# 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

from collections import OrderedDict
import copy

import paddle.fluid as fluid

from ppdet.experimental import mixed_precision_global_state
from ppdet.core.workspace import register

from .input_helper import multiscale_def

__all__ = ['MaskRCNN']


@register
class MaskRCNN(object):
    """
    Mask R-CNN architecture, see https://arxiv.org/abs/1703.06870
    Args:
        backbone (object): backbone instance
        rpn_head (object): `RPNhead` instance
        bbox_assigner (object): `BBoxAssigner` instance
        roi_extractor (object): ROI extractor instance
        bbox_head (object): `BBoxHead` instance
        mask_assigner (object): `MaskAssigner` instance
        mask_head (object): `MaskHead` instance
        fpn (object): feature pyramid network instance
    """

    __category__ = 'architecture'
    __inject__ = [
        'backbone', 'rpn_head', 'bbox_assigner', 'roi_extractor', 'bbox_head',
        'mask_assigner', 'mask_head', 'fpn'
    ]

    def __init__(self,
                 backbone,
                 rpn_head,
                 bbox_head='BBoxHead',
                 bbox_assigner='BBoxAssigner',
                 roi_extractor='RoIAlign',
                 mask_assigner='MaskAssigner',
                 mask_head='MaskHead',
                 rpn_only=False,
                 fpn=None):
        super(MaskRCNN, self).__init__()
        self.backbone = backbone
        self.rpn_head = rpn_head
        self.bbox_assigner = bbox_assigner
        self.roi_extractor = roi_extractor
        self.bbox_head = bbox_head
        self.mask_assigner = mask_assigner
        self.mask_head = mask_head
        self.rpn_only = rpn_only
        self.fpn = fpn

    def build(self, feed_vars, mode='train'):
        if mode == 'train':
            required_fields = [
                'gt_class', 'gt_bbox', 'gt_mask', 'is_crowd', 'im_info'
            ]
        else:
            required_fields = ['im_shape', 'im_info']
        self._input_check(required_fields, feed_vars)
        im = feed_vars['image']
        im_info = feed_vars['im_info']

        mixed_precision_enabled = mixed_precision_global_state() is not None
        # cast inputs to FP16
        if mixed_precision_enabled:
            im = fluid.layers.cast(im, 'float16')

        # backbone
        body_feats = self.backbone(im)

        # cast features back to FP32
        if mixed_precision_enabled:
            body_feats = OrderedDict((k, fluid.layers.cast(v, 'float32'))
                                     for k, v in body_feats.items())

        # FPN
        spatial_scale = None
        if self.fpn is not None:
            body_feats, spatial_scale = self.fpn.get_output(body_feats)

        # RPN proposals
        rois = self.rpn_head.get_proposals(body_feats, im_info, mode=mode)

        if mode == 'train':
            rpn_loss = self.rpn_head.get_loss(im_info, feed_vars['gt_bbox'],
                                              feed_vars['is_crowd'])

            outs = self.bbox_assigner(
                rpn_rois=rois,
                gt_classes=feed_vars['gt_class'],
                is_crowd=feed_vars['is_crowd'],
                gt_boxes=feed_vars['gt_bbox'],
                im_info=feed_vars['im_info'])
            rois = outs[0]
            labels_int32 = outs[1]

            if self.fpn is None:
                last_feat = body_feats[list(body_feats.keys())[-1]]
                roi_feat = self.roi_extractor(last_feat, rois)
            else:
                roi_feat = self.roi_extractor(body_feats, rois, spatial_scale)

            loss = self.bbox_head.get_loss(roi_feat, labels_int32, *outs[2:])
            loss.update(rpn_loss)

            mask_rois, roi_has_mask_int32, mask_int32 = self.mask_assigner(
                rois=rois,
                gt_classes=feed_vars['gt_class'],
                is_crowd=feed_vars['is_crowd'],
                gt_segms=feed_vars['gt_mask'],
                im_info=feed_vars['im_info'],
                labels_int32=labels_int32)
            if self.fpn is None:
                bbox_head_feat = self.bbox_head.get_head_feat()
                feat = fluid.layers.gather(bbox_head_feat, roi_has_mask_int32)
            else:
                feat = self.roi_extractor(
                    body_feats, mask_rois, spatial_scale, is_mask=True)

            mask_loss = self.mask_head.get_loss(feat, mask_int32)
            loss.update(mask_loss)

            total_loss = fluid.layers.sum(list(loss.values()))
            loss.update({'loss': total_loss})
            return loss

        else:
            if self.rpn_only:
                im_scale = fluid.layers.slice(
                    im_info, [1], starts=[2], ends=[3])
                im_scale = fluid.layers.sequence_expand(im_scale, rois)
                rois = rois / im_scale
                return {'proposal': rois}
            mask_name = 'mask_pred'
            mask_pred, bbox_pred = self.single_scale_eval(
                body_feats, mask_name, rois, im_info, feed_vars['im_shape'],
                spatial_scale)
            return {'bbox': bbox_pred, 'mask': mask_pred}

    def build_multi_scale(self, feed_vars, mask_branch=False):
        required_fields = ['image', 'im_info']
        self._input_check(required_fields, feed_vars)

        result = {}
        if not mask_branch:
            assert 'im_shape' in feed_vars, \
                "{} has no im_shape field".format(feed_vars)
            result.update(feed_vars)

        for i in range(len(self.im_info_names) // 2):
            im = feed_vars[self.im_info_names[2 * i]]
            im_info = feed_vars[self.im_info_names[2 * i + 1]]
            body_feats = self.backbone(im)

            # FPN
            if self.fpn is not None:
                body_feats, spatial_scale = self.fpn.get_output(body_feats)
            rois = self.rpn_head.get_proposals(body_feats, im_info, mode='test')
            if not mask_branch:
                im_shape = feed_vars['im_shape']
                body_feat_names = list(body_feats.keys())
                if self.fpn is None:
                    body_feat = body_feats[body_feat_names[-1]]
                    roi_feat = self.roi_extractor(body_feat, rois)
                else:
                    roi_feat = self.roi_extractor(body_feats, rois,
                                                  spatial_scale)
                pred = self.bbox_head.get_prediction(
                    roi_feat, rois, im_info, im_shape, return_box_score=True)
                bbox_name = 'bbox_' + str(i)
                score_name = 'score_' + str(i)
                if 'flip' in im.name:
                    bbox_name += '_flip'
                    score_name += '_flip'
                result[bbox_name] = pred['bbox']
                result[score_name] = pred['score']
            else:
                mask_name = 'mask_pred_' + str(i)
                bbox_pred = feed_vars['bbox']
                #result.update({im.name: im})
                if 'flip' in im.name:
                    mask_name += '_flip'
                    bbox_pred = feed_vars['bbox_flip']
                mask_pred, bbox_pred = self.single_scale_eval(
                    body_feats, mask_name, rois, im_info, feed_vars['im_shape'],
                    spatial_scale, bbox_pred)
                result[mask_name] = mask_pred
        return result

    def single_scale_eval(self,
                          body_feats,
                          mask_name,
                          rois,
                          im_info,
                          im_shape,
                          spatial_scale,
                          bbox_pred=None):
        if not bbox_pred:
            if self.fpn is None:
                last_feat = body_feats[list(body_feats.keys())[-1]]
                roi_feat = self.roi_extractor(last_feat, rois)
            else:
                roi_feat = self.roi_extractor(body_feats, rois, spatial_scale)
            bbox_pred = self.bbox_head.get_prediction(roi_feat, rois, im_info,
                                                      im_shape)
            bbox_pred = bbox_pred['bbox']

        # share weight
        bbox_shape = fluid.layers.shape(bbox_pred)
        bbox_size = fluid.layers.reduce_prod(bbox_shape)
        bbox_size = fluid.layers.reshape(bbox_size, [1, 1])
        size = fluid.layers.fill_constant([1, 1], value=6, dtype='int32')
        cond = fluid.layers.less_than(x=bbox_size, y=size)

        mask_pred = fluid.layers.create_global_var(
            shape=[1],
            value=0.0,
            dtype='float32',
            persistable=False,
            name=mask_name)

        def noop():
            fluid.layers.assign(input=bbox_pred, output=mask_pred)

        def process_boxes():
            bbox = fluid.layers.slice(bbox_pred, [1], starts=[2], ends=[6])

            im_scale = fluid.layers.slice(im_info, [1], starts=[2], ends=[3])
            im_scale = fluid.layers.sequence_expand(im_scale, bbox)

            mask_rois = bbox * im_scale
            if self.fpn is None:
                last_feat = body_feats[list(body_feats.keys())[-1]]
                mask_feat = self.roi_extractor(last_feat, mask_rois)
                mask_feat = self.bbox_head.get_head_feat(mask_feat)
            else:
                mask_feat = self.roi_extractor(
                    body_feats, mask_rois, spatial_scale, is_mask=True)

            mask_out = self.mask_head.get_prediction(mask_feat, bbox)
            fluid.layers.assign(input=mask_out, output=mask_pred)

        fluid.layers.cond(cond, noop, process_boxes)
        return mask_pred, bbox_pred

    def _input_check(self, require_fields, feed_vars):
        for var in require_fields:
            assert var in feed_vars, \
                "{} has no {} field".format(feed_vars, var)

    def _inputs_def(self, image_shape):
        im_shape = [None] + image_shape
        # yapf: disable
        inputs_def = {
            'image':    {'shape': im_shape,  'dtype': 'float32', 'lod_level': 0},
            'im_info':  {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
            'im_id':    {'shape': [None, 1], 'dtype': 'int64',   'lod_level': 0},
            'im_shape': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
            'gt_bbox':  {'shape': [None, 4], 'dtype': 'float32', 'lod_level': 1},
            'gt_class': {'shape': [None, 1], 'dtype': 'int32',   'lod_level': 1},
            'is_crowd': {'shape': [None, 1], 'dtype': 'int32',   'lod_level': 1},
            'gt_mask':  {'shape': [None, 2], 'dtype': 'float32', 'lod_level': 3}, # polygon coordinates
            'is_difficult': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
        }
        # yapf: enable
        return inputs_def

    def build_inputs(self,
                     image_shape=[3, None, None],
                     fields=[
                         'image', 'im_info', 'im_id', 'gt_bbox', 'gt_class',
                         'is_crowd', 'gt_mask'
                     ],
                     multi_scale=False,
                     num_scales=-1,
                     use_flip=None,
                     use_dataloader=True,
                     iterable=False,
                     mask_branch=False):
        inputs_def = self._inputs_def(image_shape)
        fields = copy.deepcopy(fields)
        if multi_scale:
            ms_def, ms_fields = multiscale_def(image_shape, num_scales,
                                               use_flip)
            inputs_def.update(ms_def)
            fields += ms_fields
            self.im_info_names = ['image', 'im_info'] + ms_fields
            if mask_branch:
                box_fields = ['bbox', 'bbox_flip'] if use_flip else ['bbox']
                for key in box_fields:
                    inputs_def[key] = {
                        'shape': [6],
                        'dtype': 'float32',
                        'lod_level': 1
                    }
                fields += box_fields
        feed_vars = OrderedDict([(key, fluid.data(
            name=key,
            shape=inputs_def[key]['shape'],
            dtype=inputs_def[key]['dtype'],
            lod_level=inputs_def[key]['lod_level'])) for key in fields])
        use_dataloader = use_dataloader and not mask_branch
        loader = fluid.io.DataLoader.from_generator(
            feed_list=list(feed_vars.values()),
            capacity=64,
            use_double_buffer=True,
            iterable=iterable) if use_dataloader else None
        return feed_vars, loader

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

    def eval(self, feed_vars, multi_scale=None, mask_branch=False):
        if multi_scale:
            return self.build_multi_scale(feed_vars, mask_branch)
        return self.build(feed_vars, 'test')

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