modelx = torch.nn.Sequential(*list(modelx.children())[:-1])
时间: 2024-04-08 19:35:28 浏览: 20
你的第一个问题是关于如何使用 PyTorch 将模型的最后一层移除。你可以使用 `torch.nn.Sequential` 类来构建一个新的模型,然后将原始模型的所有子模块(除了最后一层)添加到新模型中。下面是一个示例代码:
```python
import torch
# 假设原始模型为 modelx
modelx = torch.nn.Sequential(*list(modelx.children())[:-1])
# 现在 modelx 中的最后一层已被移除
```
使用 `list(modelx.children())[:-1]` 可以获取原始模型的所有子模块(除了最后一层),然后使用 `torch.nn.Sequential` 将这些子模块组合成一个新的模型。
相关问题
如何将self.conv1 = nn.Conv2d(4 * num_filters, num_filters, kernel_size=3, padding=1) self.conv_offset1 = nn.Conv2d(512, 18, kernel_size=3, stride=1, padding=1) init_offset1 = torch.Tensor(np.zeros([18, 512, 3, 3])) self.conv_offset1.weight = torch.nn.Parameter(init_offset1) # 初始化为0 self.conv_mask1 = nn.Conv2d(512, 9, kernel_size=3, stride=1, padding=1) init_mask1 = torch.Tensor(np.zeros([9, 512, 3, 3]) + np.array([0.5])) self.conv_mask1.weight = torch.nn.Parameter(init_mask1) # 初始化为0.5 与torchvision.ops.deform_conv2d,加入到:class NLayerDiscriminator(nn.Module): def init(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True): super(NLayerDiscriminator, self).init() self.use_parallel = use_parallel if type(norm_layer) == functools.partial: use_bias = norm_layer.func == nn.InstanceNorm2d else: use_bias = norm_layer == nn.InstanceNorm2d kw = 4 padw = int(np.ceil((kw-1)/2)) sequence = [ nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True) ] nf_mult = 1 for n in range(1, n_layers): nf_mult_prev = nf_mult nf_mult = min(2n, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] nf_mult_prev = nf_mult nf_mult = min(2n_layers, 8) sequence += [ nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias), norm_layer(ndf * nf_mult), nn.LeakyReLU(0.2, True) ] sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] if use_sigmoid: sequence += [nn.Sigmoid()] self.model = nn.Sequential(*sequence) def forward(self, input): return self.model(input)中,请给出修改后的代码
可以将self.conv1、self.conv_offset1和self.conv_mask1加入到NLayerDiscriminator中的nn.Sequential序列中,如下所示:
```
import torchvision
from torchvision.ops.deform_conv import DeformConv2d
class NLayerDiscriminator(nn.Module):
def __init__(self, input_nc=3, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, use_parallel=True):
super(NLayerDiscriminator, self).__init__()
self.use_parallel = use_parallel
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
kw = 4
padw = int(np.ceil((kw-1)/2))
nf_mult = 1
sequence = [
nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
nn.LeakyReLU(0.2, True)
]
for n in range(1, n_layers):
nf_mult_prev = nf_mult
nf_mult = min(2**n, 8)
sequence += [
nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
nf_mult_prev = nf_mult
nf_mult = min(2**n_layers, 8)
sequence += [
DeformConv2d(
ndf * nf_mult_prev, ndf * nf_mult, kernel_size=3, stride=1, padding=1,
dilation=1, groups=1, deformable_groups=1, bias=False
),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True),
nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)
]
if use_sigmoid:
sequence += [nn.Sigmoid()]
self.model = nn.Sequential(*sequence)
self.conv1 = nn.Conv2d(4 * ndf, ndf, kernel_size=3, padding=1)
self.conv_offset1 = nn.Conv2d(ndf, 18, kernel_size=3, stride=1, padding=1)
init_offset1 = torch.Tensor(np.zeros([18, ndf, 3, 3]))
self.conv_offset1.weight = torch.nn.Parameter(init_offset1)
self.conv_mask1 = nn.Conv2d(ndf, 9, kernel_size=3, stride=1, padding=1)
init_mask1 = torch.Tensor(np.zeros([9, ndf, 3, 3]) + np.array([0.5]))
self.conv_mask1.weight = torch.nn.Parameter(init_mask1)
def forward(self, input):
x = input
for module in self.model:
if isinstance(module, DeformConv2d):
offset_mask = torch.cat([self.conv_offset1(x), self.conv_mask1(x)], dim=1)
x = module(x, offset_mask)
else:
x = module(x)
return x
```
这里使用了torchvision.ops.deform_conv2d中的DeformConv2d作为最后一层卷积层,因此需要将self.conv1、self.conv_offset1和self.conv_mask1的通道数修改为ndf。在forward中,对于DeformConv2d层,需要将self.conv_offset1和self.conv_mask1的输出拼接起来作为offset_mask输入。
写出将结构写在一个sequtial中的代码结构与此相同 class CNN(nn.Module): # def __init__(self): # super(CNN, self).__init__() # self.conv1 = nn.Conv2d(3, 32, 3) # self.conv2 = nn.Conv2d(32, 64, 3) # self.conv3 = nn.Conv2d(64, 128, 3) # self.conv4 = nn.Conv2d(128, 256, 3) # self.fc1 = nn.Linear(6400, 1024) # self.fc2 = nn.Linear(1024, 512) # self.fc3 = nn.Linear(512, 128) # self.fc4 = nn.Linear(128, 10) # def forward(self, x): # # 3 x 32 x 32 # x = torch.relu(self.conv1(x)) # 32 x 30 x 30 # x = nn.BatchNorm2d(32).to("cuda")(x) # 32 x 30 x 30 # x = torch.relu(self.conv2(x)) # 64 x 28 x 28 # x = torch.max_pool2d(x, 2) # 64 x 14 x 14 # x = torch.relu(self.conv3(x)) # 128 x 12 x 12 # x = nn.BatchNorm2d(128).to("cuda")(x) # 128 x 12 x 12 # x = torch.relu(self.conv4(x)) # 256 x 10 x 10 # x = torch.max_pool2d(x, 2) # 256 x 5 x 5 # x = x.view(-1, 6400) # x = torch.relu(self.fc1(x)) # x = torch.tanh(self.fc2(x)) # x = torch.relu(self.fc3(x)) # x = self.fc4(x) # return x
以下是将结构写在一个Sequential中的代码:
```python
import torch
import torch.nn as nn
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.model = nn.Sequential(
nn.Conv2d(3, 32, 3),
nn.ReLU(),
nn.BatchNorm2d(32),
nn.Conv2d(32, 64, 3),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(64, 128, 3),
nn.ReLU(),
nn.BatchNorm2d(128),
nn.Conv2d(128, 256, 3),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Flatten(),
nn.Linear(6400, 1024),
nn.ReLU(),
nn.Linear(1024, 512),
nn.Tanh(),
nn.Linear(512, 128),
nn.ReLU(),
nn.Linear(128, 10)
)
def forward(self, x):
x = self.model(x)
return x
model = CNN()
```
在这个代码中,我们使用了`nn.Sequential`来将所有的层组合在一起。每个层都按照从输入到输出的顺序添加到了`nn.Sequential`中。同时,我们也将`nn.BatchNorm2d`和`nn.MaxPool2d`层直接添加到了`nn.Sequential`中。最后,我们在`forward`方法中调用了整个模型。