hunk.code_window:         python-version: ${{ matrix.python }}
       - name: Install testing dependencies
         run: |
           python -m pip install --upgrade pip
           pip install pytest pytest-timeout expecttest
       - name: Install torch on mac
         if: startsWith(matrix.os, 'macOS')
         run: pip install --no-cache-dir torch==${{ matrix.torch }} torchvision==${{ matrix.torchvision }}
       - name: Install torch on ubuntu
         if: startsWith(matrix.os, 'ubuntu')
  
hunk.labels: keep keep keep keep replace keep keep keep keep keep
hunk.after_edit:         pip install pytest pytest-timeout pytest-xdist expecttest
  
hunk.file_path: .github/workflows/tests.yml
hunk.type: replace
file: import pytest
  import torch
  import platform
  import os
  import fnmatch
  
  try:
      from torchvision.models.feature_extraction import create_feature_extractor, get_graph_node_names, NodePathTracer
      has_fx_feature_extraction = True
  except ImportError:
      has_fx_feature_extraction = False
  
  import timm
  from timm import list_models, create_model, set_scriptable, has_model_default_key, is_model_default_key, \
      get_model_default_value
  from timm.models.fx_features import _leaf_modules, _autowrap_functions    
  
  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*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*', 'crossvit_*', 'beit_*']
  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', '*50x3_bitm',
          '*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*', '*efficientnetv2_xl*',
          '*resnetrs350*', '*resnetrs420*', 'xcit_large_24_p8*']
  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
  TARGET_FWD_FX_SIZE = 128
  MAX_FWD_FX_SIZE = 224
  TARGET_BWD_FX_SIZE = 128
  MAX_BWD_FX_SIZE = 224
  
  
  def _get_input_size(model=None, model_name='', target=None):
      if model is None:
          assert model_name, "One of model or model_name must be provided"
          input_size = get_model_default_value(model_name, 'input_size')
          fixed_input_size = get_model_default_value(model_name, 'fixed_input_size')
          min_input_size = get_model_default_value(model_name, 'min_input_size')
      else:
          default_cfg = model.default_cfg
          input_size = default_cfg['input_size']
          fixed_input_size = default_cfg.get('fixed_input_size', None)
          min_input_size = default_cfg.get('min_input_size', None)
      assert input_size is not None
  
      if fixed_input_size:
          return input_size
  
      if min_input_size:
          if target and max(input_size) > target:
              input_size = 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=model, target=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, name_matches_cfg=True))
  @pytest.mark.parametrize('batch_size', [2])
  def test_model_backward(model_name, batch_size):
      """Run a single forward pass with each model"""
      input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_SIZE)
      if max(input_size) > MAX_BWD_SIZE:
          pytest.skip("Fixed input size model > limit.")
  
      model = create_model(model_name, pretrained=False, num_classes=42)
      num_params = sum([x.numel() for x in model.parameters()])
      model.train()
  
      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(300)
  @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
  
      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]
  
          if 'pruned' not in model_name:  # FIXME better pruned model handling
              # test classifier + global pool deletion via __init__
              model = create_model(model_name, pretrained=False, num_classes=0, global_pool='').eval()
              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
      classifier = cfg['classifier']
      if not isinstance(classifier, (tuple, list)):
          classifier = classifier,
      for c in classifier:
          assert c + ".weight" in state_dict.keys(), f'{c} 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'
  
  
  @pytest.mark.timeout(300)
  @pytest.mark.parametrize('model_name', list_models(filter=NON_STD_FILTERS))
  @pytest.mark.parametrize('batch_size', [1])
  def test_model_default_cfgs_non_std(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
  
      input_size = _get_input_size(model=model)
      if max(input_size) > 320:  # FIXME const
          pytest.skip("Fixed input size model > limit.")
  
      input_tensor = torch.randn((batch_size, *input_size))
  
      outputs = model.forward_features(input_tensor)
      if isinstance(outputs, (tuple, list)):
          outputs = outputs[0]
      assert outputs.shape[1] == model.num_features
  
      # test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
      model.reset_classifier(0)
      outputs = model.forward(input_tensor)
      if isinstance(outputs,  (tuple, list)):
          outputs = outputs[0]
      assert len(outputs.shape) == 2
      assert outputs.shape[1] == model.num_features
  
      model = create_model(model_name, pretrained=False, num_classes=0).eval()
      outputs = model.forward(input_tensor)
      if isinstance(outputs, (tuple, list)):
          outputs = outputs[0]
      assert len(outputs.shape) == 2
      assert outputs.shape[1] == model.num_features
  
      # check classifier name matches default_cfg
      classifier = cfg['classifier']
      if not isinstance(classifier, (tuple, list)):
          classifier = classifier,
      for c in classifier:
          assert c + ".weight" in state_dict.keys(), f'{c} 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)
          create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=0)
  
      @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
      'vit_large_*', 'vit_huge_*',
  ]
  
  
  @pytest.mark.timeout(120)
  @pytest.mark.parametrize(
      'model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS, name_matches_cfg=True))
  @pytest.mark.parametrize('batch_size', [1])
  def test_model_forward_torchscript(model_name, batch_size):
      """Run a single forward pass with each model"""
      input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
      if max(input_size) > MAX_JIT_SIZE:
          pytest.skip("Fixed input size model > limit.")
  
      with set_scriptable(True):
          model = create_model(model_name, pretrained=False)
      model.eval()
  
      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=model, target=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()
  
  
  def _create_fx_model(model, train=False):
      # This block of code does a bit of juggling to handle any case where there are multiple outputs in train mode
      # So we trace once and look at the graph, and get the indices of the nodes that lead into the original fx output
      # node. Then we use those indices to select from train_nodes returned by torchvision get_graph_node_names
      train_nodes, eval_nodes = get_graph_node_names(
          model, tracer_kwargs={'leaf_modules': list(_leaf_modules), 'autowrap_functions': list(_autowrap_functions)})
  
      eval_return_nodes = [eval_nodes[-1]]
      train_return_nodes = [train_nodes[-1]]
      if train:
          tracer = NodePathTracer(leaf_modules=list(_leaf_modules), autowrap_functions=list(_autowrap_functions))
          graph = tracer.trace(model)
          graph_nodes = list(reversed(graph.nodes))
          output_node_names = [n.name for n in graph_nodes[0]._input_nodes.keys()]
          graph_node_names = [n.name for n in graph_nodes]
          output_node_indices = [-graph_node_names.index(node_name) for node_name in output_node_names]
          train_return_nodes = [train_nodes[ix] for ix in output_node_indices]
  
      fx_model = create_feature_extractor(
          model, train_return_nodes=train_return_nodes, eval_return_nodes=eval_return_nodes,
          tracer_kwargs={'leaf_modules': list(_leaf_modules), 'autowrap_functions': list(_autowrap_functions)})
      return fx_model
  
  
  EXCLUDE_FX_FILTERS = []
  # not enough memory to run fx on more models than other tests
  if 'GITHUB_ACTIONS' in os.environ:
      EXCLUDE_FX_FILTERS += [
          'beit_large*',
          'mixer_l*',
          '*nfnet_f2*',
          '*resnext101_32x32d',
          'resnetv2_152x2*',
          'resmlp_big*',
          'resnetrs270',
          'swin_large*',
          'vgg*',
          'vit_large*',
          'vit_base_patch8*',
          'xcit_large*',
          '*evob', '*evos', # remove experimental evonorm models, seem to cause issues with dtype manipulation
      ]
  
  
  @pytest.mark.timeout(120)
  @pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FX_FILTERS))
  @pytest.mark.parametrize('batch_size', [1])
  def test_model_forward_fx(model_name, batch_size):
      """
      Symbolically trace each model and run single forward pass through the resulting GraphModule
      Also check that the output of a forward pass through the GraphModule is the same as that from the original Module
      """
      if not has_fx_feature_extraction:
          pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
  
      model = create_model(model_name, pretrained=False)
      model.eval()
  
      input_size = _get_input_size(model=model, target=TARGET_FWD_FX_SIZE)
      if max(input_size) > MAX_FWD_FX_SIZE:
          pytest.skip("Fixed input size model > limit.")
      with torch.no_grad():
          inputs = torch.randn((batch_size, *input_size))
          outputs = model(inputs)
          if isinstance(outputs, tuple):
              outputs = torch.cat(outputs)
  
          model = _create_fx_model(model)
          fx_outputs = tuple(model(inputs).values())
          if isinstance(fx_outputs, tuple):
              fx_outputs = torch.cat(fx_outputs)
  
      assert torch.all(fx_outputs == outputs)
      assert outputs.shape[0] == batch_size
      assert not torch.isnan(outputs).any(), 'Output included NaNs'
  
  
  if 'GITHUB_ACTIONS' not in os.environ:
      # FIXME this test is causing GitHub actions to run out of RAM and abruptly kill the test process
  
      @pytest.mark.timeout(120)
      @pytest.mark.parametrize('model_name', list_models(
          exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FX_FILTERS, name_matches_cfg=True))
      @pytest.mark.parametrize('batch_size', [2])
      def test_model_backward_fx(model_name, batch_size):
          """Symbolically trace each model and run single backward pass through the resulting GraphModule"""
          if not has_fx_feature_extraction:
              pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
  
          input_size = _get_input_size(model_name=model_name, target=TARGET_BWD_FX_SIZE)
          if max(input_size) > MAX_BWD_FX_SIZE:
              pytest.skip("Fixed input size model > limit.")
  
          model = create_model(model_name, pretrained=False, num_classes=42)
          model.train()
          num_params = sum([x.numel() for x in model.parameters()])
          if 'GITHUB_ACTIONS' in os.environ and num_params > 100e6:
              pytest.skip("Skipping FX backward test on model with more than 100M params.")
  
          model = _create_fx_model(model, train=True)
          outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
          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'
  
  
      # reason: model is scripted after fx tracing, but beit has torch.jit.is_scripting() control flow
      EXCLUDE_FX_JIT_FILTERS = [
          'deit_*_distilled_patch16_224',
          'levit*',
          'pit_*_distilled_224',
      ] + EXCLUDE_FX_FILTERS
  
  
      @pytest.mark.timeout(120)
      @pytest.mark.parametrize(
          'model_name', list_models(
              exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS + EXCLUDE_FX_JIT_FILTERS, name_matches_cfg=True))
      @pytest.mark.parametrize('batch_size', [1])
      def test_model_forward_fx_torchscript(model_name, batch_size):
          """Symbolically trace each model, script it, and run single forward pass"""
          if not has_fx_feature_extraction:
              pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.")
  
          input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
          if max(input_size) > MAX_JIT_SIZE:
              pytest.skip("Fixed input size model > limit.")
  
          with set_scriptable(True):
              model = create_model(model_name, pretrained=False)
          model.eval()
  
          model = torch.jit.script(_create_fx_model(model))
          with torch.no_grad():
              outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
              if isinstance(outputs, tuple):
                  outputs = torch.cat(outputs)
  
          assert outputs.shape[0] == batch_size
          assert not torch.isnan(outputs).any(), 'Output included NaNs'
  