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train_bc.py
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train_bc.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Sep 9 14:42:04 2023
@author: yexin
"""
import torch.nn as nn
import time
import torch.optim as optim
import numpy as np
import io, os
import argparse
from torch.utils.data import Dataset, DataLoader
from model import MLP_bc
from dataloader import data_train_bc, data_test_bc
import torch
from progressbar import ProgressBar
from tqdm import tqdm
import torch.nn.functional as F
import pickle
import random
from transformers import get_linear_schedule_with_warmup
import matplotlib.pyplot as plt
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--exp_dir', type=str, default='C:/Users/yexin/Desktop/liquid/', help='Please change to your experiment path')
parser.add_argument('--lr', type=float, default=2e-5, help='Learning rate')
parser.add_argument('--batch_size', type=int, default=128, help='Batch size,128')
parser.add_argument('--weightdecay', type=float, default=1e-2, help='weight decay')
parser.add_argument('--num_warmup_steps', type=int, default=100, help='num_warmup_steps')
parser.add_argument('--epoch', type=int, default=100, help='The time steps you want to subsample the dataset to,100')
parser.add_argument('--train_continue', type=bool, default= False, help='Set true if continue to train')
parser.add_argument('--test', type = bool, default = True, help = "running on test set")
parser.add_argument('--train', type = bool, default = False, help = "running on train set")
args = parser.parse_args()
if not os.path.exists(args.exp_dir + 'ckpts'):
os.makedirs(args.exp_dir + 'ckpts')
use_gpu = True
device = 'cuda'
def get_class(label, nums):
classes = []
for i in range(len(nums)):
classes.append(torch.tensor(label[nums[i]]))
return torch.stack(classes).to(device)
if args.train:
train_cap = pickle.load(open(r"C:\Users\yexin\Desktop\liquid\training data\cap_train_bc.pkl", "rb"))
train_b = pickle.load(open(r"C:\Users\yexin\Desktop\liquid\training data\b_train_bc.pkl", "rb"))
train_weight_num = pickle.load(open(r"C:\Users\yexin\Desktop\liquid\training data\weight_num_train_bc.pkl", "rb"))
train_num = pickle.load(open(r"C:\Users\yexin\Desktop\liquid\training data\num_train_bc.pkl", "rb"))
train_dataset = data_train_bc(train_cap, train_b)
print("finish load training data")
train_dataloader = DataLoader(train_dataset, batch_size=args.batch_size,shuffle=True, num_workers=0)
val_cap = pickle.load(open(r"C:\Users\yexin\Desktop\liquid\training data\cap_test_bc.pkl", "rb"))
val_b = pickle.load(open(r"C:\Users\yexin\Desktop\liquid\training data\b_test_bc.pkl", "rb"))
val_weight_num = pickle.load(open(r"C:\Users\yexin\Desktop\liquid\training data\weight_num_test_bc.pkl", "rb"))
val_num = pickle.load(open(r"C:\Users\yexin\Desktop\liquid\training data\num_test_bc.pkl", "rb"))
val_dataset = data_test_bc(val_cap, val_b)
print("finish load val data")
val_dataloader = DataLoader(val_dataset, batch_size=args.batch_size,shuffle=False, num_workers=0)
if args.test:
test_cap = pickle.load(open(r"C:\Users\yexin\Desktop\liquid\training data\cap_test_bc.pkl", "rb"))
test_b = pickle.load(open(r"C:\Users\yexin\Desktop\liquid\training data\b_test_bc.pkl", "rb"))
test_weight_num = pickle.load(open(r"C:\Users\yexin\Desktop\liquid\training data\weight_num_test_bc.pkl", "rb"))
test_num = pickle.load(open(r"C:\Users\yexin\Desktop\liquid\training data\num_test_bc.pkl", "rb"))
test_dataset = data_test_bc(test_cap, test_b)
print("finish load test data")
test_dataloader = DataLoader(test_dataset, batch_size=args.batch_size,shuffle=False, num_workers=0)
if __name__ == '__main__':
if args.train:
np.random.seed(0)
torch.manual_seed(99)
model = MLP_bc()
model.to(device)
if args.train_continue:
checkpoint = torch.load(r"C:\Users\yexin\Desktop\liquid\ckpts\best_bc.path.tar")
model.load_state_dict(checkpoint['model_state_dict'])
optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weightdecay)
scheduler = get_linear_schedule_with_warmup(optimizer, args.num_warmup_steps, len(train_dataloader) * args.epoch)
criterion = nn.BCELoss()
relu = nn.ReLU()
best_val = np.inf
for epoch in tqdm(range(args.epoch)):
loss_f = []
print ('here')
bar = ProgressBar(max_value=len(train_dataloader))
for i_batch, sample_batched in bar(enumerate(train_dataloader, 0)):
model.train(not args.test)
cap = sample_batched[0].to(device, non_blocking = True)
num = sample_batched[1].to(device, non_blocking = True)
act = sample_batched[2].to(device, non_blocking = True)
with torch.set_grad_enabled(not args.test):
label = get_class(train_num, num)
weight = get_class(train_weight_num, num)
act_out = model(cap, label, weight)
loss = criterion(act_out.float(), act.float())
if not args.test:
optimizer.zero_grad()
loss.backward()
optimizer.step()
scheduler.step()
loss_f.append(loss.data.item())
if i_batch % 50 ==0 and i_batch > 0:
print(f"loss_f: {np.array(np.mean(loss_f))}")
torch.save({
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': np.array(loss.data.item()),},
args.exp_dir + 'ckpts/' + str(epoch) + '.path.tar')
val_loss_f = []
correct_predictions = 0
total_samples = 0
bar = ProgressBar(max_value=len(val_dataloader))
for i_batch, sample_batched in bar(enumerate(val_dataloader, 0)):
model.eval()
cap = sample_batched[0].to(device, non_blocking = True)
num = sample_batched[1].to(device, non_blocking = True)
act = sample_batched[2].to(device, non_blocking = True)
with torch.set_grad_enabled(False):
label = get_class(val_num, num)
weight = get_class(val_weight_num, num)
act_out = model(cap, label, weight)
loss = criterion(act_out.float(), act.float())
val_loss_f.append(loss.data.item())
predicted_labels = (act_out > 0.5).float()
correct_predictions += (predicted_labels == act.float()).sum().item()
total_samples += act.size(0)
print(f"final accuracy = {correct_predictions / total_samples}")
print(f"val loss: {np.array(np.mean(val_loss_f))}")
if np.array(np.mean(val_loss_f)) < best_val:
torch.save({
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),},
args.exp_dir + 'ckpts/' + 'best_bc' + '.path.tar')
best_val = np.array(np.mean(val_loss_f))
elif args.test:
np.random.seed(0)
torch.manual_seed(99)
model = MLP_bc()
model.to(device)
checkpoint = torch.load(r"C:\Users\yexin\Desktop\liquid\ckpts\best_bc.path.tar")
model.load_state_dict(checkpoint['model_state_dict'])
criterion = nn.BCELoss()
relu = nn.ReLU()
val_loss_f = []
correct_predictions = 0
total_samples = 0
print ('here')
bar = ProgressBar(max_value=len(test_dataloader))
for i_batch, sample_batched in bar(enumerate(test_dataloader, 0)):
model.eval()
cap = sample_batched[0].to(device, non_blocking = True)
num = sample_batched[1].to(device, non_blocking = True)
act = sample_batched[2].to(device, non_blocking = True)
with torch.set_grad_enabled(False):
label = get_class(test_num, num)
weight = get_class(test_weight_num, num)
act_out = model(cap, label, weight)
loss = criterion(act_out.float(), act.float())
val_loss_f.append(loss.data.item())
predicted_labels = (act_out > 0.5).float()
correct_predictions += (predicted_labels == act.float()).sum().item()
total_samples += act.size(0)
print(f"final accuracy = {correct_predictions / total_samples}")
print(f"test loss: {np.array(np.mean(val_loss_f))}")