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 __future__ import print_function, division, absolute_import
  
  from collections import OrderedDict
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from .helpers import load_pretrained
  from .layers import SelectAdaptivePool2d
  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': 0.875,
          'interpolation': 'bicubic',
          'mean': (0.5, 0.5, 0.5),
          'std': (0.5, 0.5, 0.5),
          'num_classes': 1001,
          'first_conv': 'conv_0.conv',
          'classifier': 'last_linear',
      },
  }
  
  
  class MaxPool(nn.Module):
  
      def __init__(self, kernel_size, stride=1, padding=1, zero_pad=False):
          super(MaxPool, self).__init__()
          self.zero_pad = nn.ZeroPad2d((1, 0, 1, 0)) if zero_pad else None
          self.pool = nn.MaxPool2d(kernel_size, stride=stride, padding=padding)
  
      def forward(self, x):
          if self.zero_pad is not None:
              x = self.zero_pad(x)
              x = self.pool(x)
              x = x[:, :, 1:, 1:]
          else:
              x = self.pool(x)
          return x
  
  
  class SeparableConv2d(nn.Module):
  
      def __init__(self, in_channels, out_channels, dw_kernel_size, dw_stride,
                   dw_padding):
          super(SeparableConv2d, self).__init__()
          self.depthwise_conv2d = nn.Conv2d(in_channels, in_channels,
                                            kernel_size=dw_kernel_size,
                                            stride=dw_stride, padding=dw_padding,
                                            groups=in_channels, bias=False)
          self.pointwise_conv2d = nn.Conv2d(in_channels, out_channels,
                                            kernel_size=1, bias=False)
  
      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, zero_pad=False):
          super(BranchSeparables, self).__init__()
          padding = kernel_size // 2
          middle_channels = out_channels if stem_cell else in_channels
          self.zero_pad = nn.ZeroPad2d((1, 0, 1, 0)) if zero_pad else None
          self.relu_1 = nn.ReLU()
          self.separable_1 = SeparableConv2d(in_channels, middle_channels,
                                             kernel_size, dw_stride=stride,
                                             dw_padding=padding)
          self.bn_sep_1 = nn.BatchNorm2d(middle_channels, eps=0.001)
          self.relu_2 = nn.ReLU()
          self.separable_2 = SeparableConv2d(middle_channels, out_channels,
                                             kernel_size, dw_stride=1,
                                             dw_padding=padding)
          self.bn_sep_2 = nn.BatchNorm2d(out_channels, eps=0.001)
  
      def forward(self, x):
          x = self.relu_1(x)
          if self.zero_pad is not None:
              x = self.zero_pad(x)
              x = self.separable_1(x)
              x = x[:, :, 1:, 1:].contiguous()
          else:
              x = self.separable_1(x)
          x = self.bn_sep_1(x)
          x = self.relu_2(x)
          x = self.separable_2(x)
          x = self.bn_sep_2(x)
          return x
  
  
  class ReluConvBn(nn.Module):
  
      def __init__(self, in_channels, out_channels, kernel_size, stride=1):
          super(ReluConvBn, self).__init__()
          self.relu = nn.ReLU()
          self.conv = nn.Conv2d(in_channels, out_channels,
                                kernel_size=kernel_size, stride=stride,
                                bias=False)
          self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
  
      def forward(self, x):
          x = self.relu(x)
          x = self.conv(x)
          x = self.bn(x)
          return x
  
  
  class FactorizedReduction(nn.Module):
  
      def __init__(self, in_channels, out_channels):
          super(FactorizedReduction, self).__init__()
          self.relu = nn.ReLU()
          self.path_1 = nn.Sequential(OrderedDict([
              ('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)),
              ('conv', nn.Conv2d(in_channels, out_channels // 2,
                                 kernel_size=1, bias=False)),
          ]))
          self.path_2 = nn.Sequential(OrderedDict([
              ('pad', nn.ZeroPad2d((0, 1, 0, 1))),
              ('avgpool', nn.AvgPool2d(1, stride=2, count_include_pad=False)),
              ('conv', nn.Conv2d(in_channels, out_channels // 2,
                                 kernel_size=1, bias=False)),
          ]))
          self.final_path_bn = nn.BatchNorm2d(out_channels, eps=0.001)
  
      def forward(self, x):
          x = self.relu(x)
  
          x_path1 = self.path_1(x)
  
          x_path2 = self.path_2.pad(x)
          x_path2 = x_path2[:, :, 1:, 1:]
          x_path2 = self.path_2.avgpool(x_path2)
          x_path2 = self.path_2.conv(x_path2)
  
          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_channels_left, out_channels_left, in_channels_right,
                   out_channels_right):
          super(CellStem0, self).__init__()
          self.conv_1x1 = ReluConvBn(in_channels_right, out_channels_right,
                                     kernel_size=1)
          self.comb_iter_0_left = BranchSeparables(in_channels_left,
                                                   out_channels_left,
                                                   kernel_size=5, stride=2,
                                                   stem_cell=True)
          self.comb_iter_0_right = nn.Sequential(OrderedDict([
              ('max_pool', MaxPool(3, stride=2)),
              ('conv', nn.Conv2d(in_channels_left, out_channels_left,
                                 kernel_size=1, bias=False)),
              ('bn', nn.BatchNorm2d(out_channels_left, eps=0.001)),
          ]))
          self.comb_iter_1_left = BranchSeparables(out_channels_right,
                                                   out_channels_right,
                                                   kernel_size=7, stride=2)
          self.comb_iter_1_right = MaxPool(3, stride=2)
          self.comb_iter_2_left = BranchSeparables(out_channels_right,
                                                   out_channels_right,
                                                   kernel_size=5, stride=2)
          self.comb_iter_2_right = BranchSeparables(out_channels_right,
                                                    out_channels_right,
                                                    kernel_size=3, stride=2)
          self.comb_iter_3_left = BranchSeparables(out_channels_right,
                                                   out_channels_right,
                                                   kernel_size=3)
          self.comb_iter_3_right = MaxPool(3, stride=2)
          self.comb_iter_4_left = BranchSeparables(in_channels_right,
                                                   out_channels_right,
                                                   kernel_size=3, stride=2,
                                                   stem_cell=True)
          self.comb_iter_4_right = ReluConvBn(out_channels_right,
                                              out_channels_right,
                                              kernel_size=1, stride=2)
  
      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_channels_left, out_channels_left, in_channels_right,
                   out_channels_right, is_reduction=False, zero_pad=False,
                   match_prev_layer_dimensions=False):
          super(Cell, self).__init__()
  
          # If `is_reduction` is set to `True` stride 2 is used for
          # convolutional 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_dimensions
          if match_prev_layer_dimensions:
              self.conv_prev_1x1 = FactorizedReduction(in_channels_left,
                                                       out_channels_left)
          else:
              self.conv_prev_1x1 = ReluConvBn(in_channels_left,
                                              out_channels_left, kernel_size=1)
  
          self.conv_1x1 = ReluConvBn(in_channels_right, out_channels_right,
                                     kernel_size=1)
          self.comb_iter_0_left = BranchSeparables(out_channels_left,
                                                   out_channels_left,
                                                   kernel_size=5, stride=stride,
                                                   zero_pad=zero_pad)
          self.comb_iter_0_right = MaxPool(3, stride=stride, zero_pad=zero_pad)
          self.comb_iter_1_left = BranchSeparables(out_channels_right,
                                                   out_channels_right,
                                                   kernel_size=7, stride=stride,
                                                   zero_pad=zero_pad)
          self.comb_iter_1_right = MaxPool(3, stride=stride, zero_pad=zero_pad)
          self.comb_iter_2_left = BranchSeparables(out_channels_right,
                                                   out_channels_right,
                                                   kernel_size=5, stride=stride,
                                                   zero_pad=zero_pad)
          self.comb_iter_2_right = BranchSeparables(out_channels_right,
                                                    out_channels_right,
                                                    kernel_size=3, stride=stride,
                                                    zero_pad=zero_pad)
          self.comb_iter_3_left = BranchSeparables(out_channels_right,
                                                   out_channels_right,
                                                   kernel_size=3)
          self.comb_iter_3_right = MaxPool(3, stride=stride, zero_pad=zero_pad)
          self.comb_iter_4_left = BranchSeparables(out_channels_left,
                                                   out_channels_left,
                                                   kernel_size=3, stride=stride,
                                                   zero_pad=zero_pad)
          if is_reduction:
              self.comb_iter_4_right = ReluConvBn(
                  out_channels_right, out_channels_right, kernel_size=1, stride=stride)
          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=1001, in_chans=3, drop_rate=0.5, global_pool='avg'):
          super(PNASNet5Large, self).__init__()
          self.num_classes = num_classes
          self.num_features = 4320
          self.drop_rate = drop_rate
  
          self.conv_0 = nn.Sequential(OrderedDict([
              ('conv', nn.Conv2d(in_chans, 96, kernel_size=3, stride=2, bias=False)),
              ('bn', nn.BatchNorm2d(96, eps=0.001))
          ]))
          self.cell_stem_0 = CellStem0(in_channels_left=96, out_channels_left=54,
                                       in_channels_right=96,
                                       out_channels_right=54)
          self.cell_stem_1 = Cell(in_channels_left=96, out_channels_left=108,
                                  in_channels_right=270, out_channels_right=108,
                                  match_prev_layer_dimensions=True,
                                  is_reduction=True)
          self.cell_0 = Cell(in_channels_left=270, out_channels_left=216,
                             in_channels_right=540, out_channels_right=216,
                             match_prev_layer_dimensions=True)
          self.cell_1 = Cell(in_channels_left=540, out_channels_left=216,
                             in_channels_right=1080, out_channels_right=216)
          self.cell_2 = Cell(in_channels_left=1080, out_channels_left=216,
                             in_channels_right=1080, out_channels_right=216)
          self.cell_3 = Cell(in_channels_left=1080, out_channels_left=216,
                             in_channels_right=1080, out_channels_right=216)
          self.cell_4 = Cell(in_channels_left=1080, out_channels_left=432,
                             in_channels_right=1080, out_channels_right=432,
                             is_reduction=True, zero_pad=True)
          self.cell_5 = Cell(in_channels_left=1080, out_channels_left=432,
                             in_channels_right=2160, out_channels_right=432,
                             match_prev_layer_dimensions=True)
          self.cell_6 = Cell(in_channels_left=2160, out_channels_left=432,
                             in_channels_right=2160, out_channels_right=432)
          self.cell_7 = Cell(in_channels_left=2160, out_channels_left=432,
                             in_channels_right=2160, out_channels_right=432)
          self.cell_8 = Cell(in_channels_left=2160, out_channels_left=864,
                             in_channels_right=2160, out_channels_right=864,
                             is_reduction=True)
          self.cell_9 = Cell(in_channels_left=2160, out_channels_left=864,
                             in_channels_right=4320, out_channels_right=864,
                             match_prev_layer_dimensions=True)
          self.cell_10 = Cell(in_channels_left=4320, out_channels_left=864,
                              in_channels_right=4320, out_channels_right=864)
          self.cell_11 = Cell(in_channels_left=4320, out_channels_left=864,
                              in_channels_right=4320, out_channels_right=864)
          self.relu = nn.ReLU()
          self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
          self.last_linear = nn.Linear(self.num_features * self.global_pool.feat_mult(), num_classes)
  
      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 = SelectAdaptivePool2d(pool_type=global_pool)
          if num_classes:
              num_features = self.num_features * self.global_pool.feat_mult()
              self.last_linear = nn.Linear(num_features, num_classes)
          else:
              self.last_linear = nn.Identity()
  
      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.relu(x_cell_11)
          return x
  
      def forward(self, x):
          x = self.forward_features(x)
          x = self.global_pool(x).flatten(1)
          if self.drop_rate > 0:
              x = F.dropout(x, self.drop_rate, training=self.training)
          x = self.last_linear(x)
          return x
  
  
  @register_model
  def pnasnet5large(pretrained=False, num_classes=1000, in_chans=3, **kwargs):
      r"""PNASNet-5 model architecture from the
      `"Progressive Neural Architecture Search"
      <https://arxiv.org/abs/1712.00559>`_ paper.
      """
      default_cfg = default_cfgs['pnasnet5large']
      model = PNASNet5Large(num_classes=num_classes, in_chans=in_chans, **kwargs)
      model.default_cfg = default_cfg
      if pretrained:
          load_pretrained(model, default_cfg, num_classes, in_chans)
  
      return model
  