Source code for nnabla.experimental.parametric_function_class.deconvolution

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import numpy as np

import nnabla as nn
import nnabla.functions as F
from nnabla.parameter import get_parameter_or_create, get_parameter
from nnabla.initializer import (
    calc_uniform_lim_glorot,
    ConstantInitializer, NormalInitializer, UniformInitializer)

from .module import Module


[docs]class Deconvolution(Module): """ Deconvolution layer. Args: inp (~nnabla.Variable): N-D array. outmaps (int): Number of deconvolution kernels (which is equal to the number of output channels). For example, to apply deconvolution on an input with 16 types of filters, specify 16. kernel (:obj:`tuple` of :obj:`int`): Convolution kernel size. For example, to apply deconvolution on an image with a 3 (height) by 5 (width) two-dimensional kernel, specify (3,5). pad (:obj:`tuple` of :obj:`int`): Padding sizes for dimensions. stride (:obj:`tuple` of :obj:`int`): Stride sizes for dimensions. dilation (:obj:`tuple` of :obj:`int`): Dilation sizes for dimensions. group (int): Number of groups of channels. This makes connections across channels sparser by grouping connections along map direction. w_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for weight. By default, it is initialized with :obj:`nnabla.initializer.UniformInitializer` within the range determined by :obj:`nnabla.initializer.calc_uniform_lim_glorot`. b_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`): Initializer for bias. By default, it is initialized with zeros if `with_bias` is `True`. base_axis (int): Dimensions up to `base_axis` are treated as the sample dimensions. fix_parameters (bool): When set to `True`, the weights and biases will not be updated. rng (numpy.random.RandomState): Random generator for Initializer. with_bias (bool): Specify whether to include the bias term. Returns: :class:`~nnabla.Variable`: N-D array. See :obj:`~nnabla.functions.deconvolution` for the output shape. """ def __init__(self, inmaps, outmaps, kernel, pad=None, stride=None, dilation=None, group=1, w_init=None, b_init=None, base_axis=1, fix_parameters=False, rng=None, with_bias=True): if w_init is None: w_init = UniformInitializer( calc_uniform_lim_glorot(inmaps, outmaps, tuple(kernel)), rng=rng) if with_bias and b_init is None: b_init = ConstantInitializer() w_shape = (outmaps, inmaps // group) + tuple(kernel) w = nn.Variable.from_numpy_array( w_init(w_shape)).apply(need_grad=not fix_parameters) b = None if with_bias: b_shape = (outmaps, ) b = nn.Variable.from_numpy_array( b_init(b_shape)).apply(need_grad=not fix_parameters) self.W = w self.b = b self.base_axis = base_axis self.pad = pad self.stride = stride self.dilation = dilation self.group = group def __call__(self, inp): return F.deconvolution(inp, self.W, self.b, self.base_axis, self.pad, self.stride, self.dilation, self.group)
Deconv1d = Deconvolution Deconv2d = Deconvolution Deconv3d = Deconvolution DeconvNd = Deconvolution