train_classifier.py

import argparse
import os
import sys
import logging
import numpy
import numpy as np
import data
import torch
import torch.utils.data
import torchvision
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter
from tqdm import tqdm
from model import PointNet_classifier, PointNet_features


def _init_(args):
	if not os.path.exists('checkpoints'):
		os.makedirs('checkpoints')
	if not os.path.exists('checkpoints/' + args.exp_name):
		os.makedirs('checkpoints/' + args.exp_name)
	if not os.path.exists('checkpoints/' + args.exp_name + '/' + 'models'):
		os.makedirs('checkpoints/' + args.exp_name + '/' + 'models')
	os.system('cp main.py checkpoints' + '/' + args.exp_name + '/' + 'main.py.backup')
	os.system('cp model.py checkpoints' + '/' + args.exp_name + '/' + 'model.py.backup')


class IOStream:
	def __init__(self, path):
		self.f = open(path, 'a')

	def cprint(self, text):
		print(text)
		self.f.write(text + '\n')
		self.f.flush()

	def close(self):
		self.f.close()

def test_one_epoch(device, model, test_loader):
	model.eval()
	test_loss = 0.0
	pred  = 0.0
	count = 0
	for i, data in enumerate(tqdm(test_loader)):
		points, target = data

		points = points.to(device)
		target = target.to(device)

		output = model(points)
		loss_val = model.loss(output, target)

		test_loss += loss_val.item()
		count += output.size(0)

		_, pred1 = output.max(dim=1)
		ag = (pred1 == target)
		am = ag.sum()
		pred += am.item()

	test_loss = float(test_loss)/count
	accuracy = float(pred)/count
	return test_loss, accuracy

def test(args, model, test_loader, textio):
	test_loss, test_accuracy = test_one_epoch(args.device, model, test_loader)
	textio.cprint('Validation Loss: %f & Validation Accuracy: %f'%(test_loss, test_accuracy))

def train_one_epoch(device, model, train_loader, optimizer):
	model.train()
	train_loss = 0.0
	pred  = 0.0
	count = 0
	for i, data in enumerate(tqdm(train_loader)):
		points, target = data

		points = points.to(device)
		target = target.to(device)

		output = model(points)
		loss_val = model.loss(output, target)
		# print(loss_val.item())

		# forward + backward + optimize
		optimizer.zero_grad()
		loss_val.backward()
		optimizer.step()

		train_loss += loss_val.item()
		count += output.size(0)

		_, pred1 = output.max(dim=1)
		ag = (pred1 == target)
		am = ag.sum()
		pred += am.item()

	train_loss = float(train_loss)/count
	accuracy = float(pred)/count
	return train_loss, accuracy

def train(args, model, train_loader, test_loader, boardio, textio, checkpoint):
	learnable_params = filter(lambda p: p.requires_grad, model.parameters())
	if args.optimizer == 'Adam':
		optimizer = torch.optim.Adam(learnable_params)
	else:
		optimizer = torch.optim.SGD(learnable_params, lr=0.1)

	if checkpoint is not None:
		min_loss = checkpoint['min_loss']
		optimizer.load_state_dict(checkpoint['optimizer'])

	best_test_loss = np.inf

	for epoch in range(args.start_epoch, args.epochs):
		train_loss, train_accuracy = train_one_epoch(args.device, model, train_loader, optimizer)
		test_loss, test_accuracy = test_one_epoch(args.device, model, test_loader)

		if test_loss<best_test_loss:
			best_test_loss = test_loss
			snap = {'epoch': epoch + 1,
					'model': model.state_dict(),
					'min_loss': best_test_loss,
					'optimizer' : optimizer.state_dict(),}
			torch.save(snap, 'checkpoints/%s/models/best_model_snap.t7' % (args.exp_name))
			torch.save(model.state_dict(), 'checkpoints/%s/models/best_model.t7' % (args.exp_name))
			torch.save(model.ptnet.state_dict(), 'checkpoints/%s/models/best_ptnet_model.t7' % (args.exp_name))

		torch.save(snap, 'checkpoints/%s/models/model_snap.t7' % (args.exp_name))
		torch.save(model.state_dict(), 'checkpoints/%s/models/model.t7' % (args.exp_name))
		torch.save(model.ptnet.state_dict(), 'checkpoints/%s/models/ptnet_model.t7' % (args.exp_name))
		
		boardio.add_scalar('Train Loss', train_loss, epoch+1)
		boardio.add_scalar('Test Loss', test_loss, epoch+1)
		boardio.add_scalar('Best Test Loss', best_test_loss, epoch+1)
		boardio.add_scalar('Train Accuracy', train_accuracy, epoch+1)
		boardio.add_scalar('Test Accuracy', test_accuracy, epoch+1)

		textio.cprint('EPOCH:: %d, Traininig Loss: %f, Testing Loss: %f, Best Loss: %f'%(epoch+1, train_loss, test_loss, best_test_loss))
		textio.cprint('EPOCH:: %d, Traininig Accuracy: %f, Testing Accuracy: %f'%(epoch+1, train_accuracy, test_accuracy))

def options():
	parser = argparse.ArgumentParser(description='Point Cloud Registration')
	parser.add_argument('--exp_name', type=str, default='exp_classifier_debug', metavar='N',
						help='Name of the experiment')
	parser.add_argument('--dataset_path', type=str, default='ModelNet40',
						metavar='PATH', help='path to the input dataset') # like '/path/to/ModelNet40'
	parser.add_argument('-c', '--categoryfile', type=str, default='./sampledata/modelnet40_half1.txt',
						metavar='PATH', help='path to the categories to be trained') # eg. './sampledata/modelnet40_half1.txt'
	parser.add_argument('--eval', type=bool, default=False, help='Train or Evaluate the network.')

	# settings for input data
	parser.add_argument('--dataset_type', default='modelnet', choices=['modelnet', 'shapenet2'],
						metavar='DATASET', help='dataset type (default: modelnet)')
	parser.add_argument('--num_points', default=1024, type=int,
						metavar='N', help='points in point-cloud (default: 1024)')

	# settings for PointNet
	parser.add_argument('--pointnet', default='tune', type=str, choices=['fixed', 'tune'],
						help='train pointnet (default: tune)')
	parser.add_argument('--emb_dims', default=1024, type=int,
						metavar='K', help='dim. of the feature vector (default: 1024)')
	parser.add_argument('--symfn', default='max', choices=['max', 'avg'],
						help='symmetric function (default: max)')

	# settings for on training
	parser.add_argument('--seed', type=int, default=1234)
	parser.add_argument('-j', '--workers', default=4, type=int,
						metavar='N', help='number of data loading workers (default: 4)')
	parser.add_argument('-b', '--batch_size', default=32, type=int,
						metavar='N', help='mini-batch size (default: 32)')
	parser.add_argument('--epochs', default=200, type=int,
						metavar='N', help='number of total epochs to run')
	parser.add_argument('--start_epoch', default=0, type=int,
						metavar='N', help='manual epoch number (useful on restarts)')
	parser.add_argument('--optimizer', default='Adam', choices=['Adam', 'SGD'],
						metavar='METHOD', help='name of an optimizer (default: Adam)')
	parser.add_argument('--resume', default='', type=str,
						metavar='PATH', help='path to latest checkpoint (default: null (no-use))')
	parser.add_argument('--pretrained', default='', type=str,
						metavar='PATH', help='path to pretrained model file (default: null (no-use))')
	parser.add_argument('--device', default='cuda:0', type=str,
						metavar='DEVICE', help='use CUDA if available')

	args = parser.parse_args()
	return args

def main():
	args = options()
	args.dataset_path = os.path.join(os.getcwd(), os.pardir, os.pardir, 'ModelNet40', 'ModelNet40')

	torch.backends.cudnn.deterministic = True
	torch.manual_seed(args.seed)
	torch.cuda.manual_seed_all(args.seed)
	np.random.seed(args.seed)

	boardio = SummaryWriter(log_dir='checkpoints/' + args.exp_name)
	_init_(args)

	textio = IOStream('checkpoints/' + args.exp_name + '/run.log')
	textio.cprint(str(args))

	
	trainset, testset = data.get_classification_datasets(args)
	train_loader = DataLoader(trainset, batch_size=args.batch_size, shuffle=True, drop_last=True, num_workers=args.workers)
	test_loader = DataLoader(testset, batch_size=args.batch_size, shuffle=False, drop_last=False, num_workers=args.workers)

	if not torch.cuda.is_available():
		args.device = 'cpu'
	args.device = torch.device(args.device)

	# Create PointNet Model.
	ptnet = PointNet_features(emb_dims=args.emb_dims, symfn=args.symfn)
	model = PointNet_classifier(ptnet=ptnet)

	checkpoint = None
	if args.resume:
		assert os.path.isfile(args.resume)
		checkpoint = torch.load(args.resume)
		args.start_epoch = checkpoint['epoch']
		model.load_state_dict(checkpoint['model'])

	if args.pretrained:
		assert os.path.isfile(args.pretrained)
		model.load_state_dict(torch.load(args.pretrained, map_location='cpu'))
	model.to(args.device)

	if args.eval:
		test(args, model, test_loader, textio)
	else:
		train(args, model, train_loader, test_loader, boardio, textio, checkpoint)

if __name__ == '__main__':
	main()