add kernel, stride check (#35106)

* add kernel, stride check

* add unitest for param out of range

* delete max limit check

python/paddle/fluid/tests/unittests/test_pool1d_api.py

@@ -340,6 +340,36 @@ class TestPool2DError_API(unittest.TestCase):
 
         self.assertRaises(ValueError, run7)
 
+        def run_kernel_out_of_range():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = 0
+                res_pd = F.avg_pool1d(
+                    input_pd,
+                    kernel_size=-1,
+                    stride=2,
+                    padding=padding,
+                    ceil_mode=True)
+
+        self.assertRaises(ValueError, run_kernel_out_of_range)
+
+        def run_stride_out_of_range():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                padding = 0
+                res_pd = F.avg_pool1d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=0,
+                    padding=padding,
+                    ceil_mode=True)
+
+        self.assertRaises(ValueError, run_stride_out_of_range)
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_pool2d_api.py

@@ -494,6 +494,36 @@ class TestPool2DError_API(unittest.TestCase):
 
         self.assertRaises(ValueError, run9)
 
+        def run_kernel_out_of_range():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                res_pd = avg_pool2d(
+                    input_pd,
+                    kernel_size=[-1, 2],
+                    stride=2,
+                    padding=0,
+                    ceil_mode=False,
+                    data_format='NHWC')
+
+        self.assertRaises(ValueError, run_kernel_out_of_range)
+
+        def run_stride_out_of_range():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(-1, 1,
+                                             [2, 3, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                res_pd = avg_pool2d(
+                    input_pd,
+                    kernel_size=3,
+                    stride=[0, 2],
+                    padding=0,
+                    ceil_mode=False,
+                    data_format='NHWC')
+
+        self.assertRaises(ValueError, run_stride_out_of_range)
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_pool3d_api.py

@@ -471,6 +471,34 @@ class TestPool3DError_API(unittest.TestCase):
 
         self.assertRaises(ValueError, run10)
 
+        def run_kernel_out_of_range():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(
+                    -1, 1, [2, 3, 32, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                res_pd = avg_pool3d(
+                    input_pd,
+                    kernel_size=-1,
+                    stride=2,
+                    padding="VALID",
+                    ceil_mode=True)
+
+        self.assertRaises(ValueError, run_kernel_out_of_range)
+
+        def run_size_out_of_range():
+            with fluid.dygraph.guard():
+                input_np = np.random.uniform(
+                    -1, 1, [2, 3, 32, 32, 32]).astype(np.float32)
+                input_pd = fluid.dygraph.to_variable(input_np)
+                res_pd = avg_pool3d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=0,
+                    padding="VALID",
+                    ceil_mode=True)
+
+        self.assertRaises(ValueError, run_size_out_of_range)
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/nn/functional/pooling.py

@@ -42,6 +42,17 @@ def _check_instance(x, x_name, types=(int, float)):
                          format(types, x_name, type(x)))
 
 
+def _check_value_limitation(x, x_name, min_limit=1e-3):
+    def _check_value(x, x_name, min_limit=1e-3):
+        if isinstance(x, int) and min_limit is not None and x < min_limit:
+            raise ValueError(
+                "Excepted the input {} to be greater than {} but received x: {}. ".
+                format(x_name, min_limit, x))
+
+    for ele in x:
+        _check_value(ele, x_name)
+
+
 def _zero_padding_in_batch_and_channel(padding, channel_last):
     if channel_last:
         return list(padding[0]) == [0, 0] and list(padding[-1]) == [0, 0]
@@ -211,6 +222,9 @@ def avg_pool1d(x,
         stride = utils.convert_to_list(stride, 1, 'pool_stride')
         stride = [1] + stride
 
+    _check_value_limitation(kernel_size, "kernel_size", min_limit=1e-3)
+    _check_value_limitation(stride, "stride", min_limit=1e-3)
+
     channel_last = _channel_last("NCL", 1)
     padding, padding_algorithm = _update_padding_nd(
         padding, 1, channel_last=channel_last, ceil_mode=ceil_mode)
@@ -325,6 +339,9 @@ def avg_pool2d(x,
     else:
         stride = utils.convert_to_list(stride, 2, 'pool_stride')
 
+    _check_value_limitation(kernel_size, "kernel_size", min_limit=1e-3)
+    _check_value_limitation(stride, "stride", min_limit=1e-3)
+
     channel_last = _channel_last(data_format, 2)
     padding, padding_algorithm = _update_padding_nd(
         padding, 2, channel_last, ceil_mode=ceil_mode)
@@ -448,6 +465,9 @@ def avg_pool3d(x,
     padding, padding_algorithm = _update_padding_nd(
         padding, 3, channel_last=channel_last, ceil_mode=ceil_mode)
 
+    _check_value_limitation(kernel_size, "kernel_size", min_limit=1e-3)
+    _check_value_limitation(stride, "stride", min_limit=1e-3)
+
     if in_dygraph_mode():
         output = _C_ops.pool3d(
             x, 'pooling_type', 'avg', 'ksize', kernel_size, 'strides', stride,