update hapi/text/text.py

hapi/text/text.py

@@ -37,7 +37,7 @@
 import paddle.fluid as fluid
 import paddle.fluid.layers.utils as utils
 from paddle.fluid.layers.utils import map_structure, flatten, pack_sequence_as
-from paddle.fluid.dygraph import to_variable, Embedding, Linear, LayerNorm, GRUUnit
+from paddle.fluid.dygraph import to_variable, Embedding, Linear, LayerNorm, GRUUnit, Conv2D, Pool2D
 from paddle.fluid.data_feeder import convert_dtype
 
 from paddle.fluid import layers
@@ -49,7 +49,8 @@
     'BeamSearchDecoder', 'MultiHeadAttention', 'FFN',
     'TransformerEncoderLayer', 'TransformerEncoder', 'TransformerDecoderLayer',
     'TransformerDecoder', 'TransformerBeamSearchDecoder', 'Linear_chain_crf',
-    'Crf_decoding', 'SequenceTagging', 'GRUEncoderLayer'
+    'Crf_decoding', 'SequenceTagging', 'GRUEncoderLayer', 'Conv1dPoolLayer',
+    'CNNEncoder'
 ]
 
 
@@ -1896,3 +1897,203 @@ def forward(self, word, lengths, target=None):
             self.linear_chain_crf.weight = self.crf_decoding.weight
             crf_decode = self.crf_decoding(input=emission, length=lengths)
             return crf_decode, lengths
+
+class Conv1dPoolLayer(Layer):
+    """
+        This interface is used to construct a callable object of the ``Conv1DPoolLayer`` class.The ``Conv1DPoolLayer`` is composed of a ``Conv2D``  and a ``Pool2D``  .
+        For more details, refer to code examples.The ``Conv1DPoolLayer`` layer calculates the output based on the input, filter and strides, paddings, dilations, 
+        groups,global_pooling, pool_type,ceil_mode,exclusive parameters.Input and Output are in NCH format, where N is batch size, C is the number of the feature map, 
+        H is the height of the feature map.The data type of Input data and Output data is 'float32' or 'float64'.
+
+    Args:
+        input(Variable):3-D Tensor, shape is [N, C, H], data type can be float32 or float64
+        num_channels(int): The number of channels in the input data.
+        num_filters(int): The number of filters. It is the same as the output channels.
+        filter_size (int): The filter size of Conv1DPoolLayer.       
+        pool_size (int): The pooling size of Conv1DPoolLayer.
+        conv_stride (int): The stride size of the conv Layer in Conv1DPoolLayer. Default: 1
+        pool_stride (int): The stride size of the pool layer in Conv1DPoolLayer. Default: 1
+        conv_padding (int): The padding size of the conv Layer in Conv1DPoolLayer. Default: 0
+        pool_padding (int): The padding of pool layer in Conv1DPoolLayer. Default: 0
+        pool_type (str): Pooling type can be `max` for max-pooling or `avg` for average-pooling. Default: math:`max`
+        global_pooling (bool): Whether to use the global pooling. If global_pooling = true, pool_size and pool_padding while be ignored. Default: False
+        dilation (int): The dilation size of the conv Layer. Default: 1.
+        groups (int): The groups number of the conv Layer. According to grouped convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
+                the first half of the filters is only connected to the first half of the input channels, while the second half of the filters is only 
+                connected to the second half of the input channels. Default: 1.
+        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights of conv layer. If it is set to None or one attribute of 
+                ParamAttr, conv2d will create ParamAttr as param_attr. If the Initializer of the param_attr is not set, the parameter is initialized 
+                with :`Normal(0.0, std)`,and the :`std` is :`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`.Default: None.
+        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv.If it is set to False, no bias will be added to the output units.
+                If it is set to None or one attribute of ParamAttr, conv2d will create ParamAttr as bias_attr. If the Initializer of the bias_attr is not 
+                set, the bias is initialized zero. Default: None.
+        name(str, optional): The default value is None. Normally there is no need for user to set this property. Default: None
+        act (str): Activation type for conv layer, if it is set to None, activation is not appended. Default: None.
+        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn library is installed. Default: False
+        ceil_mode (bool, optional): Whether to use the ceil function to calculate output height and width.
+                False is the default. If it is set to False, the floor function will be used. Default: False.
+        exclusive (bool, optional): Whether to exclude padding points in average pooling mode. Default: True.
+
+    Return:
+            3-D Tensor, the result of input after conv and pool, with the same data type as :`input`
+
+    Return Type:
+            Variable
+
+    Example:
+    ```python
+        import paddle.fluid as fluid
+        from hapi.text import Conv1dPoolLayer
+
+        test=np.random.uniform(-1,1,[2,3,4]).astype('float32')
+        with fluid.dygraph.guard():
+            paddle_input=to_variable(test)
+            print(paddle_input.shape)
+            cov2d=Conv1dPoolLayer(3,4,2,2)
+            paddle_out=cov2d(paddle_input)
+            print(paddle_out.shape)#[2,4,2]
+
+    ```
+    """
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 pool_size,
+                 conv_stride=1,
+                 pool_stride=1,
+                 conv_padding=0,
+                 pool_padding=0,
+                 pool_type='max',
+                 global_pooling=False,
+                 dilation=1,
+                 groups=None,
+                 param_attr=None,
+                 bias_attr=None,
+                 act=None,
+                 use_cudnn=False,
+                 ceil_mode=False,
+                 exclusive=True,
+                 ):
+        super(Conv1dPoolLayer, self).__init__()
+        self._conv2d = Conv2D(num_channels=num_channels,
+                              num_filters=num_filters,
+                              filter_size=[filter_size,1],
+                              stride=[conv_stride,1],
+                              padding=[conv_padding,0],
+                              dilation=[dilation,1],
+                              groups=groups,
+                              param_attr=param_attr,
+                              bias_attr=bias_attr,
+                              use_cudnn=use_cudnn,
+                              act=act)
+        self._pool2d = Pool2D(pool_size=[pool_size,1],
+                              pool_type=pool_type,
+                              pool_stride=[pool_stride,1],
+                              pool_padding=[pool_padding,0],
+                              global_pooling=global_pooling,
+                              use_cudnn=use_cudnn,
+                              ceil_mode=ceil_mode,
+                              exclusive=exclusive
+                              )
+    def forward(self, inputs):
+        x = fluid.layers.unsqueeze(inputs,axes=[-1])
+        x = self._conv2d(x)
+        x = self._pool2d(x)
+        x = fluid.layers.squeeze(x, axes=[-1])
+        return x
+
+
+
+
+class CNNEncoder(Layer):
+    """
+        This interface is used to construct a callable object of the ``CNNEncoder`` class.The ``CNNEncoder`` is composed of a ``Embedding``  and a ``Conv1dPoolLayer``  .
+        For more details, refer to code examples. The ``CNNEncoder`` layer calculates the output based on the input, dict_size and emb_dim, filter_size, num_filters, 
+        use_cuda, is_sparse, param_attr parameters. The type of Input data is a 3-D Tensor .The data type of Input data is 'float32'. Output data are in NCH 
+        format, where N is batch size, C is the number of the feature map, H is the height of the feature map. The data type of Output data is 'float32' or 'float64'.
+
+    Args:
+    num_channels(int|list|tuple): The number of channels in the input data.If num_channels is a list or tuple, the length of num_channels must equal layer_num.If num_channels
+            is a int, all conv1dpoollayer's num_channels are the value of num_channels. 
+    num_filters(int|list|tuple): The number of filters. It is the same as the output channels. If num_filters is a list or tuple, the length of num_filters must equal layer_num.If num_filters
+            is a int, all conv1dpoollayer's num_filters are the value of num_filters. 
+    filter_size(int|list|tuple): The filter size of Conv1DPoolLayer in CNNEncoder. If filter_size is a list or tuple, the length of filter_size must equal layer_num.If filter_size
+            is a int, all conv1dpoollayer's filter_size are the value of filter_size. 
+    pool_size(int|list|tuple): The pooling size of Conv1DPoolLayer in CNNEncoder.If pool_size is a list or tuple, the length of pool_size must equal layer_num.If pool_size
+            is a int, all conv1dpoollayer's pool_size are the value of pool_size. 
+    layer_num(int): The number of conv1dpoolLayer used in CNNEncoder.
+    conv_stride(int|list|tuple): The stride size of the conv Layer in Conv1DPoolLayer. If conv_stride is a list or tuple, the length of conv_stride must equal layer_num.If conv_stride
+            is a int, all conv1dpoollayer's conv_stride are the value of conv_stride.  Default: 1
+    pool_stride(int|list|tuple): The stride size of the pool layer in Conv1DPoolLayer. If pool_stride is a list or tuple, the length of pool_stride must equal layer_num.If pool_stride
+            is a int, all conv1dpoollayer's pool_stride are the value of pool_stride. Default: 1
+    conv_padding(int|list|tuple): The padding size of the conv Layer in Conv1DPoolLayer.If conv_padding is a list or tuple, the length of conv_padding must equal layer_num.If conv_padding
+            is a int, all conv1dpoollayer's conv_padding are the value of conv_padding.  Default: 0
+    pool_padding(int|list|tuple): The padding of pool layer in Conv1DPoolLayer. If pool_padding is a list or tuple, the length of pool_padding must equal layer_num.If pool_padding
+            is a int, all conv1dpoollayer's pool_padding are the value of pool_padding. Default: 0
+    use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn library is installed. Default: False
+    act (str|list|tuple): Activation type for `Conv1dPoollayer` layer, if it is set to None, activation is not appended. Default: None.
+
+    Return:
+            3-D Tensor, the result of input after  embedding and conv1dPoollayer
+
+    Return Type:
+            Variable
+
+    Example:
+    ```python
+        import paddle.fluid as fluid
+        from hapi.text import CNNEncoder
+
+        test=np.random.uniform(1,5,[2,3,4]).astype('float32')
+        with fluid.dygraph.guard():
+            paddle_input=to_variable(test)
+            #print(paddle_input.shape)
+            cov2d=CNNEncoder(3,4,2,2,3)
+            paddle_out=cov2d(paddle_input)
+            print(paddle_out)#[2,12,2]
+
+    ```
+
+    """
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 pool_size,
+                 layer_num,
+                 conv_stride=1,
+                 pool_stride=1,
+                 conv_padding=0,
+                 pool_padding=0,
+                 use_cudnn=False,
+                 act=None
+                 ):
+        super(CNNEncoder, self).__init__()
+        self.num_channels=num_channels
+        self.num_filters=num_filters
+        self.filter_size=filter_size
+        self.pool_size=pool_size
+        self.layer_num=layer_num
+        self.conv_stride=conv_stride
+        self.pool_stride=pool_stride
+        self.conv_padding=conv_padding
+        self.pool_padding=pool_padding
+        self.use_cudnn=use_cudnn
+        self.act=act
+        self.conv_layer = fluid.dygraph.LayerList([Conv1dPoolLayer(num_channels=self.num_channels if isinstance(self.num_channels,int) else self.num_channels[i],
+            num_filters=self.num_filters if isinstance(self.num_channels,int) else self.num_filters [i],
+            filter_size=self.filter_size if isinstance(self.filter_size,int) else self.filter_size[i],
+            pool_size=self.pool_size if isinstance(self.pool_size,int) else self.pool_size[i],
+            conv_stride=self.conv_stride if isinstance(self.conv_stride,int) else self.conv_stride[i],
+            pool_stride=self.pool_stride if isinstance(self.pool_stride,int) else self.pool_stride[i],
+            conv_padding= self.conv_padding if isinstance(self.conv_padding,int) else self.conv_padding[i],
+            pool_padding=self.pool_padding if isinstance(self.pool_padding,int) else self.pool_padding[i],
+            act=self.act[i] if isinstance(self.act,(list,tuple)) else self.act,
+            use_cudnn=self.use_cudnn
+        ) for i in range(layer_num)])
+
+    def forward(self, input):
+        res=[Conv1dPoolLayer(input) for Conv1dPoolLayer in self.conv_layer]
+        out=fluid.layers.concat(input=res,axis=1)
+        return out