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main.py
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main.py
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import argparse
import sys
import os
import shutil
import time
import warnings
from random import sample
import numpy as np
from sklearn import metrics
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torch.optim.lr_scheduler import MultiStepLR
from cgcnn.model import CrystalGraphConvNet
from cgcnn.data import collate_pool, get_train_val_test_loader
from cgcnn.data import CIFData
parser = argparse.ArgumentParser(description='Crystal Graph Convolutional Neural Networks')
parser.add_argument('data_options', metavar='OPTIONS', nargs='+',
help='dataset options, started with the path to root dir, '
'then other options')
parser.add_argument('--task', choices=['regression', 'classification'],
default='regression', help='complete a regression or '
'classification task (default: regression)')
parser.add_argument('--disable-cuda', action='store_true',
help='Disable CUDA')
parser.add_argument('-j', '--workers', default=0, type=int, metavar='N',
help='number of data loading workers (default: 0)')
parser.add_argument('--epochs', default=30, type=int, metavar='N',
help='number of total epochs to run (default: 30)')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('-b', '--batch-size', default=256, type=int,
metavar='N', help='mini-batch size (default: 256)')
parser.add_argument('--lr', '--learning-rate', default=0.01, type=float,
metavar='LR', help='initial learning rate (default: '
'0.01)')
parser.add_argument('--lr-milestones', default=[100], nargs='+', type=int,
metavar='N', help='milestones for scheduler (default: '
'[100])')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum')
parser.add_argument('--weight-decay', '--wd', default=0, type=float,
metavar='W', help='weight decay (default: 0)')
parser.add_argument('--print-freq', '-p', default=10, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--train-size', default=None, type=int, metavar='N',
help='number of training data to be loaded (default none)')
parser.add_argument('--val-size', default=1000, type=int, metavar='N',
help='number of validation data to be loaded (default '
'1000)')
parser.add_argument('--test-size', default=1000, type=int, metavar='N',
help='number of test data to be loaded (default 1000)')
parser.add_argument('--optim', default='SGD', type=str, metavar='SGD',
help='choose an optimizer, SGD or Adam, (default: SGD)')
parser.add_argument('--atom-fea-len', default=64, type=int, metavar='N',
help='number of hidden atom features in conv layers')
parser.add_argument('--h-fea-len', default=128, type=int, metavar='N',
help='number of hidden features after pooling')
parser.add_argument('--n-conv', default=3, type=int, metavar='N',
help='number of conv layers')
parser.add_argument('--n-h', default=1, type=int, metavar='N',
help='number of hidden layers after pooling')
args = parser.parse_args(sys.argv[1:])
args.cuda = not args.disable_cuda and torch.cuda.is_available()
if args.task == 'regression':
best_mae_error = 1e10
else:
best_mae_error = 0.
def main():
global args, best_mae_error
# load data
dataset = CIFData(*args.data_options)
collate_fn = collate_pool
train_loader, val_loader, test_loader = get_train_val_test_loader(
dataset=dataset, collate_fn=collate_fn, batch_size=args.batch_size,
train_size=args.train_size, num_workers=args.workers,
val_size=args.val_size, test_size=args.test_size,
pin_memory=args.cuda, return_test=True)
# obtain target value normalizer
if args.task == 'classification':
normalizer = Normalizer(torch.zeros(2))
normalizer.load_state_dict({'mean': 0., 'std': 1.})
else:
if len(dataset) < 500:
warnings.warn('Dataset has less than 500 data points. '
'Lower accuracy is expected. ')
sample_data_list = [dataset[i] for i in range(len(dataset))]
else:
sample_data_list = [dataset[i] for i in
sample(range(len(dataset)), 500)]
_, sample_target, _ = collate_pool(sample_data_list)
normalizer = Normalizer(sample_target)
# build model
structures, _, _ = dataset[0]
orig_atom_fea_len = structures[0].shape[-1]
nbr_fea_len = structures[1].shape[-1]
model = CrystalGraphConvNet(orig_atom_fea_len, nbr_fea_len,
atom_fea_len=args.atom_fea_len,
n_conv=args.n_conv,
h_fea_len=args.h_fea_len,
n_h=args.n_h,
classification=True if args.task ==
'classification' else False)
if args.cuda:
model.cuda()
# define loss func and optimizer
if args.task == 'classification':
criterion = nn.NLLLoss()
else:
criterion = nn.MSELoss()
if args.optim == 'SGD':
optimizer = optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optim == 'Adam':
optimizer = optim.Adam(model.parameters(), args.lr,
weight_decay=args.weight_decay)
else:
raise NameError('Only SGD or Adam is allowed as --optim')
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_mae_error = checkpoint['best_mae_error']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
normalizer.load_state_dict(checkpoint['normalizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
scheduler = MultiStepLR(optimizer, milestones=args.lr_milestones,
gamma=0.1)
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, normalizer)
# evaluate on validation set
mae_error = validate(val_loader, model, criterion, normalizer)
if mae_error != mae_error:
print('Exit due to NaN')
sys.exit(1)
scheduler.step()
# remember the best mae_eror and save checkpoint
if args.task == 'regression':
is_best = mae_error < best_mae_error
best_mae_error = min(mae_error, best_mae_error)
else:
is_best = mae_error > best_mae_error
best_mae_error = max(mae_error, best_mae_error)
save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_mae_error': best_mae_error,
'optimizer': optimizer.state_dict(),
'normalizer': normalizer.state_dict(),
'args': vars(args)
}, is_best)
# test best model
print('---------Evaluate Model on Test Set---------------')
best_checkpoint = torch.load('model_best.pth.tar')
model.load_state_dict(best_checkpoint['state_dict'])
validate(test_loader, model, criterion, normalizer, test=True)
def train(train_loader, model, criterion, optimizer, epoch, normalizer):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
if args.task == 'regression':
mae_errors = AverageMeter()
else:
accuracies = AverageMeter()
precisions = AverageMeter()
recalls = AverageMeter()
fscores = AverageMeter()
auc_scores = AverageMeter()
# switch to train mode
model.train()
end = time.time()
for i, (input, target, _) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
if args.cuda:
input_var = (Variable(input[0].cuda(async=True)),
Variable(input[1].cuda(async=True)),
input[2].cuda(async=True),
[crys_idx.cuda(async=True) for crys_idx in input[3]])
else:
input_var = (Variable(input[0]),
Variable(input[1]),
input[2],
input[3])
# normalize target
if args.task == 'regression':
target_normed = normalizer.norm(target)
else:
target_normed = target.view(-1).long()
if args.cuda:
target_var = Variable(target_normed.cuda(async=True))
else:
target_var = Variable(target_normed)
# compute output
output = model(*input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
if args.task == 'regression':
mae_error = mae(normalizer.denorm(output.data.cpu()), target)
losses.update(loss.data.cpu()[0], target.size(0))
mae_errors.update(mae_error, target.size(0))
else:
accuracy, precision, recall, fscore, auc_score =\
class_eval(output.data.cpu(), target)
losses.update(loss.data.cpu()[0], target.size(0))
accuracies.update(accuracy, target.size(0))
precisions.update(precision, target.size(0))
recalls.update(recall, target.size(0))
fscores.update(fscore, target.size(0))
auc_scores.update(auc_score, target.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
if args.task == 'regression':
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'MAE {mae_errors.val:.3f} ({mae_errors.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, mae_errors=mae_errors)
)
else:
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Accu {accu.val:.3f} ({accu.avg:.3f})\t'
'Precision {prec.val:.3f} ({prec.avg:.3f})\t'
'Recall {recall.val:.3f} ({recall.avg:.3f})\t'
'F1 {f1.val:.3f} ({f1.avg:.3f})\t'
'AUC {auc.val:.3f} ({auc.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, accu=accuracies,
prec=precisions, recall=recalls, f1=fscores,
auc=auc_scores)
)
def validate(val_loader, model, criterion, normalizer, test=False):
batch_time = AverageMeter()
losses = AverageMeter()
if args.task == 'regression':
mae_errors = AverageMeter()
else:
accuracies = AverageMeter()
precisions = AverageMeter()
recalls = AverageMeter()
fscores = AverageMeter()
auc_scores = AverageMeter()
if test:
test_targets = []
test_preds = []
test_cif_ids = []
# switch to evaluate mode
model.eval()
end = time.time()
for i, (input, target, batch_cif_ids) in enumerate(val_loader):
if args.cuda:
input_var = (Variable(input[0].cuda(async=True), volatile=True),
Variable(input[1].cuda(async=True), volatile=True),
input[2].cuda(async=True),
[crys_idx.cuda(async=True) for crys_idx in input[3]])
else:
input_var = (Variable(input[0], volatile=True),
Variable(input[1], volatile=True),
input[2],
input[3])
if args.task == 'regression':
target_normed = normalizer.norm(target)
else:
target_normed = target.view(-1).long()
if args.cuda:
target_var = Variable(target_normed.cuda(async=True),
volatile=True)
else:
target_var = Variable(target_normed, volatile=True)
# compute output
output = model(*input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
if args.task == 'regression':
mae_error = mae(normalizer.denorm(output.data.cpu()), target)
losses.update(loss.data.cpu()[0], target.size(0))
mae_errors.update(mae_error, target.size(0))
if test:
test_pred = normalizer.denorm(output.data.cpu())
test_target = target
test_preds += test_pred.view(-1).tolist()
test_targets += test_target.view(-1).tolist()
test_cif_ids += batch_cif_ids
else:
accuracy, precision, recall, fscore, auc_score =\
class_eval(output.data.cpu(), target)
losses.update(loss.data.cpu()[0], target.size(0))
accuracies.update(accuracy, target.size(0))
precisions.update(precision, target.size(0))
recalls.update(recall, target.size(0))
fscores.update(fscore, target.size(0))
auc_scores.update(auc_score, target.size(0))
if test:
test_pred = torch.exp(output.data.cpu())
test_target = target
assert test_pred.shape[1] == 2
test_preds += test_pred[:, 1].tolist()
test_targets += test_target.view(-1).tolist()
test_cif_ids += batch_cif_ids
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
if args.task == 'regression':
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'MAE {mae_errors.val:.3f} ({mae_errors.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time, loss=losses,
mae_errors=mae_errors))
else:
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Accu {accu.val:.3f} ({accu.avg:.3f})\t'
'Precision {prec.val:.3f} ({prec.avg:.3f})\t'
'Recall {recall.val:.3f} ({recall.avg:.3f})\t'
'F1 {f1.val:.3f} ({f1.avg:.3f})\t'
'AUC {auc.val:.3f} ({auc.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time, loss=losses,
accu=accuracies, prec=precisions, recall=recalls,
f1=fscores, auc=auc_scores))
if test:
star_label = '**'
import csv
with open('test_results.csv', 'w') as f:
writer = csv.writer(f)
for cif_id, target, pred in zip(test_cif_ids, test_targets,
test_preds):
writer.writerow((cif_id, target, pred))
else:
star_label = '*'
if args.task == 'regression':
print(' {star} MAE {mae_errors.avg:.3f}'.format(star=star_label,
mae_errors=mae_errors))
return mae_errors.avg
else:
print(' {star} AUC {auc.avg:.3f}'.format(star=star_label,
auc=auc_scores))
return auc_scores.avg
class Normalizer(object):
"""Normalize a Tensor and restore it later. """
def __init__(self, tensor):
"""tensor is taken as a sample to calculate the mean and std"""
self.mean = torch.mean(tensor)
self.std = torch.std(tensor)
def norm(self, tensor):
return (tensor - self.mean) / self.std
def denorm(self, normed_tensor):
return normed_tensor * self.std + self.mean
def state_dict(self):
return {'mean': self.mean,
'std': self.std}
def load_state_dict(self, state_dict):
self.mean = state_dict['mean']
self.std = state_dict['std']
def mae(prediction, target):
"""
Computes the mean absolute error between prediction and target
Parameters
----------
prediction: torch.Tensor (N, 1)
target: torch.Tensor (N, 1)
"""
return torch.mean(torch.abs(target - prediction))
def class_eval(prediction, target):
prediction = np.exp(prediction.numpy())
target = target.numpy()
pred_label = np.argmax(prediction, axis=1)
target_label = np.squeeze(target)
if prediction.shape[1] == 2:
precision, recall, fscore, _ = metrics.precision_recall_fscore_support(
target_label, pred_label, average='binary')
auc_score = metrics.roc_auc_score(target_label, prediction[:, 1])
accuracy = metrics.accuracy_score(target_label, pred_label)
else:
raise NotImplementedError
return accuracy, precision, recall, fscore, auc_score
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, 'model_best.pth.tar')
def adjust_learning_rate(optimizer, epoch, k):
"""Sets the learning rate to the initial LR decayed by 10 every k epochs"""
assert type(k) is int
lr = args.lr * (0.1 ** (epoch // k))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
if __name__ == '__main__':
main()