# Copyright 2018 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.
# ==============================================================================
from __future__ import print_function, division, absolute_import
import torch
import torch.nn as nn
import torch.utils.model_zoo as model_zoo

from tw.utils.checkpoint import load_state_dict_from_url

__all__ = ['InceptionResNetV2', 'inceptionresnetv2']

pretrained_settings = {
    'inceptionresnetv2': {
        'imagenet': {
            'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.pth',
            'input_space': 'RGB',
            'input_size': [3, 299, 299],
            'input_range': [0, 1],
            'mean': [0.5, 0.5, 0.5],
            'std': [0.5, 0.5, 0.5],
            'num_classes': 1000
        },
        'imagenet+background': {
            'url': 'http://data.lip6.fr/cadene/pretrainedmodels/inceptionresnetv2-520b38e4.pth',
            'input_space': 'RGB',
            'input_size': [3, 299, 299],
            'input_range': [0, 1],
            'mean': [0.5, 0.5, 0.5],
            'std': [0.5, 0.5, 0.5],
            'num_classes': 1001
        }
    }
}


class BasicConv2d(nn.Module):

  def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
    super(BasicConv2d, self).__init__()
    self.conv = nn.Conv2d(in_planes, out_planes,
                          kernel_size=kernel_size, stride=stride,
                          padding=padding, bias=False)  # verify bias false
    self.bn = nn.BatchNorm2d(out_planes,
                             eps=0.001,  # value found in tensorflow
                             momentum=0.1,  # default pytorch value
                             affine=True)
    self.relu = nn.ReLU(inplace=False)

  def forward(self, x):
    x = self.conv(x)
    x = self.bn(x)
    x = self.relu(x)
    return x


class Mixed_5b(nn.Module):

  def __init__(self):
    super(Mixed_5b, self).__init__()

    self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1)

    self.branch1 = nn.Sequential(
        BasicConv2d(192, 48, kernel_size=1, stride=1),
        BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2)
    )

    self.branch2 = nn.Sequential(
        BasicConv2d(192, 64, kernel_size=1, stride=1),
        BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
        BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
    )

    self.branch3 = nn.Sequential(
        nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
        BasicConv2d(192, 64, kernel_size=1, stride=1)
    )

  def forward(self, x):
    x0 = self.branch0(x)
    x1 = self.branch1(x)
    x2 = self.branch2(x)
    x3 = self.branch3(x)
    out = torch.cat((x0, x1, x2, x3), 1)
    return out


class Block35(nn.Module):

  def __init__(self, scale=1.0):
    super(Block35, self).__init__()

    self.scale = scale

    self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1)

    self.branch1 = nn.Sequential(
        BasicConv2d(320, 32, kernel_size=1, stride=1),
        BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
    )

    self.branch2 = nn.Sequential(
        BasicConv2d(320, 32, kernel_size=1, stride=1),
        BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1),
        BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1)
    )

    self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1)
    self.relu = nn.ReLU(inplace=False)

  def forward(self, x):
    x0 = self.branch0(x)
    x1 = self.branch1(x)
    x2 = self.branch2(x)
    out = torch.cat((x0, x1, x2), 1)
    out = self.conv2d(out)
    out = out * self.scale + x
    out = self.relu(out)
    return out


class Mixed_6a(nn.Module):

  def __init__(self):
    super(Mixed_6a, self).__init__()

    self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2)

    self.branch1 = nn.Sequential(
        BasicConv2d(320, 256, kernel_size=1, stride=1),
        BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
        BasicConv2d(256, 384, kernel_size=3, stride=2)
    )

    self.branch2 = nn.MaxPool2d(3, stride=2)

  def forward(self, x):
    x0 = self.branch0(x)
    x1 = self.branch1(x)
    x2 = self.branch2(x)
    out = torch.cat((x0, x1, x2), 1)
    return out


class Block17(nn.Module):

  def __init__(self, scale=1.0):
    super(Block17, self).__init__()

    self.scale = scale

    self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1)

    self.branch1 = nn.Sequential(
        BasicConv2d(1088, 128, kernel_size=1, stride=1),
        BasicConv2d(128, 160, kernel_size=(1, 7), stride=1, padding=(0, 3)),
        BasicConv2d(160, 192, kernel_size=(7, 1), stride=1, padding=(3, 0))
    )

    self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1)
    self.relu = nn.ReLU(inplace=False)

  def forward(self, x):
    x0 = self.branch0(x)
    x1 = self.branch1(x)
    out = torch.cat((x0, x1), 1)
    out = self.conv2d(out)
    out = out * self.scale + x
    out = self.relu(out)
    return out


class Mixed_7a(nn.Module):

  def __init__(self):
    super(Mixed_7a, self).__init__()

    self.branch0 = nn.Sequential(
        BasicConv2d(1088, 256, kernel_size=1, stride=1),
        BasicConv2d(256, 384, kernel_size=3, stride=2)
    )

    self.branch1 = nn.Sequential(
        BasicConv2d(1088, 256, kernel_size=1, stride=1),
        BasicConv2d(256, 288, kernel_size=3, stride=2)
    )

    self.branch2 = nn.Sequential(
        BasicConv2d(1088, 256, kernel_size=1, stride=1),
        BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1),
        BasicConv2d(288, 320, kernel_size=3, stride=2)
    )

    self.branch3 = nn.MaxPool2d(3, stride=2)

  def forward(self, x):
    x0 = self.branch0(x)
    x1 = self.branch1(x)
    x2 = self.branch2(x)
    x3 = self.branch3(x)
    out = torch.cat((x0, x1, x2, x3), 1)
    return out


class Block8(nn.Module):

  def __init__(self, scale=1.0, noReLU=False):
    super(Block8, self).__init__()

    self.scale = scale
    self.noReLU = noReLU

    self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1)

    self.branch1 = nn.Sequential(
        BasicConv2d(2080, 192, kernel_size=1, stride=1),
        BasicConv2d(192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1)),
        BasicConv2d(224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
    )

    self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1)
    if not self.noReLU:
      self.relu = nn.ReLU(inplace=False)

  def forward(self, x):
    x0 = self.branch0(x)
    x1 = self.branch1(x)
    out = torch.cat((x0, x1), 1)
    out = self.conv2d(out)
    out = out * self.scale + x
    if not self.noReLU:
      out = self.relu(out)
    return out


class InceptionResNetV2(nn.Module):

  MEAN = [0.5, 0.5, 0.5]
  STD = [0.5, 0.5, 0.5]
  SIZE = [229, 229]
  SCALE = 299
  CROP = 0.875

  def __init__(self, num_classes=1001, output_backbone=False):
    super(InceptionResNetV2, self).__init__()
    # Special attributs
    self.input_space = None
    self.input_size = (299, 299, 3)
    self.mean = None
    self.std = None
    self.output_backbone = output_backbone
    # Modules
    self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
    self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
    self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1)
    self.maxpool_3a = nn.MaxPool2d(3, stride=2)
    self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
    self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
    self.maxpool_5a = nn.MaxPool2d(3, stride=2)
    self.mixed_5b = Mixed_5b()
    self.repeat = nn.Sequential(
        Block35(scale=0.17),
        Block35(scale=0.17),
        Block35(scale=0.17),
        Block35(scale=0.17),
        Block35(scale=0.17),
        Block35(scale=0.17),
        Block35(scale=0.17),
        Block35(scale=0.17),
        Block35(scale=0.17),
        Block35(scale=0.17)
    )
    self.mixed_6a = Mixed_6a()
    self.repeat_1 = nn.Sequential(
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10),
        Block17(scale=0.10)
    )
    self.mixed_7a = Mixed_7a()
    self.repeat_2 = nn.Sequential(
        Block8(scale=0.20),
        Block8(scale=0.20),
        Block8(scale=0.20),
        Block8(scale=0.20),
        Block8(scale=0.20),
        Block8(scale=0.20),
        Block8(scale=0.20),
        Block8(scale=0.20),
        Block8(scale=0.20)
    )
    self.block8 = Block8(noReLU=True)
    self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1)
    self.avgpool_1a = nn.AvgPool2d(8, count_include_pad=False)
    self.last_linear = nn.Linear(1536, num_classes)

  def features(self, input):
    x = self.conv2d_1a(input)
    x = self.conv2d_2a(x)
    x = self.conv2d_2b(x)
    x = self.maxpool_3a(x)
    x = self.conv2d_3b(x)
    x = self.conv2d_4a(x)
    x = self.maxpool_5a(x)
    x = self.mixed_5b(x)
    x = self.repeat(x)
    x = self.mixed_6a(x)
    x = self.repeat_1(x)
    x = self.mixed_7a(x)
    x = self.repeat_2(x)
    x = self.block8(x)
    x = self.conv2d_7b(x)
    return x

  def logits(self, features):
    x = self.avgpool_1a(features)
    x = x.view(x.size(0), -1)
    x = self.last_linear(x)
    return x

  def forward(self, input):
    x = self.features(input)
    if self.output_backbone:
        return x, x, x, x
    x = self.logits(x)
    return x


def inceptionresnetv2(num_classes=1000, pretrained='imagenet', **kwargs):
  r"""InceptionResNetV2 model architecture from the
  `"InceptionV4, Inception-ResNet..." <https://arxiv.org/abs/1602.07261>`_ paper.
  """
  if pretrained:
    settings = pretrained_settings['inceptionresnetv2'][pretrained]
    assert num_classes == settings['num_classes'], \
        "num_classes should be {}, but is {}".format(settings['num_classes'], num_classes)

    # both 'imagenet'&'imagenet+background' are loaded from same parameters
    model = InceptionResNetV2(num_classes=1001, **kwargs)

    load_state_dict_from_url(model, settings['url'])

    if pretrained == 'imagenet':
      new_last_linear = nn.Linear(1536, 1000)
      new_last_linear.weight.data = model.last_linear.weight.data[1:]
      new_last_linear.bias.data = model.last_linear.bias.data[1:]
      model.last_linear = new_last_linear

    model.input_space = settings['input_space']
    model.input_size = settings['input_size']
    model.input_range = settings['input_range']

    model.mean = settings['mean']
    model.std = settings['std']
  else:
    model = InceptionResNetV2(num_classes=num_classes, **kwargs)
  return model


'''
TEST
Run this code with:
```
cd $HOME/pretrained-models.pytorch
python -m pretrainedmodels.inceptionresnetv2
```
'''
if __name__ == '__main__':

  assert inceptionresnetv2(num_classes=10, pretrained=None)
  print('success')
  assert inceptionresnetv2(num_classes=1000, pretrained='imagenet')
  print('success')
  assert inceptionresnetv2(num_classes=1001, pretrained='imagenet+background')
  print('success')

  # fail
  assert inceptionresnetv2(num_classes=1001, pretrained='imagenet')
