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transformers/tests/pipelines/test_pipelines_image_to_image.py

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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, dtype="float32"):
+        model_id = "caidas/swin2SR-classical-sr-x2-64"
+        upscaler = pipeline("image-to-image", model=model_id, dtype=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(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))