def main():
# build train 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]
model_average = train_build_outputs[-1]
# build eval program
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)
# initialize train reader
train_reader = reader_main(config=config, mode="train")
train_loader.set_sample_list_generator(train_reader, places=place)
# initialize eval reader
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)
# dump mode structure
if config['Global']['debug']:
if train_alg_type == 'rec' and 'attention' in config['Global'][
'loss_type']:
logger.warning('Does not suport dump attention...')
else:
summary(train_program)
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,\
'model_average': model_average}
eval_info_dict = {'program':eval_program,\
'reader':eval_reader,\
'fetch_name_list':eval_fetch_name_list,\
'fetch_varname_list':eval_fetch_varname_list}
if train_alg_type == 'det':
program.train_eval_det_run(config, exe, train_info_dict, eval_info_dict)
elif train_alg_type == 'rec':
program.train_eval_rec_run(config, exe, train_info_dict, eval_info_dict)
else:
program.train_eval_cls_run(config, exe, train_info_dict, eval_info_dict)
def train(train_dataset, test_dataset):
startup_program = fluid.default_startup_program()
main_program = fluid.default_main_program()
inference_program = fluid.default_main_program().clone(for_test=True)
train_reader = paddle.batch(paddle.reader.shuffle(train_dataset,
buf_size=500),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(paddle.reader.shuffle(test_dataset,
buf_size=500),
batch_size=BATCH_SIZE)
img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
prediction, avg_loss, acc = net_conf(img, label, NUM_CLASSES)
optimizer = fluid.optimizer.RMSProp(learning_rate=0.001)
optimizer.minimize(avg_loss)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
print('Summary')
summary(main_program)
feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
exe.run(startup_program)
epochs = [epoch_id for epoch_id in range(EPOCHS)]
# train
step = 0
for epoch_id in epochs:
print("Epoch %d" % (epoch_id))
for step_id, data in enumerate(train_reader()):
metrics = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[avg_loss, acc])
if step % 100 == 0:
print("Pass %d, Cost %f" % (step, metrics[0]))
step += 1
# test
total_acc = 0
step = 0
for step_id, data in enumerate(test_reader()):
metrics = exe.run(main_program,
feed=feeder.feed(data),
fetch_list=[avg_loss, acc])
total_acc += metrics[1]
step += 1
print("Acc: %f" % (total_acc / step))
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_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)
# dump mode structure
if config['Global']['debug']:
if 'attention' in config['Global']['loss_type']:
logger.warning('Does not suport dump attention...')
else:
summary(train_program)
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)