main.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 16 15:18:34 2023

@author: jsyoonDL
"""

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import transforms

import os
import time

import numpy as np
import random

from model.Model import Model
from train import train


import torch.multiprocessing
torch.multiprocessing.set_sharing_strategy('file_system')
#%%
def set_seed(seed = 0):
    '''Sets the seed of the entire notebook so results are the same every time we run.
    This is for REPRODUCIBILITY.'''
    np.random.seed(seed)
    random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)    
    torch.backends.cudnn.enabled = False
    # When running on the CuDNN backend, two further options must be set
    # torch.backends.cudnn.deterministic = True
    # torch.backends.cudnn.benchmark = True
    # Set a fixed value for the hash seed
    os.environ['PYTHONHASHSEED'] = str(seed)
#%% model proposed
seed_num = 0
set_seed(seed_num)

os.environ["CUDA_DEVICE_ORDER"] = 'PCI_BUS_ID'
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
loss_function = nn.CrossEntropyLoss().cuda()
model_path = 'model_trained/proposed'                                                                                                                                                                                                           
    


model = Model()  
model.cuda()

save_path = model_path
os.makedirs(save_path, exist_ok=True)        

optimizer = optim.AdamW(model.parameters(),lr=1e-4, weight_decay=1e-5)  
data_path = 'Data/classification/'  
params = {
    'num_epochs': 100,
    'batch_size': 64,
    'seed_num':seed_num,
    'optimizer':optimizer,
    'loss_function':loss_function,
    'data_path': data_path,
    'model_path': model_path,
    'acc_best': 0,
    'norm': 0,
    'lambda':1e-3
    }            
                                         
train(model, params)
                                              
torch.cuda.empty_cache()