mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 05:10:06 +06:00
487dab1b2b
* single commit * correct config * fixup * dummy pt * Use ShieldGemma2Config in conversion script * Update src/transformers/models/shieldgemma2/configuration_shieldgemma2.py * Adding shieldgemma2 to models.__init__.py * Adding ShieldGemma2 to main __init__.py * Update shieldgemma2.md * Update shieldgemma2.md * Adding tests. Addressing review feedback. * Minor docs update * Fixing code quality feedback from CI * Fixing empty messages bug reported by ghunkins --------- Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com> Co-authored-by: Ren Pang <ain-soph@live.com>
62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
# coding=utf-8
|
|
# Copyright 2025 The HuggingFace Inc. 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.
|
|
"""Testing suite for the PyTorch Gemma3 model."""
|
|
|
|
import unittest
|
|
from io import BytesIO
|
|
|
|
import requests
|
|
from PIL import Image
|
|
|
|
from transformers import is_torch_available
|
|
from transformers.testing_utils import (
|
|
cleanup,
|
|
require_torch_gpu,
|
|
slow,
|
|
torch_device,
|
|
)
|
|
|
|
|
|
if is_torch_available():
|
|
import torch
|
|
|
|
from transformers import ShieldGemma2ForImageClassification, ShieldGemma2Processor
|
|
|
|
|
|
@slow
|
|
@require_torch_gpu
|
|
# @require_read_token
|
|
class ShieldGemma2IntegrationTest(unittest.TestCase):
|
|
def tearDown(self):
|
|
cleanup(torch_device, gc_collect=True)
|
|
|
|
def test_model(self):
|
|
model_id = "google/shieldgemma-2-4b-it"
|
|
|
|
processor = ShieldGemma2Processor.from_pretrained(model_id, padding_side="left")
|
|
url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png"
|
|
response = requests.get(url)
|
|
image = Image.open(BytesIO(response.content))
|
|
|
|
model = ShieldGemma2ForImageClassification.from_pretrained(
|
|
model_id, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16
|
|
).to(torch_device)
|
|
|
|
inputs = processor(images=[image]).to(torch_device)
|
|
output = model(**inputs)
|
|
self.assertEqual(len(output.probabilities), 3)
|
|
for element in output.probabilities:
|
|
self.assertEqual(len(element), 2)
|