A zamanin basirar ɗan adam da zurfin koyo, PyTorch sanannen ɗakin karatu ne na koyon injin buɗe ido don Python tare da ƙididdigar tensor da hanyoyin sadarwa mai zurfi. Ɗaya daga cikin abubuwa masu amfani da yawa shine PyTorchVideo, wanda shine kayan aiki na musamman da aka tsara don ayyukan fahimtar bidiyo. A cikin wannan labarin, za mu shiga cikin duniyar PyTorchVideo, matsalolin da zai iya taimaka mana mu magance, kuma mu bi ku ta hanyar aiwatar da shi.
PyTorchVideo: Takaitaccen Bayani
PyTorchVideo ɗakin karatu ne wanda Facebook AI ya haɓaka, an ƙirƙira shi don taimakawa masu bincike da injiniyoyi don gina ingantattun samfuran fahimtar bidiyo. Laburaren ya ƙunshi abubuwa kamar masu ɗaukar bayanan bayanan bidiyo, ƙirar da aka riga aka horar don fahimtar bidiyo, da kayan aikin awo da ƙima. Tare da PyTorchVideo, yana da sauƙi don aiki tare da bayanan bidiyo da inganta daidaiton ayyukan fahimtar bidiyo kamar rarrabawa, gano abu, da ƙari.
Magance Matsalolin Fahimtar Bidiyo
Matsalolin fahimtar bidiyo na iya zama ƙalubale sosai, saboda yawan adadin bayanai a cikin bidiyo, idan aka kwatanta da hotuna. Wannan hadaddun yana sa horarwa da sarrafa ƙirar fahimtar bidiyo ta fi cin lokaci da ƙarfi sosai. PyTorchVideo yana neman warware waɗannan batutuwa ta hanyar samar da cikakkiyar yanayin muhalli don ayyukan fahimtar bidiyo da kuma sa shi ya fi dacewa ga masu haɓakawa.
Yanzu bari mu nutse cikin aiwatar da PyTorchVideo da jagorar mataki-mataki kan yadda ake amfani da shi.
Mataki 1: Yana da mahimmanci a sanya PyTorch kafin amfani da PyTorchVideo. Hanya mafi sauƙi don samun shi ita ce ta amfani da pip:
pip install torch torchvision
Mataki 2: Sanya PyTorchVideo ta hanyar aiwatar da umarni mai zuwa:
pip install pytorchvideo
Ana Load da Bayanan Bidiyo
Ɗaya daga cikin mahimman abubuwan da PyTorchVideo ke bayarwa shine ikon yin aiki tare da bayanan bayanan bidiyo daban-daban. Bari mu bincika yadda ake loda saitin bayanai ta amfani da Module ɗin Bayanan Kinetics.
from pytorchvideo.data import KineticsDataModule
# Configure the dataloader
data_config = {
"train_path": "path/to/train/dataset",
"val_path": "path/to/validation/dataset",
"batch_size": 8,
}
# Initializing the DataModule
kinetics_data_module = KineticsDataModule.from_config_dict(data_config)
Wannan zai ɗora bayanan Kinetics, wanda za'a iya amfani dashi don horarwa da kuma inganta ƙirar fahimtar bidiyon ku.
Yin aiki tare da Model da aka riga aka horar
PyTorchVideo yana ba da nau'ikan da aka riga aka horar da su don ayyukan fahimtar bidiyo. Ana iya amfani da waɗannan samfuran ko dai don wasu ayyuka, ko kuma an daidaita su don cimma kyakkyawan aiki akan takamaiman bayanan bidiyo na ku. Ga misalin yadda ake loda samfurin da aka riga aka horar.
from pytorchvideo.models import slowfast # Load a pre-trained SlowFast model slowfast_model = slowfast.slowfast_r50()
A taƙaice, PyTorchVideo babban ɗakin karatu ne mai ban mamaki wanda ke sauƙaƙe ayyukan fahimtar bidiyo ta hanyar samar da masu lodin bayanai, ƙirar da aka riga aka horar, da kayan aiki masu amfani don ma'auni da ƙima. Tare da wannan kayan aiki, masu haɓakawa za su iya gina ingantattun samfuran fahimtar bidiyo masu inganci da inganci, suna ba da gudummawa ga ci gaban da ake samu a fagen ilimin ɗan adam da zurfin koyo. Don haka ci gaba da bincika duniyar PyTorchVideo don ɗaukar ayyukan fahimtar bidiyon ku zuwa mataki na gaba.
