# Copyright (c) 2019 PaddlePaddle Authors. 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
from paddle import fluid

from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.regularizer import L2Decay

from ppdet.core.workspace import register
from ppdet.modeling.ops import SSDOutputDecoder

__all__ = ['BlazeFace']


@register
class BlazeFace(object):
    """
    BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs,
               see https://arxiv.org/abs/1907.05047

    Args:
        backbone (object): backbone instance
        output_decoder (object): `SSDOutputDecoder` instance
        min_sizes (list|None): min sizes of generated prior boxes.
        max_sizes (list|None): max sizes of generated prior boxes. Default: None.
        num_classes (int): number of output classes
        use_density_prior_box (bool): whether or not use density_prior_box
            instead of prior_box
        densities (list|None): the densities of generated density prior boxes,
            this attribute should be a list or tuple of integers
    """

    __category__ = 'architecture'
    __inject__ = ['backbone', 'output_decoder']
    __shared__ = ['num_classes']

    def __init__(self,
                 backbone="BlazeNet",
                 output_decoder=SSDOutputDecoder().__dict__,
                 min_sizes=[[16., 24.], [32., 48., 64., 80., 96., 128.]],
                 max_sizes=None,
                 steps=[8., 16.],
                 num_classes=2,
                 use_density_prior_box=False,
                 densities=[[2, 2], [2, 1, 1, 1, 1, 1]]):
        super(BlazeFace, self).__init__()
        self.backbone = backbone
        self.num_classes = num_classes
        self.output_decoder = output_decoder
        if isinstance(output_decoder, dict):
            self.output_decoder = SSDOutputDecoder(**output_decoder)
        self.min_sizes = min_sizes
        self.max_sizes = max_sizes
        self.steps = steps
        self.use_density_prior_box = use_density_prior_box
        self.densities = densities

    def build(self, feed_vars, mode='train'):
        im = feed_vars['image']
        if mode == 'train':
            gt_box = feed_vars['gt_box']
            gt_label = feed_vars['gt_label']

        body_feats = self.backbone(im)
        locs, confs, box, box_var = self._multi_box_head(
            inputs=body_feats,
            image=im,
            num_classes=self.num_classes,
            use_density_prior_box=self.use_density_prior_box)

        if mode == 'train':
            loss = fluid.layers.ssd_loss(
                locs,
                confs,
                gt_box,
                gt_label,
                box,
                box_var,
                overlap_threshold=0.35,
                neg_overlap=0.35)
            loss = fluid.layers.reduce_sum(loss)
            loss.persistable = True
            return {'loss': loss}
        else:
            pred = self.output_decoder(locs, confs, box, box_var)
            return {'bbox': pred}

    def _multi_box_head(self,
                        inputs,
                        image,
                        num_classes=2,
                        use_density_prior_box=False):
        def permute_and_reshape(input, last_dim):
            trans = fluid.layers.transpose(input, perm=[0, 2, 3, 1])
            compile_shape = [0, -1, last_dim]
            return fluid.layers.reshape(trans, shape=compile_shape)

        def _is_list_or_tuple_(data):
            return (isinstance(data, list) or isinstance(data, tuple))

        locs, confs = [], []
        boxes, vars = [], []
        b_attr = ParamAttr(learning_rate=2., regularizer=L2Decay(0.))

        for i, input in enumerate(inputs):
            min_size = self.min_sizes[i]

            if use_density_prior_box:
                densities = self.densities[i]
                box, var = fluid.layers.density_prior_box(
                    input,
                    image,
                    densities=densities,
                    fixed_sizes=min_size,
                    fixed_ratios=[1.],
                    clip=False,
                    offset=0.5)
            else:
                box, var = fluid.layers.prior_box(
                    input,
                    image,
                    min_sizes=min_size,
                    max_sizes=None,
                    steps=[self.steps[i]] * 2,
                    aspect_ratios=[1.],
                    clip=False,
                    flip=False,
                    offset=0.5)

            num_boxes = box.shape[2]

            box = fluid.layers.reshape(box, shape=[-1, 4])
            var = fluid.layers.reshape(var, shape=[-1, 4])
            num_loc_output = num_boxes * 4
            num_conf_output = num_boxes * num_classes
            # get loc
            mbox_loc = fluid.layers.conv2d(
                input, num_loc_output, 3, 1, 1, bias_attr=b_attr)
            loc = permute_and_reshape(mbox_loc, 4)
            # get conf
            mbox_conf = fluid.layers.conv2d(
                input, num_conf_output, 3, 1, 1, bias_attr=b_attr)
            conf = permute_and_reshape(mbox_conf, 2)

            locs.append(loc)
            confs.append(conf)
            boxes.append(box)
            vars.append(var)

        face_mbox_loc = fluid.layers.concat(locs, axis=1)
        face_mbox_conf = fluid.layers.concat(confs, axis=1)
        prior_boxes = fluid.layers.concat(boxes)
        box_vars = fluid.layers.concat(vars)
        return face_mbox_loc, face_mbox_conf, prior_boxes, box_vars

    def train(self, feed_vars):
        return self.build(feed_vars, 'train')

    def eval(self, feed_vars):
        return self.build(feed_vars, 'eval')

    def test(self, feed_vars):
        return self.build(feed_vars, 'test')

    def is_bbox_normalized(self):
        return True