conf, preds = preds.max(2)
preds = preds.transpose(1, 0).contiguous().view(-1)
preds_size = Variable(torch.IntTensor([preds.size(0)]))
# raw_pred = converter.decode(preds.data, preds_size.data, raw=True)
sim_pred = converter.decode(preds.data, preds_size.data, raw=False)
return sim_pred
if __name__ == '__main__':
model_path = './weights/CRNN-0627-crop_48_901.pth'
gpu = True
if not torch.cuda.is_available():
gpu = False
model = crnn.CRNN(config.imgH, 1, len(alphabet) + 1, 256)
if gpu:
model = model.cuda()
print('loading pretrained model from %s' % model_path)
if gpu:
model.load_state_dict(torch.load(model_path))
else:
model.load_state_dict(
torch.load(model_path, map_location=lambda storage, loc: storage))
if len(sys.argv) > 1 and 'train' in sys.argv[1]:
infofile = 'data_set/infofile_updated_0627_train.txt'
print(val_model(infofile, model, gpu, '0627_train.log'))
elif len(sys.argv) > 1 and 'gen' in sys.argv[1]:
infofile = 'data_set/infofile_0627_gen_test.txt'
criterion = CTCLoss(reduction='sum')
# criterion = CTCLoss()
best_acc = 0.9
# custom weights initialization called on crnn
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
crnn = crnn.CRNN(config.imgH, config.nc, config.nclass, config.nh)
if config.pretrained_model != '' and os.path.exists(config.pretrained_model):
print('loading pretrained model from %s' % config.pretrained_model)
crnn.load_state_dict(torch.load(config.pretrained_model))
else:
crnn.apply(weights_init)
print(crnn)
# image = torch.FloatTensor(config.batchSize, 3, config.imgH, config.imgH)
# text = torch.IntTensor(config.batchSize * 5)
# length = torch.IntTensor(config.batchSize)
device = torch.device('cpu')
if config.cuda:
crnn.cuda()
# crnn = torch.nn.DataParallel(crnn, device_ids=range(opt.ngpu))
import core.utils
import core.keys
import core.dataset
import core.crnn
from PIL import Image
from torch.autograd import Variable
gpu = True
if not torch.cuda.is_available():
gpu = False
model_path = 'CRNN-0618-10w_21_990.pth'
alphabet = keys.alphabet
imgH = config.imgH
imgW = config.imgW
model = crnn.CRNN(imgH, 1, len(alphabet) + 1, 256)
print('loading pretrained model from %s, alphabet.len=%d' % (model_path, len(alphabet)))
if gpu:
model = model.cuda()
model.load_state_dict( torch.load( model_path ) )
else:
model.load_state_dict(torch.load(model_path,map_location=lambda storage,loc:storage))
converter = utils.strLabelConverter(alphabet)
transformer = mydataset.resizeNormalize((imgW, imgH),is_test=True)
def recognize_downline(img,crnn_model=model):
img = cv2.cvtColor( img, cv2.COLOR_BGR2RGB )
image = Image.fromarray(np.uint8(img)).convert('L')