add parameter of input in model.summary

[wangna11BD]

PR types

Function optimization

PR changes

APIs

Describe

Add parameter of input in model.summary
If forward (self, x, y, z), it can use input_shape; if forward(self, x_list) or forward(self， x_dict), the input of dict or list can only be input.
Examples:
.. 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.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)

        # 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)

        # 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)

[paddle-bot-old]

Thanks for your contribution!
Please wait for the result of CI firstly. See Paddle CI Manual for details.

[LielinJiang]

input can be of any type. And need to explain, if given, input_size and dtype will be ignored.

[wangna11BD]

done

[LielinJiang]

do not delete the original examples. Add new examples at the back, and add a example of dict and list input.

[wangna11BD]

done

[LielinJiang]

input can be any type as long as the network can run through

[wangna11BD]

done

[XiaoguangHu01]

LGTM