# Copyright 2022 The Authors
# 
# 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
# 
#     https://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 torch
import torch.nn as nn
from torch.hub import load_state_dict_from_url

__all__ = [
    'ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
    'resnext50_32x4d', 'resnext101_32x8d'
]


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
  """3x3 convolution with padding"""
  return nn.Conv2d(in_planes,
                   out_planes,
                   kernel_size=3,
                   stride=stride,
                   padding=dilation,
                   groups=groups,
                   bias=False,
                   dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
  """1x1 convolution"""
  return nn.Conv2d(in_planes,
                   out_planes,
                   kernel_size=1,
                   stride=stride,
                   bias=False)


class BasicBlock(nn.Module):
  expansion = 1

  def __init__(self,
      inplanes,
      planes,
      stride=1,
      downsample=None,
      groups=1,
      base_width=64,
      dilation=1,
      norm_layer=None):
    super(BasicBlock, self).__init__()
    if norm_layer is None:
      norm_layer = nn.BatchNorm2d
    if groups != 1 or base_width != 64:
      raise ValueError(
          'BasicBlock only supports groups=1 and base_width=64')
    if dilation > 1:
      raise NotImplementedError(
          "Dilation > 1 not supported in BasicBlock")
    # Both self.conv1 and self.downsample layers downsample the input when stride != 1
    self.conv1 = conv3x3(inplanes, planes, stride)
    self.bn1 = norm_layer(planes)
    self.relu = nn.ReLU(inplace=True)
    self.conv2 = conv3x3(planes, planes)
    self.bn2 = norm_layer(planes)
    self.downsample = downsample
    self.stride = stride

  def forward(self, x):
    identity = x

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

    out = self.conv2(out)
    out = self.bn2(out)

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

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

    return out


class Bottleneck(nn.Module):
  expansion = 1

  def __init__(self,
      inplanes,
      planes,
      stride=1,
      downsample=None,
      groups=1,
      base_width=64,
      dilation=1,
      norm_layer=None):
    super(Bottleneck, self).__init__()
    if norm_layer is None:
      norm_layer = nn.BatchNorm2d
    width = int(planes * (base_width / 64.)) * groups
    # Both self.conv2 and self.downsample layers downsample the input when stride != 1
    self.conv1 = conv1x1(inplanes, width)
    self.bn1 = norm_layer(width)
    self.conv2 = conv3x3(width, width, stride, groups, dilation)
    self.bn2 = norm_layer(width)
    self.conv3 = conv1x1(width, planes * self.expansion)
    self.bn3 = norm_layer(planes * self.expansion)
    self.relu = nn.ReLU(inplace=True)
    self.downsample = downsample
    self.stride = stride

  def forward(self, x):
    identity = x

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

    out = self.conv2(out)
    out = self.bn2(out)
    out = self.relu(out)

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

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

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

    return out


class ResNet(nn.Module):
  def __init__(self,
      block,
      layers,
      num_classes=1000,
      zero_init_residual=False,
      groups=1,
      width_per_group=64,
      replace_stride_with_dilation=None,
      norm_layer=None):
    super(ResNet, self).__init__()
    if norm_layer is None:
      norm_layer = nn.BatchNorm2d
    self._norm_layer = norm_layer

    self.inplanes = 64
    self.dilation = 1
    self.final_feature_dimen = 256

    if replace_stride_with_dilation is None:
      # each element in the tuple indicates if we should replace
      # the 2x2 stride with a dilated convolution instead
      replace_stride_with_dilation = [False, False, False]
    if len(replace_stride_with_dilation) != 3:
      raise ValueError("replace_stride_with_dilation should be None "
                       "or a 3-element tuple, got {}".format(
          replace_stride_with_dilation))
    self.groups = groups
    self.base_width = width_per_group
    self.conv1 = nn.Conv2d(3,
                           self.inplanes,
                           kernel_size=5,
                           stride=1,
                           padding=2,
                           bias=False)
    self.bn1 = norm_layer(self.inplanes)
    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,
                                   dilate=replace_stride_with_dilation[0])
    self.layer3 = self._make_layer(block,
                                   256,
                                   layers[2],
                                   stride=2,
                                   dilate=replace_stride_with_dilation[1])

    self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
    self.fc_dense = nn.Linear(256, 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.GroupNorm)):
        nn.init.constant_(m.weight, 1)
        nn.init.constant_(m.bias, 0)

    # Zero-initialize the last BN in each residual branch,
    # so that the residual branch starts with zeros, and each residual block behaves like an identity.
    # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
    if zero_init_residual:
      for m in self.modules():
        if isinstance(m, Bottleneck):
          nn.init.constant_(m.bn3.weight, 0)
        elif isinstance(m, BasicBlock):
          nn.init.constant_(m.bn2.weight, 0)

  def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
    norm_layer = self._norm_layer
    downsample = None
    previous_dilation = self.dilation
    if dilate:
      self.dilation *= stride
      stride = 1
    if stride != 1 or self.inplanes != planes * block.expansion:
      downsample = nn.Sequential(
          conv1x1(self.inplanes, planes * block.expansion, stride),
          norm_layer(planes * block.expansion),
      )

    layers = []
    layers.append(
        block(self.inplanes, planes, stride, downsample, self.groups,
              self.base_width, previous_dilation, norm_layer))
    self.inplanes = planes * block.expansion
    for _ in range(1, blocks):
      layers.append(
          block(self.inplanes,
                planes,
                groups=self.groups,
                base_width=self.base_width,
                dilation=self.dilation,
                norm_layer=norm_layer))

    return nn.Sequential(*layers)

  def forward(self, x, features_only=False, features_and_logits=False):
    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.reshape(x.size(0), -1)

    if features_only:
      return x

    logits = self.fc_dense(x)

    if features_and_logits:
      return x, logits

    return logits


def _resnet(arch, block, layers, pretrained, progress, **kwargs):
  model = ResNet(block, layers, **kwargs)
  if pretrained:
    state_dict = load_state_dict_from_url(model_urls[arch],
                                          progress=progress)
    model.load_state_dict(state_dict)
  return model


def resnet18(pretrained=False, progress=True, **kwargs):
  """Constructs a ResNet-18 model.
  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
      progress (bool): If True, displays a progress bar of the download to stderr
  """
  return _resnet('resnet18', BasicBlock, [3, 3, 3], pretrained, progress,
                 **kwargs)


def resnet34(pretrained=False, progress=True, **kwargs):
  """Constructs a ResNet-34 model.
  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
      progress (bool): If True, displays a progress bar of the download to stderr
  """
  return _resnet('resnet34', BasicBlock, [5, 5, 5], pretrained, progress,
                 **kwargs)


def resnet50(pretrained=False, progress=True, **kwargs):
  """Constructs a ResNet-50 model.
  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
      progress (bool): If True, displays a progress bar of the download to stderr
  """
  return _resnet('resnet50', Bottleneck, [5, 5, 5], pretrained, progress,
                 **kwargs)


def resnet101(pretrained=False, progress=True, **kwargs):
  """Constructs a ResNet-101 model.
  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
      progress (bool): If True, displays a progress bar of the download to stderr
  """
  return _resnet('resnet101', Bottleneck, [11, 11, 11], pretrained, progress,
                 **kwargs)


def resnet152(pretrained=False, progress=True, **kwargs):
  """Constructs a ResNet-152 model.
  Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
      progress (bool): If True, displays a progress bar of the download to stderr
  """
  return _resnet('resnet152', Bottleneck, [16, 18, 16], pretrained, progress,
                 **kwargs)


model_dict = {
    'resnet18': resnet18,
    'resnet34': resnet34,
    'resnet50': resnet50,
    'resnet101': resnet101,
    'resnet152': resnet152,
}