hunk.code_window:     assert not torch.isnan(outputs).any(), 'Output included NaNs'
   @pytest.mark.timeout(120)
   @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
   @pytest.mark.parametrize('batch_size', [2])
   def test_model_backward(model_name, batch_size):
       """Run a single forward pass with each model"""
  
hunk.labels: keep keep keep keep replace keep keep keep
hunk.after_edit: @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS, name_matches_cfg=True))
  
hunk.file_path: tests/test_models.py
hunk.type: replace
file: import pytest
  import torch
  import platform
  import os
  import fnmatch
  
  import timm
  from timm import list_models, create_model, set_scriptable, has_model_default_key, is_model_default_key, \
      get_model_default_value
  
  if hasattr(torch._C, '_jit_set_profiling_executor'):
      # legacy executor is too slow to compile large models for unit tests
      # no need for the fusion performance here
      torch._C._jit_set_profiling_executor(True)
      torch._C._jit_set_profiling_mode(False)
  
  # transformer models don't support many of the spatial / feature based model functionalities
  NON_STD_FILTERS = [
      'vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*',
      'convit_*', 'levit*', 'visformer*']
  NUM_NON_STD = len(NON_STD_FILTERS)
  
  # exclude models that cause specific test failures
  if 'GITHUB_ACTIONS' in os.environ:  # and 'Linux' in platform.system():
      # GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
      EXCLUDE_FILTERS = [
          '*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*101x3_bitm',
          '*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*', 
          '*resnetrs350*', '*resnetrs420*']
  else:
      EXCLUDE_FILTERS = []
  
  TARGET_FWD_SIZE = MAX_FWD_SIZE = 384
  TARGET_BWD_SIZE = 128
  MAX_BWD_SIZE = 320
  MAX_FWD_OUT_SIZE = 448
  TARGET_JIT_SIZE = 128
  MAX_JIT_SIZE = 320
  TARGET_FFEAT_SIZE = 96
  MAX_FFEAT_SIZE = 256
  
  
  def _get_input_size(model, target=None):
      default_cfg = model.default_cfg
      input_size = default_cfg['input_size']
      if 'fixed_input_size' in default_cfg and default_cfg['fixed_input_size']:
          return input_size
      if 'min_input_size' in default_cfg:
          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
  
  
  @pytest.mark.timeout(120)
  @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
  @pytest.mark.parametrize('batch_size', [1])
  def test_model_forward(model_name, batch_size):
      """Run a single forward pass with each model"""
      model = create_model(model_name, pretrained=False)
      model.eval()
  
      input_size = _get_input_size(model, TARGET_FWD_SIZE)
      if max(input_size) > MAX_FWD_SIZE:
          pytest.skip("Fixed input size model > limit.")
      inputs = torch.randn((batch_size, *input_size))
      outputs = model(inputs)
  
      assert outputs.shape[0] == batch_size
      assert not torch.isnan(outputs).any(), 'Output included NaNs'
  
  
  @pytest.mark.timeout(120)
  @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS))
  @pytest.mark.parametrize('batch_size', [2])
  def test_model_backward(model_name, batch_size):
      """Run a single forward pass with each model"""
      model = create_model(model_name, pretrained=False, num_classes=42)
      num_params = sum([x.numel() for x in model.parameters()])
      model.train()
  
      input_size = _get_input_size(model, TARGET_BWD_SIZE)
      if max(input_size) > MAX_BWD_SIZE:
          pytest.skip("Fixed input size model > limit.")
  
      inputs = torch.randn((batch_size, *input_size))
      outputs = model(inputs)
      if isinstance(outputs, tuple):
          outputs = torch.cat(outputs)
      outputs.mean().backward()
      for n, x in model.named_parameters():
          assert x.grad is not None, f'No gradient for {n}'
      num_grad = sum([x.grad.numel() for x in model.parameters() if x.grad is not None])
  
      assert outputs.shape[-1] == 42
      assert num_params == num_grad, 'Some parameters are missing gradients'
      assert not torch.isnan(outputs).any(), 'Output included NaNs'
  
  
  @pytest.mark.timeout(120)
  @pytest.mark.parametrize('model_name', list_models(exclude_filters=NON_STD_FILTERS))
  @pytest.mark.parametrize('batch_size', [1])
  def test_model_default_cfgs(model_name, batch_size):
      """Run a single forward pass with each model"""
      model = create_model(model_name, pretrained=False)
      model.eval()
      state_dict = model.state_dict()
      cfg = model.default_cfg
  
      classifier = cfg['classifier']
      pool_size = cfg['pool_size']
      input_size = model.default_cfg['input_size']
  
      if all([x <= MAX_FWD_OUT_SIZE for x in input_size]) and \
              not any([fnmatch.fnmatch(model_name, x) for x in EXCLUDE_FILTERS]):
          # output sizes only checked if default res <= 448 * 448 to keep resource down
          input_size = tuple([min(x, MAX_FWD_OUT_SIZE) for x in input_size])
          input_tensor = torch.randn((batch_size, *input_size))
  
          # test forward_features (always unpooled)
          outputs = model.forward_features(input_tensor)
          assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
  
          # test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
          model.reset_classifier(0)
          outputs = model.forward(input_tensor)
          assert len(outputs.shape) == 2
          assert outputs.shape[-1] == model.num_features
  
          # test model forward without pooling and classifier
          model.reset_classifier(0, '')  # reset classifier and set global pooling to pass-through
          outputs = model.forward(input_tensor)
          assert len(outputs.shape) == 4
          if not isinstance(model, timm.models.MobileNetV3) and not isinstance(model, timm.models.GhostNet):
              # FIXME mobilenetv3/ghostnet forward_features vs removed pooling differ
              assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
  
      # check classifier name matches default_cfg
      assert classifier + ".weight" in state_dict.keys(), f'{classifier} not in model params'
  
      # check first conv(s) names match default_cfg
      first_conv = cfg['first_conv']
      if isinstance(first_conv, str):
          first_conv = (first_conv,)
      assert isinstance(first_conv, (tuple, list))
      for fc in first_conv:
          assert fc + ".weight" in state_dict.keys(), f'{fc} not in model params'
  
  
  if 'GITHUB_ACTIONS' not in os.environ:
      @pytest.mark.timeout(120)
      @pytest.mark.parametrize('model_name', list_models(pretrained=True))
      @pytest.mark.parametrize('batch_size', [1])
      def test_model_load_pretrained(model_name, batch_size):
          """Create that pretrained weights load, verify support for in_chans != 3 while doing so."""
          in_chans = 3 if 'pruned' in model_name else 1  # pruning not currently supported with in_chans change
          create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=5)
  
      @pytest.mark.timeout(120)
      @pytest.mark.parametrize('model_name', list_models(pretrained=True, exclude_filters=NON_STD_FILTERS))
      @pytest.mark.parametrize('batch_size', [1])
      def test_model_features_pretrained(model_name, batch_size):
          """Create that pretrained weights load when features_only==True."""
          create_model(model_name, pretrained=True, features_only=True)
  
  EXCLUDE_JIT_FILTERS = [
      '*iabn*', 'tresnet*',  # models using inplace abn unlikely to ever be scriptable
      'dla*', 'hrnet*', 'ghostnet*', # hopefully fix at some point
  ]
  
  
  @pytest.mark.timeout(120)
  @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS))
  @pytest.mark.parametrize('batch_size', [1])
  def test_model_forward_torchscript(model_name, batch_size):
      """Run a single forward pass with each model"""
      with set_scriptable(True):
          model = create_model(model_name, pretrained=False)
      model.eval()
  
      input_size = _get_input_size(model, TARGET_JIT_SIZE)
      if max(input_size) > MAX_JIT_SIZE:  # NOTE using MAX_FWD_SIZE as the final limit is intentional
          pytest.skip("Fixed input size model > limit.")
  
      model = torch.jit.script(model)
      outputs = model(torch.randn((batch_size, *input_size)))
  
      assert outputs.shape[0] == batch_size
      assert not torch.isnan(outputs).any(), 'Output included NaNs'
  
  
  EXCLUDE_FEAT_FILTERS = [
      '*pruned*',  # hopefully fix at some point
  ] + NON_STD_FILTERS
  if 'GITHUB_ACTIONS' in os.environ:  # and 'Linux' in platform.system():
      # GitHub Linux runner is slower and hits memory limits sooner than MacOS, exclude bigger models
      EXCLUDE_FEAT_FILTERS += ['*resnext101_32x32d', '*resnext101_32x16d']
  
  
  @pytest.mark.timeout(120)
  @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FEAT_FILTERS))
  @pytest.mark.parametrize('batch_size', [1])
  def test_model_forward_features(model_name, batch_size):
      """Run a single forward pass with each model in feature extraction mode"""
      model = create_model(model_name, pretrained=False, features_only=True)
      model.eval()
      expected_channels = model.feature_info.channels()
      assert len(expected_channels) >= 4  # all models here should have at least 4 feature levels by default, some 5 or 6
  
      input_size = _get_input_size(model, TARGET_FFEAT_SIZE)
      if max(input_size) > MAX_FFEAT_SIZE:
          pytest.skip("Fixed input size model > limit.")
  
      outputs = model(torch.randn((batch_size, *input_size)))
      assert len(expected_channels) == len(outputs)
      for e, o in zip(expected_channels, outputs):
          assert e == o.shape[1]
          assert o.shape[0] == batch_size
          assert not torch.isnan(o).any()
  