英文:
How to change the first conv layer in the resnet 18?
问题
我有一个包含20个类别的数据，我想使用一个预训练的模型，并稍微修改一下。
我知道如果要将ResNet18的最后一个线性层更改为分类20个类别（而不是1000个），可以编写以下代码：
resnet.fc = nn.Linear(512, 20)
但是我不知道如何访问其他层？比如Basic block中的第二个卷积层？
当我调用resnet.layer1时，它返回：
Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
但是如何获取并更改conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)呢？
英文:
I have a data with 20 class, and I'd like to use pretraied model with a bit of modification.
I know if we want to change the last linear of ResNet18 to categorize 20 calss (instead of 1000); we could write the following:
resnet.fc = nn.linear(512,20)
But I don't know how to access to any other layers? Like the second convolution in Bacic block?
When I call resnet.layer1 it returns:
Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace=True)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
But how to grab and change conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)?
答案1
得分: 1
你可以按照以下方式访问到层 (conv2) 在层.1的序号 (0)：
from torchvision import datasets, transforms, models
resnet = models.resnet18(pretrained=True)
print(resnet.layer1[0].conv2)
输出结果：
Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1),
bias=False)
英文:
You can access to the layer (conv2) in sequential number (0) of layer.1 as follow:
from torchvision import datasets, transforms, models
resnet = models.resnet18(pretrained=True)
print(resnet.layer1[0].conv2)
Output:
Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1),
bias=False)
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