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ac26260436
* cast image features to model.dtype where needed to support FP16 or other precision in pipelines * Update src/transformers/pipelines/image_feature_extraction.py Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * Use .to instead * Add FP16 pipeline support for zeroshot audio classification * Remove unused torch imports * Add docs on FP16 pipeline * Remove unused import * Add FP16 tests to pipeline mixin * Add fp16 placeholder for mask_generation pipeline test * Add FP16 tests for all pipelines * Fix formatting * Remove torch_dtype arg from is_pipeline_test_to_skip* * Fix format * trigger ci --------- Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
92 lines
2.7 KiB
Python
92 lines
2.7 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from transformers import (
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MODEL_FOR_IMAGE_TO_IMAGE_MAPPING,
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AutoImageProcessor,
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AutoModelForImageToImage,
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ImageToImagePipeline,
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is_vision_available,
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pipeline,
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)
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from transformers.testing_utils import (
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is_pipeline_test,
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require_torch,
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require_vision,
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slow,
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)
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if is_vision_available():
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from PIL import Image
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else:
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class Image:
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@staticmethod
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def open(*args, **kwargs):
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pass
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@is_pipeline_test
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@require_torch
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@require_vision
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class ImageToImagePipelineTests(unittest.TestCase):
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model_mapping = MODEL_FOR_IMAGE_TO_IMAGE_MAPPING
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examples = [
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Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"),
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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]
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@require_torch
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@require_vision
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@slow
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def test_pipeline(self, torch_dtype="float32"):
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model_id = "caidas/swin2SR-classical-sr-x2-64"
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upscaler = pipeline("image-to-image", model=model_id, torch_dtype=torch_dtype)
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upscaled_list = upscaler(self.examples)
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self.assertEqual(len(upscaled_list), len(self.examples))
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for output in upscaled_list:
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self.assertIsInstance(output, Image.Image)
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self.assertEqual(upscaled_list[0].size, (1296, 976))
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self.assertEqual(upscaled_list[1].size, (1296, 976))
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@require_torch
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@require_vision
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@slow
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def test_pipeline_fp16(self):
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self.test_pipeline(torch_dtype="float16")
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@require_torch
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@require_vision
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@slow
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def test_pipeline_model_processor(self):
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model_id = "caidas/swin2SR-classical-sr-x2-64"
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model = AutoModelForImageToImage.from_pretrained(model_id)
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image_processor = AutoImageProcessor.from_pretrained(model_id)
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upscaler = ImageToImagePipeline(model=model, image_processor=image_processor)
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upscaled_list = upscaler(self.examples)
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self.assertEqual(len(upscaled_list), len(self.examples))
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for output in upscaled_list:
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self.assertIsInstance(output, Image.Image)
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self.assertEqual(upscaled_list[0].size, (1296, 976))
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self.assertEqual(upscaled_list[1].size, (1296, 976))
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