hunk.code_window:         if target and max(input_size) > target:
               input_size = default_cfg['min_input_size']
       else:
           if target and max(input_size) > target:
               input_size = tuple([min(x, target) for x in input_size])
       return input_size
  
hunk.labels: keep replace keep keep keep keep keep
hunk.after_edit:             input_size = min_input_size
  
hunk.file_path: tests/test_models.py
hunk.type: replace
file: """ Deep Layer Aggregation and DLA w/ Res2Net
  DLA original adapted from Official Pytorch impl at:
  DLA Paper: `Deep Layer Aggregation` - https://arxiv.org/abs/1707.06484
  
  Res2Net additions from: https://github.com/gasvn/Res2Net/
  Res2Net Paper: `Res2Net: A New Multi-scale Backbone Architecture` - https://arxiv.org/abs/1904.01169
  """
  import math
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from .helpers import build_model_with_cfg
  from .layers import create_classifier
  from .registry import register_model
  
  __all__ = ['DLA']
  
  
  def _cfg(url='', **kwargs):
      return {
          'url': url,
          'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
          'crop_pct': 0.875, 'interpolation': 'bilinear',
          'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
          'first_conv': 'base_layer.0', 'classifier': 'fc',
          **kwargs
      }
  
  
  default_cfgs = {
      'dla34': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla34-ba72cf86.pth'),
      'dla46_c': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla46_c-2bfd52c3.pth'),
      'dla46x_c': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla46x_c-d761bae7.pth'),
      'dla60x_c': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla60x_c-b870c45c.pth'),
      'dla60': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla60-24839fc4.pth'),
      'dla60x': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla60x-d15cacda.pth'),
      'dla102': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla102-d94d9790.pth'),
      'dla102x': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla102x-ad62be81.pth'),
      'dla102x2': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla102x2-262837b6.pth'),
      'dla169': _cfg(url='http://dl.yf.io/dla/models/imagenet/dla169-0914e092.pth'),
      'dla60_res2net': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net_dla60_4s-d88db7f9.pth'),
      'dla60_res2next': _cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2next_dla60_4s-d327927b.pth'),
  }
  
  
  class DlaBasic(nn.Module):
      """DLA Basic"""
  
      def __init__(self, inplanes, planes, stride=1, dilation=1, **_):
          super(DlaBasic, self).__init__()
          self.conv1 = nn.Conv2d(
              inplanes, planes, kernel_size=3, stride=stride, padding=dilation, bias=False, dilation=dilation)
          self.bn1 = nn.BatchNorm2d(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = nn.Conv2d(
              planes, planes, kernel_size=3, stride=1, padding=dilation, bias=False, dilation=dilation)
          self.bn2 = nn.BatchNorm2d(planes)
          self.stride = stride
  
      def forward(self, x, shortcut=None):
          if shortcut is None:
              shortcut = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
  
          out += shortcut
          out = self.relu(out)
  
          return out
  
  
  class DlaBottleneck(nn.Module):
      """DLA/DLA-X Bottleneck"""
      expansion = 2
  
      def __init__(self, inplanes, outplanes, stride=1, dilation=1, cardinality=1, base_width=64):
          super(DlaBottleneck, self).__init__()
          self.stride = stride
          mid_planes = int(math.floor(outplanes * (base_width / 64)) * cardinality)
          mid_planes = mid_planes // self.expansion
  
          self.conv1 = nn.Conv2d(inplanes, mid_planes, kernel_size=1, bias=False)
          self.bn1 = nn.BatchNorm2d(mid_planes)
          self.conv2 = nn.Conv2d(
              mid_planes, mid_planes, kernel_size=3, stride=stride, padding=dilation,
              bias=False, dilation=dilation, groups=cardinality)
          self.bn2 = nn.BatchNorm2d(mid_planes)
          self.conv3 = nn.Conv2d(mid_planes, outplanes, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(outplanes)
          self.relu = nn.ReLU(inplace=True)
  
      def forward(self, x, shortcut=None):
          if shortcut is None:
              shortcut = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
          out = self.relu(out)
  
          out = self.conv3(out)
          out = self.bn3(out)
  
          out += shortcut
          out = self.relu(out)
  
          return out
  
  
  class DlaBottle2neck(nn.Module):
      """ Res2Net/Res2NeXT DLA Bottleneck
      Adapted from https://github.com/gasvn/Res2Net/blob/master/dla.py
      """
      expansion = 2
  
      def __init__(self, inplanes, outplanes, stride=1, dilation=1, scale=4, cardinality=8, base_width=4):
          super(DlaBottle2neck, self).__init__()
          self.is_first = stride > 1
          self.scale = scale
          mid_planes = int(math.floor(outplanes * (base_width / 64)) * cardinality)
          mid_planes = mid_planes // self.expansion
          self.width = mid_planes
  
          self.conv1 = nn.Conv2d(inplanes, mid_planes * scale, kernel_size=1, bias=False)
          self.bn1 = nn.BatchNorm2d(mid_planes * scale)
  
          num_scale_convs = max(1, scale - 1)
          convs = []
          bns = []
          for _ in range(num_scale_convs):
              convs.append(nn.Conv2d(
                  mid_planes, mid_planes, kernel_size=3, stride=stride,
                  padding=dilation, dilation=dilation, groups=cardinality, bias=False))
              bns.append(nn.BatchNorm2d(mid_planes))
          self.convs = nn.ModuleList(convs)
          self.bns = nn.ModuleList(bns)
          if self.is_first:
              self.pool = nn.AvgPool2d(kernel_size=3, stride=stride, padding=1)
  
          self.conv3 = nn.Conv2d(mid_planes * scale, outplanes, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm2d(outplanes)
          self.relu = nn.ReLU(inplace=True)
  
      def forward(self, x, shortcut=None):
          if shortcut is None:
              shortcut = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          spx = torch.split(out, self.width, 1)
          spo = []
          for i, (conv, bn) in enumerate(zip(self.convs, self.bns)):
              sp = spx[i] if i == 0 or self.is_first else sp + spx[i]
              sp = conv(sp)
              sp = bn(sp)
              sp = self.relu(sp)
              spo.append(sp)
          if self.scale > 1:
              spo.append(self.pool(spx[-1]) if self.is_first else spx[-1])
          out = torch.cat(spo, 1)
  
          out = self.conv3(out)
          out = self.bn3(out)
  
          out += shortcut
          out = self.relu(out)
  
          return out
  
  
  class DlaRoot(nn.Module):
      def __init__(self, in_channels, out_channels, kernel_size, shortcut):
          super(DlaRoot, self).__init__()
          self.conv = nn.Conv2d(
              in_channels, out_channels, 1, stride=1, bias=False, padding=(kernel_size - 1) // 2)
          self.bn = nn.BatchNorm2d(out_channels)
          self.relu = nn.ReLU(inplace=True)
          self.shortcut = shortcut
  
      def forward(self, *x):
          children = x
          x = self.conv(torch.cat(x, 1))
          x = self.bn(x)
          if self.shortcut:
              x += children[0]
          x = self.relu(x)
  
          return x
  
  
  class DlaTree(nn.Module):
      def __init__(self, levels, block, in_channels, out_channels, stride=1,
                   dilation=1, cardinality=1, base_width=64,
                   level_root=False, root_dim=0, root_kernel_size=1, root_shortcut=False):
          super(DlaTree, self).__init__()
          if root_dim == 0:
              root_dim = 2 * out_channels
          if level_root:
              root_dim += in_channels
          self.downsample = nn.MaxPool2d(stride, stride=stride) if stride > 1 else nn.Identity()
          self.project = nn.Identity()
          cargs = dict(dilation=dilation, cardinality=cardinality, base_width=base_width)
          if levels == 1:
              self.tree1 = block(in_channels, out_channels, stride, **cargs)
              self.tree2 = block(out_channels, out_channels, 1, **cargs)
              if in_channels != out_channels:
                  # NOTE the official impl/weights have  project layers in levels > 1 case that are never
                  # used, I've moved the project layer here to avoid wasted params but old checkpoints will
                  # need strict=False while loading.
                  self.project = nn.Sequential(
                      nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False),
                      nn.BatchNorm2d(out_channels))
          else:
              cargs.update(dict(root_kernel_size=root_kernel_size, root_shortcut=root_shortcut))
              self.tree1 = DlaTree(
                  levels - 1, block, in_channels, out_channels, stride, root_dim=0, **cargs)
              self.tree2 = DlaTree(
                  levels - 1, block, out_channels, out_channels, root_dim=root_dim + out_channels, **cargs)
          if levels == 1:
              self.root = DlaRoot(root_dim, out_channels, root_kernel_size, root_shortcut)
          self.level_root = level_root
          self.root_dim = root_dim
          self.levels = levels
  
      def forward(self, x, shortcut=None, children=None):
          children = [] if children is None else children
          bottom = self.downsample(x)
          shortcut = self.project(bottom)
          if self.level_root:
              children.append(bottom)
          x1 = self.tree1(x, shortcut)
          if self.levels == 1:
              x2 = self.tree2(x1)
              x = self.root(x2, x1, *children)
          else:
              children.append(x1)
              x = self.tree2(x1, children=children)
          return x
  
  
  class DLA(nn.Module):
      def __init__(self, levels, channels, output_stride=32, num_classes=1000, in_chans=3,
                   cardinality=1, base_width=64, block=DlaBottle2neck, shortcut_root=False,
                   drop_rate=0.0, global_pool='avg'):
          super(DLA, self).__init__()
          self.channels = channels
          self.num_classes = num_classes
          self.cardinality = cardinality
          self.base_width = base_width
          self.drop_rate = drop_rate
          assert output_stride == 32  # FIXME support dilation
  
          self.base_layer = nn.Sequential(
              nn.Conv2d(in_chans, channels[0], kernel_size=7, stride=1, padding=3, bias=False),
              nn.BatchNorm2d(channels[0]),
              nn.ReLU(inplace=True))
          self.level0 = self._make_conv_level(channels[0], channels[0], levels[0])
          self.level1 = self._make_conv_level(channels[0], channels[1], levels[1], stride=2)
          cargs = dict(cardinality=cardinality, base_width=base_width, root_shortcut=shortcut_root)
          self.level2 = DlaTree(levels[2], block, channels[1], channels[2], 2, level_root=False, **cargs)
          self.level3 = DlaTree(levels[3], block, channels[2], channels[3], 2, level_root=True, **cargs)
          self.level4 = DlaTree(levels[4], block, channels[3], channels[4], 2, level_root=True, **cargs)
          self.level5 = DlaTree(levels[5], block, channels[4], channels[5], 2, level_root=True, **cargs)
          self.feature_info = [
              dict(num_chs=channels[0], reduction=1, module='level0'),  # rare to have a meaningful stride 1 level
              dict(num_chs=channels[1], reduction=2, module='level1'),
              dict(num_chs=channels[2], reduction=4, module='level2'),
              dict(num_chs=channels[3], reduction=8, module='level3'),
              dict(num_chs=channels[4], reduction=16, module='level4'),
              dict(num_chs=channels[5], reduction=32, module='level5'),
          ]
  
          self.num_features = channels[-1]
          self.global_pool, self.fc = create_classifier(
              self.num_features, self.num_classes, pool_type=global_pool, use_conv=True)
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                  m.weight.data.normal_(0, math.sqrt(2. / n))
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
      def _make_conv_level(self, inplanes, planes, convs, stride=1, dilation=1):
          modules = []
          for i in range(convs):
              modules.extend([
                  nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride if i == 0 else 1,
                            padding=dilation, bias=False, dilation=dilation),
                  nn.BatchNorm2d(planes),
                  nn.ReLU(inplace=True)])
              inplanes = planes
          return nn.Sequential(*modules)
  
      def get_classifier(self):
          return self.fc
  
      def reset_classifier(self, num_classes, global_pool='avg'):
          self.num_classes = num_classes
          self.global_pool, self.fc = create_classifier(
              self.num_features, self.num_classes, pool_type=global_pool, use_conv=True)
  
      def forward_features(self, x):
          x = self.base_layer(x)
          x = self.level0(x)
          x = self.level1(x)
          x = self.level2(x)
          x = self.level3(x)
          x = self.level4(x)
          x = self.level5(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.fc(x)
          if not self.global_pool.is_identity():
              x = x.flatten(1)  # conv classifier, flatten if pooling isn't pass-through (disabled)
          return x
  
  
  def _create_dla(variant, pretrained=False, **kwargs):
      return build_model_with_cfg(
          DLA, variant, pretrained,
          default_cfg=default_cfgs[variant],
          pretrained_strict=False,
          feature_cfg=dict(out_indices=(1, 2, 3, 4, 5)),
          **kwargs)
  
  
  @register_model
  def dla60_res2net(pretrained=False, **kwargs):
      model_kwargs = dict(
          levels=(1, 1, 1, 2, 3, 1), channels=(16, 32, 128, 256, 512, 1024),
          block=DlaBottle2neck, cardinality=1, base_width=28, **kwargs)
      return _create_dla('dla60_res2net', pretrained, **model_kwargs)
  
  
  @register_model
  def dla60_res2next(pretrained=False,**kwargs):
      model_kwargs = dict(
          levels=(1, 1, 1, 2, 3, 1), channels=(16, 32, 128, 256, 512, 1024),
          block=DlaBottle2neck, cardinality=8, base_width=4, **kwargs)
      return _create_dla('dla60_res2next', pretrained, **model_kwargs)
  
  
  @register_model
  def dla34(pretrained=False, **kwargs):  # DLA-34
      model_kwargs = dict(
          levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 128, 256, 512],
          block=DlaBasic, **kwargs)
      return _create_dla('dla34', pretrained, **model_kwargs)
  
  
  @register_model
  def dla46_c(pretrained=False, **kwargs):  # DLA-46-C
      model_kwargs = dict(
          levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 64, 128, 256],
          block=DlaBottleneck, **kwargs)
      return _create_dla('dla46_c', pretrained, **model_kwargs)
  
  
  @register_model
  def dla46x_c(pretrained=False, **kwargs):  # DLA-X-46-C
      model_kwargs = dict(
          levels=[1, 1, 1, 2, 2, 1], channels=[16, 32, 64, 64, 128, 256],
          block=DlaBottleneck, cardinality=32, base_width=4, **kwargs)
      return _create_dla('dla46x_c', pretrained, **model_kwargs)
  
  
  @register_model
  def dla60x_c(pretrained=False, **kwargs):  # DLA-X-60-C
      model_kwargs = dict(
          levels=[1, 1, 1, 2, 3, 1], channels=[16, 32, 64, 64, 128, 256],
          block=DlaBottleneck, cardinality=32, base_width=4, **kwargs)
      return _create_dla('dla60x_c', pretrained, **model_kwargs)
  
  
  @register_model
  def dla60(pretrained=False, **kwargs):  # DLA-60
      model_kwargs = dict(
          levels=[1, 1, 1, 2, 3, 1], channels=[16, 32, 128, 256, 512, 1024],
          block=DlaBottleneck, **kwargs)
      return _create_dla('dla60', pretrained, **model_kwargs)
  
  
  @register_model
  def dla60x(pretrained=False, **kwargs):  # DLA-X-60
      model_kwargs = dict(
          levels=[1, 1, 1, 2, 3, 1], channels=[16, 32, 128, 256, 512, 1024],
          block=DlaBottleneck, cardinality=32, base_width=4, **kwargs)
      return _create_dla('dla60x', pretrained, **model_kwargs)
  
  
  @register_model
  def dla102(pretrained=False, **kwargs):  # DLA-102
      model_kwargs = dict(
          levels=[1, 1, 1, 3, 4, 1], channels=[16, 32, 128, 256, 512, 1024],
          block=DlaBottleneck, shortcut_root=True, **kwargs)
      return _create_dla('dla102', pretrained, **model_kwargs)
  
  
  @register_model
  def dla102x(pretrained=False, **kwargs):  # DLA-X-102
      model_kwargs = dict(
          levels=[1, 1, 1, 3, 4, 1], channels=[16, 32, 128, 256, 512, 1024],
          block=DlaBottleneck, cardinality=32, base_width=4, shortcut_root=True, **kwargs)
      return _create_dla('dla102x', pretrained, **model_kwargs)
  
  
  @register_model
  def dla102x2(pretrained=False, **kwargs):  # DLA-X-102 64
      model_kwargs = dict(
          levels=[1, 1, 1, 3, 4, 1], channels=[16, 32, 128, 256, 512, 1024],
          block=DlaBottleneck, cardinality=64, base_width=4, shortcut_root=True, **kwargs)
      return _create_dla('dla102x2', pretrained, **model_kwargs)
  
  
  @register_model
  def dla169(pretrained=False, **kwargs):  # DLA-169
      model_kwargs = dict(
          levels=[1, 1, 2, 3, 5, 1], channels=[16, 32, 128, 256, 512, 1024],
          block=DlaBottleneck, shortcut_root=True, **kwargs)
      return _create_dla('dla169', pretrained, **model_kwargs)
  