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