If I’m using a seqLen of 1 and a batch size of 2, the internal loop will not be executed and thus no hooks are called:
x = torch.randn(1, 2, 3, 224, 224)
model(x)

since your loop would be:
for t in range(0, 0):

and would thus not execute the self.convNet.
Note that your current forward method also doesn’t return anything, so you might want to change that as well.
However, if I’m using a seqLen of 2, I get a shape mismatch error:
x = torch.randn(2, 2, 3, 224, 224)
output = model(x)
> RuntimeError: mat1 and mat2 shapes cannot be multiplied (17920x7 and 1280x1000)

so I guess you might be facing the first issue.
The shape mismatch error is most likely raised, since you are rewrapping the child modules in an nn.Sequential container and are thus dropping the functional calls from the original forward.
I would not recommend to wrap arbitrary models into nn.Sequential unless you are sure that all modules are just executed in a sequential way without any conditions, functional calls, loops etc.
If I avoid the shape mismatch error and use:
def forward(self, x):
   seqLen = x.size(0) - 1
   for t in range(0, seqLen):
       x1 = x[t] - x[t+1]
       x2 = self.mod(x1)

with x = torch.randn(2, 2, 3, 224, 224), the forward hook is properly called and activation will be populated.