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tests/pipelines/test_pipelines_zero_shot_object_detection.py

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+# Copyright 2021 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+from transformers import (
+    MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
+    ZeroShotObjectDetectionPipeline,
+    is_vision_available,
+    pipeline,
+)
+from transformers.testing_utils import (
+    is_pipeline_test,
+    nested_simplify,
+    require_torch,
+    require_vision,
+    slow,
+)
+
+from .test_pipelines_common import ANY
+
+
+if is_vision_available():
+    from PIL import Image
+else:
+
+    class Image:
+        @staticmethod
+        def open(*args, **kwargs):
+            pass
+
+
+@is_pipeline_test
+@require_vision
+@require_torch
+class ZeroShotObjectDetectionPipelineTests(unittest.TestCase):
+    model_mapping = MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
+
+    def get_test_pipeline(
+        self,
+        model,
+        tokenizer=None,
+        image_processor=None,
+        feature_extractor=None,
+        processor=None,
+        dtype="float32",
+    ):
+        object_detector = ZeroShotObjectDetectionPipeline(
+            model=model,
+            processor=processor,
+            tokenizer=tokenizer,
+            image_processor=image_processor,
+            dtype=dtype,
+        )
+
+        examples = [
+            {
+                "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
+                "candidate_labels": ["cat", "remote", "couch"],
+            }
+        ]
+        return object_detector, examples
+
+    def run_pipeline_test(self, object_detector, examples):
+        outputs = object_detector(examples[0].get("image"), examples[0].get("candidate_labels"), threshold=0.0)
+
+        n = len(outputs)
+        self.assertGreater(n, 0)
+        self.assertEqual(
+            outputs,
+            [
+                {
+                    "score": ANY(float),
+                    "label": ANY(str),
+                    "box": {"xmin": ANY(int), "ymin": ANY(int), "xmax": ANY(int), "ymax": ANY(int)},
+                }
+                for i in range(n)
+            ],
+        )
+
+    @require_torch
+    def test_small_model_pt(self):
+        object_detector = pipeline(
+            "zero-shot-object-detection", model="hf-internal-testing/tiny-random-owlvit-object-detection"
+        )
+
+        outputs = object_detector(
+            "./tests/fixtures/tests_samples/COCO/000000039769.png",
+            candidate_labels=["cat", "remote", "couch"],
+            threshold=0.64,
+        )
+
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
+                {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
+                {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
+                {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
+                {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
+                {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
+                {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
+                {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
+                {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
+            ],
+        )
+
+        outputs = object_detector(
+            [
+                {
+                    "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
+                    "candidate_labels": ["cat", "remote", "couch"],
+                }
+            ],
+            threshold=0.64,
+        )
+
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                [
+                    {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
+                    {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
+                    {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
+                    {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
+                    {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
+                    {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
+                    {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
+                    {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
+                    {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
+                ]
+            ],
+        )
+
+    @require_torch
+    @slow
+    def test_large_model_pt(self):
+        object_detector = pipeline("zero-shot-object-detection")
+
+        outputs = object_detector(
+            "http://images.cocodataset.org/val2017/000000039769.jpg",
+            candidate_labels=["cat", "remote", "couch"],
+        )
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
+                {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
+                {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
+                {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
+                {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
+            ],
+        )
+
+        outputs = object_detector(
+            [
+                {
+                    "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
+                    "candidate_labels": ["cat", "remote", "couch"],
+                },
+                {
+                    "image": "http://images.cocodataset.org/val2017/000000039769.jpg",
+                    "candidate_labels": ["cat", "remote", "couch"],
+                },
+            ],
+        )
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                [
+                    {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
+                    {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
+                    {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
+                    {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
+                    {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
+                ],
+                [
+                    {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
+                    {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
+                    {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
+                    {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
+                    {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
+                ],
+            ],
+        )
+
+    @require_torch
+    @slow
+    def test_threshold(self):
+        threshold = 0.2
+        object_detector = pipeline("zero-shot-object-detection")
+
+        outputs = object_detector(
+            "http://images.cocodataset.org/val2017/000000039769.jpg",
+            candidate_labels=["cat", "remote", "couch"],
+            threshold=threshold,
+        )
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
+                {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
+                {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
+            ],
+        )
+
+    @require_torch
+    @slow
+    def test_top_k(self):
+        top_k = 2
+        object_detector = pipeline("zero-shot-object-detection")
+
+        outputs = object_detector(
+            "http://images.cocodataset.org/val2017/000000039769.jpg",
+            candidate_labels=["cat", "remote", "couch"],
+            top_k=top_k,
+        )
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
+                {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
+            ],
+        )