Fix bug of Model.save (#27815)

* fix model bugs, inputs can be InputSpec instance

* correct error message

python/paddle/hapi/model.py

@@ -201,8 +201,11 @@ def prepare_distributed_context(place=None):
 
 
 def _update_input_shapes(inputs):
+    "Get input shape list by given inputs in Model initialization."
     shapes = None
-    if isinstance(inputs, list):
+    if isinstance(inputs, Input):
+        shapes = [list(inputs.shape)]
+    elif isinstance(inputs, list):
         shapes = [list(input.shape) for input in inputs]
     elif isinstance(inputs, dict):
         shapes = [list(inputs[name].shape) for name in inputs]
@@ -917,9 +920,7 @@ class Model(object):
         """
         loss = self._adapter.train_batch(inputs, labels)
         if fluid.in_dygraph_mode() and self._input_shapes is None:
-            self._input_shapes = self._adapter._input_shapes
-            self._is_shape_inferred = True
-            self._inputs = self._verify_spec(None, self._input_shapes, True)
+            self._update_inputs()
         return loss
 
     def eval_batch(self, inputs, labels=None):
@@ -967,9 +968,7 @@ class Model(object):
         """
         loss = self._adapter.eval_batch(inputs, labels)
         if fluid.in_dygraph_mode() and self._input_shapes is None:
-            self._input_shapes = self._adapter._input_shapes
-            self._is_shape_inferred = True
-            self._inputs = self._verify_spec(None, self._input_shapes, True)
+            self._update_inputs()
         return loss
 
     def test_batch(self, inputs):
@@ -1012,9 +1011,7 @@ class Model(object):
         """
         loss = self._adapter.test_batch(inputs)
         if fluid.in_dygraph_mode() and self._input_shapes is None:
-            self._input_shapes = self._adapter._input_shapes
-            self._is_shape_inferred = True
-            self._inputs = self._verify_spec(None, self._input_shapes, True)
+            self._update_inputs()
         return loss
 
     def save(self, path, training=True):
@@ -1707,7 +1704,7 @@ class Model(object):
                 layer = self.network
                 if self._input_shapes is None:  # No provided or inferred
                     raise RuntimeError(
-                        "Saving inference model needs 'inputs' or running before saving. Please specify 'inputs' in Model initialization or input training zqqdata and perform a training for shape derivation."
+                        "Saving inference model needs 'inputs' or running before saving. Please specify 'inputs' in Model initialization or input training data and perform a training for shape derivation."
                     )
                 if self._is_shape_inferred:
                     warnings.warn(
@@ -1953,3 +1950,9 @@ class Model(object):
         except Exception:
             steps = None
         return steps
+
+    def _update_inputs(self):
+        "Update self._inputs according to given inputs."
+        self._input_shapes = self._adapter._input_shapes
+        self._is_shape_inferred = True
+        self._inputs = self._verify_spec(None, self._input_shapes, True)

python/paddle/tests/test_model.py

@@ -556,9 +556,10 @@ class TestModelFunction(unittest.TestCase):
                 shutil.rmtree(save_dir)
             paddle.enable_static()
 
-    def test_dygraph_export_deploy_model_without_inputs(self):
+    def test_dygraph_export_deploy_model_about_inputs(self):
         mnist_data = MnistDataset(mode='train')
         paddle.disable_static()
+        # without inputs
         for initial in ["fit", "train_batch", "eval_batch", "test_batch"]:
             save_dir = tempfile.mkdtemp()
             if not os.path.exists(save_dir):
@@ -584,6 +585,18 @@ class TestModelFunction(unittest.TestCase):
 
             model.save(save_dir, training=False)
             shutil.rmtree(save_dir)
+        # with inputs, and the type of inputs is InputSpec
+        save_dir = tempfile.mkdtemp()
+        if not os.path.exists(save_dir):
+            os.makedirs(save_dir)
+        net = LeNet()
+        inputs = InputSpec([None, 1, 28, 28], 'float32', 'x')
+        model = Model(net, inputs)
+        optim = fluid.optimizer.Adam(
+            learning_rate=0.001, parameter_list=model.parameters())
+        model.prepare(optimizer=optim, loss=CrossEntropyLoss(reduction="sum"))
+        model.save(save_dir, training=False)
+        shutil.rmtree(save_dir)
 
 
 class TestRaiseError(unittest.TestCase):