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Add Object Detection pipeline and OwlVit models
1 parent f794e9e commit e4abee3

21 files changed

+303
-8
lines changed

‎bin/transformers

+1-1
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@@ -12,7 +12,7 @@ $application = new Application();
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try {
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$application->setName('Transformers PHP CLI');
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$application->add(new Codewithkyrian\Transformers\Commands\InitCommand());
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// $application->add(new Codewithkyrian\Transformers\Commands\InitCommand());
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$application->add(new Codewithkyrian\Transformers\Commands\DownloadModelCommand());
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$application->run();

‎examples/images/astronaut.png

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‎examples/images/beach.png

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‎examples/pipelines/image-to-text.php

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@@ -13,9 +13,9 @@
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//$captioner = pipeline('image-to-text', 'Xenova/vit-gpt2-image-captioning');
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$captioner = pipeline('image-to-text', 'Xenova/trocr-small-handwritten');
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16-
$streamer = StdOutStreamer::make($captioner->tokenizer);
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//$streamer = StdOutStreamer::make($captioner->tokenizer);
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18-
//$url = __DIR__. '/../images/cats.jpg';
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$url = __DIR__. '/../images/beach.png';
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//$url = __DIR__. '/../images/handwriting.jpg';
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//$url = __DIR__. '/../images/handwriting3.png';
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$url = __DIR__. '/../images/handwriting4.jpeg';

‎examples/pipelines/zero-shot-object-detection.php

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<?php
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declare(strict_types=1);
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namespace Codewithkyrian\Transformers\Pipelines;
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use function Codewithkyrian\Transformers\Utils\memoryUsage;
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use function Codewithkyrian\Transformers\Utils\timeUsage;
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require_once './bootstrap.php';
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ini_set('memory_limit', '-1');
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$detector = pipeline('zero-shot-object-detection', 'Xenova/owlvit-base-patch32');
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$url = __DIR__. '/../images/astronaut.png';
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$candidateLabels = ['human face', 'rocket', 'helmet', 'american flag'];
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$url = __DIR__. '/../images/beach.png';
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$candidateLabels = ['hat', 'book', 'sunglasses', 'camera'];
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$output = $detector($url, $candidateLabels, topK: 4, threshold: 0.05);
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dd($output, timeUsage(), memoryUsage());

‎src/FeatureExtractors/OwlViTFeatureExtractor.php

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<?php
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declare(strict_types=1);
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namespace Codewithkyrian\Transformers\FeatureExtractors;
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use Codewithkyrian\Transformers\Models\Output\ObjectDetectionOutput;
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use Codewithkyrian\Transformers\Processors\Processor;
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class OwlViTFeatureExtractor extends ImageFeatureExtractor
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{
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/**
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* Post-processes the outputs of the model (for object detection).
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* @param ObjectDetectionOutput $outputs The outputs of the model that must be post-processed
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* @param float $threshold The threshold to use for the scores.
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* @param array|null $targetSizes The sizes of the original images.
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* @param bool $isZeroShot Whether zero-shot object detection was performed.
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* @return array An array of objects containing the post-processed outputs.
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*/
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public function postProcessObjectDetection(ObjectDetectionOutput $outputs, float $threshold = 0.5, ?array $targetSizes = null, bool $isZeroShot = false): array
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{
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return Processor::postProcessObjectDetection($outputs, $threshold, $targetSizes, $isZeroShot);
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}
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}

‎src/FeatureExtractors/Owlv2ImageProcessor.php

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<?php
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declare(strict_types=1);
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namespace Codewithkyrian\Transformers\FeatureExtractors;
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class Owlv2ImageProcessor extends OwlViTFeatureExtractor
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{
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}

‎src/Models/Auto/AutoModel.php

+5-1
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@@ -21,6 +21,8 @@ class AutoModel extends PretrainedMixin
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'detr' => \Codewithkyrian\Transformers\Models\Pretrained\DETRModel::class,
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'yolos' => \Codewithkyrian\Transformers\Models\Pretrained\YOLOSModel::class,
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'owlvit' => \Codewithkyrian\Transformers\Models\Pretrained\OwlVitModel::class,
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'owlv2' => \Codewithkyrian\Transformers\Models\Pretrained\OwlV2Model::class,
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];
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const ENCODER_DECODER_MODEL_MAPPING = [
@@ -48,7 +50,9 @@ class AutoModel extends PretrainedMixin
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AutoModelForMaskedLM::MODEL_CLASS_MAPPING,
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AutoModelForQuestionAnswering::MODEL_CLASS_MAPPING,
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AutoModelForImageClassification::MODEL_CLASS_MAPPING,
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AutoModelForVision2Seq::MODEL_CLASS_MAPPING
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AutoModelForVision2Seq::MODEL_CLASS_MAPPING,
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AutoModelForObjectDetection::MODEL_CLASS_MAPPING,
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AutoModelForZeroShotObjectDetection::MODEL_CLASS_MAPPING,
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];
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‎src/Models/Auto/AutoModelForZeroShotObjectDetection.php

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<?php
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declare(strict_types=1);
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namespace Codewithkyrian\Transformers\Models\Auto;
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class AutoModelForZeroShotObjectDetection extends PretrainedMixin
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{
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const MODEL_CLASS_MAPPING = [
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'owlvit' => \Codewithkyrian\Transformers\Models\Pretrained\OwlViTForObjectDetection::class,
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'owlv2' => \Codewithkyrian\Transformers\Models\Pretrained\Owlv2ForObjectDetection::class,
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];
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const MODEL_CLASS_MAPPINGS = [
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self::MODEL_CLASS_MAPPING,
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];
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}

‎src/Models/Pretrained/OwlViTForObjectDetection.php

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<?php
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declare(strict_types=1);
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namespace Codewithkyrian\Transformers\Models\Pretrained;
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use Codewithkyrian\Transformers\Models\Output\ObjectDetectionOutput;
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class OwlViTForObjectDetection extends OwlViTPretrainedModel
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{
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public function __invoke(array $modelInputs): ObjectDetectionOutput
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{
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return ObjectDetectionOutput::fromOutput(parent::__invoke($modelInputs));
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}
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}

‎src/Models/Pretrained/OwlViTModel.php

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<?php
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declare(strict_types=1);
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namespace Codewithkyrian\Transformers\Models\Pretrained;
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class OwlViTModel extends OwlViTPretrainedModel
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{
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}

‎src/Models/Pretrained/OwlViTPretrainedModel.php

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<?php
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declare(strict_types=1);
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namespace Codewithkyrian\Transformers\Models\Pretrained;
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class OwlViTPretrainedModel extends PretrainedModel
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{
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}

‎src/Models/Pretrained/Owlv2ForObjectDetection.php

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<?php
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declare(strict_types=1);
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namespace Codewithkyrian\Transformers\Models\Pretrained;
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use Codewithkyrian\Transformers\Models\Output\ObjectDetectionOutput;
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class Owlv2ForObjectDetection extends Owlv2PretrainedModel
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{
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public function __invoke(array $modelInputs): ObjectDetectionOutput
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{
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return ObjectDetectionOutput::fromOutput(parent::__invoke($modelInputs));
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}
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}

‎src/Models/Pretrained/Owlv2Model.php

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<?php
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declare(strict_types=1);
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namespace Codewithkyrian\Transformers\Models\Pretrained;
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class Owlv2Model extends Owlv2PretrainedModel
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{
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}

‎src/Models/Pretrained/Owlv2PretrainedModel.php

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<?php
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declare(strict_types=1);
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namespace Codewithkyrian\Transformers\Models\Pretrained;
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class Owlv2PretrainedModel extends PretrainedModel
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{
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}

‎src/Pipelines/Task.php

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@@ -14,6 +14,7 @@
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use Codewithkyrian\Transformers\Models\Auto\AutoModelForSequenceClassification;
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use Codewithkyrian\Transformers\Models\Auto\AutoModelForTokenClassification;
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use Codewithkyrian\Transformers\Models\Auto\AutoModelForVision2Seq;
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use Codewithkyrian\Transformers\Models\Auto\AutoModelForZeroShotObjectDetection;
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use Codewithkyrian\Transformers\Models\Pretrained\PretrainedModel;
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use Codewithkyrian\Transformers\PretrainedTokenizers\AutoTokenizer;
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use Codewithkyrian\Transformers\PretrainedTokenizers\PretrainedTokenizer;
@@ -43,6 +44,7 @@ enum Task: string
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case ZeroShotImageClassification = 'zero-shot-image-classification';
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case ObjectDetection = 'object-detection';
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case ZeroShotObjectDetection = 'zero-shot-object-detection';
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public function pipeline(PretrainedModel $model, ?PretrainedTokenizer $tokenizer, ?Processor $processor): Pipeline
@@ -78,6 +80,8 @@ public function pipeline(PretrainedModel $model, ?PretrainedTokenizer $tokenizer
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self::ZeroShotImageClassification => new ZeroShotImageClassificationPipeline($this, $model, $tokenizer, $processor),
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self::ObjectDetection => new ObjectDetectionPipeline($this, $model, $tokenizer, $processor),
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self::ZeroShotObjectDetection => new ZeroShotObjectDetectionPipeline($this, $model, $tokenizer, $processor),
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};
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}
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@@ -112,6 +116,8 @@ public function defaultModelName(): string
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self::ZeroShotImageClassification => 'Xenova/clip-vit-base-patch32', // Original: 'openai/clip-vit-base-patch32'
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self::ObjectDetection => 'Xenova/detr-resnet-50', // Original: 'facebook/detr-resnet-50',
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self::ZeroShotObjectDetection => 'Xenova/owlvit-base-patch32', // Original: 'google/owlvit-base-patch32',
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};
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}
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@@ -153,6 +159,8 @@ public function autoModel(
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self::ZeroShotImageClassification => AutoModel::fromPretrained($modelNameOrPath, $quantized, $config, $cacheDir, $revision, $modelFilename, $output),
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self::ObjectDetection => AutoModelForObjectDetection::fromPretrained($modelNameOrPath, $quantized, $config, $cacheDir, $revision, $modelFilename, $output),
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self::ZeroShotObjectDetection => AutoModelForZeroShotObjectDetection::fromPretrained($modelNameOrPath, $quantized, $config, $cacheDir, $revision, $modelFilename, $output),
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};
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}
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@@ -185,7 +193,8 @@ public function autoTokenizer(
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self::TokenClassification,
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self::Ner,
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self::ImageToText,
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self::ZeroShotImageClassification => AutoTokenizer::fromPretrained($modelNameOrPath, $quantized, $config, $cacheDir, $revision, null, $output),
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self::ZeroShotImageClassification,
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self::ZeroShotObjectDetection => AutoTokenizer::fromPretrained($modelNameOrPath, $quantized, $config, $cacheDir, $revision, null, $output),
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};
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}
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@@ -202,7 +211,8 @@ public function autoProcessor(
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self::ImageToText,
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self::ImageClassification,
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self::ZeroShotImageClassification,
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self::ObjectDetection => AutoProcessor::fromPretrained($modelNameOrPath, $config, $cacheDir, $revision, $output),
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self::ObjectDetection,
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self::ZeroShotObjectDetection => AutoProcessor::fromPretrained($modelNameOrPath, $config, $cacheDir, $revision, $output),
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self::SentimentAnalysis,

‎src/Pipelines/ZeroShotObjectDetectionPipeline.php

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<?php
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declare(strict_types=1);
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namespace Codewithkyrian\Transformers\Pipelines;
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use Codewithkyrian\Transformers\Models\Output\ObjectDetectionOutput;
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use Codewithkyrian\Transformers\Utils\Tensor;
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use function Codewithkyrian\Transformers\Utils\getBoundingBox;
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use function Codewithkyrian\Transformers\Utils\prepareImages;
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/**
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* Zero-shot object detection pipeline. This pipeline predicts bounding boxes of
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* objects when you provide an image and a set of `candidate_labels`.
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*
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* **Example:** Zero-shot object detection w/ `Xenova/owlvit-base-patch32`.
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* ```php
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* $detector = pipeline('zero-shot-object-detection', 'Xenova/owlvit-base-patch32');
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* $url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/astronaut.png';
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* $candidateLabels = ['human face', 'rocket', 'helmet', 'american flag'];
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* $output = $detector($url, $candidateLabels);
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* // [
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* // [
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* // score: 0.24392342567443848,
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* // label: 'human face',
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* // box: { xmin: 180, ymin: 67, xmax: 274, ymax: 175 }
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* // ],
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* // ...
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* // ]
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* ```
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*
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* **Example:** Zero-shot object detection w/ `Xenova/owlvit-base-patch32` (returning top 4 matches and setting a threshold).
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* ```javascript
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* $detector = pipeline('zero-shot-object-detection', 'Xenova/owlvit-base-patch32');
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* $url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/beach.png';
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* $candidateLabels = ['hat', 'book', 'sunglasses', 'camera'];
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* $output = $detector($url, $candidateLabels, topK : 4, threshold : 0.05);
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* // [
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* // [
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* // score: 0.1606510728597641,
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* // label: 'sunglasses',
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* // box: { xmin: 347, ymin: 229, xmax: 429, ymax: 264 }
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* // ],
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* // ...
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* // ]
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* ```
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*/
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class ZeroShotObjectDetectionPipeline extends Pipeline
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{
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public function __invoke(array|string $inputs, ...$args): array
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{
54+
$candidateLabels = $args[0];
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$threshold = $args['threshold'] ?? 0.1;
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$topK = $args['topK'] ?? null;
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$percentage = $args['percentage'] ?? false;
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$isBatched = is_array($inputs);
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$preparedImages = prepareImages($inputs);
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// Run tokenization
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$textInputs = $this->tokenizer->tokenize($candidateLabels, padding: true, truncation: true);
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// Run processor
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$modelInputs = ($this->processor)($preparedImages);
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$toReturn = [];
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foreach ($preparedImages as $i => $image) {
71+
$imageSize = $percentage ? null : [[$image->height(), $image->width()]];
72+
$pixelValues = $modelInputs['pixel_values'][$i];
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$pixelValues = Tensor::fromNdArray($pixelValues)->unsqueeze(0);
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// Run model with both text and pixel inputs
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/** @var ObjectDetectionOutput $output */
78+
$output = $this->model->__invoke(array_merge($textInputs, ['pixel_values' => $pixelValues]));
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// Perform post-processing
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$processed = $this->processor->featureExtractor->postProcessObjectDetection($output, $threshold, $imageSize, true)[0];
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$result = [];
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foreach ($processed['boxes'] as $j => $box) {
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$result[] = [
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'score' => $processed['scores'][$j],
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'label' => $candidateLabels[$processed['classes'][$j]],
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'box' => getBoundingBox($box, !$percentage),
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];
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}
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// Sort by score
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usort($result, fn($a, $b) => $b['score'] <=> $a['score']);
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if ($topK !== null) {
96+
$result = array_slice($result, 0, $topK);
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}
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$toReturn[] = $result;
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}
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return $isBatched ? $toReturn : $toReturn[0];
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}
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}

‎src/Processors/OwlViTProcessor.php

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<?php
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declare(strict_types=1);
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namespace Codewithkyrian\Transformers\Processors;
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class OwlViTProcessor extends Processor
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{
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}

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