# Copyright (c) Chris Choy (chrischoy@ai.stanford.edu).
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
# of the Software, and to permit persons to whom the Software is furnished to do
# so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
# Please cite "4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural
# Networks", CVPR'19 (https://arxiv.org/abs/1904.08755) if you use any part
# of the code.
import torch

import MinkowskiEngine as ME
import MinkowskiEngine.MinkowskiFunctional as MF

from tests.python.common import data_loader


class UNet(ME.MinkowskiNetwork):

    def __init__(self, in_nchannel, out_nchannel, D):
        super(UNet, self).__init__(D)
        self.block1 = torch.nn.Sequential(
            ME.MinkowskiConvolution(
                in_channels=in_nchannel,
                out_channels=8,
                kernel_size=3,
                stride=1,
                dimension=D),
            ME.MinkowskiBatchNorm(8))

        self.block2 = torch.nn.Sequential(
            ME.MinkowskiConvolution(
                in_channels=8,
                out_channels=16,
                kernel_size=3,
                stride=2,
                dimension=D),
            ME.MinkowskiBatchNorm(16),
        )

        self.block3 = torch.nn.Sequential(
            ME.MinkowskiConvolution(
                in_channels=16,
                out_channels=32,
                kernel_size=3,
                stride=2,
                dimension=D),
            ME.MinkowskiBatchNorm(32))

        self.block3_tr = torch.nn.Sequential(
            ME.MinkowskiConvolutionTranspose(
                in_channels=32,
                out_channels=16,
                kernel_size=3,
                stride=2,
                dimension=D),
            ME.MinkowskiBatchNorm(16))

        self.block2_tr = torch.nn.Sequential(
            ME.MinkowskiConvolutionTranspose(
                in_channels=32,
                out_channels=16,
                kernel_size=3,
                stride=2,
                dimension=D),
            ME.MinkowskiBatchNorm(16))

        self.conv1_tr = ME.MinkowskiConvolution(
            in_channels=24,
            out_channels=out_nchannel,
            kernel_size=1,
            stride=1,
            dimension=D)

    def forward(self, x):
        out_s1 = self.block1(x)
        out = MF.relu(out_s1)

        out_s2 = self.block2(out)
        out = MF.relu(out_s2)

        out_s4 = self.block3(out)
        out = MF.relu(out_s4)

        out = MF.relu(self.block3_tr(out))
        out = ME.cat(out, out_s2)

        out = MF.relu(self.block2_tr(out))
        out = ME.cat(out, out_s1)

        return self.conv1_tr(out)


if __name__ == '__main__':
    # loss and network
    net = UNet(3, 5, D=2)
    print(net)

    # a data loader must return a tuple of coords, features, and labels.
    coords, feat, label = data_loader()
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    net = net.to(device)
    input = ME.SparseTensor(feat, coords, device=device)

    # Forward
    output = net(input)