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main.py
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import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.utils.data import DataLoader
import math
import os
import sys
from dataset import MyDataset
import numpy as np
import time
from model import LipNet
import torch.optim as optim
import re
import json
from tensorboardX import SummaryWriter
if(__name__ == '__main__'):
opt = __import__('options')
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu
writer = SummaryWriter()
def dataset2dataloader(dataset, num_workers=opt.num_workers, shuffle=True):
return DataLoader(dataset,
batch_size = opt.batch_size,
shuffle = shuffle,
num_workers = num_workers,
drop_last = False)
def show_lr(optimizer):
lr = []
for param_group in optimizer.param_groups:
lr += [param_group['lr']]
return np.array(lr).mean()
def ctc_decode(y):
result = []
y = y.argmax(-1)
return [MyDataset.ctc_arr2txt(y[_], start=1) for _ in range(y.size(0))]
def test(model, net):
with torch.no_grad():
dataset = MyDataset(opt.video_path,
opt.anno_path,
opt.val_list,
opt.vid_padding,
opt.txt_padding,
'test')
print('num_test_data:{}'.format(len(dataset.data)))
model.eval()
loader = dataset2dataloader(dataset, shuffle=False)
loss_list = []
wer = []
cer = []
crit = nn.CTCLoss()
tic = time.time()
for (i_iter, input) in enumerate(loader):
vid = input.get('vid').cuda()
txt = input.get('txt').cuda()
vid_len = input.get('vid_len').cuda()
txt_len = input.get('txt_len').cuda()
y = net(vid)
loss = crit(y.transpose(0, 1).log_softmax(-1), txt, vid_len.view(-1), txt_len.view(-1)).detach().cpu().numpy()
loss_list.append(loss)
pred_txt = ctc_decode(y)
truth_txt = [MyDataset.arr2txt(txt[_], start=1) for _ in range(txt.size(0))]
wer.extend(MyDataset.wer(pred_txt, truth_txt))
cer.extend(MyDataset.cer(pred_txt, truth_txt))
if(i_iter % opt.display == 0):
v = 1.0*(time.time()-tic)/(i_iter+1)
eta = v * (len(loader)-i_iter) / 3600.0
print(''.join(101*'-'))
print('{:<50}|{:>50}'.format('predict', 'truth'))
print(''.join(101*'-'))
for (predict, truth) in list(zip(pred_txt, truth_txt))[:10]:
print('{:<50}|{:>50}'.format(predict, truth))
print(''.join(101 *'-'))
print('test_iter={},eta={},wer={},cer={}'.format(i_iter,eta,np.array(wer).mean(),np.array(cer).mean()))
print(''.join(101 *'-'))
return (np.array(loss_list).mean(), np.array(wer).mean(), np.array(cer).mean())
def train(model, net):
dataset = MyDataset(opt.video_path,
opt.anno_path,
opt.train_list,
opt.vid_padding,
opt.txt_padding,
'train')
loader = dataset2dataloader(dataset)
optimizer = optim.Adam(model.parameters(),
lr = opt.base_lr,
weight_decay = 0.,
amsgrad = True)
print('num_train_data:{}'.format(len(dataset.data)))
crit = nn.CTCLoss()
tic = time.time()
train_wer = []
for epoch in range(opt.max_epoch):
for (i_iter, input) in enumerate(loader):
model.train()
vid = input.get('vid').cuda()
txt = input.get('txt').cuda()
vid_len = input.get('vid_len').cuda()
txt_len = input.get('txt_len').cuda()
optimizer.zero_grad()
y = net(vid)
loss = crit(y.transpose(0, 1).log_softmax(-1), txt, vid_len.view(-1), txt_len.view(-1))
loss.backward()
if(opt.is_optimize):
optimizer.step()
tot_iter = i_iter + epoch*len(loader)
pred_txt = ctc_decode(y)
truth_txt = [MyDataset.arr2txt(txt[_], start=1) for _ in range(txt.size(0))]
train_wer.extend(MyDataset.wer(pred_txt, truth_txt))
if(tot_iter % opt.display == 0):
v = 1.0*(time.time()-tic)/(tot_iter+1)
eta = (len(loader)-i_iter)*v/3600.0
writer.add_scalar('train loss', loss, tot_iter)
writer.add_scalar('train wer', np.array(train_wer).mean(), tot_iter)
print(''.join(101*'-'))
print('{:<50}|{:>50}'.format('predict', 'truth'))
print(''.join(101*'-'))
for (predict, truth) in list(zip(pred_txt, truth_txt))[:3]:
print('{:<50}|{:>50}'.format(predict, truth))
print(''.join(101*'-'))
print('epoch={},tot_iter={},eta={},loss={},train_wer={}'.format(epoch, tot_iter, eta, loss, np.array(train_wer).mean()))
print(''.join(101*'-'))
if(tot_iter % opt.test_step == 0):
(loss, wer, cer) = test(model, net)
print('i_iter={},lr={},loss={},wer={},cer={}'
.format(tot_iter,show_lr(optimizer),loss,wer,cer))
writer.add_scalar('val loss', loss, tot_iter)
writer.add_scalar('wer', wer, tot_iter)
writer.add_scalar('cer', cer, tot_iter)
savename = '{}_loss_{}_wer_{}_cer_{}.pt'.format(opt.save_prefix, loss, wer, cer)
(path, name) = os.path.split(savename)
if(not os.path.exists(path)): os.makedirs(path)
torch.save(model.state_dict(), savename)
if(not opt.is_optimize):
exit()
if(__name__ == '__main__'):
print("Loading options...")
model = LipNet()
model = model.cuda()
net = nn.DataParallel(model).cuda()
if(hasattr(opt, 'weights')):
pretrained_dict = torch.load(opt.weights)
model_dict = model.state_dict()
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict.keys() and v.size() == model_dict[k].size()}
missed_params = [k for k, v in model_dict.items() if not k in pretrained_dict.keys()]
print('loaded params/tot params:{}/{}'.format(len(pretrained_dict),len(model_dict)))
print('miss matched params:{}'.format(missed_params))
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
torch.manual_seed(opt.random_seed)
torch.cuda.manual_seed_all(opt.random_seed)
train(model, net)