def trainBatch(net, criterion, optimizer):
data = train_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
crnn.zero_grad()
cost.backward()
optimizer.step()
return cost
def main(opts):
alphabet = '0123456789.'
nclass = len(alphabet) + 1
model_name = 'crnn'
net = CRNN(nclass)
print("Using {0}".format(model_name))
if opts.cuda:
net.cuda()
learning_rate = opts.base_lr
optimizer = torch.optim.Adam(net.parameters(), lr=opts.base_lr, weight_decay=weight_decay)
if os.path.exists(opts.model):
print('loading model from %s' % args.model)
step_start, learning_rate = net_utils.load_net(args.model, net, optimizer)
## 数据集
converter = strLabelConverter(alphabet)
dataset = ImgDataset(
root='/home/yangna/deepblue/OCR/mech_demo2/dataset/imgs/image',
csv_root='/home/yangna/deepblue/OCR/mech_demo2/dataset/imgs/train_list.txt',
transform=None,
target_transform=converter.encode
)
ocrdataloader = torch.utils.data.DataLoader(
dataset, batch_size=opts.batch_size, shuffle=True, collate_fn=own_collate
)
step_start = 0
net.train()
converter = strLabelConverter(alphabet)
ctc_loss = CTCLoss()
for step in range(step_start, opts.max_iters):
try:
data = next(data_iter)
except:
data_iter = iter(ocrdataloader)
data = next(data_iter)
im_data, gt_boxes, text = data
im_data = im_data.cuda()
try:
loss= process_crnn(im_data, gt_boxes, text, net, ctc_loss, converter, training=True)
net.zero_grad()
optimizer.zero_grad()
loss.backward()
optimizer.step()
except:
import sys, traceback
traceback.print_exc(file=sys.stdout)
pass
if step % disp_interval == 0:
try:
print('step:%d || loss %.4f' % (step, loss))
except:
import sys, traceback
traceback.print_exc(file=sys.stdout)
pass
if step > step_start and (step % batch_per_epoch == 0):
save_name = os.path.join(opts.save_path, '{}_{}.h5'.format(model_name, step))
state = {'step': step,
'learning_rate': learning_rate,
'state_dict': net.state_dict(),
'optimizer': optimizer.state_dict()}
torch.save(state, save_name)
print('save model: {}'.format(save_name))
