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CRNN导出 (#1159) 

* 识别模型导出

* 识别模型inference
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zhoujun 2020-11-12 05:42:32 -06:00 committed by GitHub
parent 882ad39580
commit c708041e13
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3 changed files with 37 additions and 62 deletions
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76
tools/infer/predict_rec.py
View File

@ -26,34 +26,27 @@ import time
import paddle.fluid as fluid
import tools.infer.utility as utility
from ppocr.utils.utility import initial_logger
logger = initial_logger()
from ppocr.postprocess import build_post_process
from ppocr.utils.logging import get_logger
from ppocr.utils.utility import get_image_file_list, check_and_read_gif
from ppocr.utils.character import CharacterOps
class TextRecognizer(object):
def __init__(self, args):
self.predictor, self.input_tensor, self.output_tensors =\
utility.create_predictor(args, mode="rec")
self.rec_image_shape = [int(v) for v in args.rec_image_shape.split(",")]
self.character_type = args.rec_char_type
self.rec_batch_num = args.rec_batch_num
self.rec_algorithm = args.rec_algorithm
self.use_zero_copy_run = args.use_zero_copy_run
char_ops_params = {
postprocess_params = {
'name': 'CTCLabelDecode',
"character_type": args.rec_char_type,
"character_dict_path": args.rec_char_dict_path,
"use_space_char": args.use_space_char,
"max_text_length": args.max_text_length
"use_space_char": args.use_space_char
}
if self.rec_algorithm != "RARE":
char_ops_params['loss_type'] = 'ctc'
self.loss_type = 'ctc'
else:
char_ops_params['loss_type'] = 'attention'
self.loss_type = 'attention'
self.char_ops = CharacterOps(char_ops_params)
self.postprocess_op = build_post_process(postprocess_params)
self.predictor, self.input_tensor, self.output_tensors = \
utility.create_predictor(args, 'rec', logger)
def resize_norm_img(self, img, max_wh_ratio):
imgC, imgH, imgW = self.rec_image_shape
@ -112,48 +105,14 @@ class TextRecognizer(object):
else:
norm_img_batch = fluid.core.PaddleTensor(norm_img_batch)
self.predictor.run([norm_img_batch])
if self.loss_type == "ctc":
rec_idx_batch = self.output_tensors[0].copy_to_cpu()
rec_idx_lod = self.output_tensors[0].lod()[0]
predict_batch = self.output_tensors[1].copy_to_cpu()
predict_lod = self.output_tensors[1].lod()[0]
elapse = time.time() - starttime
predict_time += elapse
for rno in range(len(rec_idx_lod) - 1):
beg = rec_idx_lod[rno]
end = rec_idx_lod[rno + 1]
rec_idx_tmp = rec_idx_batch[beg:end, 0]
preds_text = self.char_ops.decode(rec_idx_tmp)
beg = predict_lod[rno]
end = predict_lod[rno + 1]
probs = predict_batch[beg:end, :]
ind = np.argmax(probs, axis=1)
blank = probs.shape[1]
valid_ind = np.where(ind != (blank - 1))[0]
if len(valid_ind) == 0:
continue
score = np.mean(probs[valid_ind, ind[valid_ind]])
# rec_res.append([preds_text, score])
rec_res[indices[beg_img_no + rno]] = [preds_text, score]
else:
rec_idx_batch = self.output_tensors[0].copy_to_cpu()
predict_batch = self.output_tensors[1].copy_to_cpu()
elapse = time.time() - starttime
predict_time += elapse
for rno in range(len(rec_idx_batch)):
end_pos = np.where(rec_idx_batch[rno, :] == 1)[0]
if len(end_pos) <= 1:
preds = rec_idx_batch[rno, 1:]
score = np.mean(predict_batch[rno, 1:])
else:
preds = rec_idx_batch[rno, 1:end_pos[1]]
score = np.mean(predict_batch[rno, 1:end_pos[1]])
preds_text = self.char_ops.decode(preds)
# rec_res.append([preds_text, score])
rec_res[indices[beg_img_no + rno]] = [preds_text, score]
return rec_res, predict_time
outputs = []
for output_tensor in self.output_tensors:
output = output_tensor.copy_to_cpu()
outputs.append(output)
preds = outputs[0]
rec_res = self.postprocess_op(preds)
elapse = time.time() - starttime
return rec_res, elapse
def main(args):
@ -183,9 +142,10 @@ def main(args):
exit()
for ino in range(len(img_list)):
print("Predicts of %s:%s" % (valid_image_file_list[ino], rec_res[ino]))
print("Total predict time for %d images:%.3f" %
print("Total predict time for %d images, cost: %.3f" %
(len(img_list), predict_time))
if __name__ == "__main__":
logger = get_logger()
main(utility.parse_args())

16
tools/program.py
View File

@ -323,6 +323,20 @@ def eval(model, valid_dataloader, post_process_class, eval_class):
return metirc
def save_inference_mode(model, config, logger):
model.eval()
save_path = '{}/infer/{}'.format(config['Global']['save_model_dir'],
config['Architecture']['model_type'])
if config['Architecture']['model_type'] == 'rec':
input_shape = [None, 3, 32, None]
jit_model = paddle.jit.to_static(
model, input_spec=[paddle.static.InputSpec(input_shape)])
paddle.jit.save(jit_model, save_path)
logger.info('inference model save to {}'.format(save_path))
model.train()
def preprocess():
FLAGS = ArgsParser().parse_args()
config = load_config(FLAGS.config)
@ -334,7 +348,7 @@ def preprocess():
alg = config['Architecture']['algorithm']
assert alg in [
'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN'
'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS'
]
device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'

7
tools/train.py
View File

@ -89,6 +89,7 @@ def main(config, device, logger, vdl_writer):
program.train(config, train_dataloader, valid_dataloader, device, model,
loss_class, optimizer, lr_scheduler, post_process_class,
eval_class, pre_best_model_dict, logger, vdl_writer)
program.save_inference_mode(model, config, logger)
def test_reader(config, device, logger):
@ -102,8 +103,8 @@ def test_reader(config, device, logger):
if count % 1 == 0:
batch_time = time.time() - starttime
starttime = time.time()
logger.info("reader: {}, {}, {}".format(count,
len(data), batch_time))
logger.info("reader: {}, {}, {}".format(
count, len(data[0]), batch_time))
except Exception as e:
logger.info(e)
logger.info("finish reader: {}, Success!".format(count))
@ -112,4 +113,4 @@ def test_reader(config, device, logger):
if __name__ == '__main__':
config, device, logger, vdl_writer = program.preprocess()
main(config, device, logger, vdl_writer)
# test_reader(config, device, logger)
# test_reader(config, device, logger)