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run_qm9.py
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run_qm9.py
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import os, sys
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
import os.path as osp
from shutil import copy, rmtree
import pdb
import argparse
import random
import numpy as np
from tqdm import tqdm
import torch
import torch.nn.functional as F
import torch_geometric.transforms as T
from qm9 import QM9
from dataloader import DataLoader # use a custom dataloader to handle subgraphs
from distance import Distance # custom Distance for original_edge_attr and multiple_h
from k_gnn import GraphConv, avg_pool
from k_gnn import TwoMalkin, ConnectedThreeMalkin
from qm9_models import *
# The units provided by PyG QM9 are not consistent with their original units.
# Below are meta data for unit conversion of each target task. We do unit conversion
# in order to compare with previous work (k-GNN in particular).
HAR2EV = 27.2113825435
KCALMOL2EV = 0.04336414
conversion = torch.tensor([
1., 1., HAR2EV, HAR2EV, HAR2EV, 1., HAR2EV, HAR2EV, HAR2EV, HAR2EV, HAR2EV,
1., KCALMOL2EV, KCALMOL2EV, KCALMOL2EV, KCALMOL2EV, 1., 1., 1.
])
class MyFilter(object):
def __call__(self, data):
return data.num_nodes > 6 # Remove graphs with less than 6 nodes.
class k123PreTransform(object):
def __call__(self, data):
x = data.x
data.x = data.x[:, :5]
data = TwoMalkin()(data)
data = ConnectedThreeMalkin()(data)
data.x = x
return data
class k13PreTransform(object):
def __call__(self, data):
x = data.x
data.x = data.x[:, :5]
data = ConnectedThreeMalkin()(data)
data.x = x
return data
class k12PreTransform(object):
def __call__(self, data):
x = data.x
data.x = data.x[:, :5]
data = TwoMalkin()(data)
data.x = x
return data
class MyTransform(object):
def __init__(self, pre_convert=False):
self.pre_convert = pre_convert
def __call__(self, data):
data.y = data.y[:, int(args.target)] # Specify target: 0 = mu
if self.pre_convert: # convert back to original units
data.y = data.y / conversion[int(args.target)]
return data
# General settings.
parser = argparse.ArgumentParser(description='Nested GNN for QM9 graphs')
parser.add_argument('--target', default=11)
parser.add_argument('--filter', action='store_true', default=False,
help='whether to filter graphs with less than 7 nodes')
parser.add_argument('--convert', type=str, default='post',
help='if "post", convert units after optimization; if "pre", \
convert units before optimization')
# Base GNN settings.
parser.add_argument('--model', type=str, default='NestedGIN_eff')
parser.add_argument('--layers', type=int, default=5)
# Nested GNN settings
parser.add_argument('--h', type=int, default=3, help='hop of enclosing subgraph;\
if None, will not use NestedGNN')
parser.add_argument('--max_nodes_per_hop', type=int, default=None)
parser.add_argument('--node_label', type=str, default='spd',
help='apply distance encoding to nodes within each subgraph, use node\
labels as additional node features; support "hop", "drnl", "spd", \
for "spd", you can specify number of spd to keep by "spd3", "spd4", \
"spd5", etc. Default "spd"=="spd2".')
parser.add_argument('--use_rd', action='store_true', default=False,
help='use resistance distance as additional node labels')
parser.add_argument('--subgraph_pooling', default='mean', help='support mean and center\
for some models, default mean for most models')
# Training settings.
parser.add_argument('--epochs', type=int, default=200)
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--lr', type=float, default=1E-3)
parser.add_argument('--lr_decay_factor', type=float, default=0.7)
parser.add_argument('--patience', type=int, default=5)
# Other settings.
parser.add_argument('--normalize_x', action='store_true', default=False,
help='if True, normalize non-binary node features')
parser.add_argument('--squared_dist', action='store_true', default=False,
help='use squared node distance')
parser.add_argument('--not_normalize_dist', action='store_true', default=False,
help='do not normalize node distance by max distance of a molecule')
parser.add_argument('--use_max_dist', action='store_true', default=False,
help='use maximum distance between all nodes as a global feature')
parser.add_argument('--use_pos', action='store_true', default=False,
help='use node position (3D) as continuous node features')
parser.add_argument('--RNI', action='store_true', default=False,
help='use node randomly initialized node features in [-1, 1]')
parser.add_argument('--use_relative_pos', action='store_true', default=False,
help='use relative node position (3D) as continuous edge features')
parser.add_argument('--seed', type=int, default=1)
parser.add_argument('--save_appendix', default='',
help='what to append to save-names when saving results')
parser.add_argument('--keep_old', action='store_true', default=False,
help='if True, do not overwrite old .py files in the result folder')
args = parser.parse_args()
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
if args.save_appendix == '':
args.save_appendix = '_' + time.strftime("%Y%m%d%H%M%S")
args.res_dir = 'results/QM9_{}{}'.format(args.target, args.save_appendix)
print('Results will be saved in ' + args.res_dir)
if not os.path.exists(args.res_dir):
os.makedirs(args.res_dir)
# Backup python files.
copy('run_qm9.py', args.res_dir)
copy('utils.py', args.res_dir)
copy('utils_edge_efficient.py', args.res_dir)
copy('qm9_models.py', args.res_dir)
# Save command line input.
cmd_input = 'python ' + ' '.join(sys.argv) + '\n'
with open(os.path.join(args.res_dir, 'cmd_input.txt'), 'a') as f:
f.write(cmd_input)
print('Command line input: ' + cmd_input + ' is saved.')
target = int(args.target)
print('---- Target: {} ----'.format(target))
path = 'data/QM9'
if args.model.startswith('k123_GNN'):
path = 'data/1-2-3-QM9'
elif args.model.startswith('k12_GNN'):
path = 'data/1-2-QM9'
elif args.model.startswith('k13_GNN'):
path = 'data/1-3-QM9'
if args.model.startswith('k123'):
subgraph_pretransform = k123PreTransform()
elif args.model.startswith('k13'):
subgraph_pretransform = k13PreTransform()
elif args.model.startswith('k12'):
subgraph_pretransform = k12PreTransform()
else:
subgraph_pretransform = None
pre_transform = None
if args.h is not None:
if type(args.h) == int:
path += '/ngnn_h' + str(args.h)
elif type(args.h) == list:
path += '/ngnn_h' + ''.join(str(h) for h in args.h)
path += '_' + args.node_label
if args.use_rd:
path += '_rd'
if args.max_nodes_per_hop is not None:
path += '_mnph{}'.format(args.max_nodes_per_hop)
if args.model != "NestedGIN_eff":
from utils import create_subgraphs
def pre_transform(g):
return create_subgraphs(g, args.h,
max_nodes_per_hop=args.max_nodes_per_hop,
node_label=args.node_label,
use_rd=args.use_rd,
subgraph_pretransform=subgraph_pretransform)
else:
path += '_eff'
from utils_edge_efficient import create_subgraphs
def pre_transform(g):
return create_subgraphs(g, args.h, node_label='hop', use_rd=True,
subgraph_pretransform=None, self_loop=True)
elif (args.model.startswith('k123') or args.model.startswith('k13') or
args.model.startswith('k12')):
pre_transform = subgraph_pretransform
pre_filter = None
if args.filter:
pre_filter = MyFilter()
path += '_filtered'
dataset = QM9(
path,
transform=T.Compose(
[
MyTransform(args.convert=='pre'),
Distance(norm=args.not_normalize_dist==False,
relative_pos=args.use_relative_pos,
squared=args.squared_dist)
]
),
pre_transform=pre_transform,
pre_filter=pre_filter,
skip_collate=False,
one_hot_atom=False,
)
'''
dataset = QM9(
path,
transform=MyTransform(args.convert=='pre'),
pre_transform=pre_transform,
pre_filter=pre_filter,
skip_collate=False,
one_hot_atom=False,
)
'''
dataset = dataset.shuffle()
if False: # do some statistics
loader = DataLoader(dataset, batch_size=1, shuffle=False)
n_nodes = [data.num_nodes for data in tqdm(loader)]
n_edges = [data.edge_index.shape[1]/2 for data in tqdm(loader)]
print(f'Avg #nodes: {np.mean(n_nodes)}, avg #edges: {np.mean(n_edges)}')
from torch_geometric.utils import degree
avg_deg = torch.cat(
[degree(data.edge_index[0], data.num_nodes) for data in tqdm(loader)]
).mean()
print(f'Avg node degree: {avg_deg}')
pdb.set_trace()
if False: # visualize some graphs
import networkx as nx
from torch_geometric.utils import to_networkx
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
loader = DataLoader(dataset, batch_size=1, shuffle=False)
for data in loader:
f = plt.figure(figsize=(20, 20))
limits = plt.axis('off')
data = data.to(device)
if 'name' in data.keys:
del data.name
if args.subgraph:
node_size = 100
data.x = torch.argmax(
data.x[:, :args.h+1], 1
).type(torch.int8) # only keep the hop label
with_labels = True
G = to_networkx(data, node_attrs=['x'])
labels = {i: G.nodes[i]['x'] for i in range(len(G))}
else:
node_size = 300
with_labels = False
G = to_networkx(data)
labels = None
nx.draw(G, node_size=node_size, arrows=False, with_labels=with_labels,
labels=labels)
f.savefig('tmp_vis.png')
pdb.set_trace()
# Normalize targets to mean = 0 and std = 1.
tenpercent = int(len(dataset) * 0.1)
mean = dataset.data.y[tenpercent:].mean(dim=0)
std = dataset.data.y[tenpercent:].std(dim=0)
dataset.data.y = (dataset.data.y - mean) / std
train_dataset = dataset[2 * tenpercent:]
cont_feat_start_dim = 5
if args.normalize_x:
x_mean = train_dataset.data.x[:, cont_feat_start_dim:].mean(dim=0)
x_std = train_dataset.data.x[:, cont_feat_start_dim:].std(dim=0)
x_norm = (train_dataset.data.x[:, cont_feat_start_dim:] - x_mean) / x_std
dataset.data.x = torch.cat([dataset.data.x[:, :cont_feat_start_dim], x_norm], 1)
test_dataset = dataset[:tenpercent]
val_dataset = dataset[tenpercent:2 * tenpercent]
train_dataset = dataset[2 * tenpercent:]
test_loader = DataLoader(test_dataset, batch_size=args.batch_size)
val_loader = DataLoader(val_dataset, batch_size=args.batch_size)
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)
kwargs = {
'num_layers': args.layers,
'subgraph_pooling': args.subgraph_pooling,
'use_pos': args.use_pos,
'edge_attr_dim': 8 if args.use_relative_pos else 5,
'use_max_dist': args.use_max_dist,
'use_rd': args.use_rd,
'RNI': args.RNI
}
model = eval(args.model)(dataset, **kwargs)
print('Using ' + model.__class__.__name__ + ' model')
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, factor=args.lr_decay_factor, patience=args.patience, min_lr=0.00001)
def train(epoch):
model.train()
loss_all = 0
for data in train_loader:
if type(data) == dict:
data = {key: data_.to(device) for key, data_ in data.items()}
num_graphs = data[args.h[0]].num_graphs
else:
data = data.to(device)
num_graphs = data.num_graphs
optimizer.zero_grad()
y = data.y
loss = F.mse_loss(model(data), y)
loss.backward()
loss_all += loss * num_graphs
optimizer.step()
return loss_all / len(train_loader.dataset)
def test(loader):
model.eval()
error = 0
for data in loader:
if type(data) == dict:
data = {key: data_.to(device) for key, data_ in data.items()}
else:
data = data.to(device)
y = data.y
error += ((model(data) * std[target].cuda()) -
(y * std[target].cuda())).abs().sum().item() # MAE
return error / len(loader.dataset)
def loop(start=1, best_val_error=None):
pbar = tqdm(range(start, args.epochs+start))
for epoch in pbar:
pbar.set_description('Epoch: {:03d}'.format(epoch))
lr = scheduler.optimizer.param_groups[0]['lr']
loss = train(epoch)
val_error = test(val_loader)
scheduler.step(val_error)
if best_val_error is None:
best_val_error = val_error
if val_error <= best_val_error:
test_error = test(test_loader)
best_val_error = val_error
log = (
'Epoch: {:03d}, LR: {:7f}, Loss: {:.7f}, Validation MAE: {:.7f}, ' +
'Test MAE: {:.7f}, Test MAE norm: {:.7f}, Test MAE convert: {:.7f}'
).format(
epoch, lr, loss, val_error,
test_error,
test_error / std[target].cuda(),
test_error / conversion[int(args.target)].cuda() if args.convert == 'post' else 0
)
print(log)
with open(os.path.join(args.res_dir, 'log.txt'), 'a') as f:
f.write(log + '\n')
model_name = os.path.join(args.res_dir, 'model_checkpoint{}.pth'.format(epoch))
torch.save(model.state_dict(), model_name)
start = epoch + 1
return start, best_val_error, log
best_val_error = None
start = 1
start, best_val_error, log = loop(start, best_val_error)
print(cmd_input[:-1])
print(log)
# uncomment the below to keep training even reaching epochs
'''
while True:
start, best_val_error, log = loop(start, best_val_error)
print(cmd_input[:-1])
print(log)
pdb.set_trace()
'''