pytorch在fintune时将sequential中的层输出方法,以vgg为例
作者：BinWang-cvlab
今天小编就为大家分享一篇pytorch在fintune时将sequential中的层输出方法,以vgg为例，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
有时候我们在fintune时发现pytorch把许多层都集合在一个sequential里，但是我们希望能把中间层的结果引出来做下一步操作，于是我自己琢磨了一个方法，以vgg为例，有点僵硬哈！
首先pytorch自带的vgg16模型的网络结构如下：
VGG( (features): Sequential( (0): Conv2d (3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): ReLU(inplace) (2): Conv2d (64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): ReLU(inplace) (4): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1)) (5): Conv2d (64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (6): ReLU(inplace) (7): Conv2d (128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (8): ReLU(inplace) (9): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1)) (10): Conv2d (128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (11): ReLU(inplace) (12): Conv2d (256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (13): ReLU(inplace) (14): Conv2d (256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (15): ReLU(inplace) (16): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1)) (17): Conv2d (256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (18): ReLU(inplace) (19): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (20): ReLU(inplace) (21): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (22): ReLU(inplace) (23): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1)) (24): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (25): ReLU(inplace) (26): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (27): ReLU(inplace) (28): Conv2d (512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (29): ReLU(inplace) (30): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), dilation=(1, 1)) ) (classifier): Sequential( (0): Linear(in_features=25088, out_features=4096) (1): ReLU(inplace) (2): Dropout(p=0.5) (3): Linear(in_features=4096, out_features=4096) (4): ReLU(inplace) (5): Dropout(p=0.5) (6): Linear(in_features=4096, out_features=1000) ) )
我们需要fintune vgg16的features部分，并且我希望把3，8， 15， 22， 29这五个作为输出进一步操作。我的想法是自己写一个vgg网络，这个网络参数与pytorch的网络一致但是保证我们需要的层输出在sequential外。于是我写的网络如下：
class our_vgg(nn.Module): def __init__(self): super(our_vgg, self).__init__() self.conv1 = nn.Sequential( # conv1 nn.Conv2d(3, 64, 3, padding=35), nn.ReLU(inplace=True), nn.Conv2d(64, 64, 3, padding=1), nn.ReLU(inplace=True), ) self.conv2 = nn.Sequential( # conv2 nn.MaxPool2d(2, stride=2, ceil_mode=True), # 1/2 nn.Conv2d(64, 128, 3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(128, 128, 3, padding=1), nn.ReLU(inplace=True), ) self.conv3 = nn.Sequential( # conv3 nn.MaxPool2d(2, stride=2, ceil_mode=True), # 1/4 nn.Conv2d(128, 256, 3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, 3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 256, 3, padding=1), nn.ReLU(inplace=True), ) self.conv4 = nn.Sequential( # conv4 nn.MaxPool2d(2, stride=2, ceil_mode=True), # 1/8 nn.Conv2d(256, 512, 3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(inplace=True), ) self.conv5 = nn.Sequential( # conv5 nn.MaxPool2d(2, stride=2, ceil_mode=True), # 1/16 nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(512, 512, 3, padding=1), nn.ReLU(inplace=True), ) def forward(self, x): conv1 = self.conv1(x) conv2 = self.conv2(conv1) conv3 = self.conv3(conv2) conv4 = self.conv4(conv3) conv5 = self.conv5(conv4) return conv5
接着就是copy weights了：
def convert_vgg(vgg16):#vgg16是pytorch自带的
net = our_vgg()# 我写的vgg
vgg_items = net.state_dict().items()
vgg16_items = vgg16.items()
pretrain_model = {}
j = 0
for k, v in net.state_dict().iteritems():#按顺序依次填入
v = vgg16_items[j][1]
k = vgg_items[j][0]
pretrain_model[k] = v
j += 1
return pretrain_model
## net是我们最后使用的网络，也是我们想要放置weights的网络
net = net()
print ('load the weight from vgg')
pretrained_dict = torch.load('vgg16.pth')
pretrained_dict = convert_vgg(pretrained_dict)
model_dict = net.state_dict()
# 1. 把不属于我们需要的层剔除
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
# 2. 把参数存入已经存在的model_dict
model_dict.update(pretrained_dict)
# 3. 加载更新后的model_dict
net.load_state_dict(model_dict)
print ('copy the weight sucessfully')
这样我就基本达成目标了，注意net也就是我们要使用的网络fintune部分需要和our_vgg一致。
以上这篇pytorch在fintune时将sequential中的层输出方法,以vgg为例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持脚本之家。