summary¶
通过 input_size 或 input 打印网络 net 的基础结构和参数信息。input_size 指定网络 net 输入 Tensor 的大小，而 input 指定网络 net 的输入 Tensor；如果给出 input，那么 input_size 和 dtypes 的输入将被忽略。
参数¶
net (Layer) - 网络实例，必须是
Layer的子类。input_size (tuple|InputSpec|list[tuple|InputSpec，可选) - 输入 Tensor 的大小。如果网络只有一个输入，那么该值需要设定为 tuple 或 InputSpec。如果模型有多个输入。那么该值需要设定为 list[tuple|InputSpec]，包含每个输入的 shape。默认值：None。
dtypes (str，可选) - 输入 Tensor 的数据类型，如果没有给定，默认使用
float32类型。默认值：None。input (tensor，可选) - 输入的 Tensor，如果给出
input，那么input_size和dtypes的输入将被忽略。默认值：None。
返回¶
字典，包含了总的参数量和总的可训练的参数量。
代码示例¶
>>> import paddle
>>> import paddle.nn as nn
>>> paddle.seed(2023)
>>> class LeNet(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)
...
... if self.num_classes > 0:
... x = paddle.flatten(x, 1)
... x = self.fc(x)
... return x
...
>>> lenet = LeNet()
>>> params_info = paddle.summary(lenet, (1, 1, 28, 28))
>>> print(params_info)
---------------------------------------------------------------------------
Layer (type) Input Shape Output Shape Param #
===========================================================================
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
Non-trainable params: 0
---------------------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.11
Params size (MB): 0.24
Estimated Total Size (MB): 0.35
---------------------------------------------------------------------------
{'total_params': 61610, 'trainable_params': 61610}
>>> # multi input demo
>>> class LeNetMultiInput(LeNet):
... def forward(self, inputs, y):
... x = self.features(inputs)
...
... if self.num_classes > 0:
... x = paddle.flatten(x, 1)
... x = self.fc(x + y)
... return x
...
>>> lenet_multi_input = LeNetMultiInput()
>>> params_info = paddle.summary(lenet_multi_input,
... [(1, 1, 28, 28), (1, 400)],
... dtypes=['float32', 'float32'])
>>> print(params_info)
---------------------------------------------------------------------------
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
===========================================================================
Total params: 61,610
Trainable params: 61,610
Non-trainable params: 0
---------------------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.11
Params size (MB): 0.24
Estimated Total Size (MB): 0.35
---------------------------------------------------------------------------
{'total_params': 61610, 'trainable_params': 61610}
>>> # list input demo
>>> class LeNetListInput(LeNet):
... def forward(self, inputs):
... x = self.features(inputs[0])
...
... if self.num_classes > 0:
... x = paddle.flatten(x, 1)
... x = self.fc(x + inputs[1])
... return x
...
>>> 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)
---------------------------------------------------------------------------
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
===========================================================================
Total params: 61,610
Trainable params: 61,610
Non-trainable params: 0
---------------------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.11
Params size (MB): 0.24
Estimated Total Size (MB): 0.35
---------------------------------------------------------------------------
{'total_params': 61610, 'trainable_params': 61610}
>>> # dict input demo
>>> class LeNetDictInput(LeNet):
... def forward(self, inputs):
... x = self.features(inputs['x1'])
...
... if self.num_classes > 0:
... x = paddle.flatten(x, 1)
... x = self.fc(x + inputs['x2'])
... return x
...
>>> 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)
---------------------------------------------------------------------------
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
===========================================================================
Total params: 61,610
Trainable params: 61,610
Non-trainable params: 0
---------------------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.11
Params size (MB): 0.24
Estimated Total Size (MB): 0.35
---------------------------------------------------------------------------
{'total_params': 61610, 'trainable_params': 61610}