def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0):
""" Initialize the GANLoss class.
Parameters:
gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp.
target_real_label (bool) - - label for a real image
target_fake_label (bool) - - label of a fake image
Note: Do not use sigmoid as the last layer of Discriminator.
LSGAN needs no sigmoid. vanilla GANs will handle it with BCEWithLogitsLoss.
"""
super(GANLoss, self).__init__()
# self.register_buffer('real_label', torch.tensor(target_real_label))
# self.register_buffer('fake_label', torch.tensor(target_fake_label))
self.real_label = paddle.fluid.dygraph.to_variable(
np.array(target_real_label))
self.fake_label = paddle.fluid.dygraph.to_variable(
np.array(target_fake_label))
# self.real_label.stop_gradients = True
# self.fake_label.stop_gradients = True
self.gan_mode = gan_mode
if gan_mode == 'lsgan':
self.loss = nn.MSELoss()
elif gan_mode == 'vanilla':
self.loss = nn.BCELoss() #nn.BCEWithLogitsLoss()
elif gan_mode in ['wgangp']:
self.loss = None
else:
raise NotImplementedError('gan mode %s not implemented' % gan_mode)
# 优化器
optimizerG = paddle.optimizer.Adam(
learning_rate=LR,
parameters=generator.parameters(),
beta1=0.5,
beta2=0.999)
optimizerD = paddle.optimizer.Adam(
learning_rate=LR,
parameters=discriminator.parameters(),
beta1=0.5,
beta2=0.999)
# 损失函数
bce_loss = nn.BCELoss()
l1_loss = nn.L1Loss()
# dataloader
data_loader_train = DataLoader(
paired_dataset_train,
batch_size=BATCH_SIZE,
shuffle=True,
drop_last=True
)
data_loader_test = DataLoader(
paired_dataset_test,
batch_size=BATCH_SIZE
)
results_save_path = 'work/results'
beta1,beta2=opt.beta1,opt.beta2
losses =[[],[]]
real_label = paddle.full( (opt.batch_size,1,1,1), 1., dtype='float32')
fake_label = paddle.full( (opt.batch_size,1,1,1), 0., dtype='float32')
X = 20 #窗口大小
#一行子窗口数量
num=math.sqrt(batch_size)
x=round(num) if math.fabs( math.floor(num)**2-batch_size )<1e-6 else math.floor(num)+1
print("start training: ")
print("---------------------------------")
print("num = ",num)
with paddle.fluid.dygraph.guard(place):
#损失函数
loss = nn.BCELoss()
netD = Discriminator(channels_img=3, features_d=10)
netG = Generator(z_dim=z_dim, channels_img=3, features_g=10)
optimizerD = optim.Adam(parameters=netD.parameters(), learning_rate=lr, beta1=beta1, beta2=beta2)
optimizerG = optim.Adam(parameters=netG.parameters(), learning_rate=lr, beta1=beta1, beta2=beta2)
if not os.path.exists( opt.checkpoints_path ):
os.mkdir( opt.checkpoints_path )
if not os.path.exists( opt.output_path ):
os.mkdir( opt.output_path)
last = opt.img_size
order_name = 9
model_path = opt.checkpoints_path+ f"model_{last}_{order_name}/"
def main(args):
"""
Call the configuration function of the model, build the model and load data, then start training.
model_config:
a json file with the hyperparameters,such as dropout rate ,learning rate,num tasks and so on;
num_tasks:
it means the number of task that each dataset contains, it's related to the dataset;
DownstreamModel:
It means the PretrainGNNModel for different strategies and it is an supervised GNN model which predicts the tasks.
"""
compound_encoder_config = load_json_config(args.compound_encoder_config)
model_config = load_json_config(args.model_config)
if not args.dropout_rate is None:
compound_encoder_config['dropout_rate'] = args.dropout_rate
model_config['dropout_rate'] = args.dropout_rate
task_names = get_downstream_task_names(args.dataset_name, args.data_path)
model_config['num_tasks'] = len(task_names)
### build model
compound_encoder = PretrainGNNModel(compound_encoder_config)
model = DownstreamModel(model_config, compound_encoder)
criterion = nn.BCELoss(reduction='none')
encoder_params = compound_encoder.parameters()
head_params = exempt_parameters(model.parameters(), encoder_params)
encoder_opt = paddle.optimizer.Adam(args.encoder_lr,
parameters=encoder_params)
head_opt = paddle.optimizer.Adam(args.head_lr, parameters=head_params)
print('Total param num: %s' % (len(model.parameters())))
print('Encoder param num: %s' % (len(encoder_params)))
print('Head param num: %s' % (len(head_params)))
if not args.init_model is None and not args.init_model == "":
compound_encoder.set_state_dict(paddle.load(args.init_model))
print('Load state_dict from %s' % args.init_model)
### load data
# featurizer:
# Gen features according to the raw data and return the graph data.
# Collate features about the graph data and return the feed dictionary.
# splitter:
# split type of the dataset:random,scaffold,random with scaffold. Here is randomsplit.
# `ScaffoldSplitter` will firstly order the compounds according to Bemis-Murcko scaffold,
# then take the first `frac_train` proportion as the train set, the next `frac_valid` proportion as the valid set
# and the rest as the test set. `ScaffoldSplitter` can better evaluate the generalization ability of the model on
# out-of-distribution samples. Note that other splitters like `RandomSplitter`, `RandomScaffoldSplitter`
# and `IndexSplitter` is also available."
dataset = get_dataset(args.dataset_name, args.data_path, task_names)
dataset.transform(DownstreamTransformFn(), num_workers=args.num_workers)
splitter = create_splitter(args.split_type)
train_dataset, valid_dataset, test_dataset = splitter.split(dataset,
frac_train=0.8,
frac_valid=0.1,
frac_test=0.1)
print("Train/Valid/Test num: %s/%s/%s" %
(len(train_dataset), len(valid_dataset), len(test_dataset)))
### start train
# Load the train function and calculate the train loss in each epoch.
# Here we set the epoch is in range of max epoch,you can change it if you want.
# Then we will calculate the train loss ,valid auc,test auc and print them.
# Finally we save it to the model according to the dataset.
list_val_auc, list_test_auc = [], []
collate_fn = DownstreamCollateFn(
atom_names=compound_encoder_config['atom_names'],
bond_names=compound_encoder_config['bond_names'])
for epoch_id in range(args.max_epoch):
train_loss = train(args, model, train_dataset, collate_fn, criterion,
encoder_opt, head_opt)
val_auc = evaluate(args, model, valid_dataset, collate_fn)
test_auc = evaluate(args, model, test_dataset, collate_fn)
list_val_auc.append(val_auc)
list_test_auc.append(test_auc)
test_auc_by_eval = list_test_auc[np.argmax(list_val_auc)]
print("epoch:%s train/loss:%s" % (epoch_id, train_loss))
print("epoch:%s val/auc:%s" % (epoch_id, val_auc))
print("epoch:%s test/auc:%s" % (epoch_id, test_auc))
print("epoch:%s test/auc_by_eval:%s" % (epoch_id, test_auc_by_eval))
paddle.save(
compound_encoder.state_dict(),
'%s/epoch%d/compound_encoder.pdparams' %
(args.model_dir, epoch_id))
paddle.save(model.state_dict(),
'%s/epoch%d/model.pdparams' % (args.model_dir, epoch_id))
outs = {
'model_config': basename(args.model_config).replace('.json', ''),
'metric': '',
'dataset': args.dataset_name,
'split_type': args.split_type,
'batch_size': args.batch_size,
'dropout_rate': args.dropout_rate,
'encoder_lr': args.encoder_lr,
'head_lr': args.head_lr,
'exp_id': args.exp_id,
}
best_epoch_id = np.argmax(list_val_auc)
for metric, value in [('test_auc', list_test_auc[best_epoch_id]),
('max_valid_auc', np.max(list_val_auc)),
('max_test_auc', np.max(list_test_auc))]:
outs['metric'] = metric
print('\t'.join(['FINAL'] + ["%s:%s" % (k, outs[k])
for k in outs] + [str(value)]))
