wehub-resource-sync
164 lines
4.2 KiB
Python
164 lines
4.2 KiB
Python
import argparse
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import time
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import networkx as nx
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from conf import *
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from models import *
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import dgl
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from dgl.data import load_data, register_data_args
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def get_model_and_config(name):
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name = name.lower()
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if name == "gcn":
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return GCN, GCN_CONFIG
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elif name == "gat":
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return GAT, GAT_CONFIG
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elif name == "graphsage":
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return GraphSAGE, GRAPHSAGE_CONFIG
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elif name == "appnp":
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return APPNP, APPNP_CONFIG
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elif name == "tagcn":
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return TAGCN, TAGCN_CONFIG
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elif name == "agnn":
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return AGNN, AGNN_CONFIG
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elif name == "sgc":
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return SGC, SGC_CONFIG
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elif name == "gin":
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return GIN, GIN_CONFIG
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elif name == "chebnet":
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return ChebNet, CHEBNET_CONFIG
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def evaluate(model, features, labels, mask):
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model.eval()
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with torch.no_grad():
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logits = model(features)
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logits = logits[mask]
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labels = labels[mask]
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_, indices = torch.max(logits, dim=1)
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correct = torch.sum(indices == labels)
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return correct.item() * 1.0 / len(labels)
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def main(args):
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# load and preprocess dataset
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data = load_data(args)
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g = data[0]
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if args.gpu < 0:
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cuda = False
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else:
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cuda = True
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g = g.to(args.gpu)
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features = g.ndata["feat"]
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labels = g.ndata["label"]
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train_mask = g.ndata["train_mask"]
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val_mask = g.ndata["val_mask"]
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test_mask = g.ndata["test_mask"]
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in_feats = features.shape[1]
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n_classes = data.num_classes
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n_edges = g.num_edges()
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print(
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"""----Data statistics------'
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#Edges %d
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#Classes %d
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#Train samples %d
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#Val samples %d
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#Test samples %d"""
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% (
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n_edges,
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n_classes,
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train_mask.int().sum().item(),
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val_mask.int().sum().item(),
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test_mask.int().sum().item(),
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)
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)
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# graph preprocess and calculate normalization factor
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# add self loop
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if args.self_loop:
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g = g.remove_self_loop().add_self_loop()
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n_edges = g.num_edges()
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# normalization
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degs = g.in_degrees().float()
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norm = torch.pow(degs, -0.5)
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norm[torch.isinf(norm)] = 0
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g.ndata["norm"] = norm.unsqueeze(1)
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# create GCN model
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GNN, config = get_model_and_config(args.model)
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model = GNN(g, in_feats, n_classes, *config["extra_args"])
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if cuda:
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model = model.cuda()
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print(model)
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loss_fcn = torch.nn.CrossEntropyLoss()
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# use optimizer
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optimizer = torch.optim.Adam(
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model.parameters(), lr=config["lr"], weight_decay=config["weight_decay"]
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)
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# initialize graph
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mean = 0
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for epoch in range(200):
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model.train()
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if epoch >= 3:
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t0 = time.time()
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# forward
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logits = model(features)
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loss = loss_fcn(logits[train_mask], labels[train_mask])
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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if epoch >= 3:
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mean = (mean * (epoch - 3) + (time.time() - t0)) / (epoch - 2)
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acc = evaluate(model, features, labels, val_mask)
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print(
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"Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
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"ETputs(KTEPS) {:.2f}".format(
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epoch,
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mean,
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loss.item(),
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acc,
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n_edges / mean / 1000,
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)
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)
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print()
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acc = evaluate(model, features, labels, test_mask)
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print("Test Accuracy {:.4f}".format(acc))
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Node classification on citation networks."
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)
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register_data_args(parser)
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parser.add_argument(
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"--model",
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type=str,
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default="gcn",
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help="model to use, available models are gcn, gat, graphsage, gin,"
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"appnp, tagcn, sgc, agnn",
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)
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parser.add_argument("--gpu", type=int, default=-1, help="gpu")
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parser.add_argument(
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"--self-loop",
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action="store_true",
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help="graph self-loop (default=False)",
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)
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args = parser.parse_args()
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print(args)
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main(args)
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