hunk.code_window: from contextlib import suppress
   from datetime import datetime
   import wandb 
   import torch
   import torch.nn as nn
   import torchvision.utils
   from torch.nn.parallel import DistributedDataParallel as NativeDDP
  
hunk.labels: keep keep keep replace replace keep keep keep keep
hunk.file_path: train.py
hunk.type: replace
file: """ Pytorch Inception-Resnet-V2 implementation
  Sourced from https://github.com/Cadene/tensorflow-model-zoo.torch (MIT License) which is
  based upon Google's Tensorflow implementation and pretrained weights (Apache 2.0 License)
  """
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from .helpers import build_model_with_cfg
  from .layers import create_classifier
  from .registry import register_model
  
  __all__ = ['InceptionResnetV2']
  
  default_cfgs = {
      # ported from http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz
      'inception_resnet_v2': {
          'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/inception_resnet_v2-940b1cd6.pth',
          'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
          'crop_pct': 0.8975, 'interpolation': 'bicubic',
          'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
          'first_conv': 'conv2d_1a.conv', 'classifier': 'classif',
          'label_offset': 1,  # 1001 classes in pretrained weights
      },
      # ported from http://download.tensorflow.org/models/ens_adv_inception_resnet_v2_2017_08_18.tar.gz
      'ens_adv_inception_resnet_v2': {
          'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ens_adv_inception_resnet_v2-2592a550.pth',
          'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
          'crop_pct': 0.8975, 'interpolation': 'bicubic',
          'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
          'first_conv': 'conv2d_1a.conv', 'classifier': 'classif',
          'label_offset': 1,  # 1001 classes in pretrained weights
      }
  }
  
  
  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)
          self.bn = nn.BatchNorm2d(out_planes, eps=.001)
          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, no_relu=False):
          super(Block8, self).__init__()
  
          self.scale = scale
  
          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)
          self.relu = None if no_relu else 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 self.relu is not None:
              out = self.relu(out)
          return out
  
  
  class InceptionResnetV2(nn.Module):
      def __init__(self, num_classes=1000, in_chans=3, drop_rate=0., output_stride=32, global_pool='avg'):
          super(InceptionResnetV2, self).__init__()
          self.drop_rate = drop_rate
          self.num_classes = num_classes
          self.num_features = 1536
          assert output_stride == 32
  
          self.conv2d_1a = BasicConv2d(in_chans, 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.feature_info = [dict(num_chs=64, reduction=2, module='conv2d_2b')]
  
          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.feature_info += [dict(num_chs=192, reduction=4, module='conv2d_4a')]
  
          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.feature_info += [dict(num_chs=320, reduction=8, module='repeat')]
  
          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.feature_info += [dict(num_chs=1088, reduction=16, module='repeat_1')]
  
          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(no_relu=True)
          self.conv2d_7b = BasicConv2d(2080, self.num_features, kernel_size=1, stride=1)
          self.feature_info += [dict(num_chs=self.num_features, reduction=32, module='conv2d_7b')]
  
          self.global_pool, self.classif = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
  
      def get_classifier(self):
          return self.classif
  
      def reset_classifier(self, num_classes, global_pool='avg'):
          self.num_classes = num_classes
          self.global_pool, self.classif = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
  
      def forward_features(self, x):
          x = self.conv2d_1a(x)
          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 forward(self, x):
          x = self.forward_features(x)
          x = self.global_pool(x)
          if self.drop_rate > 0:
              x = F.dropout(x, p=self.drop_rate, training=self.training)
          x = self.classif(x)
          return x
  
  
  def _create_inception_resnet_v2(variant, pretrained=False, **kwargs):
      return build_model_with_cfg(
          InceptionResnetV2, variant, pretrained,
          default_cfg=default_cfgs[variant],
          **kwargs)
  
  
  @register_model
  def inception_resnet_v2(pretrained=False, **kwargs):
      r"""InceptionResnetV2 model architecture from the
      `"InceptionV4, Inception-ResNet..." <https://arxiv.org/abs/1602.07261>` paper.
      """
      return _create_inception_resnet_v2('inception_resnet_v2', pretrained=pretrained, **kwargs)
  
  
  @register_model
  def ens_adv_inception_resnet_v2(pretrained=False, **kwargs):
      r""" Ensemble Adversarially trained InceptionResnetV2 model architecture
      As per https://arxiv.org/abs/1705.07204 and
      https://github.com/tensorflow/models/tree/master/research/adv_imagenet_models.
      """
      return _create_inception_resnet_v2('ens_adv_inception_resnet_v2', pretrained=pretrained, **kwargs)
  