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main.py
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import time
import argparse
import pickle
import os
import datetime
import torch
import torch.optim as optim
from torch.optim import lr_scheduler
from utils import *
from modules import *
parser = argparse.ArgumentParser(
'Neral relational inference for molecular dynamics simulations')
parser.add_argument('--num-residues', type=int, default=77,
help='Number of residues of the PDB.')
parser.add_argument('--save-folder', type=str, default='logs',
help='Where to save the trained model, leave empty to not save anything.')
parser.add_argument('--load-folder', type=str, default='',
help='Where to load the trained model if finetunning. ' +
'Leave empty to train from scratch')
parser.add_argument('--edge-types', type=int, default=4,
help='The number of edge types to infer.')
parser.add_argument('--dims', type=int, default=6,
help='The number of input dimensions used in study( position (X,Y,Z) + velocity (X,Y,Z) ). ')
parser.add_argument('--timesteps', type=int, default=50,
help='The number of time steps per sample. Actually is 50')
parser.add_argument('--prediction-steps', type=int, default=1, metavar='N',
help='Num steps to predict before re-using teacher forcing.')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='Disables CUDA training.')
parser.add_argument('--seed', type=int, default=42, help='Random seed.')
parser.add_argument('--epochs', type=int, default=500,
help='Number of epochs to train.')
parser.add_argument('--batch-size', type=int, default=1,
help='Number of samples per batch.')
parser.add_argument('--lr', type=float, default=0.0005,
help='Initial learning rate.')
parser.add_argument('--encoder-hidden', type=int, default=256,
help='Number of hidden units in encoder.')
parser.add_argument('--decoder-hidden', type=int, default=256,
help='Number of hidden units in decoder.')
parser.add_argument('--temp', type=float, default=0.5,
help='Temperature for Gumbel softmax.')
parser.add_argument('--encoder', type=str, default='mlp',
help='Type of path encoder model (mlp or cnn).')
parser.add_argument('--decoder', type=str, default='rnn',
help='Type of decoder model (mlp, rnn, or sim).')
parser.add_argument('--no-factor', action='store_true', default=False,
help='Disables factor graph model.')
parser.add_argument('--encoder-dropout', type=float, default=0.0,
help='Dropout rate (1 - keep probability) in encoder.')
parser.add_argument('--decoder-dropout', type=float, default=0.0,
help='Dropout rate (1 - keep probability) in decoder.')
parser.add_argument('--lr-decay', type=int, default=200,
help='After how epochs to decay LR by a factor of gamma.')
parser.add_argument('--gamma', type=float, default=0.5,
help='LR decay factor.')
parser.add_argument('--skip-first', action='store_true', default=True,
help='Skip first edge type in decoder, i.e. it represents no-edge.')
parser.add_argument('--var', type=float, default=5e-5,
help='Output variance.')
parser.add_argument('--hard', action='store_true', default=True,
help='Uses discrete samples in training forward pass.')
parser.add_argument('--prior', action='store_true', default=True,
help='Whether to use sparsity prior.')
parser.add_argument('--dynamic-graph', action='store_true', default=True,
help='Whether test with dynamically re-computed graph.')
parser.add_argument('--number-expstart', type=int, default=0,
help='start number of experiments.')
parser.add_argument('--number-exp', type=int, default=56,
help='number of experiments.')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
args.factor = not args.no_factor
# print all arguments
print(args)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
if args.dynamic_graph:
print("Testing with dynamically re-computed graph.")
# Save model and meta-data. Always saves in a new sub-folder.
if args.save_folder:
exp_counter = 0
now = datetime.datetime.now()
timestamp = now.isoformat()
save_folder = args.save_folder+'/'
if not os.path.isdir(save_folder):
os.mkdir(save_folder)
meta_file = os.path.join(save_folder, 'metadata.pkl')
encoder_file = os.path.join(save_folder, 'encoder.pt')
decoder_file = os.path.join(save_folder, 'decoder.pt')
log_file = os.path.join(save_folder, 'log.txt')
log = open(log_file, 'w')
pickle.dump({'args': args}, open(meta_file, "wb"))
else:
print("WARNING: No save_folder provided!" +
"Testing (within this script) will throw an error.")
# load data
train_loader, valid_loader, test_loader, loc_max, loc_min, vel_max, vel_min = load_dataset_train_valid_test(
args.batch_size, args.number_exp, args.number_expstart, args.dims)
# Generate off-diagonal interaction graph
off_diag = np.ones([args.num_residues, args.num_residues]
) - np.eye(args.num_residues)
rel_rec = np.array(encode_onehot(np.where(off_diag)[1]), dtype=np.float32)
rel_send = np.array(encode_onehot(np.where(off_diag)[0]), dtype=np.float32)
rel_rec = torch.FloatTensor(rel_rec)
rel_send = torch.FloatTensor(rel_send)
if args.encoder == 'mlp':
encoder = MLPEncoder(args.timesteps * args.dims, args.encoder_hidden,
args.edge_types,
args.encoder_dropout, args.factor)
elif args.encoder == 'cnn':
encoder = CNNEncoder(args.dims, args.encoder_hidden,
args.edge_types,
args.encoder_dropout, args.factor)
if args.decoder == 'mlp':
decoder = MLPDecoder(n_in_node=args.dims,
edge_types=args.edge_types,
msg_hid=args.decoder_hidden,
msg_out=args.decoder_hidden,
n_hid=args.decoder_hidden,
do_prob=args.decoder_dropout,
skip_first=args.skip_first)
elif args.decoder == 'rnn':
decoder = RNNDecoder(n_in_node=args.dims,
edge_types=args.edge_types,
n_hid=args.decoder_hidden,
do_prob=args.decoder_dropout,
skip_first=args.skip_first)
elif args.decoder == 'sim':
decoder = SimulationDecoder(
loc_max, loc_min, vel_max, vel_min, args.suffix)
if args.load_folder:
encoder_file = os.path.join(args.load_folder, 'encoder.pt')
encoder.load_state_dict(torch.load(encoder_file))
decoder_file = os.path.join(args.load_folder, 'decoder.pt')
decoder.load_state_dict(torch.load(decoder_file))
args.save_folder = False
optimizer = optim.Adam(list(encoder.parameters()) + list(decoder.parameters()),
lr=args.lr)
scheduler = lr_scheduler.StepLR(optimizer, step_size=args.lr_decay,
gamma=args.gamma)
# Linear indices of an upper triangular mx, used for acc calculation
triu_indices = get_triu_offdiag_indices(args.num_residues)
tril_indices = get_tril_offdiag_indices(args.num_residues)
if args.prior:
prior = np.array([0.91, 0.03, 0.03, 0.03]) # TODO: hard coded for now
print("Using prior")
print(prior)
log_prior = torch.FloatTensor(np.log(prior))
log_prior = torch.unsqueeze(log_prior, 0)
log_prior = torch.unsqueeze(log_prior, 0)
log_prior = Variable(log_prior)
if args.cuda:
log_prior = log_prior.cuda()
if args.cuda:
encoder.cuda()
decoder.cuda()
rel_rec = rel_rec.cuda()
rel_send = rel_send.cuda()
triu_indices = triu_indices.cuda()
tril_indices = tril_indices.cuda()
rel_rec = Variable(rel_rec)
rel_send = Variable(rel_send)
def train(epoch, best_val_loss):
t = time.time()
nll_train = []
acc_train = []
kl_train = []
mse_train = []
edges_train = []
probs_train = []
encoder.train()
decoder.train()
for batch_idx, (data, relations) in enumerate(train_loader):
if args.cuda:
data, relations = data.cuda(), relations.cuda()
data, relations = Variable(data), Variable(relations)
optimizer.zero_grad()
logits = encoder(data, rel_rec, rel_send)
edges = gumbel_softmax(logits, tau=args.temp, hard=args.hard)
prob = my_softmax(logits, -1)
if args.decoder == 'rnn':
output = decoder(data, edges, rel_rec, rel_send, 50,
burn_in=True,
burn_in_steps=args.timesteps - args.prediction_steps)
else:
output = decoder(data, edges, rel_rec, rel_send,
args.prediction_steps)
target = data[:, :, 1:, :]
loss_nll = nll_gaussian(output, target, args.var)
if args.prior:
loss_kl = kl_categorical(prob, log_prior, args.num_residues)
else:
loss_kl = kl_categorical_uniform(prob, args.num_residues,
args.edge_types)
loss = loss_nll + loss_kl
acc = edge_accuracy(logits, relations)
acc_train.append(acc)
loss.backward()
optimizer.step()
mse_train.append(F.mse_loss(output, target).item())
nll_train.append(loss_nll.item())
kl_train.append(loss_kl.item())
_, edges_t = edges.max(-1)
edges_train.append(edges_t.data.cpu().numpy())
probs_train.append(prob.data.cpu().numpy())
scheduler.step()
nll_val = []
acc_val = []
kl_val = []
mse_val = []
encoder.eval()
decoder.eval()
for batch_idx, (data, relations) in enumerate(valid_loader):
if args.cuda:
data, relations = data.cuda(), relations.cuda()
with torch.no_grad():
logits = encoder(data, rel_rec, rel_send)
edges = gumbel_softmax(logits, tau=args.temp, hard=True)
prob = my_softmax(logits, -1)
# validation output uses teacher forcing
output = decoder(data, edges, rel_rec, rel_send, 1)
target = data[:, :, 1:, :]
loss_nll = nll_gaussian(output, target, args.var)
loss_kl = kl_categorical_uniform(
prob, args.num_residues, args.edge_types)
acc = edge_accuracy(logits, relations)
acc_val.append(acc)
mse_val.append(F.mse_loss(output, target).item())
nll_val.append(loss_nll.item())
kl_val.append(loss_kl.item())
print('Epoch: {:04d}'.format(epoch),
'nll_train: {:.10f}'.format(np.mean(np.array(nll_train))),
'kl_train: {:.10f}'.format(np.mean(np.array(kl_train))),
'mse_train: {:.10f}'.format(np.mean(np.array(mse_train))),
'acc_train: {:.10f}'.format(np.mean(np.array(acc_train))),
'nll_val: {:.10f}'.format(np.mean(np.array(nll_val))),
'kl_val: {:.10f}'.format(np.mean(np.array(kl_val))),
'mse_val: {:.10f}'.format(np.mean(np.array(mse_val))),
'acc_val: {:.10f}'.format(np.mean(np.array(acc_val))),
'time: {:.4f}s'.format(time.time() - t))
edges_train = np.concatenate(edges_train)
probs_train = np.concatenate(probs_train)
if args.save_folder and np.mean(np.array(nll_val)) < best_val_loss:
torch.save(encoder.state_dict(), encoder_file)
torch.save(decoder.state_dict(), decoder_file)
print('Best model so far, saving...')
print('Epoch: {:04d}'.format(epoch),
'nll_train: {:.10f}'.format(np.mean(np.array(nll_train))),
'kl_train: {:.10f}'.format(np.mean(np.array(kl_train))),
'mse_train: {:.10f}'.format(np.mean(np.array(mse_train))),
'acc_train: {:.10f}'.format(np.mean(np.array(acc_train))),
'nll_val: {:.10f}'.format(np.mean(np.array(nll_val))),
'kl_val: {:.10f}'.format(np.mean(np.array(kl_val))),
'mse_val: {:.10f}'.format(np.mean(np.array(mse_val))),
'acc_val: {:.10f}'.format(np.mean(np.array(acc_val))),
'time: {:.4f}s'.format(time.time() - t), file=log)
log.flush()
return encoder, decoder, edges_train, probs_train, np.mean(np.array(nll_val))
def test():
acc_test = []
nll_test = []
kl_test = []
mse_test = []
edges_test = []
probs_test = []
tot_mse = 0
counter = 0
encoder.eval()
decoder.eval()
encoder.load_state_dict(torch.load(encoder_file))
decoder.load_state_dict(torch.load(decoder_file))
for batch_idx, (data, relations) in enumerate(test_loader):
if args.cuda:
data, relations = data.cuda(), relations.cuda()
with torch.no_grad():
# assert (data.size(2) - args.timesteps) >= args.timesteps
assert (data.size(2)) >= args.timesteps
data_encoder = data[:, :, :args.timesteps, :].contiguous()
data_decoder = data[:, :, -args.timesteps:, :].contiguous()
logits = encoder(data_encoder, rel_rec, rel_send)
edges = gumbel_softmax(logits, tau=args.temp, hard=True)
prob = my_softmax(logits, -1)
output = decoder(data_decoder, edges, rel_rec, rel_send, 1)
target = data_decoder[:, :, 1:, :]
loss_nll = nll_gaussian(output, target, args.var)
loss_kl = kl_categorical_uniform(
prob, args.num_residues, args.edge_types)
acc = edge_accuracy(logits, relations)
acc_test.append(acc)
mse_test.append(F.mse_loss(output, target).item())
nll_test.append(loss_nll.item())
kl_test.append(loss_kl.item())
_, edges_t = edges.max(-1)
edges_test.append(edges_t.data.cpu().numpy())
probs_test.append(prob.data.cpu().numpy())
# For plotting purposes
if args.decoder == 'rnn':
if args.dynamic_graph:
output = decoder(data, edges, rel_rec, rel_send, 50,
burn_in=False, burn_in_steps=args.timesteps,
dynamic_graph=True, encoder=encoder,
temp=args.temp)
else:
output = decoder(data, edges, rel_rec, rel_send, 50,
burn_in=True, burn_in_steps=args.timesteps)
target = data[:, :, 1:, :]
else:
data_plot = data[:, :, 0:0 + 21,
:].contiguous()
output = decoder(data_plot, edges, rel_rec, rel_send, 20)
target = data_plot[:, :, 1:, :]
mse = ((target - output) ** 2).mean(dim=0).mean(dim=0).mean(dim=-1)
tot_mse += mse.data.cpu().numpy()
counter += 1
mean_mse = tot_mse / counter
mse_str = '['
for mse_step in mean_mse[:-1]:
mse_str += " {:.12f} ,".format(mse_step)
mse_str += " {:.12f} ".format(mean_mse[-1])
mse_str += ']'
print('--------------------------------')
print('--------Testing-----------------')
print('--------------------------------')
print('nll_test: {:.10f}'.format(np.mean(nll_test)),
'kl_test: {:.10f}'.format(np.mean(kl_test)),
'mse_test: {:.10f}'.format(np.mean(mse_test)),
'acc_test: {:.10f}'.format(np.mean(acc_test)))
print('MSE: {}'.format(mse_str))
edges_test = np.concatenate(edges_test)
probs_test = np.concatenate(probs_test)
if args.save_folder:
print('--------------------------------', file=log)
print('--------Testing-----------------', file=log)
print('--------------------------------', file=log)
print('nll_test: {:.10f}'.format(np.mean(nll_test)),
'kl_test: {:.10f}'.format(np.mean(kl_test)),
'mse_test: {:.10f}'.format(np.mean(mse_test)),
'acc_test: {:.10f}'.format(np.mean(acc_test)),
file=log)
print('MSE: {}'.format(mse_str), file=log)
log.flush()
return edges_test, probs_test
# Train model
print("Start Training...")
t_total = time.time()
best_val_loss = np.inf
best_epoch = 0
for epoch in range(args.epochs):
encoder, decoder, edges_train, probs_train, val_loss = train(
epoch, best_val_loss)
# print('Epoch '+str(epoch)+' with val loss:'+str(val_loss))
if val_loss < best_val_loss:
best_val_loss = val_loss
best_epoch = epoch
np.save(str(args.save_folder)+'/out_edges_train.npy', edges_train)
np.save(str(args.save_folder)+'/out_probs_train.npy', probs_train)
print("Optimization Finished!")
print("Best Epoch: {:04d}".format(best_epoch))
if args.save_folder:
print("Best Epoch: {:04d}".format(best_epoch), file=log)
log.flush()
# Test
edges_test, probs_test = test()
if log is not None:
print(save_folder)
log.close()