init

2025-10-09 16:47:16 +08:00
parent c8feb4deb5
commit e27e3f16bb
5248 changed files with 1778505 additions and 0 deletions
--- a/transformers/tests/pipelines/test_pipelines_video_classification.py
+++ b/transformers/tests/pipelines/test_pipelines_video_classification.py
@@ -0,0 +1,124 @@
+# 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 huggingface_hub import VideoClassificationOutputElement, hf_hub_download
+
+from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
+from transformers.pipelines import VideoClassificationPipeline, pipeline
+from transformers.testing_utils import (
+    compare_pipeline_output_to_hub_spec,
+    is_pipeline_test,
+    nested_simplify,
+    require_av,
+    require_torch,
+    require_vision,
+)
+
+from .test_pipelines_common import ANY
+
+
+@is_pipeline_test
+@require_torch
+@require_vision
+@require_av
+class VideoClassificationPipelineTests(unittest.TestCase):
+    model_mapping = MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
+    example_video_filepath = None
+
+    @classmethod
+    def _load_dataset(cls):
+        # Lazy loading of the dataset. Because it is a class method, it will only be loaded once per pytest process.
+        if cls.example_video_filepath is None:
+            cls.example_video_filepath = hf_hub_download(
+                repo_id="nateraw/video-demo", filename="archery.mp4", repo_type="dataset"
+            )
+
+    def get_test_pipeline(
+        self,
+        model,
+        tokenizer=None,
+        image_processor=None,
+        feature_extractor=None,
+        processor=None,
+        dtype="float32",
+    ):
+        self._load_dataset()
+        video_classifier = VideoClassificationPipeline(
+            model=model,
+            tokenizer=tokenizer,
+            feature_extractor=feature_extractor,
+            image_processor=image_processor,
+            processor=processor,
+            dtype=dtype,
+            top_k=2,
+        )
+        examples = [
+            self.example_video_filepath,
+            # TODO: re-enable this once we have a stable hub solution for CI
+            # "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
+        ]
+        return video_classifier, examples
+
+    def run_pipeline_test(self, video_classifier, examples):
+        for example in examples:
+            outputs = video_classifier(example)
+
+            self.assertEqual(
+                outputs,
+                [
+                    {"score": ANY(float), "label": ANY(str)},
+                    {"score": ANY(float), "label": ANY(str)},
+                ],
+            )
+            for element in outputs:
+                compare_pipeline_output_to_hub_spec(element, VideoClassificationOutputElement)
+
+    @require_torch
+    def test_small_model_pt(self):
+        small_model = "hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
+        small_feature_extractor = VideoMAEFeatureExtractor(
+            size={"shortest_edge": 10}, crop_size={"height": 10, "width": 10}
+        )
+        video_classifier = pipeline(
+            "video-classification", model=small_model, feature_extractor=small_feature_extractor, frame_sampling_rate=4
+        )
+
+        video_file_path = hf_hub_download(repo_id="nateraw/video-demo", filename="archery.mp4", repo_type="dataset")
+        output = video_classifier(video_file_path, top_k=2)
+        self.assertEqual(
+            nested_simplify(output, decimals=4),
+            [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
+        )
+        for element in output:
+            compare_pipeline_output_to_hub_spec(element, VideoClassificationOutputElement)
+
+        outputs = video_classifier(
+            [
+                video_file_path,
+                video_file_path,
+            ],
+            top_k=2,
+        )
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
+                [{"score": 0.5199, "label": "LABEL_0"}, {"score": 0.4801, "label": "LABEL_1"}],
+            ],
+        )
+        for output in outputs:
+            for element in output:
+                compare_pipeline_output_to_hub_spec(element, VideoClassificationOutputElement)