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Update bros checkpoint (#26277)

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* fix bros integration test

* update bros checkpoint
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Jinho Park 2023-09-20 17:22:07 +09:00 committed by GitHub
parent 86ffd5ffa2
commit 37c205eb5d
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4 changed files with 22 additions and 25 deletions
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10
src/transformers/models/bros/configuration_bros.py
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@ -21,8 +21,8 @@ from ...utils import logging
logger = logging.get_logger(__name__)
BROS_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"naver-clova-ocr/bros-base-uncased": "https://huggingface.co/naver-clova-ocr/bros-base-uncased/resolve/main/config.json",
"naver-clova-ocr/bros-large-uncased": "https://huggingface.co/naver-clova-ocr/bros-large-uncased/resolve/main/config.json",
"jinho8345/bros-base-uncased": "https://huggingface.co/jinho8345/bros-base-uncased/blob/main/config.json",
"jinho8345/bros-large-uncased": "https://huggingface.co/jinho8345/bros-large-uncased/blob/main/config.json",
}
@ -31,7 +31,7 @@ class BrosConfig(PretrainedConfig):
This is the configuration class to store the configuration of a [`BrosModel`] or a [`TFBrosModel`]. It is used to
instantiate a Bros model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the Bros
[naver-clova-ocr/bros-base-uncased](https://huggingface.co/naver-clova-ocr/bros-base-uncased) architecture.
[jinho8345/bros-base-uncased](https://huggingface.co/jinho8345/bros-base-uncased) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
@ -81,10 +81,10 @@ class BrosConfig(PretrainedConfig):
```python
>>> from transformers import BrosConfig, BrosModel
>>> # Initializing a BROS naver-clova-ocr/bros-base-uncased style configuration
>>> # Initializing a BROS jinho8345/bros-base-uncased style configuration
>>> configuration = BrosConfig()
>>> # Initializing a model from the naver-clova-ocr/bros-base-uncased style configuration
>>> # Initializing a model from the jinho8345/bros-base-uncased style configuration
>>> model = BrosModel(configuration)
>>> # Accessing the model configuration

2
src/transformers/models/bros/convert_bros_to_pytorch.py
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@ -123,7 +123,7 @@ if __name__ == "__main__":
# Required parameters
parser.add_argument(
"--model_name",
default="naver-clova-ocr/bros-base-uncased",
default="jinho8345/bros-base-uncased",
required=False,
type=str,
help="Name of the original model you'd like to convert.",

22
src/transformers/models/bros/modeling_bros.py
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@ -44,12 +44,12 @@ from .configuration_bros import BrosConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "naver-clova-ocr/bros-base-uncased"
_CHECKPOINT_FOR_DOC = "jinho8345/bros-base-uncased"
_CONFIG_FOR_DOC = "BrosConfig"
BROS_PRETRAINED_MODEL_ARCHIVE_LIST = [
"naver-clova-ocr/bros-base-uncased",
"naver-clova-ocr/bros-large-uncased",
"jinho8345/bros-base-uncased",
"jinho8345/bros-large-uncased",
# See all Bros models at https://huggingface.co/models?filter=bros
]
@ -846,9 +846,9 @@ class BrosModel(BrosPreTrainedModel):
>>> import torch
>>> from transformers import BrosProcessor, BrosModel
>>> processor = BrosProcessor.from_pretrained("naver-clova-ocr/bros-base-uncased")
>>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")
>>> model = BrosModel.from_pretrained("naver-clova-ocr/bros-base-uncased")
>>> model = BrosModel.from_pretrained("jinho8345/bros-base-uncased")
>>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
>>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)
@ -1011,9 +1011,9 @@ class BrosForTokenClassification(BrosPreTrainedModel):
>>> import torch
>>> from transformers import BrosProcessor, BrosForTokenClassification
>>> processor = BrosProcessor.from_pretrained("naver-clova-ocr/bros-base-uncased")
>>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")
>>> model = BrosForTokenClassification.from_pretrained("naver-clova-ocr/bros-base-uncased")
>>> model = BrosForTokenClassification.from_pretrained("jinho8345/bros-base-uncased")
>>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
>>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)
@ -1130,9 +1130,9 @@ class BrosSpadeEEForTokenClassification(BrosPreTrainedModel):
>>> import torch
>>> from transformers import BrosProcessor, BrosSpadeEEForTokenClassification
>>> processor = BrosProcessor.from_pretrained("naver-clova-ocr/bros-base-uncased")
>>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")
>>> model = BrosSpadeEEForTokenClassification.from_pretrained("naver-clova-ocr/bros-base-uncased")
>>> model = BrosSpadeEEForTokenClassification.from_pretrained("jinho8345/bros-base-uncased")
>>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
>>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)
@ -1261,9 +1261,9 @@ class BrosSpadeELForTokenClassification(BrosPreTrainedModel):
>>> import torch
>>> from transformers import BrosProcessor, BrosSpadeELForTokenClassification
>>> processor = BrosProcessor.from_pretrained("naver-clova-ocr/bros-base-uncased")
>>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")
>>> model = BrosSpadeELForTokenClassification.from_pretrained("naver-clova-ocr/bros-base-uncased")
>>> model = BrosSpadeELForTokenClassification.from_pretrained("jinho8345/bros-base-uncased")
>>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
>>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)

13
tests/models/bros/test_modeling_bros.py
View File

@ -17,9 +17,8 @@
import copy
import unittest
from transformers import BrosProcessor
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from transformers.utils import is_torch_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
@ -412,13 +411,10 @@ def prepare_bros_batch_inputs():
@require_torch
class BrosModelIntegrationTest(unittest.TestCase):
@cached_property
def default_processor(self):
return BrosProcessor.from_pretrained("naver-clova-ocr/bros-base-uncased") if is_vision_available() else None
@slow
def test_inference_no_head(self):
model = BrosModel.from_pretrained("naver-clova-ocr/bros-base-uncased").to(torch_device)
model = BrosModel.from_pretrained("jinho8345/bros-base-uncased").to(torch_device)
input_ids, bbox, attention_mask = prepare_bros_batch_inputs()
with torch.no_grad():
@ -434,7 +430,8 @@ class BrosModelIntegrationTest(unittest.TestCase):
self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
expected_slice = torch.tensor(
[[-0.4027, 0.0756, -0.0647], [-0.0192, -0.0065, 0.1042], [-0.0671, 0.0214, 0.0960]]
[[-0.3074, 0.1363, 0.3143], [0.0925, -0.1155, 0.1050], [0.0221, 0.0003, 0.1285]]
).to(torch_device)
torch.set_printoptions(sci_mode=False)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))