Class: Informers::ZeroShotObjectDetectionPipeline
- Defined in:
- lib/informers/pipelines.rb
Instance Method Summary collapse
Methods inherited from Pipeline
Constructor Details
This class inherits a constructor from Informers::Pipeline
Instance Method Details
#call(images, candidate_labels, threshold: 0.1, top_k: nil, percentage: false) ⇒ Object
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# File 'lib/informers/pipelines.rb', line 693 def call( images, candidate_labels, threshold: 0.1, top_k: nil, percentage: false ) is_batched = images.is_a?(Array) prepared_images = prepare_images(images) # Run tokenization text_inputs = @tokenizer.(candidate_labels, padding: true, truncation: true ) # Run processor model_inputs = @processor.(prepared_images) # Since non-maximum suppression is performed for exporting, we need to # process each image separately. For more information, see: # https://github.com/huggingface/optimum/blob/e3b7efb1257c011db907ef40ab340e795cc5684c/optimum/exporters/onnx/model_configs.py#L1028-L1032 to_return = [] prepared_images.length.times do |i| image = prepared_images[i] image_size = percentage ? nil : [[image.height, image.width]] pixel_values = [model_inputs[:pixel_values][i]] # Run model with both text and pixel inputs output = @model.(text_inputs.merge(pixel_values: pixel_values)) # TODO remove output = @model.instance_variable_get(:@session).outputs.map { |v| v[:name].to_sym }.zip(output).to_h processed = @processor.feature_extractor.post_process_object_detection(output, threshold, image_size, true)[0] result = processed[:boxes].map.with_index do |box, i| { label: candidate_labels[processed[:classes][i]], score: processed[:scores][i], box: get_bounding_box(box, !percentage), } end result.sort_by! { |v| -v[:score] } if !top_k.nil? result = result[0...topk] end to_return << result end is_batched ? to_return : to_return[0] end |