[深度学习]PaddleClas：统计模型Flops

PaddleClas：统计模型Flops

函数接口

paddle.flops(net, input_size, custom_ops=None, print_detail=False)[源代码]

作用为：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

官方示例

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 intput 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)

---

输出：

<class 'paddle.nn.layer.conv.Conv2D'>'s flops has been counted
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

补充示例

import paddle
import paddle.nn as nn

from paddle.vision.models import resnet50

from ppcls.arch.backbone.legendary_models.resnet import ResNet50

testNet = ResNet50()

FLOPs = paddle.flops(testNet,
                     [1, 3, 224, 224],
                     print_detail=True)
print(FLOPs)

---

输出：

W0122 17:22:08.571107 1724177 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 12.2, Runtime API Version: 11.6
W0122 17:22:08.575258 1724177 gpu_resources.cc:91] device: 0, cuDNN Version: 8.4.
<class 'paddle.nn.layer.pooling.AvgPool2D'>'s flops has been counted
paddle.nn.layer.conv.Conv2D'>'s flops has been counted
<class 'paddle.fluid.dygraph.nn.BatchNorm'>'s flops has been counted
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.pooling.AdaptiveAvgPool2D'>'s flops has been counted
Cannot find suitable count function for paddle.fluid.dygraph.nn.Flatten'>. Treat it as zero FLOPs.
paddle.nn.layer.common.Linear'>'s flops has been counted
+-----------------------+-------------------+-------------------+---------+-----------+
|       Layer Name      |    Input Shape    |    Output Shape   |  Params |   Flops   |
+-----------------------+-------------------+-------------------+---------+-----------+
|        conv2d_0       |  [1, 3, 224, 224] | [1, 64, 112, 112] |   9408  | 118013952 |
|      batch_norm_0     | [1, 64, 112, 112] | [1, 64, 112, 112] |   256   |  1605632  |
|        re_lu_4        | [1, 64, 112, 112] | [1, 64, 112, 112] |    0    |     0     |
|      max_pool2d_0     | [1, 64, 112, 112] |  [1, 64, 56, 56]  |    0    |     0     |
|        conv2d_1       |  [1, 64, 56, 56]  |  [1, 64, 56, 56]  |   4096  |  12845056 |
|      batch_norm_1     |  [1, 64, 56, 56]  |  [1, 64, 56, 56]  |   256   |   401408  |
|        re_lu_5        |  [1, 64, 56, 56]  |  [1, 64, 56, 56]  |    0    |     0     |
|        conv2d_2       |  [1, 64, 56, 56]  |  [1, 64, 56, 56]  |  36864  | 115605504 |
|      batch_norm_2     |  [1, 64, 56, 56]  |  [1, 64, 56, 56]  |   256   |   401408  |
|        re_lu_6        |  [1, 64, 56, 56]  |  [1, 64, 56, 56]  |    0    |     0     |
|        conv2d_3       |  [1, 64, 56, 56]  |  [1, 256, 56, 56] |  16384  |  51380224 |
|      batch_norm_3     |  [1, 256, 56, 56] |  [1, 256, 56, 56] |   1024  |  1605632  |
|        conv2d_4       |  [1, 64, 56, 56]  |  [1, 256, 56, 56] |  16384  |  51380224 |
|      batch_norm_4     |  [1, 256, 56, 56] |  [1, 256, 56, 56] |   1024  |  1605632  |
|        re_lu_9        |  [1, 256, 56, 56] |  [1, 256, 56, 56] |    0    |     0     |
|        conv2d_5       |  [1, 256, 56, 56] |  [1, 64, 56, 56]  |  16384  |  51380224 |
|      batch_norm_5     |  [1, 64, 56, 56]  |  [1, 64, 56, 56]  |   256   |   401408  |
|        re_lu_10       |  [1, 64, 56, 56]  |  [1, 64, 56, 56]  |    0    |     0     |
|        conv2d_6       |  [1, 64, 56, 56]  |  [1, 64, 56, 56]  |  36864  | 115605504 |
|      batch_norm_6     |  [1, 64, 56, 56]  |  [1, 64, 56, 56]  |   256   |   401408  |
|        re_lu_11       |  [1, 64, 56, 56]  |  [1, 64, 56, 56]  |    0    |     0     |
|        conv2d_7       |  [1, 64, 56, 56]  |  [1, 256, 56, 56] |  16384  |  51380224 |
|      batch_norm_7     |  [1, 256, 56, 56] |  [1, 256, 56, 56] |   1024  |  1605632  |
|        re_lu_13       |  [1, 256, 56, 56] |  [1, 256, 56, 56] |    0    |     0     |
|        conv2d_8       |  [1, 256, 56, 56] |  [1, 64, 56, 56]  |  16384  |  51380224 |
|      batch_norm_8     |  [1, 64, 56, 56]  |  [1, 64, 56, 56]  |   256   |   401408  |
|        re_lu_14       |  [1, 64, 56, 56]  |  [1, 64, 56, 56]  |    0    |     0     |
|        conv2d_9       |  [1, 64, 56, 56]  |  [1, 64, 56, 56]  |  36864  | 115605504 |
|      batch_norm_9     |  [1, 64, 56, 56]  |  [1, 64, 56, 56]  |   256   |   401408  |
|        re_lu_15       |  [1, 64, 56, 56]  |  [1, 64, 56, 56]  |    0    |     0     |
|       conv2d_10       |  [1, 64, 56, 56]  |  [1, 256, 56, 56] |  16384  |  51380224 |
|     batch_norm_10     |  [1, 256, 56, 56] |  [1, 256, 56, 56] |   1024  |  1605632  |
|        re_lu_17       |  [1, 256, 56, 56] |  [1, 256, 56, 56] |    0    |     0     |
|       conv2d_11       |  [1, 256, 56, 56] |  [1, 128, 56, 56] |  32768  | 102760448 |
|     batch_norm_11     |  [1, 128, 56, 56] |  [1, 128, 56, 56] |   512   |   802816  |
|        re_lu_18       |  [1, 128, 56, 56] |  [1, 128, 56, 56] |    0    |     0     |
|       conv2d_12       |  [1, 128, 56, 56] |  [1, 128, 28, 28] |  147456 | 115605504 |
|     batch_norm_12     |  [1, 128, 28, 28] |  [1, 128, 28, 28] |   512   |   200704  |
|        re_lu_19       |  [1, 128, 28, 28] |  [1, 128, 28, 28] |    0    |     0     |
|       conv2d_13       |  [1, 128, 28, 28] |  [1, 512, 28, 28] |  65536  |  51380224 |
|     batch_norm_13     |  [1, 512, 28, 28] |  [1, 512, 28, 28] |   2048  |   802816  |
|       conv2d_14       |  [1, 256, 56, 56] |  [1, 512, 28, 28] |  131072 | 102760448 |
|     batch_norm_14     |  [1, 512, 28, 28] |  [1, 512, 28, 28] |   2048  |   802816  |
|        re_lu_22       |  [1, 512, 28, 28] |  [1, 512, 28, 28] |    0    |     0     |
|       conv2d_15       |  [1, 512, 28, 28] |  [1, 128, 28, 28] |  65536  |  51380224 |
|     batch_norm_15     |  [1, 128, 28, 28] |  [1, 128, 28, 28] |   512   |   200704  |
|        re_lu_23       |  [1, 128, 28, 28] |  [1, 128, 28, 28] |    0    |     0     |
|       conv2d_16       |  [1, 128, 28, 28] |  [1, 128, 28, 28] |  147456 | 115605504 |
|     batch_norm_16     |  [1, 128, 28, 28] |  [1, 128, 28, 28] |   512   |   200704  |
|        re_lu_24       |  [1, 128, 28, 28] |  [1, 128, 28, 28] |    0    |     0     |
|       conv2d_17       |  [1, 128, 28, 28] |  [1, 512, 28, 28] |  65536  |  51380224 |
|     batch_norm_17     |  [1, 512, 28, 28] |  [1, 512, 28, 28] |   2048  |   802816  |
|        re_lu_26       |  [1, 512, 28, 28] |  [1, 512, 28, 28] |    0    |     0     |
|       conv2d_18       |  [1, 512, 28, 28] |  [1, 128, 28, 28] |  65536  |  51380224 |
|     batch_norm_18     |  [1, 128, 28, 28] |  [1, 128, 28, 28] |   512   |   200704  |
|        re_lu_27       |  [1, 128, 28, 28] |  [1, 128, 28, 28] |    0    |     0     |
|       conv2d_19       |  [1, 128, 28, 28] |  [1, 128, 28, 28] |  147456 | 115605504 |
|     batch_norm_19     |  [1, 128, 28, 28] |  [1, 128, 28, 28] |   512   |   200704  |
|        re_lu_28       |  [1, 128, 28, 28] |  [1, 128, 28, 28] |    0    |     0     |
|       conv2d_20       |  [1, 128, 28, 28] |  [1, 512, 28, 28] |  65536  |  51380224 |
|     batch_norm_20     |  [1, 512, 28, 28] |  [1, 512, 28, 28] |   2048  |   802816  |
|        re_lu_30       |  [1, 512, 28, 28] |  [1, 512, 28, 28] |    0    |     0     |
|       conv2d_21       |  [1, 512, 28, 28] |  [1, 128, 28, 28] |  65536  |  51380224 |
|     batch_norm_21     |  [1, 128, 28, 28] |  [1, 128, 28, 28] |   512   |   200704  |
|        re_lu_31       |  [1, 128, 28, 28] |  [1, 128, 28, 28] |    0    |     0     |
|       conv2d_22       |  [1, 128, 28, 28] |  [1, 128, 28, 28] |  147456 | 115605504 |
|     batch_norm_22     |  [1, 128, 28, 28] |  [1, 128, 28, 28] |   512   |   200704  |
|        re_lu_32       |  [1, 128, 28, 28] |  [1, 128, 28, 28] |    0    |     0     |
|       conv2d_23       |  [1, 128, 28, 28] |  [1, 512, 28, 28] |  65536  |  51380224 |
|     batch_norm_23     |  [1, 512, 28, 28] |  [1, 512, 28, 28] |   2048  |   802816  |
|        re_lu_34       |  [1, 512, 28, 28] |  [1, 512, 28, 28] |    0    |     0     |
|       conv2d_24       |  [1, 512, 28, 28] |  [1, 256, 28, 28] |  131072 | 102760448 |
|     batch_norm_24     |  [1, 256, 28, 28] |  [1, 256, 28, 28] |   1024  |   401408  |
|        re_lu_35       |  [1, 256, 28, 28] |  [1, 256, 28, 28] |    0    |     0     |
|       conv2d_25       |  [1, 256, 28, 28] |  [1, 256, 14, 14] |  589824 | 115605504 |
|     batch_norm_25     |  [1, 256, 14, 14] |  [1, 256, 14, 14] |   1024  |   100352  |
|        re_lu_36       |  [1, 256, 14, 14] |  [1, 256, 14, 14] |    0    |     0     |
|       conv2d_26       |  [1, 256, 14, 14] | [1, 1024, 14, 14] |  262144 |  51380224 |
|     batch_norm_26     | [1, 1024, 14, 14] | [1, 1024, 14, 14] |   4096  |   401408  |
|       conv2d_27       |  [1, 512, 28, 28] | [1, 1024, 14, 14] |  524288 | 102760448 |
|     batch_norm_27     | [1, 1024, 14, 14] | [1, 1024, 14, 14] |   4096  |   401408  |
|        re_lu_39       | [1, 1024, 14, 14] | [1, 1024, 14, 14] |    0    |     0     |
|       conv2d_28       | [1, 1024, 14, 14] |  [1, 256, 14, 14] |  262144 |  51380224 |
|     batch_norm_28     |  [1, 256, 14, 14] |  [1, 256, 14, 14] |   1024  |   100352  |
|        re_lu_40       |  [1, 256, 14, 14] |  [1, 256, 14, 14] |    0    |     0     |
|       conv2d_29       |  [1, 256, 14, 14] |  [1, 256, 14, 14] |  589824 | 115605504 |
|     batch_norm_29     |  [1, 256, 14, 14] |  [1, 256, 14, 14] |   1024  |   100352  |
|        re_lu_41       |  [1, 256, 14, 14] |  [1, 256, 14, 14] |    0    |     0     |
|       conv2d_30       |  [1, 256, 14, 14] | [1, 1024, 14, 14] |  262144 |  51380224 |
|     batch_norm_30     | [1, 1024, 14, 14] | [1, 1024, 14, 14] |   4096  |   401408  |
|        re_lu_43       | [1, 1024, 14, 14] | [1, 1024, 14, 14] |    0    |     0     |
|       conv2d_31       | [1, 1024, 14, 14] |  [1, 256, 14, 14] |  262144 |  51380224 |
|     batch_norm_31     |  [1, 256, 14, 14] |  [1, 256, 14, 14] |   1024  |   100352  |
|        re_lu_44       |  [1, 256, 14, 14] |  [1, 256, 14, 14] |    0    |     0     |
|       conv2d_32       |  [1, 256, 14, 14] |  [1, 256, 14, 14] |  589824 | 115605504 |
|     batch_norm_32     |  [1, 256, 14, 14] |  [1, 256, 14, 14] |   1024  |   100352  |
|        re_lu_45       |  [1, 256, 14, 14] |  [1, 256, 14, 14] |    0    |     0     |
|       conv2d_33       |  [1, 256, 14, 14] | [1, 1024, 14, 14] |  262144 |  51380224 |
|     batch_norm_33     | [1, 1024, 14, 14] | [1, 1024, 14, 14] |   4096  |   401408  |
|        re_lu_47       | [1, 1024, 14, 14] | [1, 1024, 14, 14] |    0    |     0     |
|       conv2d_34       | [1, 1024, 14, 14] |  [1, 256, 14, 14] |  262144 |  51380224 |
|     batch_norm_34     |  [1, 256, 14, 14] |  [1, 256, 14, 14] |   1024  |   100352  |
|        re_lu_48       |  [1, 256, 14, 14] |  [1, 256, 14, 14] |    0    |     0     |
|       conv2d_35       |  [1, 256, 14, 14] |  [1, 256, 14, 14] |  589824 | 115605504 |
|     batch_norm_35     |  [1, 256, 14, 14] |  [1, 256, 14, 14] |   1024  |   100352  |
|        re_lu_49       |  [1, 256, 14, 14] |  [1, 256, 14, 14] |    0    |     0     |
|       conv2d_36       |  [1, 256, 14, 14] | [1, 1024, 14, 14] |  262144 |  51380224 |
|     batch_norm_36     | [1, 1024, 14, 14] | [1, 1024, 14, 14] |   4096  |   401408  |
|        re_lu_51       | [1, 1024, 14, 14] | [1, 1024, 14, 14] |    0    |     0     |
|       conv2d_37       | [1, 1024, 14, 14] |  [1, 256, 14, 14] |  262144 |  51380224 |
|     batch_norm_37     |  [1, 256, 14, 14] |  [1, 256, 14, 14] |   1024  |   100352  |
|        re_lu_52       |  [1, 256, 14, 14] |  [1, 256, 14, 14] |    0    |     0     |
|       conv2d_38       |  [1, 256, 14, 14] |  [1, 256, 14, 14] |  589824 | 115605504 |
|     batch_norm_38     |  [1, 256, 14, 14] |  [1, 256, 14, 14] |   1024  |   100352  |
|        re_lu_53       |  [1, 256, 14, 14] |  [1, 256, 14, 14] |    0    |     0     |
|       conv2d_39       |  [1, 256, 14, 14] | [1, 1024, 14, 14] |  262144 |  51380224 |
|     batch_norm_39     | [1, 1024, 14, 14] | [1, 1024, 14, 14] |   4096  |   401408  |
|        re_lu_55       | [1, 1024, 14, 14] | [1, 1024, 14, 14] |    0    |     0     |
|       conv2d_40       | [1, 1024, 14, 14] |  [1, 256, 14, 14] |  262144 |  51380224 |
|     batch_norm_40     |  [1, 256, 14, 14] |  [1, 256, 14, 14] |   1024  |   100352  |
|        re_lu_56       |  [1, 256, 14, 14] |  [1, 256, 14, 14] |    0    |     0     |
|       conv2d_41       |  [1, 256, 14, 14] |  [1, 256, 14, 14] |  589824 | 115605504 |
|     batch_norm_41     |  [1, 256, 14, 14] |  [1, 256, 14, 14] |   1024  |   100352  |
|        re_lu_57       |  [1, 256, 14, 14] |  [1, 256, 14, 14] |    0    |     0     |
|       conv2d_42       |  [1, 256, 14, 14] | [1, 1024, 14, 14] |  262144 |  51380224 |
|     batch_norm_42     | [1, 1024, 14, 14] | [1, 1024, 14, 14] |   4096  |   401408  |
|        re_lu_59       | [1, 1024, 14, 14] | [1, 1024, 14, 14] |    0    |     0     |
|       conv2d_43       | [1, 1024, 14, 14] |  [1, 512, 14, 14] |  524288 | 102760448 |
|     batch_norm_43     |  [1, 512, 14, 14] |  [1, 512, 14, 14] |   2048  |   200704  |
|        re_lu_60       |  [1, 512, 14, 14] |  [1, 512, 14, 14] |    0    |     0     |
|       conv2d_44       |  [1, 512, 14, 14] |   [1, 512, 7, 7]  | 2359296 | 115605504 |
|     batch_norm_44     |   [1, 512, 7, 7]  |   [1, 512, 7, 7]  |   2048  |   50176   |
|        re_lu_61       |   [1, 512, 7, 7]  |   [1, 512, 7, 7]  |    0    |     0     |
|       conv2d_45       |   [1, 512, 7, 7]  |  [1, 2048, 7, 7]  | 1048576 |  51380224 |
|     batch_norm_45     |  [1, 2048, 7, 7]  |  [1, 2048, 7, 7]  |   8192  |   200704  |
|       conv2d_46       | [1, 1024, 14, 14] |  [1, 2048, 7, 7]  | 2097152 | 102760448 |
|     batch_norm_46     |  [1, 2048, 7, 7]  |  [1, 2048, 7, 7]  |   8192  |   200704  |
|        re_lu_64       |  [1, 2048, 7, 7]  |  [1, 2048, 7, 7]  |    0    |     0     |
|       conv2d_47       |  [1, 2048, 7, 7]  |   [1, 512, 7, 7]  | 1048576 |  51380224 |
|     batch_norm_47     |   [1, 512, 7, 7]  |   [1, 512, 7, 7]  |   2048  |   50176   |
|        re_lu_65       |   [1, 512, 7, 7]  |   [1, 512, 7, 7]  |    0    |     0     |
|       conv2d_48       |   [1, 512, 7, 7]  |   [1, 512, 7, 7]  | 2359296 | 115605504 |
|     batch_norm_48     |   [1, 512, 7, 7]  |   [1, 512, 7, 7]  |   2048  |   50176   |
|        re_lu_66       |   [1, 512, 7, 7]  |   [1, 512, 7, 7]  |    0    |     0     |
|       conv2d_49       |   [1, 512, 7, 7]  |  [1, 2048, 7, 7]  | 1048576 |  51380224 |
|     batch_norm_49     |  [1, 2048, 7, 7]  |  [1, 2048, 7, 7]  |   8192  |   200704  |
|        re_lu_68       |  [1, 2048, 7, 7]  |  [1, 2048, 7, 7]  |    0    |     0     |
|       conv2d_50       |  [1, 2048, 7, 7]  |   [1, 512, 7, 7]  | 1048576 |  51380224 |
|     batch_norm_50     |   [1, 512, 7, 7]  |   [1, 512, 7, 7]  |   2048  |   50176   |
|        re_lu_69       |   [1, 512, 7, 7]  |   [1, 512, 7, 7]  |    0    |     0     |
|       conv2d_51       |   [1, 512, 7, 7]  |   [1, 512, 7, 7]  | 2359296 | 115605504 |
|     batch_norm_51     |   [1, 512, 7, 7]  |   [1, 512, 7, 7]  |   2048  |   50176   |
|        re_lu_70       |   [1, 512, 7, 7]  |   [1, 512, 7, 7]  |    0    |     0     |
|       conv2d_52       |   [1, 512, 7, 7]  |  [1, 2048, 7, 7]  | 1048576 |  51380224 |
|     batch_norm_52     |  [1, 2048, 7, 7]  |  [1, 2048, 7, 7]  |   8192  |   200704  |
|        re_lu_72       |  [1, 2048, 7, 7]  |  [1, 2048, 7, 7]  |    0    |     0     |
| adaptive_avg_pool2d_0 |  [1, 2048, 7, 7]  |  [1, 2048, 1, 1]  |    0    |   102400  |
|       flatten_0       |  [1, 2048, 1, 1]  |     [1, 2048]     |    0    |     0     |
|        linear_0       |     [1, 2048]     |     [1, 1000]     | 2049000 |  2048000  |
+-----------------------+-------------------+-------------------+---------+-----------+
Total Flops: 4111514624     Total Params: 25610152
4111514624

reference

@misc{BibEntry2024Jan,
title = {{flops-API文档-PaddlePaddle深度学习平台}},
year = {2024},
month = jan,
urldate = {2024-01-22},
language = {chinese},
note = {[Online; accessed 22. Jan. 2024]},
url = {https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/flops_cn.html}
}