add pool unittest (#26949) (#27097)

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

@@ -143,6 +143,27 @@ class TestPool1d_API(unittest.TestCase):
             result = avg_pool1d_dg(input)
             self.assertTrue(np.allclose(result.numpy(), result_np))
 
+    def check_avg_dygraph_padding_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = F.avg_pool1d(
+                input,
+                kernel_size=2,
+                stride=2,
+                padding=[1],
+                count_include_pad=True)
+
+            result_np = avg_pool1D_forward_naive(
+                input_np, ksize=[2], strides=[2], paddings=[1], exclusive=False)
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            avg_pool1d_dg = paddle.nn.AvgPool1d(
+                kernel_size=2, stride=None, padding=1, count_include_pad=True)
+            result = avg_pool1d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
     def check_max_static_results(self, place):
         with fluid.program_guard(fluid.Program(), fluid.Program()):
             input = fluid.data(name="input", shape=[2, 3, 32], dtype="float32")

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

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from test_pool2d_op import adaptive_start_index, adaptive_end_index, pool2D_forward_naive
+from test_pool2d_op import adaptive_start_index, adaptive_end_index, pool2D_forward_naive, avg_pool2D_forward_naive, max_pool2D_forward_naive
 import unittest
 from op_test import OpTest
 import numpy as np
@@ -68,6 +68,47 @@ class TestPool2d_API(unittest.TestCase):
             result = avg_pool2d_dg(input)
             self.assertTrue(np.allclose(result.numpy(), result_np))
 
+    def check_avg_dygraph_padding_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = avg_pool2d(
+                input, kernel_size=2, stride=2, padding=1, ceil_mode=False)
+
+            result_np = avg_pool2D_forward_naive(
+                input_np,
+                ksize=[2, 2],
+                strides=[2, 2],
+                paddings=[1, 1],
+                ceil_mode=False,
+                exclusive=False)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            avg_pool2d_dg = paddle.nn.layer.AvgPool2d(
+                kernel_size=2, stride=2, padding=1, ceil_mode=False)
+            result = avg_pool2d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_avg_dygraph_ceilmode_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = avg_pool2d(
+                input, kernel_size=2, stride=2, padding=0, ceil_mode=True)
+
+            result_np = avg_pool2D_forward_naive(
+                input_np,
+                ksize=[2, 2],
+                strides=[2, 2],
+                paddings=[0, 0],
+                ceil_mode=True)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            avg_pool2d_dg = paddle.nn.layer.AvgPool2d(
+                kernel_size=2, stride=2, padding=0, ceil_mode=True)
+            result = avg_pool2d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
     def check_max_static_results(self, place):
         with fluid.program_guard(fluid.Program(), fluid.Program()):
             input = fluid.data(
@@ -108,6 +149,70 @@ class TestPool2d_API(unittest.TestCase):
             result = max_pool2d_dg(input)
             self.assertTrue(np.allclose(result.numpy(), result_np))
 
+    def check_max_dygraph_nhwc_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(
+                np.transpose(input_np, [0, 2, 3, 1]))
+            result = max_pool2d(
+                input,
+                kernel_size=2,
+                stride=2,
+                padding=0,
+                return_indices=False,
+                data_format="NHWC")
+
+            result_np = pool2D_forward_naive(
+                input_np,
+                ksize=[2, 2],
+                strides=[2, 2],
+                paddings=[0, 0],
+                pool_type='max')
+            self.assertTrue(
+                np.allclose(
+                    np.transpose(result.numpy(), [0, 3, 1, 2]), result_np))
+
+    def check_max_dygraph_padding_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = max_pool2d(
+                input, kernel_size=2, stride=2, padding=1, ceil_mode=False)
+
+            result_np = max_pool2D_forward_naive(
+                input_np,
+                ksize=[2, 2],
+                strides=[2, 2],
+                paddings=[1, 1],
+                ceil_mode=False,
+                exclusive=False)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            max_pool2d_dg = paddle.nn.layer.MaxPool2d(
+                kernel_size=2, stride=2, padding=1, ceil_mode=False)
+            result = max_pool2d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_max_dygraph_ceilmode_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = max_pool2d(
+                input, kernel_size=2, stride=2, padding=0, ceil_mode=True)
+
+            result_np = max_pool2D_forward_naive(
+                input_np,
+                ksize=[2, 2],
+                strides=[2, 2],
+                paddings=[0, 0],
+                ceil_mode=True)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            max_pool2d_dg = paddle.nn.layer.MaxPool2d(
+                kernel_size=2, stride=2, padding=0, ceil_mode=True)
+            result = max_pool2d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
     def check_max_dygraph_stride_is_none(self, place):
         with fluid.dygraph.guard(place):
             input_np = np.random.random([2, 3, 32, 32]).astype("float32")
@@ -215,6 +320,9 @@ class TestPool2d_API(unittest.TestCase):
             self.check_avg_dygraph_stride_is_none(place)
             self.check_max_dygraph_padding(place)
             self.check_avg_divisor(place)
+            self.check_max_dygraph_padding_results(place)
+            self.check_max_dygraph_ceilmode_results(place)
+            self.check_max_dygraph_nhwc_results(place)
 
 
 class TestPool2dError_API(unittest.TestCase):
@@ -370,6 +478,22 @@ class TestPool2dError_API(unittest.TestCase):
 
         self.assertRaises(ValueError, run8)
 
+        def run9():
+            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 = max_pool2d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=0,
+                    ceil_mode=False,
+                    data_format='NHWC',
+                    return_indices=True)
+
+        self.assertRaises(ValueError, run9)
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -22,7 +22,7 @@ import paddle.fluid.core as core
 from op_test import OpTest
 import paddle.fluid as fluid
 from paddle.nn.functional import avg_pool3d, max_pool3d
-from test_pool3d_op import adaptive_start_index, adaptive_end_index, pool3D_forward_naive
+from test_pool3d_op import adaptive_start_index, adaptive_end_index, pool3D_forward_naive, avg_pool3D_forward_naive, max_pool3D_forward_naive
 
 
 class TestPool3d_API(unittest.TestCase):
@@ -73,6 +73,58 @@ class TestPool3d_API(unittest.TestCase):
             result = avg_pool3d_dg(input)
             self.assertTrue(np.allclose(result.numpy(), result_np))
 
+    def check_avg_dygraph_padding_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = avg_pool3d(
+                input,
+                kernel_size=2,
+                stride=2,
+                padding=1,
+                ceil_mode=False,
+                count_include_pad=True)
+
+            result_np = avg_pool3D_forward_naive(
+                input_np,
+                ksize=[2, 2, 2],
+                strides=[2, 2, 2],
+                paddings=[1, 1, 1],
+                ceil_mode=False,
+                exclusive=False)
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            avg_pool3d_dg = paddle.nn.layer.AvgPool3d(
+                kernel_size=2,
+                stride=None,
+                padding=1,
+                ceil_mode=False,
+                count_include_pad=True)
+            result = avg_pool3d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_avg_dygraph_ceilmode_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = avg_pool3d(
+                input, kernel_size=2, stride=2, padding=0, ceil_mode=True)
+
+            result_np = avg_pool3D_forward_naive(
+                input_np,
+                ksize=[2, 2, 2],
+                strides=[2, 2, 2],
+                paddings=[0, 0, 0],
+                ceil_mode=True)
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            avg_pool3d_dg = paddle.nn.layer.AvgPool3d(
+                kernel_size=2, stride=None, padding=0, ceil_mode=True)
+            result = avg_pool3d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
     def check_max_static_results(self, place):
         with fluid.program_guard(fluid.Program(), fluid.Program()):
             input = fluid.data(
@@ -112,6 +164,74 @@ class TestPool3d_API(unittest.TestCase):
             result = max_pool3d_dg(input)
             self.assertTrue(np.allclose(result.numpy(), result_np))
 
+    def check_max_dygraph_ndhwc_results(self, place):
+        print("run ndchw max pool3d")
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(
+                np.transpose(input_np, [0, 2, 3, 4, 1]))
+            result = max_pool3d(
+                input,
+                kernel_size=2,
+                stride=2,
+                padding=0,
+                data_format="NDHWC",
+                return_indices=False)
+
+            result_np = pool3D_forward_naive(
+                input_np,
+                ksize=[2, 2, 2],
+                strides=[2, 2, 2],
+                paddings=[0, 0, 0],
+                pool_type='max')
+
+            self.assertTrue(
+                np.allclose(
+                    np.transpose(result.numpy(), [0, 4, 1, 2, 3]), result_np))
+
+    def check_max_dygraph_ceilmode_results(self, place):
+        print("run ceil mode max pool3d")
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = max_pool3d(
+                input, kernel_size=2, stride=2, padding=0, ceil_mode=True)
+
+            result_np = max_pool3D_forward_naive(
+                input_np,
+                ksize=[2, 2, 2],
+                strides=[2, 2, 2],
+                paddings=[0, 0, 0],
+                ceil_mode=True)
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            max_pool3d_dg = paddle.nn.layer.MaxPool3d(
+                kernel_size=2, stride=None, padding=0, ceil_mode=True)
+            result = max_pool3d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+    def check_max_dygraph_padding_results(self, place):
+        with fluid.dygraph.guard(place):
+            input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
+            input = fluid.dygraph.to_variable(input_np)
+            result = max_pool3d(
+                input, kernel_size=2, stride=2, padding=1, ceil_mode=False)
+
+            result_np = max_pool3D_forward_naive(
+                input_np,
+                ksize=[2, 2, 2],
+                strides=[2, 2, 2],
+                paddings=[1, 1, 1],
+                ceil_mode=False)
+
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
+            max_pool3d_dg = paddle.nn.layer.MaxPool3d(
+                kernel_size=2, stride=None, padding=1, ceil_mode=False)
+            result = max_pool3d_dg(input)
+            self.assertTrue(np.allclose(result.numpy(), result_np))
+
     def check_max_dygraph_stride_is_none(self, place):
         with fluid.dygraph.guard(place):
             input_np = np.random.random([2, 3, 32, 32, 32]).astype("float32")
@@ -205,6 +325,8 @@ class TestPool3d_API(unittest.TestCase):
             self.check_max_dygraph_stride_is_none(place)
             self.check_max_dygraph_padding(place)
             self.check_avg_divisor(place)
+            self.check_max_dygraph_ndhwc_results(place)
+            self.check_max_dygraph_ceilmode_results(place)
 
 
 class TestPool3dError_API(unittest.TestCase):
@@ -336,6 +458,21 @@ class TestPool3dError_API(unittest.TestCase):
 
         self.assertRaises(ValueError, run9)
 
+        def run10():
+            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 = max_pool3d(
+                    input_pd,
+                    kernel_size=2,
+                    stride=2,
+                    padding=0,
+                    data_format='NDHWC',
+                    return_indices=True)
+
+        self.assertRaises(ValueError, run10)
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/nn/functional/pooling.py

@@ -168,7 +168,7 @@ def avg_pool1d(x,
                count_include_pad=True,
                ceil_mode=False,
                name=None):
-    """ 
+    """
     This API implements average pooling 1d operation,
     See more details in :ref:`api_nn_pooling_AvgPool1d` .
 
@@ -280,7 +280,7 @@ def avg_pool2d(x,
     """
     This API implements average pooling 2d operation.
     See more details in :ref:`api_nn_pooling_AvgPool2d` .
- 
+
     Args:
         x (Tensor): The input tensor of pooling operator which is a 4-D tensor with
                           shape [N, C, H, W]. The format of input tensor is `"NCHW"` or
@@ -640,7 +640,7 @@ def max_pool2d(x,
             5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
             The default value is 0.
         ceil_mode (bool): when True, will use `ceil` instead of `floor` to compute the output shape
-        return_indices (bool): Whether to return the max indices along with the outputs.
+        return_indices (bool): Whether to return the max indices along with the outputs. Default False, only support `"NCHW"` data format
         data_format (string): The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`.
                         The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
                         `[batch_size, input_channels, input_height, input_width]`.
@@ -690,15 +690,30 @@ def max_pool2d(x,
     padding, padding_algorithm = _update_padding_nd(
         padding, num_dims=2, channel_last=channel_last, ceil_mode=ceil_mode)
 
+    if data_format == "NHWC" and return_indices:
+        raise ValueError(
+            "When setting return_indices to true, data_format must be set to NCHW in API:max_pool2d"
+        )
+
     if in_dygraph_mode():
-        output = core.ops.max_pool2d_with_index(
-            x, 'ksize', kernel_size, 'global_pooling', False, 'strides', stride,
-            'paddings', padding, 'padding_algorithm', padding_algorithm,
-            'use_cudnn', True, 'ceil_mode', ceil_mode, 'use_mkldnn', False,
-            'exclusive', True, 'data_format', data_format)
-        return output if return_indices else output[0]
+        if data_format == "NCHW":
+            output = core.ops.max_pool2d_with_index(
+                x, 'ksize', kernel_size, 'global_pooling', False, 'strides',
+                stride, 'paddings', padding, 'padding_algorithm',
+                padding_algorithm, 'use_cudnn', True, 'ceil_mode', ceil_mode,
+                'use_mkldnn', False, 'exclusive', True, 'data_format',
+                data_format)
+            return output if return_indices else output[0]
+        elif data_format == "NHWC" and not return_indices:
+            output = core.ops.pool2d(
+                x, 'pooling_type', 'max', 'ksize', kernel_size,
+                'global_pooling', False, 'padding_algorithm', padding_algorithm,
+                'strides', stride, 'paddings', padding, 'use_cudnn', True,
+                'ceil_mode', ceil_mode, 'use_mkldnn', False, 'exclusive', True,
+                'data_format', data_format)
+            return output
 
-    op_type = 'max_pool2d_with_index'
+    op_type = 'max_pool2d_with_index' if data_format == "NCHW" else "max_pool2d"
     helper = LayerHelper(op_type, **locals())
     dtype = helper.input_dtype()
     pool_out = helper.create_variable_for_type_inference(dtype)
@@ -739,7 +754,7 @@ def max_pool3d(x,
     See more details in :ref:`api_nn_pooling_MaxPool3d` .
     Args:
         x (Tensor): The input tensor of pooling operator, which is a 5-D tensor with
-                          shape [N, C, D, H, W]. The format of input tensor is `"NCDHW"` or `"NDHWC"`, where N represents batch size, C represents the number of channels, D, H and W represent the depth, height and width of the feature respectively. 
+                          shape [N, C, D, H, W]. The format of input tensor is `"NCDHW"` or `"NDHWC"`, where N represents batch size, C represents the number of channels, D, H and W represent the depth, height and width of the feature respectively.
         kernel_size (int|list|tuple): The pool kernel size. If the kernel size
             is a tuple or list, it must contain three integers,
             (kernel_size_Depth, kernel_size_Height, kernel_size_Width).
@@ -755,7 +770,7 @@ def max_pool3d(x,
             5. A list or tuple of pairs of integers. It has the form [[pad_before, pad_after], [pad_before, pad_after], ...]. Note that, the batch dimension and channel dimension should be [0,0] or (0,0).
             The default value is 0.
         ceil_mode (bool): ${ceil_mode_comment}
-        return_indices (bool): Whether to return the max indices along with the outputs.
+        return_indices (bool): Whether to return the max indices along with the outputs. Default False. Only support "NDCHW" data_format.
         data_format (string): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`.
                         The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of:
                         `[batch_size, input_channels, input_depth, input_height, input_width]`.
@@ -801,15 +816,30 @@ def max_pool3d(x,
     padding, padding_algorithm = _update_padding_nd(
         padding, 3, channel_last=channel_last, ceil_mode=ceil_mode)
 
+    if data_format == "NDHWC" and return_indices:
+        raise ValueError(
+            "When setting return_indices to true, data_format must be set to NCDHW in API:max_pool3d"
+        )
+
     if in_dygraph_mode():
-        output = core.ops.max_pool3d_with_index(
-            x, 'pooling_type', 'max', 'ksize', kernel_size, 'strides', stride,
-            'paddings', padding, 'global_pooling', False, 'padding_algorithm',
-            padding_algorithm, 'use_cudnn', True, 'ceil_mode', ceil_mode,
-            'use_mkldnn', False, 'exclusive', True, 'data_format', data_format)
-        return output if return_indices else output[0]
+        if data_format == "NCDHW":
+            output = core.ops.max_pool3d_with_index(
+                x, 'pooling_type', 'max', 'ksize', kernel_size, 'strides',
+                stride, 'paddings', padding, 'global_pooling', False,
+                'padding_algorithm', padding_algorithm, 'use_cudnn', True,
+                'ceil_mode', ceil_mode, 'use_mkldnn', False, 'exclusive', True,
+                'data_format', data_format)
+            return output if return_indices else output[0]
+        elif data_format == "NDHWC" and not return_indices:
+            output = core.ops.pool3d(
+                x, 'pooling_type', 'max', 'ksize', kernel_size,
+                'global_pooling', False, 'padding_algorithm', padding_algorithm,
+                'strides', stride, 'paddings', padding, 'use_cudnn', True,
+                'ceil_mode', ceil_mode, 'use_mkldnn', False, 'exclusive', True,
+                'data_format', data_format)
+            return output
 
-    op_type = "max_pool3d_with_index"
+    op_type = "max_pool3d_with_index" if data_format == "NCDHW" else "max_pool3d"
     helper = LayerHelper(op_type, **locals())
     dtype = helper.input_dtype()
     pool_out = helper.create_variable_for_type_inference(dtype)
@@ -841,7 +871,7 @@ def adaptive_avg_pool1d(x, output_size, name=None):
     """
     This API implements adaptive average pooling 1d operation.
     See more details in :ref:`api_nn_pooling_AdaptiveAvgPool1d` .
-    
+
     Args:
         x (Tensor): The input tensor of pooling operator, which is a 3-D tensor
                               with shape [N, C, L].  The format of input tensor is NCL,

python/paddle/nn/layer/pooling.py

@@ -850,7 +850,7 @@ class AdaptiveMaxPool1d(layers.Layer):
 
        lend &= ceil((i + 1) * L_{in} / L_{out})
 
-       Output(i) &= max(Input[lstart:lend])}
+       Output(i) &= max(Input[lstart:lend])
 
     Args:
         output_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list,
@@ -932,7 +932,7 @@ class AdaptiveMaxPool2d(layers.Layer):
     Shape:
         x (Tensor): The input tensor of adaptive max pool2d operator, which is a 4-D tensor. The data type can be float32, float64.
         output (Tensor): The output tensor of adaptive max pool2d operator, which is a 4-D tensor. The data type is same as input x.
-    
+
     Returns:
         A callable object of AdaptiveMaxPool2d.
     Examples:
@@ -1032,7 +1032,7 @@ class AdaptiveMaxPool3d(layers.Layer):
             pool, indices = paddle.nn.AdaptiveMaxPool3d(output_size=3, return_indices=True)
             out = pool(x)
             # out shape: [2, 3, 4, 4, 4], indices shape: [2, 3, 4, 4, 4]
-            
+
     """
 
     def __init__(self, output_size, return_indices=False, name=None):