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https://github.com/huggingface/transformers.git
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344 lines
14 KiB
Python
344 lines
14 KiB
Python
# Copyright 2021 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 hashlib
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import unittest
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import datasets
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from datasets import load_dataset
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from transformers import (
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MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
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MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
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AutoFeatureExtractor,
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AutoModelForImageSegmentation,
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DetrForSegmentation,
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ImageSegmentationPipeline,
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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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nested_simplify,
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require_tf,
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require_timm,
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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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from .test_pipelines_common import ANY, PipelineTestCaseMeta
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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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def hashimage(image: Image) -> str:
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m = hashlib.md5(image.tobytes())
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return m.hexdigest()
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@require_vision
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@require_timm
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@require_torch
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@is_pipeline_test
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class ImageSegmentationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
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model_mapping = {
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k: v
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for k, v in (
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list(MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()) if MODEL_FOR_IMAGE_SEGMENTATION_MAPPING else []
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)
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+ (MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items() if MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING else [])
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}
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def get_test_pipeline(self, model, tokenizer, feature_extractor):
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image_segmenter = ImageSegmentationPipeline(model=model, feature_extractor=feature_extractor)
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return image_segmenter, [
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"./tests/fixtures/tests_samples/COCO/000000039769.png",
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"./tests/fixtures/tests_samples/COCO/000000039769.png",
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]
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def run_pipeline_test(self, image_segmenter, examples):
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outputs = image_segmenter("./tests/fixtures/tests_samples/COCO/000000039769.png", threshold=0.0)
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self.assertIsInstance(outputs, list)
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n = len(outputs)
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self.assertGreater(n, 1)
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# XXX: PIL.Image implements __eq__ which bypasses ANY, so we inverse the comparison
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# to make it work
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self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n, outputs)
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dataset = datasets.load_dataset("hf-internal-testing/fixtures_image_utils", "image", split="test")
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# RGBA
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outputs = image_segmenter(dataset[0]["file"])
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m = len(outputs)
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self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * m, outputs)
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# LA
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outputs = image_segmenter(dataset[1]["file"])
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m = len(outputs)
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self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * m, outputs)
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# L
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outputs = image_segmenter(dataset[2]["file"])
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m = len(outputs)
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self.assertEqual([{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * m, outputs)
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if isinstance(image_segmenter.model, DetrForSegmentation):
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# We need to test batch_size with images with the same size.
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# Detr doesn't normalize the size of the images, meaning we can have
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# 800x800 or 800x1200, meaning we cannot batch simply.
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# We simply bail on this
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batch_size = 1
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else:
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batch_size = 2
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# 5 times the same image so the output shape is predictable
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batch = [
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"./tests/fixtures/tests_samples/COCO/000000039769.png",
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"./tests/fixtures/tests_samples/COCO/000000039769.png",
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"./tests/fixtures/tests_samples/COCO/000000039769.png",
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"./tests/fixtures/tests_samples/COCO/000000039769.png",
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"./tests/fixtures/tests_samples/COCO/000000039769.png",
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]
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outputs = image_segmenter(batch, threshold=0.0, batch_size=batch_size)
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self.assertEqual(len(batch), len(outputs))
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self.assertEqual({"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}, outputs[0][0])
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self.assertEqual(len(outputs[0]), n)
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self.assertEqual(
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[
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[{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
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[{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
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[{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
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[{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
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[{"score": ANY(float, type(None)), "label": ANY(str), "mask": ANY(Image.Image)}] * n,
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],
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outputs,
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f"Expected [{n}, {n}, {n}, {n}, {n}], got {[len(item) for item in outputs]}",
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)
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@require_tf
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@unittest.skip("Image segmentation not implemented in TF")
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def test_small_model_tf(self):
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pass
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@require_torch
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def test_small_model_pt(self):
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model_id = "mishig/tiny-detr-mobilenetsv3-panoptic"
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model = AutoModelForImageSegmentation.from_pretrained(model_id)
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feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
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image_segmenter = ImageSegmentationPipeline(model=model, feature_extractor=feature_extractor)
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outputs = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg", threshold=0.0)
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for o in outputs:
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# shortening by hashing
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o["mask"] = hashimage(o["mask"])
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{
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"score": 0.004,
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"label": "LABEL_0",
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"mask": "34eecd16bbfb0f476083ef947d81bf66",
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},
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{
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"score": 0.004,
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"label": "LABEL_0",
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"mask": "34eecd16bbfb0f476083ef947d81bf66",
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},
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],
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)
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outputs = image_segmenter(
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[
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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],
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threshold=0.0,
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)
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for output in outputs:
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for o in output:
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o["mask"] = hashimage(o["mask"])
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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[
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{
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"score": 0.004,
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"label": "LABEL_0",
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"mask": "34eecd16bbfb0f476083ef947d81bf66",
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},
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{
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"score": 0.004,
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"label": "LABEL_0",
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"mask": "34eecd16bbfb0f476083ef947d81bf66",
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},
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],
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[
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{
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"score": 0.004,
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"label": "LABEL_0",
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"mask": "34eecd16bbfb0f476083ef947d81bf66",
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},
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{
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"score": 0.004,
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"label": "LABEL_0",
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"mask": "34eecd16bbfb0f476083ef947d81bf66",
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},
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],
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],
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)
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@require_torch
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def test_small_model_pt_semantic(self):
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model_id = "hf-internal-testing/tiny-random-beit-pipeline"
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image_segmenter = pipeline(model=model_id)
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outputs = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg")
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for o in outputs:
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# shortening by hashing
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o["mask"] = hashimage(o["mask"])
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{
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"score": None,
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"label": "LABEL_0",
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"mask": "6225140faf502d272af076222776d7e4",
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},
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{
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"score": None,
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"label": "LABEL_1",
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"mask": "8297c9f8eb43ddd3f32a6dae21e015a1",
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},
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],
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)
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@require_torch
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@slow
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def test_integration_torch_image_segmentation(self):
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model_id = "facebook/detr-resnet-50-panoptic"
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image_segmenter = pipeline("image-segmentation", model=model_id)
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outputs = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg")
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for o in outputs:
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o["mask"] = hashimage(o["mask"])
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.9094, "label": "blanket", "mask": "36517c16f4356f7af4b298f4eae387f9fe37eaf8"},
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{"score": 0.9941, "label": "cat", "mask": "d63196cbe08c7655c158dbabbc5e6b413cbb3b2d"},
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{"score": 0.9987, "label": "remote", "mask": "4e190e0c3934ad852aaa51aa2c54e314b9a1152e"},
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{"score": 0.9995, "label": "remote", "mask": "39dc07a07238048a06b0c2474de01ba3c09cc44f"},
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{"score": 0.9722, "label": "couch", "mask": "df5815755b6bcf328f6b6811f8794cad26f79b35"},
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{"score": 0.9994, "label": "cat", "mask": "88b37bd2202c750cc9dd191518050a9b0ca5228c"},
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],
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)
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outputs = image_segmenter(
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[
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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],
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threshold=0.0,
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)
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for output in outputs:
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for o in output:
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o["mask"] = hashlib.sha1(o["mask"].encode("UTF-8")).hexdigest()
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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[
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{"score": 0.9094, "label": "blanket", "mask": "36517c16f4356f7af4b298f4eae387f9fe37eaf8"},
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{"score": 0.9941, "label": "cat", "mask": "d63196cbe08c7655c158dbabbc5e6b413cbb3b2d"},
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{"score": 0.9987, "label": "remote", "mask": "4e190e0c3934ad852aaa51aa2c54e314b9a1152e"},
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{"score": 0.9995, "label": "remote", "mask": "39dc07a07238048a06b0c2474de01ba3c09cc44f"},
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{"score": 0.9722, "label": "couch", "mask": "df5815755b6bcf328f6b6811f8794cad26f79b35"},
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{"score": 0.9994, "label": "cat", "mask": "88b37bd2202c750cc9dd191518050a9b0ca5228c"},
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],
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[
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{"score": 0.9094, "label": "blanket", "mask": "36517c16f4356f7af4b298f4eae387f9fe37eaf8"},
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{"score": 0.9941, "label": "cat", "mask": "d63196cbe08c7655c158dbabbc5e6b413cbb3b2d"},
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{"score": 0.9987, "label": "remote", "mask": "4e190e0c3934ad852aaa51aa2c54e314b9a1152e"},
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{"score": 0.9995, "label": "remote", "mask": "39dc07a07238048a06b0c2474de01ba3c09cc44f"},
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{"score": 0.9722, "label": "couch", "mask": "df5815755b6bcf328f6b6811f8794cad26f79b35"},
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{"score": 0.9994, "label": "cat", "mask": "88b37bd2202c750cc9dd191518050a9b0ca5228c"},
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],
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],
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)
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@require_torch
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@slow
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def test_threshold(self):
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threshold = 0.999
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model_id = "facebook/detr-resnet-50-panoptic"
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image_segmenter = pipeline("image-segmentation", model=model_id)
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outputs = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg", threshold=threshold)
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for o in outputs:
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o["mask"] = hashimage(o["mask"])
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.9995, "label": "remote", "mask": "39dc07a07238048a06b0c2474de01ba3c09cc44f"},
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{"score": 0.9994, "label": "cat", "mask": "88b37bd2202c750cc9dd191518050a9b0ca5228c"},
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],
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)
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@require_torch
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@slow
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def test_maskformer(self):
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threshold = 0.999
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model_id = "facebook/maskformer-swin-base-ade"
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from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
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model = MaskFormerForInstanceSegmentation.from_pretrained(model_id)
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feature_extractor = MaskFormerFeatureExtractor.from_pretrained(model_id)
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image_segmenter = pipeline("image-segmentation", model=model, feature_extractor=feature_extractor)
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image = load_dataset("hf-internal-testing/fixtures_ade20k", split="test")
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outputs = image_segmenter(image[0]["file"], threshold=threshold)
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for o in outputs:
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o["mask"] = hashimage(o["mask"])
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"mask": "20d1b9480d1dc1501dbdcfdff483e370", "label": "wall", "score": None},
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{"mask": "0f902fbc66a0ff711ea455b0e4943adf", "label": "house", "score": None},
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{"mask": "4537bdc07d47d84b3f8634b7ada37bd4", "label": "grass", "score": None},
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{"mask": "b7ac77dfae44a904b479a0926a2acaf7", "label": "tree", "score": None},
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{"mask": "e9bedd56bd40650fb263ce03eb621079", "label": "plant", "score": None},
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{"mask": "37a609f8c9c1b8db91fbff269f428b20", "label": "road, route", "score": None},
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{"mask": "0d8cdfd63bae8bf6e4344d460a2fa711", "label": "sky", "score": None},
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],
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)
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