mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-05 22:00:09 +06:00
106 lines
4.6 KiB
Python
106 lines
4.6 KiB
Python
# Copyright 2021 The HuggingFace Team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import json
|
|
import shutil
|
|
import tempfile
|
|
import unittest
|
|
|
|
from transformers import AutoProcessor, AutoTokenizer, LlamaTokenizerFast, LlavaProcessor
|
|
from transformers.testing_utils import require_vision
|
|
from transformers.utils import is_vision_available
|
|
|
|
from ...test_processing_common import ProcessorTesterMixin
|
|
|
|
|
|
if is_vision_available():
|
|
from transformers import CLIPImageProcessor
|
|
|
|
|
|
@require_vision
|
|
class LlavaProcessorTest(ProcessorTesterMixin, unittest.TestCase):
|
|
processor_class = LlavaProcessor
|
|
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
cls.tmpdirname = tempfile.mkdtemp()
|
|
|
|
image_processor = CLIPImageProcessor(do_center_crop=False)
|
|
tokenizer = LlamaTokenizerFast.from_pretrained("huggyllama/llama-7b")
|
|
tokenizer.add_special_tokens({"additional_special_tokens": ["<image>"]})
|
|
processor_kwargs = cls.prepare_processor_dict()
|
|
processor = LlavaProcessor(image_processor, tokenizer, **processor_kwargs)
|
|
processor.save_pretrained(cls.tmpdirname)
|
|
cls.image_token = processor.image_token
|
|
|
|
def get_tokenizer(self, **kwargs):
|
|
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
|
|
|
|
def get_image_processor(self, **kwargs):
|
|
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
|
|
|
|
@classmethod
|
|
def tearDownClass(cls):
|
|
shutil.rmtree(cls.tmpdirname, ignore_errors=True)
|
|
|
|
@staticmethod
|
|
def prepare_processor_dict():
|
|
return {
|
|
"chat_template": "{% for message in messages %}{% if message['role'] != 'system' %}{{ message['role'].upper() + ': '}}{% endif %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>\n' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] + ' '}}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] + ' '}}{% endgeneration %}{% endfor %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}",
|
|
"patch_size": 128,
|
|
"vision_feature_select_strategy": "default"
|
|
} # fmt: skip
|
|
|
|
def test_chat_template_is_saved(self):
|
|
processor_loaded = self.processor_class.from_pretrained(self.tmpdirname)
|
|
processor_dict_loaded = json.loads(processor_loaded.to_json_string())
|
|
# chat templates aren't serialized to json in processors
|
|
self.assertFalse("chat_template" in processor_dict_loaded.keys())
|
|
|
|
# they have to be saved as separate file and loaded back from that file
|
|
# so we check if the same template is loaded
|
|
processor_dict = self.prepare_processor_dict()
|
|
self.assertTrue(processor_loaded.chat_template == processor_dict.get("chat_template", None))
|
|
|
|
def test_can_load_various_tokenizers(self):
|
|
for checkpoint in ["Intel/llava-gemma-2b", "llava-hf/llava-1.5-7b-hf"]:
|
|
processor = LlavaProcessor.from_pretrained(checkpoint)
|
|
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
|
|
self.assertEqual(processor.tokenizer.__class__, tokenizer.__class__)
|
|
|
|
def test_special_mm_token_truncation(self):
|
|
"""Tests that special vision tokens do not get truncated when `truncation=True` is set."""
|
|
|
|
processor = LlavaProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
|
|
|
|
input_str = self.prepare_text_inputs(batch_size=2, modality="image")
|
|
image_input = self.prepare_image_inputs(batch_size=2)
|
|
|
|
_ = processor(
|
|
text=input_str,
|
|
images=image_input,
|
|
return_tensors="pt",
|
|
truncation=None,
|
|
padding=True,
|
|
)
|
|
|
|
with self.assertRaises(ValueError):
|
|
_ = processor(
|
|
text=input_str,
|
|
images=image_input,
|
|
return_tensors="pt",
|
|
truncation=True,
|
|
padding=True,
|
|
max_length=5,
|
|
)
|