#copyright (c) 2019 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 paddle
import paddle.fluid as fluid
import math
from paddle.fluid.param_attr import ParamAttr
__all__ = [
    "Res2Net_vd", "Res2Net50_vd_48w_2s", "Res2Net50_vd_26w_4s", "Res2Net50_vd_14w_8s", "Res2Net50_vd_26w_6s",
    "Res2Net50_vd_26w_8s", "Res2Net101_vd_26w_4s", "Res2Net152_vd_26w_4s", "Res2Net200_vd_26w_4s"
]


class Res2Net_vd():
    def __init__(self, layers=50, scales=4, width=26):
        self.layers = layers
        self.scales = scales
        self.width = width

    def net(self, input, class_dim=1000):
        layers = self.layers
        supported_layers = [50, 101, 152, 200]
        assert layers in supported_layers, \
            "supported layers are {} but input layer is {}".format(supported_layers, layers)
        basic_width = self.width * self.scales
        num_filters1 = [basic_width * t for t in [1, 2, 4, 8]]
        num_filters2 = [256 * t for t in [1, 2, 4, 8]]
        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]
        conv = self.conv_bn_layer(input=input, num_filters=32, filter_size=3, stride=2, act='relu', name='conv1_1')
        conv = self.conv_bn_layer(input=conv, num_filters=32, filter_size=3, stride=1, act='relu', name='conv1_2')
        conv = self.conv_bn_layer(input=conv, num_filters=64, filter_size=3, stride=1, act='relu', name='conv1_3')

        conv = fluid.layers.pool2d(input=conv, pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
        for block in range(len(depth)):
            for i in range(depth[block]):
                if layers in [101, 152] 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)
                conv = self.bottleneck_block(
                    input=conv,
                    num_filters1=num_filters1[block],
                    num_filters2=num_filters2[block],
                    stride=2 if i == 0 and block != 0 else 1,
                    if_first=block == i == 0,
                    name=conv_name)
        pool = fluid.layers.pool2d(input=conv, pool_size=7, pool_stride=1, pool_type='avg', global_pooling=True)

        stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
        out = fluid.layers.fc(
            input=pool,
            size=class_dim,
            param_attr=fluid.param_attr.ParamAttr(
                initializer=fluid.initializer.Uniform(-stdv, stdv), name='fc_weights'),
            bias_attr=fluid.param_attr.ParamAttr(name='fc_offset'))
        return out, pool

    def conv_bn_layer(self, input, num_filters, filter_size, stride=1, groups=1, act=None, name=None):
        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"),
            bias_attr=False)
        if name == "conv1":
            bn_name = "bn_" + name
        else:
            bn_name = "bn" + name[3:]
        return fluid.layers.batch_norm(
            input=conv,
            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 conv_bn_layer_new(self, input, num_filters, filter_size, stride=1, groups=1, act=None, name=None):
        pool = fluid.layers.pool2d(
            input=input, pool_size=2, pool_stride=2, pool_padding=0, pool_type='avg', ceil_mode=True)

        conv = fluid.layers.conv2d(
            input=pool,
            num_filters=num_filters,
            filter_size=filter_size,
            stride=1,
            padding=(filter_size - 1) // 2,
            groups=groups,
            act=None,
            param_attr=ParamAttr(name=name + "_weights"),
            bias_attr=False)
        if name == "conv1":
            bn_name = "bn_" + name
        else:
            bn_name = "bn" + name[3:]
        return fluid.layers.batch_norm(
            input=conv,
            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 shortcut(self, input, ch_out, stride, name, if_first=False):
        ch_in = input.shape[1]
        if ch_in != ch_out or stride != 1:
            if if_first:
                return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
            else:
                return self.conv_bn_layer_new(input, ch_out, 1, stride, name=name)
        elif if_first:
            return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
        else:
            return input

    def bottleneck_block(self, input, num_filters1, num_filters2, stride, name, if_first):
        conv0 = self.conv_bn_layer(
            input=input, num_filters=num_filters1, filter_size=1, stride=1, act='relu', name=name + '_branch2a')

        xs = fluid.layers.split(conv0, self.scales, 1)
        ys = []
        for s in range(self.scales - 1):
            if s == 0 or stride == 2:
                ys.append(
                    self.conv_bn_layer(
                        input=xs[s],
                        num_filters=num_filters1 // self.scales,
                        stride=stride,
                        filter_size=3,
                        act='relu',
                        name=name + '_branch2b_' + str(s + 1)))
            else:
                ys.append(
                    self.conv_bn_layer(
                        input=xs[s] + ys[-1],
                        num_filters=num_filters1 // self.scales,
                        stride=stride,
                        filter_size=3,
                        act='relu',
                        name=name + '_branch2b_' + str(s + 1)))

        if stride == 1:
            ys.append(xs[-1])
        else:
            ys.append(
                fluid.layers.pool2d(input=xs[-1], pool_size=3, pool_stride=stride, pool_padding=1, pool_type='avg'))

        conv1 = fluid.layers.concat(ys, axis=1)
        conv2 = self.conv_bn_layer(
            input=conv1, num_filters=num_filters2, filter_size=1, act=None, name=name + "_branch2c")

        short = self.shortcut(input, num_filters2, stride, if_first=if_first, name=name + "_branch1")

        return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')


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


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


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


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


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


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


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


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