# 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 numbers import Integral

from paddle import fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.initializer import MSRA
from paddle.fluid.regularizer import L2Decay
from ppdet.core.workspace import register, serializable

__all__ = [
    'AnchorGenerator', 'RPNTargetAssign', 'GenerateProposals', 'MultiClassNMS',
    'BBoxAssigner', 'MaskAssigner', 'RoIAlign', 'RoIPool', 'MultiBoxHead',
    'SSDOutputDecoder', 'RetinaTargetAssign', 'RetinaOutputDecoder', 'ConvNorm'
]


def ConvNorm(input,
             num_filters,
             filter_size,
             stride=1,
             groups=1,
             norm_decay=0.,
             norm_type='affine_channel',
             freeze_norm=False,
             act=None,
             bn_name=None,
             initializer=None,
             name=None):
    fan = num_filters
    conv = fluid.layers.conv2d(
        input=input,
        num_filters=num_filters,
        filter_size=filter_size,
        stride=stride,
        padding=(filter_size - 1) // 2,
        groups=groups,
        act=None,
        param_attr=ParamAttr(
            name=name + "_weights", initializer=initializer),
        bias_attr=False,
        name=name + '.conv2d.output.1')

    norm_lr = 0. if freeze_norm else 1.
    pattr = ParamAttr(
        name=bn_name + '_scale',
        learning_rate=norm_lr,
        regularizer=L2Decay(norm_decay))
    battr = ParamAttr(
        name=bn_name + '_offset',
        learning_rate=norm_lr,
        regularizer=L2Decay(norm_decay))

    if norm_type in ['bn', 'sync_bn']:
        global_stats = True if freeze_norm else False
        out = fluid.layers.batch_norm(
            input=conv,
            act=act,
            name=bn_name + '.output.1',
            param_attr=pattr,
            bias_attr=battr,
            moving_mean_name=bn_name + '_mean',
            moving_variance_name=bn_name + '_variance',
            use_global_stats=global_stats)
        scale = fluid.framework._get_var(pattr.name)
        bias = fluid.framework._get_var(battr.name)
    elif norm_type == 'affine_channel':
        scale = fluid.layers.create_parameter(
            shape=[conv.shape[1]],
            dtype=conv.dtype,
            attr=pattr,
            default_initializer=fluid.initializer.Constant(1.))
        bias = fluid.layers.create_parameter(
            shape=[conv.shape[1]],
            dtype=conv.dtype,
            attr=battr,
            default_initializer=fluid.initializer.Constant(0.))
        out = fluid.layers.affine_channel(
            x=conv, scale=scale, bias=bias, act=act)
    if freeze_norm:
        scale.stop_gradient = True
        bias.stop_gradient = True
    return out


@register
@serializable
class AnchorGenerator(object):
    __op__ = fluid.layers.anchor_generator
    __append_doc__ = True

    def __init__(self,
                 stride=[16.0, 16.0],
                 anchor_sizes=[32, 64, 128, 256, 512],
                 aspect_ratios=[0.5, 1., 2.],
                 variance=[1., 1., 1., 1.]):
        super(AnchorGenerator, self).__init__()
        self.anchor_sizes = anchor_sizes
        self.aspect_ratios = aspect_ratios
        self.variance = variance
        self.stride = stride


@register
@serializable
class RPNTargetAssign(object):
    __op__ = fluid.layers.rpn_target_assign
    __append_doc__ = True

    def __init__(self,
                 rpn_batch_size_per_im=256,
                 rpn_straddle_thresh=0.,
                 rpn_fg_fraction=0.5,
                 rpn_positive_overlap=0.7,
                 rpn_negative_overlap=0.3,
                 use_random=True):
        super(RPNTargetAssign, self).__init__()
        self.rpn_batch_size_per_im = rpn_batch_size_per_im
        self.rpn_straddle_thresh = rpn_straddle_thresh
        self.rpn_fg_fraction = rpn_fg_fraction
        self.rpn_positive_overlap = rpn_positive_overlap
        self.rpn_negative_overlap = rpn_negative_overlap
        self.use_random = use_random


@register
@serializable
class GenerateProposals(object):
    __op__ = fluid.layers.generate_proposals
    __append_doc__ = True

    def __init__(self,
                 pre_nms_top_n=6000,
                 post_nms_top_n=1000,
                 nms_thresh=.5,
                 min_size=.1,
                 eta=1.):
        super(GenerateProposals, self).__init__()
        self.pre_nms_top_n = pre_nms_top_n
        self.post_nms_top_n = post_nms_top_n
        self.nms_thresh = nms_thresh
        self.min_size = min_size
        self.eta = eta


@register
class MaskAssigner(object):
    __op__ = fluid.layers.generate_mask_labels
    __append_doc__ = True
    __shared__ = ['num_classes']

    def __init__(self, num_classes=81, resolution=14):
        super(MaskAssigner, self).__init__()
        self.num_classes = num_classes
        self.resolution = resolution


@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
class BBoxAssigner(object):
    __op__ = fluid.layers.generate_proposal_labels
    __append_doc__ = True
    __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,
                 shuffle_before_sample=True):
        super(BBoxAssigner, 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.class_nums = num_classes
        self.use_random = shuffle_before_sample


@register
class RoIAlign(object):
    __op__ = fluid.layers.roi_align
    __append_doc__ = True

    def __init__(self, resolution=7, spatial_scale=1. / 16, sampling_ratio=0):
        super(RoIAlign, self).__init__()
        if isinstance(resolution, Integral):
            resolution = [resolution, resolution]
        self.pooled_height = resolution[0]
        self.pooled_width = resolution[1]
        self.spatial_scale = spatial_scale
        self.sampling_ratio = sampling_ratio


@register
class RoIPool(object):
    __op__ = fluid.layers.roi_pool
    __append_doc__ = True

    def __init__(self, resolution=7, spatial_scale=1. / 16):
        super(RoIPool, self).__init__()
        if isinstance(resolution, Integral):
            resolution = [resolution, resolution]
        self.pooled_height = resolution[0]
        self.pooled_width = resolution[1]
        self.spatial_scale = spatial_scale


@register
class MultiBoxHead(object):
    __op__ = fluid.layers.multi_box_head
    __append_doc__ = True

    def __init__(self,
                 min_ratio=20,
                 max_ratio=90,
                 base_size=300,
                 min_sizes=[60.0, 105.0, 150.0, 195.0, 240.0, 285.0],
                 max_sizes=[[], 150.0, 195.0, 240.0, 285.0, 300.0],
                 aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.],
                                [2., 3.]],
                 steps=None,
                 offset=0.5,
                 flip=True,
                 min_max_aspect_ratios_order=False,
                 kernel_size=1,
                 pad=0):
        super(MultiBoxHead, self).__init__()
        self.min_ratio = min_ratio
        self.max_ratio = max_ratio
        self.base_size = base_size
        self.min_sizes = min_sizes
        self.max_sizes = max_sizes
        self.aspect_ratios = aspect_ratios
        self.steps = steps
        self.offset = offset
        self.flip = flip
        self.min_max_aspect_ratios_order = min_max_aspect_ratios_order
        self.kernel_size = kernel_size
        self.pad = pad


@register
@serializable
class SSDOutputDecoder(object):
    __op__ = fluid.layers.detection_output
    __append_doc__ = True

    def __init__(self,
                 nms_threshold=0.45,
                 nms_top_k=400,
                 keep_top_k=200,
                 score_threshold=0.01,
                 nms_eta=1.0,
                 background_label=0):
        super(SSDOutputDecoder, self).__init__()
        self.nms_threshold = nms_threshold
        self.background_label = background_label
        self.nms_top_k = nms_top_k
        self.keep_top_k = keep_top_k
        self.score_threshold = score_threshold
        self.nms_eta = nms_eta


@register
@serializable
class RetinaTargetAssign(object):
    __op__ = fluid.layers.retinanet_target_assign
    __append_doc__ = True

    def __init__(self, positive_overlap=0.5, negative_overlap=0.4):
        super(RetinaTargetAssign, self).__init__()
        self.positive_overlap = positive_overlap
        self.negative_overlap = negative_overlap


@register
@serializable
class RetinaOutputDecoder(object):
    __op__ = fluid.layers.retinanet_detection_output
    __append_doc__ = True

    def __init__(self,
                 score_thresh=0.05,
                 nms_thresh=0.3,
                 pre_nms_top_n=1000,
                 detections_per_im=100,
                 nms_eta=1.0):
        super(RetinaOutputDecoder, self).__init__()
        self.score_threshold = score_thresh
        self.nms_threshold = nms_thresh
        self.nms_top_k = pre_nms_top_n
        self.keep_top_k = detections_per_im
        self.nms_eta = nms_eta