【PaddlePaddle Hackathon】6、在 Paddle 中新增 ZeroPad2d (#37151)

* add zeropad2d v0.1

* add zeropad2d v0.2

* add zeropad2d v0.3

* add zeropad2d v0.3

* add zeropad2d v0.3

* add zeropad2d v0.4

* add zeropad2d v0.5

* add zeropad2d v0.5 codestyle

* add zeropad2d v0.5 codestyle

* add zeropad2d v0.6 functional

* add zeropad2d v0.6 functional

* add zeropad2d v0.6 functional

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

@@ -146,6 +146,10 @@ class TestLayerPrint(unittest.TestCase):
             'Pad2D(padding=[1, 0, 1, 2], mode=constant, value=0.0, data_format=NCHW)'
         )
 
+        module = nn.ZeroPad2D(padding=[1, 0, 1, 2])
+        self.assertEqual(
+            str(module), 'ZeroPad2D(padding=[1, 0, 1, 2], data_format=NCHW)')
+
         module = nn.Pad3D(padding=[1, 0, 1, 2, 0, 0], mode='constant')
         self.assertEqual(
             str(module),

python/paddle/fluid/tests/unittests/test_zeropad2d.py

+#   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from paddle import to_tensor
+from paddle.nn.functional import zeropad2d
+from paddle.nn import ZeroPad2D
+
+
+class TestZeroPad2dAPIError(unittest.TestCase):
+    """
+    test paddle.zeropad2d error.
+    """
+
+    def setUp(self):
+        """
+        unsupport dtypes
+        """
+        self.shape = [4, 3, 224, 224]
+        self.unsupport_dtypes = ['bool', 'int8']
+
+    def test_unsupport_dtypes(self):
+        """
+        test unsupport dtypes.
+        """
+        for dtype in self.unsupport_dtypes:
+            pad = 2
+            x = np.random.randint(-255, 255, size=self.shape)
+            x_tensor = to_tensor(x).astype(dtype)
+            self.assertRaises(TypeError, zeropad2d, x=x_tensor, padding=pad)
+
+
+class TestZeroPad2dAPI(unittest.TestCase):
+    """
+    test paddle.zeropad2d
+    """
+
+    def setUp(self):
+        """
+        support dtypes
+        """
+        self.shape = [4, 3, 224, 224]
+        self.support_dtypes = ['float32', 'float64', 'int32', 'int64']
+
+    def test_support_dtypes(self):
+        """
+        test support types
+        """
+        for dtype in self.support_dtypes:
+            pad = 2
+            x = np.random.randint(-255, 255, size=self.shape).astype(dtype)
+            expect_res = np.pad(x, [[0, 0], [0, 0], [pad, pad], [pad, pad]])
+
+            x_tensor = to_tensor(x).astype(dtype)
+            ret_res = zeropad2d(x_tensor, [pad, pad, pad, pad]).numpy()
+            self.assertTrue(np.allclose(expect_res, ret_res))
+
+    def test_support_pad2(self):
+        """
+        test the type of 'pad' is list.
+        """
+        pad = [1, 2, 3, 4]
+        x = np.random.randint(-255, 255, size=self.shape)
+        expect_res = np.pad(
+            x, [[0, 0], [0, 0], [pad[2], pad[3]], [pad[0], pad[1]]])
+
+        x_tensor = to_tensor(x)
+        ret_res = zeropad2d(x_tensor, pad).numpy()
+        self.assertTrue(np.allclose(expect_res, ret_res))
+
+    def test_support_pad3(self):
+        """
+        test the type of 'pad' is tuple.
+        """
+        pad = (1, 2, 3, 4)
+        x = np.random.randint(-255, 255, size=self.shape)
+        expect_res = np.pad(
+            x, [[0, 0], [0, 0], [pad[2], pad[3]], [pad[0], pad[1]]])
+
+        x_tensor = to_tensor(x)
+        ret_res = zeropad2d(x_tensor, pad).numpy()
+        self.assertTrue(np.allclose(expect_res, ret_res))
+
+    def test_support_pad4(self):
+        """
+        test the type of 'pad' is paddle.Tensor.
+        """
+        pad = [1, 2, 3, 4]
+        x = np.random.randint(-255, 255, size=self.shape)
+        expect_res = np.pad(
+            x, [[0, 0], [0, 0], [pad[2], pad[3]], [pad[0], pad[1]]])
+
+        x_tensor = to_tensor(x)
+        pad_tensor = to_tensor(pad, dtype='int32')
+        ret_res = zeropad2d(x_tensor, pad_tensor).numpy()
+        self.assertTrue(np.allclose(expect_res, ret_res))
+
+
+class TestZeroPad2DLayer(unittest.TestCase):
+    """
+    test nn.ZeroPad2D
+    """
+
+    def setUp(self):
+        self.shape = [4, 3, 224, 224]
+        self.pad = [2, 2, 4, 1]
+        self.padLayer = ZeroPad2D(padding=self.pad)
+        self.x = np.random.randint(-255, 255, size=self.shape)
+        self.expect_res = np.pad(self.x,
+                                 [[0, 0], [0, 0], [self.pad[2], self.pad[3]],
+                                  [self.pad[0], self.pad[1]]])
+
+    def test_layer(self):
+        self.assertTrue(
+            np.allclose(
+                zeropad2d(to_tensor(self.x), self.pad).numpy(),
+                self.padLayer(to_tensor(self.x))))
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/nn/__init__.py

@@ -52,6 +52,7 @@ from .layer.activation import LogSoftmax  # noqa: F401
 from .layer.activation import Maxout  # noqa: F401
 from .layer.common import Pad1D  # noqa: F401
 from .layer.common import Pad2D  # noqa: F401
+from .layer.common import ZeroPad2D  # noqa: F401
 from .layer.common import Pad3D  # noqa: F401
 from .layer.common import CosineSimilarity  # noqa: F401
 from .layer.common import Embedding  # noqa: F401
@@ -293,5 +294,6 @@ __all__ = [     #noqa
            'PixelShuffle',
            'ELU',
            'ReLU6',
-           'LayerDict'
+           'LayerDict',
+           'ZeroPad2D'
 ]

python/paddle/nn/functional/__init__.py

@@ -52,6 +52,7 @@ from .common import dropout3d  # noqa: F401
 from .common import alpha_dropout  # noqa: F401
 from .common import label_smooth  # noqa: F401
 from .common import pad  # noqa: F401
+from .common import zeropad2d  # noqa: F401
 from .common import cosine_similarity  # noqa: F401
 from .common import unfold  # noqa: F401
 from .common import interpolate  # noqa: F401
@@ -162,6 +163,7 @@ __all__ = [     #noqa
            'label_smooth',
            'linear',
            'pad',
+           'zeropad2d',
            'unfold',
            'interpolate',
            'upsample',

python/paddle/nn/functional/common.py

@@ -1365,6 +1365,46 @@ def pad(x, pad, mode='constant', value=0, data_format="NCHW", name=None):
     return out
 
 
+def zeropad2d(x, padding, data_format="NCHW", name=None):
+    """
+    Pads the input tensor boundaries with zero according to 'pad'.
+
+    Args:
+        x(Tensor): The input tensor with data type float16/float32/float64/int32/int64.
+        padding(int | Tensor | List[int] | Tuple[int]): The padding size with data type int.
+            The input dimension should be 4 and pad has the form (pad_left, pad_right,
+            pad_top, pad_bottom).
+        data_format(str): An string from: "NHWC", "NCHW". Specify the data format of
+            the input data. Default: "NCHW".
+        name(str, optional): The default value is None. Normally there is no need for user
+            to set this property.
+
+    Returns：Tensor，padded with 0 according to pad and data type is same as input.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import numpy as np
+            import paddle.nn.functional as F
+
+            x_shape = (1, 1, 2, 3)
+            x = paddle.arange(np.prod(x_shape), dtype="float32").reshape(x_shape) + 1
+            y = F.zeropad2d(x, [1, 2, 1, 1])
+            # [[[[0. 0. 0. 0. 0. 0.]
+            #    [0. 1. 2. 3. 0. 0.]
+            #    [0. 4. 5. 6. 0. 0.]
+            #    [0. 0. 0. 0. 0. 0.]]]]
+    """
+
+    return pad(x,
+               pad=padding,
+               mode='constant',
+               value=0,
+               data_format=data_format,
+               name=name)
+
+
 def cosine_similarity(x1, x2, axis=1, eps=1e-8):
     """
     Compute cosine similarity between x1 and x2 along axis.

python/paddle/nn/layer/__init__.py

@@ -29,6 +29,7 @@ from .activation import LogSoftmax  # noqa: F401
 from .common import Bilinear  # noqa: F401
 from .common import Pad1D  # noqa: F401
 from .common import Pad2D  # noqa: F401
+from .common import ZeroPad2D  # noqa: F401
 from .common import Pad3D  # noqa: F401
 from .common import CosineSimilarity  # noqa: F401
 from .common import Embedding  # noqa: F401

python/paddle/nn/layer/common.py

@@ -1108,6 +1108,72 @@ class Pad2D(Layer):
             self._pad, self._mode, self._value, self._data_format, name_str)
 
 
+class ZeroPad2D(Layer):
+    """
+    This interface is used to construct a callable object of the ``ZeroPad2D`` class.
+    Pads the input tensor boundaries with zero.
+
+    Parameters:
+        padding (Tensor | List[int] | int): The padding size with data type int. If is int, use the
+            same padding in all dimensions. Else [len(padding)/2] dimensions of input will be padded.
+            The pad has the form (pad_left, pad_right, pad_top, pad_bottom).
+        data_format (str): An string from: "NCHW", "NHWC". Specify the data format of the input data.
+           Default is  "NCHW"
+        name (str, optional) : The default value is None.  Normally there is no need for
+            user to set this property.  For more information, please refer to :ref:`api_guide_Name`.
+
+    Shape:
+        - x(Tensor): The input tensor of zeropad2d operator, which is a 4-D tensor.
+          The data type can be float32, float64.
+        - output(Tensor): The output tensor of zeropad2d operator, which is a 4-D tensor.
+          The data type is same as input x.
+
+    Examples:
+        Examples are as follows.
+
+        .. code-block:: python
+
+            import paddle
+            import paddle.nn as nn
+            import numpy as np
+
+            input_shape = (1, 1, 2, 3)
+            pad = [1, 0, 1, 2]
+            data = paddle.arange(np.prod(input_shape), dtype="float32").reshape(input_shape) + 1
+
+            my_pad = nn.ZeroPad2D(padding=pad)
+            result = my_pad(data)
+
+            print(result)
+            # [[[[0. 0. 0. 0.]
+            #    [0. 1. 2. 3.]
+            #    [0. 4. 5. 6.]
+            #    [0. 0. 0. 0.]
+            #    [0. 0. 0. 0.]]]]
+    """
+
+    def __init__(self, padding, data_format="NCHW", name=None):
+        super(ZeroPad2D, self).__init__()
+        self._pad = _npairs(padding, 2)
+        self._mode = 'constant'
+        self._value = 0.
+        self._data_format = data_format
+        self._name = name
+
+    def forward(self, x):
+        return F.pad(x,
+                     pad=self._pad,
+                     mode=self._mode,
+                     value=self._value,
+                     data_format=self._data_format,
+                     name=self._name)
+
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'padding={}, data_format={}{}'.format(
+            self._pad, self._data_format, name_str)
+
+
 class Pad3D(Layer):
     """
     This interface is used to construct a callable object of the ``Pad3D`` class.