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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 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_tf,
require_torch,
require_torch_or_tf,
require_vision,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
@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,
torch_dtype="float32",
):
self._load_dataset()
video_classifier = VideoClassificationPipeline(
model=model,
tokenizer=tokenizer,
feature_extractor=feature_extractor,
image_processor=image_processor,
processor=processor,
torch_dtype=torch_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)
@require_tf
@unittest.skip
def test_small_model_tf(self):
pass
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