# Copyright 2020 The KaiJIN 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.
# ==============================================================================
r"""Code reference from:
  https://github.com/Res2Net/Res2Net-PretrainedModels/blob/master/res2net.py
"""
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch
import torch.nn.functional as F
__all__ = ['Res2Net', 'res2net50']


model_urls = {
    'res2net50_26w_4s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net50_26w_4s-06e79181.pth',
    'res2net50_48w_2s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net50_48w_2s-afed724a.pth',
    'res2net50_14w_8s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net50_14w_8s-6527dddc.pth',
    'res2net50_26w_6s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net50_26w_6s-19041792.pth',
    'res2net50_26w_8s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net50_26w_8s-2c7c9f12.pth',
    'res2net101_26w_4s': 'https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net101_26w_4s-02a759a1.pth',
}


class Bottle2neck(nn.Module):
  expansion = 4

  def __init__(self, inplanes, planes, stride=1, downsample=None, baseWidth=26, scale=4, stype='normal'):
    """ Constructor
    Args:
        inplanes: input channel dimensionality
        planes: output channel dimensionality
        stride: conv stride. Replaces pooling layer.
        downsample: None when stride = 1
        baseWidth: basic width of conv3x3
        scale: number of scale.
        type: 'normal': normal set. 'stage': first block of a new stage.
    """
    super(Bottle2neck, self).__init__()

    width = int(math.floor(planes * (baseWidth/64.0)))
    self.conv1 = nn.Conv2d(inplanes, width*scale, kernel_size=1, bias=False)
    self.bn1 = nn.BatchNorm2d(width*scale)

    if scale == 1:
      self.nums = 1
    else:
      self.nums = scale - 1
    if stype == 'stage':
      self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)
    convs = []
    bns = []
    for i in range(self.nums):
      convs.append(nn.Conv2d(width, width, kernel_size=3,
                             stride=stride, padding=1, bias=False))
      bns.append(nn.BatchNorm2d(width))
    self.convs = nn.ModuleList(convs)
    self.bns = nn.ModuleList(bns)

    self.conv3 = nn.Conv2d(width*scale, planes *
                           self.expansion, kernel_size=1, bias=False)
    self.bn3 = nn.BatchNorm2d(planes * self.expansion)

    self.relu = nn.ReLU(inplace=True)
    self.downsample = downsample
    self.stype = stype
    self.scale = scale
    self.width = width

  def forward(self, x):
    residual = x

    out = self.conv1(x)
    out = self.bn1(out)
    out = self.relu(out)

    spx = torch.split(out, self.width, 1)
    for i in range(self.nums):
      if i == 0 or self.stype == 'stage':
        sp = spx[i]
      else:
        sp = sp + spx[i]
      sp = self.convs[i](sp)
      sp = self.relu(self.bns[i](sp))
      if i == 0:
        out = sp
      else:
        out = torch.cat((out, sp), 1)
    if self.scale != 1 and self.stype == 'normal':
      out = torch.cat((out, spx[self.nums]), 1)
    elif self.scale != 1 and self.stype == 'stage':
      out = torch.cat((out, self.pool(spx[self.nums])), 1)

    out = self.conv3(out)
    out = self.bn3(out)

    if self.downsample is not None:
      residual = self.downsample(x)

    out += residual
    out = self.relu(out)

    return out


class Res2Net(nn.Module):

  def __init__(self, block, layers, baseWidth=26, scale=4, num_classes=1000):
    self.inplanes = 64
    super(Res2Net, self).__init__()
    self.baseWidth = baseWidth
    self.scale = scale
    self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                           bias=False)
    self.bn1 = nn.BatchNorm2d(64)
    self.relu = nn.ReLU(inplace=True)
    self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
    self.layer1 = self._make_layer(block, 64, layers[0])
    self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
    self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
    self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
    self.avgpool = nn.AdaptiveAvgPool2d(1)
    self.fc = nn.Linear(512 * block.expansion, num_classes)

    for m in self.modules():
      if isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
      elif isinstance(m, nn.BatchNorm2d):
        nn.init.constant_(m.weight, 1)
        nn.init.constant_(m.bias, 0)

  def _make_layer(self, block, planes, blocks, stride=1):
    downsample = None
    if stride != 1 or self.inplanes != planes * block.expansion:
      downsample = nn.Sequential(
          nn.Conv2d(self.inplanes, planes * block.expansion,
                    kernel_size=1, stride=stride, bias=False),
          nn.BatchNorm2d(planes * block.expansion),
      )

    layers = []
    layers.append(block(self.inplanes, planes, stride, downsample=downsample,
                        stype='stage', baseWidth=self.baseWidth, scale=self.scale))
    self.inplanes = planes * block.expansion
    for i in range(1, blocks):
      layers.append(block(self.inplanes, planes,
                          baseWidth=self.baseWidth, scale=self.scale))

    return nn.Sequential(*layers)

  def forward(self, x):
    x = self.conv1(x)
    x = self.bn1(x)
    x = self.relu(x)
    x = self.maxpool(x)

    x = self.layer1(x)
    x = self.layer2(x)
    x = self.layer3(x)
    x = self.layer4(x)

    x = self.avgpool(x)
    x = x.view(x.size(0), -1)
    x = self.fc(x)

    return x


def res2net50(pretrained=False, **kwargs):
  """Constructs a Res2Net-50 model.
  Res2Net-50 refers to the Res2Net-50_26w_4s.
  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
  model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=26, scale=4, **kwargs)
  if pretrained:
    model.load_state_dict(model_zoo.load_url(model_urls['res2net50_26w_4s']))
  return model


def res2net50_26w_4s(pretrained=False, **kwargs):
  """Constructs a Res2Net-50_26w_4s model.
  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
  model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=26, scale=4, **kwargs)
  if pretrained:
    model.load_state_dict(model_zoo.load_url(model_urls['res2net50_26w_4s']))
  return model


def res2net101_26w_4s(pretrained=False, **kwargs):
  """Constructs a Res2Net-50_26w_4s model.
  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
  model = Res2Net(Bottle2neck, [3, 4, 23, 3], baseWidth=26, scale=4, **kwargs)
  if pretrained:
    model.load_state_dict(model_zoo.load_url(model_urls['res2net101_26w_4s']))
  return model


def res2net50_26w_6s(pretrained=False, **kwargs):
  """Constructs a Res2Net-50_26w_4s model.
  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
  model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=26, scale=6, **kwargs)
  if pretrained:
    model.load_state_dict(model_zoo.load_url(model_urls['res2net50_26w_6s']))
  return model


def res2net50_26w_8s(pretrained=False, **kwargs):
  """Constructs a Res2Net-50_26w_4s model.
  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
  model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=26, scale=8, **kwargs)
  if pretrained:
    model.load_state_dict(model_zoo.load_url(model_urls['res2net50_26w_8s']))
  return model


def res2net50_48w_2s(pretrained=False, **kwargs):
  """Constructs a Res2Net-50_48w_2s model.
  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
  model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=48, scale=2, **kwargs)
  if pretrained:
    model.load_state_dict(model_zoo.load_url(model_urls['res2net50_48w_2s']))
  return model


def res2net50_14w_8s(pretrained=False, **kwargs):
  """Constructs a Res2Net-50_14w_8s model.
  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
  """
  model = Res2Net(Bottle2neck, [3, 4, 6, 3], baseWidth=14, scale=8, **kwargs)
  if pretrained:
    model.load_state_dict(model_zoo.load_url(model_urls['res2net50_14w_8s']))
  return model


if __name__ == '__main__':
  images = torch.rand(1, 3, 224, 224).cuda(0)
  model = res2net101_26w_4s(pretrained=True)
  model = model.cuda(0)
  print(model(images).size())
