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* remove uses of require_tf * remove redundant import guards * this class has no tests * nits * del tf rng comment
298 lines
12 KiB
Python
298 lines
12 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 unittest
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import datasets
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from huggingface_hub import ObjectDetectionOutputElement
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from transformers import (
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MODEL_FOR_OBJECT_DETECTION_MAPPING,
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AutoFeatureExtractor,
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AutoModelForObjectDetection,
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ObjectDetectionPipeline,
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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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compare_pipeline_output_to_hub_spec,
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is_pipeline_test,
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nested_simplify,
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require_pytesseract,
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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
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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_vision
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@require_timm
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@require_torch
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class ObjectDetectionPipelineTests(unittest.TestCase):
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model_mapping = MODEL_FOR_OBJECT_DETECTION_MAPPING
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_dataset = None
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@classmethod
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def _load_dataset(cls):
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# Lazy loading of the dataset. Because it is a class method, it will only be loaded once per pytest process.
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if cls._dataset is None:
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# we use revision="refs/pr/1" until the PR is merged
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# https://hf.co/datasets/hf-internal-testing/fixtures_image_utils/discussions/1
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cls._dataset = datasets.load_dataset(
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"hf-internal-testing/fixtures_image_utils", split="test", revision="refs/pr/1"
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)
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def get_test_pipeline(
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self,
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model,
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tokenizer=None,
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image_processor=None,
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feature_extractor=None,
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processor=None,
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torch_dtype="float32",
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):
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object_detector = ObjectDetectionPipeline(
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model=model,
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tokenizer=tokenizer,
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feature_extractor=feature_extractor,
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image_processor=image_processor,
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processor=processor,
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torch_dtype=torch_dtype,
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)
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return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
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def run_pipeline_test(self, object_detector, examples):
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self._load_dataset()
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outputs = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png", threshold=0.0)
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self.assertGreater(len(outputs), 0)
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for detected_object in outputs:
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self.assertEqual(
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detected_object,
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{
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"score": ANY(float),
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"label": ANY(str),
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"box": {"xmin": ANY(int), "ymin": ANY(int), "xmax": ANY(int), "ymax": ANY(int)},
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},
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)
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batch = [
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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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# RGBA
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self._dataset[0]["image"],
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# LA
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self._dataset[1]["image"],
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# L
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self._dataset[2]["image"],
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]
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batch_outputs = object_detector(batch, threshold=0.0)
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self.assertEqual(len(batch), len(batch_outputs))
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for outputs in batch_outputs:
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self.assertGreater(len(outputs), 0)
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for detected_object in outputs:
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self.assertEqual(
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detected_object,
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{
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"score": ANY(float),
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"label": ANY(str),
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"box": {"xmin": ANY(int), "ymin": ANY(int), "xmax": ANY(int), "ymax": ANY(int)},
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},
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)
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compare_pipeline_output_to_hub_spec(detected_object, ObjectDetectionOutputElement)
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@require_torch
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def test_small_model_pt(self):
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model_id = "hf-internal-testing/tiny-detr-mobilenetsv3"
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model = AutoModelForObjectDetection.from_pretrained(model_id)
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feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
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object_detector = ObjectDetectionPipeline(model=model, feature_extractor=feature_extractor)
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outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg", threshold=0.0)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
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{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
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],
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)
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outputs = object_detector(
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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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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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[
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{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
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{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
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],
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[
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{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
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{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
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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_large_model_pt(self):
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model_id = "facebook/detr-resnet-50"
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model = AutoModelForObjectDetection.from_pretrained(model_id)
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feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
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object_detector = ObjectDetectionPipeline(model=model, feature_extractor=feature_extractor)
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outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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)
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outputs = object_detector(
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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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)
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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": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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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_object_detection(self):
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model_id = "facebook/detr-resnet-50"
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object_detector = pipeline("object-detection", model=model_id)
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outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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)
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outputs = object_detector(
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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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)
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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": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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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 = 0.9985
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model_id = "facebook/detr-resnet-50"
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object_detector = pipeline("object-detection", model=model_id)
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outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg", threshold=threshold)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
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],
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)
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@require_torch
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@require_pytesseract
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@slow
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def test_layoutlm(self):
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model_id = "Narsil/layoutlmv3-finetuned-funsd"
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threshold = 0.9993
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object_detector = pipeline("object-detection", model=model_id, threshold=threshold)
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outputs = object_detector(
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"https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"
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)
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self.assertEqual(
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nested_simplify(outputs, decimals=4),
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[
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{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
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{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
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],
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)
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