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Remove static pretrained maps from the library's internals (#29112)

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* [test_all] Remove static pretrained maps from the library's internals

* Deprecate archive maps instead of removing them

* Revert init changes

* [test_all] Deprecate instead of removing

* [test_all] PVT v2 support

* [test_all] Tests should all pass

* [test_all] Style

* Address review comments

* Update src/transformers/models/deprecated/_archive_maps.py

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

* Update src/transformers/models/deprecated/_archive_maps.py

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

* [test_all] trigger tests

* [test_all] LLAVA

* [test_all] Bad rebase

---------

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>
This commit is contained in:
Lysandre Debut
2024-03-25 10:33:38 +01:00
committed by GitHub
parent 76a33a1092
commit 39114c0383
842 changed files with 4608 additions and 8613 deletions
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15
tests/models/resnet/test_modeling_resnet.py
View File

@@ -32,7 +32,6 @@ if is_torch_available():
from torch import nn
from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
@@ -273,9 +272,9 @@ class ResNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
@slow
def test_model_from_pretrained(self):
for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = ResNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)
model_name = "microsoft/resnet-50"
model = ResNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
@@ -289,15 +288,11 @@ def prepare_img():
class ResNetModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return (
AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
if is_vision_available()
else None
)
return AutoImageProcessor.from_pretrained("microsoft/resnet-50") if is_vision_available() else None
@slow
def test_inference_image_classification_head(self):
model = ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(torch_device)
model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()

15
tests/models/resnet/test_modeling_tf_resnet.py
View File

@@ -35,7 +35,6 @@ if is_tf_available():
import tensorflow as tf
from transformers import TFResNetForImageClassification, TFResNetModel
from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
@@ -214,9 +213,9 @@ class TFResNetModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCa
@slow
def test_model_from_pretrained(self):
for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = TFResNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)
model_name = "microsoft/resnet-50"
model = TFResNetModel.from_pretrained(model_name)
self.assertIsNotNone(model)
# We will verify our results on an image of cute cats
@@ -230,15 +229,11 @@ def prepare_img():
class TFResNetModelIntegrationTest(unittest.TestCase):
@cached_property
def default_image_processor(self):
return (
AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
if is_vision_available()
else None
)
return AutoImageProcessor.from_pretrained("microsoft/resnet-50") if is_vision_available() else None
@slow
def test_inference_image_classification_head(self):
model = TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
model = TFResNetForImageClassification.from_pretrained("microsoft/resnet-50")
image_processor = self.default_image_processor
image = prepare_img()