InferringIntention/keyboard_and_mouse/train.py

import pickle
import numpy as np
from torch.utils.data import Dataset, DataLoader
import torch
import torch.nn as nn
import torch.optim as optim
import shutil
import matplotlib.pyplot as plt
import argparse
from networks import ActionDemo2Predicate

print('torch version: ',torch.__version__) 
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(DEVICE)
torch.manual_seed(256)

class train_dataset(Dataset):
	def __init__(self, x, label): 
		self.x = x
		self.labels = label

	def __getitem__(self, index):
		x = self.x[index]
		label = self.labels[index]
		return  x, label #, img_idx

	def __len__(self):
		return len(self.labels)

class test_dataset(Dataset):
	def __init__(self, x, label): 
		self.x = x
		self.labels = label

	def __getitem__(self, index):
		x = self.x[index]
		label = self.labels[index]
		return  x, label #, img_idx

	def __len__(self):
		return len(self.labels)
		
def train_model(model, train_dataloader, criterion, optimizer, num_epochs, DEVICE, path, resume):
	running_loss = 0
	train_losses = 10
	is_best_acc = False
	is_best_train_loss = False

	best_train_acc = 0
	best_train_loss = 10

	start_epoch = 0
	accuracy = 0

	model.to(DEVICE)
	model.train()
	for epoch in range(start_epoch, num_epochs):
		epoch_losses = []
		train_acc = []
		epoch_loss = 0
		for iter, (x, labels) in enumerate(train_dataloader):
			x = torch.tensor(x).to(DEVICE)
			labels = torch.tensor(labels).to(DEVICE)
			optimizer.zero_grad()
			logps = model(x)
			loss = criterion(logps, labels)
			loss.backward()
			optimizer.step()
			
			epoch_loss += loss.detach().item()
			argmax_Y = torch.max(logps, 1)[1].view(-1, 1)				
			train_acc.append((labels.float().view(-1, 1) == argmax_Y.float()).sum().item() / len(labels.float().view(-1, 1)) * 100)
		epoch_loss /= (iter + 1)
		epoch_losses.append(epoch_loss)
		train_acc = np.mean(np.array(train_acc))
		print('Epoch {}, train loss {:.4f}, train acc {:.4f}'.format(epoch, epoch_loss, train_acc))

		is_best_acc = train_acc > best_train_acc
		best_train_acc = max(train_acc, best_train_acc)

		is_best_train_loss = best_train_loss < epoch_loss
		best_train_loss = min(epoch_loss, best_train_loss)
		
		if is_best_acc:
			model.save(path + '_model_best.ckpt')
		model.save(path + '_checkpoint.ckpt')
		#scheduler.step()

def save_checkpoint(state, is_best, path, filename='_checkpoint.pth.tar'):
	torch.save(state, path + filename)
	if is_best:
		shutil.copyfile(path + filename, path +'_model_best.pth.tar')
		
def main():
	# parsing parameters
	parser = argparse.ArgumentParser(description='')
	parser.add_argument('--resume', type=bool, default=False, help='resume training')
	parser.add_argument('--batch_size', type=int, default=32, help='batch size')
	parser.add_argument('--lr', type=float, default=1e-1, help='learning rate')
	parser.add_argument('--model_type', type=str, default='lstmlast', help='model type')
	parser.add_argument('--hidden_size', type=int, default=256, help='hidden_size')
	parser.add_argument('--epochs', type=int, default=100, help='training epoch')
	parser.add_argument('--dataset_path', type=str, default='dataset/strategy_dataset/', help='dataset path')
	parser.add_argument('--weight_decay', type=float, default=0.9, help='wight decay for Adam optimizer')
	parser.add_argument('--demo_hidden', type=int, default=512, help='demo_hidden')
	parser.add_argument('--dropout', type=float, default=0.5, help='dropout rate')
	parser.add_argument('--checkpoint', type=str, default='checkpoints/', help='checkpoints path')
	
	args = parser.parse_args()
	# create checkpoints path
	from pathlib import Path
	path = args.checkpoint+args.model_type+'_bs_'+str(args.batch_size)+'_lr_'+str(args.lr)+'_hidden_size_'+str(args.hidden_size)
	Path(path).mkdir(parents=True, exist_ok=True)	
	print('total epochs for training: ', args.epochs)
	
	# read dataset
	train_loader = []
	test_loader = []
	loss_funcs = []
	optimizers = []
	models = []
	parameters = []
	for i in range(7): # 7 tasks
		# train data
		with open(args.dataset_path + 'train_data_' + str(i) + '.pkl', 'rb') as f:
			data_x = pickle.load(f)			
		with open(args.dataset_path + 'train_label_' + str(i) + '.pkl', 'rb') as f:
			data_y = pickle.load(f)
		train_set = train_dataset(np.array(data_x), np.array(data_y)-1)
		train_loader.append(DataLoader(train_set, batch_size=args.batch_size, shuffle=True, num_workers=4))
		print('task', str(i), 'train data size: ', len(train_set))
		
		net = ActionDemo2Predicate(args)
		models.append(net)
		parameter = net.parameters()
		loss_funcs.append(nn.CrossEntropyLoss())
		optimizers.append(optim.Adam(parameter, lr=args.lr, weight_decay=args.weight_decay))
	
	for i in range(7):		
		path_save = path + '/task' + str(i)
		print('checkpoint save path: ', path_save)
		train_model(models[i], train_loader[i], loss_funcs[i], optimizers[i], args.epochs, DEVICE, path_save, args.resume)

if __name__ == '__main__':
	main()