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train_transductive.py
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train_transductive.py
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import copy
import logging
import os
from absl import app
from absl import flags
import torch
from torch.nn.functional import cosine_similarity
from torch.optim import AdamW
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from bgrl import *
log = logging.getLogger(__name__)
FLAGS = flags.FLAGS
flags.DEFINE_integer('model_seed', None, 'Random seed used for model initialization and training.')
flags.DEFINE_integer('data_seed', 1, 'Random seed used to generate train/val/test split.')
flags.DEFINE_integer('num_eval_splits', 3, 'Number of different train/test splits the model will be evaluated over.')
# Dataset.
flags.DEFINE_enum('dataset', 'coauthor-cs',
['amazon-computers', 'amazon-photos', 'coauthor-cs', 'coauthor-physics', 'wiki-cs'],
'Which graph dataset to use.')
flags.DEFINE_string('dataset_dir', './data', 'Where the dataset resides.')
# Architecture.
flags.DEFINE_multi_integer('graph_encoder_layer', None, 'Conv layer sizes.')
flags.DEFINE_integer('predictor_hidden_size', 512, 'Hidden size of projector.')
# Training hyperparameters.
flags.DEFINE_integer('epochs', 10000, 'The number of training epochs.')
flags.DEFINE_float('lr', 1e-5, 'The learning rate for model training.')
flags.DEFINE_float('weight_decay', 1e-5, 'The value of the weight decay for training.')
flags.DEFINE_float('mm', 0.99, 'The momentum for moving average.')
flags.DEFINE_integer('lr_warmup_epochs', 1000, 'Warmup period for learning rate.')
# Augmentations.
flags.DEFINE_float('drop_edge_p_1', 0., 'Probability of edge dropout 1.')
flags.DEFINE_float('drop_feat_p_1', 0., 'Probability of node feature dropout 1.')
flags.DEFINE_float('drop_edge_p_2', 0., 'Probability of edge dropout 2.')
flags.DEFINE_float('drop_feat_p_2', 0., 'Probability of node feature dropout 2.')
# Logging and checkpoint.
flags.DEFINE_string('logdir', None, 'Where the checkpoint and logs are stored.')
flags.DEFINE_integer('log_steps', 10, 'Log information at every log_steps.')
# Evaluation
flags.DEFINE_integer('eval_epochs', 5, 'Evaluate every eval_epochs.')
def main(argv):
# use CUDA_VISIBLE_DEVICES to select gpu
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
log.info('Using {} for training.'.format(device))
# set random seed
if FLAGS.model_seed is not None:
log.info('Random seed set to {}.'.format(FLAGS.model_seed))
set_random_seeds(random_seed=FLAGS.model_seed)
# create log directory
os.makedirs(FLAGS.logdir, exist_ok=True)
with open(os.path.join(FLAGS.logdir, 'config.cfg'), "w") as file:
file.write(FLAGS.flags_into_string()) # save config file
# load data
if FLAGS.dataset != 'wiki-cs':
dataset = get_dataset(FLAGS.dataset_dir, FLAGS.dataset)
num_eval_splits = FLAGS.num_eval_splits
else:
dataset, train_masks, val_masks, test_masks = get_wiki_cs(FLAGS.dataset_dir)
num_eval_splits = train_masks.shape[1]
data = dataset[0] # all dataset include one graph
log.info('Dataset {}, {}.'.format(dataset.__class__.__name__, data))
data = data.to(device) # permanently move in gpy memory
# prepare transforms
transform_1 = get_graph_drop_transform(drop_edge_p=FLAGS.drop_edge_p_1, drop_feat_p=FLAGS.drop_feat_p_1)
transform_2 = get_graph_drop_transform(drop_edge_p=FLAGS.drop_edge_p_2, drop_feat_p=FLAGS.drop_feat_p_2)
# build networks
input_size, representation_size = data.x.size(1), FLAGS.graph_encoder_layer[-1]
encoder = GCN([input_size] + FLAGS.graph_encoder_layer, batchnorm=True) # 512, 256, 128
predictor = MLP_Predictor(representation_size, representation_size, hidden_size=FLAGS.predictor_hidden_size)
model = BGRL(encoder, predictor).to(device)
# optimizer
optimizer = AdamW(model.trainable_parameters(), lr=FLAGS.lr, weight_decay=FLAGS.weight_decay)
# scheduler
lr_scheduler = CosineDecayScheduler(FLAGS.lr, FLAGS.lr_warmup_epochs, FLAGS.epochs)
mm_scheduler = CosineDecayScheduler(1 - FLAGS.mm, 0, FLAGS.epochs)
# setup tensorboard and make custom layout
writer = SummaryWriter(FLAGS.logdir)
layout = {'accuracy': {'accuracy/test': ['Multiline', [f'accuracy/test_{i}' for i in range(num_eval_splits)]]}}
writer.add_custom_scalars(layout)
def train(step):
model.train()
# update learning rate
lr = lr_scheduler.get(step)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# update momentum
mm = 1 - mm_scheduler.get(step)
# forward
optimizer.zero_grad()
x1, x2 = transform_1(data), transform_2(data)
q1, y2 = model(x1, x2)
q2, y1 = model(x2, x1)
loss = 2 - cosine_similarity(q1, y2.detach(), dim=-1).mean() - cosine_similarity(q2, y1.detach(), dim=-1).mean()
loss.backward()
# update online network
optimizer.step()
# update target network
model.update_target_network(mm)
# log scalars
writer.add_scalar('params/lr', lr, step)
writer.add_scalar('params/mm', mm, step)
writer.add_scalar('train/loss', loss, step)
def eval(epoch):
# make temporary copy of encoder
tmp_encoder = copy.deepcopy(model.online_encoder).eval()
representations, labels = compute_representations(tmp_encoder, dataset, device)
if FLAGS.dataset != 'wiki-cs':
scores = fit_logistic_regression(representations.cpu().numpy(), labels.cpu().numpy(),
data_random_seed=FLAGS.data_seed, repeat=FLAGS.num_eval_splits)
else:
scores = fit_logistic_regression_preset_splits(representations.cpu().numpy(), labels.cpu().numpy(),
train_masks, val_masks, test_masks)
for i, score in enumerate(scores):
writer.add_scalar(f'accuracy/test_{i}', score, epoch)
for epoch in tqdm(range(1, FLAGS.epochs + 1)):
train(epoch-1)
if epoch % FLAGS.eval_epochs == 0:
eval(epoch)
# save encoder weights
torch.save({'model': model.online_encoder.state_dict()}, os.path.join(FLAGS.logdir, 'bgrl-wikics.pt'))
if __name__ == "__main__":
log.info('PyTorch version: %s' % torch.__version__)
app.run(main)