Function flops

python/paddle/hapi/dynamic_flops.py:40–145

Print a table about the FLOPs of network. Args: net (paddle.nn.Layer||paddle.static.Program): The network which could be a instance of paddle.nn.Layer in dygraph or paddle.static.Program in static graph. input_size (list): size of input tensor. Note that the

(
    net: Layer | Program,
    input_size: list[int],
    custom_ops: _CustomOpsAlias | None = None,
    print_detail: bool = False,
)

def flops(
    net: Layer | Program,
    input_size: list[int],
    custom_ops: _CustomOpsAlias | None = None,
    print_detail: bool = False,
) -> int:
    """Print a table about the FLOPs of network.

    Args:
        net (paddle.nn.Layer||paddle.static.Program): The network which could be a instance of paddle.nn.Layer in
                    dygraph or paddle.static.Program in static graph.
        input_size (list): size of input tensor. Note that the batch_size in argument ``input_size`` only support 1.
        custom_ops (A dict of function, optional): A dictionary which key is the class of specific operation such as
                    paddle.nn.Conv2D and the value is the function used to count the FLOPs of this operation. This
                    argument only work when argument ``net`` is an instance of paddle.nn.Layer. The details could be found
                    in following example code. Default is None.
        print_detail (bool, optional): Whether to print the detail information, like FLOPs per layer, about the net FLOPs.
                    Default is False.

    Returns:
        Int: A number about the FLOPs of total network.

    Examples:
        .. code-block:: pycon

            >>> import paddle
            >>> import paddle.nn as nn

            >>> 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()
            >>> # m is the instance of nn.Layer, x is the input of layer, y is the output of layer.
            >>> def count_leaky_relu(m, x, y):