paddle.summary 英文文档修改 (#63121)

smallpoxscattered · web-flow · commit 31174be8bac1 · 2024-04-01T16:45:32.000+08:00
diff --git a/python/paddle/hapi/model_summary.py b/python/paddle/hapi/model_summary.py
@@ -45,10 +45,12 @@ def summary(net, input_size=None, dtypes=None, input=None):
 
     Examples:
         .. code-block:: python
+            :name: code-example-1
 
+            >>> # example 1: Single Input Demo
             >>> import paddle
             >>> import paddle.nn as nn
-            >>> paddle.seed(2023)
+            >>> # Define Network
             >>> class LeNet(nn.Layer):
             ...     def __init__(self, num_classes=10):
             ...         super().__init__()
@@ -76,21 +78,19 @@ def summary(net, input_size=None, dtypes=None, input=None):
             ...         return x
             ...
             >>> lenet = LeNet()
-
-            >>> params_info = paddle.summary(lenet, (1, 1, 28, 28))
-            >>> print(params_info)
+            >>> params_info = paddle.summary(lenet, (1, 1, 28, 28)) # doctest: +NORMALIZE_WHITESPACE
             ---------------------------------------------------------------------------
-            Layer (type)       Input Shape          Output Shape         Param #
+             Layer (type)       Input Shape          Output Shape         Param #
             ===========================================================================
-              Conv2D-1       [[1, 1, 28, 28]]      [1, 6, 28, 28]          60
+               Conv2D-1       [[1, 1, 28, 28]]      [1, 6, 28, 28]          60
                 ReLU-1        [[1, 6, 28, 28]]      [1, 6, 28, 28]           0
               MaxPool2D-1     [[1, 6, 28, 28]]      [1, 6, 14, 14]           0
-              Conv2D-2       [[1, 6, 14, 14]]     [1, 16, 10, 10]         2,416
+               Conv2D-2       [[1, 6, 14, 14]]     [1, 16, 10, 10]         2,416
                 ReLU-2       [[1, 16, 10, 10]]     [1, 16, 10, 10]           0
               MaxPool2D-2    [[1, 16, 10, 10]]      [1, 16, 5, 5]            0
-              Linear-1          [[1, 400]]            [1, 120]           48,120
-              Linear-2          [[1, 120]]            [1, 84]            10,164
-              Linear-3          [[1, 84]]             [1, 10]              850
+               Linear-1          [[1, 400]]            [1, 120]           48,120
+               Linear-2          [[1, 120]]            [1, 84]            10,164
+               Linear-3          [[1, 84]]             [1, 10]              850
             ===========================================================================
             Total params: 61,610
             Trainable params: 61,610
@@ -101,9 +101,34 @@ def summary(net, input_size=None, dtypes=None, input=None):
             Params size (MB): 0.24
             Estimated Total Size (MB): 0.35
             ---------------------------------------------------------------------------
+            <BLANKLINE>
+            >>> print(params_info)
             {'total_params': 61610, 'trainable_params': 61610}
-            >>> # multi input demo
-            >>> class LeNetMultiInput(LeNet):
+
+        .. code-block:: python
+            :name: code-example-2
+
+            >>> # example 2: multi input demo
+            >>> import paddle
+            >>> import paddle.nn as nn
+            >>> class LeNetMultiInput(nn.Layer):
+            ...     def __init__(self, num_classes=10):
+            ...         super().__init__()
+            ...         self.num_classes = num_classes
+            ...         self.features = nn.Sequential(
+            ...             nn.Conv2D(1, 6, 3, stride=1, padding=1),
+            ...             nn.ReLU(),
+            ...             nn.MaxPool2D(2, 2),
+            ...             nn.Conv2D(6, 16, 5, stride=1, padding=0),
+            ...             nn.ReLU(),
+            ...             nn.MaxPool2D(2, 2))
+            ...
+            ...         if num_classes > 0:
+            ...             self.fc = nn.Sequential(
+            ...                 nn.Linear(400, 120),
+            ...                 nn.Linear(120, 84),
+            ...                 nn.Linear(84, 10))
+            ...
             ...     def forward(self, inputs, y):
             ...         x = self.features(inputs)
             ...
@@ -116,20 +141,19 @@ def summary(net, input_size=None, dtypes=None, input=None):
 
             >>> params_info = paddle.summary(lenet_multi_input,
             ...                              [(1, 1, 28, 28), (1, 400)],
-            ...                              dtypes=['float32', 'float32'])
-            >>> print(params_info)
+            ...                              dtypes=['float32', 'float32']) # doctest: +NORMALIZE_WHITESPACE
             ---------------------------------------------------------------------------
-            Layer (type)       Input Shape          Output Shape         Param #
+             Layer (type)       Input Shape          Output Shape         Param #
             ===========================================================================
-              Conv2D-3       [[1, 1, 28, 28]]      [1, 6, 28, 28]          60
-                ReLU-3        [[1, 6, 28, 28]]      [1, 6, 28, 28]           0
-              MaxPool2D-3     [[1, 6, 28, 28]]      [1, 6, 14, 14]           0
-              Conv2D-4       [[1, 6, 14, 14]]     [1, 16, 10, 10]         2,416
-                ReLU-4       [[1, 16, 10, 10]]     [1, 16, 10, 10]           0
-              MaxPool2D-4    [[1, 16, 10, 10]]      [1, 16, 5, 5]            0
-              Linear-4          [[1, 400]]            [1, 120]           48,120
-              Linear-5          [[1, 120]]            [1, 84]            10,164
-              Linear-6          [[1, 84]]             [1, 10]              850
+               Conv2D-1       [[1, 1, 28, 28]]      [1, 6, 28, 28]          60
+                ReLU-1        [[1, 6, 28, 28]]      [1, 6, 28, 28]           0
+              MaxPool2D-1     [[1, 6, 28, 28]]      [1, 6, 14, 14]           0
+               Conv2D-2       [[1, 6, 14, 14]]     [1, 16, 10, 10]         2,416
+                ReLU-2       [[1, 16, 10, 10]]     [1, 16, 10, 10]           0
+              MaxPool2D-2    [[1, 16, 10, 10]]      [1, 16, 5, 5]            0
+               Linear-1          [[1, 400]]            [1, 120]           48,120
+               Linear-2          [[1, 120]]            [1, 84]            10,164
+               Linear-3          [[1, 84]]             [1, 10]              850
             ===========================================================================
             Total params: 61,610
             Trainable params: 61,610
@@ -140,9 +164,36 @@ def summary(net, input_size=None, dtypes=None, input=None):
             Params size (MB): 0.24
             Estimated Total Size (MB): 0.35
             ---------------------------------------------------------------------------
+            <BLANKLINE>
+            >>> print(params_info)
             {'total_params': 61610, 'trainable_params': 61610}
+
+        .. code-block:: python
+            :name: code-example-3
+
+            >>> # example 3: List/Dict Input Demo
+            >>> import paddle
+            >>> import paddle.nn as nn
+
             >>> # list input demo
-            >>> class LeNetListInput(LeNet):
+            >>> class LeNetListInput(nn.Layer):
+            ...     def __init__(self, num_classes=10):
+            ...         super().__init__()
+            ...         self.num_classes = num_classes
+            ...         self.features = nn.Sequential(
+            ...             nn.Conv2D(1, 6, 3, stride=1, padding=1),
+            ...             nn.ReLU(),
+            ...             nn.MaxPool2D(2, 2),
+            ...             nn.Conv2D(6, 16, 5, stride=1, padding=0),
+            ...             nn.ReLU(),
+            ...             nn.MaxPool2D(2, 2))
+            ...
+            ...         if num_classes > 0:
+            ...             self.fc = nn.Sequential(
+            ...                 nn.Linear(400, 120),
+            ...                 nn.Linear(120, 84),
+            ...                 nn.Linear(84, 10))
+            ...
             ...     def forward(self, inputs):
             ...         x = self.features(inputs[0])
             ...
@@ -153,20 +204,19 @@ def summary(net, input_size=None, dtypes=None, input=None):
             ...
             >>> lenet_list_input = LeNetListInput()
             >>> input_data = [paddle.rand([1, 1, 28, 28]), paddle.rand([1, 400])]
-            >>> params_info = paddle.summary(lenet_list_input, input=input_data)
-            >>> print(params_info)
+            >>> params_info = paddle.summary(lenet_list_input, input=input_data) # doctest: +NORMALIZE_WHITESPACE
             ---------------------------------------------------------------------------
-            Layer (type)       Input Shape          Output Shape         Param #
+             Layer (type)       Input Shape          Output Shape         Param #
             ===========================================================================
-              Conv2D-5       [[1, 1, 28, 28]]      [1, 6, 28, 28]          60
-                ReLU-5        [[1, 6, 28, 28]]      [1, 6, 28, 28]           0
-              MaxPool2D-5     [[1, 6, 28, 28]]      [1, 6, 14, 14]           0
-              Conv2D-6       [[1, 6, 14, 14]]     [1, 16, 10, 10]         2,416
-                ReLU-6       [[1, 16, 10, 10]]     [1, 16, 10, 10]           0
-              MaxPool2D-6    [[1, 16, 10, 10]]      [1, 16, 5, 5]            0
-              Linear-7          [[1, 400]]            [1, 120]           48,120
-              Linear-8          [[1, 120]]            [1, 84]            10,164
-              Linear-9          [[1, 84]]             [1, 10]              850
+               Conv2D-1       [[1, 1, 28, 28]]      [1, 6, 28, 28]          60
+                ReLU-1        [[1, 6, 28, 28]]      [1, 6, 28, 28]           0
+              MaxPool2D-1     [[1, 6, 28, 28]]      [1, 6, 14, 14]           0
+               Conv2D-2       [[1, 6, 14, 14]]     [1, 16, 10, 10]         2,416
+                ReLU-2       [[1, 16, 10, 10]]     [1, 16, 10, 10]           0
+              MaxPool2D-2    [[1, 16, 10, 10]]      [1, 16, 5, 5]            0
+               Linear-1          [[1, 400]]            [1, 120]           48,120
+               Linear-2          [[1, 120]]            [1, 84]            10,164
+               Linear-3          [[1, 84]]             [1, 10]              850
             ===========================================================================
             Total params: 61,610
             Trainable params: 61,610
@@ -177,9 +227,29 @@ def summary(net, input_size=None, dtypes=None, input=None):
             Params size (MB): 0.24
             Estimated Total Size (MB): 0.35
             ---------------------------------------------------------------------------
+            <BLANKLINE>
+            >>> print(params_info)
             {'total_params': 61610, 'trainable_params': 61610}
+
             >>> # dict input demo
-            >>> class LeNetDictInput(LeNet):
+            >>> class LeNetDictInput(nn.Layer):
+            ...     def __init__(self, num_classes=10):
+            ...         super().__init__()
+            ...         self.num_classes = num_classes
+            ...         self.features = nn.Sequential(
+            ...             nn.Conv2D(1, 6, 3, stride=1, padding=1),
+            ...             nn.ReLU(),
+            ...             nn.MaxPool2D(2, 2),
+            ...             nn.Conv2D(6, 16, 5, stride=1, padding=0),
+            ...             nn.ReLU(),
+            ...             nn.MaxPool2D(2, 2))
+            ...
+            ...         if num_classes > 0:
+            ...             self.fc = nn.Sequential(
+            ...                 nn.Linear(400, 120),
+            ...                 nn.Linear(120, 84),
+            ...                 nn.Linear(84, 10))
+            ...
             ...     def forward(self, inputs):
             ...         x = self.features(inputs['x1'])
             ...
@@ -191,20 +261,20 @@ def summary(net, input_size=None, dtypes=None, input=None):
             >>> lenet_dict_input = LeNetDictInput()
             >>> input_data = {'x1': paddle.rand([1, 1, 28, 28]),
             ...               'x2': paddle.rand([1, 400])}
-            >>> params_info = paddle.summary(lenet_dict_input, input=input_data)
-            >>> print(params_info)
+            >>> # The module suffix number indicates its sequence in modules of the same type, used for differentiation identification
+            >>> params_info = paddle.summary(lenet_dict_input, input=input_data) # doctest: +NORMALIZE_WHITESPACE
             ---------------------------------------------------------------------------
-            Layer (type)       Input Shape          Output Shape         Param #
+             Layer (type)       Input Shape          Output Shape         Param #
             ===========================================================================
-              Conv2D-7       [[1, 1, 28, 28]]      [1, 6, 28, 28]          60
-                ReLU-7        [[1, 6, 28, 28]]      [1, 6, 28, 28]           0
-              MaxPool2D-7     [[1, 6, 28, 28]]      [1, 6, 14, 14]           0
-              Conv2D-8       [[1, 6, 14, 14]]     [1, 16, 10, 10]         2,416
-                ReLU-8       [[1, 16, 10, 10]]     [1, 16, 10, 10]           0
-              MaxPool2D-8    [[1, 16, 10, 10]]      [1, 16, 5, 5]            0
-              Linear-10         [[1, 400]]            [1, 120]           48,120
-              Linear-11         [[1, 120]]            [1, 84]            10,164
-              Linear-12         [[1, 84]]             [1, 10]              850
+               Conv2D-3       [[1, 1, 28, 28]]      [1, 6, 28, 28]          60
+                ReLU-3        [[1, 6, 28, 28]]      [1, 6, 28, 28]           0
+              MaxPool2D-3     [[1, 6, 28, 28]]      [1, 6, 14, 14]           0
+               Conv2D-4       [[1, 6, 14, 14]]     [1, 16, 10, 10]         2,416
+                ReLU-4       [[1, 16, 10, 10]]     [1, 16, 10, 10]           0
+              MaxPool2D-4    [[1, 16, 10, 10]]      [1, 16, 5, 5]            0
+               Linear-4          [[1, 400]]            [1, 120]           48,120
+               Linear-5          [[1, 120]]            [1, 84]            10,164
+               Linear-6          [[1, 84]]             [1, 10]              850
             ===========================================================================
             Total params: 61,610
             Trainable params: 61,610
@@ -215,6 +285,8 @@ def summary(net, input_size=None, dtypes=None, input=None):
             Params size (MB): 0.24
             Estimated Total Size (MB): 0.35
             ---------------------------------------------------------------------------
+            <BLANKLINE>
+            >>> print(params_info)
             {'total_params': 61610, 'trainable_params': 61610}
 
     """
