# 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
from paddle.nn.initializer import Uniform

import math

__all__ = [
    "Res2Net50_vd_48w_2s", "Res2Net50_vd_26w_4s", "Res2Net50_vd_14w_8s",
    "Res2Net50_vd_48w_2s", "Res2Net50_vd_26w_6s", "Res2Net50_vd_26w_8s",
    "Res2Net101_vd_26w_4s", "Res2Net152_vd_26w_4s", "Res2Net200_vd_26w_4s"
]


class ConvBNLayer(nn.Layer):
    def __init__(
            self,
            num_channels,
            num_filters,
            filter_size,
            stride=1,
            groups=1,
            is_vd_mode=False,
            act=None,
            name=None, ):
        super(ConvBNLayer, self).__init__()

        self.is_vd_mode = is_vd_mode
        self._pool2d_avg = AvgPool2d(
            kernel_size=2, stride=2, padding=0, ceil_mode=True)
        self._conv = Conv2d(
            in_channels=num_channels,
            out_channels=num_filters,
            kernel_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
            act=None,
            weight_attr=ParamAttr(name=name + "_weights"),
            bias_attr=False)
        if name == "conv1":
            bn_name = "bn_" + name
        else:
            bn_name = "bn" + name[3:]
        self._batch_norm = BatchNorm(
            num_filters,
            act=act,
            param_attr=ParamAttr(name=bn_name + '_scale'),
            bias_attr=ParamAttr(bn_name + '_offset'),
            moving_mean_name=bn_name + '_mean',
            moving_variance_name=bn_name + '_variance')

    def forward(self, inputs):
        if self.is_vd_mode:
            inputs = self._pool2d_avg(inputs)
        y = self._conv(inputs)
        y = self._batch_norm(y)
        return y


class BottleneckBlock(nn.Layer):
    def __init__(self,
                 num_channels1,
                 num_channels2,
                 num_filters,
                 stride,
                 scales,
                 shortcut=True,
                 if_first=False,
                 name=None):
        super(BottleneckBlock, self).__init__()
        self.stride = stride
        self.scales = scales
        self.conv0 = ConvBNLayer(
            num_channels=num_channels1,
            num_filters=num_filters,
            filter_size=1,
            act='relu',
            name=name + "_branch2a")
        self.conv1_list = []
        for s in range(scales - 1):
            conv1 = self.add_sublayer(
                name + '_branch2b_' + str(s + 1),
                ConvBNLayer(
                    num_channels=num_filters // scales,
                    num_filters=num_filters // scales,
                    filter_size=3,
                    stride=stride,
                    act='relu',
                    name=name + '_branch2b_' + str(s + 1)))
            self.conv1_list.append(conv1)
        self.pool2d_avg = AvgPool2d(
            kernel_size=3, stride=stride, padding=1, ceil_mode=True)

        self.conv2 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_channels2,
            filter_size=1,
            act=None,
            name=name + "_branch2c")

        if not shortcut:
            self.short = ConvBNLayer(
                num_channels=num_channels1,
                num_filters=num_channels2,
                filter_size=1,
                stride=1,
                is_vd_mode=False if if_first else True,
                name=name + "_branch1")

        self.shortcut = shortcut

    def forward(self, inputs):
        y = self.conv0(inputs)
        xs = paddle.split(y, self.scales, 1)
        ys = []
        for s, conv1 in enumerate(self.conv1_list):
            if s == 0 or self.stride == 2:
                ys.append(conv1(xs[s]))
            else:
                ys.append(conv1(xs[s] + ys[-1]))
        if self.stride == 1:
            ys.append(xs[-1])
        else:
            ys.append(self.pool2d_avg(xs[-1]))
        conv1 = paddle.concat(ys, axis=1)
        conv2 = self.conv2(conv1)

        if self.shortcut:
            short = inputs
        else:
            short = self.short(inputs)
        y = paddle.elementwise_add(x=short, y=conv2, act='relu')
        return y


class Res2Net_vd(nn.Layer):
    def __init__(self, layers=50, scales=4, width=26, class_dim=1000):
        super(Res2Net_vd, self).__init__()

        self.layers = layers
        self.scales = scales
        self.width = width
        basic_width = self.width * self.scales
        supported_layers = [50, 101, 152, 200]
        assert layers in supported_layers, \
            "supported layers are {} but input layer is {}".format(
                supported_layers, layers)

        if layers == 50:
            depth = [3, 4, 6, 3]
        elif layers == 101:
            depth = [3, 4, 23, 3]
        elif layers == 152:
            depth = [3, 8, 36, 3]
        elif layers == 200:
            depth = [3, 12, 48, 3]
        num_channels = [64, 256, 512, 1024]
        num_channels2 = [256, 512, 1024, 2048]
        num_filters = [basic_width * t for t in [1, 2, 4, 8]]

        self.conv1_1 = ConvBNLayer(
            num_channels=3,
            num_filters=32,
            filter_size=3,
            stride=2,
            act='relu',
            name="conv1_1")
        self.conv1_2 = ConvBNLayer(
            num_channels=32,
            num_filters=32,
            filter_size=3,
            stride=1,
            act='relu',
            name="conv1_2")
        self.conv1_3 = ConvBNLayer(
            num_channels=32,
            num_filters=64,
            filter_size=3,
            stride=1,
            act='relu',
            name="conv1_3")
        self.pool2d_max = MaxPool2d(
            kernel_size=3, stride=2, padding=1, ceil_mode=True)

        self.block_list = []
        for block in range(len(depth)):
            shortcut = False
            for i in range(depth[block]):
                if layers in [101, 152, 200] and block == 2:
                    if i == 0:
                        conv_name = "res" + str(block + 2) + "a"
                    else:
                        conv_name = "res" + str(block + 2) + "b" + str(i)
                else:
                    conv_name = "res" + str(block + 2) + chr(97 + i)
                bottleneck_block = self.add_sublayer(
                    'bb_%d_%d' % (block, i),
                    BottleneckBlock(
                        num_channels1=num_channels[block]
                        if i == 0 else num_channels2[block],
                        num_channels2=num_channels2[block],
                        num_filters=num_filters[block],
                        stride=2 if i == 0 and block != 0 else 1,
                        scales=scales,
                        shortcut=shortcut,
                        if_first=block == i == 0,
                        name=conv_name))
                self.block_list.append(bottleneck_block)
                shortcut = True

        self.pool2d_avg = AdaptiveAvgPool2d(1)

        self.pool2d_avg_channels = num_channels[-1] * 2

        stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)

        self.out = Linear(
            self.pool2d_avg_channels,
            class_dim,
            weight_attr=ParamAttr(
                initializer=Uniform(-stdv, stdv), name="fc_weights"),
            bias_attr=ParamAttr(name="fc_offset"))

    def forward(self, inputs):
        y = self.conv1_1(inputs)
        y = self.conv1_2(y)
        y = self.conv1_3(y)
        y = self.pool2d_max(y)
        for block in self.block_list:
            y = block(y)
        y = self.pool2d_avg(y)
        y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
        y = self.out(y)
        return y


def Res2Net50_vd_48w_2s(**args):
    model = Res2Net_vd(layers=50, scales=2, width=48, **args)
    return model


def Res2Net50_vd_26w_4s(**args):
    model = Res2Net_vd(layers=50, scales=4, width=26, **args)
    return model


def Res2Net50_vd_14w_8s(**args):
    model = Res2Net_vd(layers=50, scales=8, width=14, **args)
    return model


def Res2Net50_vd_48w_2s(**args):
    model = Res2Net_vd(layers=50, scales=2, width=48, **args)
    return model


def Res2Net50_vd_26w_6s(**args):
    model = Res2Net_vd(layers=50, scales=6, width=26, **args)
    return model


def Res2Net50_vd_26w_8s(**args):
    model = Res2Net_vd(layers=50, scales=8, width=26, **args)
    return model


def Res2Net101_vd_26w_4s(**args):
    model = Res2Net_vd(layers=101, scales=4, width=26, **args)
    return model


def Res2Net152_vd_26w_4s(**args):
    model = Res2Net_vd(layers=152, scales=4, width=26, **args)
    return model


def Res2Net200_vd_26w_4s(**args):
    model = Res2Net_vd(layers=200, scales=4, width=26, **args)
    return model