hunk.code_window:         filter (str) - Wildcard filter string that works with fnmatch
           module (str) - Limit model selection to a specific sub-module (ie 'gen_efficientnet')
           pretrained (bool) - Include only models with pretrained weights if True
           exclude_filters (str or list[str]) - Wildcard filters to exclude models after including them with filter
       Example:
           model_list('gluon_resnet*') -- returns all models starting with 'gluon_resnet'
           model_list('*resnext*, 'resnet') -- returns all models with 'resnext' in 'resnet' module
       """
  
hunk.labels: keep keep keep add keep keep keep keep keep
hunk.after_edit:         name_matches_cfg (bool) - Include only models w/ model_name matching default_cfg name (excludes some aliases)
  
hunk.file_path: timm/models/registry.py
hunk.type: add
file: """RegNet
  
  Paper: `Designing Network Design Spaces` - https://arxiv.org/abs/2003.13678
  Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
  
  Based on original PyTorch impl linked above, but re-wrote to use my own blocks (adapted from ResNet here)
  and cleaned up with more descriptive variable names.
  
  Weights from original impl have been modified
  * first layer from BGR -> RGB as most PyTorch models are
  * removed training specific dict entries from checkpoints and keep model state_dict only
  * remap names to match the ones here
  
  Hacked together by / Copyright 2020 Ross Wightman
  """
  import numpy as np
  import torch.nn as nn
  
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from .helpers import build_model_with_cfg
  from .layers import ClassifierHead, AvgPool2dSame, ConvBnAct, SEModule, DropPath
  from .registry import register_model
  
  
  def _mcfg(**kwargs):
      cfg = dict(se_ratio=0., bottle_ratio=1., stem_width=32)
      cfg.update(**kwargs)
      return cfg
  
  
  # Model FLOPS = three trailing digits * 10^8
  model_cfgs = dict(
      regnetx_002=_mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13),
      regnetx_004=_mcfg(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22),
      regnetx_006=_mcfg(w0=48, wa=36.97, wm=2.24, group_w=24, depth=16),
      regnetx_008=_mcfg(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16),
      regnetx_016=_mcfg(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18),
      regnetx_032=_mcfg(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25),
      regnetx_040=_mcfg(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23),
      regnetx_064=_mcfg(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17),
      regnetx_080=_mcfg(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23),
      regnetx_120=_mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19),
      regnetx_160=_mcfg(w0=216, wa=55.59, wm=2.1, group_w=128, depth=22),
      regnetx_320=_mcfg(w0=320, wa=69.86, wm=2.0, group_w=168, depth=23),
      regnety_002=_mcfg(w0=24, wa=36.44, wm=2.49, group_w=8, depth=13, se_ratio=0.25),
      regnety_004=_mcfg(w0=48, wa=27.89, wm=2.09, group_w=8, depth=16, se_ratio=0.25),
      regnety_006=_mcfg(w0=48, wa=32.54, wm=2.32, group_w=16, depth=15, se_ratio=0.25),
      regnety_008=_mcfg(w0=56, wa=38.84, wm=2.4, group_w=16, depth=14, se_ratio=0.25),
      regnety_016=_mcfg(w0=48, wa=20.71, wm=2.65, group_w=24, depth=27, se_ratio=0.25),
      regnety_032=_mcfg(w0=80, wa=42.63, wm=2.66, group_w=24, depth=21, se_ratio=0.25),
      regnety_040=_mcfg(w0=96, wa=31.41, wm=2.24, group_w=64, depth=22, se_ratio=0.25),
      regnety_064=_mcfg(w0=112, wa=33.22, wm=2.27, group_w=72, depth=25, se_ratio=0.25),
      regnety_080=_mcfg(w0=192, wa=76.82, wm=2.19, group_w=56, depth=17, se_ratio=0.25),
      regnety_120=_mcfg(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, se_ratio=0.25),
      regnety_160=_mcfg(w0=200, wa=106.23, wm=2.48, group_w=112, depth=18, se_ratio=0.25),
      regnety_320=_mcfg(w0=232, wa=115.89, wm=2.53, group_w=232, depth=20, se_ratio=0.25),
  )
  
  
  def _cfg(url='', **kwargs):
      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': 'stem.conv', 'classifier': 'head.fc',
          **kwargs
      }
  
  
  default_cfgs = dict(
      regnetx_002=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_002-e7e85e5c.pth'),
      regnetx_004=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_004-7d0e9424.pth'),
      regnetx_006=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_006-85ec1baa.pth'),
      regnetx_008=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_008-d8b470eb.pth'),
      regnetx_016=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_016-65ca972a.pth'),
      regnetx_032=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_032-ed0c7f7e.pth'),
      regnetx_040=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_040-73c2a654.pth'),
      regnetx_064=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_064-29278baa.pth'),
      regnetx_080=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_080-7c7fcab1.pth'),
      regnetx_120=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_120-65d5521e.pth'),
      regnetx_160=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_160-c98c4112.pth'),
      regnetx_320=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnetx_320-8ea38b93.pth'),
      regnety_002=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_002-e68ca334.pth'),
      regnety_004=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_004-0db870e6.pth'),
      regnety_006=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_006-c67e57ec.pth'),
      regnety_008=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_008-dc900dbe.pth'),
      regnety_016=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_016-54367f74.pth'),
      regnety_032=_cfg(
          url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/regnety_032_ra-7f2439f9.pth',
          crop_pct=1.0, test_input_size=(3, 288, 288)),
      regnety_040=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_040-f0d569f9.pth'),
      regnety_064=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_064-0a48325c.pth'),
      regnety_080=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_080-e7f3eb93.pth'),
      regnety_120=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_120-721ba79a.pth'),
      regnety_160=_cfg(
          url='https://dl.fbaipublicfiles.com/deit/regnety_160-a5fe301d.pth',  # from Facebook DeiT GitHub repository
          crop_pct=1.0, test_input_size=(3, 288, 288)),
      regnety_320=_cfg(url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-regnet/regnety_320-ba464b29.pth'),
  )
  
  
  def quantize_float(f, q):
      """Converts a float to closest non-zero int divisible by q."""
      return int(round(f / q) * q)
  
  
  def adjust_widths_groups_comp(widths, bottle_ratios, groups):
      """Adjusts the compatibility of widths and groups."""
      bottleneck_widths = [int(w * b) for w, b in zip(widths, bottle_ratios)]
      groups = [min(g, w_bot) for g, w_bot in zip(groups, bottleneck_widths)]
      bottleneck_widths = [quantize_float(w_bot, g) for w_bot, g in zip(bottleneck_widths, groups)]
      widths = [int(w_bot / b) for w_bot, b in zip(bottleneck_widths, bottle_ratios)]
      return widths, groups
  
  
  def generate_regnet(width_slope, width_initial, width_mult, depth, q=8):
      """Generates per block widths from RegNet parameters."""
      assert width_slope >= 0 and width_initial > 0 and width_mult > 1 and width_initial % q == 0
      widths_cont = np.arange(depth) * width_slope + width_initial
      width_exps = np.round(np.log(widths_cont / width_initial) / np.log(width_mult))
      widths = width_initial * np.power(width_mult, width_exps)
      widths = np.round(np.divide(widths, q)) * q
      num_stages, max_stage = len(np.unique(widths)), width_exps.max() + 1
      widths, widths_cont = widths.astype(int).tolist(), widths_cont.tolist()
      return widths, num_stages, max_stage, widths_cont
  
  
  class Bottleneck(nn.Module):
      """ RegNet Bottleneck
  
      This is almost exactly the same as a ResNet Bottlneck. The main difference is the SE block is moved from
      after conv3 to after conv2. Otherwise, it's just redefining the arguments for groups/bottleneck channels.
      """
  
      def __init__(self, in_chs, out_chs, stride=1, dilation=1, bottleneck_ratio=1, group_width=1, se_ratio=0.25,
                   downsample=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None,
                   drop_block=None, drop_path=None):
          super(Bottleneck, self).__init__()
          bottleneck_chs = int(round(out_chs * bottleneck_ratio))
          groups = bottleneck_chs // group_width
  
          cargs = dict(act_layer=act_layer, norm_layer=norm_layer, aa_layer=aa_layer, drop_block=drop_block)
          self.conv1 = ConvBnAct(in_chs, bottleneck_chs, kernel_size=1, **cargs)
          self.conv2 = ConvBnAct(
              bottleneck_chs, bottleneck_chs, kernel_size=3, stride=stride, dilation=dilation,
              groups=groups, **cargs)
          if se_ratio:
              se_channels = int(round(in_chs * se_ratio))
              self.se = SEModule(bottleneck_chs, reduction_channels=se_channels)
          else:
              self.se = None
          cargs['act_layer'] = None
          self.conv3 = ConvBnAct(bottleneck_chs, out_chs, kernel_size=1, **cargs)
          self.act3 = act_layer(inplace=True)
          self.downsample = downsample
          self.drop_path = drop_path
  
      def zero_init_last_bn(self):
          nn.init.zeros_(self.conv3.bn.weight)
  
      def forward(self, x):
          shortcut = x
          x = self.conv1(x)
          x = self.conv2(x)
          if self.se is not None:
              x = self.se(x)
          x = self.conv3(x)
          if self.drop_path is not None:
              x = self.drop_path(x)
          if self.downsample is not None:
              shortcut = self.downsample(shortcut)
          x += shortcut
          x = self.act3(x)
          return x
  
  
  def downsample_conv(
          in_chs, out_chs, kernel_size, stride=1, dilation=1, norm_layer=None):
      norm_layer = norm_layer or nn.BatchNorm2d
      kernel_size = 1 if stride == 1 and dilation == 1 else kernel_size
      dilation = dilation if kernel_size > 1 else 1
      return ConvBnAct(
          in_chs, out_chs, kernel_size, stride=stride, dilation=dilation, norm_layer=norm_layer, act_layer=None)
  
  
  def downsample_avg(
          in_chs, out_chs, kernel_size, stride=1, dilation=1, norm_layer=None):
      """ AvgPool Downsampling as in 'D' ResNet variants. This is not in RegNet space but I might experiment."""
      norm_layer = norm_layer or nn.BatchNorm2d
      avg_stride = stride if dilation == 1 else 1
      pool = nn.Identity()
      if stride > 1 or dilation > 1:
          avg_pool_fn = AvgPool2dSame if avg_stride == 1 and dilation > 1 else nn.AvgPool2d
          pool = avg_pool_fn(2, avg_stride, ceil_mode=True, count_include_pad=False)
      return nn.Sequential(*[
          pool, ConvBnAct(in_chs, out_chs, 1, stride=1, norm_layer=norm_layer, act_layer=None)])
  
  
  class RegStage(nn.Module):
      """Stage (sequence of blocks w/ the same output shape)."""
  
      def __init__(self, in_chs, out_chs, stride, dilation, depth, bottle_ratio, group_width,
                   block_fn=Bottleneck, se_ratio=0., drop_path_rates=None, drop_block=None):
          super(RegStage, self).__init__()
          block_kwargs = {}  # FIXME setup to pass various aa, norm, act layer common args
          first_dilation = 1 if dilation in (1, 2) else 2
          for i in range(depth):
              block_stride = stride if i == 0 else 1
              block_in_chs = in_chs if i == 0 else out_chs
              block_dilation = first_dilation if i == 0 else dilation
              if drop_path_rates is not None and drop_path_rates[i] > 0.:
                  drop_path = DropPath(drop_path_rates[i])
              else:
                  drop_path = None
              if (block_in_chs != out_chs) or (block_stride != 1):
                  proj_block = downsample_conv(block_in_chs, out_chs, 1, block_stride, block_dilation)
              else:
                  proj_block = None
  
              name = "b{}".format(i + 1)
              self.add_module(
                  name, block_fn(
                      block_in_chs, out_chs, block_stride, block_dilation, bottle_ratio, group_width, se_ratio,
                      downsample=proj_block, drop_block=drop_block, drop_path=drop_path, **block_kwargs)
              )
  
      def forward(self, x):
          for block in self.children():
              x = block(x)
          return x
  
  
  class RegNet(nn.Module):
      """RegNet model.
  
      Paper: https://arxiv.org/abs/2003.13678
      Original Impl: https://github.com/facebookresearch/pycls/blob/master/pycls/models/regnet.py
      """
  
      def __init__(self, cfg, in_chans=3, num_classes=1000, output_stride=32, global_pool='avg', drop_rate=0.,
                   drop_path_rate=0., zero_init_last_bn=True):
          super().__init__()
          # TODO add drop block, drop path, anti-aliasing, custom bn/act args
          self.num_classes = num_classes
          self.drop_rate = drop_rate
          assert output_stride in (8, 16, 32)
  
          # Construct the stem
          stem_width = cfg['stem_width']
          self.stem = ConvBnAct(in_chans, stem_width, 3, stride=2)
          self.feature_info = [dict(num_chs=stem_width, reduction=2, module='stem')]
  
          # Construct the stages
          prev_width = stem_width
          curr_stride = 2
          stage_params = self._get_stage_params(cfg, output_stride=output_stride, drop_path_rate=drop_path_rate)
          se_ratio = cfg['se_ratio']
          for i, stage_args in enumerate(stage_params):
              stage_name = "s{}".format(i + 1)
              self.add_module(stage_name, RegStage(prev_width, **stage_args, se_ratio=se_ratio))
              prev_width = stage_args['out_chs']
              curr_stride *= stage_args['stride']
              self.feature_info += [dict(num_chs=prev_width, reduction=curr_stride, module=stage_name)]
  
          # Construct the head
          self.num_features = prev_width
          self.head = ClassifierHead(
              in_chs=prev_width, num_classes=num_classes, pool_type=global_pool, drop_rate=drop_rate)
  
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
              elif isinstance(m, nn.BatchNorm2d):
                  nn.init.ones_(m.weight)
                  nn.init.zeros_(m.bias)
              elif isinstance(m, nn.Linear):
                  nn.init.normal_(m.weight, mean=0.0, std=0.01)
                  nn.init.zeros_(m.bias)
          if zero_init_last_bn:
              for m in self.modules():
                  if hasattr(m, 'zero_init_last_bn'):
                      m.zero_init_last_bn()
  
      def _get_stage_params(self, cfg, default_stride=2, output_stride=32, drop_path_rate=0.):
          # Generate RegNet ws per block
          w_a, w_0, w_m, d = cfg['wa'], cfg['w0'], cfg['wm'], cfg['depth']
          widths, num_stages, _, _ = generate_regnet(w_a, w_0, w_m, d)
  
          # Convert to per stage format
          stage_widths, stage_depths = np.unique(widths, return_counts=True)
  
          # Use the same group width, bottleneck mult and stride for each stage
          stage_groups = [cfg['group_w'] for _ in range(num_stages)]
          stage_bottle_ratios = [cfg['bottle_ratio'] for _ in range(num_stages)]
          stage_strides = []
          stage_dilations = []
          net_stride = 2
          dilation = 1
          for _ in range(num_stages):
              if net_stride >= output_stride:
                  dilation *= default_stride
                  stride = 1
              else:
                  stride = default_stride
                  net_stride *= stride
              stage_strides.append(stride)
              stage_dilations.append(dilation)
          stage_dpr = np.split(np.linspace(0, drop_path_rate, d), np.cumsum(stage_depths[:-1]))
  
          # Adjust the compatibility of ws and gws
          stage_widths, stage_groups = adjust_widths_groups_comp(stage_widths, stage_bottle_ratios, stage_groups)
          param_names = ['out_chs', 'stride', 'dilation', 'depth', 'bottle_ratio', 'group_width', 'drop_path_rates']
          stage_params = [
              dict(zip(param_names, params)) for params in
              zip(stage_widths, stage_strides, stage_dilations, stage_depths, stage_bottle_ratios, stage_groups,
                  stage_dpr)]
          return stage_params
  
      def get_classifier(self):
          return self.head.fc
  
      def reset_classifier(self, num_classes, global_pool='avg'):
          self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate)
  
      def forward_features(self, x):
          for block in list(self.children())[:-1]:
              x = block(x)
          return x
  
      def forward(self, x):
          for block in self.children():
              x = block(x)
          return x
  
  
  def _filter_fn(state_dict):
      """ convert patch embedding weight from manual patchify + linear proj to conv"""
      if 'model' in state_dict:
          # For DeiT trained regnety_160 pretraiend model
          state_dict = state_dict['model']
      return state_dict
  
  
  def _create_regnet(variant, pretrained, **kwargs):
      return build_model_with_cfg(
          RegNet, variant, pretrained,
          default_cfg=default_cfgs[variant],
          model_cfg=model_cfgs[variant],
          pretrained_filter_fn=_filter_fn,
          **kwargs)
  
  
  @register_model
  def regnetx_002(pretrained=False, **kwargs):
      """RegNetX-200MF"""
      return _create_regnet('regnetx_002', pretrained, **kwargs)
  
  
  @register_model
  def regnetx_004(pretrained=False, **kwargs):
      """RegNetX-400MF"""
      return _create_regnet('regnetx_004', pretrained, **kwargs)
  
  
  @register_model
  def regnetx_006(pretrained=False, **kwargs):
      """RegNetX-600MF"""
      return _create_regnet('regnetx_006', pretrained, **kwargs)
  
  
  @register_model
  def regnetx_008(pretrained=False, **kwargs):
      """RegNetX-800MF"""
      return _create_regnet('regnetx_008', pretrained, **kwargs)
  
  
  @register_model
  def regnetx_016(pretrained=False, **kwargs):
      """RegNetX-1.6GF"""
      return _create_regnet('regnetx_016', pretrained, **kwargs)
  
  
  @register_model
  def regnetx_032(pretrained=False, **kwargs):
      """RegNetX-3.2GF"""
      return _create_regnet('regnetx_032', pretrained, **kwargs)
  
  
  @register_model
  def regnetx_040(pretrained=False, **kwargs):
      """RegNetX-4.0GF"""
      return _create_regnet('regnetx_040', pretrained, **kwargs)
  
  
  @register_model
  def regnetx_064(pretrained=False, **kwargs):
      """RegNetX-6.4GF"""
      return _create_regnet('regnetx_064', pretrained, **kwargs)
  
  
  @register_model
  def regnetx_080(pretrained=False, **kwargs):
      """RegNetX-8.0GF"""
      return _create_regnet('regnetx_080', pretrained, **kwargs)
  
  
  @register_model
  def regnetx_120(pretrained=False, **kwargs):
      """RegNetX-12GF"""
      return _create_regnet('regnetx_120', pretrained, **kwargs)
  
  
  @register_model
  def regnetx_160(pretrained=False, **kwargs):
      """RegNetX-16GF"""
      return _create_regnet('regnetx_160', pretrained, **kwargs)
  
  
  @register_model
  def regnetx_320(pretrained=False, **kwargs):
      """RegNetX-32GF"""
      return _create_regnet('regnetx_320', pretrained, **kwargs)
  
  
  @register_model
  def regnety_002(pretrained=False, **kwargs):
      """RegNetY-200MF"""
      return _create_regnet('regnety_002', pretrained, **kwargs)
  
  
  @register_model
  def regnety_004(pretrained=False, **kwargs):
      """RegNetY-400MF"""
      return _create_regnet('regnety_004', pretrained, **kwargs)
  
  
  @register_model
  def regnety_006(pretrained=False, **kwargs):
      """RegNetY-600MF"""
      return _create_regnet('regnety_006', pretrained, **kwargs)
  
  
  @register_model
  def regnety_008(pretrained=False, **kwargs):
      """RegNetY-800MF"""
      return _create_regnet('regnety_008', pretrained, **kwargs)
  
  
  @register_model
  def regnety_016(pretrained=False, **kwargs):
      """RegNetY-1.6GF"""
      return _create_regnet('regnety_016', pretrained, **kwargs)
  
  
  @register_model
  def regnety_032(pretrained=False, **kwargs):
      """RegNetY-3.2GF"""
      return _create_regnet('regnety_032', pretrained, **kwargs)
  
  
  @register_model
  def regnety_040(pretrained=False, **kwargs):
      """RegNetY-4.0GF"""
      return _create_regnet('regnety_040', pretrained, **kwargs)
  
  
  @register_model
  def regnety_064(pretrained=False, **kwargs):
      """RegNetY-6.4GF"""
      return _create_regnet('regnety_064', pretrained, **kwargs)
  
  
  @register_model
  def regnety_080(pretrained=False, **kwargs):
      """RegNetY-8.0GF"""
      return _create_regnet('regnety_080', pretrained, **kwargs)
  
  
  @register_model
  def regnety_120(pretrained=False, **kwargs):
      """RegNetY-12GF"""
      return _create_regnet('regnety_120', pretrained, **kwargs)
  
  
  @register_model
  def regnety_160(pretrained=False, **kwargs):
      """RegNetY-16GF"""
      return _create_regnet('regnety_160', pretrained, **kwargs)
  
  
  @register_model
  def regnety_320(pretrained=False, **kwargs):
      """RegNetY-32GF"""
      return _create_regnet('regnety_320', pretrained, **kwargs)
  