspore import context
|
|||
from mindspore import Tensor
|
||||
from mindspore.common import dtype as mstype
|
||||
import mindspore.nn as nn
|
||||
from mindspore.nn.metrics import Accuracy
|
||||
from mindspore.train.metrics import Accuracy
|
||||
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
|
||||
from mindspore import load_checkpoint, load_param_into_net, export
|
||||
from mindspore.train import Model
|
||||
|
|
|
|||
|
|
@ -25,7 +25,7 @@ import pytest
|
|||
from mindspore.common import set_seed
|
||||
from mindspore import nn, Tensor, context
|
||||
from mindspore.common.initializer import Normal
|
||||
from mindspore.nn.metrics import Loss
|
||||
from mindspore.train.metrics import Loss
|
||||
from mindspore.nn.optim import Momentum
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.train import Model
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ import pytest
|
|||
|
||||
from mindspore import nn, Tensor, context
|
||||
from mindspore.common.initializer import Normal
|
||||
from mindspore.nn.metrics import Loss
|
||||
from mindspore.train.metrics import Loss
|
||||
from mindspore.nn.optim import Momentum
|
||||
from mindspore.ops import operations as P
|
||||
from mindspore.train import Model
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ import numpy as np
|
|||
import pytest
|
||||
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.metrics import Accuracy
|
||||
from mindspore.train.metrics import Accuracy
|
||||
|
||||
|
||||
def test_classification_accuracy():
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@
|
|||
import math
|
||||
import numpy as np
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.metrics import ROC, auc
|
||||
from mindspore.train.metrics import ROC, auc
|
||||
|
||||
|
||||
def test_auc():
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@
|
|||
"""test_bleu_score"""
|
||||
import math
|
||||
import pytest
|
||||
from mindspore.nn.metrics import BleuScore
|
||||
from mindspore.train.metrics import BleuScore
|
||||
|
||||
|
||||
def test_bleu_score():
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@
|
|||
import numpy as np
|
||||
import pytest
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.metrics import ConfusionMatrix
|
||||
from mindspore.train.metrics import ConfusionMatrix
|
||||
|
||||
|
||||
def test_confusion_matrix():
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@
|
|||
import numpy as np
|
||||
import pytest
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.metrics import ConfusionMatrixMetric
|
||||
from mindspore.train.metrics import ConfusionMatrixMetric
|
||||
|
||||
|
||||
def test_confusion_matrix_metric():
|
||||
|
|
|
|||
|
|
@ -16,7 +16,7 @@
|
|||
import pytest
|
||||
import numpy as np
|
||||
from sklearn.metrics import pairwise
|
||||
from mindspore.nn.metrics import CosineSimilarity
|
||||
from mindspore.train.metrics import CosineSimilarity
|
||||
|
||||
|
||||
def test_cosine_similarity():
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@ import math
|
|||
import numpy as np
|
||||
import pytest
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.metrics import get_metric_fn, Dice
|
||||
from mindspore.train.metrics import get_metric_fn, Dice
|
||||
|
||||
|
||||
def test_classification_dice():
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ import numpy as np
|
|||
import pytest
|
||||
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.metrics import MAE, MSE
|
||||
from mindspore.train.metrics import MAE, MSE
|
||||
|
||||
|
||||
def test_MAE():
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@ import numpy as np
|
|||
import pytest
|
||||
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.metrics import get_metric_fn, Fbeta
|
||||
from mindspore.train.metrics import get_metric_fn, Fbeta
|
||||
|
||||
|
||||
def test_classification_fbeta():
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ import math
|
|||
import numpy as np
|
||||
import pytest
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.metrics import get_metric_fn, HausdorffDistance
|
||||
from mindspore.train.metrics import get_metric_fn, HausdorffDistance
|
||||
|
||||
|
||||
def test_hausdorff_distance():
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@ import numpy as np
|
|||
import pytest
|
||||
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.metrics import Loss
|
||||
from mindspore.train.metrics import Loss
|
||||
|
||||
|
||||
def test_loss_inputs_error():
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ import math
|
|||
import numpy as np
|
||||
import pytest
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.metrics import get_metric_fn, MeanSurfaceDistance
|
||||
from mindspore.train.metrics import get_metric_fn, MeanSurfaceDistance
|
||||
|
||||
|
||||
def test_mean_surface_distance():
|
||||
|
|
|
|||
|
|
@ -17,8 +17,7 @@ import math
|
|||
import numpy as np
|
||||
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.metrics import get_metric_fn
|
||||
from mindspore.nn.metrics.metric import rearrange_inputs
|
||||
from mindspore.train.metrics import get_metric_fn, rearrange_inputs
|
||||
|
||||
|
||||
def test_classification_accuracy():
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@ import pytest
|
|||
import numpy as np
|
||||
from mindspore import nn, context
|
||||
from mindspore.common.tensor import Tensor
|
||||
from mindspore.nn.metrics import OcclusionSensitivity
|
||||
from mindspore.train.metrics import OcclusionSensitivity
|
||||
context.set_context(mode=context.GRAPH_MODE)
|
||||
|
||||
class DenseNet(nn.Cell):
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ import math
|
|||
import numpy as np
|
||||
import pytest
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.metrics import get_metric_fn, Perplexity
|
||||
from mindspore.train.metrics import get_metric_fn, Perplexity
|
||||
|
||||
|
||||
def test_perplexity():
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ import numpy as np
|
|||
import pytest
|
||||
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.metrics import Precision
|
||||
from mindspore.train.metrics import Precision
|
||||
|
||||
|
||||
def test_classification_precision():
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ import numpy as np
|
|||
import pytest
|
||||
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.metrics import Recall
|
||||
from mindspore.train.metrics import Recall
|
||||
|
||||
|
||||
def test_classification_recall():
|
||||
|
|
|
|||
|
|
@ -17,7 +17,7 @@
|
|||
import numpy as np
|
||||
import pytest
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.metrics import ROC
|
||||
from mindspore.train.metrics import ROC
|
||||
|
||||
|
||||
def test_roc():
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ import math
|
|||
import numpy as np
|
||||
import pytest
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.metrics import get_metric_fn, RootMeanSquareDistance
|
||||
from mindspore.train.metrics import get_metric_fn, RootMeanSquareDistance
|
||||
|
||||
|
||||
def test_root_mean_square_distance():
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ import numpy as np
|
|||
import pytest
|
||||
|
||||
from mindspore import Tensor
|
||||
from mindspore.nn.metrics import TopKCategoricalAccuracy, Top1CategoricalAccuracy, Top5CategoricalAccuracy
|
||||
from mindspore.train.metrics import TopKCategoricalAccuracy, Top1CategoricalAccuracy, Top5CategoricalAccuracy
|
||||
|
||||
|
||||
def test_type_topk():
|
||||
|
|
|
|||
|
|
@ -20,7 +20,7 @@ import pytest
|
|||
|
||||
from mindspore.common import set_seed
|
||||
from mindspore import nn
|
||||
from mindspore.nn.metrics import Loss
|
||||
from mindspore.train.metrics import Loss
|
||||
from mindspore.train import Model
|
||||
from mindspore.train.callback import SummaryLandscape
|
||||
from tests.security_utils import security_off_wrap
|
||||
|
|
|
|||
Loading…
Reference in New Issue