141 lines
5.3 KiB
Python
141 lines
5.3 KiB
Python
from io import BytesIO
|
|
from typing import List, Union
|
|
|
|
import requests
|
|
|
|
from ..utils import (
|
|
add_end_docstrings,
|
|
is_av_available,
|
|
is_torch_available,
|
|
logging,
|
|
requires_backends,
|
|
)
|
|
from .base import Pipeline, build_pipeline_init_args
|
|
|
|
|
|
if is_av_available():
|
|
import av
|
|
import numpy as np
|
|
|
|
|
|
if is_torch_available():
|
|
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
@add_end_docstrings(build_pipeline_init_args(has_image_processor=True))
|
|
class VideoClassificationPipeline(Pipeline):
|
|
"""
|
|
Video classification pipeline using any `AutoModelForVideoClassification`. This pipeline predicts the class of a
|
|
video.
|
|
|
|
This video classification pipeline can currently be loaded from [`pipeline`] using the following task identifier:
|
|
`"video-classification"`.
|
|
|
|
See the list of available models on
|
|
[huggingface.co/models](https://huggingface.co/models?filter=video-classification).
|
|
"""
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
requires_backends(self, "av")
|
|
self.check_model_type(MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES)
|
|
|
|
def _sanitize_parameters(self, top_k=None, num_frames=None, frame_sampling_rate=None):
|
|
preprocess_params = {}
|
|
if frame_sampling_rate is not None:
|
|
preprocess_params["frame_sampling_rate"] = frame_sampling_rate
|
|
if num_frames is not None:
|
|
preprocess_params["num_frames"] = num_frames
|
|
|
|
postprocess_params = {}
|
|
if top_k is not None:
|
|
postprocess_params["top_k"] = top_k
|
|
return preprocess_params, {}, postprocess_params
|
|
|
|
def __call__(self, videos: Union[str, List[str]], **kwargs):
|
|
"""
|
|
Assign labels to the video(s) passed as inputs.
|
|
|
|
Args:
|
|
videos (`str`, `List[str]`):
|
|
The pipeline handles three types of videos:
|
|
|
|
- A string containing a http link pointing to a video
|
|
- A string containing a local path to a video
|
|
|
|
The pipeline accepts either a single video or a batch of videos, which must then be passed as a string.
|
|
Videos in a batch must all be in the same format: all as http links or all as local paths.
|
|
top_k (`int`, *optional*, defaults to 5):
|
|
The number of top labels that will be returned by the pipeline. If the provided number is higher than
|
|
the number of labels available in the model configuration, it will default to the number of labels.
|
|
num_frames (`int`, *optional*, defaults to `self.model.config.num_frames`):
|
|
The number of frames sampled from the video to run the classification on. If not provided, will default
|
|
to the number of frames specified in the model configuration.
|
|
frame_sampling_rate (`int`, *optional*, defaults to 1):
|
|
The sampling rate used to select frames from the video. If not provided, will default to 1, i.e. every
|
|
frame will be used.
|
|
|
|
Return:
|
|
A dictionary or a list of dictionaries containing result. If the input is a single video, will return a
|
|
dictionary, if the input is a list of several videos, will return a list of dictionaries corresponding to
|
|
the videos.
|
|
|
|
The dictionaries contain the following keys:
|
|
|
|
- **label** (`str`) -- The label identified by the model.
|
|
- **score** (`int`) -- The score attributed by the model for that label.
|
|
"""
|
|
return super().__call__(videos, **kwargs)
|
|
|
|
def preprocess(self, video, num_frames=None, frame_sampling_rate=1):
|
|
if num_frames is None:
|
|
num_frames = self.model.config.num_frames
|
|
|
|
if video.startswith("http://") or video.startswith("https://"):
|
|
video = BytesIO(requests.get(video).content)
|
|
|
|
container = av.open(video)
|
|
|
|
start_idx = 0
|
|
end_idx = num_frames * frame_sampling_rate - 1
|
|
indices = np.linspace(start_idx, end_idx, num=num_frames, dtype=np.int64)
|
|
|
|
video = read_video_pyav(container, indices)
|
|
video = list(video)
|
|
|
|
model_inputs = self.image_processor(video, return_tensors=self.framework)
|
|
return model_inputs
|
|
|
|
def _forward(self, model_inputs):
|
|
model_outputs = self.model(**model_inputs)
|
|
return model_outputs
|
|
|
|
def postprocess(self, model_outputs, top_k=5):
|
|
if top_k > self.model.config.num_labels:
|
|
top_k = self.model.config.num_labels
|
|
|
|
if self.framework == "pt":
|
|
probs = model_outputs.logits.softmax(-1)[0]
|
|
scores, ids = probs.topk(top_k)
|
|
else:
|
|
raise ValueError(f"Unsupported framework: {self.framework}")
|
|
|
|
scores = scores.tolist()
|
|
ids = ids.tolist()
|
|
return [{"score": score, "label": self.model.config.id2label[_id]} for score, _id in zip(scores, ids)]
|
|
|
|
|
|
def read_video_pyav(container, indices):
|
|
frames = []
|
|
container.seek(0)
|
|
start_index = indices[0]
|
|
end_index = indices[-1]
|
|
for i, frame in enumerate(container.decode(video=0)):
|
|
if i > end_index:
|
|
break
|
|
if i >= start_index and i in indices:
|
|
frames.append(frame)
|
|
return np.stack([x.to_ndarray(format="rgb24") for x in frames])
|