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Enable instantiating model with pretrained backbone weights (#28214)

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* Enable instantiating model with pretrained backbone weights

* Update tests so backbone checkpoint isn't passed in

* Remove doc updates until changes made in modeling code

* Clarify pretrained import

* Update configs - docs and validation check

* Update src/transformers/utils/backbone_utils.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Clarify exception message

* Update config init in tests

* Add test for when use_timm_backbone=True

* Small test updates

---------

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
This commit is contained in:
amyeroberts
2024-01-23 11:01:50 +00:00
committed by GitHub
parent 008a6a2208
commit 27c79a0fb4
31 changed files with 362 additions and 37 deletions
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4
tests/models/detr/test_modeling_detr.py
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@@ -130,6 +130,8 @@ class DetrModelTester:
num_labels=self.num_labels,
use_timm_backbone=False,
backbone_config=resnet_config,
backbone=None,
use_pretrained_backbone=False,
)
def prepare_config_and_inputs_for_common(self):
@@ -622,7 +624,7 @@ class DetrModelIntegrationTestsTimmBackbone(unittest.TestCase):
torch_device
)
expected_number_of_segments = 5
expected_first_segment = {"id": 1, "label_id": 17, "was_fused": False, "score": 0.994096}
expected_first_segment = {"id": 1, "label_id": 17, "was_fused": False, "score": 0.994097}
number_of_unique_segments = len(torch.unique(results["segmentation"]))
self.assertTrue(