flops¶
打印网络的基础结构和参数信息。
参数¶
net (paddle.nn.Layer|paddle.static.Program) - 网络实例，必须是 paddle.nn.Layer 的子类或者静态图下的 paddle.static.Program。
input_size (list) - 输入 Tensor 的大小。注意：仅支持 batch_size=1。
custom_ops (dict，可选) - 字典，用于实现对自定义网络层的统计。字典的 key 为自定义网络层的 class，value 为统计网络层 flops 的函数，函数实现方法见示例代码。此参数仅在
net为 paddle.nn.Layer 时生效。默认值：None。print_detail (bool，可选) - bool 值，用于控制是否打印每个网络层的细节。默认值：False。
返回¶
int，网络模型的计算量。
代码示例¶
>>> 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):
... x = x[0]
... nelements = x.numel()
... m.total_ops += int(nelements)
...
>>> FLOPs = paddle.flops(lenet,
... [1, 1, 28, 28],
... custom_ops= {nn.LeakyReLU: count_leaky_relu},
... print_detail=True)
>>> print(FLOPs)
<class 'paddle.nn.layer.conv.Conv2D'>'s flops has been counted
<class 'paddle.nn.layer.activation.ReLU'>'s flops has been counted
Cannot find suitable count function for <class 'paddle.nn.layer.pooling.MaxPool2D'>. Treat it as zero FLOPs.
<class 'paddle.nn.layer.common.Linear'>'s flops has been counted
+--------------+-----------------+-----------------+--------+--------+
| Layer Name | Input Shape | Output Shape | Params | Flops |
+--------------+-----------------+-----------------+--------+--------+
| conv2d_0 | [1, 1, 28, 28] | [1, 6, 28, 28] | 60 | 47040 |
| re_lu_0 | [1, 6, 28, 28] | [1, 6, 28, 28] | 0 | 0 |
| max_pool2d_0 | [1, 6, 28, 28] | [1, 6, 14, 14] | 0 | 0 |
| conv2d_1 | [1, 6, 14, 14] | [1, 16, 10, 10] | 2416 | 241600 |
| re_lu_1 | [1, 16, 10, 10] | [1, 16, 10, 10] | 0 | 0 |
| max_pool2d_1 | [1, 16, 10, 10] | [1, 16, 5, 5] | 0 | 0 |
| linear_0 | [1, 400] | [1, 120] | 48120 | 48000 |
| linear_1 | [1, 120] | [1, 84] | 10164 | 10080 |
| linear_2 | [1, 84] | [1, 10] | 850 | 840 |
+--------------+-----------------+-----------------+--------+--------+
Total Flops: 347560 Total Params: 61610
347560