#  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#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 paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.regularizer import L2Decay

from paddle.fluid.dygraph.nn import Conv2D, BatchNorm
from paddle.fluid.dygraph.base import to_variable


class ConvBNLayer(fluid.dygraph.Layer):

    def __init__(self,
                 ch_in,
                 ch_out,
                 filter_size=3,
                 stride=1,
                 groups=1,
                 padding=0,
                 act="leaky",
                 is_test=True):
        super(ConvBNLayer, self).__init__()

        self.conv = Conv2D(num_channels=ch_in,
                           num_filters=ch_out,
                           filter_size=filter_size,
                           stride=stride,
                           padding=padding,
                           groups=groups,
                           param_attr=ParamAttr(
                               initializer=fluid.initializer.Normal(0., 0.02)),
                           bias_attr=False,
                           act=None)
        self.batch_norm = BatchNorm(
            num_channels=ch_out,
            is_test=is_test,
            param_attr=ParamAttr(initializer=fluid.initializer.Normal(0., 0.02),
                                 regularizer=L2Decay(0.)),
            bias_attr=ParamAttr(initializer=fluid.initializer.Constant(0.0),
                                regularizer=L2Decay(0.)))

        self.act = act

    def forward(self, inputs):
        out = self.conv(inputs)
        out = self.batch_norm(out)
        if self.act == 'leaky':
            out = fluid.layers.leaky_relu(x=out, alpha=0.1)
        return out


class DownSample(fluid.dygraph.Layer):

    def __init__(self,
                 ch_in,
                 ch_out,
                 filter_size=3,
                 stride=2,
                 padding=1,
                 is_test=True):

        super(DownSample, self).__init__()

        self.conv_bn_layer = ConvBNLayer(ch_in=ch_in,
                                         ch_out=ch_out,
                                         filter_size=filter_size,
                                         stride=stride,
                                         padding=padding,
                                         is_test=is_test)
        self.ch_out = ch_out

    def forward(self, inputs):
        out = self.conv_bn_layer(inputs)
        return out


class BasicBlock(fluid.dygraph.Layer):

    def __init__(self, ch_in, ch_out, is_test=True):
        super(BasicBlock, self).__init__()

        self.conv1 = ConvBNLayer(ch_in=ch_in,
                                 ch_out=ch_out,
                                 filter_size=1,
                                 stride=1,
                                 padding=0,
                                 is_test=is_test)
        self.conv2 = ConvBNLayer(ch_in=ch_out,
                                 ch_out=ch_out * 2,
                                 filter_size=3,
                                 stride=1,
                                 padding=1,
                                 is_test=is_test)

    def forward(self, inputs):
        conv1 = self.conv1(inputs)
        conv2 = self.conv2(conv1)
        out = fluid.layers.elementwise_add(x=inputs, y=conv2, act=None)
        return out


class LayerWarp(fluid.dygraph.Layer):

    def __init__(self, ch_in, ch_out, count, is_test=True):
        super(LayerWarp, self).__init__()

        self.basicblock0 = BasicBlock(ch_in, ch_out, is_test=is_test)
        self.res_out_list = []
        for i in range(1, count):
            res_out = self.add_sublayer(
                "basic_block_%d" % (i),
                BasicBlock(ch_out * 2, ch_out, is_test=is_test))
            self.res_out_list.append(res_out)
        self.ch_out = ch_out

    def forward(self, inputs):
        y = self.basicblock0(inputs)
        for basic_block_i in self.res_out_list:
            y = basic_block_i(y)
        return y


DarkNet_cfg = {53: ([1, 2, 8, 8, 4])}


class DarkNet53_conv_body(fluid.dygraph.Layer):

    def __init__(self, ch_in=3, is_test=True):
        super(DarkNet53_conv_body, self).__init__()
        self.stages = DarkNet_cfg[53]
        self.stages = self.stages[0:5]

        self.conv0 = ConvBNLayer(ch_in=ch_in,
                                 ch_out=32,
                                 filter_size=3,
                                 stride=1,
                                 padding=1,
                                 is_test=is_test)

        self.downsample0 = DownSample(ch_in=32, ch_out=32 * 2, is_test=is_test)
        self.darknet53_conv_block_list = []
        self.downsample_list = []
        ch_in = [64, 128, 256, 512, 1024]
        for i, stage in enumerate(self.stages):
            conv_block = self.add_sublayer(
                "stage_%d" % (i),
                LayerWarp(int(ch_in[i]), 32 * (2**i), stage, is_test=is_test))
            self.darknet53_conv_block_list.append(conv_block)
        for i in range(len(self.stages) - 1):
            downsample = self.add_sublayer(
                "stage_%d_downsample" % i,
                DownSample(ch_in=32 * (2**(i + 1)),
                           ch_out=32 * (2**(i + 2)),
                           is_test=is_test))
            self.downsample_list.append(downsample)

    def forward(self, inputs):

        out = self.conv0(inputs)
        out = self.downsample0(out)
        blocks = []
        for i, conv_block_i in enumerate(self.darknet53_conv_block_list):
            out = conv_block_i(out)
            blocks.append(out)
            if i < len(self.stages) - 1:
                out = self.downsample_list[i](out)
        return blocks[-1:-4:-1]