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train_ocr_crnn.py
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train_ocr_crnn.py
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'''
Created on Sep 29, 2017
@author: Michal.Busta at gmail.com
'''
import numpy as np
import torch.nn.functional as F
import os
import torch
import net_utils
import argparse
import time
import ocr_gen
import torch.nn as nn
from models_crnn import ModelResNetSep_crnn
from ocr_test_utils import print_seq_ext
from utils import E2Ecollate,E2Edataset,alignCollate,ocrDataset
from torchvision import transforms
from net_eval import strLabelConverter,eval_ocr_crnn
import matplotlib.pyplot as plt
device = 'cuda'
f = open('codec.txt', 'r')
codec = f.readlines()[0]
f.close()
print(len(codec))
base_lr = 0.001
lr_decay = 0.99
momentum = 0.9
weight_decay = 0.0005
batch_per_epoch = 1000
disp_interval = 200
def main(opts):
train_loss = 0
train_loss_lr = 0
cnt = 1
cntt = 0
time_total = 0
now = time.time()
converter = strLabelConverter(codec)
model_name = 'E2E-MLT'
net = ModelResNetSep_crnn(attention=True, multi_scale=True, num_classes=400, fixed_height=opts.norm_height,
net='densenet', )
ctc_loss = nn.CTCLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=base_lr, weight_decay=weight_decay)
# scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,factor=0.5 ,patience=5,verbose=True)
scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.00007, max_lr=0.0003, step_size_up=3000,
cycle_momentum=False)
step_start = 0
if opts.cuda:
net.to(device)
ctc_loss.to(device)
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)
else:
learning_rate = base_lr
for param_group in optimizer.param_groups:
param_group['lr'] = base_lr
learning_rate = param_group['lr']
print(param_group['lr'])
step_start = 0
net.train()
# data_generator = ocr_gen.get_batch(num_workers=opts.num_readers,
# batch_size=opts.batch_size,
# train_list=opts.train_list, in_train=True, norm_height=opts.norm_height, rgb = True)
data_dataset = ocrDataset(root=opts.train_list, norm_height=opts.norm_height , in_train=True)
data_generator1 = torch.utils.data.DataLoader(data_dataset, batch_size=opts.batch_size, shuffle=True,
collate_fn=alignCollate())
val_dataset = ocrDataset(root=opts.valid_list, norm_height=opts.norm_height , in_train=False)
val_generator1 = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=False,
collate_fn=alignCollate())
for step in range(step_start, 300000):
# images, labels, label_length = next(data_generator)
# im_data = net_utils.np_to_variable(images, is_cuda=opts.cuda).permute(0, 3, 1, 2)
try:
images, label = next(dataloader_iterator)
except:
dataloader_iterator = iter(data_generator1)
images, label = next(dataloader_iterator)
labels, label_length = converter.encode(label)
im_data = images.to(device)
labels_pred = net.forward_ocr(im_data)
# backward
probs_sizes = torch.IntTensor( [(labels_pred.size()[0])] * (labels_pred.size()[1]) )
label_sizes = torch.IntTensor( torch.from_numpy(np.array(label_length)).int() )
labels = torch.IntTensor( torch.from_numpy(np.array(labels)).int() )
loss = ctc_loss(labels_pred, labels, probs_sizes, label_sizes) / im_data.size(0) # change 1.9.
optimizer.zero_grad()
loss.backward()
clipping_value = 1.0
torch.nn.utils.clip_grad_norm_(net.parameters(),clipping_value)
if not (torch.isnan(loss) or torch.isinf(loss)):
optimizer.step()
scheduler.step()
train_loss += loss.data.cpu().numpy() #net.bbox_loss.data.cpu().numpy()[0]
# train_loss += loss.data.cpu().numpy()[0] #net.bbox_loss.data.cpu().numpy()[0]
cnt += 1
if opts.debug:
dbg = labels_pred.permute(1, 2, 0).data.cpu().numpy()
ctc_f = dbg.swapaxes(1, 2)
labels = ctc_f.argmax(2)
det_text, conf, dec_s,_ = print_seq_ext(labels[0, :], codec)
print('{0} \t'.format(det_text))
if step % disp_interval == 0:
for param_group in optimizer.param_groups:
learning_rate = param_group['lr']
train_loss /= cnt
train_loss_lr += train_loss
cntt += 1
time_now = time.time() - now
time_total += time_now
now = time.time()
save_log = os.path.join(opts.save_path, 'loss_ocr.txt')
# f = open('content/drive/My_Drive/DATA_OCR/backup/ca ca/loss.txt','a')
f = open(save_log, 'a')
f.write(
'epoch %d[%d], loss_ctc: %.3f,time: %.2f s, lr: %.5f, cnt: %d\n' % (
step / batch_per_epoch, step, train_loss, time_now,learning_rate, cnt))
f.close()
print('epoch %d[%d], loss_ctc: %.3f,time: %.2f s, lr: %.5f, cnt: %d\n' % (
step / batch_per_epoch, step, train_loss, time_now,learning_rate, cnt))
train_loss = 0
cnt = 1
if step > step_start and (step % batch_per_epoch == 0):
for param_group in optimizer.param_groups:
learning_rate = param_group['lr']
# print(learning_rate)
save_name = os.path.join(opts.save_path, 'OCR_{}_{}.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)
# scheduler.step(train_loss_lr / cntt)
# evaluate
CER, WER = eval_ocr_crnn(val_generator1, net)
# scheduler.step(CER)
f = open(save_log, 'a')
f.write('time epoch [%d]: %.2f s, loss_total: %.3f, CER = %f, WER = %f' % (step / batch_per_epoch, time_total, train_loss_lr / cntt, CER, WER))
f.close()
print('time epoch [%d]: %.2f s, loss_total: %.3f, CER = %f, WER = %f \n' % (step / batch_per_epoch, time_total, train_loss_lr / cntt, CER, WER))
print('save model: {}'.format(save_name))
net.train()
time_total = 0
cntt = 0
train_loss_lr = 0
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-train_list', default='/content/data_MLT_crop/gt_vi.txt')
parser.add_argument('-valid_list', default='/content/data_MLT_crop/gt_vi_eval.txt')
parser.add_argument('-save_path', default='/content/drive/My Drive/DATA_OCR/ocr_lstm')
parser.add_argument('-model', default='E2E-MLT_69000.h5')
parser.add_argument('-debug', type=int, default=0)
parser.add_argument('-batch_size', type=int, default=8)
parser.add_argument('-num_readers', type=int, default=2)
parser.add_argument('-cuda', type=bool, default=True)
parser.add_argument('-norm_height', type=int, default=64)
args = parser.parse_args()
main(args)