clean redundant API alias in 2.0 - part 1 (#29928)

* rm check_import_scipy, rm chunk_eval and mean_iou in paddle.metric.__init__.py

* Revert "rm check_import_scipy, rm chunk_eval and mean_iou in paddle.metric.__init__.py"

This reverts commit 179ba8c2b22bc31fe8d8a126e31820792cbd0f4e.

* delete paddle.metric.chunk_eval and paddle.metric.mean_iou

* delete paddle.nn.clip and paddle.nn.clip_by_norm

* delete paddle.nn.functional.activation.hard_sigmoid and paddle.nn.functional.activation.hard_swish

* delete paddle.nn.Pool2D, paddle.nn.BilinearTensorProduct, paddle.nn.RowConv, paddle.nn.functional.row_conv

* fix extension import error

* fix unittest for row_conv and Pool2D

python/paddle/fluid/contrib/slim/tests/test_imperative_qat_user_defined.py

@@ -26,7 +26,7 @@ from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware
 from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
 from paddle.nn import Sequential
 from paddle.fluid.dygraph import Conv2D
-from paddle.nn import Pool2D
+from paddle.fluid.dygraph import Pool2D
 from paddle.fluid.dygraph import Linear
 from paddle.fluid.log_helper import get_logger
 

python/paddle/fluid/layers/nn.py

@@ -12352,10 +12352,11 @@ def clip_by_norm(x, max_norm, name=None):
         .. code-block:: python
 
             import paddle
-            import numpy as np
+            import paddle.fluid as fluid
 
-            input = paddle.to_tensor(data=np.array([[0.1, 0.2], [0.3, 0.4]]), dtype="float32")
-            reward = paddle.nn.clip_by_norm(x=input, max_norm=1.0)
+            input = paddle.to_tensor([[2.0, 2.0], [2.0, 2.0]], dtype='float32')
+            reward = fluid.layers.clip_by_norm(x=input, max_norm=1.0)
+            # [[0.5, 0.5], [0.5, 0.5]]
     """
 
     if in_dygraph_mode():

python/paddle/fluid/tests/unittests/test_row_conv.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 numpy as np
-from paddle import fluid, nn
-import paddle.fluid.dygraph as dg
-import paddle.fluid.initializer as I
-import paddle.nn.functional as F
-import unittest
-
-
-class RowConvTestCase(unittest.TestCase):
-    def __init__(self,
-                 methodName='runTest',
-                 batch_size=4,
-                 num_channels=8,
-                 time_steps=12,
-                 context_size=3,
-                 act=None,
-                 dtype="float32"):
-        super(RowConvTestCase, self).__init__(methodName=methodName)
-        self.batch_size = batch_size
-        self.num_channels = num_channels
-        self.time_steps = time_steps
-        self.context_size = context_size
-        self.act = act
-        self.dtype = dtype
-
-    def setUp(self):
-        input_shape = (self.batch_size, self.time_steps, self.num_channels)
-        self.input = np.random.uniform(size=input_shape).astype(self.dtype)
-        self.weight_shape = weight_shape = (self.context_size + 1,
-                                            self.num_channels)
-        self.weight = np.random.uniform(size=weight_shape).astype(self.dtype)
-
-    def fluid_layer(self, place):
-        main = fluid.Program()
-        start = fluid.Program()
-        with fluid.unique_name.guard():
-            with fluid.program_guard(main, start):
-                x = fluid.data(
-                    "input", [-1, -1, self.num_channels], dtype=self.dtype)
-                y = fluid.layers.row_conv(
-                    x,
-                    self.context_size,
-                    param_attr=I.NumpyArrayInitializer(self.weight),
-                    act=self.act)
-        exe = fluid.Executor(place)
-        exe.run(start)
-        y_np, = exe.run(main, feed={"input": self.input}, fetch_list=[y])
-        return y_np
-
-    def functional_declarative(self, place):
-        main = fluid.Program()
-        start = fluid.Program()
-        with fluid.unique_name.guard():
-            with fluid.program_guard(main, start):
-                x = fluid.data(
-                    "input", [-1, -1, self.num_channels], dtype=self.dtype)
-                w = fluid.data("weight", self.weight_shape, dtype=self.dtype)
-                y = F.extension.row_conv(x, w, act=self.act)
-        exe = fluid.Executor(place)
-        exe.run(start)
-        y_np, = exe.run(main,
-                        feed={"input": self.input,
-                              "weight": self.weight},
-                        fetch_list=[y])
-        return y_np
-
-    def functional_imperative(self, place):
-        with dg.guard(place):
-            x_var = dg.to_variable(self.input)
-            w_var = dg.to_variable(self.weight)
-            y_var = F.extension.row_conv(x_var, w_var, act=self.act)
-            y_np = y_var.numpy()
-        return y_np
-
-    def nn_layer(self, place):
-        with dg.guard(place):
-            x_var = dg.to_variable(self.input)
-            conv = nn.RowConv(
-                self.num_channels,
-                self.context_size,
-                param_attr=I.NumpyArrayInitializer(self.weight),
-                act=self.act,
-                dtype=self.dtype)
-            y_var = conv(x_var)
-            y_np = y_var.numpy()
-        return y_np
-
-    def _test_equivalence(self, place):
-        result1 = self.fluid_layer(place)
-        result2 = self.functional_declarative(place)
-        result3 = self.functional_imperative(place)
-        result4 = self.nn_layer(place)
-        np.testing.assert_array_almost_equal(result1, result2)
-        np.testing.assert_array_almost_equal(result2, result3)
-        np.testing.assert_array_almost_equal(result3, result4)
-
-    def runTest(self):
-        place = fluid.CPUPlace()
-        self._test_equivalence(place)
-
-        if fluid.core.is_compiled_with_cuda():
-            palce = fluid.CUDAPlace(0)
-            self._test_equivalence(place)
-
-
-def load_tests(loader, standard_tests, pattern):
-    suite = unittest.TestSuite()
-    suite.addTest(RowConvTestCase(methodName="runTest"))
-    suite.addTest(RowConvTestCase(methodName="runTest", act="sigmoid"))
-    suite.addTest(
-        RowConvTestCase(
-            methodName="runTest", context_size=5, act="sigmoid"))
-    return suite
-
-
-if __name__ == "__main__":
-    unittest.main()

python/paddle/metric/__init__.py

@@ -15,9 +15,4 @@
 from .metrics import *
 from . import metrics
 
-from ..fluid.layers.nn import chunk_eval, mean_iou
-
-__all__ = metrics.__all__ + [
-    'chunk_eval',
-    'mean_iou',
-]
+__all__ = metrics.__all__

python/paddle/nn/__init__.py

@@ -34,9 +34,6 @@ __all__ += weight_norm_hook.__all__
 from .clip import ClipGradByGlobalNorm  #DEFINE_ALIAS
 from .clip import ClipGradByNorm  #DEFINE_ALIAS
 from .clip import ClipGradByValue  #DEFINE_ALIAS
-# from .clip import set_gradient_clip        #DEFINE_ALIAS
-from .clip import clip  #DEFINE_ALIAS
-from .clip import clip_by_norm  #DEFINE_ALIAS
 # from .control_flow import cond  #DEFINE_ALIAS
 # from .control_flow import DynamicRNN        #DEFINE_ALIAS
 # from .control_flow import StaticRNN        #DEFINE_ALIAS
@@ -71,8 +68,6 @@ from .layer.activation import Tanhshrink  #DEFINE_ALIAS
 from .layer.activation import ThresholdedReLU  #DEFINE_ALIAS
 from .layer.activation import LogSoftmax  #DEFINE_ALIAS
 from .layer.activation import Maxout  #DEFINE_ALIAS
-from .layer.common import BilinearTensorProduct  #DEFINE_ALIAS
-from .layer.common import Pool2D  #DEFINE_ALIAS
 from .layer.common import Pad1D  #DEFINE_ALIAS
 from .layer.common import Pad2D  #DEFINE_ALIAS
 from .layer.common import Pad3D  #DEFINE_ALIAS
@@ -108,7 +103,6 @@ from .layer.conv import Conv2DTranspose  #DEFINE_ALIAS
 from .layer.conv import Conv3DTranspose  #DEFINE_ALIAS
 # from .layer.conv import TreeConv        #DEFINE_ALIAS
 # from .layer.conv import Conv1D        #DEFINE_ALIAS
-from .layer.extension import RowConv  #DEFINE_ALIAS
 from .layer.common import Linear
 # from .layer.loss import NCELoss        #DEFINE_ALIAS
 from .layer.loss import BCEWithLogitsLoss  #DEFINE_ALIAS

python/paddle/nn/clip.py

@@ -16,16 +16,5 @@
 from ..fluid.clip import ClipGradByGlobalNorm  #DEFINE_ALIAS
 from ..fluid.clip import ClipGradByNorm  #DEFINE_ALIAS
 from ..fluid.clip import ClipGradByValue  #DEFINE_ALIAS
-from ..fluid.layers import clip  #DEFINE_ALIAS
 
-from ..fluid.layers import clip_by_norm  #DEFINE_ALIAS
-
-__all__ = [
-    #       'ErrorClipByValue',
-    'ClipGradByGlobalNorm',
-    'ClipGradByNorm',
-    'ClipGradByValue',
-    #       'set_gradient_clip',
-    'clip',
-    'clip_by_norm'
-]
+__all__ = ['ClipGradByGlobalNorm', 'ClipGradByNorm', 'ClipGradByValue']

python/paddle/nn/functional/__init__.py

@@ -88,7 +88,6 @@ from .conv import conv3d_transpose  #DEFINE_ALIAS
 # from .extension import multiclass_nms  #DEFINE_ALIAS
 # from .extension import polygon_box_transform  #DEFINE_ALIAS
 # from .extension import random_crop  #DEFINE_ALIAS
-# from .extension import row_conv  #DEFINE_ALIAS
 # from .extension import rpn_target_assign  #DEFINE_ALIAS
 # from .extension import similarity_focus  #DEFINE_ALIAS
 # from .extension import target_assign  #DEFINE_ALIAS

python/paddle/nn/functional/activation.py

@@ -15,8 +15,6 @@
 # TODO: define activation functions of neural network
 from ...fluid.layers import brelu  #DEFINE_ALIAS
 # from ...fluid.layers import erf  #DEFINE_ALIAS
-from ...fluid.layers import hard_sigmoid  #DEFINE_ALIAS
-from ...fluid.layers import hard_swish  #DEFINE_ALIAS
 from ...fluid.layers import maxout  #DEFINE_ALIAS
 # from ...fluid.layers import soft_relu  #DEFINE_ALIAS
 from ...fluid.layers import swish  #DEFINE_ALIAS
@@ -24,6 +22,7 @@ from ...fluid.layers import sigmoid  #DEFINE_ALIAS
 from ...tensor.math import tanh  #DEFINE_ALIAS
 
 __all__ = [
+    'brelu',
     'elu',
     'gelu',
     'hardshrink',

python/paddle/nn/functional/extension.py

@@ -14,7 +14,7 @@
 
 # TODO: define the extention functions
 
-__all__ = ['diag_embed', 'row_conv']
+__all__ = ['diag_embed']
 
 import numpy as np
 from ...fluid.data_feeder import check_dtype
@@ -138,64 +138,3 @@ def diag_embed(input, offset=0, dim1=-2, dim2=-1):
         outputs={'Out': [out]})
     out.stop_gradient = True
     return out
-
-
-@templatedoc()
-def row_conv(input, weight, act=None):
-    """
-
-    ${comment}
-
-    Args:
-        input (Tensor):  the input(X) is a LodTensor or tensor, LodTensor(X) 
-            supports variable time-length input sequences. The underlying 
-            tensor in this LoDTensor is a matrix with shape (T, D), where 
-            T is the total time steps in this mini-batch and D is the input 
-            data dimension. 
-            If the input is a padded minibatch, the shape of the input is 
-            (N, T, D), N is batch size, T is the max time steps in the batch,
-             D is the input data dimension.
-        weight (Tensor): The weight. A Tensor with shape 
-            (future_context_size + 1, D), where future_context_size is the 
-            context size of the RowConv operator.
-        act (str): Non-linear activation to be applied to output variable.
-
-    Returns:
-        ${out_comment}.
-
-    Examples:
-        .. code-block:: python
-
-            from paddle import fluid, nn
-            import paddle.nn.functional as F
-            import numpy as np
-
-            batch_size = 4
-            time_steps = 8
-            feature_size = 6
-            context_size = 4
-            x = np.random.randn(batch_size, time_steps, feature_size).astype(np.float32)
-            weight = np.random.randn(context_size + 1, feature_size).astype(np.float32)
-
-            x_var = paddle.to_tensor(x)
-            w_var = paddle.to_tensor(weight)
-            y_var = F.extension.row_conv(x_var, w_var)
-            print(y_var.shape)
-
-            # [4, 8, 6]
-    """
-
-    if in_dygraph_mode():
-        pre_act = core.ops.row_conv(input, weight)
-        out = dygraph_utils._append_activation_in_dygraph(pre_act, act)
-        return out
-    else:
-        helper = LayerHelper('row_conv', **locals())
-        dtype = helper.input_dtype()
-
-        inputs = {'X': [input], 'Filter': [weight]}
-        pre_act = helper.create_variable_for_type_inference(dtype)
-        outputs = {'Out': [pre_act]}
-        helper.append_op(type='row_conv', inputs=inputs, outputs=outputs)
-        out = helper.append_activation(pre_act)
-    return out

python/paddle/nn/layer/__init__.py

@@ -17,7 +17,6 @@
 from . import activation
 from . import loss
 from . import conv
-from . import extension
 from . import activation
 from . import norm
 from . import rnn
@@ -28,7 +27,6 @@ from . import transformer
 from .activation import *
 from .loss import *
 from .conv import *
-from .extension import *
 from .activation import *
 from .norm import *
 from .rnn import *
@@ -41,9 +39,7 @@ from .activation import LeakyReLU  #DEFINE_ALIAS
 from .activation import Sigmoid  #DEFINE_ALIAS
 from .activation import Softmax  #DEFINE_ALIAS
 from .activation import LogSoftmax  #DEFINE_ALIAS
-from .common import BilinearTensorProduct  #DEFINE_ALIAS
 from .common import Bilinear  #DEFINE_ALIAS
-from .common import Pool2D  #DEFINE_ALIAS
 from .common import Pad1D  #DEFINE_ALIAS
 from .common import Pad2D  #DEFINE_ALIAS
 from .common import Pad3D  #DEFINE_ALIAS
@@ -79,7 +75,6 @@ from .conv import Conv2DTranspose  #DEFINE_ALIAS
 from .conv import Conv3DTranspose  #DEFINE_ALIAS
 # from .conv import TreeConv        #DEFINE_ALIAS
 # from .conv import Conv1D        #DEFINE_ALIAS
-from .extension import RowConv  #DEFINE_ALIAS
 # from .loss import NCELoss        #DEFINE_ALIAS
 from .loss import BCEWithLogitsLoss  #DEFINE_ALIAS
 from .loss import CrossEntropyLoss  #DEFINE_ALIAS

python/paddle/nn/layer/common.py

@@ -14,16 +14,12 @@
 
 # TODO: define the common classes to build a neural network
 import paddle
-from ...fluid.dygraph import BilinearTensorProduct  #DEFINE_ALIAS
-from ...fluid.dygraph import Pool2D  #DEFINE_ALIAS
 from ...fluid.dygraph import Flatten  #DEFINE_ALIAS
 from ...fluid.dygraph import layers
 from .. import functional as F
 from ...fluid.framework import _dygraph_tracer
 
 __all__ = [
-    'BilinearTensorProduct',
-    'Pool2D',
     'Embedding',
     'Linear',
     'Upsample',

python/paddle/nn/layer/extension.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.
-
-__all__ = ['RowConv']
-
-from ...fluid.dygraph import layers
-from .. import functional as F
-
-
-class RowConv(layers.Layer):
-    """
-    **Row-convolution operator**
-
-    The row convolution is called lookahead convolution.  This operator was 
-    introduced in the following paper for 
-    `DeepSpeech2 <http://www.cs.cmu.edu/~dyogatam/papers/wang+etal.iclrworkshop2016.pdf>`_.
-
-    The main motivation is that a bidirectional RNN, useful in DeepSpeech like 
-    speech models, learns representation for a sequence by performing a
-    forward and a backward pass through the entire sequence. However, unlike
-    unidirectional RNNs, bidirectional RNNs are challenging to deploy in an online
-    and low-latency setting. The lookahead convolution incorporates information
-    from future subsequences in a computationally efficient manner to improve
-    unidirectional recurrent neural networks. The row convolution operator is
-    different from the 1D sequence convolution, and is computed as follows:
-
-    Given an input sequence X of length t and input dimension D, and a filter 
-    (W) of size context * D.
-
-    More details about row_conv please refer to the design document 
-    `<https://github.com/PaddlePaddle/Paddle/issues/2228#issuecomment-303903645>`_ .
-
-    Parameters:
-        num_channels (int): input data's feature size.
-        future_context_size (int): Future context size. Please note, the shape
-            of convolution kernel is [future_context_size + 1, D].
-        param_attr (ParamAttr): Attributes of parameters, including
-            name, initializer etc. Default: None.
-        act (str): Non-linear activation to be applied to output tensor. Default: None.
-        dtype (str, optional): Data type, it can be "float32". Default: "float32".
-
-    Attributes:
-        weight (Parameter): shape [future_context_size + 1, D], the learnable 
-            weight (convolution kernel) of this layer.
-
-    Returns:
-        None
-
-    Examples:
-        .. code-block:: python
-
-          from paddle import nn
-          import paddle.nn.functional as F
-          import numpy as np
-
-          batch_size = 4
-          time_steps = 8
-          feature_size = 6
-          context_size = 4
-
-          x = np.random.randn(batch_size, time_steps, feature_size).astype(np.float32)
-
-          x = paddle.to_tensor(x)
-          conv = nn.RowConv(feature_size, context_size)
-          y = conv(x)
-          print(y.shape)
-
-          # [4, 8, 6]
-    """
-
-    def __init__(self,
-                 num_channels,
-                 future_context_size,
-                 param_attr=None,
-                 act=None,
-                 dtype="float32"):
-        super(RowConv, self).__init__()
-        self._dtype = dtype
-        self._param_attr = param_attr
-        self._act = act
-
-        filter_shape = [future_context_size + 1, num_channels]
-        self.weight = self.create_parameter(
-            filter_shape, attr=param_attr, dtype=dtype)
-
-    def forward(self, input):
-        out = F.extension.row_conv(input, self.weight, act=self._act)
-        return out