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Fix initialization of OneFormer (#38901)

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* fix initialization of OneFormer

* remove redundant initializations

* remove redundant initializations

* remove redundant initializations

* keep BC
This commit is contained in:
BUI Van Tuan
2025-06-27 12:39:37 +02:00
committed by GitHub
parent 540a10848c
commit 371c471113
2 changed files with 57 additions and 52 deletions
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58
tests/models/oneformer/test_modeling_oneformer.py
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@@ -13,14 +13,13 @@
# limitations under the License.
"""Testing suite for the PyTorch OneFormer model."""
import copy
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import OneFormerConfig, is_torch_available, is_vision_available
from transformers import AutoModelForImageClassification, OneFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import (
is_flaky,
require_timm,
@@ -35,7 +34,7 @@ from transformers.testing_utils import (
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_modeling_common import ModelTesterMixin, _config_zero_init
from ...test_pipeline_mixin import PipelineTesterMixin
@@ -51,14 +50,6 @@ if is_vision_available():
from PIL import Image
def _config_zero_init(config):
configs_no_init = copy.deepcopy(config)
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(configs_no_init, key, 1e-10)
return configs_no_init
class OneFormerModelTester:
def __init__(
self,
@@ -375,6 +366,7 @@ class OneFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCas
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.is_training = True
config.contrastive_temperature = 1
configs_no_init = _config_zero_init(config)
@@ -382,12 +374,56 @@ class OneFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCas
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
if (
"self_attn.sampling_offsets.bias" in name
or "self_attn.value_proj.weight" in name
or "self_attn.output_proj.weight" in name
or "self_attn.in_proj_weight" in name
or "self_attn.out_proj.weight" in name
or "mlp.fc1.weight" in name
or "mlp.fc2.weight" in name
or "text_mapper.text_encoder.positional_embedding" in name
or "text_mapper.text_encoder.token_embedding.weight" in name
):
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item(),
[0.0, 1.0],
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
)
def test_initialization_pretrained_backbone(self):
backbone_name = "microsoft/resnet-18"
# load OneFormerConfig config with a pretrained backbone
config = OneFormerConfig(
backbone=backbone_name,
use_pretrained_backbone=True,
)
# load pretrained backbone
backbone_model = AutoModelForImageClassification.from_pretrained(backbone_name, device_map=torch_device)
def params_match(params1, params2):
return all((p1 == p2).all() for p1, p2 in zip(params1, params2))
for model_class in self.all_model_classes:
model = model_class(config).to(torch_device).eval()
if model.__class__.__name__ == "OneFormerModel":
self.assertTrue(
params_match(
backbone_model.base_model.encoder.parameters(),
model.pixel_level_module.encoder.encoder.parameters(),
)
)
elif model.__class__.__name__ == "OneFormerForUniversalSegmentation":
self.assertTrue(
params_match(
backbone_model.base_model.encoder.parameters(),
model.model.pixel_level_module.encoder.encoder.parameters(),
)
)
def test_training(self):
if not self.model_tester.is_training:
self.skipTest(reason="model_tester.is_training is set to False")