visualize model that not in the model zoo

HI! as you said:

If you want to port this code to use it on your model that does not have such separation, you just need to do some editing on parts where it calls model.features and model.classifier.

I try to modify the model.features or .classifier part , but i get confused how to do it. Below is part of my script , hope that you can give some details about how to visualize own trained model.

model =torch.load(model_save_path)
for index,(layer,_) in enumerate(model.items()):
      # model.items() return the weight of this layer, eg, model.items()[0]=model.0.1.weight
      x = layer(x)
        ......

but i get the TypeError that the object is not callable. i wonder how to read the layer from the trained model that didn’t have features attribute. thanks~

[utkuozbulak]

Hey, it doesn’t have to contain features, what is important is for the model to contain nn.Sequential so that you can iterate.

Let me elaborate.

You can have an __ init __() function like this:

...
def __init__(self):
  self.my_layers = nn.Sequential(
    nn.Conv2d(1, 6, (5, 5), padding=2),
    nn.Conv2d(1, 6, (5, 5), padding=2),
    nn.Conv2d(1, 6, (5, 5), padding=2),
    .
    .
    .
 )

def forward(self, input):
  output = self.my_layers(input)

or this

...
def __init__(self):
    self.layer1 = nn.Conv2d(1, 6, (5, 5), padding=2)
    self.layer2 = nn.Conv2d(1, 6, (5, 5), padding=2)
    self.layer3 = nn.Conv2d(1, 6, (5, 5), padding=2)
    .
    .
    .
 
def forward(self, x):
  x = self.layer1(x)
  x = self.layer2(x)
  x = self.layer3(x)
  ...

If you have a model that wraps up layers with nn.Sequential then iterating through layers one by one is super easy.

x = input
for single_layer in model.my_layers():
   x = single_layer(x)

If there is no nn.Sequential wrapping it up, you can’t use this form (becase there is no pre-defined iterative process) but you can use .modules() or .named_children().

Have a look at these: https://discuss.pytorch.org/t/module-children-vs-module-modules/4551 https://discuss.pytorch.org/t/how-to-loop-over-all-the-variable-in-a-nn-module/912

Hope it helps.

[utkuozbulak]

I had one done for one of my projects but did not put it in this repository.

Adapting the code to resnet, or for any other architecture that has nested blocks, is not hard. You only need to change two or three lines of code where it hooks the layers. Because resnet has residual blocks, what you want is to hook the layers inside these blocks and not the blocks themselves.