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train_charge.py
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train_charge.py
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import time
import torch
import pickle
import torch.nn as nn
from random import sample
from model.GCN_E import GCN
import torch.optim as optim
from model.GCN_charge import SemiFullGN
from torch.optim.lr_scheduler import ExponentialLR
from model.data_charge import collate_pool, get_data_loader, CIFData
from model.utils import Normalizer,save_checkpoint,AverageMeter,mae
def main():
model_folder = './pth/'
chk_name = model_folder+'chk_cm5/checkpoint.pth'
best_name = model_folder+'best_cm5/cm5.pth'
root_dir ='./data/json/'
root_dir_pos ='./data/npy/pos/'
root_dir_cm5 ='./data/npy/cm5/'
radius = 6
dmin = 0
step = 0.2
random_seed = 1126
batch_size = 32
num_workers = 0
pin_memory = False
atom_fea_len = 128
n_feature = 256
n_conv = 8
lr_decay_rate = 0.99
lr = 0.0005
weight_decay = 0
best_mae_error = 1e10
start_epoch = 0
epochs = 500
train_csv = root_dir+'id_prop_train_cm5.csv'
val_csv = root_dir+'id_prop_val_cm5.csv'
test_csv = root_dir+'id_prop_test_cm5.csv'
train_dataset = CIFData(root_dir,root_dir_pos,root_dir_cm5,train_csv,radius,dmin,step,random_seed)
val_dataset = CIFData(root_dir,root_dir_pos,root_dir_cm5,val_csv,radius,dmin,step,random_seed)
test_dataset = CIFData(root_dir,root_dir_pos,root_dir_cm5,test_csv,radius,dmin,step,random_seed)
collate_fn = collate_pool
train_loader = get_data_loader(train_dataset,collate_fn,batch_size,num_workers,pin_memory,False)
val_loader = get_data_loader(val_dataset,collate_fn,batch_size,num_workers,pin_memory,True)
test_loader= get_data_loader(test_dataset,collate_fn,batch_size,num_workers,pin_memory,True)
print('# of trainset: ',len(train_loader.dataset))
print('# of valset: ',len(val_loader.dataset))
print('# of testset: ',len(test_loader.dataset))
sample_data_list = [train_dataset[i] for i in sample(range(len(train_dataset)), 500)]
_,sample_target_charge, _ = collate_pool(sample_data_list)
normalizer = Normalizer(sample_target_charge)
with open(model_folder + 'best_cm5/normalizer-cm5.pkl', 'wb') as f:
pickle.dump(normalizer, f)
structures, _,_,_ = train_dataset[0]
orig_atom_fea_len = structures[0].shape[-1] + 3
nbr_fea_len = structures[1].shape[-1]
gcn = GCN(orig_atom_fea_len-3, nbr_fea_len, 128, 7, 256,5)
gcn.cuda()
model = SemiFullGN(orig_atom_fea_len,nbr_fea_len,atom_fea_len,n_conv,n_feature)
model.cuda()
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(),lr,weight_decay=weight_decay)
scheduler = ExponentialLR(optimizer, gamma=lr_decay_rate)
t0 = time.time()
for epoch in range(start_epoch,epochs):
train(train_loader,model,gcn,criterion,optimizer,epoch,normalizer)
mae_error = validate(val_loader,model,gcn,criterion,normalizer)
scheduler.step()
is_best = mae_error < best_mae_error
best_mae_error = min(mae_error, best_mae_error)
save_checkpoint({'epoch': epoch,'state_dict': model.state_dict(),'best_mae_error': best_mae_error,
'optimizer': optimizer.state_dict(),'normalizer': normalizer.state_dict()},
is_best,chk_name,best_name)
t1 = time.time()
print('--------Training time in sec-------------')
print(t1-t0)
print('---------Best Model on Validation Set---------------')
best_checkpoint = torch.load(best_name)
print(best_checkpoint['best_mae_error'].cpu().numpy())
print('---------Evaluate Model on Test Set---------------')
model.load_state_dict(best_checkpoint['state_dict'])
validate(test_loader,model,gcn,criterion,normalizer)
def train(train_loader, model, gcn, criterion, optimizer, epoch, normalizer):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
mae_errors = AverageMeter()
model.train()
end = time.time()
for i, (input,target_charge,_) in enumerate(train_loader):
data_time.update(time.time() - end)
with torch.no_grad():
input_var = (input[0].cuda(),
input[1].cuda(),
input[2].cuda(),
input[3].cuda(),
input[4].cuda(),
input[5].cuda())
structure_feature = gcn.Encoding(*input_var)
atoms_fea = torch.cat((input[0],input[7]),dim=-1)
input_var2 = (atoms_fea.cuda(),
input[1].cuda(),
input[2].cuda(),
input[3].cuda(),
input[4].cuda(),
input[5].cuda(),
structure_feature
) ; target = target_charge
target_normed = normalizer.norm(target)
target_var = target_normed.cuda()
output = model(*input_var2)
loss = criterion(output, target_var)
mae_error = mae(normalizer.denorm(output.data.cpu()), target)
losses.update(loss.item(), target.size(0))
mae_errors.update(mae_error, target.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
batch_time.update(time.time() - end)
end = time.time()
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'MAE {mae_errors.val:.3f} ({mae_errors.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, mae_errors=mae_errors))
def validate(val_loader,model,gcn,criterion,normalizer):
batch_time = AverageMeter()
losses = AverageMeter()
mae_errors = AverageMeter()
model.eval()
end = time.time()
for i, (input,target_charge,_) in enumerate(val_loader):
with torch.no_grad():
input_var = (input[0].cuda(),
input[1].cuda(),
input[2].cuda(),
input[3].cuda(),
input[4].cuda(),
input[5].cuda())
structure_feature = gcn.Encoding(*input_var)
atoms_fea = torch.cat((input[0],input[7]),dim=-1)
input_var2 = (atoms_fea.cuda(),
input[1].cuda(),
input[2].cuda(),
input[3].cuda(),
input[4].cuda(),
input[5].cuda(),
structure_feature
) ; target = target_charge
target_normed = normalizer.norm(target)
target_var = target_normed.cuda()
output = model(*input_var2)
loss = criterion(output, target_var)
mae_error = mae(normalizer.denorm(output.data.cpu()),target)
losses.update(loss.item(), target.size(0))
mae_errors.update(mae_error, target.size(0))
batch_time.update(time.time() - end)
end = time.time()
print('Test: [{0}/{1}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'MAE {mae_errors.val:.3f} ({mae_errors.avg:.3f})'.format(
i, len(val_loader), batch_time=batch_time, loss=losses,
mae_errors=mae_errors))
star_label = '*'
print(' {star} MAE {mae_errors.avg:.3f}'.format(star=star_label,mae_errors=mae_errors))
print(normalizer.denorm(output)[0:10])
print(target[0:10])
return mae_errors.avg
if __name__ == '__main__':
main()