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Avoid using uncessary get_values(MODEL_MAPPING) (#29362)

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* more fixes

* more fixes

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2024-02-29 17:19:17 +08:00
committed by GitHub
parent b647acdb53
commit 44fe1a1cc4
14 changed files with 94 additions and 85 deletions
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8
tests/models/dpt/test_modeling_dpt.py
View File

@@ -19,7 +19,6 @@ import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
@@ -31,7 +30,8 @@ if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers import DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
@@ -214,7 +214,7 @@ class DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
if model_class in get_values(MODEL_MAPPING):
if model_class.__name__ in MODEL_MAPPING_NAMES.values():
continue
model = model_class(config)
@@ -233,7 +233,7 @@ class DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
config.use_cache = False
config.return_dict = True
if model_class in get_values(MODEL_MAPPING) or not model_class.supports_gradient_checkpointing:
if model_class.__name__ in MODEL_MAPPING_NAMES.values() or not model_class.supports_gradient_checkpointing:
continue
model = model_class(config)
model.to(torch_device)

8
tests/models/dpt/test_modeling_dpt_auto_backbone.py
View File

@@ -19,7 +19,6 @@ import unittest
from transformers import Dinov2Config, DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
@@ -30,7 +29,8 @@ from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MODEL_MAPPING, DPTForDepthEstimation
from transformers import DPTForDepthEstimation
from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
@@ -166,7 +166,7 @@ class DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
if model_class in get_values(MODEL_MAPPING):
if model_class.__name__ in MODEL_MAPPING_NAMES.values():
continue
model = model_class(config)
@@ -185,7 +185,7 @@ class DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
config.use_cache = False
config.return_dict = True
if model_class in get_values(MODEL_MAPPING) or not model_class.supports_gradient_checkpointing:
if model_class.__name__ in MODEL_MAPPING_NAMES.values() or not model_class.supports_gradient_checkpointing:
continue
model = model_class(config)
model.to(torch_device)

8
tests/models/dpt/test_modeling_dpt_hybrid.py
View File

@@ -19,7 +19,6 @@ import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
@@ -31,7 +30,8 @@ if is_torch_available():
import torch
from torch import nn
from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers import DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
@@ -229,7 +229,7 @@ class DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
if model_class in get_values(MODEL_MAPPING):
if model_class.__name__ in MODEL_MAPPING_NAMES.values():
continue
model = model_class(config)
@@ -248,7 +248,7 @@ class DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
config.use_cache = False
config.return_dict = True
if model_class in get_values(MODEL_MAPPING) or not model_class.supports_gradient_checkpointing:
if model_class.__name__ in MODEL_MAPPING_NAMES.values() or not model_class.supports_gradient_checkpointing:
continue
model = model_class(config)
model.to(torch_device)