fix quant module

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)
 

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__':

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:
+                        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)
-                        else:
-                            save_model(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:
+            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)
-            else:
-                save_model(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:
+            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)
-            else:
-                save_model(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"
+            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_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"
+            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_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