add save infermodel with train steps

paddlepalm/mtl_controller.py

@@ -348,7 +348,6 @@ class Controller(object):
         task_attrs = []
         pred_task_attrs = []
         for inst in instances:
-
             train_reader = inst.Reader(inst.config, phase='train')
             inst.reader['train'] = train_reader
             train_parad = inst.Paradigm(inst.config, phase='train', backbone_config=bb_conf)
@@ -593,7 +592,7 @@ class Controller(object):
             cur_task.cur_train_step += 1
 
             if cur_task.save_infermodel_every_n_steps > 0 and cur_task.cur_train_step % cur_task.save_infermodel_every_n_steps == 0:
-                cur_task.save(suffix='-step'+str(cur_task.cur_train_step))
+                cur_task.save(suffix='.step'+str(cur_task.cur_train_step))
 
             if global_step % main_conf.get('print_every_n_steps', 5) == 0:
                 loss = rt_outputs[cur_task.name+'/loss']

paddlepalm/task_instance.py

@@ -18,6 +18,8 @@ from paddlepalm.interface import task_paradigm as base_paradigm
 import os
 import json
 from paddle import fluid
+import importlib
+from paddlepalm.default_settings import *
 
 
 def check_req_args(conf, name):
@@ -33,7 +35,7 @@ class TaskInstance(object):
         self._config = config
         self._verbose = verbose
 
-        check_req_args(config)
+        check_req_args(config, name)
 
         # parse Reader and Paradigm
         reader_name = config['reader']
@@ -49,6 +51,7 @@ class TaskInstance(object):
 
         self._save_infermodel_path = os.path.join(self._config['save_path'], self._name, 'infer_model')
         self._save_ckpt_path = os.path.join(self._config['save_path'], 'ckpt')
+        self._save_infermodel_every_n_steps = config.get('save_infermodel_every_n_steps', -1)
 
         # following flags can be fetch from instance config file
         self._is_target = config.get('is_target', True)
@@ -77,9 +80,6 @@ class TaskInstance(object):
         self._pred_fetch_name_list = []
         self._pred_fetch_var_list = []
 
-        self._Reader = None
-        self._Paradigm = None
-
         self._exe = fluid.Executor(fluid.CPUPlace())
 
         self._save_protocol = {
@@ -108,7 +108,9 @@ class TaskInstance(object):
         dirpath = self._save_infermodel_path + suffix
         self._pred_input_varname_list = [str(i) for i in self._pred_input_varname_list]
 
-        fluid.io.save_inference_model(dirpath, self._pred_input_varname_list, self._pred_fetch_var_list, self._exe, export_for_deployment = True)
+        # fluid.io.save_inference_model(dirpath, self._pred_input_varname_list, self._pred_fetch_var_list, self._exe, export_for_deployment = True)
+        prog = fluid.default_main_program().clone()
+        fluid.io.save_inference_model(dirpath, self._pred_input_varname_list, self._pred_fetch_var_list, self._exe, prog)
 
         conf = {}
         for k, strv in self._save_protocol.items():
@@ -222,6 +224,10 @@ class TaskInstance(object):
         if self._verbose:
             print('{}: mix_ratio is set to {}'.format(self._name, self._mix_ratio))
 
+    @property
+    def save_infermodel_every_n_steps(self):
+        return self._save_infermodel_every_n_steps
+
     @property
     def expected_train_steps(self):
         return self._expected_train_steps