【cherry-pick2.0】Polish and Optimize the print/repr information of Layer (#29998) (#30893)

cherry-pick #29998
* Polish and Optimize the print/repr message of all layer
* fix some code format

paddle/fluid/platform/dynload/dynamic_loader.cc

@@ -267,7 +267,7 @@ void* GetCUDNNDsoHandle() {
       "For instance, download cudnn-10.0-windows10-x64-v7.6.5.32.zip from "
       "NVIDIA's official website, \n"
       "then, unzip it and copy it into C:\\Program Files\\NVIDIA GPU Computing "
-      "Toolkit\\CUDA/v10.0\n"
+      "Toolkit\\CUDA\\v10.0\n"
       "You should do this according to your CUDA installation directory and "
       "CUDNN version.");
   return GetDsoHandleFromSearchPath(FLAGS_cudnn_dir, win_cudnn_lib, true,

python/paddle/fluid/dygraph/layers.py

@@ -46,6 +46,17 @@ def _convert_camel_to_snake(name):
     return _all_cap_re.sub(r'\1_\2', s1).lower()
 
 
+def _addindent(string, indent):
+    s1 = string.split('\n')
+    if len(s1) == 1:
+        return string
+    s2 = []
+    for idx, line in enumerate(s1):
+        if idx > 0:
+            s2.append(str((indent * ' ') + line))
+    return s1[0] + '\n' + '\n'.join(s2)
+
+
 class HookRemoveHelper(object):
     """ A HookRemoveHelper that can be used to remove hook. """
 
@@ -1166,6 +1177,35 @@ class Layer(core.Layer):
 
         return keys
 
+    def extra_repr(self):
+        """
+        Extra representation of this layer, you can have custom implementation
+        of your own layer.
+        """
+        return ''
+
+    def __repr__(self):
+        extra_lines = []
+        extra_repr = self.extra_repr()
+        extra_lines = extra_repr.split('\n')
+        sublayer_lines = []
+        for name, layer in self._sub_layers.items():
+            sublayer_str = repr(layer)
+            sublayer_str = _addindent(sublayer_str, 2)
+            sublayer_lines.append('(' + name + '): ' + sublayer_str)
+
+        final_str = self.__class__.__name__ + '('
+        if extra_lines:
+            if len(extra_lines) > 1:
+                final_str += '\n  ' + '\n  '.join(extra_lines) + '\n'
+            elif len(extra_lines) == 1:
+                final_str += extra_lines[0]
+        if sublayer_lines:
+            final_str += '\n  ' + '\n  '.join(sublayer_lines) + '\n'
+
+        final_str += ')'
+        return final_str
+
     def state_dict(self,
                    destination=None,
                    include_sublayers=True,

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

+# Copyright (c) 2020 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.
+
+import unittest
+import paddle
+import paddle.nn as nn
+
+
+class TestLayerPrint(unittest.TestCase):
+    def test_layer_str(self):
+        module = nn.ELU(0.2)
+        self.assertEqual(str(module), 'ELU(alpha=0.2)')
+
+        module = nn.GELU(True)
+        self.assertEqual(str(module), 'GELU(approximate=True)')
+
+        module = nn.Hardshrink()
+        self.assertEqual(str(module), 'Hardshrink(threshold=0.5)')
+
+        module = nn.Hardswish(name="Hardswish")
+        self.assertEqual(str(module), 'Hardswish(name=Hardswish)')
+
+        module = nn.Tanh(name="Tanh")
+        self.assertEqual(str(module), 'Tanh(name=Tanh)')
+
+        module = nn.Hardtanh(name="Hardtanh")
+        self.assertEqual(
+            str(module), 'Hardtanh(min=-1.0, max=1.0, name=Hardtanh)')
+
+        module = nn.PReLU(1, 0.25, name="PReLU")
+        self.assertEqual(
+            str(module),
+            'PReLU(num_parameters=1, init=0.25, dtype=float32, name=PReLU)')
+
+        module = nn.ReLU()
+        self.assertEqual(str(module), 'ReLU()')
+
+        module = nn.ReLU6()
+        self.assertEqual(str(module), 'ReLU6()')
+
+        module = nn.SELU()
+        self.assertEqual(
+            str(module),
+            'SELU(scale=1.0507009873554805, alpha=1.6732632423543772)')
+
+        module = nn.LeakyReLU()
+        self.assertEqual(str(module), 'LeakyReLU(negative_slope=0.01)')
+
+        module = nn.Sigmoid()
+        self.assertEqual(str(module), 'Sigmoid()')
+
+        module = nn.Hardsigmoid()
+        self.assertEqual(str(module), 'Hardsigmoid()')
+
+        module = nn.Softplus()
+        self.assertEqual(str(module), 'Softplus(beta=1, threshold=20)')
+
+        module = nn.Softshrink()
+        self.assertEqual(str(module), 'Softshrink(threshold=0.5)')
+
+        module = nn.Softsign()
+        self.assertEqual(str(module), 'Softsign()')
+
+        module = nn.Swish()
+        self.assertEqual(str(module), 'Swish()')
+
+        module = nn.Tanhshrink()
+        self.assertEqual(str(module), 'Tanhshrink()')
+
+        module = nn.ThresholdedReLU()
+        self.assertEqual(str(module), 'ThresholdedReLU(threshold=1.0)')
+
+        module = nn.LogSigmoid()
+        self.assertEqual(str(module), 'LogSigmoid()')
+
+        module = nn.Softmax()
+        self.assertEqual(str(module), 'Softmax(axis=-1)')
+
+        module = nn.LogSoftmax()
+        self.assertEqual(str(module), 'LogSoftmax(axis=-1)')
+
+        module = nn.Maxout(groups=2)
+        self.assertEqual(str(module), 'Maxout(groups=2, axis=1)')
+
+        module = nn.Linear(2, 4, name='linear')
+        self.assertEqual(
+            str(module),
+            'Linear(in_features=2, out_features=4, dtype=float32, name=linear)')
+
+        module = nn.Upsample(size=[12, 12])
+        self.assertEqual(
+            str(module),
+            'Upsample(size=[12, 12], mode=nearest, align_corners=False, align_mode=0, data_format=NCHW)'
+        )
+
+        module = nn.UpsamplingNearest2D(size=[12, 12])
+        self.assertEqual(
+            str(module), 'UpsamplingNearest2D(size=[12, 12], data_format=NCHW)')
+
+        module = nn.UpsamplingBilinear2D(size=[12, 12])
+        self.assertEqual(
+            str(module),
+            'UpsamplingBilinear2D(size=[12, 12], data_format=NCHW)')
+
+        module = nn.Bilinear(in1_features=5, in2_features=4, out_features=1000)
+        self.assertEqual(
+            str(module),
+            'Bilinear(in1_features=5, in2_features=4, out_features=1000, dtype=float32)'
+        )
+
+        module = nn.Dropout(p=0.5)
+        self.assertEqual(
+            str(module), 'Dropout(p=0.5, axis=None, mode=upscale_in_train)')
+
+        module = nn.Dropout2D(p=0.5)
+        self.assertEqual(str(module), 'Dropout2D(p=0.5, data_format=NCHW)')
+
+        module = nn.Dropout3D(p=0.5)
+        self.assertEqual(str(module), 'Dropout3D(p=0.5, data_format=NCDHW)')
+
+        module = nn.AlphaDropout(p=0.5)
+        self.assertEqual(str(module), 'AlphaDropout(p=0.5)')
+
+        module = nn.Pad1D(padding=[1, 2], mode='constant')
+        self.assertEqual(
+            str(module),
+            'Pad1D(padding=[1, 2], mode=constant, value=0.0, data_format=NCL)')
+
+        module = nn.Pad2D(padding=[1, 0, 1, 2], mode='constant')
+        self.assertEqual(
+            str(module),
+            'Pad2D(padding=[1, 0, 1, 2], mode=constant, value=0.0, data_format=NCHW)'
+        )
+
+        module = nn.Pad3D(padding=[1, 0, 1, 2, 0, 0], mode='constant')
+        self.assertEqual(
+            str(module),
+            'Pad3D(padding=[1, 0, 1, 2, 0, 0], mode=constant, value=0.0, data_format=NCDHW)'
+        )
+
+        module = nn.CosineSimilarity(axis=0)
+        self.assertEqual(str(module), 'CosineSimilarity(axis=0, eps=1e-08)')
+
+        module = nn.Embedding(10, 3, sparse=True)
+        self.assertEqual(str(module), 'Embedding(10, 3, sparse=True)')
+
+        module = nn.Conv1D(3, 2, 3)
+        self.assertEqual(
+            str(module), 'Conv1D(3, 2, kernel_size=[3], data_format=NCL)')
+
+        module = nn.Conv1DTranspose(2, 1, 2)
+        self.assertEqual(
+            str(module),
+            'Conv1DTranspose(2, 1, kernel_size=[2], data_format=NCL)')
+
+        module = nn.Conv2D(4, 6, (3, 3))
+        self.assertEqual(
+            str(module), 'Conv2D(4, 6, kernel_size=[3, 3], data_format=NCHW)')
+
+        module = nn.Conv2DTranspose(4, 6, (3, 3))
+        self.assertEqual(
+            str(module),
+            'Conv2DTranspose(4, 6, kernel_size=[3, 3], data_format=NCHW)')
+
+        module = nn.Conv3D(4, 6, (3, 3, 3))
+        self.assertEqual(
+            str(module),
+            'Conv3D(4, 6, kernel_size=[3, 3, 3], data_format=NCDHW)')
+
+        module = nn.Conv3DTranspose(4, 6, (3, 3, 3))
+        self.assertEqual(
+            str(module),
+            'Conv3DTranspose(4, 6, kernel_size=[3, 3, 3], data_format=NCDHW)')
+
+        module = nn.PairwiseDistance()
+        self.assertEqual(str(module), 'PairwiseDistance(p=2.0)')
+
+        module = nn.InstanceNorm1D(2)
+        self.assertEqual(
+            str(module), 'InstanceNorm1D(num_features=2, epsilon=1e-05)')
+
+        module = nn.InstanceNorm2D(2)
+        self.assertEqual(
+            str(module), 'InstanceNorm2D(num_features=2, epsilon=1e-05)')
+
+        module = nn.InstanceNorm3D(2)
+        self.assertEqual(
+            str(module), 'InstanceNorm3D(num_features=2, epsilon=1e-05)')
+
+        module = nn.GroupNorm(num_channels=6, num_groups=6)
+        self.assertEqual(
+            str(module),
+            'GroupNorm(num_groups=6, num_channels=6, epsilon=1e-05)')
+
+        module = nn.LayerNorm([2, 2, 3])
+        self.assertEqual(
+            str(module), 'LayerNorm(normalized_shape=[2, 2, 3], epsilon=1e-05)')
+
+        module = nn.BatchNorm1D(1)
+        self.assertEqual(
+            str(module),
+            'BatchNorm1D(num_features=1, momentum=0.9, epsilon=1e-05)')
+
+        module = nn.BatchNorm2D(1)
+        self.assertEqual(
+            str(module),
+            'BatchNorm2D(num_features=1, momentum=0.9, epsilon=1e-05)')
+
+        module = nn.BatchNorm3D(1)
+        self.assertEqual(
+            str(module),
+            'BatchNorm3D(num_features=1, momentum=0.9, epsilon=1e-05)')
+
+        module = nn.SyncBatchNorm(2)
+        self.assertEqual(
+            str(module),
+            'SyncBatchNorm(num_features=2, momentum=0.9, epsilon=1e-05)')
+
+        module = nn.LocalResponseNorm(size=5)
+        self.assertEqual(
+            str(module),
+            'LocalResponseNorm(size=5, alpha=0.0001, beta=0.75, k=1.0)')
+
+        module = nn.AvgPool1D(kernel_size=2, stride=2, padding=0)
+        self.assertEqual(
+            str(module), 'AvgPool1D(kernel_size=2, stride=2, padding=0)')
+
+        module = nn.AvgPool2D(kernel_size=2, stride=2, padding=0)
+        self.assertEqual(
+            str(module), 'AvgPool2D(kernel_size=2, stride=2, padding=0)')
+
+        module = nn.AvgPool3D(kernel_size=2, stride=2, padding=0)
+        self.assertEqual(
+            str(module), 'AvgPool3D(kernel_size=2, stride=2, padding=0)')
+
+        module = nn.MaxPool1D(kernel_size=2, stride=2, padding=0)
+        self.assertEqual(
+            str(module), 'MaxPool1D(kernel_size=2, stride=2, padding=0)')
+
+        module = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
+        self.assertEqual(
+            str(module), 'MaxPool2D(kernel_size=2, stride=2, padding=0)')
+
+        module = nn.MaxPool3D(kernel_size=2, stride=2, padding=0)
+        self.assertEqual(
+            str(module), 'MaxPool3D(kernel_size=2, stride=2, padding=0)')
+
+        module = nn.AdaptiveAvgPool1D(output_size=16)
+        self.assertEqual(str(module), 'AdaptiveAvgPool1D(output_size=16)')
+
+        module = nn.AdaptiveAvgPool2D(output_size=3)
+        self.assertEqual(str(module), 'AdaptiveAvgPool2D(output_size=3)')
+
+        module = nn.AdaptiveAvgPool3D(output_size=3)
+        self.assertEqual(str(module), 'AdaptiveAvgPool3D(output_size=3)')
+
+        module = nn.AdaptiveMaxPool1D(output_size=16, return_mask=True)
+        self.assertEqual(
+            str(module), 'AdaptiveMaxPool1D(output_size=16, return_mask=True)')
+
+        module = nn.AdaptiveMaxPool2D(output_size=3, return_mask=True)
+        self.assertEqual(
+            str(module), 'AdaptiveMaxPool2D(output_size=3, return_mask=True)')
+
+        module = nn.AdaptiveMaxPool3D(output_size=3, return_mask=True)
+        self.assertEqual(
+            str(module), 'AdaptiveMaxPool3D(output_size=3, return_mask=True)')
+
+        module = nn.SimpleRNNCell(16, 32)
+        self.assertEqual(str(module), 'SimpleRNNCell(16, 32)')
+
+        module = nn.LSTMCell(16, 32)
+        self.assertEqual(str(module), 'LSTMCell(16, 32)')
+
+        module = nn.GRUCell(16, 32)
+        self.assertEqual(str(module), 'GRUCell(16, 32)')
+
+        module = nn.PixelShuffle(3)
+        self.assertEqual(str(module), 'PixelShuffle(upscale_factor=3)')
+
+        module = nn.SimpleRNN(16, 32, 2)
+        self.assertEqual(
+            str(module),
+            'SimpleRNN(16, 32, num_layers=2\n  (0): RNN(\n    (cell): SimpleRNNCell(16, 32)\n  )\n  (1): RNN(\n    (cell): SimpleRNNCell(32, 32)\n  )\n)'
+        )
+
+        module = nn.LSTM(16, 32, 2)
+        self.assertEqual(
+            str(module),
+            'LSTM(16, 32, num_layers=2\n  (0): RNN(\n    (cell): LSTMCell(16, 32)\n  )\n  (1): RNN(\n    (cell): LSTMCell(32, 32)\n  )\n)'
+        )
+
+        module = nn.GRU(16, 32, 2)
+        self.assertEqual(
+            str(module),
+            'GRU(16, 32, num_layers=2\n  (0): RNN(\n    (cell): GRUCell(16, 32)\n  )\n  (1): RNN(\n    (cell): GRUCell(32, 32)\n  )\n)'
+        )
+
+        module1 = nn.Sequential(
+            ('conv1', nn.Conv2D(1, 20, 5)), ('relu1', nn.ReLU()),
+            ('conv2', nn.Conv2D(20, 64, 5)), ('relu2', nn.ReLU()))
+        self.assertEqual(
+            str(module1),
+            'Sequential(\n  '\
+            '(conv1): Conv2D(1, 20, kernel_size=[5, 5], data_format=NCHW)\n  '\
+            '(relu1): ReLU()\n  '\
+            '(conv2): Conv2D(20, 64, kernel_size=[5, 5], data_format=NCHW)\n  '\
+            '(relu2): ReLU()\n)'
+        )
+
+        module2 = nn.Sequential(
+            nn.Conv3DTranspose(4, 6, (3, 3, 3)),
+            nn.AvgPool3D(
+                kernel_size=2, stride=2, padding=0),
+            nn.Tanh(name="Tanh"),
+            module1,
+            nn.Conv3D(4, 6, (3, 3, 3)),
+            nn.MaxPool3D(
+                kernel_size=2, stride=2, padding=0),
+            nn.GELU(True))
+        self.assertEqual(
+            str(module2),
+            'Sequential(\n  '\
+            '(0): Conv3DTranspose(4, 6, kernel_size=[3, 3, 3], data_format=NCDHW)\n  '\
+            '(1): AvgPool3D(kernel_size=2, stride=2, padding=0)\n  '\
+            '(2): Tanh(name=Tanh)\n  '\
+            '(3): Sequential(\n    (conv1): Conv2D(1, 20, kernel_size=[5, 5], data_format=NCHW)\n    (relu1): ReLU()\n'\
+            '    (conv2): Conv2D(20, 64, kernel_size=[5, 5], data_format=NCHW)\n    (relu2): ReLU()\n  )\n  '\
+            '(4): Conv3D(4, 6, kernel_size=[3, 3, 3], data_format=NCDHW)\n  '\
+            '(5): MaxPool3D(kernel_size=2, stride=2, padding=0)\n  '\
+            '(6): GELU(approximate=True)\n)'
+        )
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/nn/layer/activation.py

@@ -86,6 +86,10 @@ class ELU(layers.Layer):
     def forward(self, x):
         return F.elu(x, self._alpha, self._name)
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'alpha={}{}'.format(self._alpha, name_str)
+
 
 class GELU(layers.Layer):
     r"""
@@ -135,6 +139,10 @@ class GELU(layers.Layer):
     def forward(self, x):
         return F.gelu(x, self._approximate, self._name)
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'approximate={}{}'.format(self._approximate, name_str)
+
 
 class Hardshrink(layers.Layer):
     r"""
@@ -179,6 +187,10 @@ class Hardshrink(layers.Layer):
     def forward(self, x):
         return F.hardshrink(x, self._threshold, self._name)
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'threshold={}{}'.format(self._threshold, name_str)
+
 
 class Hardswish(layers.Layer):
     r"""
@@ -225,6 +237,10 @@ class Hardswish(layers.Layer):
     def forward(self, x):
         return F.hardswish(x, self._name)
 
+    def extra_repr(self):
+        name_str = 'name={}'.format(self._name) if self._name else ''
+        return name_str
+
 
 class Tanh(layers.Layer):
     r"""
@@ -262,6 +278,10 @@ class Tanh(layers.Layer):
     def forward(self, x):
         return F.tanh(x, self._name)
 
+    def extra_repr(self):
+        name_str = 'name={}'.format(self._name) if self._name else ''
+        return name_str
+
 
 class Hardtanh(layers.Layer):
     r"""
@@ -304,6 +324,10 @@ class Hardtanh(layers.Layer):
     def forward(self, x):
         return F.hardtanh(x, self._min, self._max, self._name)
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'min={}, max={}{}'.format(self._min, self._max, name_str)
+
 
 class PReLU(layers.Layer):
     """
@@ -371,6 +395,11 @@ class PReLU(layers.Layer):
     def forward(self, x):
         return F.prelu(x, self._weight)
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'num_parameters={}, init={}, dtype={}{}'.format(
+            self._num_parameters, self._init, self._dtype, name_str)
+
 
 class ReLU(layers.Layer):
     """
@@ -405,6 +434,10 @@ class ReLU(layers.Layer):
     def forward(self, x):
         return F.relu(x, self._name)
 
+    def extra_repr(self):
+        name_str = 'name={}'.format(self._name) if self._name else ''
+        return name_str
+
 
 class ReLU6(layers.Layer):
     """
@@ -440,6 +473,10 @@ class ReLU6(layers.Layer):
     def forward(self, x):
         return F.relu6(x, self._name)
 
+    def extra_repr(self):
+        name_str = 'name={}'.format(self._name) if self._name else ''
+        return name_str
+
 
 class SELU(layers.Layer):
     r"""
@@ -486,6 +523,11 @@ class SELU(layers.Layer):
     def forward(self, x):
         return F.selu(x, self._scale, self._alpha, self._name)
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'scale={:.16f}, alpha={:.16f}{}'.format(self._scale, self._alpha,
+                                                       name_str)
+
 
 class LeakyReLU(layers.Layer):
     r"""
@@ -530,6 +572,10 @@ class LeakyReLU(layers.Layer):
     def forward(self, x):
         return F.leaky_relu(x, self._negative_slope, self._name)
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'negative_slope={}{}'.format(self._negative_slope, name_str)
+
 
 class Sigmoid(layers.Layer):
     """
@@ -566,6 +612,10 @@ class Sigmoid(layers.Layer):
     def forward(self, x):
         return F.sigmoid(x, self.name)
 
+    def extra_repr(self):
+        name_str = 'name={}'.format(self.name) if self.name else ''
+        return name_str
+
 
 class Hardsigmoid(layers.Layer):
     r"""
@@ -613,6 +663,10 @@ class Hardsigmoid(layers.Layer):
     def forward(self, x):
         return F.hardsigmoid(x, name=self.name)
 
+    def extra_repr(self):
+        name_str = 'name={}'.format(self.name) if self.name else ''
+        return name_str
+
 
 class Softplus(layers.Layer):
     r"""
@@ -653,6 +707,11 @@ class Softplus(layers.Layer):
     def forward(self, x):
         return F.softplus(x, self._beta, self._threshold, self._name)
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'beta={}, threshold={}{}'.format(self._beta, self._threshold,
+                                                name_str)
+
 
 class Softshrink(layers.Layer):
     r"""
@@ -694,6 +753,10 @@ class Softshrink(layers.Layer):
     def forward(self, x):
         return F.softshrink(x, self._threshold, self._name)
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'threshold={}{}'.format(self._threshold, name_str)
+
 
 class Softsign(layers.Layer):
     r"""
@@ -729,6 +792,10 @@ class Softsign(layers.Layer):
     def forward(self, x):
         return F.softsign(x, self._name)
 
+    def extra_repr(self):
+        name_str = 'name={}'.format(self._name) if self._name else ''
+        return name_str
+
 
 class Swish(layers.Layer):
     r"""
@@ -764,6 +831,10 @@ class Swish(layers.Layer):
     def forward(self, x):
         return F.swish(x, self._name)
 
+    def extra_repr(self):
+        name_str = 'name={}'.format(self._name) if self._name else ''
+        return name_str
+
 
 class Tanhshrink(layers.Layer):
     """
@@ -799,6 +870,10 @@ class Tanhshrink(layers.Layer):
     def forward(self, x):
         return F.tanhshrink(x, self._name)
 
+    def extra_repr(self):
+        name_str = 'name={}'.format(self._name) if self._name else ''
+        return name_str
+
 
 class ThresholdedReLU(layers.Layer):
     r"""
@@ -839,6 +914,10 @@ class ThresholdedReLU(layers.Layer):
     def forward(self, x):
         return F.thresholded_relu(x, self._threshold, self._name)
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'threshold={}{}'.format(self._threshold, name_str)
+
 
 class LogSigmoid(layers.Layer):
     r"""
@@ -874,6 +953,10 @@ class LogSigmoid(layers.Layer):
     def forward(self, x):
         return F.log_sigmoid(x, self._name)
 
+    def extra_repr(self):
+        name_str = 'name={}'.format(self._name) if self._name else ''
+        return name_str
+
 
 class Softmax(layers.Layer):
     r"""
@@ -997,6 +1080,10 @@ class Softmax(layers.Layer):
     def forward(self, x):
         return F.softmax(x, self._axis, self._dtype, self._name)
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'axis={}{}'.format(self._axis, name_str)
+
 
 class LogSoftmax(layers.Layer):
     r"""
@@ -1051,6 +1138,10 @@ class LogSoftmax(layers.Layer):
     def forward(self, x):
         return F.log_softmax(x, self._axis)
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'axis={}{}'.format(self._axis, name_str)
+
 
 class Maxout(layers.Layer):
     r"""
@@ -1111,3 +1202,7 @@ class Maxout(layers.Layer):
 
     def forward(self, x):
         return F.maxout(x, self._groups, self._axis, self._name)
+
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'groups={}, axis={}{}'.format(self._groups, self._axis, name_str)

python/paddle/nn/layer/common.py

@@ -120,7 +120,6 @@ class Linear(layers.Layer):
         self._dtype = self._helper.get_default_dtype()
         self._weight_attr = weight_attr
         self._bias_attr = bias_attr
-        self.name = name
         self.weight = self.create_parameter(
             shape=[in_features, out_features],
             attr=self._weight_attr,
@@ -138,6 +137,11 @@ class Linear(layers.Layer):
             x=input, weight=self.weight, bias=self.bias, name=self.name)
         return out
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self.name) if self.name else ''
+        return 'in_features={}, out_features={}, dtype={}{}'.format(
+            self.weight.shape[0], self.weight.shape[1], self._dtype, name_str)
+
 
 class Upsample(layers.Layer):
     """
@@ -378,6 +382,16 @@ class Upsample(layers.Layer):
 
         return out
 
+    def extra_repr(self):
+        if self.scale_factor is not None:
+            main_str = 'scale_factor={}'.format(self.scale_factor)
+        else:
+            main_str = 'size={}'.format(self.size)
+        name_str = ', name={}'.format(self.name) if self.name else ''
+        return '{}, mode={}, align_corners={}, align_mode={}, data_format={}{}'.format(
+            main_str, self.mode, self.align_corners, self.align_mode,
+            self.data_format, name_str)
+
 
 class UpsamplingNearest2D(layers.Layer):
     """
@@ -454,6 +468,15 @@ class UpsamplingNearest2D(layers.Layer):
 
         return out
 
+    def extra_repr(self):
+        if self.scale_factor is not None:
+            main_str = 'scale_factor={}'.format(self.scale_factor)
+        else:
+            main_str = 'size={}'.format(self.size)
+        name_str = ', name={}'.format(self.name) if self.name else ''
+        return '{}, data_format={}{}'.format(main_str, self.data_format,
+                                             name_str)
+
 
 class UpsamplingBilinear2D(layers.Layer):
     """
@@ -531,6 +554,15 @@ class UpsamplingBilinear2D(layers.Layer):
 
         return out
 
+    def extra_repr(self):
+        if self.scale_factor is not None:
+            main_str = 'scale_factor={}'.format(self.scale_factor)
+        else:
+            main_str = 'size={}'.format(self.size)
+        name_str = ', name={}'.format(self.name) if self.name else ''
+        return '{}, data_format={}{}'.format(main_str, self.data_format,
+                                             name_str)
+
 
 class Bilinear(layers.Layer):
     r"""
@@ -620,6 +652,12 @@ class Bilinear(layers.Layer):
     def forward(self, x1, x2):
         return F.bilinear(x1, x2, self.weight, self.bias, self._name)
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'in1_features={}, in2_features={}, out_features={}, dtype={}{}'.format(
+            self._in1_features, self._in2_features, self._out_features,
+            self._dtype, name_str)
+
 
 class Dropout(layers.Layer):
     """
@@ -689,6 +727,11 @@ class Dropout(layers.Layer):
             name=self.name)
         return out
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self.name) if self.name else ''
+        return 'p={}, axis={}, mode={}{}'.format(self.p, self.axis, self.mode,
+                                                 name_str)
+
 
 class Dropout2D(layers.Layer):
     """
@@ -745,6 +788,11 @@ class Dropout2D(layers.Layer):
             name=self.name)
         return out
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self.name) if self.name else ''
+        return 'p={}, data_format={}{}'.format(self.p, self.data_format,
+                                               name_str)
+
 
 class Dropout3D(layers.Layer):
     """
@@ -801,6 +849,11 @@ class Dropout3D(layers.Layer):
             name=self.name)
         return out
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self.name) if self.name else ''
+        return 'p={}, data_format={}{}'.format(self.p, self.data_format,
+                                               name_str)
+
 
 class AlphaDropout(layers.Layer):
     """
@@ -850,6 +903,10 @@ class AlphaDropout(layers.Layer):
             input, p=self.p, training=self.training, name=self.name)
         return out
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self.name) if self.name else ''
+        return 'p={}{}'.format(self.p, name_str)
+
 
 class Pad1D(layers.Layer):
     """
@@ -925,6 +982,11 @@ class Pad1D(layers.Layer):
                      data_format=self._data_format,
                      name=self._name)
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'padding={}, mode={}, value={}, data_format={}{}'.format(
+            self._pad, self._mode, self._value, self._data_format, name_str)
+
 
 class Pad2D(layers.Layer):
     """
@@ -1003,6 +1065,11 @@ class Pad2D(layers.Layer):
                      data_format=self._data_format,
                      name=self._name)
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'padding={}, mode={}, value={}, data_format={}{}'.format(
+            self._pad, self._mode, self._value, self._data_format, name_str)
+
 
 class Pad3D(layers.Layer):
     """
@@ -1081,6 +1148,11 @@ class Pad3D(layers.Layer):
                      data_format=self._data_format,
                      name=self._name)
 
+    def extra_repr(self):
+        name_str = ', name={}'.format(self._name) if self._name else ''
+        return 'padding={}, mode={}, value={}, data_format={}{}'.format(
+            self._pad, self._mode, self._value, self._data_format, name_str)
+
 
 class CosineSimilarity(layers.Layer):
     """
@@ -1135,6 +1207,9 @@ class CosineSimilarity(layers.Layer):
     def forward(self, x1, x2):
         return F.cosine_similarity(x1, x2, axis=self._axis, eps=self._eps)
 
+    def extra_repr(self):
+        return 'axis={_axis}, eps={_eps}'.format(**self.__dict__)
+
 
 class Embedding(layers.Layer):
     r"""
@@ -1288,3 +1363,12 @@ class Embedding(layers.Layer):
             padding_idx=self._padding_idx,
             sparse=self._sparse,
             name=self._name)
+
+    def extra_repr(self):
+        main_str = '{_num_embeddings}, {_embedding_dim}'
+        if self._padding_idx is not None:
+            main_str += ', padding_idx={_padding_idx}'
+        main_str += ', sparse={_sparse}'
+        if self._name is not None:
+            main_str += ', name={_name}'
+        return main_str.format(**self.__dict__)

python/paddle/nn/layer/conv.py

@@ -148,6 +148,23 @@ class _ConvNd(layers.Layer):
             self._op_type = 'depthwise_conv2d'
             self._use_cudnn = False
 
+    def extra_repr(self):
+        main_str = '{_in_channels}, {_out_channels}, kernel_size={_kernel_size}'
+        if self._stride != [1] * len(self._stride):
+            main_str += ', stride={_stride}'
+        if self._padding != 0:
+            main_str += ', padding={_padding}'
+        if self._padding_mode is not 'zeros':
+            main_str += ', padding_mode={_padding_mode}'
+        if self.output_padding != 0:
+            main_str += ', output_padding={_output_padding}'
+        if self._dilation != [1] * len(self._dilation):
+            main_str += ', dilation={_dilation}'
+        if self._groups != 1:
+            main_str += ', groups={_groups}'
+        main_str += ', data_format={_data_format}'
+        return main_str.format(**self.__dict__)
+
 
 class Conv1D(_ConvNd):
     r"""

python/paddle/nn/layer/distance.py

@@ -100,3 +100,13 @@ class PairwiseDistance(layers.Layer):
             type='p_norm', inputs={'X': sub}, outputs={'Out': out}, attrs=attrs)
 
         return out
+
+    def extra_repr(self):
+        main_str = 'p={p}'
+        if self.epsilon != 1e-6:
+            main_str += ', epsilon={epsilon}'
+        if self.keepdim != False:
+            main_str += ', keepdim={keepdim}'
+        if self.name != None:
+            main_str += ', name={name}'
+        return main_str.format(**self.__dict__)

python/paddle/nn/layer/norm.py

@@ -107,6 +107,10 @@ class _InstanceNormBase(layers.Layer):
         return instance_norm(
             input, weight=self.scale, bias=self.bias, eps=self._epsilon)
 
+    def extra_repr(self):
+        return 'num_features={}, epsilon={}'.format(self.scale.shape[0],
+                                                    self._epsilon)
+
 
 class InstanceNorm1D(_InstanceNormBase):
     r"""
@@ -433,6 +437,10 @@ class GroupNorm(layers.Layer):
 
         return self._helper.append_activation(group_norm_out, None)
 
+    def extra_repr(self):
+        return 'num_groups={}, num_channels={}, epsilon={}'.format(
+            self._num_groups, self._num_channels, self._epsilon)
+
 
 class LayerNorm(layers.Layer):
     r"""
@@ -537,6 +545,10 @@ class LayerNorm(layers.Layer):
             bias=self.bias,
             epsilon=self._epsilon)
 
+    def extra_repr(self):
+        return 'normalized_shape={}, epsilon={}'.format(self._normalized_shape,
+                                                        self._epsilon)
+
 
 class _BatchNormBase(layers.Layer):
     """
@@ -647,6 +659,15 @@ class _BatchNormBase(layers.Layer):
             data_format=self._data_format,
             use_global_stats=self._use_global_stats)
 
+    def extra_repr(self):
+        main_str = 'num_features={}, momentum={}, epsilon={}'.format(
+            self._num_features, self._momentum, self._epsilon)
+        if self._data_format is not 'NCHW':
+            main_str += ', data_format={}'.format(self._data_format)
+        if self._name is not None:
+            main_str += ', name={}'.format(self._name)
+        return main_str
+
 
 class BatchNorm1D(_BatchNormBase):
     r"""
@@ -1186,3 +1207,12 @@ class LocalResponseNorm(layers.Layer):
         out = F.local_response_norm(input, self.size, self.alpha, self.beta,
                                     self.k, self.data_format, self.name)
         return out
+
+    def extra_repr(self):
+        main_str = 'size={}, alpha={}, beta={}, k={}'.format(
+            self.size, self.alpha, self.beta, self.k)
+        if self.data_format is not 'NCHW':
+            main_str += ', data_format={}'.format(self.data_format)
+        if self.name is not None:
+            main_str += ', name={}'.format(self.name)
+        return main_str

python/paddle/nn/layer/pooling.py

@@ -119,6 +119,10 @@ class AvgPool1D(layers.Layer):
                            self.exclusive, self.ceil_mode, self.name)
         return out
 
+    def extra_repr(self):
+        return 'kernel_size={kernel_size}, stride={stride}, padding={padding}'.format(
+            **self.__dict__)
+
 
 class AvgPool2D(layers.Layer):
     r"""
@@ -222,6 +226,10 @@ class AvgPool2D(layers.Layer):
             data_format=self.data_format,
             name=self.name)
 
+    def extra_repr(self):
+        return 'kernel_size={ksize}, stride={stride}, padding={padding}'.format(
+            **self.__dict__)
+
 
 class AvgPool3D(layers.Layer):
     """
@@ -313,6 +321,10 @@ class AvgPool3D(layers.Layer):
             data_format=self.data_format,
             name=self.name)
 
+    def extra_repr(self):
+        return 'kernel_size={ksize}, stride={stride}, padding={padding}'.format(
+            **self.__dict__)
+
 
 class MaxPool1D(layers.Layer):
     """
@@ -401,6 +413,10 @@ class MaxPool1D(layers.Layer):
                            self.return_mask, self.ceil_mode, self.name)
         return out
 
+    def extra_repr(self):
+        return 'kernel_size={kernel_size}, stride={stride}, padding={padding}'.format(
+            **self.__dict__)
+
 
 class MaxPool2D(layers.Layer):
     r"""
@@ -504,6 +520,10 @@ class MaxPool2D(layers.Layer):
             data_format=self.data_format,
             name=self.name)
 
+    def extra_repr(self):
+        return 'kernel_size={ksize}, stride={stride}, padding={padding}'.format(
+            **self.__dict__)
+
 
 class MaxPool3D(layers.Layer):
     """
@@ -595,6 +615,10 @@ class MaxPool3D(layers.Layer):
             data_format=self.data_format,
             name=self.name)
 
+    def extra_repr(self):
+        return 'kernel_size={ksize}, stride={stride}, padding={padding}'.format(
+            **self.__dict__)
+
 
 class AdaptiveAvgPool1D(layers.Layer):
     r"""
@@ -664,6 +688,9 @@ class AdaptiveAvgPool1D(layers.Layer):
     def forward(self, input):
         return F.adaptive_avg_pool1d(input, self.output_size, self.name)
 
+    def extra_repr(self):
+        return 'output_size={}'.format(self.output_size)
+
 
 class AdaptiveAvgPool2D(layers.Layer):
     r"""
@@ -746,6 +773,9 @@ class AdaptiveAvgPool2D(layers.Layer):
             data_format=self._data_format,
             name=self._name)
 
+    def extra_repr(self):
+        return 'output_size={}'.format(self._output_size)
+
 
 class AdaptiveAvgPool3D(layers.Layer):
     r"""
@@ -834,6 +864,9 @@ class AdaptiveAvgPool3D(layers.Layer):
             data_format=self._data_format,
             name=self._name)
 
+    def extra_repr(self):
+        return 'output_size={}'.format(self._output_size)
+
 
 class AdaptiveMaxPool1D(layers.Layer):
     """
@@ -913,6 +946,10 @@ class AdaptiveMaxPool1D(layers.Layer):
         return F.adaptive_max_pool1d(input, self.output_size, self.return_mask,
                                      self.name)
 
+    def extra_repr(self):
+        return 'output_size={}, return_mask={}'.format(self.output_size,
+                                                       self.return_mask)
+
 
 class AdaptiveMaxPool2D(layers.Layer):
     """
@@ -985,6 +1022,10 @@ class AdaptiveMaxPool2D(layers.Layer):
             return_mask=self._return_mask,
             name=self._name)
 
+    def extra_repr(self):
+        return 'output_size={}, return_mask={}'.format(self._output_size,
+                                                       self._return_mask)
+
 
 class AdaptiveMaxPool3D(layers.Layer):
     """
@@ -1067,3 +1108,7 @@ class AdaptiveMaxPool3D(layers.Layer):
             output_size=self._output_size,
             return_mask=self._return_mask,
             name=self._name)
+
+    def extra_repr(self):
+        return 'output_size={}, return_mask={}'.format(self._output_size,
+                                                       self._return_mask)

python/paddle/nn/layer/rnn.py

@@ -390,6 +390,12 @@ class SimpleRNNCell(RNNCellBase):
     def state_shape(self):
         return (self.hidden_size, )
 
+    def extra_repr(self):
+        s = '{input_size}, {hidden_size}'
+        if self.activation is not "tanh":
+            s += ', activation={activation}'
+        return s.format(**self.__dict__)
+
 
 class LSTMCell(RNNCellBase):
     r"""
@@ -540,6 +546,9 @@ class LSTMCell(RNNCellBase):
         """
         return ((self.hidden_size, ), (self.hidden_size, ))
 
+    def extra_repr(self):
+        return '{input_size}, {hidden_size}'.format(**self.__dict__)
+
 
 class GRUCell(RNNCellBase):
     r"""
@@ -684,6 +693,9 @@ class GRUCell(RNNCellBase):
         """
         return (self.hidden_size, )
 
+    def extra_repr(self):
+        return '{input_size}, {hidden_size}'.format(**self.__dict__)
+
 
 class RNN(Layer):
     r"""
@@ -1053,6 +1065,16 @@ class RNNBase(LayerList):
                                      self.state_components)
         return outputs, final_states
 
+    def extra_repr(self):
+        main_str = '{input_size}, {hidden_size}'
+        if self.num_layers != 1:
+            main_str += ', num_layers={num_layers}'
+        if self.time_major != False:
+            main_str += ', time_major={time_major}'
+        if self.dropout != 0:
+            main_str += ', dropout={dropout}'
+        return main_str.format(**self.__dict__)
+
 
 class SimpleRNN(RNNBase):
     r"""

python/paddle/nn/layer/vision.py

@@ -79,3 +79,11 @@ class PixelShuffle(layers.Layer):
     def forward(self, x):
         return functional.pixel_shuffle(x, self._upscale_factor,
                                         self._data_format, self._name)
+
+    def extra_repr(self):
+        main_str = 'upscale_factor={}'.format(self._upscale_factor)
+        if self._data_format is not 'NCHW':
+            main_str += ', data_format={}'.format(self._data_format)
+        if self._name is not None:
+            main_str += ', name={}'.format(self._name)
+        return main_str