Fix conv and summary api bug (#27023)

* fix conv output_size has no default value bug
* fix summary bug

python/paddle/hapi/model.py

@@ -1868,8 +1868,13 @@ class Model(object):
               print(params_info)
 
         """
-
-        return summary(self.network, self._inputs, batch_size, dtype)
+        assert (input_size is not None or self._inputs is not None
+                ), "'input_size' or 'self._input' must be set"
+        if input_size is not None:
+            _input_size = input_size
+        else:
+            _input_size = self._inputs
+        return summary(self.network, _input_size, batch_size, dtype)
 
     def _verify_spec(self, specs, is_input=False):
         out_specs = []

python/paddle/hapi/model_summary.py

@@ -86,8 +86,10 @@ def summary(net, input_size, batch_size=None, dtypes=None):
     elif isinstance(input_size, list):
         _input_size = []
         for item in input_size:
+            if isinstance(item, int):
+                item = (item, )
             assert isinstance(item,
-                              (list, InputSpec)), 'When input_size is list, \
+                              (tuple, InputSpec)), 'When input_size is list, \
             expect item in input_size is a tuple or InputSpec, but got {}'.format(
                                   type(item))
 
@@ -97,6 +99,8 @@ def summary(net, input_size, batch_size=None, dtypes=None):
                     batch_size = item.shape[0]
             else:
                 _input_size.append(item)
+    elif isinstance(input_size, int):
+        _input_size = (input_size, )
     else:
         _input_size = input_size
 
@@ -138,11 +142,11 @@ def summary_string(model, input_size, batch_size=-1, dtypes=None):
                 summary[m_key]["output_shape"][0] = batch_size
 
             params = 0
-            if hasattr(module, "weight"):
+            if hasattr(module, "weight") and hasattr(module.weight, "shape"):
                 params += np.prod(module.weight.shape)
                 summary[m_key]["trainable"] = module.weight.trainable or (
                     not module.weight.stop_gradient)
-            if hasattr(module, "bias"):
+            if hasattr(module, "bias") and hasattr(module.bias, "shape"):
                 params += np.prod(module.bias.shape)
             summary[m_key]["nb_params"] = params
 

python/paddle/nn/layer/conv.py

@@ -1084,7 +1084,7 @@ class ConvTranspose3d(_ConvNd):
             bias_attr=bias_attr,
             data_format=data_format)
 
-    def forward(self, x, output_size):
+    def forward(self, x, output_size=None):
         if output_size is None:
             output_padding = self.output_padding
         else:

python/paddle/tests/test_model.py

@@ -519,6 +519,10 @@ class TestModelFunction(unittest.TestCase):
             np.testing.assert_allclose(params_info['total_params'], gt_params)
             print(params_info)
 
+            model.summary(input_size=(20))
+            model.summary(input_size=[(20)])
+            model.summary(input_size=(20), batch_size=2)
+
     def test_export_deploy_model(self):
         for dynamic in [True, False]:
             fluid.enable_dygraph() if dynamic else None