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import argparse
import os
import dgl
import torch.utils.data
from dgl.dataloading import GraphDataLoader
from torch import optim
from tqdm import tqdm
from QM9_dataset_class import PreprocessedQM9Dataset
from model import SimpleGnn, GMae
import torch.nn as nn
def train_epoch(epoch, graphLoader: torch.utils.data.DataLoader,
model: nn.Module,device, optimizer:torch.optim.Optimizer,
save_dir:str
):
print(f"epoch {epoch} started!")
model.train()
model.encoder.train()
model.decoder.train()
model.to(device)
loss_epoch = 0
for batch in tqdm(graphLoader):
optimizer.zero_grad()
batch_g, _ = batch
R = batch_g.ndata["R"].to(device)
# Z_index = batch_g.ndata["Z_index"].to(device)
Z_index = batch_g.ndata["Z_index"].to(device)
Z_emb = model.encode_atom_index(Z_index)
feat = torch.cat([R, Z_emb], dim=1)
batch_g = batch_g.to(device)
loss = model.mask_attr_prediction(batch_g, feat)
loss.backward()
optimizer.step()
loss_epoch += loss.item()
return loss_epoch
def train_loop(dataset_path, epochs, batch_size,device,save_dir):
device = torch.device(device)
dataset = PreprocessedQM9Dataset(None)
dataset.load_dataset(dataset_path)
print("Dataset loaded:", dataset_path, "Total samples:", len(dataset))
print("Initializing dataloader")
myGLoader = GraphDataLoader(dataset, batch_size=batch_size, pin_memory=True,shuffle=False)
sage_enc = SimpleGnn(in_feats=7, hid_feats=4, out_feats=4) # 7 = R_dim(3)+Z_embedding_dim(4)
sage_dec = SimpleGnn(in_feats=4, hid_feats=4, out_feats=7)
gmae = GMae(sage_enc, sage_dec, 7, 4, 7, replace_rate=0)
optimizer = optim.Adam(gmae.parameters(), lr=1e-3)
print("Start training", "epochs:", epochs, "batch_size:", batch_size)
for epoch in range(epochs):
loss_epoch = train_epoch(epoch, myGLoader,gmae,device,optimizer,save_dir)
formatted_loss_epoch = f"{loss_epoch:.3f}"
save_path = os.path.join(save_dir,f"epoch_{epoch}",f"gmae_{formatted_loss_epoch}.pt")
save_subdir = os.path.dirname(save_path)
if not os.path.exists(save_subdir):
os.makedirs(save_subdir, exist_ok=True)
torch.save(gmae.state_dict(), save_path)
print(f"Epoch:{epoch},loss:{loss_epoch},Model saved:{save_path}")
with torch.no_grad():
embedded_graphs = []
print(f"Epoch:{epoch},start embedding")
gmae.eval()
gmae.encoder.eval()
for batch in tqdm(myGLoader):
batch_g, _ = batch
R = batch_g.ndata["R"].to(device)
Z_index = batch_g.ndata["Z_index"].to(device)
Z_emb = gmae.encode_atom_index(Z_index)
feat = torch.cat([R, Z_emb], dim=1)
batch_g = batch_g.to(device)
batch_g.ndata["embedding"] = gmae.embed(batch_g,feat)
unbatched_graphs = dgl.unbatch(batch_g)
embedded_graphs.extend(unbatched_graphs)
for idx,embedded_graph in enumerate(embedded_graphs):
embeddings_save_path = os.path.join(save_dir, f"epoch_{epoch}", f"embedding_{idx}.dgl")
dgl.save_graphs(embeddings_save_path, [embedded_graph])
print(f"epoch:{epoch},embedding saved:{embeddings_save_path},total_graphs:{len(embedded_graphs)}")
def main():
parser = argparse.ArgumentParser(description="Prepare QM9 dataset")
parser.add_argument('--dataset_path', type=str, default='dataset/QM9_dataset_processed.pt')
parser.add_argument('--batch_size', type=int, default=4)
parser.add_argument('--epochs', type=int, default=10, help='number of epochs')
parser.add_argument("--device", type=str, default='cuda:0')
parser.add_argument("--save_dir", type=str, default='./model')
args = parser.parse_args()
train_loop(args.dataset_path, args.epochs, args.batch_size,args.device,args.save_dir)
if __name__ == '__main__':
main()
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