Fix paddle.flops AttributeError (#38850)

* Fix AttributeError when output y is a tuple which has no attribute 'shape'

* Add unit test for dynamic_flops with multiple outputs

* Add unit test for dynamic_flops with multiple outputs

python/paddle/hapi/dynamic_flops.py

@@ -181,6 +181,9 @@ def count_parameters(m, x, y):
 
 def count_io_info(m, x, y):
     m.register_buffer('input_shape', paddle.to_tensor(x[0].shape))
+    if isinstance(y, (list, tuple)):
+        m.register_buffer('output_shape', paddle.to_tensor(y[0].shape))
+    else:
         m.register_buffer('output_shape', paddle.to_tensor(y.shape))
 
 
@@ -258,8 +261,8 @@ def dynamic_flops(model, inputs, custom_ops=None, print_detail=False):
     for m in model.sublayers():
         if len(list(m.children())) > 0:
             continue
-        if set(['total_ops', 'total_params', 'input_shape',
-                'output_shape']).issubset(set(list(m._buffers.keys()))):
+        if {'total_ops', 'total_params', 'input_shape',
+                'output_shape'}.issubset(set(list(m._buffers.keys()))):
             total_ops += m.total_ops
             total_params += m.total_params
 
@@ -274,8 +277,8 @@ def dynamic_flops(model, inputs, custom_ops=None, print_detail=False):
     for n, m in model.named_sublayers():
         if len(list(m.children())) > 0:
             continue
-        if set(['total_ops', 'total_params', 'input_shape',
-                'output_shape']).issubset(set(list(m._buffers.keys()))):
+        if {'total_ops', 'total_params', 'input_shape',
+                'output_shape'}.issubset(set(list(m._buffers.keys()))):
             table.add_row([
                 m.full_name(), list(m.input_shape.numpy()),
                 list(m.output_shape.numpy()), int(m.total_params),

python/paddle/tests/test_model.py

@@ -732,6 +732,18 @@ class TestModelFunction(unittest.TestCase):
             custom_ops={paddle.nn.Dropout: customize_dropout},
             print_detail=True)
 
+    def test_dynamic_flops_with_multiple_outputs(self):
+        net = paddle.nn.MaxPool2D(
+            kernel_size=2, stride=2, padding=0, return_mask=True)
+
+        def customize_dropout(m, x, y):
+            m.total_ops += 0
+
+        paddle.flops(
+            net, [1, 2, 32, 32],
+            custom_ops={paddle.nn.Dropout: customize_dropout},
+            print_detail=True)
+
     def test_export_deploy_model(self):
         self.set_seed()
         np.random.seed(201)