hunk.code_window: torch>=1.4.0
   torchvision>=0.5.0
   pyyaml
   wandb
  
hunk.labels: keep keep replace replace
hunk.after_edit: pyyaml
  
hunk.file_path: requirements.txt
hunk.type: replace
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 re
  from collections import OrderedDict
  from functools import partial
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  import torch.utils.checkpoint as cp
  from torch.jit.annotations import List
  
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from .helpers import build_model_with_cfg
  from .layers import BatchNormAct2d, create_norm_act, BlurPool2d, create_classifier
  from .registry import register_model
  
  __all__ = ['DenseNet']
  
  
  def _cfg(url=''):
      return {
          'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
          'crop_pct': 0.875, 'interpolation': 'bicubic',
          'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
          'first_conv': 'features.conv0', 'classifier': 'classifier',
      }
  
  
  default_cfgs = {
      'densenet121': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/densenet121_ra-50efcf5c.pth'),
      'densenet121d': _cfg(url=''),
      'densenetblur121d': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/densenetblur121d_ra-100dcfbc.pth'),
      'densenet169': _cfg(url='https://download.pytorch.org/models/densenet169-b2777c0a.pth'),
      'densenet201': _cfg(url='https://download.pytorch.org/models/densenet201-c1103571.pth'),
      'densenet161': _cfg(url='https://download.pytorch.org/models/densenet161-8d451a50.pth'),
      'densenet264': _cfg(url=''),
      'densenet264d_iabn': _cfg(url=''),
      'tv_densenet121': _cfg(url='https://download.pytorch.org/models/densenet121-a639ec97.pth'),
  }
  
  
  class DenseLayer(nn.Module):
      def __init__(self, num_input_features, growth_rate, bn_size, norm_layer=BatchNormAct2d,
                   drop_rate=0., memory_efficient=False):
          super(DenseLayer, self).__init__()
          self.add_module('norm1', norm_layer(num_input_features)),
          self.add_module('conv1', nn.Conv2d(
              num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False)),
          self.add_module('norm2', norm_layer(bn_size * growth_rate)),
          self.add_module('conv2', nn.Conv2d(
              bn_size * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False)),
          self.drop_rate = float(drop_rate)
          self.memory_efficient = memory_efficient
  
      def bottleneck_fn(self, xs):
          # type: (List[torch.Tensor]) -> torch.Tensor
          concated_features = torch.cat(xs, 1)
          bottleneck_output = self.conv1(self.norm1(concated_features))  # noqa: T484
          return bottleneck_output
  
      # todo: rewrite when torchscript supports any
      def any_requires_grad(self, x):
          # type: (List[torch.Tensor]) -> bool
          for tensor in x:
              if tensor.requires_grad:
                  return True
          return False
  
      @torch.jit.unused  # noqa: T484
      def call_checkpoint_bottleneck(self, x):
          # type: (List[torch.Tensor]) -> torch.Tensor
          def closure(*xs):
              return self.bottleneck_fn(xs)
  
          return cp.checkpoint(closure, *x)
  
      @torch.jit._overload_method  # noqa: F811
      def forward(self, x):
          # type: (List[torch.Tensor]) -> (torch.Tensor)
          pass
  
      @torch.jit._overload_method  # noqa: F811
      def forward(self, x):
          # type: (torch.Tensor) -> (torch.Tensor)
          pass
  
      # torchscript does not yet support *args, so we overload method
      # allowing it to take either a List[Tensor] or single Tensor
      def forward(self, x):  # noqa: F811
          if isinstance(x, torch.Tensor):
              prev_features = [x]
          else:
              prev_features = x
  
          if self.memory_efficient and self.any_requires_grad(prev_features):
              if torch.jit.is_scripting():
                  raise Exception("Memory Efficient not supported in JIT")
              bottleneck_output = self.call_checkpoint_bottleneck(prev_features)
          else:
              bottleneck_output = self.bottleneck_fn(prev_features)
  
          new_features = self.conv2(self.norm2(bottleneck_output))
          if self.drop_rate > 0:
              new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
          return new_features
  
  
  class DenseBlock(nn.ModuleDict):
      _version = 2
  
      def __init__(self, num_layers, num_input_features, bn_size, growth_rate, norm_layer=nn.ReLU,
                   drop_rate=0., memory_efficient=False):
          super(DenseBlock, self).__init__()
          for i in range(num_layers):
              layer = DenseLayer(
                  num_input_features + i * growth_rate,
                  growth_rate=growth_rate,
                  bn_size=bn_size,
                  norm_layer=norm_layer,
                  drop_rate=drop_rate,
                  memory_efficient=memory_efficient,
              )
              self.add_module('denselayer%d' % (i + 1), layer)
  
      def forward(self, init_features):
          features = [init_features]
          for name, layer in self.items():
              new_features = layer(features)
              features.append(new_features)
          return torch.cat(features, 1)
  
  
  class DenseTransition(nn.Sequential):
      def __init__(self, num_input_features, num_output_features, norm_layer=nn.BatchNorm2d, aa_layer=None):
          super(DenseTransition, self).__init__()
          self.add_module('norm', norm_layer(num_input_features))
          self.add_module('conv', nn.Conv2d(
              num_input_features, num_output_features, kernel_size=1, stride=1, bias=False))
          if aa_layer is not None:
              self.add_module('pool', aa_layer(num_output_features, stride=2))
          else:
              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
          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
          memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
            but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
      """
  
      def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), bn_size=4, stem_type='',
                   num_classes=1000, in_chans=3, global_pool='avg',
                   norm_layer=BatchNormAct2d, aa_layer=None, drop_rate=0, memory_efficient=False,
                   aa_stem_only=True):
          self.num_classes = num_classes
          self.drop_rate = drop_rate
          super(DenseNet, self).__init__()
  
          # Stem
          deep_stem = 'deep' in stem_type  # 3x3 deep stem
          num_init_features = growth_rate * 2
          if aa_layer is None:
              stem_pool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
          else:
              stem_pool = nn.Sequential(*[
                  nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
                  aa_layer(channels=num_init_features, stride=2)])
          if deep_stem:
              stem_chs_1 = stem_chs_2 = growth_rate
              if 'tiered' in stem_type:
                  stem_chs_1 = 3 * (growth_rate // 4)
                  stem_chs_2 = num_init_features if 'narrow' in stem_type else 6 * (growth_rate // 4)
              self.features = nn.Sequential(OrderedDict([
                  ('conv0', nn.Conv2d(in_chans, stem_chs_1, 3, stride=2, padding=1, bias=False)),
                  ('norm0', norm_layer(stem_chs_1)),
                  ('conv1', nn.Conv2d(stem_chs_1, stem_chs_2, 3, stride=1, padding=1, bias=False)),
                  ('norm1', norm_layer(stem_chs_2)),
                  ('conv2', nn.Conv2d(stem_chs_2, num_init_features, 3, stride=1, padding=1, bias=False)),
                  ('norm2', norm_layer(num_init_features)),
                  ('pool0', stem_pool),
              ]))
          else:
              self.features = nn.Sequential(OrderedDict([
                  ('conv0', nn.Conv2d(in_chans, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
                  ('norm0', norm_layer(num_init_features)),
                  ('pool0', stem_pool),
              ]))
          self.feature_info = [
              dict(num_chs=num_init_features, reduction=2, module=f'features.norm{2 if deep_stem else 0}')]
          current_stride = 4
  
          # DenseBlocks
          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,
                  norm_layer=norm_layer,
                  drop_rate=drop_rate,
                  memory_efficient=memory_efficient
              )
              module_name = f'denseblock{(i + 1)}'
              self.features.add_module(module_name, block)
              num_features = num_features + num_layers * growth_rate
              transition_aa_layer = None if aa_stem_only else aa_layer
              if i != len(block_config) - 1:
                  self.feature_info += [
                      dict(num_chs=num_features, reduction=current_stride, module='features.' + module_name)]
                  current_stride *= 2
                  trans = DenseTransition(
                      num_input_features=num_features, num_output_features=num_features // 2,
                      norm_layer=norm_layer, aa_layer=transition_aa_layer)
                  self.features.add_module(f'transition{i + 1}', trans)
                  num_features = num_features // 2
  
          # Final batch norm
          self.features.add_module('norm5', norm_layer(num_features))
  
          self.feature_info += [dict(num_chs=num_features, reduction=current_stride, module='features.norm5')]
          self.num_features = num_features
  
          # Linear layer
          self.global_pool, self.classifier = create_classifier(
              self.num_features, self.num_classes, pool_type=global_pool)
  
          # Official init from torch repo.
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(m.weight)
              elif isinstance(m, nn.BatchNorm2d):
                  nn.init.constant_(m.weight, 1)
                  nn.init.constant_(m.bias, 0)
              elif isinstance(m, nn.Linear):
                  nn.init.constant_(m.bias, 0)
  
      def get_classifier(self):
          return self.classifier
  
      def reset_classifier(self, num_classes, global_pool='avg'):
          self.num_classes = num_classes
          self.global_pool, self.classifier = create_classifier(
              self.num_features, self.num_classes, pool_type=global_pool)
  
      def forward_features(self, x):
          return self.features(x)
  
      def forward(self, x):
          x = self.forward_features(x)
          x = self.global_pool(x)
          # both classifier and block drop?
          # if self.drop_rate > 0.:
          #     x = F.dropout(x, p=self.drop_rate, training=self.training)
          x = self.classifier(x)
          return x
  
  
  def _filter_torchvision_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 _create_densenet(variant, growth_rate, block_config, pretrained, **kwargs):
      kwargs['growth_rate'] = growth_rate
      kwargs['block_config'] = block_config
      return build_model_with_cfg(
          DenseNet, variant, pretrained,
          default_cfg=default_cfgs[variant],
          feature_cfg=dict(flatten_sequential=True), pretrained_filter_fn=_filter_torchvision_pretrained,
          **kwargs)
  
  
  @register_model
  def densenet121(pretrained=False, **kwargs):
      r"""Densenet-121 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
      """
      model = _create_densenet(
          'densenet121', growth_rate=32, block_config=(6, 12, 24, 16), pretrained=pretrained, **kwargs)
      return model
  
  
  @register_model
  def densenetblur121d(pretrained=False, **kwargs):
      r"""Densenet-121 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
      """
      model = _create_densenet(
          'densenetblur121d', growth_rate=32, block_config=(6, 12, 24, 16), pretrained=pretrained, stem_type='deep',
          aa_layer=BlurPool2d, **kwargs)
      return model
  
  
  @register_model
  def densenet121d(pretrained=False, **kwargs):
      r"""Densenet-121 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
      """
      model = _create_densenet(
          'densenet121d', growth_rate=32, block_config=(6, 12, 24, 16), stem_type='deep',
          pretrained=pretrained, **kwargs)
      return model
  
  
  @register_model
  def densenet169(pretrained=False, **kwargs):
      r"""Densenet-169 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
      """
      model = _create_densenet(
          'densenet169', growth_rate=32, block_config=(6, 12, 32, 32), pretrained=pretrained, **kwargs)
      return model
  
  
  @register_model
  def densenet201(pretrained=False, **kwargs):
      r"""Densenet-201 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
      """
      model = _create_densenet(
          'densenet201', growth_rate=32, block_config=(6, 12, 48, 32), pretrained=pretrained, **kwargs)
      return model
  
  
  @register_model
  def densenet161(pretrained=False, **kwargs):
      r"""Densenet-161 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
      """
      model = _create_densenet(
          'densenet161', growth_rate=48, block_config=(6, 12, 36, 24), pretrained=pretrained, **kwargs)
      return model
  
  
  @register_model
  def densenet264(pretrained=False, **kwargs):
      r"""Densenet-264 model from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
      """
      model = _create_densenet(
          'densenet264', growth_rate=48, block_config=(6, 12, 64, 48), pretrained=pretrained, **kwargs)
      return model
  
  
  @register_model
  def densenet264d_iabn(pretrained=False, **kwargs):
      r"""Densenet-264 model with deep stem and Inplace-ABN
      """
      def norm_act_fn(num_features, **kwargs):
          return create_norm_act('iabn', num_features, **kwargs)
      model = _create_densenet(
          'densenet264d_iabn', growth_rate=48, block_config=(6, 12, 64, 48), stem_type='deep',
          norm_layer=norm_act_fn, pretrained=pretrained, **kwargs)
      return model
  
  
  @register_model
  def tv_densenet121(pretrained=False, **kwargs):
      r"""Densenet-121 model with original Torchvision weights, from
      `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
      """
      model = _create_densenet(
          'tv_densenet121', growth_rate=32, block_config=(6, 12, 24, 16), pretrained=pretrained, **kwargs)
      return model
  