m.bias.data.fill_(0)
crnn = crnn.CRNN(opt.imgH, nc, nclass, opt.nh)
crnn.apply(weights_init)
if opt.pretrained != '':
print('loading pretrained model from %s' % opt.pretrained)
crnn.load_state_dict(torch.load(opt.pretrained))
print(crnn)
image = torch.FloatTensor(opt.batchSize, 3, opt.imgH, opt.imgH)
text = torch.IntTensor(opt.batchSize * 5)
length = torch.IntTensor(opt.batchSize)
if opt.cuda:
crnn.cuda()
crnn = torch.nn.DataParallel(crnn, device_ids=range(opt.ngpu))
image = image.cuda()
criterion = criterion.cuda()
image = Variable(image)
text = Variable(text)
length = Variable(length)
# loss averager
loss_avg = utils.averager()
# setup optimizer
if opt.adam:
optimizer = optim.Adam(crnn.parameters(),
lr=opt.lr,
# new_state_dict[name] = v
# for name, module in crnn.named_children():
# if name == 'cnn':
# module_dict = module.state_dict()
# new_state_dict = {k: v for k, v in new_state_dict.items() if k in module_dict}
# module_dict.update(new_state_dict)
# module.load_state_dict(module_dict)
image = torch.FloatTensor(opt.batchSize, 3, opt.imgH, opt.imgH)
text = torch.IntTensor(opt.batchSize * 5)
length = torch.IntTensor(opt.batchSize)
if opt.cuda:
crnn = crnn.cuda(id_gpu)
image = image.cuda(id_gpu)
criterion = criterion.cuda(id_gpu)
image = Variable(image)
text = Variable(text)
length = Variable(length)
# loss averager
loss_avg = util.averager()
# setup optimizer
if opt.adam:
optimizer = optim.Adam(crnn.parameters(),
lr=opt.lr,
betas=(opt.beta1, 0.999))
try:
with io.open(args.alphabet, 'r', encoding='utf-8') as myfile:
alphabet = myfile.read().split()
alphabet.append(u' ')
alphabet = ''.join(alphabet)
converter = utils.strLabelConverter(alphabet, attention=False)
nclass = converter.num_classes
crnn = models.crnn.CRNN(imgH, nc, nclass, num_hidden)
crnn.apply(weights_init)
if args.cuda:
crnn = crnn.cuda()
crnn = torch.nn.DataParallel(crnn)
logger.info("Loading pretrained model from {}".format(args.ckpt))
file_weights = torch.load(args.ckpt)
crnn.load_state_dict(file_weights)
print("The oracle network:",
crnn) # Logging can't print torch models :thinking:
image = Image.open(args.input[0]).convert('L')
attack = CarliniAttack(crnn, alphabet, image.size, args.target,
file_weights)
attack.execute(args.input, args.out)
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
crnn = crnn.CRNN(opt.imgH, nc, nclass, nh, ngpu)
crnn.apply(weights_init)
if opt.crnn != '':
print('loading pretrained model from %s' % opt.crnn)
crnn.load_state_dict(torch.load(opt.crnn))
print(crnn)
image = torch.FloatTensor(opt.batchSize, 3, opt.imgH, opt.imgH)
text = torch.IntTensor(opt.batchSize * 5)
length = torch.IntTensor(opt.batchSize)
if opt.cuda:
crnn.cuda()
image = image.cuda()
criterion = criterion.cuda()
image = Variable(image)
text = Variable(text)
length = Variable(length)
# loss averager
loss_avg = utils.averager()
# setup optimizer
if opt.adam:
optimizer = optim.Adam(crnn.parameters(), lr=opt.lr,
betas=(opt.beta1, 0.999))
elif opt.adadelta:
