# 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 math
from numbers import Integral

import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.distributed.fleet.utils import recompute
from ppdet.core.workspace import register, serializable
from paddle.regularizer import L2Decay
from paddle.nn.initializer import Uniform
from paddle import ParamAttr
from paddle.nn.initializer import Constant
from paddle.vision.ops import DeformConv2D
from .name_adapter import NameAdapter
from ..shape_spec import ShapeSpec

__all__ = ['ResNet', 'Res5Head', 'Blocks', 'BasicBlock', 'BottleNeck']

ResNet_cfg = {
    18: [2, 2, 2, 2],
    34: [3, 4, 6, 3],
    50: [3, 4, 6, 3],
    101: [3, 4, 23, 3],
    152: [3, 8, 36, 3],
}


class ConvNormLayer(nn.Layer):
    def __init__(self,
                 ch_in,
                 ch_out,
                 filter_size,
                 stride,
                 groups=1,
                 act=None,
                 norm_type='bn',
                 norm_decay=0.,
                 freeze_norm=True,
                 lr=1.0,
                 dcn_v2=False):
        super(ConvNormLayer, self).__init__()
        assert norm_type in ['bn', 'sync_bn']
        self.norm_type = norm_type
        self.act = act
        self.dcn_v2 = dcn_v2

        if not self.dcn_v2:
            self.conv = nn.Conv2D(
                in_channels=ch_in,
                out_channels=ch_out,
                kernel_size=filter_size,
                stride=stride,
                padding=(filter_size - 1) // 2,
                groups=groups,
                weight_attr=ParamAttr(learning_rate=lr),
                bias_attr=False)
        else:
            self.offset_channel = 2 * filter_size**2
            self.mask_channel = filter_size**2

            self.conv_offset = nn.Conv2D(
                in_channels=ch_in,
                out_channels=3 * filter_size**2,
                kernel_size=filter_size,
                stride=stride,
                padding=(filter_size - 1) // 2,
                weight_attr=ParamAttr(initializer=Constant(0.)),
                bias_attr=ParamAttr(initializer=Constant(0.)))
            self.conv = DeformConv2D(
                in_channels=ch_in,
                out_channels=ch_out,
                kernel_size=filter_size,
                stride=stride,
                padding=(filter_size - 1) // 2,
                dilation=1,
                groups=groups,
                weight_attr=ParamAttr(learning_rate=lr),
                bias_attr=False)

        norm_lr = 0. if freeze_norm else lr
        param_attr = ParamAttr(
            learning_rate=norm_lr,
            regularizer=L2Decay(norm_decay),
            trainable=False if freeze_norm else True)
        bias_attr = ParamAttr(
            learning_rate=norm_lr,
            regularizer=L2Decay(norm_decay),
            trainable=False if freeze_norm else True)

        global_stats = True if freeze_norm else None
        if norm_type in ['sync_bn', 'bn']:
            self.norm = nn.BatchNorm2D(
                ch_out,
                weight_attr=param_attr,
                bias_attr=bias_attr,
                use_global_stats=global_stats)
        norm_params = self.norm.parameters()

        if freeze_norm:
            for param in norm_params:
                param.stop_gradient = True

    def forward(self, inputs):
        if not self.dcn_v2:
            out = self.conv(inputs)
        else:
            offset_mask = self.conv_offset(inputs)
            offset, mask = paddle.split(
                offset_mask,
                num_or_sections=[self.offset_channel, self.mask_channel],
                axis=1)
            mask = F.sigmoid(mask)
            out = self.conv(inputs, offset, mask=mask)

        if self.norm_type in ['bn', 'sync_bn']:
            out = self.norm(out)
        if self.act:
            out = getattr(F, self.act)(out)
        return out


class SELayer(nn.Layer):
    def __init__(self, ch, reduction_ratio=16):
        super(SELayer, self).__init__()
        self.pool = nn.AdaptiveAvgPool2D(1)
        stdv = 1.0 / math.sqrt(ch)
        c_ = ch // reduction_ratio
        self.squeeze = nn.Linear(
            ch,
            c_,
            weight_attr=paddle.ParamAttr(initializer=Uniform(-stdv, stdv)),
            bias_attr=True)

        stdv = 1.0 / math.sqrt(c_)
        self.extract = nn.Linear(
            c_,
            ch,
            weight_attr=paddle.ParamAttr(initializer=Uniform(-stdv, stdv)),
            bias_attr=True)

    def forward(self, inputs):
        out = self.pool(inputs)
        out = paddle.squeeze(out, axis=[2, 3])
        out = self.squeeze(out)
        out = F.relu(out)
        out = self.extract(out)
        out = F.sigmoid(out)
        out = paddle.unsqueeze(out, axis=[2, 3])
        scale = out * inputs
        return scale


class BasicBlock(nn.Layer):

    expansion = 1

    def __init__(self,
                 ch_in,
                 ch_out,
                 stride,
                 shortcut,
                 variant='b',
                 groups=1,
                 base_width=64,
                 lr=1.0,
                 norm_type='bn',
                 norm_decay=0.,
                 freeze_norm=True,
                 dcn_v2=False,
                 std_senet=False):
        super(BasicBlock, self).__init__()
        assert groups == 1 and base_width == 64, 'BasicBlock only supports groups=1 and base_width=64'

        self.shortcut = shortcut
        if not shortcut:
            if variant == 'd' and stride == 2:
                self.short = nn.Sequential()
                self.short.add_sublayer(
                    'pool',
                    nn.AvgPool2D(
                        kernel_size=2, stride=2, padding=0, ceil_mode=True))
                self.short.add_sublayer(
                    'conv',
                    ConvNormLayer(
                        ch_in=ch_in,
                        ch_out=ch_out,
                        filter_size=1,
                        stride=1,
                        norm_type=norm_type,
                        norm_decay=norm_decay,
                        freeze_norm=freeze_norm,
                        lr=lr))
            else:
                self.short = ConvNormLayer(
                    ch_in=ch_in,
                    ch_out=ch_out,
                    filter_size=1,
                    stride=stride,
                    norm_type=norm_type,
                    norm_decay=norm_decay,
                    freeze_norm=freeze_norm,
                    lr=lr)

        self.branch2a = ConvNormLayer(
            ch_in=ch_in,
            ch_out=ch_out,
            filter_size=3,
            stride=stride,
            act='relu',
            norm_type=norm_type,
            norm_decay=norm_decay,
            freeze_norm=freeze_norm,
            lr=lr)

        self.branch2b = ConvNormLayer(
            ch_in=ch_out,
            ch_out=ch_out,
            filter_size=3,
            stride=1,
            act=None,
            norm_type=norm_type,
            norm_decay=norm_decay,
            freeze_norm=freeze_norm,
            lr=lr,
            dcn_v2=dcn_v2)

        self.std_senet = std_senet
        if self.std_senet:
            self.se = SELayer(ch_out)

    def forward(self, inputs):
        out = self.branch2a(inputs)
        out = self.branch2b(out)
        if self.std_senet:
            out = self.se(out)

        if self.shortcut:
            short = inputs
        else:
            short = self.short(inputs)

        out = paddle.add(x=out, y=short)
        out = F.relu(out)

        return out


class BottleNeck(nn.Layer):

    expansion = 4

    def __init__(self,
                 ch_in,
                 ch_out,
                 stride,
                 shortcut,
                 variant='b',
                 groups=1,
                 base_width=4,
                 lr=1.0,
                 norm_type='bn',
                 norm_decay=0.,
                 freeze_norm=True,
                 dcn_v2=False,
                 std_senet=False):
        super(BottleNeck, self).__init__()
        if variant == 'a':
            stride1, stride2 = stride, 1
        else:
            stride1, stride2 = 1, stride

        # ResNeXt
        width = int(ch_out * (base_width / 64.)) * groups

        self.branch2a = ConvNormLayer(
            ch_in=ch_in,
            ch_out=width,
            filter_size=1,
            stride=stride1,
            groups=1,
            act='relu',
            norm_type=norm_type,
            norm_decay=norm_decay,
            freeze_norm=freeze_norm,
            lr=lr)

        self.branch2b = ConvNormLayer(
            ch_in=width,
            ch_out=width,
            filter_size=3,
            stride=stride2,
            groups=groups,
            act='relu',
            norm_type=norm_type,
            norm_decay=norm_decay,
            freeze_norm=freeze_norm,
            lr=lr,
            dcn_v2=dcn_v2)

        self.branch2c = ConvNormLayer(
            ch_in=width,
            ch_out=ch_out * self.expansion,
            filter_size=1,
            stride=1,
            groups=1,
            norm_type=norm_type,
            norm_decay=norm_decay,
            freeze_norm=freeze_norm,
            lr=lr)

        self.shortcut = shortcut
        if not shortcut:
            if variant == 'd' and stride == 2:
                self.short = nn.Sequential()
                self.short.add_sublayer(
                    'pool',
                    nn.AvgPool2D(
                        kernel_size=2, stride=2, padding=0, ceil_mode=True))
                self.short.add_sublayer(
                    'conv',
                    ConvNormLayer(
                        ch_in=ch_in,
                        ch_out=ch_out * self.expansion,
                        filter_size=1,
                        stride=1,
                        norm_type=norm_type,
                        norm_decay=norm_decay,
                        freeze_norm=freeze_norm,
                        lr=lr))
            else:
                self.short = ConvNormLayer(
                    ch_in=ch_in,
                    ch_out=ch_out * self.expansion,
                    filter_size=1,
                    stride=stride,
                    norm_type=norm_type,
                    norm_decay=norm_decay,
                    freeze_norm=freeze_norm,
                    lr=lr)

        self.std_senet = std_senet
        if self.std_senet:
            self.se = SELayer(ch_out * self.expansion)

    def forward(self, inputs):

        out = self.branch2a(inputs)
        out = self.branch2b(out)
        out = self.branch2c(out)

        if self.std_senet:
            out = self.se(out)

        if self.shortcut:
            short = inputs
        else:
            short = self.short(inputs)

        out = paddle.add(x=out, y=short)
        out = F.relu(out)

        return out


class Blocks(nn.Layer):
    def __init__(self,
                 block,
                 ch_in,
                 ch_out,
                 count,
                 name_adapter,
                 stage_num,
                 variant='b',
                 groups=1,
                 base_width=64,
                 lr=1.0,
                 norm_type='bn',
                 norm_decay=0.,
                 freeze_norm=True,
                 dcn_v2=False,
                 std_senet=False,
                 with_cp=False):
        super(Blocks, self).__init__()
        self.with_cp = with_cp

        self.blocks = []
        for i in range(count):
            conv_name = name_adapter.fix_layer_warp_name(stage_num, count, i)
            layer = self.add_sublayer(
                conv_name,
                block(
                    ch_in=ch_in,
                    ch_out=ch_out,
                    stride=2 if i == 0 and stage_num != 2 else 1,
                    shortcut=False if i == 0 else True,
                    variant=variant,
                    groups=groups,
                    base_width=base_width,
                    lr=lr,
                    norm_type=norm_type,
                    norm_decay=norm_decay,
                    freeze_norm=freeze_norm,
                    dcn_v2=dcn_v2,
                    std_senet=std_senet))
            self.blocks.append(layer)
            if i == 0:
                ch_in = ch_out * block.expansion

    def forward(self, inputs):
        block_out = inputs
        for block in self.blocks:
            if self.training and self.with_cp:
                block_out = recompute(block, block_out)
            else:
                block_out = block(block_out)
        return block_out


@register
@serializable
class ResNet(nn.Layer):
    __shared__ = ['norm_type']

    def __init__(self,
                 depth=50,
                 ch_in=64,
                 variant='b',
                 lr_mult_list=[1.0, 1.0, 1.0, 1.0],
                 groups=1,
                 base_width=64,
                 norm_type='bn',
                 norm_decay=0,
                 freeze_norm=True,
                 freeze_at=0,
                 return_idx=[0, 1, 2, 3],
                 dcn_v2_stages=[-1],
                 num_stages=4,
                 std_senet=False,
                 freeze_stem_only=False,
                 with_cp=False):
        """
        Residual Network, see https://arxiv.org/abs/1512.03385
        
        Args:
            depth (int): ResNet depth, should be 18, 34, 50, 101, 152.
            ch_in (int): output channel of first stage, default 64
            variant (str): ResNet variant, supports 'a', 'b', 'c', 'd' currently
            lr_mult_list (list): learning rate ratio of different resnet stages(2,3,4,5),
                                 lower learning rate ratio is need for pretrained model 
                                 got using distillation(default as [1.0, 1.0, 1.0, 1.0]).
            groups (int): group convolution cardinality
            base_width (int): base width of each group convolution
            norm_type (str): normalization type, 'bn', 'sync_bn' or 'affine_channel'
            norm_decay (float): weight decay for normalization layer weights
            freeze_norm (bool): freeze normalization layers
            freeze_at (int): freeze the backbone at which stage
            return_idx (list): index of the stages whose feature maps are returned
            dcn_v2_stages (list): index of stages who select deformable conv v2
            num_stages (int): total num of stages
            std_senet (bool): whether use senet, default True
        """
        super(ResNet, self).__init__()
        self._model_type = 'ResNet' if groups == 1 else 'ResNeXt'
        assert num_stages >= 1 and num_stages <= 4
        self.depth = depth
        self.variant = variant
        self.groups = groups
        self.base_width = base_width
        self.norm_type = norm_type
        self.norm_decay = norm_decay
        self.freeze_norm = freeze_norm
        self.freeze_at = freeze_at
        if isinstance(return_idx, Integral):
            return_idx = [return_idx]
        assert max(return_idx) < num_stages, \
            'the maximum return index must smaller than num_stages, ' \
            'but received maximum return index is {} and num_stages ' \
            'is {}'.format(max(return_idx), num_stages)
        self.return_idx = return_idx
        self.num_stages = num_stages
        assert len(lr_mult_list) == 4, \
            "lr_mult_list length must be 4 but got {}".format(len(lr_mult_list))
        if isinstance(dcn_v2_stages, Integral):
            dcn_v2_stages = [dcn_v2_stages]
        assert max(dcn_v2_stages) < num_stages

        if isinstance(dcn_v2_stages, Integral):
            dcn_v2_stages = [dcn_v2_stages]
        assert max(dcn_v2_stages) < num_stages
        self.dcn_v2_stages = dcn_v2_stages

        block_nums = ResNet_cfg[depth]
        na = NameAdapter(self)

        conv1_name = na.fix_c1_stage_name()
        if variant in ['c', 'd']:
            conv_def = [
                [3, ch_in // 2, 3, 2, "conv1_1"],
                [ch_in // 2, ch_in // 2, 3, 1, "conv1_2"],
                [ch_in // 2, ch_in, 3, 1, "conv1_3"],
            ]
        else:
            conv_def = [[3, ch_in, 7, 2, conv1_name]]
        self.conv1 = nn.Sequential()
        for (c_in, c_out, k, s, _name) in conv_def:
            self.conv1.add_sublayer(
                _name,
                ConvNormLayer(
                    ch_in=c_in,
                    ch_out=c_out,
                    filter_size=k,
                    stride=s,
                    groups=1,
                    act='relu',
                    norm_type=norm_type,
                    norm_decay=norm_decay,
                    freeze_norm=freeze_norm,
                    lr=1.0))

        self.ch_in = ch_in
        ch_out_list = [64, 128, 256, 512]
        block = BottleNeck if depth >= 50 else BasicBlock

        self._out_channels = [block.expansion * v for v in ch_out_list]
        self._out_strides = [4, 8, 16, 32]

        self.res_layers = []
        for i in range(num_stages):
            lr_mult = lr_mult_list[i]
            stage_num = i + 2
            res_name = "res{}".format(stage_num)
            res_layer = self.add_sublayer(
                res_name,
                Blocks(
                    block,
                    self.ch_in,
                    ch_out_list[i],
                    count=block_nums[i],
                    name_adapter=na,
                    stage_num=stage_num,
                    variant=variant,
                    groups=groups,
                    base_width=base_width,
                    lr=lr_mult,
                    norm_type=norm_type,
                    norm_decay=norm_decay,
                    freeze_norm=freeze_norm,
                    dcn_v2=(i in self.dcn_v2_stages),
                    std_senet=std_senet,
                    with_cp=with_cp))
            self.res_layers.append(res_layer)
            self.ch_in = self._out_channels[i]

        if freeze_at >= 0:
            self._freeze_parameters(self.conv1)
            if not freeze_stem_only:
                for i in range(min(freeze_at + 1, num_stages)):
                    self._freeze_parameters(self.res_layers[i])

    def _freeze_parameters(self, m):
        for p in m.parameters():
            p.stop_gradient = True

    @property
    def out_shape(self):
        return [
            ShapeSpec(
                channels=self._out_channels[i], stride=self._out_strides[i])
            for i in self.return_idx
        ]

    def forward(self, inputs):
        x = inputs['image']
        conv1 = self.conv1(x)
        x = F.max_pool2d(conv1, kernel_size=3, stride=2, padding=1)
        outs = []
        for idx, stage in enumerate(self.res_layers):
            x = stage(x)
            if idx in self.return_idx:
                outs.append(x)
        return outs


@register
class Res5Head(nn.Layer):
    def __init__(self, depth=50):
        super(Res5Head, self).__init__()
        feat_in, feat_out = [1024, 512]
        if depth < 50:
            feat_in = 256
        na = NameAdapter(self)
        block = BottleNeck if depth >= 50 else BasicBlock
        self.res5 = Blocks(
            block, feat_in, feat_out, count=3, name_adapter=na, stage_num=5)
        self.feat_out = feat_out if depth < 50 else feat_out * 4

    @property
    def out_shape(self):
        return [ShapeSpec(
            channels=self.feat_out,
            stride=16, )]

    def forward(self, roi_feat, stage=0):
        y = self.res5(roi_feat)
        return y
