我想改变resnet50,这样我就可以切换到4通道输入,使用相同的权重为rgb通道和初始化最后一个通道与正常的平均值0和方差0.01。
下面是我代码:
import torch.nn as nn
import torch
from torchvision import models
from misc.layer import Conv2d, FC
import torch.nn.functional as F
from misc.utils import *
import pdb
class Res50(nn.Module):
def __init__(self, pretrained=True):
super(Res50, self).__init__()
self.de_pred = nn.Sequential(Conv2d(1024, 128, 1, same_padding=True, NL='relu'),
Conv2d(128, 1, 1, same_padding=True, NL='relu'))
self._initialize_weights()
res = models.resnet50(pretrained=pretrained)
pretrained_weights = res.conv1.weight
res.conv1 = nn.Conv2d(4, 64, kernel_size=7, stride=2, padding=3,bias=False)
res.conv1.weight[:,:3,:,:] = pretrained_weights
res.conv1.weight[:,3,:,:].data.normal_(0.0, std=0.01)
self.frontend = nn.Sequential(
res.conv1, res.bn1, res.relu, res.maxpool, res.layer1, res.layer2
)
self.own_reslayer_3 = make_res_layer(Bottleneck, 256, 6, stride=1)
self.own_reslayer_3.load_state_dict(res.layer3.state_dict())
def forward(self,x):
x = self.frontend(x)
x = self.own_reslayer_3(x)
x = self.de_pred(x)
x = F.upsample(x,scale_factor=8)
return x
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
m.weight.data.normal_(0.0, std=0.01)
if m.bias is not None:
m.bias.data.fill_(0)
elif isinstance(m, nn.BatchNorm2d):
m.weight.fill_(1)
m.bias.data.fill_(0)
但它产生了以下错误,有人有什么建议吗?
/usr/local/lib/python3.6/dist-packages/torch/tensor.py:746: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the gradient for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations.
warnings.warn("The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad "
Traceback (most recent call last):
File "train.py", line 62, in <module>
cc_trainer = Trainer(loading_data,cfg_data,pwd)
File "/content/drive/My Drive/Folder/Code/trainer.py", line 28, in __init__
self.optimizer = optim.Adam(self.net.CCN.parameters(), lr=cfg.LR, weight_decay=1e-4) #remenber was 1e-4
File "/usr/local/lib/python3.6/dist-packages/torch/optim/adam.py", line 44, in __init__
super(Adam, self).__init__(params, defaults)
File "/usr/local/lib/python3.6/dist-packages/torch/optim/optimizer.py", line 51, in __init__
self.add_param_group(param_group)
File "/usr/local/lib/python3.6/dist-packages/torch/optim/optimizer.py", line 206, in add_param_group
raise ValueError("can't optimize a non-leaf Tensor")
ValueError: can't optimize a non-leaf Tensor
3条答案
按热度按时间o7jaxewo1#
理想情况下,ResNet接受3通道输入。要使其工作于4通道输入,您必须添加一个额外的层(2D conv),将4通道输入通过该层,以使该层的输出适合ResNet架构。
步骤
1.复制模型权重
1.为4通道输入添加额外的2D转换
1.您可以在额外的con2d上添加Relu和BatchNorm。在本例中,我没有使用。
1.将额外的cov2d与ResNet模型(之前复制的权重)连接
1.已完成
抱歉,我没有修改您的程式码。您可以调整程式码中的变更。
6tr1vspr2#
尝试设置第一个通道的
.data
:0pizxfdo3#
我想我已经解决了这个问题,但我不明白为什么。有人能给我解释一下nn.参数的作用吗?为什么它能起作用?