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- import torch
- import transform
- from model import SimSiam, MoCo
- def get_transformation(model):
- if model == SimSiam:
- transformation = transform.SimSiamTransform
- elif model == MoCo:
- transformation = transform.MoCoTransform
- else:
- transformation = transform.SimCLRTransform
- return transformation
- def calculate_model_distance(m1, m2):
- distance, count = 0, 0
- d1, d2 = m1.state_dict(), m2.state_dict()
- for name, param in m1.named_parameters():
- if 'conv' in name and 'weight' in name:
- distance += torch.dist(d1[name].detach().clone().view(1, -1), d2[name].detach().clone().view(1, -1), 2)
- count += 1
- return distance / count
- def normalize(arr):
- maxx = max(arr)
- minn = min(arr)
- diff = maxx - minn
- if diff == 0:
- return arr
- return [(x - minn) / diff for x in arr]
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