hunk.code_window:         np_img = np.rollaxis(np_img, 2)  # HWC to CHW
           return torch.from_numpy(np_img).to(dtype=self.dtype)
   def transforms_imagenet_train(
           img_size=224,
           scale=(0.1, 1.0),
  
hunk.labels: keep keep keep add keep keep keep
hunk.after_edit: def _pil_interp(method):
       if method == 'bicubic':
           return Image.BICUBIC
       elif method == 'lanczos':
           return Image.LANCZOS
       elif method == 'hamming':
           return Image.HAMMING
       else:
           # default bilinear, do we want to allow nearest?
           return Image.BILINEAR
  
hunk.file_path: data/transforms.py
hunk.type: add
file: """Pytorch Densenet implementation w/ tweaks
  This file is a copy of https://github.com/pytorch/vision 'densenet.py' (BSD-3-Clause) with
  fixed kwargs passthrough and addition of dynamic global avg/max pool.
  """
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from collections import OrderedDict
  
  from models.helpers import load_pretrained
  from models.adaptive_avgmax_pool import *
  from data.transforms import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  import re
  
  __all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161']
  
  
  def _cfg(url=''):
      return {
          'url': url, 'num_classes': 1000, 'input_size': (3, 224, 244), 'pool_size': (7, 7),
          'crop_pct': 0.875, 'interpolation': 'bilinear',
          'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
          'first_conv': 'features.conv0', 'classifier': 'classifier',
      }
  
  
  default_cfgs = {
      'densenet121': _cfg(url='https://download.pytorch.org/models/densenet121-241335ed.pth'),
      'densenet169': _cfg(url='https://download.pytorch.org/models/densenet169-6f0f7f60.pth'),
      'densenet201': _cfg(url='https://download.pytorch.org/models/densenet201-4c113574.pth'),
      'densenet161': _cfg(url='https://download.pytorch.org/models/densenet161-17b70270.pth'),
  }
  
  
  def _filter_pretrained(state_dict):
      pattern = re.compile(
          r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
  
      for key in list(state_dict.keys()):
          res = pattern.match(key)
          if res:
              new_key = res.group(1) + res.group(2)
              state_dict[new_key] = state_dict[key]
              del state_dict[key]
      return state_dict
  
  
  def densenet121(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
      r"""Densenet-121 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
      """
      default_cfg = default_cfgs['densenet121']
      model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16),
                       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, filter_fn=_filter_pretrained)
      return model
  
  
  def densenet169(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
      r"""Densenet-169 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
      """
      default_cfg = default_cfgs['densenet169']
      model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32),
                       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, filter_fn=_filter_pretrained)
      return model
  
  
  def densenet201(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
      r"""Densenet-201 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
      """
      default_cfg = default_cfgs['densenet201']
      model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32),
                       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, filter_fn=_filter_pretrained)
      return model
  
  
  def densenet161(num_classes=1000, in_chans=3, pretrained=False, **kwargs):
      r"""Densenet-201 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
      """
      print(num_classes, in_chans, pretrained)
      default_cfg = default_cfgs['densenet161']
      model = DenseNet(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24),
                       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, filter_fn=_filter_pretrained)
      return model
  
  
  class _DenseLayer(nn.Sequential):
      def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
          super(_DenseLayer, self).__init__()
          self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
          self.add_module('relu1', nn.ReLU(inplace=True)),
          self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *
                          growth_rate, kernel_size=1, stride=1, bias=False)),
          self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
          self.add_module('relu2', nn.ReLU(inplace=True)),
          self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,
                          kernel_size=3, stride=1, padding=1, bias=False)),
          self.drop_rate = drop_rate
  
      def forward(self, x):
          new_features = super(_DenseLayer, self).forward(x)
          if self.drop_rate > 0:
              new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
          return torch.cat([x, new_features], 1)
  
  
  class _DenseBlock(nn.Sequential):
      def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):
          super(_DenseBlock, self).__init__()
          for i in range(num_layers):
              layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate)
              self.add_module('denselayer%d' % (i + 1), layer)
  
  
  class _Transition(nn.Sequential):
      def __init__(self, num_input_features, num_output_features):
          super(_Transition, self).__init__()
          self.add_module('norm', nn.BatchNorm2d(num_input_features))
          self.add_module('relu', nn.ReLU(inplace=True))
          self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,
                                            kernel_size=1, stride=1, bias=False))
          self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
  
  
  class DenseNet(nn.Module):
      r"""Densenet-BC model class, based on
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
  
      Args:
          growth_rate (int) - how many filters to add each layer (`k` in paper)
          block_config (list of 4 ints) - how many layers in each pooling block
          num_init_features (int) - the number of filters to learn in the first convolution layer
          bn_size (int) - multiplicative factor for number of bottle neck layers
            (i.e. bn_size * k features in the bottleneck layer)
          drop_rate (float) - dropout rate after each dense layer
          num_classes (int) - number of classification classes
      """
      def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
                   num_init_features=64, bn_size=4, drop_rate=0,
                   num_classes=1000, in_chans=3, global_pool='avg'):
          self.global_pool = global_pool
          self.num_classes = num_classes
          super(DenseNet, self).__init__()
  
          # First convolution
          self.features = nn.Sequential(OrderedDict([
              ('conv0', nn.Conv2d(in_chans, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
              ('norm0', nn.BatchNorm2d(num_init_features)),
              ('relu0', nn.ReLU(inplace=True)),
              ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
          ]))
  
          # Each denseblock
          num_features = num_init_features
          for i, num_layers in enumerate(block_config):
              block = _DenseBlock(num_layers=num_layers, num_input_features=num_features,
                                  bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate)
              self.features.add_module('denseblock%d' % (i + 1), block)
              num_features = num_features + num_layers * growth_rate
              if i != len(block_config) - 1:
                  trans = _Transition(
                      num_input_features=num_features, num_output_features=num_features // 2)
                  self.features.add_module('transition%d' % (i + 1), trans)
                  num_features = num_features // 2
  
          # Final batch norm
          self.features.add_module('norm5', nn.BatchNorm2d(num_features))
  
          # Linear layer
          self.classifier = nn.Linear(num_features, num_classes)
  
          self.num_features = num_features
  
      def get_classifier(self):
          return self.classifier
  
      def reset_classifier(self, num_classes, global_pool='avg'):
          self.global_pool = global_pool
          self.num_classes = num_classes
          del self.classifier
          if num_classes:
              self.classifier = nn.Linear(self.num_features, num_classes)
          else:
              self.classifier = None
  
      def forward_features(self, x, pool=True):
          x = self.features(x)
          x = F.relu(x, inplace=True)
          if pool:
              x = select_adaptive_pool2d(x, self.global_pool)
              x = x.view(x.size(0), -1)
          return x
  
      def forward(self, x):
          return self.classifier(self.forward_features(x, pool=True))
  
  