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# Copyright 2023 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 unittest
from transformers import (
MODEL_FOR_IMAGE_TO_IMAGE_MAPPING,
AutoImageProcessor,
AutoModelForImageToImage,
ImageToImagePipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class Image:
@staticmethod
def open(*args, **kwargs):
pass
@is_pipeline_test
@require_torch
@require_vision
class ImageToImagePipelineTests(unittest.TestCase):
model_mapping = MODEL_FOR_IMAGE_TO_IMAGE_MAPPING
examples = [
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
"http://images.cocodataset.org/val2017/000000039769.jpg",
]
@require_torch
@require_vision
@slow
def test_pipeline(self, torch_dtype="float32"):
model_id = "caidas/swin2SR-classical-sr-x2-64"
upscaler = pipeline("image-to-image", model=model_id, torch_dtype=torch_dtype)
upscaled_list = upscaler(self.examples)
self.assertEqual(len(upscaled_list), len(self.examples))
for output in upscaled_list:
self.assertIsInstance(output, Image.Image)
self.assertEqual(upscaled_list[0].size, (1296, 976))
self.assertEqual(upscaled_list[1].size, (1296, 976))
@require_torch
@require_vision
@slow
def test_pipeline_fp16(self):
self.test_pipeline(torch_dtype="float16")
@require_torch
@require_vision
@slow
def test_pipeline_model_processor(self):
model_id = "caidas/swin2SR-classical-sr-x2-64"
model = AutoModelForImageToImage.from_pretrained(model_id)
image_processor = AutoImageProcessor.from_pretrained(model_id)
upscaler = ImageToImagePipeline(model=model, image_processor=image_processor)
upscaled_list = upscaler(self.examples)
self.assertEqual(len(upscaled_list), len(self.examples))
for output in upscaled_list:
self.assertIsInstance(output, Image.Image)
self.assertEqual(upscaled_list[0].size, (1296, 976))
self.assertEqual(upscaled_list[1].size, (1296, 976))
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