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提交 25b01fae 作者: 朱学凯
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add attention_mask

上级 94ca8a36
正在显示 包含 161 行增加 和 30 行删除
.idea/CPI.iml
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
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.idea/misc.xml
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.idea/shelf/Uncommitted_changes_before_Update_at_2021_4_16,_11_22_上午_[Default_Changelist]/shelved.patch 0 → 100644
Index: .idea/workspace.xml
Index: .idea/workspace.xml
IDEA additional info:
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dataset.py
...@@ -142,7 +142,8 @@ class Data_Encoder(data.Dataset): ...@@ -142,7 +142,8 @@ class Data_Encoder(data.Dataset):
token_type_ids = np.concatenate((np.zeros((len(d) + 2), dtype=np.int), np.ones((len(p) + 1), dtype=np.int))) token_type_ids = np.concatenate((np.zeros((len(d) + 2), dtype=np.int), np.ones((len(p) + 1), dtype=np.int)))
token_type_ids = np.pad(token_type_ids, (0, self.max_len-len(input_seq)), 'constant', constant_values=0) token_type_ids = np.pad(token_type_ids, (0, self.max_len-len(input_seq)), 'constant', constant_values=0)
input, input_mask = seq2emb_encoder(input_seq, self.max_len, self.vocab) input, input_mask = seq2emb_encoder(input_seq, self.max_len, self.vocab)
return torch.from_numpy(input).long(), torch.from_numpy(token_type_ids).long(), y return torch.from_numpy(input).long(), torch.from_numpy(token_type_ids).long(), torch.from_numpy(input_mask).long(), y
# return len(d), len(p)
if __name__ == "__main__": if __name__ == "__main__":
...@@ -162,18 +163,26 @@ if __name__ == "__main__": ...@@ -162,18 +163,26 @@ if __name__ == "__main__":
"smile": './data/train_smile', "smile": './data/train_smile',
"affinity": './data/train_ic50', "affinity": './data/train_ic50',
} }
df_test = {"sps": './data/test_sps',
"smile": './data/test_smile',
"affinity": './data/test_ic50',
}
tokenizer_config = {"vocab_file": './config/vocab.txt', tokenizer_config = {"vocab_file": './config/vocab.txt',
"vocab_pair": './config/drug_codes_chembl.txt', "vocab_pair": './config/drug_codes_chembl.txt',
"begin_id": '[CLS]', "begin_id": '[CLS]',
"separate_id": "[SEP]", "separate_id": "[SEP]",
"max_len": 256 "max_len": 256
} }
params = {'batch_size': 5, params = {'batch_size': 1,
'shuffle': True, 'shuffle': False,
'num_workers': 0, 'num_workers': 0
'drop_last': True} }
# trainset = Data_Encoder(df_train, tokenizer_config) # trainset = Data_Encoder(df_train, tokenizer_config)
# training_generator = data.DataLoader(trainset, **params) # training_generator = data.DataLoader(trainset, **params)
# for i, (input, affinity) in tqdm(enumerate(training_generator)): # with open('utils/train_data_analyse.csv', 'w', newline='') as f:
# print(input.size()) # csv_f = csv.writer(f)
# csv_f.writerow(['drup_len', 'protein_len'])
# for i, (len_d, len_p) in tqdm(enumerate(training_generator)):
# d = len_d.numpy()[0]
# p = len_p.numpy()[0]
# csv_f.writerow([str(d), str(p)])
run_interaction.py
...@@ -51,7 +51,7 @@ def train(args, model, dataset): ...@@ -51,7 +51,7 @@ def train(args, model, dataset):
writer = SummaryWriter('./log/' + args.savedir) writer = SummaryWriter('./log/' + args.savedir)
num_step = args.epochs * len(data_generator) num_step = args.epochs * len(data_generator)
step = 0 step = 0
save_step = num_step // 5 save_step = num_step // 10
# detect GPU # detect GPU
if torch.cuda.is_available(): if torch.cuda.is_available():
model.cuda() model.cuda()
...@@ -63,14 +63,14 @@ def train(args, model, dataset): ...@@ -63,14 +63,14 @@ def train(args, model, dataset):
print('begin training') print('begin training')
# training # training
for epoch in range(args.epochs): for epoch in range(args.epochs):
for i, (input, token_type_ids, affinity) in enumerate(data_generator): for i, (input, token_type_ids, input_mask, affinity) in enumerate(data_generator):
# use cuda # use cuda
# input model # input model
if torch.cuda.is_available(): if torch.cuda.is_available():
pred_affinity = model(input_ids=input.cuda(), token_type_ids=token_type_ids.cuda()) pred_affinity = model(input_ids=input.cuda(), token_type_ids=token_type_ids.cuda(), attention_mask=input_mask.cuda())
loss = loss_fct(pred_affinity, affinity.cuda().unsqueeze(-1)) loss = loss_fct(pred_affinity, affinity.cuda().unsqueeze(-1))
else: else:
pred_affinity = model(input_ids=input, token_type_ids=token_type_ids) pred_affinity = model(input_ids=input, token_type_ids=token_type_ids, attention_mask=input_mask)
loss = loss_fct(pred_affinity, affinity.unsqueeze(-1)) loss = loss_fct(pred_affinity, affinity.unsqueeze(-1))
step += 1 step += 1
writer.add_scalar('loss', loss, global_step=step) writer.add_scalar('loss', loss, global_step=step)
...@@ -109,12 +109,13 @@ def test(args, model, dataset): ...@@ -109,12 +109,13 @@ def test(args, model, dataset):
result = args.output + '/' + '{}.txt'.format(args.task) result = args.output + '/' + '{}.txt'.format(args.task)
print('begin predicting') print('begin predicting')
with open(result, 'w') as f: with open(result, 'w') as f:
for i, (input, affinity) in enumerate(tqdm(data_generator)): for i, (input, token_type_ids, input_mask, affinity) in enumerate(tqdm(data_generator)):
if torch.cuda.is_available(): if torch.cuda.is_available():
model.cuda() model.cuda()
pred_affinity = model(input.cuda().long()).detach().cpu().numpy() pred_affinity = model(input_ids=input.cuda(), token_type_ids=token_type_ids.cuda(),
attention_mask=input_mask.cuda())
else: else:
pred_affinity = model(input.long()).numpy() pred_affinity = model(input_ids=input, token_type_ids=token_type_ids, attention_mask=input_mask)
for res in range(args.batch_size): for res in range(args.batch_size):
f.write(str(pred_affinity[res, :][0]) + '\n') f.write(str(pred_affinity[res, :][0]) + '\n')
...@@ -176,7 +177,7 @@ if __name__ == '__main__': ...@@ -176,7 +177,7 @@ if __name__ == '__main__':
# local test # local test
args.task = 'train' args.task = 'train'
args.savedir='local_test_train' args.savedir = 'local_test_train'
args.epochs = 10 args.epochs = 10
args.lr = 1e-5 args.lr = 1e-5
args.config = './config/config_layer_3.json' args.config = './config/config_layer_3.json'
......
test.py 0 → 100644
from transformers import BertTokenizer, BertModel
import torch
a = torch.Tensor([0])
if a > -1:
print('------')
\ No newline at end of file
utils/data_analyse_train.tsv deleted 100644 → 0
This source diff could not be displayed because it is too large. You can view the blob instead.
utils/test_data_analyse.csv 0 → 100644
This source diff could not be displayed because it is too large. You can view the blob instead.
utils/train_data_analyse.csv 0 → 100644
This source diff could not be displayed because it is too large. You can view the blob instead.
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