def forward(self, x, last_cont=None):
x = self.model(x)
if self.use_dcl:
mask = self.Convmask(x)
mask = self.avgpool2(mask)
mask = torch.tanh(mask)
mask = mask.view(mask.size(0), -1)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
out = []
out.append(self.classifier(x))
if self.use_dcl:
out.append(self.classifier_swap(x))
out.append(mask)
1、 for name, module in model._modules.items():
print (name," : ",module)
这里的名字模型定义的时候,前向传播的一个大块,每个大块里面的是多个小块包含在module中
for name, module in model._modules.items():
print (name," : ",module)
print ("**********")
for name, module in model._modules.items():
print (name)
打印##################################
(relu): ReLU(inplace)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(2048, 128, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace)
(fc2): Conv2d(128, 2048, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
)
)
)
avgpool : AdaptiveAvgPool2d(output_size=1)
classifier : Linear(in_features=2048, out_features=402, bias=False)
classifier_swap : Linear(in_features=2048, out_features=804, bias=False)
Convmask : Conv2d(2048, 1, kernel_size=(1, 1), stride=(1, 1))
avgpool2 : AvgPool2d(kernel_size=2, stride=2, padding=0)
**********
model
avgpool
classifier
classifier_swap
Convmask
avgpool2
1、 for n in model.named_modules():
print (n)
打印是一个元组,层的名字和对应的类型:
...
('model.4.2.se_module.avg_pool', AdaptiveAvgPool2d(output_size=1))
('model.4.2.se_module.fc1', Conv2d(2048, 128, kernel_size=(1, 1), stride=(1, 1)))
('model.4.2.se_module.relu', ReLU(inplace))
('model.4.2.se_module.fc2', Conv2d(128, 2048, kernel_size=(1, 1), stride=(1, 1)))
('model.4.2.se_module.sigmoid', Sigmoid())
('avgpool', AdaptiveAvgPool2d(output_size=1))
('classifier', Linear(in_features=2048, out_features=402, bias=False))
('classifier_swap', Linear(in_features=2048, out_features=804, bias=False))
('Convmask', Conv2d(2048, 1, kernel_size=(1, 1), stride=(1, 1)))
('avgpool2', AvgPool2d(kernel_size=2, stride=2, padding=0))
2、 for n in (model.children()):
print (n)
打印的是所有层的类型,以及对应输入输出维度,参数
(2): SEResNetBottleneck(
(conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(2048, 128, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace)
(fc2): Conv2d(128, 2048, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
)
)
)
AdaptiveAvgPool2d(output_size=1)
Linear(in_features=2048, out_features=402, bias=False)
Linear(in_features=2048, out_features=804, bias=False)
Conv2d(2048, 1, kernel_size=(1, 1), stride=(1, 1))
AvgPool2d(kernel_size=2, stride=2, padding=0)
3、 for n in (model.modules()):
print (n)
(se_module): SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(2048, 128, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace)
(fc2): Conv2d(128, 2048, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
)
Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
ReLU(inplace)
SEModule(
(avg_pool): AdaptiveAvgPool2d(output_size=1)
(fc1): Conv2d(2048, 128, kernel_size=(1, 1), stride=(1, 1))
(relu): ReLU(inplace)
(fc2): Conv2d(128, 2048, kernel_size=(1, 1), stride=(1, 1))
(sigmoid): Sigmoid()
)
AdaptiveAvgPool2d(output_size=1)
Conv2d(2048, 128, kernel_size=(1, 1), stride=(1, 1))
ReLU(inplace)
Conv2d(128, 2048, kernel_size=(1, 1), stride=(1, 1))
Sigmoid()
AdaptiveAvgPool2d(output_size=1)
Linear(in_features=2048, out_features=402, bias=False)
Linear(in_features=2048, out_features=804, bias=False)
Conv2d(2048, 1, kernel_size=(1, 1), stride=(1, 1))
AvgPool2d(kernel_size=2, stride=2, padding=0)
4、 for ind,i in model.state_dict().items():
print (ind,i.shape)
打印的是权重的层的名字和对应形状,顺序可能不是对的
model.4.2.bn1.num_batches_tracked torch.Size([])
model.4.2.conv2.weight torch.Size([512, 512, 3, 3])
model.4.2.bn2.weight torch.Size([512])
model.4.2.bn2.bias torch.Size([512])
model.4.2.bn2.running_mean torch.Size([512])
model.4.2.bn2.running_var torch.Size([512])
model.4.2.bn2.num_batches_tracked torch.Size([])
model.4.2.conv3.weight torch.Size([2048, 512, 1, 1])
model.4.2.bn3.weight torch.Size([2048])
model.4.2.bn3.bias torch.Size([2048])
model.4.2.bn3.running_mean torch.Size([2048])
model.4.2.bn3.running_var torch.Size([2048])
model.4.2.bn3.num_batches_tracked torch.Size([])
model.4.2.se_module.fc1.weight torch.Size([128, 2048, 1, 1])
model.4.2.se_module.fc1.bias torch.Size([128])
model.4.2.se_module.fc2.weight torch.Size([2048, 128, 1, 1])
model.4.2.se_module.fc2.bias torch.Size([2048])
classifier.weight torch.Size([402, 2048])
classifier_swap.weight torch.Size([804, 2048])
Convmask.weight torch.Size([1, 2048, 1, 1])
Convmask.bias torch.Size([1])
module 和 children返回的区别,mododule更多
最后删除层的方式两种
#resnet = models.resnet50(pretrained=True)
modules = list(model.children())[:-4] # #删除最后四个个层 【-1】删除最后一个层
model = torch.nn.Sequential(*modules)
这种方式最后的层的名字会变成数字
(‘model.4.2.se_module.relu’, ReLU(inplace)) 会变成(‘0.4.2.se_module.relu’, ReLU(inplace))
(‘avgpool’, AdaptiveAvgPool2d(output_size=1))会变成(‘1’, AdaptiveAvgPool2d(output_size=1))
model>>0
avgpool>>1
把名字的 点 的第一个名字变成数字,没有点就是整体的名字变成数字
。。。。
('0.4.2.se_module.relu', ReLU(inplace))
('0.4.2.se_module.fc2', Conv2d(128, 2048, kernel_size=(1, 1), stride=(1, 1)))
('0.4.2.se_module.sigmoid', Sigmoid())
('1', AdaptiveAvgPool2d(output_size=1))
('2', Linear(in_features=2048, out_features=402, bias=False))
('3', Linear(in_features=2048, out_features=804, bias=False))
('4', Conv2d(2048, 1, kernel_size=(1, 1), stride=(1, 1)))
####原来是按照模型结构定义的名字
('model.4.2.se_module.relu', ReLU(inplace))
('model.4.2.se_module.fc2', Conv2d(128, 2048, kernel_size=(1, 1), stride=(1, 1)))
('model.4.2.se_module.sigmoid', Sigmoid())
('avgpool', AdaptiveAvgPool2d(output_size=1))
('classifier', Linear(in_features=2048, out_features=402, bias=False))
('classifier_swap', Linear(in_features=2048, out_features=804, bias=False))
('Convmask', Conv2d(2048, 1, kernel_size=(1, 1), stride=(1, 1)))
('avgpool2', AvgPool2d(kernel_size=2, stride=2, padding=0))
方法2打印模型名字,不改变其他层名字
# del model.classifier
# del model.classifier_swap
# del model.Convmask
# del model.avgpool2
直接对模型进行del ,不知道名字,先打印,名字,然后直接删除
for n in model.named_modules():
print (n)