init
This commit is contained in:
@@ -0,0 +1,57 @@
|
||||
# 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_read_token,
|
||||
require_torch_accelerator,
|
||||
slow,
|
||||
torch_device,
|
||||
)
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
from transformers import ShieldGemma2ForImageClassification, ShieldGemma2Processor
|
||||
|
||||
|
||||
@slow
|
||||
@require_torch_accelerator
|
||||
@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, load_in_4bit=True)
|
||||
|
||||
inputs = processor(images=[image], return_tensors="pt").to(torch_device)
|
||||
output = model(**inputs)
|
||||
self.assertEqual(len(output.probabilities), 3)
|
||||
for element in output.probabilities:
|
||||
self.assertEqual(len(element), 2)
|
||||
Reference in New Issue
Block a user