mirror of
https://github.com/huggingface/transformers.git
synced 2025-07-04 13:20:12 +06:00
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.
|
|
#
|
|
# 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))
|