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!13669 modify example 

From: @lijiaqi0612
Reviewed-by: @kingxian,@kisnwang
Signed-off-by: @kingxian
This commit is contained in:
mindspore-ci-bot 2021-03-22 10:42:41 +08:00 committed by Gitee
parent 5a70d2aa6e 9f33381425
commit 958ec1c85b
12 changed files with 78 additions and 28 deletions
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24
mindspore/nn/loss/loss.py
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@ -436,7 +436,7 @@ class DiceLoss(_Loss):
>>> y = Tensor(np.array([[0, 1], [1, 0], [0, 1]]), mstype.float32)
>>> output = loss(y_pred, y)
>>> print(output)
[0.38596618]
0.38596618
"""
def __init__(self, smooth=1e-5):
super(DiceLoss, self).__init__()
@ -1031,7 +1031,11 @@ class FocalLoss(_Loss):
r"""
The loss function proposed by Kaiming team in their paper ``Focal Loss for Dense Object Detection`` improves the
effect of image object detection. It is a loss function to solve the imbalance of categories and the difference of
classification difficulty.
classification difficulty. If you want to learn more, please refer to the paper.
`https://arxiv.org/pdf/1708.02002.pdf`. The function is shown as follows:
.. math::
FL(p_t) = -(1-p_t)^\gamma log(p_t)
Args:
gamma (float): Gamma is used to adjust the steepness of weight curve in focal loss. Default: 2.0.
@ -1042,25 +1046,25 @@ class FocalLoss(_Loss):
Inputs:
- **predict** (Tensor) - Tensor of shape should be (B, C) or (B, C, H) or (B, C, H, W). Where C is the number
of classes. Its value is greater than 1. If the shape is (B, C, H, W) or (B, C, H), the H or product of H
and W should be the same as target.
of classes. Its value is greater than 1. If the shape is (B, C, H, W) or (B, C, H), the H or product of H
and W should be the same as target.
- **target** (Tensor) - Tensor of shape should be (B, C) or (B, C, H) or (B, C, H, W). The value of C is 1 or
it needs to be the same as predict's C. If C is not 1, the shape of target should be the same as that of
predict, where C is the number of classes. If the shape is (B, C, H, W) or (B, C, H), the H or product of H
and W should be the same as predict.
it needs to be the same as predict's C. If C is not 1, the shape of target should be the same as that of
predict, where C is the number of classes. If the shape is (B, C, H, W) or (B, C, H), the H or product of H
and W should be the same as predict.
Outputs:
Tensor, it's a tensor with the same shape and type as input `predict`.
Raises:
TypeError: If the data type of ``gamma`` is not float..
TypeError: If ``weight`` is not a Parameter.
TypeError: If the data type of ``gamma`` is not float.
TypeError: If ``weight`` is not a Tensor.
ValueError: If ``target`` dim different from ``predict``.
ValueError: If ``target`` channel is not 1 and ``target`` shape is different from ``predict``.
ValueError: If ``reduction`` is not one of 'none', 'mean', 'sum'.
Supported Platforms:
``Ascend`` ``GPU``
``Ascend``
Example:
>>> predict = Tensor([[0.8, 1.4], [0.5, 0.9], [1.2, 0.9]], mstype.float32)

6
mindspore/nn/metrics/auc.py
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@ -32,14 +32,18 @@ def auc(x, y, reorder=False):
Returns:
area (float): Compute result.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> 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 = ROC(pos_label=2)
>>> metric = nn.ROC(pos_label=2)
>>> metric.clear()
>>> metric.update(y_pred, y)
>>> fpr, tpr, thre = metric.eval()
>>> output = auc(fpr, tpr)
>>> print(output)
0.5357142857142857
"""
if not isinstance(x, np.ndarray) or not isinstance(y, np.ndarray):

4
mindspore/nn/metrics/bleu_score.py
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@ -27,6 +27,9 @@ class BleuScore(Metric):
n_gram (int): The n_gram value ranged from 1 to 4. Default: 4
smooth (bool): Whether or not to apply smoothing. Default: False
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Example:
>>> candidate_corpus = [['i', 'have', 'a', 'pen', 'on', 'my', 'desk']]
>>> reference_corpus = [[['i', 'have', 'a', 'pen', 'in', 'my', 'desk'],
@ -35,6 +38,7 @@ class BleuScore(Metric):
>>> metric.clear()
>>> metric.update(candidate_corpus, reference_corpus)
>>> bleu_score = metric.eval()
>>> print(output)
0.5946035575013605
"""
def __init__(self, n_gram=4, smooth=False):

10
mindspore/nn/metrics/confusion_matrix.py
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@ -39,10 +39,13 @@ class ConfusionMatrix(Metric):
threshold (float): A threshold, which is used to compare with the input tensor. Default: 0.5.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> 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 = nn.ConfusionMatrix(num_classes=2, normalize='no_norm', threshold=0.5)
>>> metric.clear()
>>> metric.update(x, y)
>>> output = metric.eval()
@ -165,9 +168,12 @@ class ConfusionMatrixMetric(Metric):
calculation_method is True. Default: "mean". Choose from:
["none", "mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel"].
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> metric = ConfusionMatrixMetric(skip_channel=True, metric_name="tpr",
>>> calculation_method=False, decrease="mean")
... calculation_method=False, decrease="mean")
>>> metric.clear()
>>> x = Tensor(np.array([[[0], [1]], [[1], [0]]]))
>>> y = Tensor(np.array([[[0], [1]], [[0], [1]]]))

14
mindspore/nn/metrics/cosine_similarity.py
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@ -29,17 +29,21 @@ class CosineSimilarity(Metric):
Return:
A square matrix (input1, input1) with the similarity scores between all elements.
If sum or mean are used, then returns (b, 1) with the reduced value for each row
If sum or mean are used, then returns (b, 1) with the reduced value for each row.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Example:
>>> test_data = np.random.randn(4, 8)
>>> test_data = np.array([[1, 3, 4, 7], [2, 4, 2, 5], [3, 1, 5, 8]])
>>> metric = CosineSimilarity()
>>> metric.clear()
>>> metric.update(test_data)
>>> square_matrix = metric.eval()
[[0. -0.14682831 0.19102288 -0.36204537]
...
]
>>> print(square_matrix)
[[0. 0.94025615 0.95162452]
[0.94025615 0. 0.86146098]
[0.95162452 0.86146098 0.]]
"""
def __init__(self, similarity='cosine', reduction='none', zero_diagonal=True):
super().__init__()

3
mindspore/nn/metrics/dice.py
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@ -32,6 +32,9 @@ class Dice(Metric):
smooth (float): A term added to the denominator to improve numerical stability. Should be greater than 0.
Default: 1e-5.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> 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]]))

5
mindspore/nn/metrics/hausdorff_distance.py
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@ -86,10 +86,13 @@ class HausdorffDistance(Metric):
here the bounding box is achieved by (y_pred | y) which represents the union set of two images.
Default: True.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> 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 = nn.HausdorffDistance()
>>> metric.clear()
>>> metric.update(x, y, 0)
>>> mean_average_distance = metric.eval()

3
mindspore/nn/metrics/mean_surface_distance.py
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@ -32,6 +32,9 @@ class MeanSurfaceDistance(Metric):
if sets ``symmetric = True``, the average symmetric surface distance between these two inputs
will be returned. Defaults: False.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> 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]]))

21
mindspore/nn/metrics/occlusion_sensitivity.py
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@ -13,7 +13,6 @@
# limitations under the License.
# ============================================================================
"""OcclusionSensitivity."""
from collections.abc import Sequence
import numpy as np
from mindspore import nn
from mindspore.common.tensor import Tensor
@ -47,16 +46,19 @@ class OcclusionSensitivity(Metric):
as the input image. If a bounding box is used, the output image will be cropped to this size.
Default: None.
Supported Platforms:
``Ascend`` ``GPU``
Example:
>>> class DenseNet(nn.Cell):
>>> def init(self):
>>> super(DenseNet, self).init()
>>> w = np.array([[0.1, 0.8, 0.1, 0.1],[1, 1, 1, 1]]).astype(np.float32)
>>> b = np.array([0.3, 0.6]).astype(np.float32)
>>> self.dense = nn.Dense(4, 2, weight_init=Tensor(w), bias_init=Tensor(b))
>>>
>>> def construct(self, x):
>>> return self.dense(x)
... def init(self):
... super(DenseNet, self).init()
... w = np.array([[0.1, 0.8, 0.1, 0.1],[1, 1, 1, 1]]).astype(np.float32)
... b = np.array([0.3, 0.6]).astype(np.float32)
... self.dense = nn.Dense(4, 2, weight_init=Tensor(w), bias_init=Tensor(b))
...
... def construct(self, x):
... return self.dense(x)
>>>
>>> model = DenseNet()
>>> test_data = np.array([[0.1, 0.2, 0.3, 0.4]]).astype(np.float32)
@ -65,6 +67,7 @@ class OcclusionSensitivity(Metric):
>>> metric.clear()
>>> metric.update(model, test_data, label)
>>> score = metric.eval()
>>> print(score)
[0.29999995 0.6 1 0.9]
"""
def __init__(self, pad_val=0.0, margin=2, n_batch=128, b_box=None):

4
mindspore/nn/metrics/perplexity.py
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@ -32,6 +32,9 @@ class Perplexity(Metric):
ignore_label (int): Index of an invalid label to be ignored when counting. If set to `None`, it will include all
entries. Default: -1.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
>>> y = Tensor(np.array([1, 0, 1]))
@ -39,6 +42,7 @@ class Perplexity(Metric):
>>> metric.clear()
>>> metric.update(x, y)
>>> perplexity = metric.eval()
>>> print(perplexity)
2.231443166940565
"""

9
mindspore/nn/metrics/roc.py
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@ -30,6 +30,9 @@ class ROC(Metric):
to 1. For multiclass problems, this argument should not be set, as it is iteratively changed in the
range [0,num_classes-1]. Default: None.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> # 1) binary classification example
>>> x = Tensor(np.array([3, 1, 4, 2]))
@ -38,8 +41,11 @@ class ROC(Metric):
>>> metric.clear()
>>> metric.update(x, y)
>>> fpr, tpr, thresholds = metric.eval()
>>> print(fpr)
[0., 0., 0.33333333, 0.6666667, 1.]
>>> print(tpr)
[0., 1, 1., 1., 1.]
>>> print(thresholds)
[5, 4, 3, 2, 1]
>>>
>>> # 2) multiclass classification example
@ -50,9 +56,12 @@ class ROC(Metric):
>>> metric.clear()
>>> metric.update(x, y)
>>> fpr, tpr, thresholds = metric.eval()
>>> print(fpr)
[array([0., 0., 0.33333333, 0.66666667, 1.]), array([0., 0.33333333, 0.33333333, 1.]),
array([0., 0.33333333, 1.]), array([0., 0., 1.])]
>>> print(tpr)
[array([0., 1., 1., 1., 1.]), array([0., 0., 1., 1.]), array([0., 1., 1.]), array([0., 1., 1.])]
>>> print(thresholds)
[array([1.28, 0.28, 0.2, 0.1, 0.05]), array([1.55, 0.55, 0.2, 0.05]), array([1.15, 0.15, 0.05]),
array([1.75, 0.75, 0.05])]
"""

3
mindspore/nn/metrics/root_mean_square_surface_distance.py
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@ -33,6 +33,9 @@ class RootMeanSquareDistance(Metric):
if sets ``symmetric = True``, the average symmetric surface distance between these two inputs
will be returned. Defaults: False.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> 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]]))