ireland-news-headlines-year.../roberta_regressor_head/train.py
2021-09-19 11:43:31 +02:00

180 lines
5.7 KiB
Python

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 = 3000
BATCH_SIZE = 25
model_type = 'base' # base or large
roberta = torch.hub.load('pytorch/fairseq', f'roberta.{model_type}')
roberta.cuda()
device='cuda'
# LOAD DATA
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
# SHUFFLE DATA
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)
# SCALE DATA
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__()
in_dim = 768 if model_type == 'base' else 1024
self.linear = torch.nn.Linear(in_dim, 1)
self.m = torch.nn.LeakyReLU(0.1)
def forward(self, x):
x = self.linear(x)
x = self.m(x)
x = - self.m(-x + 1 ) +1
return x
def get_features_and_year(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)
features = roberta.extract_features(batch).mean(1)
years = torch.FloatTensor(dataset_y[i:i+BATCH_SIZE]).to(device)
yield features, years
def eval_dev(short=False):
criterion_eval = torch.nn.MSELoss(reduction='sum')
roberta.eval()
regressor_head.eval()
loss = 0.0
loss_clipped = 0.0
loss_scaled = 0.0
if short:
dataset_in = dev_in[:1000]
dataset_years = dev_year_scaled[:1000]
else:
dataset_in = dev_in
dataset_years = dev_year_scaled
predictions_sum = 0
for batch, year in tqdm(get_features_and_year(dataset_in, dataset_years)):
predictions_sum += year.shape[0]
x = regressor_head(batch.to(device))
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/predictions_sum)))
print('valid loss: ' + str(np.sqrt(loss/predictions_sum)))
print('valid loss clipped: ' + str(np.sqrt(loss_clipped/predictions_sum)))
def train_one_epoch():
roberta.train()
regressor_head.train()
loss_value=0.0
iteration = 0
for batch, year in get_features_and_year(train_in,train_year_scaled):
iteration +=1
roberta.zero_grad()
regressor_head.zero_grad()
predictions = regressor_head(batch.to(device))
loss = criterion(predictions, 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*BATCH_SIZE))))
eval_dev(True)
roberta.train()
regressor_head.train()
loss_value = 0.0
def predict(dataset='dev'):
if dataset=='dev':
f_out_path = '../dev-0/out.tsv'
dataset_in_not_shuffled = dev_in_not_shuffled
elif dataset=='test':
f_out_path = '../test-A/out.tsv'
dataset_in_not_shuffled = test_in
roberta.eval()
regressor_head.eval()
f_out = open(f_out_path,'w')
for batch, year in tqdm(get_features_and_year(dataset_in_not_shuffled, dev_year_scaled)):
x = regressor_head(batch)
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()
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)
for i in range(100):
print('epoch ' + str(i))
train_one_epoch()
print(f'epoch {i} done, EVALUATION ON FULL DEV:')
eval_dev()
print('evaluation done')
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')
roberta.load_state_dict(torch.load('checkpoints/roberta_to_regressor1.pt'))
regressor_head.load_state_dict(torch.load('checkpoints/regressor_head1.pt'))
predict('dev')
predict('test')