修改 COPY-FROM No.6 metric (#5971)

* update code-block to COPY-FROM * fix COPY-FROM format
PaddlePaddle · Jul 6, 2023 · eb2ead6 · eb2ead6
1 parent a60b04a
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diff --git a/docs/api/paddle/metric/Accuracy__upper_cn.rst b/docs/api/paddle/metric/Accuracy__upper_cn.rst
@@ -16,49 +16,15 @@ Accuracy
 代码示例
 :::::::::
 
-**独立使用示例:**
+独立使用示例
 
-    .. code-block:: python
+COPY-FROM: paddle.metric.Accuracy:code-standalone-example
 
-        import numpy as np
-        import paddle
-        x = paddle.to_tensor(np.array([
-            [0.1, 0.2, 0.3, 0.4],
-            [0.1, 0.4, 0.3, 0.2],
-            [0.1, 0.2, 0.4, 0.3],
-            [0.1, 0.2, 0.3, 0.4]]))
-        y = paddle.to_tensor(np.array([[0], [1], [2], [3]]))
-        m = paddle.metric.Accuracy()
-        correct = m.compute(x, y)
-        m.update(correct)
-        res = m.accumulate()
-        print(res) # 0.75
-
-
-**在 Model API 中的示例**
-
-    .. code-block:: python
-
-        import paddle
-        from paddle.static import InputSpec
-        import paddle.vision.transforms as T
-        from paddle.vision.datasets import MNIST
-
-        input = InputSpec([None, 1, 28, 28], 'float32', 'image')
-        label = InputSpec([None, 1], 'int64', 'label')
-        transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
-        train_dataset = MNIST(mode='train', transform=transform)
-
-        model = paddle.Model(paddle.vision.models.LeNet(), input, label)
-        optim = paddle.optimizer.Adam(
-            learning_rate=0.001, parameters=model.parameters())
-        model.prepare(
-            optim,
-            loss=paddle.nn.CrossEntropyLoss(),
-            metrics=paddle.metric.Accuracy())
-
-        model.fit(train_dataset, batch_size=64)
+代码示例 2
+::::::::::::
+在 Model API 中的示例
 
+COPY-FROM: paddle.metric.Accuracy:code-model-api-example
 
 compute(pred, label, *args)
 :::::::::

diff --git a/docs/api/paddle/metric/Auc_cn.rst b/docs/api/paddle/metric/Auc_cn.rst
@@ -26,63 +26,14 @@ Auc
 
 独立使用示例
 
-    .. code-block:: python
-
-        import numpy as np
-        import paddle
-
-        m = paddle.metric.Auc()
-
-        n = 8
-        class0_preds = np.random.random(size = (n, 1))
-        class1_preds = 1 - class0_preds
-
-        preds = np.concatenate((class0_preds, class1_preds), axis=1)
-        labels = np.random.randint(2, size = (n, 1))
-
-        m.update(preds=preds, labels=labels)
-        res = m.accumulate()
+COPY-FROM: paddle.metric.Auc:code-standalone-example
 
 代码示例 2
 ::::::::::::
 
 在 Model API 中的示例
 
-    .. code-block:: python
-
-        import numpy as np
-        import paddle
-        import paddle.nn as nn
-
-        class Data(paddle.io.Dataset):
-            def __init__(self):
-                super().__init__()
-                self.n = 1024
-                self.x = np.random.randn(self.n, 10).astype('float32')
-                self.y = np.random.randint(2, size=(self.n, 1)).astype('int64')
-
-            def __getitem__(self, idx):
-                return self.x[idx], self.y[idx]
-
-            def __len__(self):
-                return self.n
-
-        model = paddle.Model(nn.Sequential(
-            nn.Linear(10, 2), nn.Softmax())
-        )
-        optim = paddle.optimizer.Adam(
-            learning_rate=0.001, parameters=model.parameters())
-
-        def loss(x, y):
-            return nn.functional.nll_loss(paddle.log(x), y)
-
-        model.prepare(
-            optim,
-            loss=loss,
-            metrics=paddle.metric.Auc())
-        data = Data()
-        model.fit(data, batch_size=16)
-
+COPY-FROM: paddle.metric.Auc:code-model-api-example
 
 方法
 ::::::::::::

diff --git a/docs/api/paddle/metric/Metric_cn.rst b/docs/api/paddle/metric/Metric_cn.rst
@@ -51,31 +51,14 @@ Python/NumPy，这样可以加速计算。`update` 接口将 `compute` 的输出
 可以在 `compute` 中计算每个样本的 top-5 得分，正确预测的矩阵的 shape 是[N, 5]。
 
 
-    .. code-block:: python
-
-        def compute(pred, label):
-            # sort prediction and slice the top-5 scores
-            pred = paddle.argsort(pred, descending=True)[:, :5]
-            # calculate whether the predictions are correct
-            correct = pred == label
-            return paddle.cast(correct, dtype='float32')
+COPY-FROM: paddle.metric.Metric:code-compute-example
 
 代码示例 2
 ::::::::::::
 
 在 `compute` 中的计算，使用内置的算子(可以跑在 GPU 上，使得速度更快)。作为 `update` 的输入，该接口计算如下：
 
-    .. code-block:: python
-
-        def update(self, correct):
-            accs = []
-            for i, k in enumerate(self.topk):
-                num_corrects = correct[:, :k].sum()
-                num_samples = len(correct)
-                accs.append(float(num_corrects) / num_samples)
-                self.total[i] += num_corrects
-                self.count[i] += num_samples
-            return accs
+COPY-FROM: paddle.metric.Metric:code-update-example
 
 方法
 ::::::::::::

diff --git a/docs/api/paddle/metric/Precision_cn.rst b/docs/api/paddle/metric/Precision_cn.rst
@@ -24,57 +24,14 @@ Precision
 
 独立使用示例
 
-    .. code-block:: python
-
-        import numpy as np
-        import paddle
-
-        x = np.array([0.1, 0.5, 0.6, 0.7])
-        y = np.array([0, 1, 1, 1])
-
-        m = paddle.metric.Precision()
-        m.update(x, y)
-        res = m.accumulate()
-        print(res) # 1.0
+COPY-FROM: paddle.metric.Precision:code-standalone-example
 
 代码示例 2
 ::::::::::::
 
 在 Model API 中的示例
 
-    .. code-block:: python
-
-        import numpy as np
-
-        import paddle
-        import paddle.nn as nn
-
-        class Data(paddle.io.Dataset):
-            def __init__(self):
-                super().__init__()
-                self.n = 1024
-                self.x = np.random.randn(self.n, 10).astype('float32')
-                self.y = np.random.randint(2, size=(self.n, 1)).astype('float32')
-
-            def __getitem__(self, idx):
-                return self.x[idx], self.y[idx]
-
-            def __len__(self):
-                return self.n
-
-        model = paddle.Model(nn.Sequential(
-            nn.Linear(10, 1),
-            nn.Sigmoid()
-        ))
-        optim = paddle.optimizer.Adam(
-            learning_rate=0.001, parameters=model.parameters())
-        model.prepare(
-            optim,
-            loss=nn.BCELoss(),
-            metrics=paddle.metric.Precision())
-
-        data = Data()
-        model.fit(data, batch_size=16)
+COPY-FROM: paddle.metric.Precision:code-model-api-example
 
 方法
 ::::::::::::

diff --git a/docs/api/paddle/metric/Recall_cn.rst b/docs/api/paddle/metric/Recall_cn.rst
@@ -25,56 +25,13 @@ Recall
 
 独立使用示例
 
-    .. code-block:: python
-
-        import numpy as np
-        import paddle
-
-        x = np.array([0.1, 0.5, 0.6, 0.7])
-        y = np.array([1, 0, 1, 1])
-
-        m = paddle.metric.Recall()
-        m.update(x, y)
-        res = m.accumulate()
-        print(res) # 2.0 / 3.0
+COPY-FROM: paddle.metric.Recall:code-standalone-example
 
 代码示例 2
 ::::::::::::
 在 Model API 中的示例
 
-    .. code-block:: python
-
-        import numpy as np
-
-        import paddle
-        import paddle.nn as nn
-
-        class Data(paddle.io.Dataset):
-            def __init__(self):
-                super().__init__()
-                self.n = 1024
-                self.x = np.random.randn(self.n, 10).astype('float32')
-                self.y = np.random.randint(2, size=(self.n, 1)).astype('float32')
-
-            def __getitem__(self, idx):
-                return self.x[idx], self.y[idx]
-
-            def __len__(self):
-                return self.n
-
-        model = paddle.Model(nn.Sequential(
-            nn.Linear(10, 1),
-            nn.Sigmoid()
-        ))
-        optim = paddle.optimizer.Adam(
-            learning_rate=0.001, parameters=model.parameters())
-        model.prepare(
-            optim,
-            loss=nn.BCELoss(),
-            metrics=[paddle.metric.Precision(), paddle.metric.Recall()])
-
-        data = Data()
-        model.fit(data, batch_size=16)
+COPY-FROM: paddle.metric.Recall:code-model-api-example
 
 方法
 ::::::::::::