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BiTransDPI
提交 dc50914b 作者: 朱学凯
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adding predict

上级 ef8fe61a
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.idea/inspectionProfiles/Project_Default.xml deleted 100644 → 0
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.idea/misc.xml
<?xml version="1.0" encoding="UTF-8"?>
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\ No newline at end of file
.idea/workspace.xml
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<change beforePath="$PROJECT_DIR$/log/train/events.out.tfevents.1617283406.DESKTOP-K4GGDLG.1656.0" beforeDir="false" />
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<change beforePath="$PROJECT_DIR$/run_interaction.py" beforeDir="false" afterPath="$PROJECT_DIR$/run_interaction.py" afterDir="false" />
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......@@ -17,24 +24,18 @@
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<recent name="E:\CPI\project\CPI" />
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<recent name="E:\CPI\project\CPI\log\lr-5e-6-batch-30-layer3" />
</key>
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<component name="RunManager">
<configuration name="run_interaction" type="PythonConfigurationType" factoryName="Python" temporary="true" nameIsGenerated="true">
<module name="CPI" />
......@@ -67,12 +68,12 @@
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<servers />
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......@@ -90,18 +91,7 @@
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\ No newline at end of file
experment_result/loss.svg 0 → 100644
This source diff could not be displayed because it is too large. You can view the blob instead.
modeling_bert.py
......@@ -1828,18 +1828,20 @@ class BertForQuestionAnswering(BertPreTrainedModel):
class Multilayer_perceptron(nn.Module):
def __init__(self, config):
super(Multilayer_perceptron, self).__init__()
self.layer_1 = nn.Linear(config.hidden_size, 512)
self.layer_1 = nn.Linear(config.hidden_size, 256)
self.layer_2 = nn.Linear(512, 256)
self.layer_3 = nn.Linear(256, 1)
self.drop_out = nn.Dropout(0.5)
def forward(self, bert_output):
x1 = self.layer_1(bert_output)
x1 = F.relu(x1, inplace=True)
x2 = self.layer_2(x1)
x2 = F.relu(x2, inplace=True)
x3 = self.layer_3(x2)
x1 = self.drop_out(x1)
# x2 = self.layer_2(x1)
# x2 = F.relu(x2, inplace=True)
# x2 = self.drop_out(x2)
x2 = self.layer_3(x1)
return x3
return x2
class BertAffinityModel(BertPreTrainedModel):
......
predict/test/results.txt 0 → 100644
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run_interaction.py
......@@ -6,6 +6,7 @@ from configuration_bert import BertConfig
from modeling_bert import BertAffinityModel
from torch.utils.tensorboard import SummaryWriter
import os
from tqdm import tqdm
def get_task(task_name):
if task_name.lower() == 'train':
......@@ -35,23 +36,7 @@ def get_task(task_name):
return df_test, tokenizer_config
def main(args):
# load data
data_file, tokenizer_config = get_task(args.task)
dataset = Data_Encoder(data_file, tokenizer_config)
data_loder_para = {'batch_size': args.batch_size,
'shuffle': False,
'num_workers': args.workers,
'drop_last': True
}
data_generator = DataLoader(dataset, **data_loder_para)
# creat model
print('------------------creat model---------------------------')
config = BertConfig.from_pretrained(args.config)
model = BertAffinityModel(config)
print('model name : BertAffinity')
if args.task == 'train':
def train(args, model, data_generator):
opt = torch.optim.Adam(model.parameters(), lr=args.lr)
loss_fct = torch.nn.MSELoss()
writer = SummaryWriter('./log/' + args.savedir)
......@@ -64,9 +49,6 @@ def main(args):
for epoch in range(args.epochs):
for i, (input, affinity) in enumerate(data_generator):
# use cuda
# if torch.cuda.is_available():
# input.cuda()
# affinity.cuda()
# input model
if torch.cuda.is_available():
pred_affinity = model(input.cuda().long())
......@@ -74,13 +56,13 @@ def main(args):
else:
pred_affinity = model(input.long())
loss = loss_fct(pred_affinity, affinity.unsqueeze(-1))
writer.add_scalar('loss', loss, num_step)
writer.add_scalar('loss', loss, global_step=num_step)
# Update gradient
opt.zero_grad()
loss.backward()
opt.step()
# if (i % 100 == 0):
# if (i % 100 == 0):
print('Training at Epoch ' + str(epoch + 1) + ' step ' + str(i) + ' with loss ' + str(
loss.cpu().detach().numpy()))
# save
......@@ -89,6 +71,49 @@ def main(args):
if not os.path.exists(save_path):
os.mkdir(save_path)
torch.save(model.state_dict(), save_path + 'epoch-{}-step-{}-loss-{}.pth'.format(epoch, i, loss))
print('training over')
writer.close()
def test(args, model, data_generator):
with torch.no_grad():
model.load_state_dict(torch.load(args.init, map_location=torch.device('cpu')))
model.eval()
if not os.path.exists(args.output):
os.mkdir(args.output)
result = args.output + '/' + 'results.txt'
print('begin predicting')
with open(result, 'w') as f:
for i, (input, affinity) in enumerate(tqdm(data_generator)):
if torch.cuda.is_available():
model.cuda()
pred_affinity = model(input.cuda().long()).detach().cpu().numpy()
else:
pred_affinity = model(input.long()).numpy()
for res in range(args.batch_size):
f.write(str(pred_affinity[res, :]) + '\n')
def main(args):
# load data
data_file, tokenizer_config = get_task(args.task)
dataset = Data_Encoder(data_file, tokenizer_config)
data_loder_para = {'batch_size': args.batch_size,
'shuffle': False,
'num_workers': args.workers,
'drop_last': True
}
data_generator = DataLoader(dataset, **data_loder_para)
# creat model
print('------------------creat model---------------------------')
config = BertConfig.from_pretrained(args.config)
model = BertAffinityModel(config)
print('model name : BertAffinity')
if args.task == 'train':
train(args, model, data_generator)
elif args.task == 'test':
test(args, model, data_generator)
......@@ -114,12 +139,19 @@ if __name__ == '__main__':
parser.add_argument('--lr', '--learning-rate', default=1e-4, type=float,
metavar='LR', help='initial learning rate', dest='lr')
parser.add_argument('--config', default='./config/config.json', type=str, help='model config file path')
# parser.add_argument('--log', default='training_log', type=str, help='training log')
# parser.add_argument('--log', default='training_log', type=str, help='training log')
parser.add_argument('--savedir', default='train', type=str, help='log and model save path')
# parser.add_argument('--device', default='0', type=str, help='name of GPU')
args = parser.parse_args()
parser.add_argument('--init', default='model', type=str, help='init checkpoint')
parser.add_argument('--output', default='predict', type=str, help='result save path')
args = parser.parse_args()
args.task = 'test'
args.init = './model/lr-1e-5-batch-64-layer3/epoch-4-step-2000-loss-2.9310436248779297.pth'
args.output = './predict/test'
args.config = './config/config_layer_3.json'
# input dict
main(args)
# load_config(args.config)
......
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