计算模型的GFLOPs和参数量 & 举例VGG16和DETR

近期忙于写论文，分享一下论文中表格数据的计算方法。

目录

一、FLOPS、FLOPs和GFLOPs的概念

• FLOPS：注意S是大写，是 “每秒所执行的浮点运算次数”（floating-point operations per second）的缩写。它常被用来估算电脑的执行效能，尤其是在使用到大量浮点运算的科学计算领域中。正因为FLOPS字尾的那个S，代表秒，而不是复数，所以不能省略掉。
• FLOPs：注意s小写，是floating point operations的缩写（s表复数），意指浮点运算数，理解为计算量。可以用来衡量算法/模型的复杂度。
• GFLOPs：一个GFLOPs等于每秒十亿（=10^9）次的浮点运算。

二、计算VGG16的GFLOPs和参数量

from thop import profile
import torch
import torchvision.models as models

model = models.vgg16()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
input = torch.zeros((1, 3, 224, 224)).to(device)
flops, params = profile(model.to(device), inputs=(input,))

print("参数量：", params)
print("FLOPS：", flops)

>>>output

参数量： 138357544.0
FLOPS： 15470314496.0

三、计算DETR的GFLOPs和参数量

1. 首先，访问网址：GitHub – facebookresearch/detr: End-to-End Object Detection with Transformers
2. 然后，下载DETR源码压缩包，调通源码。
3. 最后，把下面的代码封装到py文件中，放到DETR源码的根目录即可。

import os
import time

from PIL import Image
import matplotlib.pyplot as plt

import torch
import torchvision.transforms as T
torch.set_grad_enabled(False)

from models import build_model
import argparse

from torch.nn.functional import dropout,linear,softmax


def get_args_parser():
    parser = argparse.ArgumentParser('Set transformer detector', add_help=False)
    parser.add_argument('--lr', default=1e-4, type=float)
    parser.add_argument('--lr_backbone', default=1e-5, type=float)
    parser.add_argument('--batch_size', default=1, type=int)
    parser.add_argument('--weight_decay', default=1e-4, type=float)
    # parser.add_argument('--epochs', default=300, type=int)
    parser.add_argument('--epochs', default=100, type=int)
    parser.add_argument('--lr_drop', default=200, type=int)
    parser.add_argument('--clip_max_norm', default=0.1, type=float,
                        help='gradient clipping max norm')

    # Model parameters
    parser.add_argument('--frozen_weights', type=str, default=None,
                        help="Path to the pretrained model. If set, only the mask head will be trained")
    # * Backbone
    parser.add_argument('--backbone', default='resnet50', type=str,
                        help="Name of the convolutional backbone to use")
    parser.add_argument('--dilation', action='store_true',
                        help="If true, we replace stride with dilation in the last convolutional block (DC5)")
    parser.add_argument('--position_embedding', default='sine', type=str, choices=('sine', 'learned'),
                        help="Type of positional embedding to use on top of the image features")

    # * Transformer
    parser.add_argument('--enc_layers', default=6, type=int,
                        help="Number of encoding layers in the transformer")
    parser.add_argument('--dec_layers', default=6, type=int,
                        help="Number of decoding layers in the transformer")
    parser.add_argument('--dim_feedforward', default=2048, type=int,
                        help="Intermediate size of the feedforward layers in the transformer blocks")
    parser.add_argument('--hidden_dim', default=256, type=int,
                        help="Size of the embeddings (dimension of the transformer)")
    parser.add_argument('--dropout', default=0.1, type=float,
                        help="Dropout applied in the transformer")
    parser.add_argument('--nheads', default=8, type=int,
                        help="Number of attention heads inside the transformer's attentions")
    parser.add_argument('--num_queries', default=40, type=int,
                        help="Number of query slots")  # 论文中对象查询为100
    parser.add_argument('--pre_norm', action='store_true')

    # * Segmentation
    parser.add_argument('--masks', action='store_true',
                        help="Train segmentation head if the flag is provided")

    # Loss
    parser.add_argument('--no_aux_loss', dest='aux_loss', action='store_false',
                        help="Disables auxiliary decoding losses (loss at each layer)")
    # * Matcher
    parser.add_argument('--set_cost_class', default=1, type=float,
                        help="Class coefficient in the matching cost")
    parser.add_argument('--set_cost_bbox', default=5, type=float,
                        help="L1 box coefficient in the matching cost")
    parser.add_argument('--set_cost_giou', default=2, type=float,
                        help="giou box coefficient in the matching cost")
    # * Loss coefficients
    parser.add_argument('--mask_loss_coef', default=1, type=float)
    parser.add_argument('--dice_loss_coef', default=1, type=float)
    parser.add_argument('--bbox_loss_coef', default=5, type=float)
    parser.add_argument('--giou_loss_coef', default=2, type=float)
    parser.add_argument('--eos_coef', default=0.1, type=float,
                        help="Relative classification weight of the no-object class")

    # dataset parameters
    parser.add_argument('--dataset_file', default='coco')
    parser.add_argument('--coco_path', default='', type=str)
    parser.add_argument('--coco_panoptic_path', type=str)
    parser.add_argument('--remove_difficult', action='store_true')

    parser.add_argument('--output_dir', default='E:\project_yd\paper_sci_one_yd\Transformer\DETR\detr\\runs\\train',
                        help='path where to save, empty for no saving')
    parser.add_argument('--device', default='cuda',
                        help='device to use for training / testing')
    parser.add_argument('--seed', default=42, type=int)

    # ============================================================================= #
    parser.add_argument('--resume', default='', help='resume from checkpoint')
    # ============================================================================= #

    parser.add_argument('--start_epoch', default=0, type=int, metavar='N',
                        help='start epoch')
    parser.add_argument('--eval', action='store_true')
    parser.add_argument('--num_workers', default=2, type=int)

    # distributed training parameters
    parser.add_argument('--world_size', default=1, type=int,
                        help='number of distributed processes')
    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
    return parser



if __name__ == '__main__':
    parser = argparse.ArgumentParser('DETR training and evaluation script', parents=[get_args_parser()])
    args = parser.parse_args()

    # 建立模型
    model, criterion, postprocessors = build_model(args)
    model.to('cuda:0')
    url = r'detr-r50-dc5-f0fb7ef5.pth'
    state_dict = torch.load(url)
    # print(state_dict)

    # 加载模型参数，以字典的形式表示
    model.load_state_dict(state_dict['model'])
    model.eval()  # 把字符串类型转换成字典类型

    # ==================================================== #
    from thop import profile
    import torchsummary

    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    model.to(device)
    input = torch.zeros((1, 3, 800, 1422)).to(device)
    flops, params = profile(model.to(device), inputs=(input,))

    print("参数量：", params)
    print("FLOPS：", flops)
    # ==================================================== #

>>> output

参数量： 36739785.0
FLOPS： 100937364480.0

四、整理数据表格

>>> 如有疑问，欢迎评论区一起探讨！