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generate.py
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generate.py
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import argparse
import os
import time
import dgl
import numpy as np
import tensorboard_logger as tb_logger
import torch
import time
from gcc.contrastive.criterions import NCESoftmaxLoss, NCESoftmaxLossNS
from gcc.contrastive.memory_moco import MemoryMoCo
from gcc.datasets import (
GRAPH_CLASSIFICATION_DSETS,
GraphClassificationDataset,
GraphClassificationDatasetLabeled,
LoadBalanceGraphDataset,
NodeClassificationDataset,
NodeClassificationDatasetLabeled,
worker_init_fn,
)
from gcc.datasets.data_util import batcher
from gcc.models import GraphEncoder
from gcc.utils.misc import AverageMeter, adjust_learning_rate, warmup_linear
def test_moco(train_loader, model, opt):
"""
one epoch training for moco
"""
model.eval()
emb_list = []
for idx, batch in enumerate(train_loader):
graph_q, graph_k = batch
bsz = graph_q.batch_size
graph_q.to(opt.device)
graph_k.to(opt.device)
with torch.no_grad():
feat_q = model(graph_q)
feat_k = model(graph_k)
assert feat_q.shape == (bsz, opt.hidden_size)
emb_list.append(((feat_q + feat_k) / 2).detach().cpu())
return torch.cat(emb_list)
def main(args_test):
print("test_path", args_test.load_path)
print("dataset", args_test.dataset)
print("#", args_test.num_workers)
if os.path.isfile(args_test.load_path):
print("=> loading checkpoint '{}'".format(args_test.load_path))
checkpoint = torch.load(args_test.load_path, map_location="cpu")
print(
"=> loaded successfully '{}' (epoch {})".format(
args_test.load_path, checkpoint["epoch"]
)
)
else:
print("=> no checkpoint found at '{}'".format(args_test.load_path))
args = checkpoint["opt"]
print(args)
assert args_test.gpu is None or torch.cuda.is_available()
print("Use GPU: {} for generation".format(args_test.gpu))
args.gpu = args_test.gpu
args.device = torch.device("cpu") if args.gpu is None else torch.device(args.gpu)
if args_test.dataset in GRAPH_CLASSIFICATION_DSETS:
train_dataset = GraphClassificationDataset(
dataset=args_test.dataset,
rw_hops=args.rw_hops,
subgraph_size=args.subgraph_size,
restart_prob=args.restart_prob,
positional_embedding_size=args.positional_embedding_size,
)
else:
train_dataset = NodeClassificationDataset(
dataset=args_test.dataset,
rw_hops=args.rw_hops,
subgraph_size=args.subgraph_size,
restart_prob=args.restart_prob,
positional_embedding_size=args.positional_embedding_size,
)
args.batch_size = len(train_dataset)
print("final num_workers:", args.num_workers)
train_loader = torch.utils.data.DataLoader(
dataset=train_dataset,
batch_size=args.batch_size,
collate_fn=batcher(),
shuffle=False,
num_workers=args.num_workers,
)
# create model and optimizer
model = GraphEncoder(
positional_embedding_size=args.positional_embedding_size,
max_node_freq=args.max_node_freq,
max_edge_freq=args.max_edge_freq,
max_degree=args.max_degree,
freq_embedding_size=args.freq_embedding_size,
degree_embedding_size=args.degree_embedding_size,
output_dim=args.hidden_size,
node_hidden_dim=args.hidden_size,
edge_hidden_dim=args.hidden_size,
num_layers=args.num_layer,
num_step_set2set=args.set2set_iter,
num_layer_set2set=args.set2set_lstm_layer,
gnn_model=args.model,
norm=args.norm,
degree_input=True,
)
model = model.to(args.device)
model.load_state_dict(checkpoint["model"])
del checkpoint
emb = test_moco(train_loader, model, args)
print(args.model_folder)
np.save(os.path.join(args.model_folder, args_test.dataset), emb.numpy())
if __name__ == "__main__":
parser = argparse.ArgumentParser("argument for training")
# fmt: off
parser.add_argument("--load-path",
default="saved/apt/current.pth")
parser.add_argument("--dataset", type=str, default="imdb-binary",
choices=["dgl", "wikipedia", "blogcatalog", "usa_airport", "brazil_airport", "europe_airport",
"cora", "citeseer", "pubmed", "squirrel", "texas", "cornell", "wisconsin", "youtube",
"flickr", "blogcatalog", "kdd", "chameleon", "icdm", "chameleon", "wiki", "facebook",
"sigir", "cikm", "sigmod", "icde", "cs", "phy", "cora_full", "photo", "computer",
"h-index-rand-1", "h-index-top-1", "h-index", "polblogs", "DD242", "DD68", "DD687",
"academia", "p2p25",
"gene"] + GRAPH_CLASSIFICATION_DSETS)
parser.add_argument("--gpu", default="0", type=int, help="GPU id to use.")
parser.add_argument("--num-workers", type=int, default=1, help="num of workers to use")
parser.add_argument("--num-copies", type=int, default=1, help="num of dataset copies that fit in memory")
# fmt: on
t1 = time.time()
main(parser.parse_args())
t2 = time.time()
print("total time:", t2 - t1)