I ran into the same problem. I cloned the latest code repos of
pytorchvision and built both manually.
Or you can refer to this issue
I ran into the same problem. I cloned the latest code repos of
Hmm, I didn’t try the code yesterday and just changed the line from a residual network.
Now I tried it, and got the same error as you. It seems that you can get just the model, but not the weights of it. Which actually contradicts the documentation when it is written that you can give the pretrained argument.
As @Crazyai said, you can refer to that issue and download the model from github. However, bear in mind that they are not planning to release the version with batch normalization. Alternately, use a residual network and finetune it, probably getting even better results than with VGG.
Pretrained VGG models are a new thing. Updating torchvision to the newest version should fix it.
I uninstalled the current torchvision, and rebulid the newest version, and it worked. Thank you very much!
My network initialized as below:
#VGG19 import torchvision import torch.nn as nn import torch.nn.functional as F vgg19 = torchvision.models.vgg19(pretrained=True) for param in vgg19.parameters(): param.requires_grad = False requires_grad=True by default vgg19.fc = nn.Linear(1000, 8) neurons, otherwise change it vgg19.cuda() import torch.optim as optim criterion = nn.CrossEntropyLoss() optimizer = optim.SGD(vgg19.fc.parameters(), lr=0.001, momentum=0.9)#lr 0.001
But when I train with code as below, there is a error in loss.backward():
inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda()) # zero the parameter gradients optimizer.zero_grad() # forward + backward + optimize outputs = vgg19(inputs) #print(type(outputs),type(inputs)) loss = criterion(outputs, labels) #loss = F.nll_loss(outputs, labels) loss.backward() optimizer.step() step += 1 # print statistics running_loss += loss.data
RuntimeError: there are no graph nodes that require computing gradients
That’s because vgg19 doesn’t have a
fc member variable. Instead, it has a
(classifier): Sequential ( (0): Dropout (p = 0.5) (1): Linear (25088 -> 4096) (2): ReLU (inplace) (3): Dropout (p = 0.5) (4): Linear (4096 -> 4096) (5): ReLU (inplace) (6): Linear (4096 -> 100) )
To replace the last linear layer, a temporary solution would be
vgg19.classifier._modules['6'] = nn.Linear(4096, 8)
Thank you, then how should I change the last layer to param.requires_grad = True
Newly constructed layer has
requires_grad=True by default. You don’t need to do it manually.
thank you. what should I change if I want to add dropout in ‘fc’? the last classifier layer has been changed in terms of the number of classes and I want to add dropout before it.
There’s already a dropout layer before the final FC layer, the code is
self.classifier = nn.Sequential( nn.Linear(512 * 7 * 7, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, num_classes), )
you only need to replace the last 4096, num_classes to your own fc layer.
i am trying to convert the classification network to regression netowork by replacing the last layer with number of outputs as one can you please suggest any solution ay help would be greatly appreciated i am new to pytorch
You can just do exactly this by setting the last layer as:
In your training procedure you probably want to use
nn.MSELoss as your criterion.
Thank you for your response. However, i tried that here is my code but the model returns nothing
class FineTuneModel(nn.Module): def __init__(self, original_model, num_classes): super(FineTuneModel, self).__init__() # Everything except the last linear layer self.features = nn.Sequential(*list(original_model.children())[:-1]) self.classifier = nn.Sequential( nn.Linear(512, 1) ) self.modelName = 'LightCNN-29' # Freeze those weights for p in self.features.parameters(): p.requires_grad = False def forward(self, x): f = self.features(x) f = f.view(f.size(0), -1) y = self.classifier(f) return y model = FineTuneModel(original_model, args.num_classes) print(model)
FineTuneModel( (features): Sequential() (classifier): Sequential( (0): Linear(in_features=512, out_features=1, bias=True) ) )
original model is pretrained resnet model
class FineTuneModel(nn.Module): def __init__(self, original_model, num_classes): super(FineTuneModel, self).__init__() # Everything except the last linear layer self.features = nn.Sequential(*list(original_model.children())[:-1]) self.classifier = nn.Sequential( nn.Linear(512, 1) ) self.modelName = 'LightCNN-29' # Freeze those weights for p in self.features.parameters(): p.requires_grad = False def forward(self, x): f = self.features(x) f = f.view(f.size(0), -1) y = self.classifier(f) return y
self.features seems to be empty.
Could you check, that
oroginal_model is a valid model?
PS: You can add code with three backticks `. I’ve formatted your code for better readability.
I can do this with ResNet easily but apparently VGG has no fc attribute to call.
If I build:
resnet_baseline = models.resnet50(pretrained=True) vgg_baseline = models.vgg16(pretrained=True)
I can see the last fc layer of ResNet with
But I just can see the last fc layer with
I even need the second index because the way the modules are constructed is different from ResNet.
I have attempt to recreate the functionality by:
vgg_baseline.add_module(module=nn.Linear(list(vgg_baseline.children())[-1][-1].in_features, 75), name='fc')
And now I can call
Any ideas why VGG behaves like that?
Just wanna say more…
suppose use resnet18:
import torchvision.models as models net = models.resnet18()
as you know the output has 1000 classes:
(fc): Linear(in_features=512, out_features=1000, bias=True)
if you want to change class to 10, someone may do:
net.fc.out_features = 10
I know it’s not working, but if you print the net, it gives you a changed output:
(fc): Linear(in_features=512, out_features=3, bias=True)
that is interesting. However you will get an error when training. The correct approach is:
net.fc = nn.Linear(512, 10)
so, if you want change the input channel from 3 to 1, use:
net.conv1 = nn.Conv2d(1, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
net.conv1.in_channels = 1
That’s it. It bothered me somehow, when I learned PyTorch. Just wanna point out~~~
How to set gradients enabled for a particular class in the fc layer?
Consider a resnet18 model with 10 classes.
Shape of the fc.weight layer is [10,512]
I want to train the only the weights corresponds to class0.
meaning only one vector of 512 elements.
How to do that?
You cannot set the
requires_grad attribute on slices of a parameter and would need to zero out the gradients of the frozen part of the parameter.
Alternatively you could also create two parameters (frozen and trainable), concatenate them and use the functional API for the layer operation.
However, the first approach might be a bit simpler.
Thank you very much!