def test(model, net):
#transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
# composed = transforms.Compose([Rescale(114), transforms.CenterCrop((112, 224),ToTensor(),ColorNormalize()])
with torch.no_grad():
# 引入测试集数据
dataset = MyDataset(opt.video_path,
opt.val_list,
max_frame_len=opt.max_frame_len,
trainflag=False)
# 输出测试集大小
print('num_test_data:{}'.format(len(dataset.data)))
# ？
model.eval()
# 初始化数据加载器
loader = dataset2dataloader(dataset, shuffle=False)
loss_list = []
cer = []
crit = nn.CTCLoss(blank=0, reduction='mean')
tic = time.time()
for (i_iter, input) in enumerate(loader):
# 存储图片id集、标签集
# 记录图片数和标签数
video = input.get('video').to(device)
txt = input.get('txt').to(device)
vid_len = input.get('vid_len').to(device)
txt_len = input.get('txt_len').to(device)
y = net(video)
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[_]) for _ in range(txt.size(0))]
cer.extend(MyDataset.cer(pred_txt, truth_txt))
if (i_iter % opt.display == 0):
# 剩余时间
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={},cer={}'.format(
i_iter, eta,
np.array(cer).mean()))
print(''.join(101 * '-'))
return (np.array(loss_list).mean(), np.array(cer).mean())
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()
def train(model, net):
#composed = transforms.Compose([Rescale(115), RandomCrop((112, 224)),ToTensor(),ColorNormalize()])
dataset = MyDataset(opt.video_path,
opt.train_list,
max_frame_len=opt.max_frame_len)
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(blank=0, reduction='mean')
tic = time.time()
train_cer = []
for epoch in range(opt.max_epoch):
for (i_iter, input) in enumerate(loader):
model.train()
video = input.get('video').to(device)
txt = input.get('txt').to(device)
vid_len = input.get('vid_len').to(device)
txt_len = input.get('txt_len').to(device)
optimizer.zero_grad()
y = net(video)
y_trans_log_soft = y.transpose(0, 1).log_softmax(-1)
loss = crit(y_trans_log_soft, 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[_]) for _ in range(txt.size(0))]
train_cer.extend(MyDataset.cer(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 cer',
np.array(train_cer).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_cer={}'.format(
epoch, tot_iter, eta, loss,
np.array(train_cer).mean()))
print(''.join(101 * '-'))
if (tot_iter % opt.test_step == 0):
(loss, cer) = test(model, net)
print('i_iter={},lr={},loss={},cer={}'.format(
tot_iter, show_lr(optimizer), loss, cer))
writer.add_scalar('val loss', loss, tot_iter)
writer.add_scalar('cer', cer, tot_iter)
writer.add_graph(model, video)
savename = '{}_loss_{}_cer_{}.pt'.format(
opt.save_prefix, loss, 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()
def eval(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)
wer = []
cer = []
wla = []
total_sentences = 0.0
correct_sentences = 0.0
for (i_iter, input) in enumerate(loader):
vid = input.get('vid').cuda()
txt = input.get('txt').cuda()
y = net(vid)
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))
wla.extend(MyDataset.wla(pred_txt, truth_txt))
batch_correct_sentences, batch_total_sentences = MyDataset.sentences(
pred_txt, truth_txt)
correct_sentences = correct_sentences + batch_correct_sentences
total_sentences = total_sentences + batch_total_sentences
sla = correct_sentences / total_sentences
if (i_iter % opt.display == 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={}, wer={}, cer={}, wla={} , sla={}'.format(
i_iter,
np.array(wer).mean(),
np.array(cer).mean(),
np.array(wla).mean(), sla))
print(''.join(101 * '-'))
writer.add_scalar('wer', np.array(wer).mean(), i_iter)
writer.add_scalar('cer', np.array(cer).mean(), i_iter)
writer.add_scalar('wla', np.array(wla).mean(), i_iter)
writer.add_scalar('bla', sla, i_iter)
return np.array(wer).mean(), np.array(cer).mean(), np.array(
wla).mean(), sla
