Spaces:
Paused
Paused
File size: 2,686 Bytes
1cac303 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | import torchvision.models as models
import torch.nn as nn
import torch
from thop import profile
df = 15
def VGG16(df, num_class=1,):
net = models.vgg16()
net.classifier = nn.Sequential(
nn.Linear(7*7*512, df),
nn.ReLU(inplace=True),
nn.Linear(df, num_class)
)
return net
def Mobilenetv2(df, num_class=1,):
net = models.mobilenet_v2()
net.classifier = nn.Sequential(
nn.Linear(1280, df),
nn.ReLU(inplace=True),
nn.Linear(df, num_class)
)
return net
def Densenet121(df, num_class=1,):
net = models.densenet121()
net.classifier = nn.Sequential(
nn.Linear(1024, df),
nn.ReLU(inplace=True),
nn.Linear(df, num_class)
)
return net
def Resnext50(df, num_class=1,):
net = models.resnext50_32x4d()
net.classifier = nn.Sequential(
nn.Linear(2048, df),
nn.ReLU(inplace=True),
nn.Linear(df, num_class)
)
return net
def AlexNet(df, num_class=1,):
net = models.AlexNet()
net.classifier = nn.Sequential(
nn.Linear(9216, df),
nn.ReLU(inplace=True),
nn.Linear(df, num_class)
)
return net
def Mnasnet(df, num_class=1,):
net = models.mnasnet1_0()
net.classifier = nn.Sequential(
nn.Linear(1280, df),
nn.ReLU(inplace=True),
nn.Linear(df, num_class)
)
return net
def Resnet101(df, num_class=1,):
net = models.resnet101()
net.fc = nn.Sequential(
nn.Linear(2048, df),
nn.ReLU(inplace=True),
nn.Linear(df, num_class)
)
return net
def Resnet50(df, num_class=1,):
net = models.resnet50()
net.fc = nn.Sequential(
nn.Linear(2048, df),
nn.ReLU(inplace=True),
nn.Linear(df, num_class)
)
return net
# # test
# print(resnet50(df=df))
# net = vgg16(df)
# input = torch.randn(1, 3, 224, 224)
# out = net(input)
# print("构建网络成功")
# 计算 flops 和 parmas
# print(VGG16(df=df))
# print(Mobilenetv2(df=df))
# print(Densenet121(df=df))
# print(Resnext50(df=df))
# print(AlexNet(df=df))
# print(Mnasnet(df=df))
# print(Resnet50(df=df))
# Nets = [Densenet121(df), AlexNet(df), Mobilenetv2(df), VGG16(df), Mnasnet(df), Resnext50(df), Resnet50(df)]
# Nets_names = ['Densenet121()', 'AlexNet()', 'Mobilenetv2()', 'VGG16()', 'Mnasnet()', 'Resnext50()', 'Resnet50()']
# input = torch.randn(1, 3, 224, 224)
# for Net, Name in zip(Nets, Nets_names):
# num_params = 0
# for param in Net.parameters():
# num_params += param.numel()
# # print(Name, num_params / 1e6)
# flops, params = profile(Net, inputs=(input,))
# print(Name, params / 1e6, flops / 1e9)
|