# Copyright (c) 2017 Sony Corporation. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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


class Convolution(Module):
    """N-D Convolution with a bias term.

    For Dilated Convolution (a.k.a. Atrous Convolution), refer to:

    - Chen et al., DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. https://arxiv.org/abs/1606.00915

    - Yu et al., Multi-Scale Context Aggregation by Dilated Convolutions. https://arxiv.org/abs/1511.07122

    Note:

        Convolution is a computationally intensive operation that
        should preferably be run with the `cudnn` backend. NNabla
        then uses CuDNN library functions to determine and cache the
        fastest algorithm for the given set of convolution parameters,
        which results in additional memory consumption which may pose
        a problem for GPUs with insufficient memory size. In that
        case, the `NNABLA_CUDNN_WORKSPACE_LIMIT` environment variable
        can be used to restrict the choice of algorithms to those that
        fit the given workspace memory limit, expressed in bytes. In
        some cases it may also be desired to restrict the automatic
        search to algorithms that produce deterministic (reproducable)
        results. This can be requested by setting the the environment
        variable `NNABLA_CUDNN_DETERMINISTIC` to a non-zero value.

    Args:
        inp (~nnabla.Variable): N-D array.
        outmaps (int): Number of convolution kernels (which is equal to the number of output channels). For example, to apply convolution on an input with 16 types of filters, specify 16.
        kernel (:obj:`tuple` of :obj:`int`): Convolution kernel size. For example, to apply convolution 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 more sparse 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.convolution` 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.convolution(inp, self.W, self.b, self.base_axis,
                             self.pad, self.stride, self.dilation, self.group)


Conv1d = Convolution
Conv2d = Convolution
Conv3d = Convolution
ConvNd = Convolution
