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8e777b3ba4
* [Proposal] Breaking change `zero-shot-object-detection` for improved
consistency.
This is a proposal to modify the output of `zero-shot-object-detection`
to provide better alignment with other pipelines.
The output is now strictly the same as `object-detection` whereas before
it would output lists of lists.
The name `candidate_labels` is used throughout for consistency with
other `zero-shot` pipelines.
The pipeline is changed to `ChunkPipeline` to support batching cleanly.
This removes all the lists and list of lists shenanigans, it's now a
matter of the base pipeline handling all this not this specific one.
**Breaking change**: It did remove complex calls potentials `pipe(images = [image1, image2],
text_queries=[candidates1, candidates2])` to support only
`pipe([{"image": image1, "candidate_labels": candidates1}, {"image": image2, "candidate_labels": candidates2}])`
when dealing with lists and/or datasets.
We could keep them, but it will add a lot of complexity to the code
base, since the pipeline is rather young, I'd rather break to keep the
code simpler, but we can revert this.
**Breaking change**: The name of the argument is now `image` instead of
`images` since it expects by default only 1 image. This is revertable
like the previous one.
**Breaking change**: The types is now simplified and flattened:
`pipe(inputs) == [{**object1}, {**object2}]`
instead of the previous
`pipe(inputs) == [[{**object1}, {**object1}], [{**object2}]]`
Where the different instances would be grouped by candidate labels
within lists.
IMHO this is not really desirable, since it would output empty lists and
is only adding superflous indirection compared to
`zero-shot-object-detection`.
It is relatively change free in terms of how the results, it does change
computation however since now the batching is handled by the pipeline
itself. It **did** change the results for the small models so there
seems to be a real difference in how the models handle this.
* Fixing the doctests.
* Behind is_torch_available.
222 lines
9.5 KiB
Python
222 lines
9.5 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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from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
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from transformers.testing_utils import nested_simplify, require_tf, require_torch, require_vision, slow
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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_torch
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class ZeroShotObjectDetectionPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
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model_mapping = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
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def get_test_pipeline(self, model, tokenizer, feature_extractor):
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object_detector = pipeline(
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"zero-shot-object-detection", model="hf-internal-testing/tiny-random-owlvit-object-detection"
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)
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examples = [
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{
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"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
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"candidate_labels": ["cat", "remote", "couch"],
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}
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]
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return object_detector, examples
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def run_pipeline_test(self, object_detector, examples):
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outputs = object_detector(examples[0], threshold=0.0)
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n = len(outputs)
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self.assertGreater(n, 0)
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self.assertEqual(
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outputs,
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[
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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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for i in range(n)
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],
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)
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@require_tf
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@unittest.skip("Zero Shot 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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object_detector = pipeline(
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"zero-shot-object-detection", model="hf-internal-testing/tiny-random-owlvit-object-detection"
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)
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outputs = object_detector(
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"./tests/fixtures/tests_samples/COCO/000000039769.png",
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candidate_labels=["cat", "remote", "couch"],
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threshold=0.64,
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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": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
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{"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
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{"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
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{"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
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{"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
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{"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
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{"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
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{"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
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{"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
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],
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)
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outputs = object_detector(
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[
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{
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"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
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"candidate_labels": ["cat", "remote", "couch"],
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}
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],
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threshold=0.64,
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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": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
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{"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
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{"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
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{"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
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{"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
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{"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
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{"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
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{"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
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{"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
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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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object_detector = pipeline("zero-shot-object-detection")
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outputs = object_detector(
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"http://images.cocodataset.org/val2017/000000039769.jpg", candidate_labels=["cat", "remote", "couch"]
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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": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
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{"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
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{"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
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{"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
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{"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
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],
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)
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outputs = object_detector(
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[
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{
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"image": "http://images.cocodataset.org/val2017/000000039769.jpg",
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"candidate_labels": ["cat", "remote", "couch"],
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},
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{
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"image": "http://images.cocodataset.org/val2017/000000039769.jpg",
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"candidate_labels": ["cat", "remote", "couch"],
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},
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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": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
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{"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
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{"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
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{"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
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{"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
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],
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[
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{"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
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{"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
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{"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
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{"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
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{"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
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],
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],
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)
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@require_tf
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@unittest.skip("Zero Shot Object Detection not implemented in TF")
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def test_large_model_tf(self):
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pass
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@require_torch
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@slow
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def test_threshold(self):
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threshold = 0.2
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object_detector = pipeline("zero-shot-object-detection")
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outputs = object_detector(
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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candidate_labels=["cat", "remote", "couch"],
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threshold=threshold,
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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": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
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{"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
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{"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
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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_top_k(self):
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top_k = 2
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object_detector = pipeline("zero-shot-object-detection")
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outputs = object_detector(
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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candidate_labels=["cat", "remote", "couch"],
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top_k=top_k,
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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": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
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{"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
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],
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)
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