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Add TimmBackbone model (#22619)

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* Add test_backbone for convnext

* Add TimmBackbone model

* Add check for backbone type

* Tidying up - config checks

* Update convnextv2

* Tidy up

* Fix indices & clearer comment

* Exceptions for config checks

* Correclty update config for tests

* Safer imports

* Safer safer imports

* Fix where decorators go

* Update import logic and backbone tests

* More import fixes

* Fixup

* Only import all_models if torch available

* Fix kwarg updates in from_pretrained & main rebase

* Tidy up

* Add tests for AutoBackbone

* Tidy up

* Fix import error

* Fix up

* Install nattan in doc_test_job

* Revert back to setting self._out_xxx directly

* Bug fix - out_indices mapping from out_features

* Fix tests

* Dont accept output_loading_info for Timm models

* Set out_xxx and don't remap

* Use smaller checkpoint for test

* Don't remap timm indices - check out_indices based on stage names

* Skip test as it's n/a

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Cleaner imports / spelling is hard

---------

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
amyeroberts
2023-06-06 17:11:30 +01:00
committed by GitHub
parent b8935980a2
commit a717e0318c
29 changed files with 753 additions and 88 deletions
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39
tests/models/auto/test_modeling_auto.py
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@@ -45,6 +45,7 @@ if is_torch_available():
from test_module.custom_modeling import CustomModel
from transformers import (
AutoBackbone,
AutoConfig,
AutoModel,
AutoModelForCausalLM,
@@ -66,11 +67,13 @@ if is_torch_available():
FunnelModel,
GPT2Config,
GPT2LMHeadModel,
ResNetBackbone,
RobertaForMaskedLM,
T5Config,
T5ForConditionalGeneration,
TapasConfig,
TapasForQuestionAnswering,
TimmBackbone,
)
from transformers.models.auto.modeling_auto import (
MODEL_FOR_CAUSAL_LM_MAPPING,
@@ -224,6 +227,42 @@ class AutoModelTest(unittest.TestCase):
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForTokenClassification)
@slow
def test_auto_backbone_timm_model_from_pretrained(self):
# Configs can't be loaded for timm models
model = AutoBackbone.from_pretrained("resnet18", use_timm_backbone=True)
with pytest.raises(ValueError):
# We can't pass output_loading_info=True as we're loading from timm
AutoBackbone.from_pretrained("resnet18", use_timm_backbone=True, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TimmBackbone)
# Check kwargs are correctly passed to the backbone
model = AutoBackbone.from_pretrained("resnet18", use_timm_backbone=True, out_indices=(-1, -2))
self.assertEqual(model.out_indices, (-1, -2))
# Check out_features cannot be passed to Timm backbones
with self.assertRaises(ValueError):
_ = AutoBackbone.from_pretrained("resnet18", use_timm_backbone=True, out_features=["stage1"])
@slow
def test_auto_backbone_from_pretrained(self):
model = AutoBackbone.from_pretrained("microsoft/resnet-18")
model, loading_info = AutoBackbone.from_pretrained("microsoft/resnet-18", output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, ResNetBackbone)
# Check kwargs are correctly passed to the backbone
model = AutoBackbone.from_pretrained("microsoft/resnet-18", out_indices=[-1, -2])
self.assertEqual(model.out_indices, [-1, -2])
self.assertEqual(model.out_features, ["stage4", "stage3"])
model = AutoBackbone.from_pretrained("microsoft/resnet-18", out_features=["stage2", "stage4"])
self.assertEqual(model.out_indices, [2, 4])
self.assertEqual(model.out_features, ["stage2", "stage4"])
def test_from_pretrained_identifier(self):
model = AutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER)
self.assertIsInstance(model, BertForMaskedLM)