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train.py
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train.py
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#!/usr/bin/env python3
'''
Trainer
Author: Oyesh Mann Singh
'''
import os
from utils.eval import Evaluator
from tqdm import tqdm, tqdm_notebook, tnrange
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.metrics import accuracy_score
torch.manual_seed(163)
tqdm.pandas(desc='Progress')
# Decay functions to be used with lr_scheduler
def lr_decay_noam(config):
return lambda t: (
10.0 * config.hidden_dim ** -0.5 * min(
(t + 1) * config.learning_rate_warmup_steps ** -1.5, (t + 1) ** -0.5))
def lr_decay_exp(config):
return lambda t: config.learning_rate_falloff ** t
# Map names to lr decay functions
lr_decay_map = {
'noam': lr_decay_noam,
'exp': lr_decay_exp
}
class Trainer:
def __init__(self, config, logger, dataloader, model, k):
self.config = config
self.logger = logger
self.dataloader = dataloader
self.verbose = config.verbose
self.use_pos = config.use_pos
self.train_dl, self.val_dl, self.test_dl = dataloader.load_data(batch_size=config.batch_size)
### DO NOT DELETE
### DEBUGGING PURPOSE
# sample = next(iter(self.train_dl))
# print(sample.TEXT)
# print(sample.LABEL)
# print(sample.POS)
self.train_dlen = len(self.train_dl)
self.val_dlen = len(self.val_dl)
self.test_dlen = len(self.test_dl)
self.model = model
self.epochs = config.epochs
self.loss_fn = nn.NLLLoss()
self.opt = optim.Adam(filter(lambda p: p.requires_grad, self.model.parameters()),
lr=config.learning_rate,
weight_decay=config.weight_decay)
self.lr_scheduler_step = self.lr_scheduler_epoch = None
# Set up learing rate decay scheme
if config.use_lr_decay:
if '_' not in config.lr_rate_decay:
raise ValueError("Malformed learning_rate_decay")
lrd_scheme, lrd_range = config.lr_rate_decay.split('_')
if lrd_scheme not in lr_decay_map:
raise ValueError("Unknown lr decay scheme {}".format(lrd_scheme))
lrd_func = lr_decay_map[lrd_scheme]
lr_scheduler = optim.lr_scheduler.LambdaLR(
self.opt,
lrd_func(config),
last_epoch=-1
)
# For each scheme, decay can happen every step or every epoch
if lrd_range == 'epoch':
self.lr_scheduler_epoch = lr_scheduler
elif lrd_range == 'step':
self.lr_scheduler_step = lr_scheduler
else:
raise ValueError("Unknown lr decay range {}".format(lrd_range))
self.k = k
self.model_name = config.model_name + self.k
self.file_name = self.model_name + '.pth'
self.model_file = os.path.join(config.output_dir, self.file_name)
self.total_train_loss = []
self.total_train_acc = []
self.total_val_loss = []
self.total_val_acc = []
self.early_max_patience = config.early_max_patience
def load_checkpoint(self):
checkpoint = torch.load(self.model_file)
self.model.load_state_dict(checkpoint['state_dict'])
self.opt = checkpoint['opt']
self.opt.load_state_dict(checkpoint['opt_state'])
self.total_train_loss = checkpoint['train_loss']
self.total_train_acc = checkpoint['train_acc']
self.total_val_loss = checkpoint['val_loss']
self.total_val_acc = checkpoint['val_acc']
self.epochs = checkpoint['epochs']
def save_checkpoint(self):
save_parameters = {'state_dict': self.model.state_dict(),
'opt': self.opt,
'opt_state': self.opt.state_dict(),
'train_loss': self.total_train_loss,
'train_acc': self.total_train_acc,
'val_loss': self.total_val_loss,
'val_acc': self.total_val_acc,
'epochs': self.epochs}
torch.save(save_parameters, self.model_file)
def fit(self):
prev_lstm_val_acc = 0.0
prev_val_loss = 100.0
counter = 0
patience_limit = 10
for epoch in tnrange(0, self.epochs):
y_true_train = list()
y_pred_train = list()
total_loss_train = 0
t = tqdm(iter(self.train_dl), leave=False, total=self.train_dlen)
for (k, v) in t:
t.set_description(f'Epoch {epoch + 1}')
self.model.train()
self.opt.zero_grad()
if self.use_pos:
(X, p, y) = k
pred = self.model(X, p)
else:
(X, y) = k
pred = self.model(X, None)
y = y.view(-1)
loss = self.loss_fn(pred, y)
loss.backward()
self.opt.step()
if self.lr_scheduler_step:
self.lr_scheduler_step.step()
t.set_postfix(loss=loss.item())
pred_idx = torch.max(pred, dim=1)[1]
y_true_train += list(y.cpu().data.numpy())
y_pred_train += list(pred_idx.cpu().data.numpy())
total_loss_train += loss.item()
train_acc = accuracy_score(y_true_train, y_pred_train)
train_loss = total_loss_train / self.train_dlen
self.total_train_loss.append(train_loss)
self.total_train_acc.append(train_acc)
if self.val_dl:
y_true_val = list()
y_pred_val = list()
total_loss_val = 0
v = tqdm(iter(self.val_dl), leave=False)
for (k, v) in v:
if self.use_pos:
(X, p, y) = k
pred = self.model(X, p)
else:
(X, y) = k
pred = self.model(X, None)
y = y.view(-1)
loss = self.loss_fn(pred, y)
pred_idx = torch.max(pred, 1)[1]
y_true_val += list(y.cpu().data.numpy())
y_pred_val += list(pred_idx.cpu().data.numpy())
total_loss_val += loss.item()
valacc = accuracy_score(y_true_val, y_pred_val)
valloss = total_loss_val / self.val_dlen
self.logger.info(
f'Epoch {epoch + 1}: train_loss: {train_loss:.4f} train_acc: {train_acc:.4f} | val_loss: {valloss:.4f} val_acc: {valacc:.4f}')
else:
self.logger.info(f'Epoch {epoch + 1}: train_loss: {train_loss:.4f} train_acc: {train_acc:.4f}')
self.total_val_loss.append(valloss)
self.total_val_acc.append(valacc)
if self.lr_scheduler_epoch:
self.lr_scheduler_epoch.step()
if valloss < prev_val_loss:
self.save_checkpoint()
prev_val_loss = valloss
counter = 0
self.logger.info("Best model saved!!!")
else:
counter += 1
if counter >= self.early_max_patience:
self.logger.info("Training stopped because maximum tolerance reached!!!")
break
# Predict
def predict(self):
self.model.eval()
evaluate = Evaluator(self.config, self.logger, self.model, self.dataloader, self.model_name)
self.logger.info("Writing results")
evaluate.write_results()
self.logger.info("Evaluate results")
acc, prec, rec, f1 = evaluate.conll_eval()
return (acc, prec, rec, f1)
# Infer
def infer(self, sent):
"""
Prints the result
"""
evaluate = Evaluator(self.config, self.logger, self.model, self.dataloader, self.model_name)
return evaluate.infer(sent)