#   Copyright (c) 2020 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.

import numpy as np
from numbers import Integral
import paddle.fluid as fluid
from paddle.fluid.dygraph.base import to_variable
from ppdet.core.workspace import register, serializable
from ppdet.py_op.target import generate_rpn_anchor_target, generate_proposal_target, generate_mask_target
from ppdet.py_op.post_process import bbox_post_process


@register
@serializable
class AnchorGeneratorRPN(object):
    def __init__(self,
                 anchor_sizes=[32, 64, 128, 256, 512],
                 aspect_ratios=[0.5, 1.0, 2.0],
                 stride=[16.0, 16.0],
                 variance=[1.0, 1.0, 1.0, 1.0],
                 anchor_start_size=None):
        super(AnchorGeneratorRPN, self).__init__()
        self.anchor_sizes = anchor_sizes
        self.aspect_ratios = aspect_ratios
        self.stride = stride
        self.variance = variance
        self.anchor_start_size = anchor_start_size

    def __call__(self, input, level=None):
        anchor_sizes = self.anchor_sizes if (
            level is None or self.anchor_start_size is None) else (
                self.anchor_start_size * 2**level)
        stride = self.stride if (
            level is None or self.anchor_start_size is None) else (
                self.stride[0] * (2.**level), self.stride[1] * (2.**level))
        anchor, var = fluid.layers.anchor_generator(
            input=input,
            anchor_sizes=anchor_sizes,
            aspect_ratios=self.aspect_ratios,
            stride=stride,
            variance=self.variance)
        return anchor, var


@register
@serializable
class AnchorTargetGeneratorRPN(object):
    def __init__(self,
                 batch_size_per_im=256,
                 straddle_thresh=0.,
                 fg_fraction=0.5,
                 positive_overlap=0.7,
                 negative_overlap=0.3,
                 use_random=True):
        super(AnchorTargetGeneratorRPN, self).__init__()
        self.batch_size_per_im = batch_size_per_im
        self.straddle_thresh = straddle_thresh
        self.fg_fraction = fg_fraction
        self.positive_overlap = positive_overlap
        self.negative_overlap = negative_overlap
        self.use_random = use_random

    def __call__(self, cls_logits, bbox_pred, anchor_box, gt_boxes, is_crowd,
                 im_info):
        anchor_box = anchor_box.numpy()
        gt_boxes = gt_boxes.numpy()
        is_crowd = is_crowd.numpy()
        im_info = im_info.numpy()
        loc_indexes, score_indexes, tgt_labels, tgt_bboxes, bbox_inside_weights = generate_rpn_anchor_target(
            anchor_box, gt_boxes, is_crowd, im_info, self.straddle_thresh,
            self.batch_size_per_im, self.positive_overlap,
            self.negative_overlap, self.fg_fraction, self.use_random)

        loc_indexes = to_variable(loc_indexes)
        score_indexes = to_variable(score_indexes)
        tgt_labels = to_variable(tgt_labels)
        tgt_bboxes = to_variable(tgt_bboxes)
        bbox_inside_weights = to_variable(bbox_inside_weights)

        loc_indexes.stop_gradient = True
        score_indexes.stop_gradient = True
        tgt_labels.stop_gradient = True

        cls_logits = fluid.layers.reshape(x=cls_logits, shape=(-1, ))
        bbox_pred = fluid.layers.reshape(x=bbox_pred, shape=(-1, 4))
        pred_cls_logits = fluid.layers.gather(cls_logits, score_indexes)
        pred_bbox_pred = fluid.layers.gather(bbox_pred, loc_indexes)

        return pred_cls_logits, pred_bbox_pred, tgt_labels, tgt_bboxes, bbox_inside_weights


@register
@serializable
class AnchorGeneratorYOLO(object):
    def __init__(self,
                 anchors=[
                     10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90,
                     156, 198, 373, 326
                 ],
                 anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]]):
        super(AnchorGeneratorYOLO, self).__init__()
        self.anchors = anchors
        self.anchor_masks = anchor_masks

    def __call__(self):
        anchor_num = len(self.anchors)
        mask_anchors = []
        for i in range(len(self.anchor_masks)):
            mask_anchor = []
            for m in self.anchor_masks[i]:
                assert m < anchor_num, "anchor mask index overflow"
                mask_anchor.extend(self.anchors[2 * m:2 * m + 2])
            mask_anchors.append(mask_anchor)
        return self.anchors, self.anchor_masks, mask_anchors


@register
@serializable
class AnchorTargetGeneratorYOLO(object):
    def __init__(self,
                 ignore_thresh=0.7,
                 downsample_ratio=32,
                 label_smooth=True):
        super(AnchorTargetGeneratorYOLO, self).__init__()
        self.ignore_thresh = ignore_thresh
        self.downsample_ratio = downsample_ratio
        self.label_smooth = label_smooth

    def __call__(self, ):
        # TODO: split yolov3_loss into here 
        outs = {
            'ignore_thresh': self.ignore_thresh,
            'downsample_ratio': self.downsample_ratio,
            'label_smooth': self.label_smooth
        }
        return outs


@register
@serializable
class ProposalGenerator(object):
    __append_doc__ = True

    def __init__(self,
                 train_pre_nms_top_n=12000,
                 train_post_nms_top_n=2000,
                 infer_pre_nms_top_n=6000,
                 infer_post_nms_top_n=1000,
                 nms_thresh=.5,
                 min_size=.1,
                 eta=1.):
        super(ProposalGenerator, self).__init__()
        self.train_pre_nms_top_n = train_pre_nms_top_n
        self.train_post_nms_top_n = train_post_nms_top_n
        self.infer_pre_nms_top_n = infer_pre_nms_top_n
        self.infer_post_nms_top_n = infer_post_nms_top_n
        self.nms_thresh = nms_thresh
        self.min_size = min_size
        self.eta = eta

    def __call__(self,
                 scores,
                 bbox_deltas,
                 anchors,
                 variances,
                 im_info,
                 mode='train'):
        pre_nms_top_n = self.train_pre_nms_top_n if mode == 'train' else self.infer_pre_nms_top_n
        post_nms_top_n = self.train_post_nms_top_n if mode == 'train' else self.infer_post_nms_top_n
        rpn_rois, rpn_rois_prob, rpn_rois_num = fluid.layers.generate_proposals(
            scores,
            bbox_deltas,
            im_info,
            anchors,
            variances,
            pre_nms_top_n=pre_nms_top_n,
            post_nms_top_n=post_nms_top_n,
            nms_thresh=self.nms_thresh,
            min_size=self.min_size,
            eta=self.eta,
            return_rois_num=True)
        return rpn_rois, rpn_rois_prob, rpn_rois_num, post_nms_top_n


@register
@serializable
class ProposalTargetGenerator(object):
    __shared__ = ['num_classes']

    def __init__(self,
                 batch_size_per_im=512,
                 fg_fraction=.25,
                 fg_thresh=[.5, ],
                 bg_thresh_hi=[.5, ],
                 bg_thresh_lo=[0., ],
                 bbox_reg_weights=[[0.1, 0.1, 0.2, 0.2]],
                 num_classes=81,
                 use_random=True,
                 is_cls_agnostic=False,
                 is_cascade_rcnn=False):
        super(ProposalTargetGenerator, self).__init__()
        self.batch_size_per_im = batch_size_per_im
        self.fg_fraction = fg_fraction
        self.fg_thresh = fg_thresh
        self.bg_thresh_hi = bg_thresh_hi
        self.bg_thresh_lo = bg_thresh_lo
        self.bbox_reg_weights = bbox_reg_weights
        self.num_classes = num_classes
        self.use_random = use_random
        self.is_cls_agnostic = is_cls_agnostic
        self.is_cascade_rcnn = is_cascade_rcnn

    def __call__(self,
                 rpn_rois,
                 rpn_rois_num,
                 gt_classes,
                 is_crowd,
                 gt_boxes,
                 im_info,
                 stage=0):
        rpn_rois = rpn_rois.numpy()
        rpn_rois_num = rpn_rois_num.numpy()
        gt_classes = gt_classes.numpy()
        gt_boxes = gt_boxes.numpy()
        is_crowd = is_crowd.numpy()
        im_info = im_info.numpy()
        outs = generate_proposal_target(
            rpn_rois, rpn_rois_num, gt_classes, is_crowd, gt_boxes, im_info,
            self.batch_size_per_im, self.fg_fraction, self.fg_thresh[stage],
            self.bg_thresh_hi[stage], self.bg_thresh_lo[stage],
            self.bbox_reg_weights[stage], self.num_classes, self.use_random,
            self.is_cls_agnostic, self.is_cascade_rcnn)
        outs = [to_variable(v) for v in outs]
        for v in outs:
            v.stop_gradient = True
        return outs


@register
@serializable
class MaskTargetGenerator(object):
    __shared__ = ['num_classes', 'mask_resolution']

    def __init__(self, num_classes=81, mask_resolution=14):
        super(MaskTargetGenerator, self).__init__()
        self.num_classes = num_classes
        self.mask_resolution = mask_resolution

    def __call__(self, im_info, gt_classes, is_crowd, gt_segms, rois, rois_num,
                 labels_int32):
        im_info = im_info.numpy()
        gt_classes = gt_classes.numpy()
        is_crowd = is_crowd.numpy()
        gt_segms = gt_segms.numpy()
        rois = rois.numpy()
        rois_num = rois_num.numpy()
        labels_int32 = labels_int32.numpy()
        outs = generate_mask_target(im_info, gt_classes, is_crowd, gt_segms,
                                    rois, rois_num, labels_int32,
                                    self.num_classes, self.mask_resolution)

        outs = [to_variable(v) for v in outs]
        for v in outs:
            v.stop_gradient = True
        return outs


@register
class RoIExtractor(object):
    def __init__(self,
                 resolution=14,
                 sampling_ratio=0,
                 canconical_level=4,
                 canonical_size=224,
                 start_level=0,
                 end_level=3):
        super(RoIExtractor, self).__init__()
        self.resolution = resolution
        self.sampling_ratio = sampling_ratio
        self.canconical_level = canconical_level
        self.canonical_size = canonical_size
        self.start_level = start_level
        self.end_level = end_level

    def __call__(self, feats, rois, spatial_scale):
        roi, rois_num = rois
        cur_l = 0
        if self.start_level == self.end_level:
            rois_feat = fluid.layers.roi_align(
                feats[self.start_level],
                roi,
                self.resolution,
                self.resolution,
                spatial_scale,
                rois_num=rois_num)
            return rois_feat
        offset = 2
        k_min = self.start_level + offset
        k_max = self.end_level + offset
        rois_dist, restore_index, rois_num_dist = fluid.layers.distribute_fpn_proposals(
            roi,
            k_min,
            k_max,
            self.canconical_level,
            self.canonical_size,
            rois_num=rois_num)

        rois_feat_list = []
        for lvl in range(self.start_level, self.end_level + 1):
            roi_feat = fluid.layers.roi_align(
                feats[lvl],
                rois_dist[lvl],
                self.resolution,
                self.resolution,
                spatial_scale[lvl],
                sampling_ratio=self.sampling_ratio,
                rois_num=rois_num_dist[lvl])
            rois_feat_list.append(roi_feat)
        rois_feat_shuffle = fluid.layers.concat(rois_feat_list)
        rois_feat = fluid.layers.gather(rois_feat_shuffle, restore_index)

        return rois_feat


@register
@serializable
class DecodeClipNms(object):
    __shared__ = ['num_classes']

    def __init__(
            self,
            num_classes=81,
            keep_top_k=100,
            score_threshold=0.05,
            nms_threshold=0.5, ):
        super(DecodeClipNms, self).__init__()
        self.num_classes = num_classes
        self.keep_top_k = keep_top_k
        self.score_threshold = score_threshold
        self.nms_threshold = nms_threshold

    def __call__(self, bboxes, bbox_prob, bbox_delta, im_info):
        bboxes_np = (i.numpy() for i in bboxes)
        # bbox, bbox_num
        outs = bbox_post_process(bboxes_np,
                                 bbox_prob.numpy(),
                                 bbox_delta.numpy(),
                                 im_info.numpy(), self.keep_top_k,
                                 self.score_threshold, self.nms_threshold,
                                 self.num_classes)
        outs = [to_variable(v) for v in outs]
        for v in outs:
            v.stop_gradient = True
        return outs


@register
@serializable
class MultiClassNMS(object):
    __op__ = fluid.layers.multiclass_nms
    __append_doc__ = True

    def __init__(self,
                 score_threshold=.05,
                 nms_top_k=-1,
                 keep_top_k=100,
                 nms_threshold=.5,
                 normalized=False,
                 nms_eta=1.0,
                 background_label=0):
        super(MultiClassNMS, self).__init__()
        self.score_threshold = score_threshold
        self.nms_top_k = nms_top_k
        self.keep_top_k = keep_top_k
        self.nms_threshold = nms_threshold
        self.normalized = normalized
        self.nms_eta = nms_eta
        self.background_label = background_label


@register
@serializable
class YOLOBox(object):
    def __init__(
            self,
            conf_thresh=0.005,
            downsample_ratio=32,
            clip_bbox=True, ):
        self.conf_thresh = conf_thresh
        self.downsample_ratio = downsample_ratio
        self.clip_bbox = clip_bbox

    def __call__(self, x, img_size, anchors, num_classes, stage=0):
        outs = fluid.layers.yolo_box(x, img_size, anchors, num_classes,
                                     self.conf_thresh, self.downsample_ratio //
                                     2**stage, self.clip_bbox)
        return outs


@register
@serializable
class AnchorGrid(object):
    """Generate anchor grid

    Args:
        image_size (int or list): input image size, may be a single integer or
            list of [h, w]. Default: 512
        min_level (int): min level of the feature pyramid. Default: 3
        max_level (int): max level of the feature pyramid. Default: 7
        anchor_base_scale: base anchor scale. Default: 4
        num_scales: number of anchor scales. Default: 3
        aspect_ratios: aspect ratios. default: [[1, 1], [1.4, 0.7], [0.7, 1.4]]
    """

    def __init__(self,
                 image_size=512,
                 min_level=3,
                 max_level=7,
                 anchor_base_scale=4,
                 num_scales=3,
                 aspect_ratios=[[1, 1], [1.4, 0.7], [0.7, 1.4]]):
        super(AnchorGrid, self).__init__()
        if isinstance(image_size, Integral):
            self.image_size = [image_size, image_size]
        else:
            self.image_size = image_size
        for dim in self.image_size:
            assert dim % 2 ** max_level == 0, \
                "image size should be multiple of the max level stride"
        self.min_level = min_level
        self.max_level = max_level
        self.anchor_base_scale = anchor_base_scale
        self.num_scales = num_scales
        self.aspect_ratios = aspect_ratios

    @property
    def base_cell(self):
        if not hasattr(self, '_base_cell'):
            self._base_cell = self.make_cell()
        return self._base_cell

    def make_cell(self):
        scales = [2**(i / self.num_scales) for i in range(self.num_scales)]
        scales = np.array(scales)
        ratios = np.array(self.aspect_ratios)
        ws = np.outer(scales, ratios[:, 0]).reshape(-1, 1)
        hs = np.outer(scales, ratios[:, 1]).reshape(-1, 1)
        anchors = np.hstack((-0.5 * ws, -0.5 * hs, 0.5 * ws, 0.5 * hs))
        return anchors

    def make_grid(self, stride):
        cell = self.base_cell * stride * self.anchor_base_scale
        x_steps = np.arange(stride // 2, self.image_size[1], stride)
        y_steps = np.arange(stride // 2, self.image_size[0], stride)
        offset_x, offset_y = np.meshgrid(x_steps, y_steps)
        offset_x = offset_x.flatten()
        offset_y = offset_y.flatten()
        offsets = np.stack((offset_x, offset_y, offset_x, offset_y), axis=-1)
        offsets = offsets[:, np.newaxis, :]
        return (cell + offsets).reshape(-1, 4)

    def generate(self):
        return [
            self.make_grid(2**l)
            for l in range(self.min_level, self.max_level + 1)
        ]

    def __call__(self):
        if not hasattr(self, '_anchor_vars'):
            anchor_vars = []
            helper = LayerHelper('anchor_grid')
            for idx, l in enumerate(range(self.min_level, self.max_level + 1)):
                stride = 2**l
                anchors = self.make_grid(stride)
                var = helper.create_parameter(
                    attr=ParamAttr(name='anchors_{}'.format(idx)),
                    shape=anchors.shape,
                    dtype='float32',
                    stop_gradient=True,
                    default_initializer=NumpyArrayInitializer(anchors))
                anchor_vars.append(var)
                var.persistable = True
            self._anchor_vars = anchor_vars

        return self._anchor_vars