import torch
import torch.nn as nn
import torchvision
import numpy as np
from torchsummary import summary
# Define model
class TheModelClass(nn.Module):
  def __init__(self):
    super(TheModelClass, self).__init__()
    self.conv1 = nn.Conv2d(3, 6, 5)
    self.pool = nn.MaxPool2d(2, 2)
    self.conv2 = nn.Conv2d(6, 16, 5)
    self.fc1 = nn.Linear(16 * 5 * 5, 120)
    self.fc2 = nn.Linear(120, 84)
    self.fc3 = nn.Linear(84, 10)
  def forward(self, x):
    x = self.pool(F.relu(self.conv1(x)))
    x = self.pool(F.relu(self.conv2(x)))
    x = x.view(-1, 16 * 5 * 5)
    x = F.relu(self.fc1(x))
    x = F.relu(self.fc2(x))
    x = self.fc3(x)
    return x
# Initialize model
model = TheModelClass()
# Initialize optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
# Print model's state_dict
print("Model's state_dict:")
for param_tensor in model.state_dict():
  print(param_tensor,"\t", model.state_dict()[param_tensor].size())
# Print optimizer's state_dict
print("Optimizer's state_dict:")
for var_name in optimizer.state_dict():
  print(var_name, "\t", optimizer.state_dict()[var_name])

Model's state_dict:
conv1.weight  torch.Size([6, 3, 5, 5])
conv1.bias  torch.Size([6])
conv2.weight  torch.Size([16, 6, 5, 5])
conv2.bias  torch.Size([16])
fc1.weight  torch.Size([120, 400])
fc1.bias  torch.Size([120])
fc2.weight  torch.Size([84, 120])
fc2.bias  torch.Size([84])
fc3.weight  torch.Size([10, 84])
fc3.bias  torch.Size([10])
Optimizer's state_dict:
state  {}
param_groups  [{'lr': 0.001, 'momentum': 0.9, 'dampening': 0, 'weight_decay': 0, 'nesterov': False, 'params': [2238501264336, 2238501329800, 2238501330016, 2238501327136, 2238501328576, 2238501329728, 2238501327928, 2238501327064, 2238501330808, 2238501328288]}]

net=BaseNet()

torch.save(net.state_dict(), models_dir)

class BaseNet(object):
  def __init__(self):

class BaseNet(nn.Module):
  def __init__(self):
    super(BaseNet, self).__init__()