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import os
import os.path as osp
from datetime import datetime
import torch
from ogb.nodeproppred import PygNodePropPredDataset
from tqdm import tqdm
import torch_geometric.transforms as T
from torch_geometric.data import HeteroData
from torch_geometric.datasets import OGB_MAG, Reddit
from torch_geometric.nn import GAT, GCN, PNA, EdgeCNN, GraphSAGE
from torch_geometric.utils import index_to_mask
from .hetero_gat import HeteroGAT
from .hetero_sage import HeteroGraphSAGE
try:
from torch.autograd.profiler import emit_itt
except ImportError:
from contextlib import contextmanager
@contextmanager
def emit_itt(*args, **kwargs):
yield
models_dict = {
'edge_cnn': EdgeCNN,
'gat': GAT,
'gcn': GCN,
'pna': PNA,
'sage': GraphSAGE,
'rgat': HeteroGAT,
'rgcn': HeteroGraphSAGE,
}
def get_dataset_with_transformation(name, root, use_sparse_tensor=False,
bf16=False):
path = osp.join(osp.dirname(osp.realpath(__file__)), root, name)
transform = T.ToSparseTensor(
remove_edge_index=False) if use_sparse_tensor else None
if name == 'ogbn-mag':
if transform is None:
transform = T.ToUndirected(merge=True)
else:
transform = T.Compose([T.ToUndirected(merge=True), transform])
dataset = OGB_MAG(root=path, preprocess='metapath2vec',
transform=transform)
elif name == 'ogbn-products':
if transform is None:
transform = T.RemoveDuplicatedEdges()
else:
transform = T.Compose([T.RemoveDuplicatedEdges(), transform])
dataset = PygNodePropPredDataset('ogbn-products', root=path,
transform=transform)
elif name == 'Reddit':
dataset = Reddit(root=path, transform=transform)
data = dataset[0]
if name == 'ogbn-products':
split_idx = dataset.get_idx_split()
data.train_mask = index_to_mask(split_idx['train'],
size=data.num_nodes)
data.val_mask = index_to_mask(split_idx['valid'], size=data.num_nodes)
data.test_mask = index_to_mask(split_idx['test'], size=data.num_nodes)
data.y = data.y.squeeze()
if bf16:
if isinstance(data, HeteroData):
for node_type in data.node_types:
data[node_type].x = data[node_type].x.to(torch.bfloat16)
else:
data.x = data.x.to(torch.bfloat16)
return data, dataset.num_classes, transform
def get_dataset(name, root, use_sparse_tensor=False, bf16=False):
data, num_classes, _ = get_dataset_with_transformation(
name, root, use_sparse_tensor, bf16)
return data, num_classes
def get_model(name, params, metadata=None):
Model = models_dict.get(name, None)
assert Model is not None, f'Model {name} not supported!'
if name == 'rgat':
return Model(metadata, params['hidden_channels'], params['num_layers'],
params['output_channels'], params['num_heads'])
if name == 'rgcn':
return Model(metadata, params['hidden_channels'], params['num_layers'],
params['output_channels'])
if name == 'gat':
return Model(params['inputs_channels'], params['hidden_channels'],
params['num_layers'], params['output_channels'],
heads=params['num_heads'])
if name == 'pna':
return Model(params['inputs_channels'], params['hidden_channels'],
params['num_layers'], params['output_channels'],
aggregators=['mean', 'min', 'max', 'std'],
scalers=['identity', 'amplification',
'attenuation'], deg=params['degree'])
return Model(params['inputs_channels'], params['hidden_channels'],
params['num_layers'], params['output_channels'])
def get_split_masks(data, dataset_name):
if dataset_name == 'ogbn-mag':
train_mask = ('paper', data['paper'].train_mask)
test_mask = ('paper', data['paper'].test_mask)
val_mask = ('paper', data['paper'].val_mask)
else:
train_mask = data.train_mask
val_mask = data.val_mask
test_mask = data.test_mask
return train_mask, val_mask, test_mask
def save_benchmark_data(csv_data, batch_size, layers, num_neighbors,
hidden_channels, total_time, model_name, dataset_name,
use_sparse_tensor):
config = f'Batch size={batch_size}, ' \
f'#Layers={layers}, ' \
f'#Neighbors={num_neighbors}, ' \
f'#Hidden features={hidden_channels}'
csv_data['DATE'].append(datetime.now().date())
csv_data['TIME (s)'].append(round(total_time, 2))
csv_data['MODEL'].append(model_name)
csv_data['DATASET'].append(dataset_name)
csv_data['CONFIG'].append(config)
csv_data['SPARSE'].append(use_sparse_tensor)
def write_to_csv(csv_data, write_csv='bench', training=False):
import pandas as pd
results_path = osp.join(osp.dirname(osp.realpath(__file__)), '../results/')
os.makedirs(results_path, exist_ok=True)
name = 'training' if training else 'inference'
if write_csv == 'bench':
csv_file_name = f'TOTAL_{name}_benchmark.csv'
else:
csv_file_name = f'TOTAL_prof_{name}_benchmark.csv'
csv_path = osp.join(results_path, csv_file_name)
index_label = 'TEST_ID' if write_csv == 'bench' else 'ID'
with_header = not osp.exists(csv_path)
df = pd.DataFrame(csv_data)
df.to_csv(csv_path, mode='a', index_label=index_label, header=with_header)
@torch.no_grad()
def test(model, loader, device, hetero, progress_bar=True,
desc="Evaluation") -> None:
if progress_bar:
loader = tqdm(loader, desc=desc)
total_examples = total_correct = 0
if hetero:
for batch in loader:
batch = batch.to(device)
if 'adj_t' in batch:
edge_index_dict = batch.adj_t_dict
else:
edge_index_dict = batch.edge_index_dict
out = model(batch.x_dict, edge_index_dict)
batch_size = batch['paper'].batch_size
out = out['paper'][:batch_size]
pred = out.argmax(dim=-1)
total_examples += batch_size
total_correct += int((pred == batch['paper'].y[:batch_size]).sum())
else:
for batch in loader:
batch = batch.to(device)
if 'adj_t' in batch:
edge_index = batch.adj_t
else:
edge_index = batch.edge_index
out = model(batch.x, edge_index)
batch_size = batch.batch_size
out = out[:batch_size]
pred = out.argmax(dim=-1)
total_examples += batch_size
total_correct += int((pred == batch.y[:batch_size]).sum())
return total_correct / total_examples
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