roberta with regression layer on top

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kubapok 2021-06-09 22:53:53 +02:00
parent c7fb59e7da
commit 18adc7f49e
4 changed files with 73950 additions and 73549 deletions

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import os
import torch
import random
import copy
from fairseq.models.roberta import RobertaModel, RobertaHubInterface
from fairseq import hub_utils
from fairseq.data.data_utils import collate_tokens
from tqdm import tqdm
import numpy as np
from sklearn.preprocessing import MinMaxScaler
EVAL_OFTEN = True
EVAL_EVERY = 10000
roberta = torch.hub.load('pytorch/fairseq', 'roberta.base')
roberta.cuda()
device='cuda'
train_in = [l.rstrip('\n') for l in open('../train/in.tsv',newline='\n').readlines()] # shuffled
dev_in = [l.rstrip('\n') for l in open('../dev-0/in.tsv',newline='\n').readlines()] # shuffled
train_year = [float(l.rstrip('\n')) for l in open('../train/expected.tsv',newline='\n').readlines()]
dev_year = [float(l.rstrip('\n')) for l in open('../dev-0/expected.tsv',newline='\n').readlines()]
dev_in_not_shuffled = copy.deepcopy(dev_in) # not shuffled
test_in = [l.rstrip('\n') for l in open('../test-A/in.tsv',newline='\n').readlines()] # not shuffled
c = list(zip(train_in,train_year))
random.shuffle(c)
train_in, train_year = zip(*c)
c = list(zip(dev_in,dev_year))
random.shuffle(c)
dev_in, dev_year = zip(*c)
scaler = MinMaxScaler()
train_year_scaled = scaler.fit_transform(np.array(train_year).reshape(-1,1))
dev_year_scaled = scaler.transform(np.array(dev_year).reshape(-1,1))
class RegressorHead(torch.nn.Module):
def __init__(self):
super(RegressorHead, self).__init__()
self.linear1 = torch.nn.Linear(768,300)
self.linear2 = torch.nn.Linear(300,1)
self.linearxxx = torch.nn.Linear(768,1)
self.dropout1 = torch.nn.Dropout(0.0)
self.dropout2 = torch.nn.Dropout(0.0)
self.m = torch.nn.LeakyReLU(0.1)
def forward(self,x):
#x = self.dropout1(x)
#x = self.linear1(x)
#x = self.dropout2(x)
x = self.linearxxx(x)
x = self.m(x)
x = -self.m(-x +1 ) +1
return x
regressor_head = RegressorHead().to(device)
optimizer = torch.optim.Adam(list(roberta.parameters()) + list(regressor_head.parameters()), lr = 1e-6)
criterion = torch.nn.MSELoss(reduction='sum').to(device)
BATCH_SIZE = 1
def get_train_batch(dataset_in,dataset_y):
for i in tqdm(range(0,len(dataset_in), BATCH_SIZE)):
batch_of_text = dataset_in[i:i+BATCH_SIZE]
#batch = collate_tokens([roberta.encode(p)[:512] for p in batch_of_text], pad_idx=1)
batch = roberta.encode(batch_of_text[0])
output= None
for j in range(0,1,512): # only first 512 tokens instead of all
if output is None:
output = roberta.extract_features(batch[j:j+512])
else:
output_new = roberta.extract_features(batch[j:j+512])
output = torch.cat((output, output_new),1)
features = torch.mean(output,1)
years = torch.FloatTensor(dataset_y[i:i+BATCH_SIZE]).to(device).squeeze()
yield features, years
def eval():
criterion_eval = torch.nn.MSELoss(reduction='sum')
roberta.eval()
regressor_head.eval()
loss = 0.0
loss_clipped = 0.0
loss_scaled = 0.0
for batch, year in tqdm(get_train_batch(dev_in,dev_year_scaled)):
x = regressor_head(batch.to(device)).squeeze()
x_clipped = torch.clamp(x,0.0,1.0)
original_x = torch.FloatTensor(scaler.inverse_transform(x.detach().cpu().numpy().reshape(1,-1)))
original_x_clipped = torch.FloatTensor(scaler.inverse_transform(x_clipped.detach().cpu().numpy().reshape(1,-1)))
original_year = torch.FloatTensor(scaler.inverse_transform(year.detach().cpu().numpy().reshape(1,-1)))
loss_scaled += criterion_eval(x, year).item()
loss += criterion_eval(original_x, original_year).item()
loss_clipped += criterion_eval(original_x_clipped, original_year).item()
print(' full valid loss scaled: ' + str(np.sqrt(loss_scaled/len(dev_year))))
print(' full valid loss: ' + str(np.sqrt(loss/len(dev_year))))
print(' full valid loss clipped: ' + str(np.sqrt(loss_clipped/len(dev_year))))
def eval_short():
criterion_eval = torch.nn.MSELoss(reduction='sum')
roberta.eval()
regressor_head.eval()
loss = 0.0
loss_clipped = 0.0
loss_scaled = 0.0
for batch, year in tqdm(get_train_batch(dev_in[:1000],dev_year_scaled[:1000])):
x = regressor_head(batch.to(device)).squeeze()
x_clipped = torch.clamp(x,0.0,1.0)
original_x = torch.FloatTensor(scaler.inverse_transform(x.detach().cpu().numpy().reshape(1,-1)))
original_x_clipped = torch.FloatTensor(scaler.inverse_transform(x_clipped.detach().cpu().numpy().reshape(1,-1)))
original_year = torch.FloatTensor(scaler.inverse_transform(year.detach().cpu().numpy().reshape(1,-1)))
loss_scaled += criterion_eval(x, year).item()
loss += criterion_eval(original_x, original_year).item()
loss_clipped += criterion_eval(original_x_clipped, original_year).item()
print('valid loss scaled: ' + str(np.sqrt(loss_scaled/1000)))
print('valid loss: ' + str(np.sqrt(loss/1000)))
print('valid loss clipped: ' + str(np.sqrt(loss_clipped/len(dev_year))))
def train_one_epoch():
roberta.train()
regressor_head.train()
loss_value=0.0
iteration = 0
for batch, year in get_train_batch(train_in,train_year_scaled):
iteration +=1
roberta.zero_grad()
regressor_head.zero_grad()
#import pdb; pdb.set_trace()
x = regressor_head(batch.to(device)).squeeze()
loss = criterion(x, year)
loss_value += loss.item()
loss.backward()
optimizer.step()
roberta.zero_grad()
regressor_head.zero_grad()
if EVAL_OFTEN and (iteration > 1) and (iteration % EVAL_EVERY == 1):
print('train loss: ' + str(np.sqrt(loss_value / EVAL_EVERY)))
eval_short()
roberta.train()
regressor_head.train()
loss_value = 0.0
#print('train loss: ' + str(loss_value/len(train_year)))
def predict_dev():
roberta.eval()
regressor_head.eval()
f_out = open('../dev-0/out.tsv','w')
for batch, year in tqdm(get_train_batch(dev_in_not_shuffled,dev_year_scaled)):
#batch_first = roberta.extract_features(batch)[:,0].to(device)
x = regressor_head(batch).squeeze()
x_clipped = torch.clamp(x,0.0,1.0)
original_x_clipped = scaler.inverse_transform(x_clipped.detach().cpu().numpy().reshape(1,-1))
for y in original_x_clipped[0]:
f_out.write(str(y) + '\n')
f_out.close()
def predict_test():
roberta.eval()
regressor_head.eval()
f_out = open('../test-A/out.tsv','w')
for batch, year in tqdm(get_train_batch(test_in,dev_year_scaled)):
#batch_first = roberta.extract_features(batch)[:,0].to(device)
x = regressor_head(batch).squeeze()
x_clipped = torch.clamp(x,0.0,1.0)
original_x_clipped = scaler.inverse_transform(x_clipped.detach().cpu().numpy().reshape(1,-1))
for y in original_x_clipped[0]:
f_out.write(str(y) + '\n')
f_out.close()
roberta.load_state_dict(torch.load('checkpoints/roberta_to_regressor3.pt'))
regressor_head.load_state_dict(torch.load('checkpoints/regressor_head3.pt'))
predict_dev()
predict_test()

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import os
import torch
import random
import copy
from fairseq.models.roberta import RobertaModel, RobertaHubInterface
from fairseq import hub_utils
from fairseq.data.data_utils import collate_tokens
from tqdm import tqdm
import numpy as np
from sklearn.preprocessing import MinMaxScaler
EVAL_OFTEN = True
EVAL_EVERY = 10000
roberta = torch.hub.load('pytorch/fairseq', 'roberta.base')
roberta.cuda()
device='cuda'
EXPECTED='expected2.tsv'
OUT='out2.tsv'
train_in = [l.rstrip('\n') for l in open('../train/in.tsv',newline='\n').readlines()] # shuffled
dev_in = [l.rstrip('\n') for l in open('../dev-0/in.tsv',newline='\n').readlines()] # shuffled
train_year = [float(l.rstrip('\n')) for l in open(f'../train/{EXPECTED}',newline='\n').readlines()]
dev_year = [float(l.rstrip('\n')) for l in open(f'../dev-0/{EXPECTED}',newline='\n').readlines()]
dev_in_not_shuffled = copy.deepcopy(dev_in) # not shuffled
test_in = [l.rstrip('\n') for l in open('../test-A/in.tsv',newline='\n').readlines()] # not shuffled
c = list(zip(train_in,train_year))
random.shuffle(c)
train_in, train_year = zip(*c)
c = list(zip(dev_in,dev_year))
random.shuffle(c)
dev_in, dev_year = zip(*c)
scaler = MinMaxScaler()
train_year_scaled = scaler.fit_transform(np.array(train_year).reshape(-1,1))
dev_year_scaled = scaler.transform(np.array(dev_year).reshape(-1,1))
class RegressorHead(torch.nn.Module):
def __init__(self):
super(RegressorHead, self).__init__()
self.linear1 = torch.nn.Linear(768,300)
self.linear2 = torch.nn.Linear(300,1)
self.linearxxx = torch.nn.Linear(768,1)
self.dropout1 = torch.nn.Dropout(0.0)
self.dropout2 = torch.nn.Dropout(0.0)
self.m = torch.nn.LeakyReLU(0.1)
def forward(self,x):
#x = self.dropout1(x)
#x = self.linear1(x)
#x = self.dropout2(x)
x = self.linearxxx(x)
x = self.m(x)
x = -self.m(-x +1 ) +1
return x
regressor_head = RegressorHead().to(device)
optimizer = torch.optim.Adam(list(roberta.parameters()) + list(regressor_head.parameters()), lr=1e-6)
criterion = torch.nn.MSELoss(reduction='sum').to(device)
BATCH_SIZE = 1
def get_train_batch(dataset_in,dataset_y):
for i in tqdm(range(0,len(dataset_in), BATCH_SIZE)):
batch_of_text = dataset_in[i:i+BATCH_SIZE]
#batch = collate_tokens([roberta.encode(p)[:512] for p in batch_of_text], pad_idx=1)
batch = roberta.encode(batch_of_text[0])
output= None
for j in range(0,1,512): # only first 512 tokens instead of all
if output is None:
output = roberta.extract_features(batch[j:j+512])
else:
output_new = roberta.extract_features(batch[j:j+512])
output = torch.cat((output, output_new),1)
features = torch.mean(output,1)
years = torch.FloatTensor(dataset_y[i:i+BATCH_SIZE]).to(device).squeeze()
yield features, years
def eval():
criterion_eval = torch.nn.MSELoss(reduction='sum')
roberta.eval()
regressor_head.eval()
loss = 0.0
loss_clipped = 0.0
loss_scaled = 0.0
for batch, year in tqdm(get_train_batch(dev_in,dev_year_scaled)):
x = regressor_head(batch.to(device)).squeeze()
x_clipped = torch.clamp(x,0.0,1.0)
original_x = torch.FloatTensor(scaler.inverse_transform(x.detach().cpu().numpy().reshape(1,-1)))
original_x_clipped = torch.FloatTensor(scaler.inverse_transform(x_clipped.detach().cpu().numpy().reshape(1,-1)))
original_year = torch.FloatTensor(scaler.inverse_transform(year.detach().cpu().numpy().reshape(1,-1)))
loss_scaled += criterion_eval(x, year).item()
loss += criterion_eval(original_x, original_year).item()
loss_clipped += criterion_eval(original_x_clipped, original_year).item()
print(' full valid loss scaled: ' + str(np.sqrt(loss_scaled/len(dev_year))))
print(' full valid loss: ' + str(np.sqrt(loss/len(dev_year))))
print(' full valid loss clipped: ' + str(np.sqrt(loss_clipped/len(dev_year))))
def eval_short():
criterion_eval = torch.nn.MSELoss(reduction='sum')
roberta.eval()
regressor_head.eval()
loss = 0.0
loss_clipped = 0.0
loss_scaled = 0.0
for batch, year in tqdm(get_train_batch(dev_in[:1000],dev_year_scaled[:1000])):
x = regressor_head(batch.to(device)).squeeze()
x_clipped = torch.clamp(x,0.0,1.0)
original_x = torch.FloatTensor(scaler.inverse_transform(x.detach().cpu().numpy().reshape(1,-1)))
original_x_clipped = torch.FloatTensor(scaler.inverse_transform(x_clipped.detach().cpu().numpy().reshape(1,-1)))
original_year = torch.FloatTensor(scaler.inverse_transform(year.detach().cpu().numpy().reshape(1,-1)))
loss_scaled += criterion_eval(x, year).item()
loss += criterion_eval(original_x, original_year).item()
loss_clipped += criterion_eval(original_x_clipped, original_year).item()
print('valid loss scaled: ' + str(np.sqrt(loss_scaled/1000)))
print('valid loss: ' + str(np.sqrt(loss/1000)))
print('valid loss clipped: ' + str(np.sqrt(loss_clipped/len(dev_year))))
def train_one_epoch():
roberta.train()
regressor_head.train()
loss_value=0.0
iteration = 0
for batch, year in get_train_batch(train_in,train_year_scaled):
iteration +=1
roberta.zero_grad()
regressor_head.zero_grad()
#import pdb; pdb.set_trace()
x = regressor_head(batch.to(device)).squeeze()
loss = criterion(x, year)
loss_value += loss.item()
loss.backward()
optimizer.step()
roberta.zero_grad()
regressor_head.zero_grad()
if EVAL_OFTEN and (iteration > 1) and (iteration % EVAL_EVERY == 1):
print('train loss: ' + str(np.sqrt(loss_value / EVAL_EVERY)))
eval_short()
roberta.train()
regressor_head.train()
loss_value = 0.0
#print('train loss: ' + str(loss_value/len(train_year)))
def predict_dev():
roberta.eval()
regressor_head.eval()
f_out = open(f'../dev-0/{OUT}','w')
for batch, year in tqdm(get_train_batch(dev_in_not_shuffled,dev_year_scaled)):
#batch_first = roberta.extract_features(batch)[:,0].to(device)
x = regressor_head(batch).squeeze()
x_clipped = torch.clamp(x,0.0,1.0)
original_x_clipped = scaler.inverse_transform(x_clipped.detach().cpu().numpy().reshape(1,-1))
for y in original_x_clipped[0]:
f_out.write(str(y) + '\n')
f_out.close()
def predict_test():
roberta.eval()
regressor_head.eval()
f_out = open(f'../test-A/{OUT}','w')
for batch, year in tqdm(get_train_batch(test_in,dev_year_scaled)):
#batch_first = roberta.extract_features(batch)[:,0].to(device)
x = regressor_head(batch).squeeze()
x_clipped = torch.clamp(x,0.0,1.0)
original_x_clipped = scaler.inverse_transform(x_clipped.detach().cpu().numpy().reshape(1,-1))
for y in original_x_clipped[0]:
f_out.write(str(y) + '\n')
f_out.close()
for i in range(100):
print('epoch ' + str(i))
train_one_epoch()
eval()
predict_dev()
predict_test()
torch.save(roberta.state_dict(),'checkpoints/roberta_to_regressor' + str(i) + '.pt')
torch.save(regressor_head.state_dict(),'checkpoints/regressor_head' + str(i) + '.pt')
predict_dev()
predict_test()

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