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train.py
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train.py
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import os
import time
import shutil
import time
import json
import random
import time
import argparse
import numpy as np
## torch packages
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from torch.utils.tensorboard import SummaryWriter
import torch.nn as nn
from transformers import get_linear_schedule_with_warmup
from easydict import EasyDict as edict
## for visualisation
import matplotlib.pyplot as plt
## custom
from eval import eval_model
from select_model_input import select_model,select_input
import dataset
import config as train_config
from label_dict import ed_emo_dict
from utils import clip_gradient,save_checkpoint
def train_epoch(model, train_iter, epoch,loss_fn,optimizer,log_dict):
total_epoch_loss = 0
total_epoch_acc = 0
model.cuda()
steps = 0
model.train()
start_train_time = time.time()
for idx, batch in enumerate(train_iter):
text, attn, target = select_input(batch,log_dict.param)
target = torch.autograd.Variable(target).long()
if (target.size()[0] is not log_dict.param.batch_size):# Last batch may have length different than log_dict.param.batch_size
continue
if torch.cuda.is_available():
text = [text[0].cuda(),text[1].cuda(),text[2].cuda(),text[3].cuda()]
attn = attn.cuda()
target = target.cuda()
## model prediction
model.zero_grad()
optimizer.zero_grad()
# print("Prediction")
prediction = model(text,attn)
# print("computing loss")
loss = loss_fn(prediction, target)
## evaluation
num_corrects = (torch.max(prediction, 1)[1].view(target.size()).data == target.data).float().sum()
acc = 100.0 * num_corrects/log_dict.param.batch_size
# print("Loss backward")
startloss = time.time()
loss.backward()
# print(time.time()-startloss,"Finish loss")
clip_gradient(model, 1e-1)
# torch.nn.utils.clip_grad_norm_(model.parameters(),1)
optimizer.step()
# print("=====================")
steps += 1
if steps % 100 == 0:
print (f'Epoch: {epoch+1:02}, Idx: {idx+1}, Training Loss: {loss.item():.4f}, Training Accuracy: {acc.item(): .2f}%, Time taken: {((time.time()-start_train_time)/60): .2f} min')
start_train_time = time.time()
total_epoch_loss += loss.item()
total_epoch_acc += acc.item()
return total_epoch_loss/len(train_iter), total_epoch_acc/len(train_iter)
def train_model(log_dict,data,model,loss_fn,optimizer,lr_scheduler,writer,save_home):
best_acc1 = 0
patience_flag = 0
train_iter,valid_iter,test_iter = data[0],data[1],data[2] # data is a tuple of three iterators
# print("Start Training")
for epoch in range(0,log_dict.param.nepoch):
## train and validation
train_loss, train_acc = train_epoch(model, train_iter, epoch,loss_fn,optimizer,log_dict)
val_loss, val_acc ,val_f1_score,val_w_f1_score,val_top3_acc= eval_model(model, valid_iter,loss_fn,log_dict)
print(f'Epoch: {epoch+1:02}, Train Loss: {train_loss:.3f}, Train Acc: {train_acc:.2f}%, Val. Loss: {val_loss:3f}, Val. Acc: {val_acc:.2f}%')
## testing
test_loss, test_acc,test_f1_score,test_w_f1_score,test_top3_acc = eval_model(model, test_iter,loss_fn,log_dict)
print(f'Test Loss: {test_loss:.3f}, Test Acc: {test_acc:.2f}% Test F1 score: {test_f1_score:.4f}')
## save best model
is_best = val_acc > best_acc1
os.makedirs(save_home,exist_ok=True)
save_checkpoint({'epoch': epoch + 1,'arch': log_dict.param.arch_name,'state_dict': model.state_dict(),'train_acc':train_acc,"val_acc":val_acc,'param':dict(log_dict.param),'optimizer' : optimizer.state_dict()},is_best,save_home+"/model_best.pth.tar")
best_acc1 = max(val_acc, best_acc1)
if log_dict.param.step_size != None:
lr_scheduler.step()
## tensorboard runs
writer.add_scalar('Loss/train',train_loss,epoch)
writer.add_scalar('Accuracy/train',train_acc,epoch)
writer.add_scalar('Loss/val',val_loss,epoch)
writer.add_scalar('Accuracy/val',val_acc,epoch)
## save logs
if is_best:
patience_flag = 0
log_dict.train_acc = train_acc
log_dict.test_acc = test_acc
log_dict.valid_acc = val_acc
log_dict.test_f1_score = test_f1_score
log_dict.valid_f1_score = val_f1_score
log_dict.valid_top3_acc = val_top3_acc
log_dict.test_top3_acc = test_top3_acc
log_dict.train_loss = train_loss
log_dict.test_loss = test_loss
log_dict.valid_loss = val_loss
log_dict.epoch = epoch+1
log_dict.weighted_test_f1_score = test_w_f1_score
log_dict.weighted_valid_f1_score = val_w_f1_score
with open(save_home+"/log.json", 'w') as fp:
json.dump(dict(log_dict), fp,indent=4)
fp.close()
else:
patience_flag += 1
## early stopping
if patience_flag == log_dict.param.patience or epoch == log_dict.param.nepoch-1:
print(log_dict)
break
if __name__ == '__main__':
# note = "without dom" ## to note any changes
log_dict = edict({})
log_dict.param = train_config.param
## Loading data
if train_config.tuning:
for learning_rate in [3e-05]: ## for tuning
log_dict.param.learning_rate = learning_rate
np.random.seed(0)
random.seed(0)
torch.manual_seed(0)
torch.cuda.manual_seed(0)
torch.cuda.manual_seed_all(0)
print('Loading dataset')
start_time = time.time()
train_iter, valid_iter ,test_iter= dataset.get_dataloader(log_dict.param.batch_size,log_dict.param.dataset,log_dict.param.arch_name)
data = (train_iter,valid_iter,test_iter)
finish_time = time.time()
print('Finished loading. Time taken:{:06.3f} sec'.format(finish_time-start_time))
## Initialising model, loss, optimizer, lr_scheduler
model = select_model(log_dict.param)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),lr=learning_rate)
if log_dict.param.step_size != None:
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,log_dict.param.step_size, gamma=0.5)
## Filepaths for saving the model and the tensorboard runs
model_run_time = time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())
writer = SummaryWriter("./runs/"+log_dict.param.arch_name+"/")
save_home = "./save/"+log_dict.param.dataset+"/"+log_dict.param.arch_name+"/"+model_run_time
train_model(log_dict,data,model,loss_fn,optimizer,lr_scheduler,writer,save_home)
else:
np.random.seed(0)
random.seed(0)
torch.manual_seed(0)
torch.cuda.manual_seed(0)
torch.cuda.manual_seed_all(0)
print('Loading dataset')
start_time = time.time()
train_iter, valid_iter ,test_iter= dataset.get_dataloader(log_dict.param.batch_size,log_dict.param.dataset,log_dict.param.arch_name)
data = (train_iter,valid_iter,test_iter)
finish_time = time.time()
print('Finished loading. Time taken:{:06.3f} sec'.format(finish_time-start_time))
## Initialising model, loss, optimizer, lr_scheduler
model = select_model(log_dict.param)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),lr=log_dict.param.learning_rate)
if log_dict.param.step_size != None:
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,log_dict.param.step_size, gamma=0.5)
## Filepaths for saving the model and the tensorboard runs
model_run_time = time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())
writer = SummaryWriter("./runs/"+log_dict.param.arch_name+"/")
save_home = "./save/"+log_dict.param.dataset+"/"+log_dict.param.arch_name+"/"+model_run_time
train_model(log_dict,data,model,loss_fn,optimizer,lr_scheduler,writer,save_home)