# 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 collections import namedtuple
import warnings
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.jit.annotations import Optional
from torch import Tensor


__all__ = ['Inception3', 'inception_v3',
           'InceptionOutputs', '_InceptionOutputs']


model_urls = {
    # Inception v3 ported from TensorFlow
    'inception_v3_google': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth',
}

InceptionOutputs = namedtuple('InceptionOutputs', ['logits', 'aux_logits'])
InceptionOutputs.__annotations__ = {
    'logits': torch.Tensor, 'aux_logits': Optional[torch.Tensor]}

# Script annotations failed with _GoogleNetOutputs = namedtuple ...
# _InceptionOutputs set here for backwards compat
_InceptionOutputs = InceptionOutputs


def inception_v3(pretrained=False, progress=True, **kwargs):
  r"""Inception v3 model architecture from
  `"Rethinking the Inception Architecture for Computer Vision" <http://arxiv.org/abs/1512.00567>`_.

  .. note::
      **Important**: In contrast to the other models the inception_v3 expects tensors with a size of
      N x 3 x 299 x 299, so ensure your images are sized accordingly.

  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
      aux_logits (bool): If True, add an auxiliary branch that can improve training.
          Default: *True*
      transform_input (bool): If True, preprocesses the input according to the method with which it
          was trained on ImageNet. Default: *False*
  """
  if pretrained:
    if 'transform_input' not in kwargs:
      kwargs['transform_input'] = True
    if 'aux_logits' in kwargs:
      original_aux_logits = kwargs['aux_logits']
      kwargs['aux_logits'] = True
    else:
      original_aux_logits = True
    model = Inception3(**kwargs)
    state_dict = load_state_dict_from_url(model_urls['inception_v3_google'],
                                          progress=progress)
    model.load_state_dict(state_dict)
    if not original_aux_logits:
      model.aux_logits = False
      del model.AuxLogits
    return model

  return Inception3(**kwargs)


class Inception3(nn.Module):

  def __init__(self, num_classes=1000, aux_logits=True, transform_input=False,
               inception_blocks=None):
    super(Inception3, self).__init__()
    if inception_blocks is None:
      inception_blocks = [
          BasicConv2d, InceptionA, InceptionB, InceptionC,
          InceptionD, InceptionE, InceptionAux
      ]
    assert len(inception_blocks) == 7
    conv_block = inception_blocks[0]
    inception_a = inception_blocks[1]
    inception_b = inception_blocks[2]
    inception_c = inception_blocks[3]
    inception_d = inception_blocks[4]
    inception_e = inception_blocks[5]
    inception_aux = inception_blocks[6]

    self.aux_logits = aux_logits
    self.transform_input = transform_input
    self.Conv2d_1a_3x3 = conv_block(3, 32, kernel_size=3, stride=2)
    self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3)
    self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1)
    self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1)
    self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3)
    self.Mixed_5b = inception_a(192, pool_features=32)
    self.Mixed_5c = inception_a(256, pool_features=64)
    self.Mixed_5d = inception_a(288, pool_features=64)
    self.Mixed_6a = inception_b(288)
    self.Mixed_6b = inception_c(768, channels_7x7=128)
    self.Mixed_6c = inception_c(768, channels_7x7=160)
    self.Mixed_6d = inception_c(768, channels_7x7=160)
    self.Mixed_6e = inception_c(768, channels_7x7=192)
    if aux_logits:
      self.AuxLogits = inception_aux(768, num_classes)
    self.Mixed_7a = inception_d(768)
    self.Mixed_7b = inception_e(1280)
    self.Mixed_7c = inception_e(2048)
    self.fc = nn.Linear(2048, num_classes)

    for m in self.modules():
      if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
        import scipy.stats as stats
        stddev = m.stddev if hasattr(m, 'stddev') else 0.1
        X = stats.truncnorm(-2, 2, scale=stddev)
        values = torch.as_tensor(X.rvs(m.weight.numel()), dtype=m.weight.dtype)
        values = values.view(m.weight.size())
        with torch.no_grad():
          m.weight.copy_(values)
      elif isinstance(m, nn.BatchNorm2d):
        nn.init.constant_(m.weight, 1)
        nn.init.constant_(m.bias, 0)

  def _transform_input(self, x):
    if self.transform_input:
      x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
      x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
      x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
      x = torch.cat((x_ch0, x_ch1, x_ch2), 1)
    return x

  def _forward(self, x):
    # N x 3 x 299 x 299
    x = self.Conv2d_1a_3x3(x)
    # N x 32 x 149 x 149
    x = self.Conv2d_2a_3x3(x)
    # N x 32 x 147 x 147
    x = self.Conv2d_2b_3x3(x)
    # N x 64 x 147 x 147
    x = F.max_pool2d(x, kernel_size=3, stride=2)
    # N x 64 x 73 x 73
    x = self.Conv2d_3b_1x1(x)
    # N x 80 x 73 x 73
    x = self.Conv2d_4a_3x3(x)
    # N x 192 x 71 x 71
    x = F.max_pool2d(x, kernel_size=3, stride=2)
    # N x 192 x 35 x 35
    x = self.Mixed_5b(x)
    # N x 256 x 35 x 35
    x = self.Mixed_5c(x)
    # N x 288 x 35 x 35
    x = self.Mixed_5d(x)
    # N x 288 x 35 x 35
    x = self.Mixed_6a(x)
    # N x 768 x 17 x 17
    x = self.Mixed_6b(x)
    # N x 768 x 17 x 17
    x = self.Mixed_6c(x)
    # N x 768 x 17 x 17
    x = self.Mixed_6d(x)
    # N x 768 x 17 x 17
    x = self.Mixed_6e(x)
    # N x 768 x 17 x 17
    aux_defined = self.training and self.aux_logits
    if aux_defined:
      aux = self.AuxLogits(x)
    else:
      aux = None
    # N x 768 x 17 x 17
    x = self.Mixed_7a(x)
    # N x 1280 x 8 x 8
    x = self.Mixed_7b(x)
    # N x 2048 x 8 x 8
    x = self.Mixed_7c(x)
    # N x 2048 x 8 x 8
    # Adaptive average pooling
    x = F.adaptive_avg_pool2d(x, (1, 1))
    # N x 2048 x 1 x 1
    x = F.dropout(x, training=self.training)
    # N x 2048 x 1 x 1
    x = torch.flatten(x, 1)
    # N x 2048
    x = self.fc(x)
    # N x 1000 (num_classes)
    return x, aux

  @torch.jit.unused
  def eager_outputs(self, x, aux):
    # type: (Tensor, Optional[Tensor]) -> InceptionOutputs
    if self.training and self.aux_logits:
      return InceptionOutputs(x, aux)
    else:
      return x

  def forward(self, x):
    x = self._transform_input(x)
    x, aux = self._forward(x)
    aux_defined = self.training and self.aux_logits
    if torch.jit.is_scripting():
      if not aux_defined:
        warnings.warn("Scripted Inception3 always returns Inception3 Tuple")
      return InceptionOutputs(x, aux)
    else:
      return self.eager_outputs(x, aux)


class InceptionA(nn.Module):

  def __init__(self, in_channels, pool_features, conv_block=None):
    super(InceptionA, self).__init__()
    if conv_block is None:
      conv_block = BasicConv2d
    self.branch1x1 = conv_block(in_channels, 64, kernel_size=1)

    self.branch5x5_1 = conv_block(in_channels, 48, kernel_size=1)
    self.branch5x5_2 = conv_block(48, 64, kernel_size=5, padding=2)

    self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
    self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
    self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, padding=1)

    self.branch_pool = conv_block(in_channels, pool_features, kernel_size=1)

  def _forward(self, x):
    branch1x1 = self.branch1x1(x)

    branch5x5 = self.branch5x5_1(x)
    branch5x5 = self.branch5x5_2(branch5x5)

    branch3x3dbl = self.branch3x3dbl_1(x)
    branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
    branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)

    branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
    branch_pool = self.branch_pool(branch_pool)

    outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
    return outputs

  def forward(self, x):
    outputs = self._forward(x)
    return torch.cat(outputs, 1)


class InceptionB(nn.Module):

  def __init__(self, in_channels, conv_block=None):
    super(InceptionB, self).__init__()
    if conv_block is None:
      conv_block = BasicConv2d
    self.branch3x3 = conv_block(in_channels, 384, kernel_size=3, stride=2)

    self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1)
    self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1)
    self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, stride=2)

  def _forward(self, x):
    branch3x3 = self.branch3x3(x)

    branch3x3dbl = self.branch3x3dbl_1(x)
    branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
    branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)

    branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)

    outputs = [branch3x3, branch3x3dbl, branch_pool]
    return outputs

  def forward(self, x):
    outputs = self._forward(x)
    return torch.cat(outputs, 1)


class InceptionC(nn.Module):

  def __init__(self, in_channels, channels_7x7, conv_block=None):
    super(InceptionC, self).__init__()
    if conv_block is None:
      conv_block = BasicConv2d
    self.branch1x1 = conv_block(in_channels, 192, kernel_size=1)

    c7 = channels_7x7
    self.branch7x7_1 = conv_block(in_channels, c7, kernel_size=1)
    self.branch7x7_2 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3))
    self.branch7x7_3 = conv_block(c7, 192, kernel_size=(7, 1), padding=(3, 0))

    self.branch7x7dbl_1 = conv_block(in_channels, c7, kernel_size=1)
    self.branch7x7dbl_2 = conv_block(
        c7, c7, kernel_size=(7, 1), padding=(3, 0))
    self.branch7x7dbl_3 = conv_block(
        c7, c7, kernel_size=(1, 7), padding=(0, 3))
    self.branch7x7dbl_4 = conv_block(
        c7, c7, kernel_size=(7, 1), padding=(3, 0))
    self.branch7x7dbl_5 = conv_block(
        c7, 192, kernel_size=(1, 7), padding=(0, 3))

    self.branch_pool = conv_block(in_channels, 192, kernel_size=1)

  def _forward(self, x):
    branch1x1 = self.branch1x1(x)

    branch7x7 = self.branch7x7_1(x)
    branch7x7 = self.branch7x7_2(branch7x7)
    branch7x7 = self.branch7x7_3(branch7x7)

    branch7x7dbl = self.branch7x7dbl_1(x)
    branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
    branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
    branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
    branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)

    branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
    branch_pool = self.branch_pool(branch_pool)

    outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
    return outputs

  def forward(self, x):
    outputs = self._forward(x)
    return torch.cat(outputs, 1)


class InceptionD(nn.Module):

  def __init__(self, in_channels, conv_block=None):
    super(InceptionD, self).__init__()
    if conv_block is None:
      conv_block = BasicConv2d
    self.branch3x3_1 = conv_block(in_channels, 192, kernel_size=1)
    self.branch3x3_2 = conv_block(192, 320, kernel_size=3, stride=2)

    self.branch7x7x3_1 = conv_block(in_channels, 192, kernel_size=1)
    self.branch7x7x3_2 = conv_block(
        192, 192, kernel_size=(1, 7), padding=(0, 3))
    self.branch7x7x3_3 = conv_block(
        192, 192, kernel_size=(7, 1), padding=(3, 0))
    self.branch7x7x3_4 = conv_block(192, 192, kernel_size=3, stride=2)

  def _forward(self, x):
    branch3x3 = self.branch3x3_1(x)
    branch3x3 = self.branch3x3_2(branch3x3)

    branch7x7x3 = self.branch7x7x3_1(x)
    branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
    branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
    branch7x7x3 = self.branch7x7x3_4(branch7x7x3)

    branch_pool = F.max_pool2d(x, kernel_size=3, stride=2)
    outputs = [branch3x3, branch7x7x3, branch_pool]
    return outputs

  def forward(self, x):
    outputs = self._forward(x)
    return torch.cat(outputs, 1)


class InceptionE(nn.Module):

  def __init__(self, in_channels, conv_block=None):
    super(InceptionE, self).__init__()
    if conv_block is None:
      conv_block = BasicConv2d
    self.branch1x1 = conv_block(in_channels, 320, kernel_size=1)

    self.branch3x3_1 = conv_block(in_channels, 384, kernel_size=1)
    self.branch3x3_2a = conv_block(
        384, 384, kernel_size=(1, 3), padding=(0, 1))
    self.branch3x3_2b = conv_block(
        384, 384, kernel_size=(3, 1), padding=(1, 0))

    self.branch3x3dbl_1 = conv_block(in_channels, 448, kernel_size=1)
    self.branch3x3dbl_2 = conv_block(448, 384, kernel_size=3, padding=1)
    self.branch3x3dbl_3a = conv_block(
        384, 384, kernel_size=(1, 3), padding=(0, 1))
    self.branch3x3dbl_3b = conv_block(
        384, 384, kernel_size=(3, 1), padding=(1, 0))

    self.branch_pool = conv_block(in_channels, 192, kernel_size=1)

  def _forward(self, x):
    branch1x1 = self.branch1x1(x)

    branch3x3 = self.branch3x3_1(x)
    branch3x3 = [
        self.branch3x3_2a(branch3x3),
        self.branch3x3_2b(branch3x3),
    ]
    branch3x3 = torch.cat(branch3x3, 1)

    branch3x3dbl = self.branch3x3dbl_1(x)
    branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
    branch3x3dbl = [
        self.branch3x3dbl_3a(branch3x3dbl),
        self.branch3x3dbl_3b(branch3x3dbl),
    ]
    branch3x3dbl = torch.cat(branch3x3dbl, 1)

    branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
    branch_pool = self.branch_pool(branch_pool)

    outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
    return outputs

  def forward(self, x):
    outputs = self._forward(x)
    return torch.cat(outputs, 1)


class InceptionAux(nn.Module):

  def __init__(self, in_channels, num_classes, conv_block=None):
    super(InceptionAux, self).__init__()
    if conv_block is None:
      conv_block = BasicConv2d
    self.conv0 = conv_block(in_channels, 128, kernel_size=1)
    self.conv1 = conv_block(128, 768, kernel_size=5)
    self.conv1.stddev = 0.01
    self.fc = nn.Linear(768, num_classes)
    self.fc.stddev = 0.001

  def forward(self, x):
    # N x 768 x 17 x 17
    x = F.avg_pool2d(x, kernel_size=5, stride=3)
    # N x 768 x 5 x 5
    x = self.conv0(x)
    # N x 128 x 5 x 5
    x = self.conv1(x)
    # N x 768 x 1 x 1
    # Adaptive average pooling
    x = F.adaptive_avg_pool2d(x, (1, 1))
    # N x 768 x 1 x 1
    x = torch.flatten(x, 1)
    # N x 768
    x = self.fc(x)
    # N x 1000
    return x


class BasicConv2d(nn.Module):

  def __init__(self, in_channels, out_channels, **kwargs):
    super(BasicConv2d, self).__init__()
    self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
    self.bn = nn.BatchNorm2d(out_channels, eps=0.001)

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