From 02ae18abf9a0b0b5ec14e3a65dc80d06abd8db88 Mon Sep 17 00:00:00 2001 From: baiyfbupt Date: Thu, 24 Sep 2020 17:11:22 +0800 Subject: [PATCH] fix quant module --- deploy/slim/prune/pruning_and_finetune.py | 2 +- deploy/slim/quantization/quant.py | 11 +- tools/program.py | 123 +++++++++++++++++----- 3 files changed, 104 insertions(+), 32 deletions(-) diff --git a/deploy/slim/prune/pruning_and_finetune.py b/deploy/slim/prune/pruning_and_finetune.py index bf54b799..77ba604a 100644 --- a/deploy/slim/prune/pruning_and_finetune.py +++ b/deploy/slim/prune/pruning_and_finetune.py @@ -135,7 +135,7 @@ def main(): if alg in ['EAST', 'DB']: program.train_eval_det_run( - config, exe, train_info_dict, eval_info_dict, is_pruning=True) + config, exe, train_info_dict, eval_info_dict, is_slim="prune") else: program.train_eval_rec_run(config, exe, train_info_dict, eval_info_dict) diff --git a/deploy/slim/quantization/quant.py b/deploy/slim/quantization/quant.py index f54a328d..b1003ca9 100755 --- a/deploy/slim/quantization/quant.py +++ b/deploy/slim/quantization/quant.py @@ -155,14 +155,13 @@ def main(): act_preprocess_func=act_preprocess_func, optimizer_func=optimizer_func, executor=executor, - for_test=False, - return_program=True) + for_test=False) # compile program for multi-devices train_compile_program = program.create_multi_devices_program( quant_train_program, train_opt_loss_name, for_quant=True) - init_model(config, quant_train_program, exe) + init_model(config, train_program, exe) train_info_dict = {'compile_program':train_compile_program,\ 'train_program':quant_train_program,\ @@ -177,9 +176,11 @@ def main(): '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) + program.train_eval_det_run( + config, exe, train_info_dict, eval_info_dict, is_slim="quant") else: - program.train_eval_rec_run(config, exe, train_info_dict, eval_info_dict) + program.train_eval_rec_run( + config, exe, train_info_dict, eval_info_dict, is_slim="quant") if __name__ == '__main__': diff --git a/tools/program.py b/tools/program.py index 2ef203f4..7c892e7e 100755 --- a/tools/program.py +++ b/tools/program.py @@ -241,9 +241,11 @@ def create_multi_devices_program(program, loss_var_name, for_quant=False): build_strategy.enable_inplace = True if for_quant: build_strategy.fuse_all_reduce_ops = False + else: + program = fluid.CompiledProgram(program) exec_strategy = fluid.ExecutionStrategy() exec_strategy.num_iteration_per_drop_scope = 1 - compile_program = fluid.CompiledProgram(program).with_data_parallel( + compile_program = program.with_data_parallel( loss_name=loss_var_name, build_strategy=build_strategy, exec_strategy=exec_strategy) @@ -254,7 +256,7 @@ def train_eval_det_run(config, exe, train_info_dict, eval_info_dict, - is_pruning=False): + is_slim=None): ''' main program of evaluation for detection ''' @@ -313,14 +315,17 @@ def train_eval_det_run(config, best_batch_id = train_batch_id best_epoch = epoch save_path = save_model_dir + "/best_accuracy" - if is_pruning: - import paddleslim as slim - slim.prune.save_model( - exe, train_info_dict['train_program'], - save_path) - else: + if is_slim is None: save_model(train_info_dict['train_program'], save_path) + else: + import paddleslim as slim + if is_slim == "prune": + slim.prune.save_model( + exe, train_info_dict['train_program'], + save_path) + elif is_slim == "quant": + save_model(eval_info_dict['program'], save_path) strs = 'Test iter: {}, metrics:{}, best_hmean:{:.6f}, best_epoch:{}, best_batch_id:{}'.format( train_batch_id, metrics, best_eval_hmean, best_epoch, best_batch_id) @@ -331,24 +336,34 @@ def train_eval_det_run(config, train_loader.reset() if epoch == 0 and save_epoch_step == 1: save_path = save_model_dir + "/iter_epoch_0" - if is_pruning: - import paddleslim as slim - slim.prune.save_model(exe, train_info_dict['train_program'], - save_path) - else: + if is_slim is None: save_model(train_info_dict['train_program'], save_path) + else: + import paddleslim as slim + if is_slim == "prune": + slim.prune.save_model(exe, train_info_dict['train_program'], + save_path) + elif is_slim == "quant": + save_model(eval_info_dict['program'], save_path) if epoch > 0 and epoch % save_epoch_step == 0: save_path = save_model_dir + "/iter_epoch_%d" % (epoch) - if is_pruning: - import paddleslim as slim - slim.prune.save_model(exe, train_info_dict['train_program'], - save_path) - else: + if is_slim is None: save_model(train_info_dict['train_program'], save_path) + else: + import paddleslim as slim + if is_slim == "prune": + slim.prune.save_model(exe, train_info_dict['train_program'], + save_path) + elif is_slim == "quant": + save_model(eval_info_dict['program'], save_path) return -def train_eval_rec_run(config, exe, train_info_dict, eval_info_dict): +def train_eval_rec_run(config, + exe, + train_info_dict, + eval_info_dict, + is_slim=None): ''' main program of evaluation for recognition ''' @@ -428,7 +443,17 @@ def train_eval_rec_run(config, exe, train_info_dict, eval_info_dict): best_batch_id = train_batch_id best_epoch = epoch save_path = save_model_dir + "/best_accuracy" - save_model(train_info_dict['train_program'], save_path) + if is_slim is None: + save_model(train_info_dict['train_program'], + save_path) + else: + import paddleslim as slim + if is_slim == "prune": + slim.prune.save_model( + exe, train_info_dict['train_program'], + save_path) + elif is_slim == "quant": + save_model(eval_info_dict['program'], save_path) strs = 'Test iter: {}, acc:{:.6f}, best_acc:{:.6f}, best_epoch:{}, best_batch_id:{}, eval_sample_num:{}'.format( train_batch_id, eval_acc, best_eval_acc, best_epoch, best_batch_id, eval_sample_num) @@ -439,14 +464,34 @@ def train_eval_rec_run(config, exe, train_info_dict, eval_info_dict): train_loader.reset() if epoch == 0 and save_epoch_step == 1: save_path = save_model_dir + "/iter_epoch_0" - save_model(train_info_dict['train_program'], save_path) + if is_slim is None: + save_model(train_info_dict['train_program'], save_path) + else: + import paddleslim as slim + if is_slim == "prune": + slim.prune.save_model(exe, train_info_dict['train_program'], + save_path) + elif is_slim == "quant": + save_model(eval_info_dict['program'], save_path) if epoch > 0 and epoch % save_epoch_step == 0: save_path = save_model_dir + "/iter_epoch_%d" % (epoch) - save_model(train_info_dict['train_program'], save_path) + if is_slim is None: + save_model(train_info_dict['train_program'], save_path) + else: + import paddleslim as slim + if is_slim == "prune": + slim.prune.save_model(exe, train_info_dict['train_program'], + save_path) + elif is_slim == "quant": + save_model(eval_info_dict['program'], save_path) return -def train_eval_cls_run(config, exe, train_info_dict, eval_info_dict): +def train_eval_cls_run(config, + exe, + train_info_dict, + eval_info_dict, + is_slim=None): train_batch_id = 0 log_smooth_window = config['Global']['log_smooth_window'] epoch_num = config['Global']['epoch_num'] @@ -509,7 +554,17 @@ def train_eval_cls_run(config, exe, train_info_dict, eval_info_dict): best_batch_id = train_batch_id best_epoch = epoch save_path = save_model_dir + "/best_accuracy" - save_model(train_info_dict['train_program'], save_path) + if is_slim is None: + save_model(train_info_dict['train_program'], + save_path) + else: + import paddleslim as slim + if is_slim == "prune": + slim.prune.save_model( + exe, train_info_dict['train_program'], + save_path) + elif is_slim == "quant": + save_model(eval_info_dict['program'], save_path) strs = 'Test iter: {}, acc:{:.6f}, best_acc:{:.6f}, best_epoch:{}, best_batch_id:{}, eval_sample_num:{}'.format( train_batch_id, eval_acc, best_eval_acc, best_epoch, best_batch_id, eval_sample_num) @@ -520,10 +575,26 @@ def train_eval_cls_run(config, exe, train_info_dict, eval_info_dict): train_loader.reset() if epoch == 0 and save_epoch_step == 1: save_path = save_model_dir + "/iter_epoch_0" - save_model(train_info_dict['train_program'], save_path) + if is_slim is None: + save_model(train_info_dict['train_program'], save_path) + else: + import paddleslim as slim + if is_slim == "prune": + slim.prune.save_model(exe, train_info_dict['train_program'], + save_path) + elif is_slim == "quant": + save_model(eval_info_dict['program'], save_path) if epoch > 0 and epoch % save_epoch_step == 0: save_path = save_model_dir + "/iter_epoch_%d" % (epoch) - save_model(train_info_dict['train_program'], save_path) + if is_slim is None: + save_model(train_info_dict['train_program'], save_path) + else: + import paddleslim as slim + if is_slim == "prune": + slim.prune.save_model(exe, train_info_dict['train_program'], + save_path) + elif is_slim == "quant": + save_model(eval_info_dict['program'], save_path) return -- GitLab