common.py

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#     http://www.apache.org/licenses/LICENSE-2.0
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# TODO: define the common classes to build a neural network  
from ...fluid.dygraph import BilinearTensorProduct  #DEFINE_ALIAS
from ...fluid.dygraph import Pool2D  #DEFINE_ALIAS
from ...fluid.dygraph import Embedding  #DEFINE_ALIAS
from ...fluid.dygraph import Linear  #DEFINE_ALIAS
from ...fluid.dygraph import layers
from .. import functional as F

__all__ = ['BilinearTensorProduct', 'Pool2D', 'Embedding', 'Linear', 'UpSample']


class UpSample(layers.Layer):
    """
    This op resizes a batch of images.
    The input must be a 3-D Tensor of the shape (num_batches, channels, in_w)
    or 4-D (num_batches, channels, in_h, in_w), or a 5-D Tensor of the shape
    (num_batches, channels, in_d, in_h, in_w) or (num_batches, in_d, in_h, in_w, channels),
    and the resizing only applies on the three dimensions(depth, height and width).
    **Warning:** the parameter :attr:`actual_shape` will be deprecated in the
    future and only use :attr:`out_shape` instead.
    Supporting resample methods:
        'LINEAR' : linear interpolation
        'BILINEAR' : Bilinear interpolation
        'TRILINEAR' : Trilinear interpolation
        'NEAREST' : Nearest neighbor interpolation
        'BICUBIC' : Bicubic interpolation
    
    Linear interpolation is the method of using a line connecting two known quantities 
    to determine the value of an unknown quantity between the two known quantities. 
    
    Nearest neighbor interpolation is to perform nearest neighbor interpolation
    in both the 3rd dimension(in height direction) and the 4th dimension(in width
    direction) on input tensor.

    Bilinear interpolation is an extension of linear interpolation for
    interpolating functions of two variables (e.g. H-direction and
    W-direction in this op) on a rectilinear 2D grid. The key idea is
    to perform linear interpolation first in one direction, and then
    again in the other direction.

    Trilinear interpolation is an extension of linear interpolation for
    interpolating functions of three variables (e.g. D-direction,
    H-direction and W-direction in this op) on a rectilinear 3D grid.
    The linear interpolation is performed on three directions.
    Align_corners and align_mode are optional parameters,the calculation method
    of interpolation can be selected by them.

    Bicubic interpolation is an extension of cubic interpolation for interpolating
    data points on a two-dimensional regular grid. The interpolated surface is
    smoother than corresponding surfaces obtained by bilinear interpolation or
    nearest-neighbor interpolation.

    Example:

    .. code-block:: text

        For scale:

            if align_corners = True && out_size > 1 :
              scale_factor = (in_size-1.0)/(out_size-1.0)

            else:

              scale_factor = float(in_size/out_size)


        Nearest neighbor interpolation:
          if:
              align_corners = False
              input : (N,C,H_in,W_in)
              output: (N,C,H_out,W_out) where:
              H_out = floor (H_{in} * scale_{factor})
              W_out = floor (W_{in} * scale_{factor})
          else:
              align_corners = True
              input : (N,C,H_in,W_in)
              output: (N,C,H_out,W_out) where:
              H_out = round(H_{in} * scale_{factor})
              W_out = round(W_{in} * scale_{factor})

        Linear interpolation:
          if:
              align_corners = False , align_mode = 0
              input : (N,C,W_in)
              output: (N,C,W_out) where:
              W_out = (W_{in}+0.5) * scale_{factor} - 0.5
          else:
              input : (N,C,W_in)
              output: (N,C,W_out) where:
              W_out = W_{in} * scale_{factor}

        Bilinear interpolation:
          if:
              align_corners = False , align_mode = 0
              input : (N,C,H_in,W_in)
              output: (N,C,H_out,W_out) where:
              H_out = (H_{in}+0.5) * scale_{factor} - 0.5
              W_out = (W_{in}+0.5) * scale_{factor} - 0.5
          else:
              input : (N,C,H_in,W_in)
              output: (N,C,H_out,W_out) where:
              H_out = H_{in} * scale_{factor}
              W_out = W_{in} * scale_{factor}

        Bicubic interpolation:
          if:
              align_corners = False
              input : (N,C,H_in,W_in)
              output: (N,C,H_out,W_out) where:
              H_out = (H_{in}+0.5) * scale_{factor} - 0.5
              W_out = (W_{in}+0.5) * scale_{factor} - 0.5
          else:
              input : (N,C,H_in,W_in)
              output: (N,C,H_out,W_out) where:
              H_out = H_{in} * scale_{factor}
              W_out = W_{in} * scale_{factor}

        Trilinear interpolation:
          if:
              align_corners = False , align_mode = 0
              input : (N,C,D_in,H_in,W_in)
              output: (N,C,D_out,H_out,W_out) where:
              D_out = (D_{in}+0.5) * scale_{factor} - 0.5
              H_out = (H_{in}+0.5) * scale_{factor} - 0.5
              W_out = (W_{in}+0.5) * scale_{factor} - 0.5
          else:
              input : (N,C,D_in,H_in,W_in)
              output: (N,C,D_out,H_out,W_out) where:
              D_out = D_{in} * scale_{factor}
              H_out = H_{in} * scale_{factor}
              W_out = W_{in} * scale_{factor}

    For details of nearest neighbor interpolation, please refer to Wikipedia:
    https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation.
    For details of bilinear interpolation, please refer to Wikipedia:
    https://en.wikipedia.org/wiki/Linear_interpolation.
    For details of bilinear interpolation, please refer to Wikipedia:
    https://en.wikipedia.org/wiki/Bilinear_interpolation.
    For details of trilinear interpolation, please refer to Wikipedia:
    https://en.wikipedia.org/wiki/Trilinear_interpolation.
    For details of bicubic interpolation, please refer to Wikipedia:
    https://en.wikipedia.org/wiki/Bicubic_interpolation 

    Parameters:
        input (Variable): 3-D, 4-D or 5-D Tensor, its data type is float32, float64, or uint8,
                          its data format is specified by :attr:`data_format`.
        out_shape(list|tuple|Variable|None): Output shape of image resize
             layer, the shape is (out_w, ) when input is 3-D Tensor ,
             the shape is (out_h, out_w) when input is a 4-D Tensor and is
             (out_d, out_h, out_w) when input is a 5-D Tensor. Default: None. If
             a list, each element can be an integer or a Tensor Variable of shape: [1].
             If a Tensor Variable, its dimensions size should be a 1.
        scale(float|Variable|None): The multiplier for the input height or width. At
             least one of :attr:`out_shape` or :attr:`scale` must be set.
             And :attr:`out_shape` has a higher priority than :attr:`scale`.
             Default: None.
        name(str|None): A name for this layer(optional). If set None, the layer
                        will be named automatically.
        resample(str): The resample method. It supports 'LINEAR',  'BILINEAR', 'TRILINEAR' ,
                       'BICUBIC' and 'NEAREST' currently. Default: 'BILINEAR'
        align_corners(bool) :  An optional bool, If True, the centers of the 4 corner pixels of the
                               input and output tensors are aligned, preserving the values at the
                               corner pixels.
                               Default: True
        align_mode(int)  :  An optional for bilinear interpolation. can be \'0\'
                            for src_idx = scale*(dst_indx+0.5)-0.5 , can be \'1\' for
                            src_idx = scale*dst_index.
        data_format (str, optional): Specify the data format of the input, and the data format of the output
            will be consistent with that of the input. An optional string from:'NCW', `"NCHW"`, `"NHWC"`, `"NCDHW"`,
            `"NDHWC"`. The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
            `[batch_size, input_channels, input_height, input_width]`. When it is `"NCHW"`, the data is stored
            in the order of: `[batch_size, input_channels, input_depth, input_height, input_width]`.
    
    Returns:
        A 3-D Tensor of the shape (num_batches, channels, out_w) or (num_batches, out_w, channels),
        A 4-D Tensor of the shape (num_batches, channels, out_h, out_w) or (num_batches, out_h, out_w, channels),
        or 5-D Tensor of the shape (num_batches, channels, out_d, out_h, out_w) or (num_batches, out_d, out_h, out_w, channels). 

    Raises:
        TypeError: out_shape should be a list or tuple or Variable.
        TypeError: actual_shape should either be Variable or None.
        ValueError: The 'resample' of image_resize can only be 'BILINEAR',
                    'TRILINEAR', 'BICUBIC', or 'NEAREST' currently.
        ValueError: 'BILINEAR', 'BICUBIC' and 'NEAREST' only support 4-D tensor.
        ValueError: 'TRILINEAR' only support 5-D tensor.
        ValueError: One of out_shape and scale must not be None.
        ValueError: out_shape length should be 2 for input 4-D tensor.
        ValueError: out_shape length should be 3 for input 5-D tensor.
        ValueError: scale should be greater than zero.
        TypeError: align_corners should be a bool value
        ValueError: align_mode can only be '0' or '1'
        ValueError: data_format can only be 'NCW', 'NCHW', 'NHWC', 'NCDHW' or 'NDHWC'. 

    Examples:
        .. code-block:: python
            import paddle
            import numpy as np
            import paddle.fluid.dygraph as dg
            upsample_op = paddle.nn.UpSample(out_shape=[12,12])
            input_data = np.random.rand(2,3,6,10).astype("float32")
            place = paddle.fluid.CPUPlace()
            with dg.guard(place) as g:
                input = dg.to_variable(input_data)
                output = upsample_op(input=input)
                print(output.shape)
                # [2L, 3L, 12L, 12L]
    """

    def __init__(self,
                 out_shape=None,
                 scale=None,
                 resample='BILINEAR',
                 align_corners=True,
                 align_mode=1,
                 data_format='NCHW'):
        super(UpSample, self).__init__()
        self.out_shape = out_shape
        self.scale = scale
        self.resample = resample
        self.align_corners = align_corners
        self.align_mode = align_mode
        self.data_format = data_format

    def forward(self, input):
        out = F.interpolate(
            input,
            out_shape=self.out_shape,
            scale=self.scale,
            resample=self.resample,
            align_corners=self.align_corners,
            align_mode=self.align_mode,
            data_format=self.data_format)

        return out