#! /usr/bin/python # -*- coding: utf-8 -*- import tensorlayer as tl from tensorlayer import logging from tensorlayer.layers.core import Module __all__ = [ 'UpSampling2d', 'DownSampling2d', ] class UpSampling2d(Module): """The :class:`UpSampling2d` class is a up-sampling 2D layer. See `tf.image.resize_images `__. Parameters ---------- scale : int/float or tuple of int/float (height, width) scale factor. method : str The resize method selected through the given string. Default 'bilinear'. - 'bilinear', Bilinear interpolation. - 'nearest', Nearest neighbor interpolation. - 'bicubic', Bicubic interpolation. - 'area', Area interpolation. antialias : boolean Whether to use an anti-aliasing filter when downsampling an image. data_format : str channels_last 'channel_last' (default) or channels_first. name : None or str A unique layer name. Examples --------- With TensorLayer >>> ni = tl.layers.Input([10, 50, 50, 32], name='input') >>> ni = tl.layers.UpSampling2d(scale=(2, 2))(ni) >>> output shape : [10, 100, 100, 32] """ def __init__(self, scale, method='bilinear', antialias=False, data_format='channels_last', name=None, ksize=None): super(UpSampling2d, self).__init__(name) self.method = method self.antialias = antialias self.data_format = data_format self.ksize = ksize logging.info( "UpSampling2d %s: scale: %s method: %s antialias: %s" % (self.name, scale, self.method, self.antialias) ) if isinstance(scale, (list, tuple)) and len(scale) != 2: raise ValueError("scale must be int or tuple/list of length 2") self.scale = (scale, scale) if isinstance(scale, int) else scale self.build(None) self._built = True def __repr__(self): s = '{classname}(scale={scale}, method={method}' if self.name is not None: s += ', name=\'{name}\'' s += ')' return s.format(classname=self.__class__.__name__, scale=self.scale, method=self.method, name=self.name) def build(self, inputs_shape): self.resize = tl.ops.Resize( scale=self.scale, method=self.method, antialias=self.antialias, data_format=self.data_format, ksize=self.ksize ) def forward(self, inputs): """ Parameters ------------ inputs : :class:`Tensor` Inputs tensors with 4-D Tensor of the shape (batch, height, width, channels) """ outputs = self.resize(inputs) return outputs class DownSampling2d(Module): """The :class:`DownSampling2d` class is down-sampling 2D layer. See `tf.image.resize_images `__. Parameters ---------- scale : int/float or tuple of int/float (height, width) scale factor. method : str The resize method selected through the given string. Default 'bilinear'. - 'bilinear', Bilinear interpolation. - 'nearest', Nearest neighbor interpolation. - 'bicubic', Bicubic interpolation. - 'area', Area interpolation. antialias : boolean Whether to use an anti-aliasing filter when downsampling an image. data_format : str channels_last 'channel_last' (default) or channels_first. name : None or str A unique layer name. Examples --------- With TensorLayer >>> ni = tl.layers.Input([10, 50, 50, 32], name='input') >>> ni = tl.layers.DownSampling2d(scale=(2, 2))(ni) >>> output shape : [10, 25, 25, 32] """ def __init__(self, scale, method='bilinear', antialias=False, data_format='channels_last', name=None, ksize=None): super(DownSampling2d, self).__init__(name) self.method = method self.antialias = antialias self.data_format = data_format self.ksize = ksize logging.info( "DownSampling2d %s: scale: %s method: %s antialias: %s" % (self.name, scale, self.method, self.antialias) ) if isinstance(scale, (list, tuple)) and len(scale) != 2: raise ValueError("scale must be int or tuple/list of length 2") self.scale = (scale, scale) if isinstance(scale, int) else scale self.build(None) self._built = True def __repr__(self): s = '{classname}(scale={scale}, method={method}' if self.name is not None: s += ', name=\'{name}\'' s += ')' return s.format(classname=self.__class__.__name__, scale=self.scale, method=self.method, name=self.name) def build(self, inputs_shape): scale = [1.0 / self.scale[0], 1.0 / self.scale[1]] self.resize = tl.ops.Resize( scale=scale, method=self.method, antialias=self.antialias, data_format=self.data_format, ksize=self.ksize ) def forward(self, inputs): """ Parameters ------------ inputs : :class:`Tensor` Inputs tensors with 4-D Tensor of the shape (batch, height, width, channels) """ outputs = self.resize(inputs) return outputs