I create two neural network models m1 and m2, however when I modify the weights in m1, the weights in m2 are modified as well. I suspected I am not instantiating them properly, however they do have different memory addresses. I have also tried creating a custom class based on nn.module instead of using nn.sequential but the issue remains. Any help much appreciated.
The issue is demonstrated as follows:
from collections import namedtuple, OrderedDict
import torch
import torch.nn as nn
# if gpu is to be used
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def init_weights(m):
    if type(m) == nn.Linear:
        m.weight.data.fill_(0.2)
        m.bias.data.fill_(1.0)
        
D_in = 4
H = 8
D_out = 2    
net_spec = OrderedDict([('0', torch.nn.Linear(D_in, H)),
                    ('1', torch.nn.ReLU()),
                    ('2', torch.nn.Linear(H, D_out))])

m1 = torch.nn.Sequential(net_spec)
m2 = torch.nn.Sequential(net_spec)
print('object addresses:')
print('   m1   :',hex(id(m1)))
print('   m2   :',hex(id(m2)))
print("\nm1 weights:\n",m1.state_dict()['0.weight'])
print("\nm2 weights:\n",m2.state_dict()['0.weight'])
m1.apply(init_weights)
print('m1 weights updated')
print("\nm1 weights:\n",m1.state_dict()['0.weight'])
print("\nm2 weights:\n",m2.state_dict()['0.weight'])

output:
object addresses:
   m1   : 0x7f893cbe91d0
   m2   : 0x7f893cbe92e8

m1 weights:
 tensor([[-0.4047,  0.1752, -0.1989, -0.2917],
        [-0.0466, -0.0044,  0.1561,  0.2465],
        [ 0.0700, -0.3357, -0.4978, -0.0837],
        [-0.0248, -0.2826, -0.4564, -0.0516],
        [-0.0024, -0.1732, -0.4144, -0.3790],
        [-0.3075,  0.2768, -0.2676,  0.4495],
        [-0.4929,  0.1328, -0.3153, -0.4591],
        [-0.0597,  0.3718,  0.0522,  0.0899]])

m2 weights:
 tensor([[-0.4047,  0.1752, -0.1989, -0.2917],
        [-0.0466, -0.0044,  0.1561,  0.2465],
        [ 0.0700, -0.3357, -0.4978, -0.0837],
        [-0.0248, -0.2826, -0.4564, -0.0516],
        [-0.0024, -0.1732, -0.4144, -0.3790],
        [-0.3075,  0.2768, -0.2676,  0.4495],
        [-0.4929,  0.1328, -0.3153, -0.4591],
        [-0.0597,  0.3718,  0.0522,  0.0899]])
m1 weights updated

m1 weights:
 tensor([[ 0.2000,  0.2000,  0.2000,  0.2000],
        [ 0.2000,  0.2000,  0.2000,  0.2000],
        [ 0.2000,  0.2000,  0.2000,  0.2000],
        [ 0.2000,  0.2000,  0.2000,  0.2000],
        [ 0.2000,  0.2000,  0.2000,  0.2000],
        [ 0.2000,  0.2000,  0.2000,  0.2000],
        [ 0.2000,  0.2000,  0.2000,  0.2000],
        [ 0.2000,  0.2000,  0.2000,  0.2000]])

m2 weights:
 tensor([[ 0.2000,  0.2000,  0.2000,  0.2000],
        [ 0.2000,  0.2000,  0.2000,  0.2000],
        [ 0.2000,  0.2000,  0.2000,  0.2000],
        [ 0.2000,  0.2000,  0.2000,  0.2000],
        [ 0.2000,  0.2000,  0.2000,  0.2000],
        [ 0.2000,  0.2000,  0.2000,  0.2000],
        [ 0.2000,  0.2000,  0.2000,  0.2000],
        [ 0.2000,  0.2000,  0.2000,  0.2000]])

Solution Found:
I found two ways to create the second model m2 that does not have the issue.
m2 = torch.nn.Sequential(copy.deepcopy(net_spec))

or
m2 = copy.deepcopy(m1)

It seems strange to me that the instantiation of the network is tied to the instance OrderedDict() passed to the class to instantiate the model. If anyone can enlighten me it would be appreciated.