def val(net, data_loader, criterion):
print('Start val')
for p in net.parameters():
p.requires_grad = False
net.eval()
val_iter = iter(data_loader)
val_loss_avg = utils.averager()
val_cer_avg = utils.averager()
max_iter = len(data_loader)
print('Total files:', num_files, ', Number of iters:', max_iter)
with torch.no_grad():
for i in range(max_iter):
if i % 10 == 0:
print('iter', i)
data = val_iter.next()
cpu_images, cpu_texts, imgpath = data
batch_sz = cpu_images.size(0)
utils.loadData(image, cpu_images)
t, l = converter.encode(cpu_texts)
utils.loadData(text, t)
utils.loadData(length, l)
preds = net(image)
preds_size = Variable(torch.IntTensor([preds.size(0)] * batch_sz))
cost = criterion(preds, text, preds_size, length) / batch_sz
cost = cost.detach().item()
val_loss_avg.add(cost)
_, preds = preds.max(2)
preds = preds.transpose(1, 0).contiguous().view(-1)
sim_preds = converter.decode(preds.data, preds_size.data, raw=False)
if batch_size == 1:
sim_preds = [sim_preds]
cer_loss = utils.cer_loss(sim_preds, cpu_texts)
if debug and cer_loss[0] > 0 and batch_size == 1:
print(i,'\nimg path', imgpath)
print('sim pred', sim_preds)
print('cpu text', cpu_texts)
print('cer', cer_loss)
inv_tensor = inv_normalize(cpu_images[0])
cv_img = inv_tensor.permute(1, 2, 0).numpy()
cv_img_convert = cv2.cvtColor(cv_img, cv2.COLOR_BGR2RGB)
cv2.imshow('image data', cv_img_convert)
ch = cv2.waitKey(0)
if ch == 27:
break
val_cer_avg.add(cer_loss)
raw_preds = converter.decode(preds.data, preds_size.data, raw=True)[:opt.n_test_disp]
for raw_pred, pred, gt in zip(raw_preds, sim_preds, cpu_texts):
print('\nraw: %-30s \nsim: %-30s\n gt: %-30s' % (raw_pred, pred, gt))
test_loss = val_loss_avg.val()
test_cer = val_cer_avg.val()
print('\nTest loss: %f - test cer %f' % (test_loss, test_cer))
return test_loss, test_cer
def val(net, data_loader, criterion, max_iter=1000):
print('Start val')
for p in crnn.parameters():
p.requires_grad = False
net.eval()
val_iter = iter(data_loader)
val_loss_avg = utils.averager()
val_cer_avg = utils.averager()
max_iter = min(max_iter, len(data_loader))
with torch.no_grad():
for i in range(max_iter):
data = val_iter.next()
cpu_images, cpu_texts = data
batch_size = cpu_images.size(0)
utils.loadData(image, cpu_images)
t, l = converter.encode(cpu_texts)
utils.loadData(text, t)
utils.loadData(length, l)
preds = crnn(image)
preds_size = Variable(torch.IntTensor([preds.size(0)] *
batch_size))
cost = criterion(preds, text, preds_size, length) / batch_size
cost = cost.detach().item()
val_loss_avg.add(cost)
_, preds = preds.max(2)
preds = preds.transpose(1, 0).contiguous().view(-1)
sim_preds = converter.decode(preds.data,
preds_size.data,
raw=False)
cer_loss = utils.cer_loss(sim_preds, cpu_texts)
val_cer_avg.add(cer_loss)
raw_preds = converter.decode(preds.data, preds_size.data,
raw=True)[:opt.n_test_disp]
for raw_pred, pred, gt in zip(raw_preds, sim_preds, cpu_texts):
print('%-30s => %-30s, gt: %-30s' % (raw_pred, pred, gt))
print('Test loss: %f - cer loss %f' %
(val_loss_avg.val(), val_cer_avg.val()))
def trainBatch(net, data, criterion, optimizer):
cpu_images, cpu_texts = data
batch_size = cpu_images.size(0)
utils.loadData(image, cpu_images)
t, l = converter.encode(cpu_texts)
utils.loadData(text, t)
utils.loadData(length, l)
preds = crnn(image)
preds_size = Variable(torch.IntTensor([preds.size(0)] * batch_size))
cost = criterion(preds, text, preds_size, length) / batch_size
crnn.zero_grad()
cost.backward()
optimizer.step()
cost = cost.detach().item()
_, preds = preds.max(2)
preds = preds.transpose(1, 0).contiguous().view(-1)
sim_preds = converter.decode(preds.data, preds_size.data, raw=False)
cer_loss = utils.cer_loss(sim_preds, cpu_texts)
return cost, cer_loss, len(cpu_images)
def predict(dir, batch_sz, max_iter=10000):
print('Init CRNN classifier')
image = torch.FloatTensor(batch_sz, 3, imgH, imgH)
model = crnn.CRNN64(imgH, nc, nclass, 256)
#model = crnn128.CRNN128(imgH, nc, nclass, 256)
if gpu != None:
print('Use GPU', gpu)
os.environ['CUDA_VISIBLE_DEVICES'] = gpu
model = model.cuda()
image = image.cuda()
print('loading pretrained model from %s' % pretrained)
model.load_state_dict(torch.load(pretrained, map_location='cpu'))
converter = strLabelConverter(alphabet, ignore_case=False)
val_dataset = ImageFileLoader(dir,
flist=test_list,
label=label,
transform=transform_test)
num_files = len(val_dataset)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=batch_sz,
num_workers=workers,
shuffle=False)
image = Variable(image)
# for p in crnn.parameters():
# p.requires_grad = False
model.eval()
print('Start predict in folder', img_dir)
val_iter = iter(val_loader)
max_iter = min(max_iter, len(val_loader))
print('Number of samples', num_files)
begin = time.time()
with torch.no_grad():
for i in range(max_iter):
data = val_iter.next()
cpu_images, cpu_texts, img_paths = data
batch_size = cpu_images.size(0)
utils.loadData(image, cpu_images)
preds = model(image)
preds = preds.squeeze(1)
raw_pred, sim_pred, sent_prob = decode(converter, preds)
# preds_size = Variable(torch.IntTensor([preds.size(0)] * batch_size))
# _, preds = preds.max(2)
# preds = preds.transpose(1, 0).contiguous().view(-1)
# sim_pred = converter.decode(preds.data, preds_size.data, raw=False)
# #print(sim_pred)
# raw_pred = converter.decode(preds.data, preds_size.data, raw=True)
#print(cpu_texts[0])
if debug:
print('\n', raw_pred)
print('\n', round(sent_prob, 3), sim_pred, img_paths)
inv_tensor = inv_normalize(cpu_images[0])
cv_img = inv_tensor.permute(1, 2, 0).numpy()
cv_img_convert = cv2.cvtColor(cv_img, cv2.COLOR_BGR2RGB)
cv2.imshow('image data', cv_img_convert)
ch = cv2.waitKey(0)
if ch == 27:
break
end = time.time()
processing_time = end - begin
print('Processing time:', processing_time)
print('Speed:', num_files / processing_time, 'fps')