Fix the bug of export quantized inference model (#2300)

* Fix the bug of export quantized inference model

* Fix the bug of export quantized inference model

dygraph/ppdet/engine/trainer.py

@@ -50,6 +50,7 @@ class Trainer(object):
                 "mode should be 'train', 'eval' or 'test'"
         self.mode = mode.lower()
         self.optimizer = None
+        self.slim = None
 
         # build model
         self.model = create(cfg.architecture)
@@ -58,8 +59,8 @@ class Trainer(object):
         if 'slim' in cfg and cfg.slim:
             if self.mode == 'train':
                 self.load_weights(cfg.pretrain_weights, cfg.weight_type)
-            slim = create(cfg.slim)
-            slim(self.model)
+            self.slim = create(cfg.slim)
+            self.slim(self.model)
 
         # build data loader
         self.dataset = cfg['{}Dataset'.format(self.mode.capitalize())]
@@ -385,7 +386,9 @@ class Trainer(object):
         }]
 
         # dy2st and save model
-        static_model = paddle.jit.to_static(self.model, input_spec=input_spec)
+        if self.slim is None or self.cfg['slim'] != 'quant':
+            static_model = paddle.jit.to_static(
+                self.model, input_spec=input_spec)
             # NOTE: dy2st do not pruned program, but jit.save will prune program
             # input spec, prune input spec here and save with pruned input spec
             pruned_input_spec = self._prune_input_spec(
@@ -396,6 +399,11 @@ class Trainer(object):
                 os.path.join(save_dir, 'model'),
                 input_spec=pruned_input_spec)
             logger.info("Export model and saved in {}".format(save_dir))
+        else:
+            self.slim.save_quantized_model(
+                self.model,
+                os.path.join(save_dir, 'model'),
+                input_spec=input_spec)
 
     def _prune_input_spec(self, input_spec, program, targets):
         # try to prune static program to figure out pruned input spec

dygraph/ppdet/slim/quant.py

@@ -46,3 +46,7 @@ class QAT(object):
             logger.info(model)
 
         return model
+
+    def save_quantized_model(self, layer, path, input_spec=None, **config):
+        self.quanter.save_quantized_model(
+            model=layer, path=path, input_spec=input_spec, **config)