def main():
config = program.load_config(FLAGS.config)
program.merge_config(FLAGS.opt)
logger.info(config)
# check if set use_gpu=True in paddlepaddle cpu version
use_gpu = config['Global']['use_gpu']
program.check_gpu(True)
alg = config['Global']['algorithm']
assert alg in ['EAST', 'DB', 'Rosetta', 'CRNN', 'STARNet', 'RARE']
if alg in ['Rosetta', 'CRNN', 'STARNet', 'RARE']:
config['Global']['char_ops'] = CharacterOps(config['Global'])
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
startup_prog = fluid.Program()
eval_program = fluid.Program()
feeded_var_names, target_vars, fetches_var_name = program.build_export(
config, eval_program, startup_prog)
eval_program = eval_program.clone(for_test=True)
exe = fluid.Executor(place)
exe.run(startup_prog)
init_model(config, eval_program, exe)
fluid.io.save_inference_model(dirname="./output/",
feeded_var_names=feeded_var_names,
main_program=eval_program,
target_vars=target_vars,
executor=exe,
model_filename='model',
params_filename='params')
print("save success, output_name_list:", fetches_var_name)
def main():
config = program.load_config(FLAGS.config)
program.merge_config(FLAGS.opt)
logger.info(config)
# check if set use_gpu=True in paddlepaddle cpu version
use_gpu = config['Global']['use_gpu']
program.check_gpu(use_gpu)
alg = config['Global']['algorithm']
assert alg in ['EAST', 'DB', 'Rosetta', 'CRNN', 'STARNet', 'RARE']
if alg in ['Rosetta', 'CRNN', 'STARNet', 'RARE']:
config['Global']['char_ops'] = CharacterOps(config['Global'])
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
startup_prog = fluid.Program()
eval_program = fluid.Program()
eval_build_outputs = program.build(config,
eval_program,
startup_prog,
mode='test')
eval_fetch_name_list = eval_build_outputs[1]
eval_fetch_varname_list = eval_build_outputs[2]
eval_program = eval_program.clone(for_test=True)
exe = fluid.Executor(place)
exe.run(startup_prog)
init_model(config, eval_program, exe)
if alg in ['EAST', 'DB']:
eval_reader = reader_main(config=config, mode="eval")
eval_info_dict = {'program':eval_program,\
'reader':eval_reader,\
'fetch_name_list':eval_fetch_name_list,\
'fetch_varname_list':eval_fetch_varname_list}
metrics = eval_det_run(exe, config, eval_info_dict, "eval")
logger.info("Eval result: {}".format(metrics))
else:
reader_type = config['Global']['reader_yml']
if "benchmark" not in reader_type:
eval_reader = reader_main(config=config, mode="eval")
eval_info_dict = {'program': eval_program, \
'reader': eval_reader, \
'fetch_name_list': eval_fetch_name_list, \
'fetch_varname_list': eval_fetch_varname_list}
metrics = eval_rec_run(exe, config, eval_info_dict, "eval")
logger.info("Eval result: {}".format(metrics))
else:
eval_info_dict = {'program':eval_program,\
'fetch_name_list':eval_fetch_name_list,\
'fetch_varname_list':eval_fetch_varname_list}
test_rec_benchmark(exe, config, eval_info_dict)
def load_model():
config = program.load_config('./configs/det/det_r18_vd_db_v1.1.yml')
# check if set use_gpu=True in paddlepaddle cpu version
use_gpu = config['Global']['use_gpu']
program.check_gpu(use_gpu)
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
det_model = create_module(
config['Architecture']['function'])(params=config)
startup_prog = fluid.Program()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
_, eval_outputs = det_model(mode="test")
fetch_name_list = list(eval_outputs.keys())
eval_fetch_list = [eval_outputs[v].name for v in fetch_name_list]
eval_prog = eval_prog.clone(for_test=True)
exe.run(startup_prog)
# load checkpoints
checkpoints = config['Global'].get('checkpoints')
if checkpoints:
path = checkpoints
fluid.load(eval_prog, path, exe)
logger.info("Finish initing model from {}".format(path))
else:
raise Exception("{} not exists!".format(checkpoints))
config_ocr = Cfg.load_config_from_name('vgg_seq2seq')
config_ocr['weights'] = './my_weights/transformer.pth'
config_ocr['cnn']['pretrained'] = False
config_ocr['device'] = 'cpu'
config_ocr['predictor']['beamsearch'] = False
detector = Predictor(config_ocr)
return detector, exe, config, eval_prog, eval_fetch_list
def main():
config = program.load_config(FLAGS.config)
program.merge_config(FLAGS.opt)
print(config)
# check if set use_gpu=True in paddlepaddle cpu version
use_gpu = config['Global']['use_gpu']
program.check_gpu(use_gpu)
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
det_model = create_module(
config['Architecture']['function'])(params=config)
startup_prog = fluid.Program()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
_, eval_outputs = det_model(mode="test")
fetch_name_list = list(eval_outputs.keys())
eval_fetch_list = [eval_outputs[v].name for v in fetch_name_list]
eval_prog = eval_prog.clone(for_test=True)
exe.run(startup_prog)
# load checkpoints
checkpoints = config['Global'].get('checkpoints')
if checkpoints:
path = checkpoints
fluid.load(eval_prog, path, exe)
logger.info("Finish initing model from {}".format(path))
else:
raise Exception("{} not exists!".format(checkpoints))
save_res_path = config['Global']['save_res_path']
if not os.path.exists(os.path.dirname(save_res_path)):
os.makedirs(os.path.dirname(save_res_path))
with open(save_res_path, "wb") as fout:
test_reader = reader_main(config=config, mode='test')
tackling_num = 0
for data in test_reader():
img_num = len(data)
tackling_num = tackling_num + img_num
logger.info("tackling_num:%d", tackling_num)
img_list = []
ratio_list = []
img_name_list = []
for ino in range(img_num):
img_list.append(data[ino][0])
ratio_list.append(data[ino][1])
img_name_list.append(data[ino][2])
img_list = np.concatenate(img_list, axis=0)
outs = exe.run(eval_prog,\
feed={'image': img_list},\
fetch_list=eval_fetch_list)
global_params = config['Global']
postprocess_params = deepcopy(config["PostProcess"])
postprocess_params.update(global_params)
postprocess = create_module(postprocess_params['function'])\
(params=postprocess_params)
if config['Global']['algorithm'] == 'EAST':
dic = {'f_score': outs[0], 'f_geo': outs[1]}
elif config['Global']['algorithm'] == 'DB':
dic = {'maps': outs[0]}
else:
raise Exception("only support algorithm: ['EAST', 'DB']")
dt_boxes_list = postprocess(dic, ratio_list)
for ino in range(img_num):
dt_boxes = dt_boxes_list[ino]
img_name = img_name_list[ino]
dt_boxes_json = []
for box in dt_boxes:
tmp_json = {"transcription": ""}
tmp_json['points'] = box.tolist()
dt_boxes_json.append(tmp_json)
otstr = img_name + "\t" + json.dumps(dt_boxes_json) + "\n"
fout.write(otstr.encode())
src_img = cv2.imread(img_name)
draw_det_res(dt_boxes, config, src_img, img_name)
logger.info("success!")
def main():
config = program.load_config(FLAGS.config)
program.merge_config(FLAGS.opt)
logger.info(config)
# check if set use_gpu=True in paddlepaddle cpu version
use_gpu = config['Global']['use_gpu']
program.check_gpu(True)
alg = config['Global']['algorithm']
assert alg in ['EAST', 'DB', 'Rosetta', 'CRNN', 'STARNet', 'RARE']
if alg in ['Rosetta', 'CRNN', 'STARNet', 'RARE']:
config['Global']['char_ops'] = CharacterOps(config['Global'])
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
startup_program = fluid.Program()
train_program = fluid.Program()
train_build_outputs = program.build(config,
train_program,
startup_program,
mode='train')
train_loader = train_build_outputs[0]
train_fetch_name_list = train_build_outputs[1]
train_fetch_varname_list = train_build_outputs[2]
train_opt_loss_name = train_build_outputs[3]
eval_program = fluid.Program()
eval_build_outputs = program.build(config,
eval_program,
startup_program,
mode='eval')
eval_fetch_name_list = eval_build_outputs[1]
eval_fetch_varname_list = eval_build_outputs[2]
eval_program = eval_program.clone(for_test=True)
train_reader = reader_main(config=config, mode="train")
train_loader.set_sample_list_generator(train_reader, places=place)
eval_reader = reader_main(config=config, mode="eval")
exe = fluid.Executor(place)
exe.run(startup_program)
# compile program for multi-devices
train_compile_program = program.create_multi_devices_program(
train_program, train_opt_loss_name)
init_model(config, train_program, exe)
train_info_dict = {'compile_program':train_compile_program,\
'train_program':train_program,\
'reader':train_loader,\
'fetch_name_list':train_fetch_name_list,\
'fetch_varname_list':train_fetch_varname_list}
eval_info_dict = {'program':eval_program,\
'reader':eval_reader,\
'fetch_name_list':eval_fetch_name_list,\
'fetch_varname_list':eval_fetch_varname_list}
if alg in ['EAST', 'DB']:
program.train_eval_det_run(config, exe, train_info_dict,
eval_info_dict)
else:
program.train_eval_rec_run(config, exe, train_info_dict,
eval_info_dict)
def main():
config = program.load_config(FLAGS.config)
program.merge_config(FLAGS.opt)
logger.info(config)
char_ops = CharacterOps(config['Global'])
config['Global']['char_ops'] = char_ops
# check if set use_gpu=True in paddlepaddle cpu version
use_gpu = config['Global']['use_gpu']
# check_gpu(use_gpu)
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
rec_model = create_module(config['Architecture']['function'])(params=config)
startup_prog = fluid.Program()
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
_, outputs = rec_model(mode="test")
fetch_name_list = list(outputs.keys())
fetch_varname_list = [outputs[v].name for v in fetch_name_list]
eval_prog = eval_prog.clone(for_test=True)
exe.run(startup_prog)
init_model(config, eval_prog, exe)
blobs = reader_main(config, 'test')
imgs = next(blobs())
for img in imgs:
predict = exe.run(program=eval_prog,
feed={"image": img},
fetch_list=fetch_varname_list,
return_numpy=False)
preds = np.array(predict[0])
if preds.shape[1] == 1:
preds = preds.reshape(-1)
preds_lod = predict[0].lod()[0]
preds_text = char_ops.decode(preds)
else:
end_pos = np.where(preds[0, :] == 1)[0]
if len(end_pos) <= 1:
preds_text = preds[0, 1:]
else:
preds_text = preds[0, 1:end_pos[1]]
preds_text = preds_text.reshape(-1)
preds_text = char_ops.decode(preds_text)
print(preds)
print(preds_text)
# save for inference model
target_var = []
for key, values in outputs.items():
target_var.append(values)
fluid.io.save_inference_model(
"./output/",
feeded_var_names=['image'],
target_vars=target_var,
executor=exe,
main_program=eval_prog,
model_filename="model",
params_filename="params")