text: """
   pnasnet5large implementation grabbed from Cadene's pretrained models
   Additional credit to https://github.com/creafz
  
   https://github.com/Cadene/pretrained-models.pytorch/blob/master/pretrainedmodels/models/pnasnet.py
  
  """
  from collections import OrderedDict
  from functools import partial
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from .helpers import build_model_with_cfg
  from .layers import ConvBnAct, create_conv2d, create_pool2d, create_classifier
  from .registry import register_model
  
  __all__ = ['PNASNet5Large']
  
  default_cfgs = {
      'pnasnet5large': {
          'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/pnasnet5large-bf079911.pth',
          'input_size': (3, 331, 331),
          'pool_size': (11, 11),
          'crop_pct': 0.911,
          'interpolation': 'bicubic',
          'mean': (0.5, 0.5, 0.5),
          'std': (0.5, 0.5, 0.5),
          'num_classes': 1000,
          'first_conv': 'conv_0.conv',
          'classifier': 'last_linear',
          'label_offset': 1,  # 1001 classes in pretrained weights
      },
  }
  
  
  class SeparableConv2d(nn.Module):
  
      def __init__(self, in_channels, out_channels, kernel_size, stride, padding=''):
          super(SeparableConv2d, self).__init__()
          self.depthwise_conv2d = create_conv2d(
              in_channels, in_channels, kernel_size=kernel_size,
              stride=stride, padding=padding, groups=in_channels)
          self.pointwise_conv2d = create_conv2d(
              in_channels, out_channels, kernel_size=1, padding=padding)
  
      def forward(self, x):
          x = self.depthwise_conv2d(x)
          x = self.pointwise_conv2d(x)
          return x
  
  
  class BranchSeparables(nn.Module):
  
      def __init__(self, in_channels, out_channels, kernel_size, stride=1, stem_cell=False, padding=''):
          super(BranchSeparables, self).__init__()
          middle_channels = out_channels if stem_cell else in_channels
          self.act_1 = nn.ReLU()
          self.separable_1 = SeparableConv2d(
              in_channels, middle_channels, kernel_size, stride=stride, padding=padding)
          self.bn_sep_1 = nn.BatchNorm2d(middle_channels, eps=0.001)
          self.act_2 = nn.ReLU()
          self.separable_2 = SeparableConv2d(
              middle_channels, out_channels, kernel_size, stride=1, padding=padding)
          self.bn_sep_2 = nn.BatchNorm2d(out_channels, eps=0.001)
  
      def forward(self, x):
          x = self.act_1(x)
          x = self.separable_1(x)
          x = self.bn_sep_1(x)
          x = self.act_2(x)
          x = self.separable_2(x)
          x = self.bn_sep_2(x)
          return x
  
  
  class ActConvBn(nn.Module):
  
      def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=''):
          super(ActConvBn, self).__init__()
          self.act = nn.ReLU()
          self.conv = create_conv2d(
              in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding)
          self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
  
      def forward(self, x):
          x = self.act(x)
          x = self.conv(x)
          x = self.bn(x)
          return x
  
  
  class FactorizedReduction(nn.Module):
  
      def __init__(self, in_channels, out_channels, padding=''):
          super(FactorizedReduction, self).__init__()
          self.act = nn.ReLU()
          self.path_1 = nn.Sequential(OrderedDict([
              ('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)),
              ('conv', create_conv2d(in_channels, out_channels // 2, kernel_size=1, padding=padding)),
          ]))
          self.path_2 = nn.Sequential(OrderedDict([
              ('pad', nn.ZeroPad2d((-1, 1, -1, 1))),  # shift
              ('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)),
              ('conv', create_conv2d(in_channels, out_channels // 2, kernel_size=1, padding=padding)),
          ]))
          self.final_path_bn = nn.BatchNorm2d(out_channels, eps=0.001)
  
      def forward(self, x):
          x = self.act(x)
          x_path1 = self.path_1(x)
          x_path2 = self.path_2(x)
          out = self.final_path_bn(torch.cat([x_path1, x_path2], 1))
          return out
  
  
  class CellBase(nn.Module):
  
      def cell_forward(self, x_left, x_right):
          x_comb_iter_0_left = self.comb_iter_0_left(x_left)
          x_comb_iter_0_right = self.comb_iter_0_right(x_left)
          x_comb_iter_0 = x_comb_iter_0_left + x_comb_iter_0_right
  
          x_comb_iter_1_left = self.comb_iter_1_left(x_right)
          x_comb_iter_1_right = self.comb_iter_1_right(x_right)
          x_comb_iter_1 = x_comb_iter_1_left + x_comb_iter_1_right
  
          x_comb_iter_2_left = self.comb_iter_2_left(x_right)
          x_comb_iter_2_right = self.comb_iter_2_right(x_right)
          x_comb_iter_2 = x_comb_iter_2_left + x_comb_iter_2_right
  
          x_comb_iter_3_left = self.comb_iter_3_left(x_comb_iter_2)
          x_comb_iter_3_right = self.comb_iter_3_right(x_right)
          x_comb_iter_3 = x_comb_iter_3_left + x_comb_iter_3_right
  
          x_comb_iter_4_left = self.comb_iter_4_left(x_left)
          if self.comb_iter_4_right is not None:
              x_comb_iter_4_right = self.comb_iter_4_right(x_right)
          else:
              x_comb_iter_4_right = x_right
          x_comb_iter_4 = x_comb_iter_4_left + x_comb_iter_4_right
  
          x_out = torch.cat([x_comb_iter_0, x_comb_iter_1, x_comb_iter_2, x_comb_iter_3, x_comb_iter_4], 1)
          return x_out
  
  
  class CellStem0(CellBase):
  
      def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type=''):
          super(CellStem0, self).__init__()
          self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, kernel_size=1, padding=pad_type)
  
          self.comb_iter_0_left = BranchSeparables(
              in_chs_left, out_chs_left, kernel_size=5, stride=2, stem_cell=True, padding=pad_type)
          self.comb_iter_0_right = nn.Sequential(OrderedDict([
              ('max_pool', create_pool2d('max', 3, stride=2, padding=pad_type)),
              ('conv', create_conv2d(in_chs_left, out_chs_left, kernel_size=1, padding=pad_type)),
              ('bn', nn.BatchNorm2d(out_chs_left, eps=0.001)),
          ]))
  
          self.comb_iter_1_left = BranchSeparables(
              out_chs_right, out_chs_right, kernel_size=7, stride=2, padding=pad_type)
          self.comb_iter_1_right = create_pool2d('max', 3, stride=2, padding=pad_type)
  
          self.comb_iter_2_left = BranchSeparables(
              out_chs_right, out_chs_right, kernel_size=5, stride=2, padding=pad_type)
          self.comb_iter_2_right = BranchSeparables(
              out_chs_right, out_chs_right, kernel_size=3, stride=2, padding=pad_type)
  
          self.comb_iter_3_left = BranchSeparables(
              out_chs_right, out_chs_right, kernel_size=3, padding=pad_type)
          self.comb_iter_3_right = create_pool2d('max', 3, stride=2, padding=pad_type)
  
          self.comb_iter_4_left = BranchSeparables(
              in_chs_right, out_chs_right, kernel_size=3, stride=2, stem_cell=True, padding=pad_type)
          self.comb_iter_4_right = ActConvBn(
              out_chs_right, out_chs_right, kernel_size=1, stride=2, padding=pad_type)
  
      def forward(self, x_left):
          x_right = self.conv_1x1(x_left)
          x_out = self.cell_forward(x_left, x_right)
          return x_out
  
  
  class Cell(CellBase):
  
      def __init__(self, in_chs_left, out_chs_left, in_chs_right, out_chs_right, pad_type='',
                   is_reduction=False, match_prev_layer_dims=False):
          super(Cell, self).__init__()
  
          # If `is_reduction` is set to `True` stride 2 is used for
          # convolution and pooling layers to reduce the spatial size of
          # the output of a cell approximately by a factor of 2.
          stride = 2 if is_reduction else 1
  
          # If `match_prev_layer_dimensions` is set to `True`
          # `FactorizedReduction` is used to reduce the spatial size
          # of the left input of a cell approximately by a factor of 2.
          self.match_prev_layer_dimensions = match_prev_layer_dims
          if match_prev_layer_dims:
              self.conv_prev_1x1 = FactorizedReduction(in_chs_left, out_chs_left, padding=pad_type)
          else:
              self.conv_prev_1x1 = ActConvBn(in_chs_left, out_chs_left, kernel_size=1, padding=pad_type)
          self.conv_1x1 = ActConvBn(in_chs_right, out_chs_right, kernel_size=1, padding=pad_type)
  
          self.comb_iter_0_left = BranchSeparables(
              out_chs_left, out_chs_left, kernel_size=5, stride=stride, padding=pad_type)
          self.comb_iter_0_right = create_pool2d('max', 3, stride=stride, padding=pad_type)
  
          self.comb_iter_1_left = BranchSeparables(
              out_chs_right, out_chs_right, kernel_size=7, stride=stride, padding=pad_type)
          self.comb_iter_1_right = create_pool2d('max', 3, stride=stride, padding=pad_type)
  
          self.comb_iter_2_left = BranchSeparables(
              out_chs_right, out_chs_right, kernel_size=5, stride=stride, padding=pad_type)
          self.comb_iter_2_right = BranchSeparables(
              out_chs_right, out_chs_right, kernel_size=3, stride=stride, padding=pad_type)
  
          self.comb_iter_3_left = BranchSeparables(out_chs_right, out_chs_right, kernel_size=3)
          self.comb_iter_3_right = create_pool2d('max', 3, stride=stride, padding=pad_type)
  
          self.comb_iter_4_left = BranchSeparables(
              out_chs_left, out_chs_left, kernel_size=3, stride=stride, padding=pad_type)
          if is_reduction:
              self.comb_iter_4_right = ActConvBn(
                  out_chs_right, out_chs_right, kernel_size=1, stride=stride, padding=pad_type)
          else:
              self.comb_iter_4_right = None
  
      def forward(self, x_left, x_right):
          x_left = self.conv_prev_1x1(x_left)
          x_right = self.conv_1x1(x_right)
          x_out = self.cell_forward(x_left, x_right)
          return x_out
  
  
  class PNASNet5Large(nn.Module):
      def __init__(self, num_classes=1000, in_chans=3, output_stride=32, drop_rate=0., global_pool='avg', pad_type=''):
          super(PNASNet5Large, self).__init__()
          self.num_classes = num_classes
          self.drop_rate = drop_rate
          self.num_features = 4320
          assert output_stride == 32
  
          self.conv_0 = ConvBnAct(
              in_chans, 96, kernel_size=3, stride=2, padding=0,
              norm_layer=partial(nn.BatchNorm2d, eps=0.001, momentum=0.1), apply_act=False)
  
          self.cell_stem_0 = CellStem0(
              in_chs_left=96, out_chs_left=54, in_chs_right=96, out_chs_right=54, pad_type=pad_type)
  
          self.cell_stem_1 = Cell(
              in_chs_left=96, out_chs_left=108, in_chs_right=270, out_chs_right=108, pad_type=pad_type,
              match_prev_layer_dims=True, is_reduction=True)
          self.cell_0 = Cell(
              in_chs_left=270, out_chs_left=216, in_chs_right=540, out_chs_right=216, pad_type=pad_type,
              match_prev_layer_dims=True)
          self.cell_1 = Cell(
              in_chs_left=540, out_chs_left=216, in_chs_right=1080, out_chs_right=216, pad_type=pad_type)
          self.cell_2 = Cell(
              in_chs_left=1080, out_chs_left=216, in_chs_right=1080, out_chs_right=216, pad_type=pad_type)
          self.cell_3 = Cell(
              in_chs_left=1080, out_chs_left=216, in_chs_right=1080, out_chs_right=216, pad_type=pad_type)
  
          self.cell_4 = Cell(
              in_chs_left=1080, out_chs_left=432, in_chs_right=1080, out_chs_right=432, pad_type=pad_type,
              is_reduction=True)
          self.cell_5 = Cell(
              in_chs_left=1080, out_chs_left=432, in_chs_right=2160, out_chs_right=432, pad_type=pad_type,
              match_prev_layer_dims=True)
          self.cell_6 = Cell(
              in_chs_left=2160, out_chs_left=432, in_chs_right=2160, out_chs_right=432, pad_type=pad_type)
          self.cell_7 = Cell(
              in_chs_left=2160, out_chs_left=432, in_chs_right=2160, out_chs_right=432, pad_type=pad_type)
  
          self.cell_8 = Cell(
              in_chs_left=2160, out_chs_left=864, in_chs_right=2160, out_chs_right=864, pad_type=pad_type,
              is_reduction=True)
          self.cell_9 = Cell(
              in_chs_left=2160, out_chs_left=864, in_chs_right=4320, out_chs_right=864, pad_type=pad_type,
              match_prev_layer_dims=True)
          self.cell_10 = Cell(
              in_chs_left=4320, out_chs_left=864, in_chs_right=4320, out_chs_right=864, pad_type=pad_type)
          self.cell_11 = Cell(
              in_chs_left=4320, out_chs_left=864, in_chs_right=4320, out_chs_right=864, pad_type=pad_type)
          self.act = nn.ReLU()
          self.feature_info = [
              dict(num_chs=96, reduction=2, module='conv_0'),
              dict(num_chs=270, reduction=4, module='cell_stem_1.conv_1x1.act'),
              dict(num_chs=1080, reduction=8, module='cell_4.conv_1x1.act'),
              dict(num_chs=2160, reduction=16, module='cell_8.conv_1x1.act'),
              dict(num_chs=4320, reduction=32, module='act'),
          ]
  
          self.global_pool, self.last_linear = create_classifier(
              self.num_features, self.num_classes, pool_type=global_pool)
  
      def get_classifier(self):
          return self.last_linear
  
      def reset_classifier(self, num_classes, global_pool='avg'):
          self.num_classes = num_classes
          self.global_pool, self.last_linear = create_classifier(
              self.num_features, self.num_classes, pool_type=global_pool)
  
      def forward_features(self, x):
          x_conv_0 = self.conv_0(x)
          x_stem_0 = self.cell_stem_0(x_conv_0)
          x_stem_1 = self.cell_stem_1(x_conv_0, x_stem_0)
          x_cell_0 = self.cell_0(x_stem_0, x_stem_1)
          x_cell_1 = self.cell_1(x_stem_1, x_cell_0)
          x_cell_2 = self.cell_2(x_cell_0, x_cell_1)
          x_cell_3 = self.cell_3(x_cell_1, x_cell_2)
          x_cell_4 = self.cell_4(x_cell_2, x_cell_3)
          x_cell_5 = self.cell_5(x_cell_3, x_cell_4)
          x_cell_6 = self.cell_6(x_cell_4, x_cell_5)
          x_cell_7 = self.cell_7(x_cell_5, x_cell_6)
          x_cell_8 = self.cell_8(x_cell_6, x_cell_7)
          x_cell_9 = self.cell_9(x_cell_7, x_cell_8)
          x_cell_10 = self.cell_10(x_cell_8, x_cell_9)
          x_cell_11 = self.cell_11(x_cell_9, x_cell_10)
          x = self.act(x_cell_11)
          return x
  
      def forward(self, x):
          x = self.forward_features(x)
          x = self.global_pool(x)
          if self.drop_rate > 0:
              x = F.dropout(x, self.drop_rate, training=self.training)
          x = self.last_linear(x)
          return x
  
  
  def _create_pnasnet(variant, pretrained=False, **kwargs):
      return build_model_with_cfg(
          PNASNet5Large, variant, pretrained,
          default_cfg=default_cfgs[variant],
          feature_cfg=dict(feature_cls='hook', no_rewrite=True),  # not possible to re-write this model
          **kwargs)
  
  
  @register_model
  def pnasnet5large(pretrained=False, **kwargs):
      r"""PNASNet-5 model architecture from the
      `"Progressive Neural Architecture Search"
      <https://arxiv.org/abs/1712.00559>`_ paper.
      """
      model_kwargs = dict(pad_type='same', **kwargs)
      return _create_pnasnet('pnasnet5large', pretrained, **model_kwargs)
  