fix summary trainable_params bug

[wangna11BD]

PR types

Bug fixes

PR changes

APIs

Describe

Some network layers contain both trainable parameters and non-trainable parameters. This PR fixes that paddle.summary could only record the parameters of the entire layer as trainable or non-trainable parameters, resulting in incorrect calculation of trainable parameters.

Example:
.. code-block:: python

import paddle
import paddle.nn as nn

class LeNet(nn.Layer):
    def __init__(self, num_classes=10):
        super(LeNet, self).__init__()
        self.num_classes = num_classes
        self.features = nn.Sequential(
            nn.Conv2D(1, 6, 3, stride=1, padding=1),
            nn.BatchNorm2D(6),
            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()
status = lenet.state_dict()
params_info = paddle.summary(lenet, (1, 1, 28, 28))
print(params_info)

previous incorrect output :

correct output :

Corresponding issue #40057

[paddle-bot-old]

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