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e9a49babee
* Add ZeroShotObjectDetectionPipeline (#18445) * Add AutoModelForZeroShotObjectDetection task This commit also adds the following - Add explicit _processor method for ZeroShotObjectDetectionPipeline. This is necessary as pipelines don't auto infer processors yet and `OwlVitProcessor` wraps tokenizer and feature_extractor together, to process multiple images at once - Add auto tests and other tests for ZeroShotObjectDetectionPipeline * Add AutoModelForZeroShotObjectDetection task This commit also adds the following - Add explicit _processor method for ZeroShotObjectDetectionPipeline. This is necessary as pipelines don't auto infer processors yet and `OwlVitProcessor` wraps tokenizer and feature_extractor together, to process multiple images at once - Add auto tests and other tests for ZeroShotObjectDetectionPipeline * Add batching for ZeroShotObjectDetectionPipeline * Fix doc-string ZeroShotObjectDetectionPipeline * Fix output format: ZeroShotObjectDetectionPipeline
264 lines
11 KiB
Python
264 lines
11 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 (
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is_pipeline_test,
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nested_simplify,
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require_tf,
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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_torch
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@is_pipeline_test
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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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"images": "./tests/fixtures/tests_samples/COCO/000000039769.png",
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"text_queries": ["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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batch_outputs = object_detector(examples, threshold=0.0)
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self.assertEqual(len(examples), len(batch_outputs))
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for outputs in batch_outputs:
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for output_per_image in outputs:
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self.assertGreater(len(output_per_image), 0)
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for detected_object in output_per_image:
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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("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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text_queries=["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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[
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{"score": 0.7235, "label": "cat", "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": 0.6456, "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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]
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],
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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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text_queries=["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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[
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{"score": 0.7235, "label": "cat", "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": 0.6456, "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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]
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],
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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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text_queries=[["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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[
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{"score": 0.7235, "label": "cat", "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": 0.6456, "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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]
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],
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)
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outputs = object_detector(
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[
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"./tests/fixtures/tests_samples/COCO/000000039769.png",
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"http://images.cocodataset.org/val2017/000000039769.jpg",
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],
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text_queries=[["cat", "remote", "couch"], ["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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[
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{"score": 0.7235, "label": "cat", "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": 0.6456, "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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],
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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": 0.6748, "label": "remote", "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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],
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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", text_queries=["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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[
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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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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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text_queries=[["cat", "remote", "couch"], ["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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[
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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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text_queries=["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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[
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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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)
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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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text_queries=["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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[
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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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)
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