Object detection pipeline (#12886)
* Implement object-detection pipeline * Define threshold const * Add `threshold` argument * Refactor * Uncomment test inputs * `rm Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Fix typo Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Fix typo Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Chore better doc Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Rm unnecessary lines Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Chore better naming Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/pipelines/object_detection.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/pipelines/object_detection.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Fix typo * Add `detr-tiny` for tests * Add `ObjectDetectionPipeline` to `trnsfrmrs/init` * Implement new bbox format * Update detr post_process * Update `load_img` method obj det pipeline * make style * Implement new testing format for obj det pipeln * Add guard pytorch specific code in pipeline * Add doc * Make pipeline_obj_tet tests deterministic * Revert some changes to `post_process` COCO api * Chore * Update src/transformers/pipelines/object_detection.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/pipelines/object_detection.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/pipelines/object_detection.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/pipelines/object_detection.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/pipelines/object_detection.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/pipelines/object_detection.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Rm timm requirement * make fixup * Add timm requirement to test * Make fixup * Guard torch.Tensor * Chore * Delete unnecessary comment Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
This commit is contained in:
Mishig Davaadorj
253
tests/test_pipelines_object_detection.py
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253
tests/test_pipelines_object_detection.py
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# 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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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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is_pipeline_test,
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nested_simplify,
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require_datasets,
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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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@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 ObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
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model_mapping = MODEL_FOR_OBJECT_DETECTION_MAPPING
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@require_datasets
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def run_pipeline_test(self, model, tokenizer, feature_extractor):
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object_detector = ObjectDetectionPipeline(model=model, feature_extractor=feature_extractor)
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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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import datasets
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dataset = datasets.load_dataset("Narsil/image_dummy", "image", split="test")
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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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dataset[0]["file"],
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# LA
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dataset[1]["file"],
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# L
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dataset[2]["file"],
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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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@require_tf
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@unittest.skip("Object detection 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"
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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": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
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{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
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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": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
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{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
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],
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[
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{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
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{"score": 0.3432, "label": "LABEL_0", "box": {"xmin": 266, "ymin": 200, "xmax": 799, "ymax": 599}},
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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": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
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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": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
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],
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
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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": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
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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": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
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],
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[
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{"score": 0.9982, "label": "remote", "box": {"xmin": 66, "ymin": 118, "xmax": 292, "ymax": 196}},
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{"score": 0.9960, "label": "remote", "box": {"xmin": 555, "ymin": 120, "xmax": 613, "ymax": 312}},
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{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 1065, "ymax": 789}},
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{"score": 0.9988, "label": "cat", "box": {"xmin": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
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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": 22, "ymin": 86, "xmax": 523, "ymax": 784}},
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{"score": 0.9987, "label": "cat", "box": {"xmin": 575, "ymin": 39, "xmax": 1066, "ymax": 614}},
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],
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)
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