Welcome to Pytorch Discussion Forum. I answered your question at Stackoverflow 3 as well.
As per the discussion here 2, update your code to include torch.nn.Parameter(), which basically makes the weight recognizable as a parameter in optimizer.
import torch
from torch import nn
conv = nn.Conv1d(1,1,kernel_size=2)
K = torch.tensor([[[0.5, 0.5]]]) #use one dimensional as per your conv layer
conv.weight = nn.Parameter(K) #use nn.parameters

for Bigger and complex models you can see this toy example which uses pytorch state_dict() method.
import torch
import torch.nn as nn
import torchvision

net = torchvision.models.resnet18(pretrained=True)

pretrained_dict = net.state_dict()
conv_weights = pretrained_dict['conv1.weight'] #64,3,7,7

new = torch.tensor((), dtype=torch.int32)
new = new.new_ones(conv_weights.shape) #assigning all ones

pretrained_dict['conv1.weight'] = new


net.load_state_dict(pretrained_dict)
param = list(net.parameters())

print(param[0])