common.py

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import paddle
from paddle.fluid.framework import static_only

__all__ = []


@static_only
def fc(x,
       size,
       num_flatten_dims=1,
       weight_attr=None,
       bias_attr=None,
       activation=None,
       name=None):
    r"""

    Fully-Connected layer can take a tensor or a list of tensor as its inputs.
    It creates a 2-D weight tensor for each input tensor, which represents its
    weight matrix from each input unit to each output unit. The fully connected
    layer multiplies each input tensor with its corresponding weight to produce
    an output tensor with shape :math:`[batch\_size, *, size]` , where :math:`*`
    means any number of additional dimensions. If a list of tensor is given,
    the results of multiple output tensors with shape :math:`[batch\_size, *, size]`
    will be summed up. If :attr:`bias_attr` is not False, a 1-D bias tensor will
    be created and added to the output. Finally, if :attr:`activation` is not None,
    it will be applied to the output as well.

    For a single input tensor :math:`X` , the equation is:

    .. math::

        Out = Act({XW + b})

    For a list of input tensor, the equation is:

    .. math::

        Out = Act({\sum_{i=0}^{N-1}X_iW_i + b})

    where:

    * :math:`N`: The number of the input tensors. :math:`N` equals to :math:`len(X)` if :math:`X` is list of tensor.
    * :math:`X_i`: The i-th input tensor.
    * :math:`W_i`: The i-th weight matrix corresponding i-th input tensor.
    * :math:`b`: The bias created by this layer (if needed).
    * :math:`Act`: The activation function.
    * :math:`Out`: The output tensor.

    .. code-block:: text

        # Case 1, input is a single tensor:
        x.data = [[[0.1, 0.2],
                   [0.3, 0.4]]]
        x.shape = (1, 2, 2) # 1 is batch_size

        out = paddle.static.nn.fc(x=x, size=1, num_flatten_dims=2)

        # Get the output:
        out.data = [[0.83234344], [0.34936576]]
        out.shape = (1, 2, 1)

        # Case 2, input is a list of tensor:
        x0.data = [[[0.1, 0.2],
                    [0.3, 0.4]]]
        x0.shape = (1, 2, 2) # 1 is batch_size

        x1.data = [[[0.1, 0.2, 0.3]]]
        x1.shape = (1, 1, 3)

        out = paddle.static.nn.fc(x=[x0, x1], size=2)

        # Get the output:
        out.data = [[0.18669507, 0.1893476]]
        out.shape = (1, 2)

    Args:
        x (Tensor|list[Tensor]|tuple[Tensor]): A tensor or a list/tuple of tensors. The number of dimensions
            of each tensor is at least 2. The data type should be float16, float32 or float64.
        size (int): The number of output units in this layer, which also means the feature
            size of output tensor.
        num_flatten_dims (int, optional): The fc layer can accept an input tensor with more than
            two dimensions. If this happens, the multi-dimensional tensor will first be flattened
            into a 2-D matrix. The parameter :attr:`num_flatten_dims` determines how the input
            tensor is flattened: the first :math:`num\_flatten\_dims` (inclusive, index starts from 1)
            dimensions will be flatten to form the first dimension of the final matrix (height of
            the matrix), and the rest :math:`rank(x) - num\_flatten\_dims` dimensions are
            flattened to form the second dimension of the final matrix (width of the matrix).
            For example, assuming that :attr:`x` is a 5-dimensional tensor with a shape
            :math:`[2, 3, 4, 5, 6]` , and :attr:`num_flatten_dims` = 3.
            Then, the flattened matrix will have a shape :math:`[2 * 3 * 4, 5 * 6] = [24, 30]` .
            Default: 1.
        weight_attr (ParamAttr, optional): The attribute for the learnable weight.
            The default value is None, and the weight will be initialized to zero.
            For detailed information, please refer to :attr:`paddle.ParamAttr`.
            Warning, if x is a list of tensor, weight_attr should also be a list of same length.
        bias_attr (ParamAttr|bool, optional): The attribute of the learnable bias.
            If it is set to False, no bias will be added to the output.
            If it is set to None or one kind of ParamAttr, a bias parameter will
            be created according to ParamAttr. For detailed information, please refer
            to :attr:`paddle.ParamAttr`. The default value is None and the bias will be
            initialized to zero.
        activation (str, optional): Activation to be applied to the output of
            this layer, such as tanh, softmax, sigmoid, relu. For more information,
            please refer to :ref:`api_guide_activations_en` . Default: None.
        name (str, optional): The default value is None. Normally there is no need for user to set
            it. For more information, please refer to :ref:`api_guide_Name` .

    Returns:
        Tensor, its shape is :math:`[batch\_size, *, size]` , and the data type is same with input.

    Examples:
        .. code-block:: python

          import paddle
          paddle.enable_static()

          # When input is a single tensor
          x = paddle.static.data(name="x", shape=[1, 2, 2], dtype="float32")
          # x: [[[0.1 0.2]
          #      [0.3 0.4]]]
          out = paddle.static.nn.fc(
              x=x,
              size=1,
              num_flatten_dims=2,
              weight_attr=paddle.ParamAttr(initializer=paddle.nn.initializer.Constant(value=0.5)),
              bias_attr=paddle.ParamAttr(initializer=paddle.nn.initializer.Constant(value=1.0)))
          # out: [[[1.15]
          #        [1.35]]]

          # When input is multiple tensors
          x0 = paddle.static.data(name="x0", shape=[1, 2, 2], dtype="float32")
          # x0: [[[0.1 0.2]
          #       [0.3 0.4]]]
          x1 = paddle.static.data(name="x1", shape=[1, 1, 3], dtype="float32")
          # x1: [[[0.1 0.2 0.3]]]
          out = paddle.static.nn.fc(
              x=[x0, x1],
              size=2,
              weight_attr=paddle.ParamAttr(initializer=paddle.nn.initializer.Constant(value=0.5)),
              bias_attr=paddle.ParamAttr(initializer=paddle.nn.initializer.Constant(value=1.0)))
          # out: [[1.8 1.8]]
    """
    return paddle.fluid.layers.fc(input=x,
                                  size=size,
                                  num_flatten_dims=num_flatten_dims,
                                  param_attr=weight_attr,
                                  bias_attr=bias_attr,
                                  act=activation,
                                  name=name)


@static_only
def deform_conv2d(x,
                  offset,
                  mask,
                  num_filters,
                  filter_size,
                  stride=1,
                  padding=0,
                  dilation=1,
                  groups=1,
                  deformable_groups=1,
                  im2col_step=1,
                  weight_attr=None,
                  bias_attr=None,
                  name=None):
    r"""

    Compute 2-D deformable convolution on 4-D input.
    Given input image x, output feature map y, the deformable convolution operation can be expressed as follow:


    Deformable Convolution v2:

    .. math::

        y(p) = \sum_{k=1}^{K}{w_k * x(p + p_k + \Delta p_k) * \Delta m_k}

    Deformable Convolution v1:

    .. math::

        y(p) = \sum_{k=1}^{K}{w_k * x(p + p_k + \Delta p_k)}

    Where :math:`\Delta p_k` and :math:`\Delta m_k` are the learnable offset and modulation scalar for the k-th location,
    Which :math:`\Delta m_k` is one in deformable convolution v1. Please refer to `Deformable ConvNets v2: More Deformable, Better Results
    <https://arxiv.org/abs/1811.11168v2>`_ and `Deformable Convolutional Networks <https://arxiv.org/abs/1703.06211>`_.

    Example:
        - Input:

          X shape: :math:`(N, C_{in}, H_{in}, W_{in})`

          Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)`

          Offset shape: :math:`(N, 2 * deformable\_groups * H_f * H_w, H_{in}, W_{in})`

          Mask shape: :math:`(N, deformable\_groups * H_f * H_w, H_{in}, W_{in})`

        - Output:

          Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`

        Where

        .. math::

            H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\
            W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1

    Args:
        x (Tensor): The input image with [N, C, H, W] format. A Tensor with type
            float32, float64.
        offset (Tensor): The input coordinate offset of deformable convolution layer.
            A Tensor with type float32, float64.
        mask (Tensor, Optional): The input mask of deformable convolution layer.
            A Tensor with type float32, float64. It should be None when you use
            deformable convolution v1.
        num_filters(int): The number of filter. It is as same as the output
            image channel.
        filter_size (int|list|tuple): The filter size. If filter_size is a list/tuple,
            it must contain two integers, (filter_size_H, filter_size_W).
            Otherwise, the filter will be a square.
        stride (int|list|tuple, Optional): The stride size. If stride is a list/tuple, it must
            contain two integers, (stride_H, stride_W). Otherwise, the
            stride_H = stride_W = stride. Default: stride = 1.
        padding (int|list|tuple, Optional): The padding size. If padding is a list/tuple, it must
            contain two integers, (padding_H, padding_W). Otherwise, the
            padding_H = padding_W = padding. Default: padding = 0.
        dilation (int|list|tuple, Optional): The dilation size. If dilation is a list/tuple, it must
            contain two integers, (dilation_H, dilation_W). Otherwise, the
            dilation_H = dilation_W = dilation. Default: dilation = 1.
        groups (int, Optional): The groups number of the deformable 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: groups=1.
        deformable_groups (int, Optional): The number of deformable group partitions.
            Default: deformable_groups = 1.
        im2col_step (int, Optional): Maximum number of images per im2col computation;
            The total batch size should be devisable by this value or smaller
            than this value; if you face out of memory problem, you can try
            to use a smaller value here.
            Default: im2col_step = 1.
        weight_attr (ParamAttr, Optional): The parameter attribute for learnable parameters/weights
            of deformable conv. If it is set to None or one attribute of ParamAttr,
            deformable conv will create ParamAttr as weight_attr.
            If the Initializer of the weight_attr is not set, the parameter is
            initialized with :math:`Normal(0.0, std)`, and the
            :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None.
        bias_attr (ParamAttr|bool, Optional): The parameter attribute for the bias of
            deformable conv layer. 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): For details, please refer to :ref:`api_guide_Name`.
                        Generally, no setting is required. Default: None.
    Returns:
        Tensor: The tensor storing the deformable convolution \
                  result. A Tensor with type float32, float64.

    Examples:
        .. code-block:: python

          #deformable conv v2:

          import paddle
          paddle.enable_static()

          C_in, H_in, W_in = 3, 32, 32
          filter_size, deformable_groups = 3, 1
          data = paddle.static.data(name='data', shape=[None, C_in, H_in, W_in], dtype='float32')
          offset = paddle.static.data(name='offset', shape=[None, 2*deformable_groups*filter_size**2, H_in, W_in], dtype='float32')
          mask = paddle.static.data(name='mask', shape=[None, deformable_groups*filter_size**2, H_in, W_in], dtype='float32')
          out = paddle.static.nn.deform_conv2d(x=data, offset=offset, mask=mask,
                                             num_filters=2, filter_size=filter_size, padding=1)

          #deformable conv v1:

          import paddle
          paddle.enable_static()

          C_in, H_in, W_in = 3, 32, 32
          filter_size, deformable_groups = 3, 1
          data = paddle.static.data(name='data', shape=[None, C_in, H_in, W_in], dtype='float32')
          offset = paddle.static.data(name='offset', shape=[None, 2*deformable_groups*filter_size**2, H_in, W_in], dtype='float32')
          out = paddle.static.nn.deform_conv2d(x=data, offset=offset, mask=None,
                                             num_filters=2, filter_size=filter_size, padding=1)
    """

    if mask is None:
        return paddle.fluid.layers.deformable_conv(
            input=x,
            offset=offset,
            mask=mask,
            num_filters=num_filters,
            filter_size=filter_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=groups,
            deformable_groups=deformable_groups,
            im2col_step=im2col_step,
            param_attr=weight_attr,
            bias_attr=bias_attr,
            modulated=False,
            name=name)
    else:
        return paddle.fluid.layers.deformable_conv(
            input=x,
            offset=offset,
            mask=mask,
            num_filters=num_filters,
            filter_size=filter_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=groups,
            deformable_groups=deformable_groups,
            im2col_step=im2col_step,
            param_attr=weight_attr,
            bias_attr=bias_attr,
            modulated=True,
            name=name)