add parameter of input in model.summary

python/paddle/hapi/model.py

@@ -2145,7 +2145,7 @@ def summary(self, input_size=None, dtype=None):
             _input_size = input_size
         else:
             _input_size = self._inputs
-        return summary(self.network, _input_size, dtype)
+        return summary(self.network, _input_size, dtypes=dtype)
 
     def _verify_spec(self, specs, shapes=None, dtypes=None, is_input=False):
         out_specs = []

python/paddle/hapi/model_summary.py

@@ -25,7 +25,7 @@
 __all__ = []
 
 
-def summary(net, input_size, dtypes=None):
+def summary(net, input_size=None, dtypes=None, input=None):
     """Prints a string summary of the network.
 
     Args:
@@ -34,8 +34,10 @@ def summary(net, input_size, dtypes=None):
                     have one input, input_size can be tuple or InputSpec. if model
                     have multiple input, input_size must be a list which contain 
                     every input's shape. Note that input_size only dim of
-                    batch_size can be None or -1.
+                    batch_size can be None or -1. Default: None. Note that 
+                    input_size and input cannot be None at the same time.
         dtypes (str, optional): if dtypes is None, 'float32' will be used, Default: None.
+        input: the input tensor. if input is given, input_size and dtype will be ignored, Default: None.
 
     Returns:
         Dict: a summary of the network including total params and total trainable params.
@@ -94,10 +96,62 @@ def forward(self, inputs, y):
             lenet_multi_input = LeNetMultiInput()
 
             params_info = paddle.summary(lenet_multi_input, [(1, 1, 28, 28), (1, 400)], 
-                                        ['float32', 'float32'])
+                                        dtypes=['float32', 'float32'])
+            print(params_info)
+
+            # list input demo
+            class LeNetListInput(LeNet):
+
+                def forward(self, inputs):
+                    x = self.features(inputs[0])
+
+                    if self.num_classes > 0:
+                        x = paddle.flatten(x, 1)
+                        x = self.fc(x + inputs[1])
+                    return x
+
+            lenet_list_input = LeNetListInput()
+            input_data = [paddle.rand([1, 1, 28, 28]), paddle.rand([1, 400])]
+            params_info = paddle.summary(lenet_list_input, input=input_data)
+            print(params_info)
+
+            # dict input demo
+            class LeNetDictInput(LeNet):
+
+                def forward(self, inputs):
+                    x = self.features(inputs['x1'])
+
+                    if self.num_classes > 0:
+                        x = paddle.flatten(x, 1)
+                        x = self.fc(x + inputs['x2'])
+                    return x
+
+            lenet_dict_input = LeNetDictInput()
+            input_data = {'x1': paddle.rand([1, 1, 28, 28]),
+                          'x2': paddle.rand([1, 400])}
+            params_info = paddle.summary(lenet_dict_input, input=input_data)
             print(params_info)
 
     """
+    if input_size is None and input is None:
+        raise ValueError("input_size and input cannot be None at the same time")
+
+    if input_size is None and input is not None:
+        if paddle.is_tensor(input):
+            input_size = tuple(input.shape)
+        elif isinstance(input, (list, tuple)):
+            input_size = []
+            for x in input:
+                input_size.append(tuple(x.shape))
+        elif isinstance(input, dict):
+            input_size = []
+            for key in input.keys():
+                input_size.append(tuple(input[key].shape))
+        else:
+            raise ValueError(
+                "Input is not tensor, list, tuple and dict, unable to determine input_size, please input input_size."
+            )
+
     if isinstance(input_size, InputSpec):
         _input_size = tuple(input_size.shape)
     elif isinstance(input_size, list):
@@ -163,7 +217,8 @@ def _check_input(input_size):
             return [_check_input(i) for i in input_size]
 
     _input_size = _check_input(_input_size)
-    result, params_info = summary_string(net, _input_size, dtypes)
+
+    result, params_info = summary_string(net, _input_size, dtypes, input)
     print(result)
 
     if in_train_mode:
@@ -173,7 +228,7 @@ def _check_input(input_size):
 
 
 @paddle.no_grad()
-def summary_string(model, input_size, dtypes=None):
+def summary_string(model, input_size=None, dtypes=None, input=None):
     def _all_is_numper(items):
         for item in items:
             if not isinstance(item, numbers.Number):
@@ -280,17 +335,18 @@ def build_input(input_size, dtypes):
                 build_input(i, dtype) for i, dtype in zip(input_size, dtypes)
             ]
 
-    x = build_input(input_size, dtypes)
-
     # create properties
     summary = OrderedDict()
     hooks = []
-
     # register hook
     model.apply(register_hook)
-
-    # make a forward pass
-    model(*x)
+    if input is not None:
+        x = input
+        model(x)
+    else:
+        x = build_input(input_size, dtypes)
+        # make a forward pass
+        model(*x)
 
     # remove these hooks
     for h in hooks:

python/paddle/tests/test_model.py

@@ -68,6 +68,27 @@ def forward(self, inputs):
         return x
 
 
+class LeNetListInput(LeNetDygraph):
+    def forward(self, inputs):
+        x = inputs[0]
+        x = self.features(x)
+
+        if self.num_classes > 0:
+            x = paddle.flatten(x, 1)
+            x = self.fc(x + inputs[1])
+        return x
+
+
+class LeNetDictInput(LeNetDygraph):
+    def forward(self, inputs):
+        x = self.features(inputs['x1'])
+
+        if self.num_classes > 0:
+            x = paddle.flatten(x, 1)
+            x = self.fc(x + inputs['x2'])
+        return x
+
+
 class MnistDataset(MNIST):
     def __init__(self, mode, return_label=True, sample_num=None):
         super(MnistDataset, self).__init__(mode=mode)
@@ -615,6 +636,22 @@ def _get_param_from_state_dict(state_dict):
         gt_params = _get_param_from_state_dict(rnn.state_dict())
         np.testing.assert_allclose(params_info['total_params'], gt_params / 2.0)
 
+    def test_summary_input(self):
+        rnn = paddle.nn.SimpleRNN(16, 32, 2, direction='bidirectional')
+        input_data = paddle.rand([4, 23, 16])
+        paddle.summary(rnn, input=input_data)
+
+        lenet_List_input = LeNetListInput()
+        input_data = [paddle.rand([1, 1, 28, 28]), paddle.rand([1, 400])]
+        paddle.summary(lenet_List_input, input=input_data)
+
+        lenet_dict_input = LeNetDictInput()
+        input_data = {
+            'x1': paddle.rand([1, 1, 28, 28]),
+            'x2': paddle.rand([1, 400])
+        }
+        paddle.summary(lenet_dict_input, input=input_data)
+
     def test_summary_dtype(self):
         input_shape = (3, 1)
         net = paddle.nn.Embedding(10, 3, sparse=True)