def main():
global args, model_args, best_mae_error
# load data
dataset = CIFData(args.cifpath)
collate_fn = collate_pool
test_loader = DataLoader(
dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
collate_fn=collate_fn,
pin_memory=args.cuda,
)
# build model
structures, _, _ = dataset[0]
orig_atom_fea_len = structures[0].shape[-1]
nbr_fea_len = structures[1].shape[-1]
model = CrystalGraphConvNet(
orig_atom_fea_len,
nbr_fea_len,
atom_fea_len=model_args.atom_fea_len,
n_conv=model_args.n_conv,
h_fea_len=model_args.h_fea_len,
n_h=model_args.n_h,
classification=True if model_args.task == "classification" else False,
)
if args.cuda:
model.cuda()
# define loss func and optimizer
if model_args.task == "classification":
criterion = nn.NLLLoss()
else:
criterion = nn.MSELoss()
normalizer = Normalizer(torch.zeros(3))
# optionally resume from a checkpoint
if os.path.isfile(args.modelpath):
print("=> loading model '{}'".format(args.modelpath))
checkpoint = torch.load(args.modelpath,
map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint["state_dict"])
normalizer.load_state_dict(checkpoint["normalizer"])
print("=> loaded model '{}' (epoch {}, validation {})".format(
args.modelpath, checkpoint["epoch"], checkpoint["best_mae_error"]))
else:
print("=> no model found at '{}'".format(args.modelpath))
validate(test_loader,
model,
criterion,
normalizer,
test=True,
fname="predictions")
print(dataset.bad_indices)
def main():
global args, model_args, best_mae_error
# load data
dataset = CIFData(args.cifpath)
collate_fn = collate_pool
test_loader = DataLoader(dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
collate_fn=collate_fn,
pin_memory=args.cuda)
# build model
structures, _, _ = dataset[0]
orig_atom_fea_len = structures[0].shape[-1]
nbr_fea_len = structures[1].shape[-1]
model = CrystalGraphConvNet(
orig_atom_fea_len,
nbr_fea_len,
atom_fea_len=model_args.atom_fea_len,
n_conv=model_args.n_conv,
h_fea_len=model_args.h_fea_len,
n_h=model_args.n_h,
classification=True if model_args.task == 'classification' else False)
if args.cuda:
model.cuda()
# define loss func and optimizer
if model_args.task == 'classification':
criterion = nn.NLLLoss()
else:
criterion = nn.MSELoss()
# if args.optim == 'SGD':
# optimizer = optim.SGD(model.parameters(), args.lr,
# momentum=args.momentum,
# weight_decay=args.weight_decay)
# elif args.optim == 'Adam':
# optimizer = optim.Adam(model.parameters(), args.lr,
# weight_decay=args.weight_decay)
# else:
# raise NameError('Only SGD or Adam is allowed as --optim')
normalizer = Normalizer(torch.zeros(3))
# optionally resume from a checkpoint
if os.path.isfile(args.modelpath):
print("=> loading model '{}'".format(args.modelpath))
checkpoint = torch.load(args.modelpath,
map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint['state_dict'])
normalizer.load_state_dict(checkpoint['normalizer'])
print("=> loaded model '{}' (epoch {}, validation {})".format(
args.modelpath, checkpoint['epoch'], checkpoint['best_mae_error']))
else:
print("=> no model found at '{}'".format(args.modelpath))
validate(test_loader, model, criterion, normalizer, test=True)
def predict(args, dataset, collate_fn, test_loader):
# build model
structures, _, _ = dataset[0]
orig_atom_fea_len = structures[0].shape[-1]
nbr_fea_len = structures[1].shape[-1]
model = CrystalGraphConvNet(
orig_atom_fea_len,
nbr_fea_len,
atom_fea_len=model_args.atom_fea_len,
n_conv=model_args.n_conv,
h_fea_len=model_args.h_fea_len,
n_h=model_args.n_h,
classification=True if model_args.task == 'classification' else False)
if args.cuda:
model.cuda()
# define loss func and optimizer
if model_args.task == 'classification':
criterion = nn.NLLLoss()
else:
criterion = nn.MSELoss()
normalizer = Normalizer(torch.zeros(3))
# optionally resume from a checkpoint
if os.path.isfile(args.modelpath):
print("=> loading model '{}'".format(args.modelpath))
checkpoint = torch.load(args.modelpath,
map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint['state_dict'])
normalizer.load_state_dict(checkpoint['normalizer'])
print("=> loaded model '{}' (epoch {}, validation {})".format(
args.modelpath, checkpoint['epoch'], checkpoint['best_mae_error']))
else:
print("=> no model found at '{}'".format(args.modelpath))
validate(test_loader, model, criterion, normalizer, test=True)
def main():
global args, best_mae_error
# load data
dataset = CIFData(*args.data_options)
collate_fn = collate_pool
train_loader, val_loader, test_loader = get_train_val_test_loader(
dataset=dataset,
collate_fn=collate_fn,
batch_size=args.batch_size,
train_ratio=args.train_ratio,
num_workers=args.workers,
val_ratio=args.val_ratio,
test_ratio=args.test_ratio,
pin_memory=args.cuda,
train_size=args.train_size,
val_size=args.val_size,
test_size=args.test_size,
return_test=True)
# obtain target value normalizer
if args.task == 'classification':
normalizer = Normalizer(torch.zeros(2))
normalizer.load_state_dict({'mean': 0., 'std': 1.})
else:
if len(dataset) < 500:
warnings.warn('Dataset has less than 500 data points. '
'Lower accuracy is expected. ')
sample_data_list = [dataset[i] for i in range(len(dataset))]
else:
sample_data_list = [
dataset[i] for i in sample(range(len(dataset)), 500)
]
_, sample_target, _ = collate_pool(sample_data_list)
normalizer = Normalizer(sample_target)
# build model
structures, _, _ = dataset[0]
orig_atom_fea_len = structures[0].shape[-1]
nbr_fea_len = structures[1].shape[-1]
model = CrystalGraphConvNet(
orig_atom_fea_len,
nbr_fea_len,
atom_fea_len=args.atom_fea_len,
n_conv=args.n_conv,
h_fea_len=args.h_fea_len,
n_h=args.n_h,
classification=True if args.task == 'classification' else False)
if args.cuda:
model.cuda()
# define loss func and optimizer
if args.task == 'classification':
criterion = nn.NLLLoss()
else:
criterion = nn.MSELoss()
if args.optim == 'SGD':
optimizer = optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optim == 'Adam':
optimizer = optim.Adam(model.parameters(),
args.lr,
weight_decay=args.weight_decay)
else:
raise NameError('Only SGD or Adam is allowed as --optim')
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_mae_error = checkpoint['best_mae_error']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
normalizer.load_state_dict(checkpoint['normalizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
scheduler = MultiStepLR(optimizer,
milestones=args.lr_milestones,
gamma=0.1)
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, normalizer)
# evaluate on validation set
mae_error = validate(val_loader, model, criterion, normalizer)
if mae_error != mae_error:
print('Exit due to NaN')
sys.exit(1)
scheduler.step()
# remember the best mae_eror and save checkpoint
if args.task == 'regression':
is_best = mae_error < best_mae_error
best_mae_error = min(mae_error, best_mae_error)
else:
is_best = mae_error > best_mae_error
best_mae_error = max(mae_error, best_mae_error)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_mae_error': best_mae_error,
'optimizer': optimizer.state_dict(),
'normalizer': normalizer.state_dict(),
'args': vars(args)
}, is_best)
# test best model
print('---------Evaluate Model on Test Set---------------')
best_checkpoint = torch.load('model_best.pth.tar')
model.load_state_dict(best_checkpoint['state_dict'])
validate(test_loader, model, criterion, normalizer, test=True)
print(target)
collate_fn = collate_pool
train_loader, val_loader, test_loader = get_train_val_test_loader(
dataset=dataset,
collate_fn=collate_fn,
batch_size=3,
train_ratio=0.5,
num_workers=2,
val_ratio=0.2,
test_ratio=0.3,
pin_memory=False,
train_size=5,
val_size=3,
test_size=2,
return_test=True)
_, sample_target, _ = collate_pool(sample_data_list)
normalizer = Normalizer(sample_target)
# build model
structures, _, _ = dataset[0]
orig_atom_fea_len = structures[0].shape[-1]
nbr_fea_len = structures[1].shape[-1]
model = CrystalGraphConvNet(orig_atom_fea_len,
nbr_fea_len,
atom_fea_len=4,
n_conv=3,
h_fea_len=32,
n_h=1,
classification=False)
def main():
global args, best_mae_error
# load data
dataset = CIFData(*args.data_options)
collate_fn = collate_pool
train_loader, val_loader, test_loader = get_train_val_test_loader(
dataset=dataset,
collate_fn=collate_fn,
batch_size=args.batch_size,
train_ratio=args.train_ratio,
num_workers=args.workers,
val_ratio=args.val_ratio,
test_ratio=args.test_ratio,
pin_memory=args.cuda,
train_size=args.train_size,
val_size=args.val_size,
test_size=args.test_size,
return_val=True,
return_test=True,
)
# obtain target value normalizer
if args.task == "classification":
normalizer = Normalizer(torch.zeros(2))
normalizer.load_state_dict({"mean": 0.0, "std": 1.0})
else:
if len(dataset) < 500:
warnings.warn(
"Dataset has less than 500 data points. " "Lower accuracy is expected. "
)
sample_data_list = [dataset[i] for i in range(len(dataset))]
else:
sample_data_list = [dataset[i] for i in sample(range(len(dataset)), 500)]
_, sample_target, _ = collate_pool(sample_data_list)
normalizer = Normalizer(sample_target)
# build model
structures, _, _ = dataset[0]
orig_atom_fea_len = structures[0].shape[-1]
nbr_fea_len = structures[1].shape[-1]
model = CrystalGraphConvNet(
orig_atom_fea_len,
nbr_fea_len,
atom_fea_len=args.atom_fea_len,
n_conv=args.n_conv,
h_fea_len=args.h_fea_len,
n_h=args.n_h,
classification=True if args.task == "classification" else False,
dropout_rate=args.dropout_rate,
)
if args.cuda:
model.cuda()
# define loss func and optimizer
if args.task == "classification":
criterion = nn.NLLLoss()
else:
criterion = nn.MSELoss()
if args.optim == "SGD":
optimizer = optim.SGD(
model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
)
elif args.optim == "Adam":
optimizer = optim.Adam(
model.parameters(), args.lr, weight_decay=args.weight_decay
)
else:
raise NameError("Only SGD or Adam is allowed as --optim")
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint["epoch"]
best_mae_error = checkpoint["best_mae_error"]
model.load_state_dict(checkpoint["state_dict"])
optimizer.load_state_dict(checkpoint["optimizer"])
normalizer.load_state_dict(checkpoint["normalizer"])
print(
"=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint["epoch"]
)
)
else:
print("=> no checkpoint found at '{}'".format(args.resume))
scheduler = MultiStepLR(optimizer, milestones=args.lr_milestones, gamma=0.1)
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, normalizer)
# evaluate on validation set
mae_error = validate(val_loader, model, criterion, normalizer)
if mae_error != mae_error:
print("Exit due to NaN")
sys.exit(1)
scheduler.step()
# remember the best mae_eror and save checkpoint
if args.task == "regression":
is_best = mae_error < best_mae_error
best_mae_error = min(mae_error, best_mae_error)
else:
is_best = mae_error > best_mae_error
best_mae_error = max(mae_error, best_mae_error)
save_checkpoint(
{
"epoch": epoch + 1,
"state_dict": model.state_dict(),
"best_mae_error": best_mae_error,
"optimizer": optimizer.state_dict(),
"normalizer": normalizer.state_dict(),
"args": vars(args),
},
is_best,
)
# test best model
best_checkpoint = torch.load("model_best.pth.tar")
model.load_state_dict(best_checkpoint["state_dict"])
validate(
train_loader, model, criterion, normalizer, test=True, fname="train_results"
)
validate(val_loader, model, criterion, normalizer, test=True, fname="val_results")
validate(test_loader, model, criterion, normalizer, test=True, fname="test_results")
def cv():
global args, best_mae_error
if not os.path.exists("./checkpoints"):
os.mkdir("./checkpoints")
# load data
dataset = CIFData(*args.data_options)
collate_fn = collate_pool
i = 0
train_maes = []
val_maes = []
test_maes = []
for train_loader, val_loader, test_loader in get_cv_loader(
dataset=dataset,
collate_fn=collate_fn,
batch_size=args.batch_size,
train_ratio=args.train_ratio,
num_workers=args.workers,
test_ratio=args.test_ratio,
pin_memory=args.cuda,
train_size=args.train_size,
val_size=args.val_size,
test_size=args.test_size,
cross_validation=args.cross_validation,
):
i += 1
# obtain target value normalizer
if args.task == "classification":
normalizer = Normalizer(torch.zeros(2))
normalizer.load_state_dict({"mean": 0.0, "std": 1.0})
else:
if len(dataset) < 500:
warnings.warn(
"Dataset has less than 500 data points. "
"Lower accuracy is expected. "
)
sample_data_list = [dataset[i] for i in range(len(dataset))]
else:
sample_data_list = [
dataset[i] for i in sample(range(len(dataset)), 500)
]
_, sample_target, _ = collate_pool(sample_data_list)
normalizer = Normalizer(sample_target)
# build model
structures, _, _ = dataset[0]
orig_atom_fea_len = structures[0].shape[-1]
nbr_fea_len = structures[1].shape[-1]
model = CrystalGraphConvNet(
orig_atom_fea_len,
nbr_fea_len,
atom_fea_len=args.atom_fea_len,
n_conv=args.n_conv,
h_fea_len=args.h_fea_len,
n_h=args.n_h,
classification=True if args.task == "classification" else False,
dropout_rate=args.dropout_rate,
)
if args.cuda:
model.cuda()
# define loss func and optimizer
if args.task == "classification":
criterion = nn.NLLLoss()
else:
criterion = nn.MSELoss()
if args.optim == "SGD":
optimizer = optim.SGD(
model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
)
elif args.optim == "Adam":
optimizer = optim.Adam(
model.parameters(), args.lr, weight_decay=args.weight_decay
)
else:
raise NameError("Only SGD or Adam is allowed as --optim")
scheduler = MultiStepLR(optimizer, milestones=args.lr_milestones, gamma=0.1)
print(f"Split {i}")
if args.task == "regression":
best_mae_error = 1e10
else:
best_mae_error = 0.0
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, normalizer)
# evaluate on validation set
mae_error = validate(val_loader, model, criterion, normalizer)
if mae_error != mae_error:
print("Exit due to NaN")
sys.exit(1)
scheduler.step()
# remember the best mae_eror and save checkpoint
if args.task == "regression":
is_best = mae_error < best_mae_error
best_mae_error = min(mae_error, best_mae_error)
else:
is_best = mae_error > best_mae_error
best_mae_error = max(mae_error, best_mae_error)
save_checkpoint(
{
"epoch": epoch + 1,
"state_dict": model.state_dict(),
"best_mae_error": best_mae_error,
"optimizer": optimizer.state_dict(),
"normalizer": normalizer.state_dict(),
"args": vars(args),
},
is_best,
pad_string=f"./checkpoints/{i}",
)
# test best model
best_checkpoint = torch.load(f"./checkpoints/{i}_model_best.pth.tar")
model.load_state_dict(best_checkpoint["state_dict"])
train_mae = validate(
train_loader,
model,
criterion,
normalizer,
test=True,
split=i,
fname="train",
to_save=False,
)
val_mae = validate(
val_loader,
model,
criterion,
normalizer,
test=True,
fname="val",
to_save=False,
)
test_mae = validate(
test_loader,
model,
criterion,
normalizer,
test=True,
fname="test",
to_save=False,
)
train_maes.append(train_mae.detach().item())
val_maes.append(val_mae.detach().item())
test_maes.append(test_mae.detach().item())
with open("results.out", "a+") as fw:
fw.write("\n")
fw.write(f"Avg Train MAE: {np.mean(train_maes):.4f}\n")
fw.write(f"Avg Val MAE: {np.mean(val_maes):.4f}\n")
fw.write(f"Avg Test MAE: {np.mean(test_maes):.4f}\n")
from cgcnn.data import collate_pool, get_train_val_test_loader
if __name__ == '__main__':
PATH = r"C:\Users\10989\PycharmProjects\8_CGCNN\cgcnn\pre-trained\band-gap.pth.tar"
model_dict = torch.load(PATH, map_location=torch.device('cpu'))
dict_name = list(model_dict)
for i, p in enumerate(dict_name):
print(i, p)
for i, p in enumerate(model_dict["state_dict"]):
print(i, p,'\t',model_dict["state_dict"][p].size())
from cgcnn.model import CrystalGraphConvNet
from cgcnn.data import CIFData
model = CrystalGraphConvNet(92, 41,
atom_fea_len=64,
n_conv=4,
h_fea_len=32,
n_h=1,
classification=False)
model.load_state_dict(model_dict["state_dict"])
# load data
cif_data = CIFData(r"C:\Users\10989\PycharmProjects\8_CGCNN\cgcnn\data\sample-classification")
dataset = CIFData(r"C:\Users\10989\PycharmProjects\8_CGCNN\cgcnn\data\sample-classification")
collate_fn = collate_pool
train_loader, val_loader, test_loader = get_train_val_test_loader(
dataset=dataset,
collate_fn=collate_fn,
batch_size=3,
train_ratio=0.5,
num_workers=2,
val_ratio=0.2,
test_ratio=0.3,
def main():
global args, model_args, best_mae_error
# load data
dataset = CIFData(args.cifpath,
max_num_nbr=model_args.max_num_nbr,
radius=model_args.radius,
nn_method=model_args.nn_method,
disable_save_torch=args.disable_save_torch)
collate_fn = collate_pool
if args.train_val_test:
train_loader, val_loader, test_loader = get_train_val_test_loader(
dataset=dataset,
collate_fn=collate_fn,
batch_size=model_args.batch_size,
train_ratio=model_args.train_ratio,
num_workers=args.workers,
val_ratio=model_args.val_ratio,
test_ratio=model_args.test_ratio,
pin_memory=args.cuda,
train_size=model_args.train_size,
val_size=model_args.val_size,
test_size=model_args.test_size,
return_test=True)
else:
test_loader = DataLoader(dataset,
batch_size=model_args.batch_size,
shuffle=True,
num_workers=args.workers,
collate_fn=collate_fn,
pin_memory=args.cuda)
# make and clean torch files if needed
torch_data_path = os.path.join(args.cifpath, 'cifdata')
if args.clean_torch and os.path.exists(torch_data_path):
shutil.rmtree(torch_data_path)
if os.path.exists(torch_data_path):
if not args.clean_torch:
warnings.warn('Found torch .json files at ' + torch_data_path +
'. Will read in .jsons as-available')
else:
os.mkdir(torch_data_path)
# build model
structures, _, _ = dataset[0]
orig_atom_fea_len = structures[0].shape[-1]
nbr_fea_len = structures[1].shape[-1]
model = CrystalGraphConvNet(
orig_atom_fea_len,
nbr_fea_len,
atom_fea_len=model_args.atom_fea_len,
n_conv=model_args.n_conv,
h_fea_len=model_args.h_fea_len,
n_h=model_args.n_h,
classification=True if model_args.task == 'classification' else False,
enable_tanh=model_args.enable_tanh)
if args.cuda:
model.cuda()
# define loss func and optimizer
if model_args.task == 'classification':
criterion = nn.NLLLoss()
else:
criterion = nn.MSELoss()
normalizer = Normalizer(torch.zeros(3))
# optionally resume from a checkpoint
if os.path.isfile(args.modelpath):
print("=> loading model '{}'".format(args.modelpath))
checkpoint = torch.load(args.modelpath,
map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint['state_dict'])
normalizer.load_state_dict(checkpoint['normalizer'])
print("=> loaded model '{}' (epoch {}, validation {})".format(
args.modelpath, checkpoint['epoch'], checkpoint['best_mae_error']))
else:
print("=> no model found at '{}'".format(args.modelpath))
if args.train_val_test:
print('---------Evaluate Model on Train Set---------------')
validate(train_loader,
model,
criterion,
normalizer,
test=True,
csv_name='train_results.csv')
print('---------Evaluate Model on Val Set---------------')
validate(val_loader,
model,
criterion,
normalizer,
test=True,
csv_name='val_results.csv')
print('---------Evaluate Model on Test Set---------------')
validate(test_loader,
model,
criterion,
normalizer,
test=True,
csv_name='test_results.csv')
else:
print('---------Evaluate Model on Dataset---------------')
validate(test_loader,
model,
criterion,
normalizer,
test=True,
csv_name='predictions.csv')
def tc_trans2():
global args, best_mae_error
# load data
dataset = CIFData(*args.data_options)
collate_fn = collate_pool
# obtain target value normalizer
if args.task == 'classification':
normalizer = Normalizer(torch.zeros(2))
normalizer.load_state_dict({'mean': 0., 'std': 1.})
else:
if len(dataset) < 500:
warnings.warn('Dataset has less than 500 data points. '
'Lower accuracy is expected. ')
sample_data_list = [dataset[i] for i in range(len(dataset))]
else:
sample_data_list = [
dataset[i] for i in sample(range(len(dataset)), 500)
]
_, sample_target, _ = collate_pool(sample_data_list)
normalizer = Normalizer(sample_target)
# build model
structures, _, _ = dataset[0]
orig_atom_fea_len = structures[0].shape[-1]
nbr_fea_len = structures[1].shape[-1]
model_a = CrystalGraphConvNet(
orig_atom_fea_len,
nbr_fea_len,
atom_fea_len=args.atom_fea_len,
n_conv=args.n_conv,
h_fea_len=args.h_fea_len,
n_h=args.n_h,
classification=True if args.task == 'classification' else False)
model_b = CrystalGraphConvNet(
orig_atom_fea_len,
nbr_fea_len,
atom_fea_len=args.atom_fea_len,
n_conv=args.n_conv,
h_fea_len=args.h_fea_len,
n_h=args.n_h,
classification=True if args.task == 'classification' else False)
model = SimpleNN(in_feature=256, out_feature=1)
# pretrained model path
model_a_path = '../pre-trained/research-model/bulk_moduli-model_best.pth.tar'
model_b_path = '../pre-trained/research-model/sps-model_best.pth.tar'
# load latest model state
ckpt_a = torch.load(model_a_path)
ckpt_b = torch.load(model_b_path)
# load model
model_a.load_state_dict(ckpt_a['state_dict'])
model_b.load_state_dict(ckpt_b['state_dict'])
def get_activation_a(name, activation_a):
def hook(model, input, output):
activation_a[name] = output.detach()
return hook
def get_activation_b(name, activation_b):
def hook(model, input, output):
activation_b[name] = output.detach()
return hook
if args.cuda:
model_a.cuda()
model_b.cuda()
model.cuda()
activation_a = {}
activation_b = {}
# hook the activation function
model_a.conv_to_fc.register_forward_hook(
get_activation_a('conv_to_fc', activation_a))
model_b.conv_to_fc.register_forward_hook(
get_activation_b('conv_to_fc', activation_b))
# define loss func and optimizer
if args.task == 'classification':
criterion = nn.NLLLoss()
else:
criterion = nn.MSELoss()
if args.optim == 'SGD':
optimizer = optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optim == 'Adam':
optimizer = optim.Adam(model.parameters(),
args.lr,
weight_decay=args.weight_decay)
else:
raise NameError('Only SGD or Adam is allowed as --optim')
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_mae_error = checkpoint['best_mae_error']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
normalizer.load_state_dict(checkpoint['normalizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
scheduler = MultiStepLR(optimizer,
milestones=args.lr_milestones,
gamma=0.1)
X = torch.Tensor()
T = torch.Tensor()
for i in range(5):
total_size = len(dataset)
indices = list(range(total_size))
batch_size = args.batch_size
num_workers = args.workers
pin_memory = args.cuda
if i == 0:
train_sampler = SubsetRandomSampler(indices[:61])
test_sampler = SubsetRandomSampler(indices[-16:])
if i == 1:
x = indices[:45]
y = x.extend(indices[-16:])
train_samplre = SubsetRandomSampler(y)
test_sampler = SubsetRandomSampler(indices[45:-16])
if i == 2:
x = indices[:29]
y = x.extend(indices[-32:])
train_samplre = SubsetRandomSampler(y)
test_sampler = SubsetRandomSampler(indices[29:-32])
if i == 3:
x = indices[:13]
y = x.extend(indices[-48:])
train_samplre = SubsetRandomSampler(y)
test_sampler = SubsetRandomSampler(indices[13:-48])
if i == 4:
y = indices[-64:]
train_samplre = SubsetRandomSampler(y)
test_sampler = SubsetRandomSampler(indices[:-64])
train_loader = DataLoader(dataset,
batch_size=batch_size,
sampler=train_sampler,
num_workers=num_workers,
collate_fn=collate_fn,
pin_memory=pin_memory)
test_loader = DataLoader(dataset,
batch_size=batch_size,
sampler=test_sampler,
num_workers=num_workers,
collate_fn=collate_fn,
pin_memory=pin_memory)
print(test_sampler)
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
train(args, train_loader, model_a, model_b, model, activation_a,
activation_b, criterion, optimizer, epoch, normalizer)
# evaluate on validation set
mae_error = validate(args, train_loader, model_a, model_b, model,
activation_a, activation_b, criterion,
normalizer)
if mae_error != mae_error:
print('Exit due to NaN')
sys.exit(1)
scheduler.step()
# remember the best mae_eror and save checkpoint
if args.task == 'regression':
is_best = mae_error < best_mae_error
best_mae_error = min(mae_error, best_mae_error)
else:
is_best = mae_error > best_mae_error
best_mae_error = max(mae_error, best_mae_error)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_mae_error': best_mae_error,
'optimizer': optimizer.state_dict(),
'normalizer': normalizer.state_dict(),
'args': vars(args)
},
is_best,
prop=args.property)
# test best model
print('---------Evaluate Model on Test Set---------------')
best_checkpoint = torch.load('../result/' + args.property +
'-model_best.pth.tar')
model.load_state_dict(best_checkpoint['state_dict'])
x, t = validate(args,
test_loader,
model_a,
model_b,
model,
activation_a,
activation_b,
criterion,
normalizer,
test=True,
tc=True)
X = torch.cat((X, x), dim=0)
T = torch.cat((T, t), dim=0)
x, t = X.numpy(), T.numpy()
n_max = max(np.max(x), np.max(t))
n_min = min(np.min(x), np.min(t))
a = np.linspace(n_min - abs(n_max), n_max + abs(n_max))
b = a
plt.rcParams["font.family"] = "Times New Roman"
plt.plot(a, b, color='blue')
plt.scatter(t, x, marker=".", color='red', edgecolors='black')
plt.xlim(n_min - abs(n_min), n_max + abs(n_min))
plt.ylim(n_min - abs(n_min), n_max + abs(n_min))
plt.title(
"Thermal Conductivity Prediction by CGCNN with Combined Model Transfer Learning"
)
plt.xlabel("observation")
plt.ylabel("prediction")
plt.show()
def main():
global args, best_mae_error
# load data
dataset = CIFData(*args.data_options,
disable_save_torch=args.disable_save_torch)
collate_fn = collate_pool
train_loader, val_loader, test_loader = get_train_val_test_loader(
dataset=dataset,
collate_fn=collate_fn,
batch_size=args.batch_size,
train_ratio=args.train_ratio,
num_workers=args.workers,
val_ratio=args.val_ratio,
test_ratio=args.test_ratio,
pin_memory=args.cuda,
train_size=args.train_size,
val_size=args.val_size,
test_size=args.test_size,
return_test=True)
# Make sure >1 class is present
if args.task == 'classification':
total_train = 0
total_val = 0
total_test = 0
for i, (_, target, _) in enumerate(train_loader):
for target_i in target.squeeze():
total_train += target_i
if bool(total_train == 0):
raise ValueError('All 0s in train')
elif bool(total_train == 1):
raise ValueError('All 1s in train')
for i, (_, target, _) in enumerate(val_loader):
if len(target) == 1:
raise ValueError('Only single entry in val')
for target_i in target.squeeze():
total_val += target_i
if bool(total_val == 0):
raise ValueError('All 0s in val')
elif bool(total_val == 1):
raise ValueError('All 1s in val')
for i, (_, target, _) in enumerate(test_loader):
if len(target) == 1:
raise ValueError('Only single entry in test')
for target_i in target.squeeze():
total_test += target_i
if bool(total_test == 0):
raise ValueError('All 0s in test')
elif bool(total_test == 1):
raise ValueError('All 1s in test')
# make output folder if needed
if not os.path.exists('output'):
os.mkdir('output')
# make and clean torch files if needed
torch_data_path = os.path.join(args.data_options[0], 'cifdata')
if args.clean_torch and os.path.exists(torch_data_path):
shutil.rmtree(torch_data_path)
if os.path.exists(torch_data_path):
if not args.clean_torch:
warnings.warn('Found cifdata folder at ' +
torch_data_path+'. Will read in .jsons as-available')
else:
os.mkdir(torch_data_path)
# obtain target value normalizer
if args.task == 'classification':
normalizer = Normalizer(torch.zeros(2))
normalizer.load_state_dict({'mean': 0., 'std': 1.})
else:
if len(dataset) < 500:
warnings.warn('Dataset has less than 500 data points. '
'Lower accuracy is expected. ')
sample_data_list = [dataset[i] for i in range(len(dataset))]
else:
sample_data_list = [dataset[i] for i in
sample(range(len(dataset)), 500)]
_, sample_target, _ = collate_pool(sample_data_list)
normalizer = Normalizer(sample_target)
# build model
structures, _, _ = dataset[0]
orig_atom_fea_len = structures[0].shape[-1]
nbr_fea_len = structures[1].shape[-1]
model = CrystalGraphConvNet(orig_atom_fea_len, nbr_fea_len,
atom_fea_len=args.atom_fea_len,
n_conv=args.n_conv,
h_fea_len=args.h_fea_len,
n_h=args.n_h,
classification=True if args.task ==
'classification' else False)
if args.cuda:
model.cuda()
# define loss func and optimizer
if args.task == 'classification':
criterion = nn.NLLLoss()
else:
criterion = nn.MSELoss()
if args.optim == 'SGD':
optimizer = optim.SGD(model.parameters(), args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optim == 'Adam':
optimizer = optim.Adam(model.parameters(), args.lr,
weight_decay=args.weight_decay)
else:
raise NameError('Only SGD or Adam is allowed as --optim')
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_mae_error = checkpoint['best_mae_error']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
normalizer.load_state_dict(checkpoint['normalizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
scheduler = MultiStepLR(optimizer, milestones=args.lr_milestones,
gamma=0.1)
for epoch in range(args.start_epoch, args.epochs):
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, normalizer)
# evaluate on validation set
mae_error = validate(val_loader, model, criterion, normalizer)
if mae_error != mae_error:
print('Exit due to NaN')
sys.exit(1)
scheduler.step()
# remember the best mae_eror and save checkpoint
if args.task == 'regression':
is_best = mae_error < best_mae_error
best_mae_error = min(mae_error, best_mae_error)
else:
is_best = mae_error > best_mae_error
best_mae_error = max(mae_error, best_mae_error)
save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_mae_error': best_mae_error,
'optimizer': optimizer.state_dict(),
'normalizer': normalizer.state_dict(),
'args': vars(args)
}, is_best)
# test best model
best_checkpoint = torch.load(os.path.join('output', 'model_best.pth.tar'))
model.load_state_dict(best_checkpoint['state_dict'])
print('---------Evaluate Best Model on Train Set---------------')
validate(train_loader, model, criterion, normalizer, test=True,
csv_name='train_results.csv')
print('---------Evaluate Best Model on Val Set---------------')
validate(val_loader, model, criterion, normalizer, test=True,
csv_name='val_results.csv')
print('---------Evaluate Best Model on Test Set---------------')
validate(test_loader, model, criterion, normalizer, test=True,
csv_name='test_results.csv')
def main():
global args, best_mae_error
# load dataset: (atom_fea, nbr_fea, nbr_fea_idx), target, cif_id
dataset = CIFData(args.root + args.target)
collate_fn = collate_pool
train_loader, val_loader, test_loader = get_train_val_test_loader(
dataset=dataset,
collate_fn=collate_fn,
batch_size=args.batch_size,
train_ratio=args.train_ratio,
num_workers=args.workers,
val_ratio=args.val_ratio,
test_ratio=args.test_ratio,
pin_memory=args.cuda,
return_test=True)
# obtain target value normalizer
if args.task == 'classification':
normalizer = Normalizer(torch.zeros(2))
normalizer.load_state_dict({'mean': 0., 'std': 1.})
else:
sample_data_list = [dataset[i] for i in \
sample(range(len(dataset)), 500)]
_, sample_target, _ = collate_pool(sample_data_list)
normalizer = Normalizer(sample_target)
# build model
structures, _, _ = dataset[0]
orig_atom_fea_len = structures[0].shape[-1]
nbr_fea_len = structures[1].shape[-1]
model = CrystalGraphConvNet(
orig_atom_fea_len,
nbr_fea_len,
atom_fea_len=args.atom_fea_len,
n_conv=args.n_conv,
h_fea_len=args.h_fea_len,
n_h=args.n_h,
classification=True if args.task == 'classification' else False)
# pring number of trainable model parameters
trainable_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print('=> number of trainable model parameters: {:d}'.format(
trainable_params))
if args.cuda:
model.cuda()
# define loss func and optimizer
if args.task == 'classification':
criterion = nn.NLLLoss()
else:
criterion = nn.MSELoss()
if args.optim == 'SGD':
optimizer = optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
elif args.optim == 'Adam':
optimizer = optim.Adam(model.parameters(),
args.lr,
weight_decay=args.weight_decay)
else:
raise NameError('Only SGD or Adam is allowed as --optim')
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume,
map_location=torch.device('cpu'))
args.start_epoch = checkpoint['epoch']
best_mae_error = checkpoint['best_mae_error']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
normalizer.load_state_dict(checkpoint['normalizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# TensorBoard writer
summary_root = './runs/'
if not os.path.exists(summary_root):
os.mkdir(summary_root)
summary_file = summary_root + args.target
if os.path.exists(summary_file):
shutil.rmtree(summary_file)
writer = SummaryWriter(summary_file)
scheduler = MultiStepLR(optimizer,
milestones=args.lr_milestones,
gamma=0.1)
for epoch in range(args.start_epoch, args.start_epoch + args.epochs):
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, normalizer,
writer)
# evaluate on validation set
mae_error = validate(val_loader, model, criterion, epoch, normalizer,
writer)
scheduler.step()
# remember the best mae_eror and save checkpoint
if args.task == 'regression':
is_best = mae_error < best_mae_error
best_mae_error = min(mae_error, best_mae_error)
else:
is_best = mae_error > best_mae_error
best_mae_error = max(mae_error, best_mae_error)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_mae_error': best_mae_error,
'optimizer': optimizer.state_dict(),
'normalizer': normalizer.state_dict(),
'args': vars(args)
}, args.target, is_best)