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Would like to pass GitHub tests again disabling both FX feature extract backward and torchscript tests

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Ross Wightman 2021-11-23 22:24:58 -08:00
parent a22b85c1b9
commit f83b0b01e3
1 changed files with 28 additions and 28 deletions
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tests/test_models.py
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@ -422,37 +422,37 @@ if 'GITHUB_ACTIONS' not in os.environ:
assert not torch.isnan(outputs).any(), 'Output included NaNs' 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 # reason: model is scripted after fx tracing, but beit has torch.jit.is_scripting() control flow
EXCLUDE_FX_JIT_FILTERS = [ EXCLUDE_FX_JIT_FILTERS = [
'deit_*_distilled_patch16_224', 'deit_*_distilled_patch16_224',
'levit*', 'levit*',
'pit_*_distilled_224', 'pit_*_distilled_224',
] + EXCLUDE_FX_FILTERS ] + EXCLUDE_FX_FILTERS
@pytest.mark.timeout(120) @pytest.mark.timeout(120)
@pytest.mark.parametrize( @pytest.mark.parametrize(
'model_name', list_models( 'model_name', list_models(
exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS + EXCLUDE_FX_JIT_FILTERS, name_matches_cfg=True)) exclude_filters=EXCLUDE_FILTERS + EXCLUDE_JIT_FILTERS + EXCLUDE_FX_JIT_FILTERS, name_matches_cfg=True))
@pytest.mark.parametrize('batch_size', [1]) @pytest.mark.parametrize('batch_size', [1])
def test_model_forward_fx_torchscript(model_name, batch_size): def test_model_forward_fx_torchscript(model_name, batch_size):
"""Symbolically trace each model, script it, and run single forward pass""" """Symbolically trace each model, script it, and run single forward pass"""
if not has_fx_feature_extraction: if not has_fx_feature_extraction:
pytest.skip("Can't test FX. Torch >= 1.10 and Torchvision >= 0.11 are required.") 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) input_size = _get_input_size(model_name=model_name, target=TARGET_JIT_SIZE)
if max(input_size) > MAX_JIT_SIZE: if max(input_size) > MAX_JIT_SIZE:
pytest.skip("Fixed input size model > limit.") pytest.skip("Fixed input size model > limit.")
with set_scriptable(True): with set_scriptable(True):
model = create_model(model_name, pretrained=False) model = create_model(model_name, pretrained=False)
model.eval() model.eval()
model = torch.jit.script(_create_fx_model(model)) model = torch.jit.script(_create_fx_model(model))
with torch.no_grad(): with torch.no_grad():
outputs = tuple(model(torch.randn((batch_size, *input_size))).values()) outputs = tuple(model(torch.randn((batch_size, *input_size))).values())
if isinstance(outputs, tuple): if isinstance(outputs, tuple):
outputs = torch.cat(outputs) outputs = torch.cat(outputs)
assert outputs.shape[0] == batch_size assert outputs.shape[0] == batch_size
assert not torch.isnan(outputs).any(), 'Output included NaNs' assert not torch.isnan(outputs).any(), 'Output included NaNs'